Visual perception as retrospective Bayesian decoding from high- to low-level features

PubMed Central

Ding, Stephanie; Cueva, Christopher J.; Tsodyks, Misha; Qian, Ning

2017-01-01

When a stimulus is presented, its encoding is known to progress from low- to high-level features. How these features are decoded to produce perception is less clear, and most models assume that decoding follows the same low- to high-level hierarchy of encoding. There are also theories arguing for global precedence, reversed hierarchy, or bidirectional processing, but they are descriptive without quantitative comparison with human perception. Moreover, observers often inspect different parts of a scene sequentially to form overall perception, suggesting that perceptual decoding requires working memory, yet few models consider how working-memory properties may affect decoding hierarchy. We probed decoding hierarchy by comparing absolute judgments of single orientations and relative/ordinal judgments between two sequentially presented orientations. We found that lower-level, absolute judgments failed to account for higher-level, relative/ordinal judgments. However, when ordinal judgment was used to retrospectively decode memory representations of absolute orientations, striking aspects of absolute judgments, including the correlation and forward/backward aftereffects between two reported orientations in a trial, were explained. We propose that the brain prioritizes decoding of higher-level features because they are more behaviorally relevant, and more invariant and categorical, and thus easier to specify and maintain in noisy working memory, and that more reliable higher-level decoding constrains less reliable lower-level decoding. PMID:29073108

Network-based high level data classification.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-06-01

Traditional supervised data classification considers only physical features (e.g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

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

Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features

NASA Astrophysics Data System (ADS)

Wan, Xiaoqing; Zhao, Chunhui; Wang, Yanchun; Liu, Wu

2017-11-01

This paper proposes a novel classification paradigm for hyperspectral image (HSI) using feature-level fusion and deep learning-based methodologies. Operation is carried out in three main steps. First, during a pre-processing stage, wave atoms are introduced into bilateral filter to smooth HSI, and this strategy can effectively attenuate noise and restore texture information. Meanwhile, high quality spectral-spatial features can be extracted from HSI by taking geometric closeness and photometric similarity among pixels into consideration simultaneously. Second, higher order statistics techniques are firstly introduced into hyperspectral data classification to characterize the phase correlations of spectral curves. Third, multifractal spectrum features are extracted to characterize the singularities and self-similarities of spectra shapes. To this end, a feature-level fusion is applied to the extracted spectral-spatial features along with higher order statistics and multifractal spectrum features. Finally, stacked sparse autoencoder is utilized to learn more abstract and invariant high-level features from the multiple feature sets, and then random forest classifier is employed to perform supervised fine-tuning and classification. Experimental results on two real hyperspectral data sets demonstrate that the proposed method outperforms some traditional alternatives.

Feature diagnosticity and task context shape activity in human scene-selective cortex.

PubMed

Lowe, Matthew X; Gallivan, Jason P; Ferber, Susanne; Cant, Jonathan S

2016-01-15

Scenes are constructed from multiple visual features, yet previous research investigating scene processing has often focused on the contributions of single features in isolation. In the real world, features rarely exist independently of one another and likely converge to inform scene identity in unique ways. Here, we utilize fMRI and pattern classification techniques to examine the interactions between task context (i.e., attend to diagnostic global scene features; texture or layout) and high-level scene attributes (content and spatial boundary) to test the novel hypothesis that scene-selective cortex represents multiple visual features, the importance of which varies according to their diagnostic relevance across scene categories and task demands. Our results show for the first time that scene representations are driven by interactions between multiple visual features and high-level scene attributes. Specifically, univariate analysis of scene-selective cortex revealed that task context and feature diagnosticity shape activity differentially across scene categories. Examination using multivariate decoding methods revealed results consistent with univariate findings, but also evidence for an interaction between high-level scene attributes and diagnostic visual features within scene categories. Critically, these findings suggest visual feature representations are not distributed uniformly across scene categories but are shaped by task context and feature diagnosticity. Thus, we propose that scene-selective cortex constructs a flexible representation of the environment by integrating multiple diagnostically relevant visual features, the nature of which varies according to the particular scene being perceived and the goals of the observer. Copyright © 2015 Elsevier Inc. All rights reserved.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

PubMed

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

Combined 18F-Fluciclovine PET/MRI Shows Potential for Detection and Characterization of High-Risk Prostate Cancer.

PubMed

Elschot, Mattijs; Selnæs, Kirsten M; Sandsmark, Elise; Krüger-Stokke, Brage; Størkersen, Øystein; Giskeødegård, Guro F; Tessem, May-Britt; Moestue, Siver A; Bertilsson, Helena; Bathen, Tone F

2018-05-01

The objective of this study was to investigate whether quantitative imaging features derived from combined 18 F-fluciclovine PET/multiparametric MRI show potential for detection and characterization of primary prostate cancer. Methods: Twenty-eight patients diagnosed with high-risk prostate cancer underwent simultaneous 18 F-fluciclovine PET/MRI before radical prostatectomy. Volumes of interest (VOIs) for prostate tumors, benign prostatic hyperplasia (BPH) nodules, prostatitis, and healthy tissue were delineated on T2-weighted images, using histology as a reference. Tumor VOIs were marked as high-grade (≥Gleason grade group 3) or not. MRI and PET features were extracted on the voxel and VOI levels. Partial least-squared discriminant analysis (PLS-DA) with double leave-one-patient-out cross-validation was performed to distinguish tumors from benign tissue (BPH, prostatitis, or healthy tissue) and high-grade tumors from other tissue (low-grade tumors or benign tissue). The performance levels of PET, MRI, and combined PET/MRI features were compared using the area under the receiver-operating-characteristic curve (AUC). Results: Voxel and VOI features were extracted from 40 tumor VOIs (26 high-grade), 36 BPH VOIs, 6 prostatitis VOIs, and 37 healthy-tissue VOIs. PET/MRI performed better than MRI and PET alone for distinguishing tumors from benign tissue (AUCs of 87%, 81%, and 83%, respectively, at the voxel level and 96%, 93%, and 93%, respectively, at the VOI level) and high-grade tumors from other tissue (AUCs of 85%, 79%, and 81%, respectively, at the voxel level and 93%, 93%, and 91%, respectively, at the VOI level). T2-weighted MRI, diffusion-weighted MRI, and PET features were the most important for classification. Conclusion: Combined 18 F-fluciclovine PET/multiparametric MRI shows potential for improving detection and characterization of high-risk prostate cancer, in comparison to MRI and PET alone. © 2018 by the Society of Nuclear Medicine and Molecular Imaging.

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

PubMed

Altazi, Baderaldeen A; Zhang, Geoffrey G; Fernandez, Daniel C; Montejo, Michael E; Hunt, Dylan; Werner, Joan; Biagioli, Matthew C; Moros, Eduardo G

2017-11-01

Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18 Flourine-fluorodeoxyglucose ( 18 F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board-certified radiation oncologists manually segmented the metabolic tumor volume (MTV 1 and MTV 2 ) for each patient. For comparison, we used a graphical-based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down-sampled the tumor volumes into three gray-levels: 32, 64, and 128 from the original gray-level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D-reconstruction algorithms: maximum likelihood-ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning-ML-OSEM (FOREIR), FORE-filtered back projection (FOREFBP), and 3D-Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray-levels of down-sampled volumes, and PET reconstruction algorithms. The features were extracted using gray-level co-occurrence matrices (GLCM), gray-level size zone matrices (GLSZM), gray-level run-length matrices (GLRLM), neighborhood gray-tone difference matrices (NGTDM), shape-based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV 1 -MTV 2 , MTV 1 -GBSV, MTV 2 -GBSV; gray-levels: 64-32, 64-128, and 64-256; reconstruction algorithms: OSEM-FORE-OSEM, OSEM-FOREFBP, and OSEM-3DRP). We used |d¯| as a measure of radiomic feature reproducibility level, where any feature scored |d¯| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland-Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High- ±1% ≤ U/LRL ≤ ±30%; Intermediate- ±30% < U/LRL ≤ ±45%; Low- ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic feature reproducibility among segmentation pair MTV 1 -GBSV than MTV 2 -GBSV, gray-level pairs of 64-32 and 64-128 than 64-256, and reconstruction algorithm pairs of OSEM-FOREIR and OSEM-FOREFBP than OSEM-3DRP. Although the choice of cervical tumor segmentation method, gray-level value, and reconstruction algorithm may affect radiomic features, some features were characterized by high reproducibility through all testing parameters. The number of radiomic features that showed insensitivity to variations in segmentation methods, gray-level discretization, and reconstruction algorithms was 10 (13%), 4 (5%), and 1 (1%), respectively. These results suggest that a careful analysis of the effects of these parameters is essential prior to any radiomics clinical application. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Visual perception as retrospective Bayesian decoding from high- to low-level features.

PubMed

Ding, Stephanie; Cueva, Christopher J; Tsodyks, Misha; Qian, Ning

2017-10-24

When a stimulus is presented, its encoding is known to progress from low- to high-level features. How these features are decoded to produce perception is less clear, and most models assume that decoding follows the same low- to high-level hierarchy of encoding. There are also theories arguing for global precedence, reversed hierarchy, or bidirectional processing, but they are descriptive without quantitative comparison with human perception. Moreover, observers often inspect different parts of a scene sequentially to form overall perception, suggesting that perceptual decoding requires working memory, yet few models consider how working-memory properties may affect decoding hierarchy. We probed decoding hierarchy by comparing absolute judgments of single orientations and relative/ordinal judgments between two sequentially presented orientations. We found that lower-level, absolute judgments failed to account for higher-level, relative/ordinal judgments. However, when ordinal judgment was used to retrospectively decode memory representations of absolute orientations, striking aspects of absolute judgments, including the correlation and forward/backward aftereffects between two reported orientations in a trial, were explained. We propose that the brain prioritizes decoding of higher-level features because they are more behaviorally relevant, and more invariant and categorical, and thus easier to specify and maintain in noisy working memory, and that more reliable higher-level decoding constrains less reliable lower-level decoding. Published under the PNAS license.

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

NASA Astrophysics Data System (ADS)

Lee, Jongpil; Nam, Juhan

2017-08-01

Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification.

PubMed

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification

PubMed Central

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database. PMID:26640813

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.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

PubMed Central

Zhang, Xin; Cui, Jintian; Wang, Weisheng; Lin, Chao

2017-01-01

To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. PMID:28640181

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Accurate predictions of population-level changes in sequence and structural properties of HIV-1 Env using a volatility-controlled diffusion model

PubMed Central

DeLeon, Orlando; Hodis, Hagit; O’Malley, Yunxia; Johnson, Jacklyn; Salimi, Hamid; Zhai, Yinjie; Winter, Elizabeth; Remec, Claire; Eichelberger, Noah; Van Cleave, Brandon; Puliadi, Ramya; Harrington, Robert D.; Stapleton, Jack T.; Haim, Hillel

2017-01-01

The envelope glycoproteins (Envs) of HIV-1 continuously evolve in the host by random mutations and recombination events. The resulting diversity of Env variants circulating in the population and their continuing diversification process limit the efficacy of AIDS vaccines. We examined the historic changes in Env sequence and structural features (measured by integrity of epitopes on the Env trimer) in a geographically defined population in the United States. As expected, many Env features were relatively conserved during the 1980s. From this state, some features diversified whereas others remained conserved across the years. We sought to identify “clues” to predict the observed historic diversification patterns. Comparison of viruses that cocirculate in patients at any given time revealed that each feature of Env (sequence or structural) exists at a defined level of variance. The in-host variance of each feature is highly conserved among individuals but can vary between different HIV-1 clades. We designate this property “volatility” and apply it to model evolution of features as a linear diffusion process that progresses with increasing genetic distance. Volatilities of different features are highly correlated with their divergence in longitudinally monitored patients. Volatilities of features also correlate highly with their population-level diversification. Using volatility indices measured from a small number of patient samples, we accurately predict the population diversity that developed for each feature over the course of 30 years. Amino acid variants that evolved at key antigenic sites are also predicted well. Therefore, small “fluctuations” in feature values measured in isolated patient samples accurately describe their potential for population-level diversification. These tools will likely contribute to the design of population-targeted AIDS vaccines by effectively capturing the diversity of currently circulating strains and addressing properties of variants expected to appear in the future. PMID:28384158

On application of kernel PCA for generating stimulus features for fMRI during continuous music listening.

PubMed

Tsatsishvili, Valeri; Burunat, Iballa; Cong, Fengyu; Toiviainen, Petri; Alluri, Vinoo; Ristaniemi, Tapani

2018-06-01

There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. NEW METHOD: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing. Copyright © 2018 Elsevier B.V. All rights reserved.

Internal curvature signal and noise in low- and high-level vision

PubMed Central

Grabowecky, Marcia; Kim, Yee Joon; Suzuki, Satoru

2011-01-01

How does internal processing contribute to visual pattern perception? By modeling visual search performance, we estimated internal signal and noise relevant to perception of curvature, a basic feature important for encoding of three-dimensional surfaces and objects. We used isolated, sparse, crowded, and face contexts to determine how internal curvature signal and noise depended on image crowding, lateral feature interactions, and level of pattern processing. Observers reported the curvature of a briefly flashed segment, which was presented alone (without lateral interaction) or among multiple straight segments (with lateral interaction). Each segment was presented with no context (engaging low-to-intermediate-level curvature processing), embedded within a face context as the mouth (engaging high-level face processing), or embedded within an inverted-scrambled-face context as a control for crowding. Using a simple, biologically plausible model of curvature perception, we estimated internal curvature signal and noise as the mean and standard deviation, respectively, of the Gaussian-distributed population activity of local curvature-tuned channels that best simulated behavioral curvature responses. Internal noise was increased by crowding but not by face context (irrespective of lateral interactions), suggesting prevention of noise accumulation in high-level pattern processing. In contrast, internal curvature signal was unaffected by crowding but modulated by lateral interactions. Lateral interactions (with straight segments) increased curvature signal when no contextual elements were added, but equivalent interactions reduced curvature signal when each segment was presented within a face. These opposing effects of lateral interactions are consistent with the phenomena of local-feature contrast in low-level processing and global-feature averaging in high-level processing. PMID:21209356

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior

PubMed Central

Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

2018-01-01

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information. PMID:29513219

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.

PubMed

Groen, Iris Ia; Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

2018-03-07

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

Exploring Secondary Students' Epistemological Features Depending on the Evaluation Levels of the Group Model on Blood Circulation

ERIC Educational Resources Information Center

Lee, Shinyoung; Kim, Heui-Baik

2014-01-01

The purpose of this study is to identify the epistemological features and model qualities depending on model evaluation levels and to explore the reasoning process behind high-level evaluation through small group interaction about blood circulation. Nine groups of three to four students in the eighth grade participated in the modeling practice.…

Health information seeking on the Internet: The role of involvement in searching for and assessing online health information.

PubMed

Park, Sun-Young; Go, Eun

2016-01-01

This study focuses on how young people with differing levels of involvement seek and evaluate information about the human papillomavirus online. The results, which are drawn from an experiment and a self-administered survey, suggest that compared to people with a low level of involvement, people with a high level of involvement engage in more information search activity. The results also indicate that those with a high level of involvement in a given subject place a higher value on a website's message features than on its structural features. Implications, limitations, and suggestions for future research are discussed.

Angular difference feature extraction for urban scene classification using ZY-3 multi-angle high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Huang, Xin; Chen, Huijun; Gong, Jianya

2018-01-01

Spaceborne multi-angle images with a high-resolution are capable of simultaneously providing spatial details and three-dimensional (3D) information to support detailed and accurate classification of complex urban scenes. In recent years, satellite-derived digital surface models (DSMs) have been increasingly utilized to provide height information to complement spectral properties for urban classification. However, in such a way, the multi-angle information is not effectively exploited, which is mainly due to the errors and difficulties of the multi-view image matching and the inaccuracy of the generated DSM over complex and dense urban scenes. Therefore, it is still a challenging task to effectively exploit the available angular information from high-resolution multi-angle images. In this paper, we investigate the potential for classifying urban scenes based on local angular properties characterized from high-resolution ZY-3 multi-view images. Specifically, three categories of angular difference features (ADFs) are proposed to describe the angular information at three levels (i.e., pixel, feature, and label levels): (1) ADF-pixel: the angular information is directly extrapolated by pixel comparison between the multi-angle images; (2) ADF-feature: the angular differences are described in the feature domains by comparing the differences between the multi-angle spatial features (e.g., morphological attribute profiles (APs)). (3) ADF-label: label-level angular features are proposed based on a group of urban primitives (e.g., buildings and shadows), in order to describe the specific angular information related to the types of primitive classes. In addition, we utilize spatial-contextual information to refine the multi-level ADF features using superpixel segmentation, for the purpose of alleviating the effects of salt-and-pepper noise and representing the main angular characteristics within a local area. The experiments on ZY-3 multi-angle images confirm that the proposed ADF features can effectively improve the accuracy of urban scene classification, with a significant increase in overall accuracy (3.8-11.7%) compared to using the spectral bands alone. Furthermore, the results indicated the superiority of the proposed ADFs in distinguishing between the spectrally similar and complex man-made classes, including roads and various types of buildings (e.g., high buildings, urban villages, and residential apartments).

Vehicle logo recognition using multi-level fusion model

NASA Astrophysics Data System (ADS)

Ming, Wei; Xiao, Jianli

2018-04-01

Vehicle logo recognition plays an important role in manufacturer identification and vehicle recognition. This paper proposes a new vehicle logo recognition algorithm. It has a hierarchical framework, which consists of two fusion levels. At the first level, a feature fusion model is employed to map the original features to a higher dimension feature space. In this space, the vehicle logos become more recognizable. At the second level, a weighted voting strategy is proposed to promote the accuracy and the robustness of the recognition results. To evaluate the performance of the proposed algorithm, extensive experiments are performed, which demonstrate that the proposed algorithm can achieve high recognition accuracy and work robustly.

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.

Tongue Images Classification Based on Constrained High Dispersal Network.

PubMed

Meng, Dan; Cao, Guitao; Duan, Ye; Zhu, Minghua; Tu, Liping; Xu, Dong; Xu, Jiatuo

2017-01-01

Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM). However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN), we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet) to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

Use of Visual Metaphors for Navigation in Educational Hypermedia: Effects on the Navigational Performance

ERIC Educational Resources Information Center

Firat, Mehmet; Kabakci, Isil

2010-01-01

The interactional feature of hypermedia that allows high-level student-control is considered as one of the most important advantages that hypermedia provides for learning and teaching. However, high-level student control in hypermedia might not always lead to high-level learning performance. The learner is likely to experience navigation problems…

Deconvolution single shot multibox detector for supermarket commodity detection and classification

NASA Astrophysics Data System (ADS)

Li, Dejian; Li, Jian; Nie, Binling; Sun, Shouqian

2017-07-01

This paper proposes an image detection model to detect and classify supermarkets shelves' commodity. Based on the principle of the features directly affects the accuracy of the final classification, feature maps are performed to combine high level features with bottom level features. Then set some fixed anchors on those feature maps, finally the label and the position of commodity is generated by doing a box regression and classification. In this work, we proposed a model named Deconvolutiuon Single Shot MultiBox Detector, we evaluated the model using 300 images photographed from real supermarket shelves. Followed the same protocol in other recent methods, the results showed that our model outperformed other baseline methods.

Entropy-Based Adaptive Nuclear Texture Features are Independent Prognostic Markers in a Total Population of Uterine Sarcomas

PubMed Central

Nielsen, Birgitte; Hveem, Tarjei Sveinsgjerd; Kildal, Wanja; Abeler, Vera M; Kristensen, Gunnar B; Albregtsen, Fritz; Danielsen, Håvard E; Rohde, Gustavo K

2015-01-01

Nuclear texture analysis measures the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image and is a promising quantitative tool for prognosis of cancer. The aim of this study was to evaluate the prognostic value of entropy-based adaptive nuclear texture features in a total population of 354 uterine sarcomas. Isolated nuclei (monolayers) were prepared from 50 µm tissue sections and stained with Feulgen-Schiff. Local gray level entropy was measured within small windows of each nuclear image and stored in gray level entropy matrices, and two superior adaptive texture features were calculated from each matrix. The 5-year crude survival was significantly higher (P < 0.001) for patients with high texture feature values (72%) than for patients with low feature values (36%). When combining DNA ploidy classification (diploid/nondiploid) and texture (high/low feature value), the patients could be stratified into three risk groups with 5-year crude survival of 77, 57, and 34% (Hazard Ratios (HR) of 1, 2.3, and 4.1, P < 0.001). Entropy-based adaptive nuclear texture was an independent prognostic marker for crude survival in multivariate analysis including relevant clinicopathological features (HR = 2.1, P = 0.001), and should therefore be considered as a potential prognostic marker in uterine sarcomas. © The Authors. Published 2014 International Society for Advancement of Cytometry PMID:25483227

Relevance feedback-based building recognition

NASA Astrophysics Data System (ADS)

Li, Jing; Allinson, Nigel M.

2010-07-01

Building recognition is a nontrivial task in computer vision research which can be utilized in robot localization, mobile navigation, etc. However, existing building recognition systems usually encounter the following two problems: 1) extracted low level features cannot reveal the true semantic concepts; and 2) they usually involve high dimensional data which require heavy computational costs and memory. Relevance feedback (RF), widely applied in multimedia information retrieval, is able to bridge the gap between the low level visual features and high level concepts; while dimensionality reduction methods can mitigate the high-dimensional problem. In this paper, we propose a building recognition scheme which integrates the RF and subspace learning algorithms. Experimental results undertaken on our own building database show that the newly proposed scheme appreciably enhances the recognition accuracy.

K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited

NASA Astrophysics Data System (ADS)

Wang, Dong

2016-03-01

Gears are the most commonly used components in mechanical transmission systems. Their failures may cause transmission system breakdown and result in economic loss. Identification of different gear crack levels is important to prevent any unexpected gear failure because gear cracks lead to gear tooth breakage. Signal processing based methods mainly require expertize to explain gear fault signatures which is usually not easy to be achieved by ordinary users. In order to automatically identify different gear crack levels, intelligent gear crack identification methods should be developed. The previous case studies experimentally proved that K-nearest neighbors based methods exhibit high prediction accuracies for identification of 3 different gear crack levels under different motor speeds and loads. In this short communication, to further enhance prediction accuracies of existing K-nearest neighbors based methods and extend identification of 3 different gear crack levels to identification of 5 different gear crack levels, redundant statistical features are constructed by using Daubechies 44 (db44) binary wavelet packet transform at different wavelet decomposition levels, prior to the use of a K-nearest neighbors method. The dimensionality of redundant statistical features is 620, which provides richer gear fault signatures. Since many of these statistical features are redundant and highly correlated with each other, dimensionality reduction of redundant statistical features is conducted to obtain new significant statistical features. At last, the K-nearest neighbors method is used to identify 5 different gear crack levels under different motor speeds and loads. A case study including 3 experiments is investigated to demonstrate that the developed method provides higher prediction accuracies than the existing K-nearest neighbors based methods for recognizing different gear crack levels under different motor speeds and loads. Based on the new significant statistical features, some other popular statistical models including linear discriminant analysis, quadratic discriminant analysis, classification and regression tree and naive Bayes classifier, are compared with the developed method. The results show that the developed method has the highest prediction accuracies among these statistical models. Additionally, selection of the number of new significant features and parameter selection of K-nearest neighbors are thoroughly investigated.

Learning discriminative functional network features of schizophrenia

NASA Astrophysics Data System (ADS)

Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

2017-03-01

Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

Coastal barrier stratigraphy for Holocene high-resolution sea-level reconstruction

PubMed Central

Costas, Susana; Ferreira, Óscar; Plomaritis, Theocharis A.; Leorri, Eduardo

2016-01-01

The uncertainties surrounding present and future sea-level rise have revived the debate around sea-level changes through the deglaciation and mid- to late Holocene, from which arises a need for high-quality reconstructions of regional sea level. Here, we explore the stratigraphy of a sandy barrier to identify the best sea-level indicators and provide a new sea-level reconstruction for the central Portuguese coast over the past 6.5 ka. The selected indicators represent morphological features extracted from coastal barrier stratigraphy, beach berm and dune-beach contact. These features were mapped from high-resolution ground penetrating radar images of the subsurface and transformed into sea-level indicators through comparison with modern analogs and a chronology based on optically stimulated luminescence ages. Our reconstructions document a continuous but slow sea-level rise after 6.5 ka with an accumulated change in elevation of about 2 m. In the context of SW Europe, our results show good agreement with previous studies, including the Tagus isostatic model, with minor discrepancies that demand further improvement of regional models. This work reinforces the potential of barrier indicators to accurately reconstruct high-resolution mid- to late Holocene sea-level changes through simple approaches. PMID:27929122

Coastal barrier stratigraphy for Holocene high-resolution sea-level reconstruction.

PubMed

Costas, Susana; Ferreira, Óscar; Plomaritis, Theocharis A; Leorri, Eduardo

2016-12-08

The uncertainties surrounding present and future sea-level rise have revived the debate around sea-level changes through the deglaciation and mid- to late Holocene, from which arises a need for high-quality reconstructions of regional sea level. Here, we explore the stratigraphy of a sandy barrier to identify the best sea-level indicators and provide a new sea-level reconstruction for the central Portuguese coast over the past 6.5 ka. The selected indicators represent morphological features extracted from coastal barrier stratigraphy, beach berm and dune-beach contact. These features were mapped from high-resolution ground penetrating radar images of the subsurface and transformed into sea-level indicators through comparison with modern analogs and a chronology based on optically stimulated luminescence ages. Our reconstructions document a continuous but slow sea-level rise after 6.5 ka with an accumulated change in elevation of about 2 m. In the context of SW Europe, our results show good agreement with previous studies, including the Tagus isostatic model, with minor discrepancies that demand further improvement of regional models. This work reinforces the potential of barrier indicators to accurately reconstruct high-resolution mid- to late Holocene sea-level changes through simple approaches.

Temporal distance and person memory: thinking about the future changes memory for the past.

PubMed

Wyer, Natalie A; Perfect, Timothy J; Pahl, Sabine

2010-06-01

Psychological distance has been shown to influence how people construe an event such that greater distance produces high-level construal (characterized by global or holistic processing) and lesser distance produces low-level construal (characterized by detailed or feature-based processing). The present research tested the hypothesis that construal level has carryover effects on how information about an event is retrieved from memory. Two experiments manipulated temporal distance and found that greater distance (high-level construal) improves face recognition and increases retrieval of the abstract features of an event, whereas lesser distance (low-level construal) impairs face recognition and increases retrieval of the concrete details of an event. The findings have implications for transfer-inappropriate processing accounts of face recognition and event memory, and suggest potential applications in forensic settings.

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

NASA Astrophysics Data System (ADS)

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

2017-11-01

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features

PubMed Central

Corredor, Germán; Whitney, Jon; Arias, Viviana; Madabhushi, Anant; Romero, Eduardo

2017-01-01

Abstract. Computational histomorphometric approaches typically use low-level image features for building machine learning classifiers. However, these approaches usually ignore high-level expert knowledge. A computational model (M_im) combines low-, mid-, and high-level image information to predict the likelihood of cancer in whole slide images. Handcrafted low- and mid-level features are computed from area, color, and spatial nuclei distributions. High-level information is implicitly captured from the recorded navigations of pathologists while exploring whole slide images during diagnostic tasks. This model was validated by predicting the presence of cancer in a set of unseen fields of view. The available database was composed of 24 cases of basal-cell carcinoma, from which 17 served to estimate the model parameters and the remaining 7 comprised the evaluation set. A total of 274 fields of view of size 1024×1024  pixels were extracted from the evaluation set. Then 176 patches from this set were used to train a support vector machine classifier to predict the presence of cancer on a patch-by-patch basis while the remaining 98 image patches were used for independent testing, ensuring that the training and test sets do not comprise patches from the same patient. A baseline model (M_ex) estimated the cancer likelihood for each of the image patches. M_ex uses the same visual features as M_im, but its weights are estimated from nuclei manually labeled as cancerous or noncancerous by a pathologist. M_im achieved an accuracy of 74.49% and an F-measure of 80.31%, while M_ex yielded corresponding accuracy and F-measures of 73.47% and 77.97%, respectively. PMID:28382314

The perceptual features of vocal fatigue as self-reported by a group of actors and singers.

PubMed

Kitch, J A; Oates, J

1994-09-01

Performers (10 actors/10 singers) rated via a self-report questionnaire the severity of their voice-related changes when vocally fatigued. Similar frequency patterns and perceptual features of vocal fatigue were found across subjects. Actors rated "power" aspects (e.g., voice projection) and singers rated vocal dynamic aspects (e.g., pitch range) of their voices as most affected when vocally fatigued. Vocal fatigue was evidenced by changes in kinesthetic/proprioceptive sensations and vocal dynamics. The causes and context of vocal fatigue were vocal misuse, being "run down," high performance demands, and using high pitch/volume levels. Further research is needed to delineate the perceptual features of "normal" levels of vocal fatigue and its possible causes.

[Genetic polymorphism of Gentiana lutea L. (Gentianaceae) populations from Chornohora Ridge of Ukrainian Carpathians].

PubMed

Mosula, M Z; Konvaliuk, I I; Mel'nyk, V M; Drobyk, N M; Tsaryk, I V; Nesteruk, Iu I; Kunakh, V A

2014-01-01

The features of genetic structure and level of diversity were investigated for G. lutea populations from Chornohora Ridge of Ukrainian Carpathians using RAPD- and ISSR-PCR. We have shown a high level of genetic diversity for investigated populations. The differences between populations account for 59-72% of the total genetic variation, whereas intrapopulation polymorphism makes up 28-41%. The relationships among genetic variability level and ecological-geographical conditions as well as biological features of the species were assumed to be possible. The obtained results indicate the genetic isolation of G. lutea Chornohora populations from Ukrainian Carpathians. Pozhyzhevska agropopulation was characterized by a high level of polymorphism that means the possibility to use artificial plantings of the investigated species for its conservation.

Analyzing the Language of Therapist Empathy in Motivational Interview based Psychotherapy

PubMed Central

Xiao, Bo; Can, Dogan; Georgiou, Panayiotis G.; Atkins, David; Narayanan, Shrikanth S.

2016-01-01

Empathy is an important aspect of social communication, especially in medical and psychotherapy applications. Measures of empathy can offer insights into the quality of therapy. We use an N-gram language model based maximum likelihood strategy to classify empathic versus non-empathic utterances and report the precision and recall of classification for various parameters. High recall is obtained with unigram while bigram features achieved the highest F1-score. Based on the utterance level models, a group of lexical features are extracted at the therapy session level. The effectiveness of these features in modeling session level annotator perceptions of empathy is evaluated through correlation with expert-coded session level empathy scores. Our combined feature set achieved a correlation of 0.558 between predicted and expert-coded empathy scores. Results also suggest that the longer term empathy perception process may be more related to isolated empathic salient events. PMID:27602411

Component-Level Tuning of Kinematic Features from Composite Therapist Impressions of Movement Quality

PubMed Central

Venkataraman, Vinay; Turaga, Pavan; Baran, Michael; Lehrer, Nicole; Du, Tingfang; Cheng, Long; Rikakis, Thanassis; Wolf, Steven L.

2016-01-01

In this paper, we propose a general framework for tuning component-level kinematic features using therapists’ overall impressions of movement quality, in the context of a Home-based Adaptive Mixed Reality Rehabilitation (HAMRR) system. We propose a linear combination of non-linear kinematic features to model wrist movement, and propose an approach to learn feature thresholds and weights using high-level labels of overall movement quality provided by a therapist. The kinematic features are chosen such that they correlate with the quality of wrist movements to clinical assessment scores. Further, the proposed features are designed to be reliably extracted from an inexpensive and portable motion capture system using a single reflective marker on the wrist. Using a dataset collected from ten stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment in HAMRR systems. The system is currently being deployed for large-scale evaluations, and will represent an increasingly important application area of motion capture and activity analysis. PMID:25438331

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

PubMed

Liu, Manhua; Cheng, Danni; Wang, Kundong; Wang, Yaping

2018-03-23

Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for classification pMCI vs. NC, demonstrating the promising classification performance.

Exploring multiple feature combination strategies with a recurrent neural network architecture for off-line handwriting recognition

NASA Astrophysics Data System (ADS)

Mioulet, L.; Bideault, G.; Chatelain, C.; Paquet, T.; Brunessaux, S.

2015-01-01

The BLSTM-CTC is a novel recurrent neural network architecture that has outperformed previous state of the art algorithms in tasks such as speech recognition or handwriting recognition. It has the ability to process long term dependencies in temporal signals in order to label unsegmented data. This paper describes different ways of combining features using a BLSTM-CTC architecture. Not only do we explore the low level combination (feature space combination) but we also explore high level combination (decoding combination) and mid-level (internal system representation combination). The results are compared on the RIMES word database. Our results show that the low level combination works best, thanks to the powerful data modeling of the LSTM neurons.

EEG oscillations entrain their phase to high-level features of speech sound.

PubMed

Zoefel, Benedikt; VanRullen, Rufin

2016-01-01

Phase entrainment of neural oscillations, the brain's adjustment to rhythmic stimulation, is a central component in recent theories of speech comprehension: the alignment between brain oscillations and speech sound improves speech intelligibility. However, phase entrainment to everyday speech sound could also be explained by oscillations passively following the low-level periodicities (e.g., in sound amplitude and spectral content) of auditory stimulation-and not by an adjustment to the speech rhythm per se. Recently, using novel speech/noise mixture stimuli, we have shown that behavioral performance can entrain to speech sound even when high-level features (including phonetic information) are not accompanied by fluctuations in sound amplitude and spectral content. In the present study, we report that neural phase entrainment might underlie our behavioral findings. We observed phase-locking between electroencephalogram (EEG) and speech sound in response not only to original (unprocessed) speech but also to our constructed "high-level" speech/noise mixture stimuli. Phase entrainment to original speech and speech/noise sound did not differ in the degree of entrainment, but rather in the actual phase difference between EEG signal and sound. Phase entrainment was not abolished when speech/noise stimuli were presented in reverse (which disrupts semantic processing), indicating that acoustic (rather than linguistic) high-level features play a major role in the observed neural entrainment. Our results provide further evidence for phase entrainment as a potential mechanism underlying speech processing and segmentation, and for the involvement of high-level processes in the adjustment to the rhythm of speech. Copyright © 2015 Elsevier Inc. All rights reserved.

Computer Based Behavioral Biometric Authentication via Multi-Modal Fusion

DTIC Science & Technology

2013-03-01

the decisions made by each individual modality. Fusion of features is the simple concatenation of feature vectors from multiple modalities to be...of Features BayesNet MDL 330 LibSVM PCA 80 J48 Wrapper Evaluator 11 3.5.3 Ensemble Based Decision Level Fusion. In ensemble learning multiple ...The high fusion percentages validate our hypothesis that by combining features from multiple modalities, classification accuracy can be improved. As

Visual Saliency Detection Based on Multiscale Deep CNN Features.

PubMed

Guanbin Li; Yizhou Yu

2016-11-01

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving the state-of-the-art performance on all public benchmarks, improving the F-measure by 6.12% and 10%, respectively, on the DUT-OMRON data set and our new data set (HKU-IS), and lowering the mean absolute error by 9% and 35.3%, respectively, on these two data sets.

Fast and efficient indexing approach for object recognition

NASA Astrophysics Data System (ADS)

Hefnawy, Alaa; Mashali, Samia A.; Rashwan, Mohsen; Fikri, Magdi

1999-08-01

This paper introduces a fast and efficient indexing approach for both 2D and 3D model-based object recognition in the presence of rotation, translation, and scale variations of objects. The indexing entries are computed after preprocessing the data by Haar wavelet decomposition. The scheme is based on a unified image feature detection approach based on Zernike moments. A set of low level features, e.g. high precision edges, gray level corners, are estimated by a set of orthogonal Zernike moments, calculated locally around every image point. A high dimensional, highly descriptive indexing entries are then calculated based on the correlation of these local features and employed for fast access to the model database to generate hypotheses. A list of the most candidate models is then presented by evaluating the hypotheses. Experimental results are included to demonstrate the effectiveness of the proposed indexing approach.

Joint trajectories for social and physical aggression as predictors of adolescent maladjustment: internalizing symptoms, rule-breaking behaviors, and borderline and narcissistic personality features.

PubMed

Underwood, Marion K; Beron, Kurt J; Rosen, Lisa H

2011-05-01

This investigation examined the relation between developmental trajectories jointly estimated for social and physical aggression and adjustment problems at age 14. Teachers provided ratings of children's social and physical aggression in Grades 3, 4, 5, 6, and 7 for a sample of 255 children (131 girls, 21% African American, 52% European American, 21% Mexican American). Participants, parents, and teachers completed measures of the adolescent's adjustment to assess internalizing symptoms, rule-breaking behaviors, and borderline and narcissistic personality features. Results showed that membership in a high and rising trajectory group predicted rule-breaking behaviors and borderline personality features. Membership in a high desister group predicted internalizing symptoms, rule-breaking behaviors, and borderline and narcissistic personality features. The findings suggest that although low levels of social and physical aggression may not bode poorly for adjustment, individuals engaging in high levels of social and physical aggression in middle childhood may be at greatest risk for adolescent psychopathology, whether they increase or desist in their aggression through early adolescence.

Joint trajectories for social and physical aggression as predictors of adolescent maladjustment: Internalizing symptoms, rule-breaking behaviors, and borderline and narcissistic personality features

PubMed Central

UNDERWOOD, MARION K.; BERON, KURT J.; ROSEN, LISA H.

2011-01-01

This investigation examined the relation between developmental trajectories jointly estimated for social and physical aggression and adjustment problems at age 14. Teachers provided ratings of children's social and physical aggression in Grades 3, 4, 5, 6, and 7 for a sample of 255 children (131 girls, 21% African American, 52% European American, 21% Mexican American). Participants, parents, and teachers completed measures of the adolescent's adjustment to assess internalizing symptoms, rule-breaking behaviors, and borderline and narcissistic personality features. Results showed that membership in a high and rising trajectory group predicted rule-breaking behaviors and borderline personality features. Membership in a high desister group predicted internalizing symptoms, rule-breaking behaviors, and borderline and narcissistic personality features. The findings suggest that although low levels of social and physical aggression may not bode poorly for adjustment, individuals engaging in high levels of social and physical aggression in middle childhood may be at greatest risk for adolescent psychopathology, whether they increase or desist in their aggression through early adolescence. PMID:21532919

Cognitive Fingerprints

DTIC Science & Technology

2015-03-25

is another cognitive fingerprint that has been used extensively for authorship . This work has been ex- tended to authentication by relating keyboard...this work is the inference of high-level features such as personality, gender , and dominant hand but those features have not been integrated to date

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

Benchmark Problems for Spacecraft Formation Flying Missions

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell; Leitner, Jesse A.; Burns, Richard D.; Folta, David C.

2003-01-01

To provide high-level focus to distributed space system flight dynamics and control research, several benchmark problems are suggested. These problems are not specific to any current or proposed mission, but instead are intended to capture high-level features that would be generic to many similar missions.

Memory Scanning, Introversion-Extraversion, and Levels of Processing.

ERIC Educational Resources Information Center

Eysenck, Michael W.; Eysenck, M. Christine

1979-01-01

Investigated was the hypothesis that high arousal increases processing of physical characteristics and reduces processing of semantic characteristics. While introverts and extroverts had equivalent scanning rates for physical features, introverts were significantly slower in searching for semantic features of category membership, indicating…

Enhanced Perceptual Processing of Speech in Autism

ERIC Educational Resources Information Center

Jarvinen-Pasley, Anna; Wallace, Gregory L.; Ramus, Franck; Happe, Francesca; Heaton, Pamela

2008-01-01

Theories of autism have proposed that a bias towards low-level perceptual information, or a featural/surface-biased information-processing style, may compromise higher-level language processing in such individuals. Two experiments, utilizing linguistic stimuli with competing low-level/perceptual and high-level/semantic information, tested…

Schizophrenia classification using functional network features

NASA Astrophysics Data System (ADS)

Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle

2012-03-01

This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.

Very high resolution Earth observation features for monitoring plant and animal community structure across multiple spatial scales in protected areas

NASA Astrophysics Data System (ADS)

Mairota, Paola; Cafarelli, Barbara; Labadessa, Rocco; Lovergine, Francesco; Tarantino, Cristina; Lucas, Richard M.; Nagendra, Harini; Didham, Raphael K.

2015-05-01

Monitoring the status and future trends in biodiversity can be prohibitively expensive using ground-based surveys. Consequently, significant effort is being invested in the use of satellite remote sensing to represent aspects of the proximate mechanisms (e.g., resource availability) that can be related to biodiversity surrogates (BS) such as species community descriptors. We explored the potential of very high resolution (VHR) satellite Earth observation (EO) features as proxies for habitat structural attributes that influence spatial variation in habitat quality and biodiversity change. In a semi-natural grassland mosaic of conservation concern in southern Italy, we employed a hierarchical nested sampling strategy to collect field and VHR-EO data across three spatial extent levels (landscape, patch and plot). Species incidence and abundance data were collected at the plot level for plant, insect and bird functional groups. Spectral and textural VHR-EO image features were derived from a Worldview-2 image. Three window sizes (grains) were tested for analysis and computation of textural features, guided by the perception limits of different organisms. The modelled relationships between VHR-EO features and BS responses differed across scales, suggesting that landscape, patch and plot levels are respectively most appropriate when dealing with birds, plants and insects. This research demonstrates the potential of VHR-EO for biodiversity mapping and habitat modelling, and highlights the importance of identifying the appropriate scale of analysis for specific taxonomic groups of interest. Further, textural features are important in the modelling of functional group-specific indices which represent BS in high conservation value habitat types, and provide a more direct link to species interaction networks and ecosystem functioning, than provided by traditional taxonomic diversity indices.

Associations Between PET Textural Features and GLUT1 Expression, and the Prognostic Significance of Textural Features in Lung Adenocarcinoma.

PubMed

Koh, Young Wha; Park, Seong Yong; Hyun, Seung Hyup; Lee, Su Jin

2018-02-01

We evaluated the association between positron emission tomography (PET) textural features and glucose transporter 1 (GLUT1) expression level and further investigated the prognostic significance of textural features in lung adenocarcinoma. We evaluated 105 adenocarcinoma patients. We extracted texture-based PET parameters of primary tumors. Conventional PET parameters were also measured. The relationships between PET parameters and GLUT1 expression levels were evaluated. The association between PET parameters and overall survival (OS) was assessed using Cox's proportional hazard regression models. In terms of PET textural features, tumors expressing high levels of GLUT1 exhibited significantly lower coarseness, contrast, complexity, and strength, but significantly higher busyness. On univariate analysis, the metabolic tumor volume, total lesion glycolysis, contrast, busyness, complexity, and strength were significant predictors of OS. Multivariate analysis showed that lower complexity (HR=2.017, 95%CI=1.032-3.942, p=0.040) was independently associated with poorer survival. PET textural features may aid risk stratification in lung adenocarcinoma patients. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Generic decoding of seen and imagined objects using hierarchical visual features.

PubMed

Horikawa, Tomoyasu; Kamitani, Yukiyasu

2017-05-22

Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, decoding of imagined objects reveals progressive recruitment of higher-to-lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.

Extracting intrinsic functional networks with feature-based group independent component analysis.

PubMed

Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.

Comparisons of observed seasonal climate features with a winter and summer numerical simulation produced with the GLAS general circulation model

NASA Technical Reports Server (NTRS)

Halem, M.; Shukla, J.; Mintz, Y.; Wu, M. L.; Godbole, R.; Herman, G.; Sud, Y.

1979-01-01

Results are presented from numerical simulations performed with the general circulation model (GCM) for winter and summer. The monthly mean simulated fields for each integration are compared with observed geographical distributions and zonal averages. In general, the simulated sea level pressure and upper level geopotential height field agree well with the observations. Well simulated features are the winter Aleutian and Icelandic lows, the summer southwestern U.S. low, the summer and winter oceanic subtropical highs in both hemispheres, and the summer upper level Tibetan high and Atlantic ridge. The surface and upper air wind fields in the low latitudes are in good agreement with the observations. The geographical distirbutions of the Earth-atmosphere radiation balance and of the precipitation rates over the oceans are well simulated, but not all of the intensities of these features are correct. Other comparisons are shown for precipitation along the ITCZ, rediation balance, zonally averaged temperatures and zonal winds, and poleward transports of momentum and sensible heat.

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

PubMed

Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

Behavioral model of visual perception and recognition

NASA Astrophysics Data System (ADS)

Rybak, Ilya A.; Golovan, Alexander V.; Gusakova, Valentina I.

1993-09-01

In the processes of visual perception and recognition human eyes actively select essential information by way of successive fixations at the most informative points of the image. A behavioral program defining a scanpath of the image is formed at the stage of learning (object memorizing) and consists of sequential motor actions, which are shifts of attention from one to another point of fixation, and sensory signals expected to arrive in response to each shift of attention. In the modern view of the problem, invariant object recognition is provided by the following: (1) separated processing of `what' (object features) and `where' (spatial features) information at high levels of the visual system; (2) mechanisms of visual attention using `where' information; (3) representation of `what' information in an object-based frame of reference (OFR). However, most recent models of vision based on OFR have demonstrated the ability of invariant recognition of only simple objects like letters or binary objects without background, i.e. objects to which a frame of reference is easily attached. In contrast, we use not OFR, but a feature-based frame of reference (FFR), connected with the basic feature (edge) at the fixation point. This has provided for our model, the ability for invariant representation of complex objects in gray-level images, but demands realization of behavioral aspects of vision described above. The developed model contains a neural network subsystem of low-level vision which extracts a set of primary features (edges) in each fixation, and high- level subsystem consisting of `what' (Sensory Memory) and `where' (Motor Memory) modules. The resolution of primary features extraction decreases with distances from the point of fixation. FFR provides both the invariant representation of object features in Sensor Memory and shifts of attention in Motor Memory. Object recognition consists in successive recall (from Motor Memory) and execution of shifts of attention and successive verification of the expected sets of features (stored in Sensory Memory). The model shows the ability of recognition of complex objects (such as faces) in gray-level images invariant with respect to shift, rotation, and scale.

Neural Tuning to Low-Level Features of Speech throughout the Perisylvian Cortex.

PubMed

Berezutskaya, Julia; Freudenburg, Zachary V; Güçlü, Umut; van Gerven, Marcel A J; Ramsey, Nick F

2017-08-16

Despite a large body of research, we continue to lack a detailed account of how auditory processing of continuous speech unfolds in the human brain. Previous research showed the propagation of low-level acoustic features of speech from posterior superior temporal gyrus toward anterior superior temporal gyrus in the human brain (Hullett et al., 2016). In this study, we investigate what happens to these neural representations past the superior temporal gyrus and how they engage higher-level language processing areas such as inferior frontal gyrus. We used low-level sound features to model neural responses to speech outside of the primary auditory cortex. Two complementary imaging techniques were used with human participants (both males and females): electrocorticography (ECoG) and fMRI. Both imaging techniques showed tuning of the perisylvian cortex to low-level speech features. With ECoG, we found evidence of propagation of the temporal features of speech sounds along the ventral pathway of language processing in the brain toward inferior frontal gyrus. Increasingly coarse temporal features of speech spreading from posterior superior temporal cortex toward inferior frontal gyrus were associated with linguistic features such as voice onset time, duration of the formant transitions, and phoneme, syllable, and word boundaries. The present findings provide the groundwork for a comprehensive bottom-up account of speech comprehension in the human brain. SIGNIFICANCE STATEMENT We know that, during natural speech comprehension, a broad network of perisylvian cortical regions is involved in sound and language processing. Here, we investigated the tuning to low-level sound features within these regions using neural responses to a short feature film. We also looked at whether the tuning organization along these brain regions showed any parallel to the hierarchy of language structures in continuous speech. Our results show that low-level speech features propagate throughout the perisylvian cortex and potentially contribute to the emergence of "coarse" speech representations in inferior frontal gyrus typically associated with high-level language processing. These findings add to the previous work on auditory processing and underline a distinctive role of inferior frontal gyrus in natural speech comprehension. Copyright © 2017 the authors 0270-6474/17/377906-15$15.00/0.

Large-scale circulation departures related to wet episodes in north-east Brazil

NASA Technical Reports Server (NTRS)

Sikdar, Dhirendra N.; Elsner, James B.

1987-01-01

Large scale circulation features are presented as related to wet spells over northeast Brazil (Nordeste) during the rainy season (March and April) of 1979. The rainy season is divided into dry and wet periods; the FGGE and geostationary satellite data was averaged; and mean and departure fields of basic variables and cloudiness were studied. Analysis of seasonal mean circulation features show: lowest sea level easterlies beneath upper level westerlies; weak meridional winds; high relative humidity over the Amazon basin and relatively dry conditions over the South Atlantic Ocean. A fluctuation was found in the large scale circulation features on time scales of a few weeks or so over Nordeste and the South Atlantic sector. Even the subtropical High SLPs have large departures during wet episodes, implying a short period oscillation in the Southern Hemisphere Hadley circulation.

Large-scale circulation departures related to wet episodes in northeast Brazil

NASA Technical Reports Server (NTRS)

Sikdar, D. N.; Elsner, J. B.

1985-01-01

Large scale circulation features are presented as related to wet spells over northeast Brazil (Nordeste) during the rainy season (March and April) of 1979. The rainy season season is devided into dry and wet periods, the FGGE and geostationary satellite data was averaged and mean and departure fields of basic variables and cloudiness were studied. Analysis of seasonal mean circulation features show: lowest sea level easterlies beneath upper level westerlies; weak meridional winds; high relative humidity over the Amazon basin and relatively dry conditions over the South Atlantic Ocean. A fluctuation was found in the large scale circulation features on time scales of a few weeks or so over Nordeste and the South Atlantic sector. Even the subtropical High SLP's have large departures during wet episodes, implying a short period oscillation in the Southern Hemisphere Hadley circulation.

Inattention in primary school is not good for your future school achievement—A pattern classification study

PubMed Central

Bøe, Tormod; Lundervold, Arvid

2017-01-01

Inattention in childhood is associated with academic problems later in life. The contribution of specific aspects of inattentive behaviour is, however, less known. We investigated feature importance of primary school teachers’ reports on nine aspects of inattentive behaviour, gender and age in predicting future academic achievement. Primary school teachers of n = 2491 children (7–9 years) rated nine items reflecting different aspects of inattentive behaviour in 2002. A mean academic achievement score from the previous semester in high school (2012) was available for each youth from an official school register. All scores were at a categorical level. Feature importances were assessed by using multinominal logistic regression, classification and regression trees analysis, and a random forest algorithm. Finally, a comprehensive pattern classification procedure using k-fold cross-validation was implemented. Overall, inattention was rated as more severe in boys, who also obtained lower academic achievement scores in high school than girls. Problems related to sustained attention and distractibility were together with age and gender defined as the most important features to predict future achievement scores. Using these four features as input to a collection of classifiers employing k-fold cross-validation for prediction of academic achievement level, we obtained classification accuracy, precision and recall that were clearly better than chance levels. Primary school teachers’ reports of problems related to sustained attention and distractibility were identified as the two most important features of inattentive behaviour predicting academic achievement in high school. Identification and follow-up procedures of primary school children showing these characteristics should be prioritised to prevent future academic failure. PMID:29182663

Inattention in primary school is not good for your future school achievement-A pattern classification study.

PubMed

Lundervold, Astri J; Bøe, Tormod; Lundervold, Arvid

2017-01-01

Inattention in childhood is associated with academic problems later in life. The contribution of specific aspects of inattentive behaviour is, however, less known. We investigated feature importance of primary school teachers' reports on nine aspects of inattentive behaviour, gender and age in predicting future academic achievement. Primary school teachers of n = 2491 children (7-9 years) rated nine items reflecting different aspects of inattentive behaviour in 2002. A mean academic achievement score from the previous semester in high school (2012) was available for each youth from an official school register. All scores were at a categorical level. Feature importances were assessed by using multinominal logistic regression, classification and regression trees analysis, and a random forest algorithm. Finally, a comprehensive pattern classification procedure using k-fold cross-validation was implemented. Overall, inattention was rated as more severe in boys, who also obtained lower academic achievement scores in high school than girls. Problems related to sustained attention and distractibility were together with age and gender defined as the most important features to predict future achievement scores. Using these four features as input to a collection of classifiers employing k-fold cross-validation for prediction of academic achievement level, we obtained classification accuracy, precision and recall that were clearly better than chance levels. Primary school teachers' reports of problems related to sustained attention and distractibility were identified as the two most important features of inattentive behaviour predicting academic achievement in high school. Identification and follow-up procedures of primary school children showing these characteristics should be prioritised to prevent future academic failure.

Library Development Handbook. Central Archive for Reusable Defense Software (CARDS)

DTIC Science & Technology

1993-10-29

features. This feature benefits the individual not versed in the terminology of the domain. When class requirements become part of the domain criteria, they... franchisee - Group to whom a franchise is granted. generic architecture - A collection of high-level paradigms and constraints that characterize the

The sensory components of high-capacity iconic memory and visual working memory.

PubMed

Bradley, Claire; Pearson, Joel

2012-01-01

EARLY VISUAL MEMORY CAN BE SPLIT INTO TWO PRIMARY COMPONENTS: a high-capacity, short-lived iconic memory followed by a limited-capacity visual working memory that can last many seconds. Whereas a large number of studies have investigated visual working memory for low-level sensory features, much research on iconic memory has used more "high-level" alphanumeric stimuli such as letters or numbers. These two forms of memory are typically examined separately, despite an intrinsic overlap in their characteristics. Here, we used a purely sensory paradigm to examine visual short-term memory for 10 homogeneous items of three different visual features (color, orientation and motion) across a range of durations from 0 to 6 s. We found that the amount of information stored in iconic memory is smaller for motion than for color or orientation. Performance declined exponentially with longer storage durations and reached chance levels after ∼2 s. Further experiments showed that performance for the 10 items at 1 s was contingent on unperturbed attentional resources. In addition, for orientation stimuli, performance was contingent on the location of stimuli in the visual field, especially for short cue delays. Overall, our results suggest a smooth transition between an automatic, high-capacity, feature-specific sensory-iconic memory, and an effortful "lower-capacity" visual working memory.

Antisense sequences of the nbl gene induce apoptosis in the human promyelocytic leukemia cell line HL-60.

PubMed

Naora, H; Nishida, T; Shindo, Y; Adachi, M; Naora, H

1998-04-01

Apoptosis is induced by the transcriptional inhibitor actinomycin D (Act D) in various cell types, particularly many leukemic cell lines such as HL-60. A common feature of these cell lines is their high constitutive expression level of the nbl gene, which was originally isolated by virtue of its abundance in a Namalwa Burkitt lymphoma cDNA library. In contrast, cell lines which constitutively express nbl at low levels appear not to undergo typical apoptotic death in response to Act D. Apoptotic induction by Act D in cells which normally express nbl at high levels was found in this study to be closely associated with a decline in nbl mRNA levels, raising the possibility that apoptosis could be induced by lowering nbl expression levels in such cells. Transient expression of nbl antisense sequences in HL-60 cells decreased cell viability, and induced typical apoptotic morphology such as cell shrinkage, chromatin condensation and nuclear fragmentation. Incubation with nbl antisense oligomers also induced similar features in HL-60 cells and in another high nb-expressing cell line, Jurkat, but had little effect in HepG2 cells which constitutively express nbl at low levels. These findings suggest that lowering constitutively high levels of nbl expression can induce apoptosis.

The myositis autoantibody phenotypes of the juvenile idiopathic inflammatory myopathies.

PubMed

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

2013-07-01

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

The Myositis Autoantibody Phenotypes of the Juvenile Idiopathic Inflammatory Myopathies

PubMed Central

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

2013-01-01

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

Cloud field classification based upon high spatial resolution textural features. I - Gray level co-occurrence matrix approach

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Chen, D. W.

1988-01-01

Stratocumulus, cumulus, and cirrus clouds were identified on the basis of cloud textural features which were derived from a single high-resolution Landsat MSS NIR channel using a stepwise linear discriminant analysis. It is shown that, using this method, it is possible to distinguish high cirrus clouds from low clouds with high accuracy on the basis of spatial brightness patterns. The largest probability of misclassification is associated with confusion between the stratocumulus breakup regions and the fair-weather cumulus.

On-Demand Single Photons with High Extraction Efficiency and Near-Unity Indistinguishability from a Resonantly Driven Quantum Dot in a Micropillar.

PubMed

Ding, Xing; He, Yu; Duan, Z-C; Gregersen, Niels; Chen, M-C; Unsleber, S; Maier, S; Schneider, Christian; Kamp, Martin; Höfling, Sven; Lu, Chao-Yang; Pan, Jian-Wei

2016-01-15

Scalable photonic quantum technologies require on-demand single-photon sources with simultaneously high levels of purity, indistinguishability, and efficiency. These key features, however, have only been demonstrated separately in previous experiments. Here, by s-shell pulsed resonant excitation of a Purcell-enhanced quantum dot-micropillar system, we deterministically generate resonance fluorescence single photons which, at π pulse excitation, have an extraction efficiency of 66%, single-photon purity of 99.1%, and photon indistinguishability of 98.5%. Such a single-photon source for the first time combines the features of high efficiency and near-perfect levels of purity and indistinguishabilty, and thus opens the way to multiphoton experiments with semiconductor quantum dots.

Recovery of a crowded object by masking the flankers: Determining the locus of feature integration

PubMed Central

Chakravarthi, Ramakrishna; Cavanagh, Patrick

2009-01-01

Object recognition is a central function of the visual system. As a first step, the features of an object are registered; these independently encoded features are then bound together to form a single representation. Here we investigate the locus of this “feature integration” by examining crowding, a striking breakdown of this process. Crowding, an inability to identify a peripheral target surrounded by flankers, results from “excessive integration” of target and flanker features. We presented a standard crowding display with a target C flanked by four flanker C's in the periphery. We then masked only the flankers (but not the target) with one of three kinds of masks—noise, metacontrast, and object substitution—each of which interferes at progressively higher levels of visual processing. With noise and metacontrast masks (low-level masking), the crowded target was recovered, whereas with object substitution masks (high-level masking), it was not. This places a clear upper bound on the locus of interference in crowding suggesting that crowding is not a low-level phenomenon. We conclude that feature integration, which underlies crowding, occurs prior to the locus of object substitution masking. Further, our results indicate that the integrity of the flankers, but not their identification, is crucial for crowding to occur. PMID:19810785

Classification of CT examinations for COPD visual severity analysis

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Pu, Jiantao; Gur, David; Sciurba, Frank C.; Leader, J. Ken

2012-03-01

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Cyber Foraging for Improving Survivability of Mobile Systems

DTIC Science & Technology

2016-02-10

environments—such as dynamic context, limited computing resources, disconnected- intermittent - limited (DIL) network connectivity, and high levels of stress...environments, such as dynamic context, limited computing resources, disconnected- intermittent -limited (DIL) network connectivity, and high levels of...Table 1: Mapping of Cloudlet Features to Survivability Requirements Threats Intermittent Cloudlet- Enterprise Connectivity Mobility Limited

YASS: A System Simulator for Operating System and Computer Architecture Teaching and Learning

ERIC Educational Resources Information Center

Mustafa, Besim

2013-01-01

A highly interactive, integrated and multi-level simulator has been developed specifically to support both the teachers and the learners of modern computer technologies at undergraduate level. The simulator provides a highly visual and user configurable environment with many pedagogical features aimed at facilitating deep understanding of concepts…

Typewriter Modifications for Persons Who Are High-Level Quadriplegics.

ERIC Educational Resources Information Center

O'Reagan, James R.; And Others

Standard, common electric typewriters are not completely suited to the needs of a high-level quadriplegic typing with a mouthstick. Experiences show that for complete control of a typewriter a mouthstick user needs the combined features of one-button correction, electric forward and reverse indexing, and easy character viewing. To modify a…

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

Sorensen, J; Duran, C; Stingo, F

Purpose: To characterize the effect of virtual monochromatic reconstructions on several commonly used texture analysis features in DECT of the chest. Further, to assess the effect of monochromatic energy levels on the ability of these textural features to identify tissue types. Methods: 20 consecutive patients underwent chest CTs for evaluation of lung nodules using Siemens Somatom Definition Flash DECT. Virtual monochromatic images were constructed at 10keV intervals from 40–190keV. For each patient, an ROI delineated the lesion under investigation, and cylindrical ROI’s were placed within 5 different healthy tissues (blood, fat, muscle, lung, and liver). Several histogram- and Grey Levelmore » Cooccurrence Matrix (GLCM)-based texture features were then evaluated in each ROI at each energy level. As a means of validation, these feature values were then used in a random forest classifier to attempt to identify the tissue types present within each ROI. Their predictive accuracy at each energy level was recorded. Results: All textural features changed considerably with virtual monochromatic energy, particularly below 70keV. Most features exhibited a global minimum or maximum around 80keV, and while feature values changed with energy above this, patient ranking was generally unaffected. As expected, blood demonstrated the lowest inter-patient variability, for all features, while lung lesions (encompassing many different pathologies) exhibited the highest. The accuracy of these features in identifying tissues (76% accuracy) was highest at 80keV, but no clear relationship between energy and classification accuracy was found. Two common misclassifications (blood vs liver and muscle vs fat) accounted for the majority (24 of the 28) errors observed. Conclusion: All textural features were highly dependent on virtual monochromatic energy level, especially below 80keV, and were more stable above this energy. However, in a random forest model, these commonly used features were able to reliably differentiate between most tissues types regardless of energy level. Dr Godoy has received a dual-energy CT research grant from Siemens Healthcare. That grant did not directly fund this research.« less

Semantic classification of business images

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

PubMed

Ortiz-Ramón, Rafael; Larroza, Andrés; Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

2018-05-14

To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

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

Honorio, J.; Goldstein, R.; Honorio, J.

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statisticalmore » theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.« less

Progress In Fresnel-Köhler Concentrators

NASA Astrophysics Data System (ADS)

Mohedano, Rubén; Cvetković, Aleksandra; Benítez, Pablo; Chaves, Julio; Miñano, Juan C.; Zamora, Pablo; Hernandez, Maikel; Vilaplana, Juan

2011-12-01

The Fresnel Köhler (FK) concentrator was first presented in 2008. Since then, various CPV companies have adopted this technology as base for their future commercial product. The key for this rapid penetration is a mixture of simplicity (the FK is essentially a Fresnel lens concentrator, a technology that dominates the market) and excellent performance: high concentration without giving up large manufacturing/aiming tolerances, enabling high efficiency even at the array level. All these features together have a great potential to lower energy costs. This work shows recent results and progress regarding this device, covering new design features, measurements and tests along with first performance achievements at the array level (pilot 6.5 Kwp plant). The work also discusses the potential impact of the FK enhanced performance on the Levelized Cost Of Electricity (LCOE).

Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?

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

Fave, Xenia, E-mail: xjfave@mdanderson.org; Fried, David; Mackin, Dennis

Purpose: Increasing evidence suggests radiomics features extracted from computed tomography (CT) images may be useful in prognostic models for patients with nonsmall cell lung cancer (NSCLC). This study was designed to determine whether such features can be reproducibly obtained from cone-beam CT (CBCT) images taken using medical Linac onboard-imaging systems in order to track them through treatment. Methods: Test-retest CBCT images of ten patients previously enrolled in a clinical trial were retrospectively obtained and used to determine the concordance correlation coefficient (CCC) for 68 different texture features. The volume dependence of each feature was also measured using the Spearman rankmore » correlation coefficient. Features with a high reproducibility (CCC > 0.9) that were not due to volume dependence in the patient test-retest set were further examined for their sensitivity to differences in imaging protocol, level of scatter, and amount of motion by using two phantoms. The first phantom was a texture phantom composed of rectangular cartridges to represent different textures. Features were measured from two cartridges, shredded rubber and dense cork, in this study. The texture phantom was scanned with 19 different CBCT imagers to establish the features’ interscanner variability. The effect of scatter on these features was studied by surrounding the same texture phantom with scattering material (rice and solid water). The effect of respiratory motion on these features was studied using a dynamic-motion thoracic phantom and a specially designed tumor texture insert of the shredded rubber material. The differences between scans acquired with different Linacs and protocols, varying amounts of scatter, and with different levels of motion were compared to the mean intrapatient difference from the test-retest image set. Results: Of the original 68 features, 37 had a CCC >0.9 that was not due to volume dependence. When the Linac manufacturer and imaging protocol were kept consistent, 4–13 of these 37 features passed our criteria for reproducibility more than 50% of the time, depending on the manufacturer-protocol combination. Almost all of the features changed substantially when scatter material was added around the phantom. For the dense cork, 23 features passed in the thoracic scans and 11 features passed in the head scans when the differences between one and two layers of scatter were compared. Using the same test for the shredded rubber, five features passed the thoracic scans and eight features passed the head scans. Motion substantially impacted the reproducibility of the features. With 4 mm of motion, 12 features from the entire volume and 14 features from the center slice measurements were reproducible. With 6–8 mm of motion, three features (Laplacian of Gaussian filtered kurtosis, gray-level nonuniformity, and entropy), from the entire volume and seven features (coarseness, high gray-level run emphasis, gray-level nonuniformity, sum-average, information measure correlation, scaled mean, and entropy) from the center-slice measurements were considered reproducible. Conclusions: Some radiomics features are robust to the noise and poor image quality of CBCT images when the imaging protocol is consistent, relative changes in the features are used, and patients are limited to those with less than 1 cm of motion.« less

The nature-disorder paradox: A perceptual study on how nature is disorderly yet aesthetically preferred.

PubMed

Kotabe, Hiroki P; Kardan, Omid; Berman, Marc G

2017-08-01

Natural environments have powerful aesthetic appeal linked to their capacity for psychological restoration. In contrast, disorderly environments are aesthetically aversive, and have various detrimental psychological effects. But in our research, we have repeatedly found that natural environments are perceptually disorderly. What could explain this paradox? We present 3 competing hypotheses: the aesthetic preference for naturalness is more powerful than the aesthetic aversion to disorder (the nature-trumps-disorder hypothesis ); disorder is trivial to aesthetic preference in natural contexts (the harmless-disorder hypothesis ); and disorder is aesthetically preferred in natural contexts (the beneficial-disorder hypothesis ). Utilizing novel methods of perceptual study and diverse stimuli, we rule in the nature-trumps-disorder hypothesis and rule out the harmless-disorder and beneficial-disorder hypotheses. In examining perceptual mechanisms, we find evidence that high-level scene semantics are both necessary and sufficient for the nature-trumps-disorder effect. Necessity is evidenced by the effect disappearing in experiments utilizing only low-level visual stimuli (i.e., where scene semantics have been removed) and experiments utilizing a rapid-scene-presentation procedure that obscures scene semantics. Sufficiency is evidenced by the effect reappearing in experiments utilizing noun stimuli which remove low-level visual features. Furthermore, we present evidence that the interaction of scene semantics with low-level visual features amplifies the nature-trumps-disorder effect-the effect is weaker both when statistically adjusting for quantified low-level visual features and when using noun stimuli which remove low-level visual features. These results have implications for psychological theories bearing on the joint influence of low- and high-level perceptual inputs on affect and cognition, as well as for aesthetic design. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Cross-Category Adaptation: Objects Produce Gender Adaptation in the Perception of Faces

PubMed Central

Javadi, Amir Homayoun; Wee, Natalie

2012-01-01

Adaptation aftereffects have been found for low-level visual features such as colour, motion and shape perception, as well as higher-level features such as gender, race and identity in domains such as faces and biological motion. It is not yet clear if adaptation effects in humans extend beyond this set of higher order features. The aim of this study was to investigate whether objects highly associated with one gender, e.g. high heels for females or electric shavers for males can modulate gender perception of a face. In two separate experiments, we adapted subjects to a series of objects highly associated with one gender and subsequently asked participants to judge the gender of an ambiguous face. Results showed that participants are more likely to perceive an ambiguous face as male after being exposed to objects highly associated to females and vice versa. A gender adaptation aftereffect was obtained despite the adaptor and test stimuli being from different global categories (objects and faces respectively). These findings show that our perception of gender from faces is highly affected by our environment and recent experience. This suggests two possible mechanisms: (a) that perception of the gender associated with an object shares at least some brain areas with those responsible for gender perception of faces and (b) adaptation to gender, which is a high-level concept, can modulate brain areas that are involved in facial gender perception through top-down processes. PMID:23049942

High spectral resolution lidar based on quad mach zehnder interferometer for aerosols and wind measurements on board space missions

NASA Astrophysics Data System (ADS)

Mariscal, Jean-François; Bruneau, Didier; Pelon, Jacques; Van Haecke, Mathilde; Blouzon, Frédéric; Montmessin, Franck; Chepfer, Hélène

2018-04-01

We present the measurement principle and the optical design of a Quad Mach Zehnder (QMZ) interferometer as HSRL technique, allowing simultaneous measurements of particle backscattering and wind velocity. Key features of this concept is to operate with a multimodal laser and do not require any frequency stabilization. These features are relevant especially for space applications for which high technical readiness level is required.

Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Liu, Yuan; Liang, Fuxun; Wang, Yongjun

2017-04-01

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects.

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System

PubMed Central

Kwon, Sungjun; Lee, Dongseok; Kim, Jeehoon; Lee, Youngki; Kang, Seungwoo; Seo, Sangwon; Park, Kwangsuk

2016-01-01

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user’s ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user’s high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user’s daily smartphone use. PMID:26978364

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System.

PubMed

Kwon, Sungjun; Lee, Dongseok; Kim, Jeehoon; Lee, Youngki; Kang, Seungwoo; Seo, Sangwon; Park, Kwangsuk

2016-03-11

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user's ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user's high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user's daily smartphone use.

A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

PubMed

Bao, Wei; Yue, Jun; Rao, Yulei

2017-01-01

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

Clinical features of body dysmorphic disorder in adolescents and adults

PubMed Central

Phillips, Katharine A.; Didie, Elizabeth R.; Menard, William; Pagano, Maria E.; Fay, Christina; Weisberg, Risa B.

2006-01-01

Body dysmorphic disorder (BDD) usually begins during adolescence, but its clinical features have received little investigation in this age group. Two hundred individuals with BDD (36 adolescents; 164 adults) completed interviewer-administered and self-report measures. Adolescents were preoccupied with numerous aspects of their appearance, most often their skin, hair, and stomach. Among the adolescents, 94.3% reported moderate, severe, or extreme distress due to BDD, 80.6% had a history of suicidal ideation, and 44.4% had attempted suicide. Adolescents experienced high rates and levels of impairment in school, work, and other aspects of psychosocial functioning. Adolescents and adults were comparable on most variables, although adolescents had significantly more delusional BDD beliefs and a higher lifetime rate of suicide attempts. Thus, adolescents with BDD have high levels of distress and rates of functional impairment, suicidal ideation, and suicide attempts. BDD’s clinical features in adolescents appear largely similar to those in adults. PMID:16499973

High-Level Data-Abstraction System

NASA Technical Reports Server (NTRS)

Fishwick, P. A.

1986-01-01

Communication with data-base processor flexible and efficient. High Level Data Abstraction (HILDA) system is three-layer system supporting data-abstraction features of Intel data-base processor (DBP). Purpose of HILDA establishment of flexible method of efficiently communicating with DBP. Power of HILDA lies in its extensibility with regard to syntax and semantic changes. HILDA's high-level query language readily modified. Offers powerful potential to computer sites where DBP attached to DEC VAX-series computer. HILDA system written in Pascal and FORTRAN 77 for interactive execution.

SU-F-R-31: Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease

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

Choi, W; Riyahi, S; Lu, W

Purpose: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation fibrosis). For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume. Methods: The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres placed at the upper lobe and lower lobe respectively in the normal lung (contralateral to tumor). 27 texture features (9 from intensity histogram, 8 from grey-level co-occurrence matrix [GLCM] and 10 from grey-level run-length matrix [GLRM])more » were extracted from [normal lung-GTV]. To measure the variability of a feature F, the relative difference D=|Fref -Fsim|/Fref*100% was calculated, where Fref was for the entire normal lung and Fsim was for [normal lung-GTV]. A feature was considered as robust if the largest non-outlier (Q3+1.5*IQR) D was less than 5%, and considered as not correlated with normal lung volume when their Pearson correlation was lower than 0.50. Results: Only 11 features were robust. All first-order intensity-histogram features (mean, max, etc.) were robust, while most higher-order features (skewness, kurtosis, etc.) were unrobust. Only two of the GLCM and four of the GLRM features were robust. Larger GTV resulted greater feature variation, this was particularly true for unrobust features. All robust features were not correlated with normal lung volume while three unrobust features showed high correlation. Excessive variations were observed in two low grey-level run features and were later identified to be from one patient with local lung diseases (atelectasis) in the normal lung. There was no dependence on GTV location. Conclusion: We identified 11 robust normal lung CT texture features that can be further examined for the prediction of radiation-induced lung disease. Interestingly, low grey-level run features identified normal lung diseases. This work was supported in part by the National Cancer Institute Grants R01CA172638.« less

Resonant-phonon-assisted THz quantum cascade lasers with metal-metal waveguides.

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

Callebaut, Hans; Kohen, Stephen; Kumar, Sushil

2004-06-01

We report our development of terahertz (THz) quantum-cascade lasers (QCLs) based on two novel features. First, the depopulation of the lower radiative level is achieved through resonant longitudinal optical (LO-)phonon scattering. This depopulation mechanism is robust at high temperatures and high injection levels. In contrast to infrared QCLs that also use LO-phonon scattering for depopulation, in our THz lasers the selectivity of the depopulation scattering is achieved through a combination of resonant tunneling and LO-phonon scattering, hence the term resonant phonon. This resonant-phonon scheme allows a highly selective depopulation of the lower radiative level with a sub-picosecond lifetime, while maintainingmore » a relatively long upper level lifetime (>5 ps) that is due to upper-to-ground-state scattering. The second feature of our lasers is that mode confinement is achieved by using a novel double-sided metal-metal waveguide, which yields an essentially unity mode confinement factor and therefore a low total cavity loss at THz frequencies. Based on these two unique features, we have achieved some record performance, including, but not limited to, the highest pulsed operating temperature of 137 K, the highest continuous-wave operating temperature of 97 K, and the longest wavelength of 141 {micro}m (corresponding to 2.1 THz) without the assistance of a magnetic field.« less

Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream

PubMed Central

Egner, Tobias; Monti, Jim M.; Summerfield, Christopher

2014-01-01

Visual cortex is traditionally viewed as a hierarchy of neural feature detectors, with neural population responses being driven by bottom-up stimulus features. Conversely, “predictive coding” models propose that each stage of the visual hierarchy harbors two computationally distinct classes of processing unit: representational units that encode the conditional probability of a stimulus and provide predictions to the next lower level; and error units that encode the mismatch between predictions and bottom-up evidence, and forward prediction error to the next higher level. Predictive coding therefore suggests that neural population responses in category-selective visual regions, like the fusiform face area (FFA), reflect a summation of activity related to prediction (“face expectation”) and prediction error (“face surprise”), rather than a homogenous feature detection response. We tested the rival hypotheses of the feature detection and predictive coding models by collecting functional magnetic resonance imaging data from the FFA while independently varying both stimulus features (faces vs houses) and subjects’ perceptual expectations regarding those features (low vs medium vs high face expectation). The effects of stimulus and expectation factors interacted, whereby FFA activity elicited by face and house stimuli was indistinguishable under high face expectation and maximally differentiated under low face expectation. Using computational modeling, we show that these data can be explained by predictive coding but not by feature detection models, even when the latter are augmented with attentional mechanisms. Thus, population responses in the ventral visual stream appear to be determined by feature expectation and surprise rather than by stimulus features per se. PMID:21147999

Real-time UNIX in HEP data acquisition

NASA Astrophysics Data System (ADS)

Buono, S.; Gaponenko, I.; Jones, R.; Mapelli, L.; Mornacchi, G.; Prigent, D.; Sanchez-Corral, E.; Skiadelli, M.; Toppers, A.; Duval, P. Y.; Ferrato, D.; Le Van Suu, A.; Qian, Z.; Rondot, C.; Ambrosini, G.; Fumagalli, G.; Aguer, M.; Huet, M.

1994-12-01

Today's experimentation in high energy physics is characterized by an increasing need for sensitivity to rare phenomena and complex physics signatures, which require the use of huge and sophisticated detectors and consequently a high performance readout and data acquisition. Multi-level triggering, hierarchical data collection and an always increasing amount of processing power, distributed throughout the data acquisition layers, will impose a number of features on the software environment, especially the need for a high level of standardization. Real-time UNIX seems, today, the best solution for the platform independence, operating system interface standards and real-time features necessary for data acquisition in HEP experiments. We present the results of the evaluation, in a realistic application environment, of a Real-Time UNIX operating system: the EP/LX real-time UNIX system.

Clinical features and growth hormone receptor gene mutations of patients with Laron syndrome from a Chinese family.

PubMed

Ying, Yan-Qin; Wei, Hong; Cao, Li-Zhi; Lu, Juan-Juan; Luo, Xiao-Ping

2007-08-01

Laron syndrome is an autosomal recessive disorder caused by defects of growth hormone receptor (GHR) gene. It is characterized by severe postnatal growth retardation and characteristic facial features as well as high circulating levels of growth hormone (GH) and low levels of insulin-like growth factor I (IGF-I) and insulin-like growth factor binding protein-3 (IGFBP-3). This report described the clinical features and GHR gene mutations in 2 siblings with Laron syndrome in a Chinese family. Their heights and weights were in the normal range at birth, but the growth was retarded after birth. When they presented to the clinic, the heights of the boy (8 years old) and his sister (11 years old) were 80.0 cm (-8.2 SDS) and 96.6 cm (-6.8 SDS) respectively. They had typical appearance features of Laron syndrome such as short stature and obesity, with protruding forehead, saddle nose, large eyes, sparse and thin silky hair and high-pitched voice. They had higher basal serum GH levels and lower serum levels of IGF-I, IGFBP-3 and growth hormone binding protein (GHBP) than normal controls. The peak serum GH level after colonidine and insulin stimulations in the boy was over 350 ng/mL. After one-year rhGH treatment, the boy's height increased from 80.0 cm to 83.3 cm. The gene mutation analysis revealed that two patients had same homozygous mutation of S65H (TCA -->CCA) in exon 4, which is a novel gene mutation. It was concluded that a definite diagnosis of Laron syndrome can be made based on characteristic appearance features and serum levels of GH, IGF-I, IGFBP-3 and GHBP. The S65H mutation might be the cause of Laron syndrome in the two patients.

SU-F-R-36: Validating Quantitative Radiomic Texture Features for Oncologic PET: A Digital Phantom Study

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

Yang, F; Yang, Y; Young, L

Purpose: Radiomic texture features derived from the oncologic PET have recently been brought under intense investigation within the context of patient stratification and treatment outcome prediction in a variety of cancer types; however, their validity has not yet been examined. This work is aimed to validate radiomic PET texture metrics through the use of realistic simulations in the ground truth setting. Methods: Simulation of FDG-PET was conducted by applying the Zubal phantom as an attenuation map to the SimSET software package that employs Monte Carlo techniques to model the physical process of emission imaging. A total of 15 irregularly-shaped lesionsmore » featuring heterogeneous activity distribution were simulated. For each simulated lesion, 28 texture features in relation to the intensity histograms (GLIH), grey-level co-occurrence matrices (GLCOM), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated and compared with their respective values extracted from the ground truth activity map. Results: In reference to the values from the ground truth images, texture parameters appearing on the simulated data varied with a range of 0.73–3026.2% for GLIH-based, 0.02–100.1% for GLCOM-based, 1.11–173.8% for GLNDM-based, and 0.35–66.3% for GLZSM-based. For majority of the examined texture metrics (16/28), their values on the simulated data differed significantly from those from the ground truth images (P-value ranges from <0.0001 to 0.04). Features not exhibiting significant difference comprised of GLIH-based standard deviation, GLCO-based energy and entropy, GLND-based coarseness and contrast, and GLZS-based low gray-level zone emphasis, high gray-level zone emphasis, short zone low gray-level emphasis, long zone low gray-level emphasis, long zone high gray-level emphasis, and zone size nonuniformity. Conclusion: The extent to which PET imaging disturbs texture appearance is feature-dependent and could be substantial. It is thus advised that use of PET texture parameters for predictive and prognostic measurements in oncologic setting awaits further systematic and critical evaluation.« less

Comparison of the Cartoons Created by the Gifted and Non-Gifted Students

ERIC Educational Resources Information Center

Kurnaz, Ahmet; Genç, Mehmet Ali

2017-01-01

When compared to their non-gifted peers, gifted students who have high-level talent, motivation and creativity are significantly different from other students in many respects. In addition to their distinct mental, physical and educational features, developed sense of humor is another distinct feature of these students. Also, currently no…

Repetitive Behaviors in Autism: Relationships with Associated Clinical Features

ERIC Educational Resources Information Center

Gabriels, Robin L.; Cuccaro, Michael L.; Hill, Dina E.; Ivers, Bonnie J.; Goldson, Edward

2005-01-01

Relationships between repetitive behaviors (RBs) and associated clinical features (i.e., cognitive and adaptive functioning levels, sleep problems, medication use, and other behavioral problems) were examined in two groups (High nonverbal IQ greater than or equal to 97 versus Low nonverbal IQ less than or equal to 56) of children with autism…

Why Is Parkinsonism Not a Feature of Human Methamphetamine Users?

ERIC Educational Resources Information Center

Moszczynska, Anna; Fitzmaurice, Paul; Ang, Lee; Kalasinsky, Kathryn S.; Schmunk, Gregory A.; Peretti, Frank J.; Aiken, Sally S.; Wickham, Dennis J.; Kish, Stephen J.

2004-01-01

For more than 50 years, methamphetamine has been a widely used stimulant drug taken to maintain wakefulness and performance and, in high doses, to cause intense euphoria. Animal studies show that methamphetamine can cause short-term and even persistent depletion of brain levels of the neurotransmitter dopamine. However, the clinical features of…

The Next Generation Science Standards and the Life Sciences

ERIC Educational Resources Information Center

Bybee, Rodger W.

2013-01-01

Using the life sciences, this article first reviews essential features of the "NRC Framework for K-12 Science Education" that provided a foundation for the new standards. Second, the article describes the important features of life science standards for elementary, middle, and high school levels. Special attention is paid to the teaching…

Geographical topic learning for social images with a deep neural network

NASA Astrophysics Data System (ADS)

Feng, Jiangfan; Xu, Xin

2017-03-01

The use of geographical tagging in social-media images is becoming a part of image metadata and a great interest for geographical information science. It is well recognized that geographical topic learning is crucial for geographical annotation. Existing methods usually exploit geographical characteristics using image preprocessing, pixel-based classification, and feature recognition. How to effectively exploit the high-level semantic feature and underlying correlation among different types of contents is a crucial task for geographical topic learning. Deep learning (DL) has recently demonstrated robust capabilities for image tagging and has been introduced into geoscience. It extracts high-level features computed from a whole image component, where the cluttered background may dominate spatial features in the deep representation. Therefore, a method of spatial-attentional DL for geographical topic learning is provided and we can regard it as a special case of DL combined with various deep networks and tuning tricks. Results demonstrated that the method is discriminative for different types of geographical topic learning. In addition, it outperforms other sequential processing models in a tagging task for a geographical image dataset.

TOMML: A Rule Language for Structured Data

NASA Astrophysics Data System (ADS)

Cirstea, Horatiu; Moreau, Pierre-Etienne; Reilles, Antoine

We present the TOM language that extends JAVA with the purpose of providing high level constructs inspired by the rewriting community. TOM bridges thus the gap between a general purpose language and high level specifications based on rewriting. This approach was motivated by the promotion of rule based techniques and their integration in large scale applications. Powerful matching capabilities along with a rich strategy language are among TOM's strong features that make it easy to use and competitive with respect to other rule based languages. TOM is thus a natural choice for querying and transforming structured data and in particular XML documents [1]. We present here its main XML oriented features and illustrate its use on several examples.

Self-esteem instability and personality: the connections between feelings of self-worth and the big five dimensions of personality.

PubMed

Zeigler-Hill, Virgil; Holden, Christopher J; Enjaian, Brian; Southard, Ashton C; Besser, Avi; Li, Haijiang; Zhang, Qinglin

2015-02-01

Relatively few studies have focused on the connections between self-esteem and basic personality dimensions. The purpose of the present studies was to examine whether self-esteem level and self-esteem instability were associated with the Big Five personality dimensions and whether self-esteem instability moderated the associations that self-esteem level had with these personality features. This was accomplished by conducting a series of studies that included samples from the United States, Israel, and China. Across these studies, self-esteem level was associated with high levels of extraversion, emotional stability, agreeableness, conscientiousness, and openness, whereas self-esteem instability was associated with low levels of emotional stability, agreeableness, and conscientiousness. Individuals with stable high self-esteem reported the highest levels of emotional stability, agreeableness, and conscientiousness, whereas those with stable low self-esteem had the lowest levels of openness. The results of these studies suggest that feelings of self-worth are associated with self-reported and perceived personality features. © 2014 by the Society for Personality and Social Psychology, Inc.

Measurement of inflammatory cytokines and thrombomodulin in chronic subdural hematoma.

PubMed

Kitazono, Masatoshi; Yokota, Hiroyuki; Satoh, Hidetaka; Onda, Hidetaka; Matsumoto, Gaku; Fuse, Akira; Teramoto, Akira

2012-01-01

Inflammation and the coagulation system may influence the genesis of chronic subdural hematoma (CSDH). The appearance of CSDH on computed tomography (CT) varies with the stage of the hematoma. This study investigated the pathogenesis and the recurrence of CSDH by comparing cytokine levels with the CT features of CSDH in 26 patients with 34 CSDHs who underwent single burr-hole surgery at our hospital between October 2004 and November 2006. The hematoma components removed during the procedure were examined, and the hematoma serum levels of cytokines measured such as thrombomodulin (TM), interleukin-6 (IL-6), tumor necrosis factor-α (TNFα), and interleukin-10 (IL-10). Using CT, mixed density hematomas were distinguished from other homogeneous hematomas, and found that the TM level was significantly higher in mixed density hematomas than in homogeneous hematomas (p = 0.043). Mixed density hematomas were classified into three subtypes (laminar, separated, and trabecular hematomas). The TM level was significantly higher in laminar and separated hematomas than in other hematomas (p = 0.01). The levels of IL-6, TNFα, and IL-10 were extremely high, but showed no significant differences in relation to the CT features. Mixed density hematomas had high recurrence rate, as reported previously, and TM level was high in mixed density hematomas such as laminar and separated mixed density hematomas. The present findings suggest that the types of CSDH associated with high TM levels tend to have higher recurrence rate.

In vivo placental MRI shape and textural features predict fetal growth restriction and postnatal outcome.

PubMed

Dahdouh, Sonia; Andescavage, Nickie; Yewale, Sayali; Yarish, Alexa; Lanham, Diane; Bulas, Dorothy; du Plessis, Adre J; Limperopoulos, Catherine

2018-02-01

To investigate the ability of three-dimensional (3D) MRI placental shape and textural features to predict fetal growth restriction (FGR) and birth weight (BW) for both healthy and FGR fetuses. We recruited two groups of pregnant volunteers between 18 and 39 weeks of gestation; 46 healthy subjects and 34 FGR. Both groups underwent fetal MR imaging on a 1.5 Tesla GE scanner using an eight-channel receiver coil. We acquired T2-weighted images on either the coronal or the axial plane to obtain MR volumes with a slice thickness of either 4 or 8 mm covering the full placenta. Placental shape features (volume, thickness, elongation) were combined with textural features; first order textural features (mean, variance, kurtosis, and skewness of placental gray levels), as well as, textural features computed on the gray level co-occurrence and run-length matrices characterizing placental homogeneity, symmetry, and coarseness. The features were used in two machine learning frameworks to predict FGR and BW. The proposed machine-learning based method using shape and textural features identified FGR pregnancies with 86% accuracy, 77% precision and 86% recall. BW estimations were 0.3 ± 13.4% (mean percentage error ± standard error) for healthy fetuses and -2.6 ± 15.9% for FGR. The proposed FGR identification and BW estimation methods using in utero placental shape and textural features computed on 3D MR images demonstrated high accuracy in our healthy and high-risk cohorts. Future studies to assess the evolution of each feature with regard to placental development are currently underway. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:449-458. © 2017 International Society for Magnetic Resonance in Medicine.

Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis

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

Volkova, Svitlana; Bell, Eric B.

Social networks are dynamically changing over time e.g., some accounts are being created and some are being deleted or become private. This ephemerality at both an account level and content level results from a combination of privacy concerns, spam, and deceptive behaviors. In this study we analyze a large dataset of 180,340 accounts active during the Russian-Ukrainian crisis to discover a series of predictive features for the removal or shutdown of a suspicious account. We find that unlike previously reported profile and net- work features, lexical features form the basis for highly accurate prediction of the deletion of an account.

Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

NASA Astrophysics Data System (ADS)

Fan, Lei

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems.

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

[Clinical features of Enterococcus faecium meningitis in children].

PubMed

Wang, Li-Yuan; Cai, Xiao-Tang; Wang, Zhi-Ling; Liu, Shun-Li; Xie, Yong-Mei; Zhou, Hui

2018-03-01

To summarize the clinical features of Enterococcus faecium meningitis in children. The clinical data of nine children with Enterococcus faecium meningitis were analyzed. In all the nine children, Enterococcus faecium was isolated from blood, cerebrospinal fluid, or peripherally inserted central catheters; 6 (67%) patients were neonates, 2 (22%) patients were younger than 6 months, and 1 (11%) patient was three years and four months of age. In those patients, 56% had high-risk factors before onset, which included intestinal infection, resettlement of drainage tube after surgery for hydrocephalus, skull fracture, perinatal maternal infection history, and catheter-related infection. The main symptoms were fever and poor response. In those patients, 22% had seizures; no child had meningeal irritation sign or disturbance of consciousness. The white blood cell count and level of C-reactive protein were normal or increased; the nucleated cell count in cerebrospinal fluid was normal or mildly elevated; the protein level was substantially elevated; the glucose level was decreased. The drug sensitivity test showed that bacteria were all sensitive to vancomycin and the vancomycin treatment was effective. Only one child had the complication of hydrocephalus. Enterococcus faecium meningitis occurs mainly in neonates and infants. The patients have atypical clinical features. A high proportion of patients with Enterococcus faecium meningitis have high-risk factors. Enterococcus faecium is sensitive to vancomycin.

Automatic rule generation for high-level vision

NASA Technical Reports Server (NTRS)

Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

1992-01-01

Many high-level vision systems use rule-based approaches to solving problems such as autonomous navigation and image understanding. The rules are usually elaborated by experts. However, this procedure may be rather tedious. In this paper, we propose a method to generate such rules automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Association between background parenchymal enhancement of breast MRI and BIRADS rating change in the subsequent screening

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

2018-03-01

Although breast magnetic resonance imaging (MRI) has been used as a breast cancer screening modality for high-risk women, its cancer detection yield remains low (i.e., <= 3%). Thus, increasing breast MRI screening efficacy and cancer detection yield is an important clinical issue in breast cancer screening. In this study, we investigated association between the background parenchymal enhancement (BPE) of breast MRI and the change of diagnostic (BIRADS) status in the next subsequent breast MRI screening. A dataset with 65 breast MRI screening cases was retrospectively assembled. All cases were rated BIRADS-2 (benign findings). In the subsequent screening, 4 cases were malignant (BIRADS-6), 48 remained BIRADS-2 and 13 were downgraded to negative (BIRADS-1). A computer-aided detection scheme was applied to process images of the first set of breast MRI screening. Total of 33 features were computed including texture feature and global BPE features. Texture features were computed from either a gray-level co-occurrence matrix or a gray level run length matrix. Ten global BPE features were also initially computed from two breast regions and bilateral difference between the left and right breasts. Box-plot based analysis shows positive association between texture features and BIRADS rating levels in the second screening. Furthermore, a logistic regression model was built using optimal features selected by a CFS based feature selection method. Using a leave-one-case-out based cross-validation method, classification yielded an overall 75% accuracy in predicting the improvement (or downgrade) of diagnostic status (to BIRAD-1) in the subsequent breast MRI screening. This study demonstrated potential of developing a new quantitative imaging marker to predict diagnostic status change in the short-term, which may help eliminate a high fraction of unnecessary repeated breast MRI screenings and increase the cancer detection yield.

A quick response four decade logarithmic high-voltage stepping supply

NASA Technical Reports Server (NTRS)

Doong, H.

1978-01-01

An improved high-voltage stepping supply, for space instrumentation is described where low power consumption and fast settling time between steps are required. The high-voltage stepping supply, utilizing an average power of 750 milliwatts, delivers a pair of mirror images with 64 level logarithmic outputs. It covers a four decade range of + or - 2500 to + or - 0.29 volts having an output stability of + or - 0.5 percent or + or - 20 millivolts for all line load and temperature variations. The supply provides a typical step setting time of 1 millisecond with 100 microseconds for the lower two decades. The versatile design features of the high-voltage stepping supply provides a quick response staircase generator as described or a fixed voltage with the option to change levels as required over large dynamic ranges without circuit modifications. The concept can be implemented up to + or - 5000 volts. With these design features, the high-voltage stepping supply should find numerous applications where charged particle detection, electro-optical systems, and high voltage scientific instruments are used.

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

DOE PAGES

Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; ...

2017-12-20

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differentialmore » equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.« less

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

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

Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differentialmore » equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.« less

Cognitive architecture of perceptual organization: from neurons to gnosons.

PubMed

van der Helm, Peter A

2012-02-01

What, if anything, is cognitive architecture and how is it implemented in neural architecture? Focusing on perceptual organization, this question is addressed by way of a pluralist approach which, supported by metatheoretical considerations, combines complementary insights from representational, connectionist, and dynamic systems approaches to cognition. This pluralist approach starts from a representationally inspired model which implements the intertwined but functionally distinguishable subprocesses of feedforward feature encoding, horizontal feature binding, and recurrent feature selection. As sustained by a review of neuroscientific evidence, these are the subprocesses that are believed to take place in the visual hierarchy in the brain. Furthermore, the model employs a special form of processing, called transparallel processing, whose neural signature is proposed to be gamma-band synchronization in transient horizontal neural assemblies. In neuroscience, such assemblies are believed to mediate binding of similar features. Their formal counterparts in the model are special input-dependent distributed representations, called hyperstrings, which allow many similar features to be processed in a transparallel fashion, that is, simultaneously as if only one feature were concerned. This form of processing does justice to both the high combinatorial capacity and the high speed of the perceptual organization process. A naturally following proposal is that those temporarily synchronized neural assemblies are "gnosons", that is, constituents of flexible self-organizing cognitive architecture in between the relatively rigid level of neurons and the still elusive level of consciousness.

Red spinach (Amaranthus tricolor L.) ethanolic extract as prevention against atherosclerosis based on the level of Low-Density Lipoprotein and histopathological feature of aorta in male Sprague-Dawley rats

NASA Astrophysics Data System (ADS)

Pradana, Dimas Adhi; Pondawinata, Marizki; Widyarini, Sitarina

2017-03-01

This study aimed to determine the potential activity of standardized ethanolic extract of red spinach as prevention against atherosclerosis based on the level of Low-Density Lipoprotein (LDL) and histopathological feature of aorta in male Sprague-Dawley rats induced by high-fat, high-cholesterol diet. A total of 42 animals was divided into 6 groups: normal control group, negative control group, positive control group (0.9 mg/kgBW of simvastatin), first intervention group (200 mg/kgBW of red spinach extract), second intervention group (400 mg/kgBW of red spinach extract), and third intervention group (800 mg/kgBW of red spinach extract). From the first day up to the 66th day, all the groups, except the normal control group and negative control group, were administered simvastatin (positive control) and extract of amaranth (intervention). Then, from the eighth day until Day 66, induction of high-fat and high-cholesterol diet was given in two hours after the simvastatin and red spinach extract administration. The determination of LDL parameters was conducted on Day 0, Day 35, and Day 67. On the 67th day, the animals were dissected to examine the aortic histopathological parameters. The results showed that the ethanolic extract of red spinach with a dose of 200 mg/kgBW, 400 mg/kgBW, and 800 mg/kgBW statistically demonstrated a significant difference (p<0.05). The histopathological feature of the aorta in the treatment indicated the absence of fat in the blood vessel walls or even of foam cells supporting thereby the result of LDL level. This means there was a significant effect of ethanolic extract of red spinach on the prevention against atherosclerosis based on the level of Low-Density Lipoprotein and the histopathological feature of aorta in male Sprague-Dawley rats.

Forest Stand Segmentation Using Airborne LIDAR Data and Very High Resolution Multispectral Imagery

NASA Astrophysics Data System (ADS)

Dechesne, Clément; Mallet, Clément; Le Bris, Arnaud; Gouet, Valérie; Hervieu, Alexandre

2016-06-01

Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., ≥ 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Borderline Personality Features and Implicit Shame-Prone Self-Concept in Middle Childhood and Early Adolescence

ERIC Educational Resources Information Center

Hawes, David J.; Helyer, Rebekah; Herlianto, Eugene C.; Willing, Jonah

2013-01-01

This study tested if children and adolescents with high levels of borderline personality features (BPF) exhibit the same shame-prone self-concept previously found to characterize adults with borderline personality disorder (Rusch et al., 2007). Self-concept was indexed using the Implicit Association Test, in a community sample of…

Dialogic and Hortatory Features in the Writing of Chinese Candidates for the IELTS Test

ERIC Educational Resources Information Center

Mayor, Barbara M.

2006-01-01

Research conducted in the context of the IELTS Research Program indicates that there are recurrent features in the writing under test conditions of candidates from Chinese language backgrounds, particularly in terms of interpersonal tenor. These include a high level of interpersonal reference, combined with a heavily dialogic and hortatory style.…

Maritime English Vocabulary in Feature Films: "The Perfect Storm" (2000) and "Master and Commander" (2003)

ERIC Educational Resources Information Center

Jurkovic, Violeta

2016-01-01

The teaching content of Maritime English is dictated by the 1995 International Convention on Standards of Training, Certification, and Watchkeeping, as amended, which sets qualification standards for masters, officers, and officers of the watch on merchant ships, including a high proficiency level in maritime English. Feature films have an…

categoryCompare, an analytical tool based on feature annotations

PubMed Central

Flight, Robert M.; Harrison, Benjamin J.; Mohammad, Fahim; Bunge, Mary B.; Moon, Lawrence D. F.; Petruska, Jeffrey C.; Rouchka, Eric C.

2014-01-01

Assessment of high-throughput—omics data initially focuses on relative or raw levels of a particular feature, such as an expression value for a transcript, protein, or metabolite. At a second level, analyses of annotations including known or predicted functions and associations of each individual feature, attempt to distill biological context. Most currently available comparative- and meta-analyses methods are dependent on the availability of identical features across data sets, and concentrate on determining features that are differentially expressed across experiments, some of which may be considered “biomarkers.” The heterogeneity of measurement platforms and inherent variability of biological systems confounds the search for robust biomarkers indicative of a particular condition. In many instances, however, multiple data sets show involvement of common biological processes or signaling pathways, even though individual features are not commonly measured or differentially expressed between them. We developed a methodology, categoryCompare, for cross-platform and cross-sample comparison of high-throughput data at the annotation level. We assessed the utility of the approach using hypothetical data, as well as determining similarities and differences in the set of processes in two instances: (1) denervated skin vs. denervated muscle, and (2) colon from Crohn's disease vs. colon from ulcerative colitis (UC). The hypothetical data showed that in many cases comparing annotations gave superior results to comparing only at the gene level. Improved analytical results depended as well on the number of genes included in the annotation term, the amount of noise in relation to the number of genes expressing in unenriched annotation categories, and the specific method in which samples are combined. In the skin vs. muscle denervation comparison, the tissues demonstrated markedly different responses. The Crohn's vs. UC comparison showed gross similarities in inflammatory response in the two diseases, with particular processes specific to each disease. PMID:24808906

Investigating the role of executive attentional control to self-harm in a non-clinical cohort with borderline personality features

PubMed Central

Drabble, Jennifer; Bowles, David P.; Barker, Lynne Ann

2014-01-01

Self-injurious behavior (or self-harm) is a frequently reported maladaptive behavior in the general population and a key feature of borderline personality disorder (BPD). Poor affect regulation is strongly linked to a propensity to self-harm, is a core component of BPD, and is linked with reduced attentional control abilities. The idea that attentional control difficulties may provide a link between BPD, negative affect and self-harm has yet to be established, however. The present study explored the putative relationship between levels of BPD features, three aspects of attentional/executive control, affect, and self-harm history in a sample of 340 non-clinical participants recruited online from self-harm forums and social networking sites. Analyses showed that self-reported levels of BPD features and attentional focusing predicted self-harm incidence, and high attentional focusing increased the likelihood of a prior self-harm history in those with high BPD features. Ability to shift attention was associated with a reduced likelihood of self-harm, suggesting that good attentional switching ability may provide a protective buffer against self-harm behavior for some individuals. These attentional control differences mediated the association between negative affect and self-harm, but the relationship between BPD and self-harm appears independent. PMID:25191235

High-Precision Image Aided Inertial Navigation with Known Features: Observability Analysis and Performance Evaluation

PubMed Central

Jiang, Weiping; Wang, Li; Niu, Xiaoji; Zhang, Quan; Zhang, Hui; Tang, Min; Hu, Xiangyun

2014-01-01

A high-precision image-aided inertial navigation system (INS) is proposed as an alternative to the carrier-phase-based differential Global Navigation Satellite Systems (CDGNSSs) when satellite-based navigation systems are unavailable. In this paper, the image/INS integrated algorithm is modeled by a tightly-coupled iterative extended Kalman filter (IEKF). Tightly-coupled integration ensures that the integrated system is reliable, even if few known feature points (i.e., less than three) are observed in the images. A new global observability analysis of this tightly-coupled integration is presented to guarantee that the system is observable under the necessary conditions. The analysis conclusions were verified by simulations and field tests. The field tests also indicate that high-precision position (centimeter-level) and attitude (half-degree-level)-integrated solutions can be achieved in a global reference. PMID:25330046

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

Single-trial laser-evoked potentials feature extraction for prediction of pain perception.

PubMed

Huang, Gan; Xiao, Ping; Hu, Li; Hung, Yeung Sam; Zhang, Zhiguo

2013-01-01

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.

Category inference as a function of correlational structure, category discriminability, and number of available cues.

PubMed

Lancaster, Matthew E; Shelhamer, Ryan; Homa, Donald

2013-04-01

Two experiments investigated category inference when categories were composed of correlated or uncorrelated dimensions and the categories overlapped minimally or moderately. When the categories minimally overlapped, the dimensions were strongly correlated with the category label. Following a classification learning phase, subsequent transfer required the selection of either a category label or a feature when one, two, or three features were missing. Experiments 1 and 2 differed primarily in the number of learning blocks prior to transfer. In each experiment, the inference of the category label or category feature was influenced by both dimensional and category correlations, as well as their interaction. The number of cues available at test impacted performance more when the dimensional correlations were zero and category overlap was high. However, a minimal number of cues were sufficient to produce high levels of inference when the dimensions were highly correlated; additional cues had a positive but reduced impact, even when overlap was high. Subjects were generally more accurate in inferring the category label than a category feature regardless of dimensional correlation, category overlap, or number of cues available at test. Whether the category label functioned as a special feature or not was critically dependent upon these embedded correlations, with feature inference driven more strongly by dimensional correlations.

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

PubMed

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

2017-06-09

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

Convolutional neural network features based change detection in satellite images

NASA Astrophysics Data System (ADS)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Health System Features That Enhance Access to Comprehensive Primary Care for Women Living with HIV in High-Income Settings: A Systematic Mixed Studies Review.

PubMed

O'Brien, Nadia; Hong, Quan Nha; Law, Susan; Massoud, Sarah; Carter, Allison; Kaida, Angela; Loutfy, Mona; Cox, Joseph; Andersson, Neil; de Pokomandy, Alexandra

2018-04-01

Women living with HIV in high-income settings continue to experience modifiable barriers to care. We sought to determine the features of care that facilitate access to comprehensive primary care, inclusive of HIV, comorbidity, and sexual and reproductive healthcare. Using a systematic mixed studies review design, we reviewed qualitative, mixed methods, and quantitative studies identified in Ovid MEDLINE, EMBASE, and CINAHL databases (January 2000 to August 2017). Eligibility criteria included women living with HIV; high-income countries; primary care; and healthcare accessibility. We performed a thematic synthesis using NVivo. After screening 3466 records, we retained 44 articles and identified 13 themes. Drawing on a social-ecological framework on engagement in HIV care, we situated the themes across three levels of the healthcare system: care providers, clinical care environments, and social and institutional factors. At the care provider level, features enhancing access to comprehensive primary care included positive patient-provider relationships and availability of peer support, case managers, and/or nurse navigators. Within clinical care environments, facilitators to care were appointment reminder systems, nonidentifying clinic signs, women and family spaces, transportation services, and coordination of care to meet women's HIV, comorbidity, and sexual and reproductive healthcare needs. Finally, social and institutional factors included healthcare insurance, patient and physician education, and dispelling HIV-related stigma. This review highlights several features of care that are particularly relevant to the care-seeking experience of women living with HIV. Improving their health through comprehensive care requires a variety of strategies at the provider, clinic, and greater social and institutional levels.

Introducing Graduate Students to High-Resolution Mass Spectrometry (HRMS) Using a Hands-On Approach

ERIC Educational Resources Information Center

Stock, Naomi L.

2017-01-01

High-resolution mass spectrometry (HRMS) features both high resolution and high mass accuracy and is a powerful tool for the analysis and quantitation of compounds, determination of elemental compositions, and identification of unknowns. A hands-on laboratory experiment for upper-level undergraduate and graduate students to investigate HRMS is…

Computer-aided diagnosis for phase-contrast X-ray computed tomography: quantitative characterization of human patellar cartilage with high-dimensional geometric features.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismüller, Axel

2014-02-01

Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2) changes to chondrocyte organization induced by osteoarthritis. This study investigates the use of high-dimensional geometric features in characterizing such chondrocyte patterns in the presence or absence of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and statistical features derived from gray-level co-occurrence matrices were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic curve (AUC). SIM-derived geometrical features exhibited the best classification performance (AUC, 0.95 ± 0.06) and were most robust to changes in ROI size. These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated and non-subjective manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.

Technology and equipment based on induction melters with ``cold'' crucible for reprocessing active metal waste

NASA Astrophysics Data System (ADS)

Pastushkov, V. G.; Molchanov, A. V.; Serebryakov, V. P.; Smelova, T. V.; Shestoperov, I. N.

2000-07-01

The paper discusses specific features of technology, equipment and control of a single stage RAMW decontamination and melting process in an induction furnace equipped with a "cold" crucible. The calculated and experimental data are given on melting high activity level stainless steel and Zr simulating high activity level metal waste. The work is under way in SSC RF VNIINM.

Pyomo v5.0

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

Woodruff, David; Hackebeil, Gabe; Laird, Carl Damon

Pyomo supports the formulation and analysis of mathematical models for complex optimization applications. This capability is commonly associated with algebraic modeling languages (AMLs), which support the description and analysis of mathematical models with a high-level language. Although most AMLs are implemented in custom modeling languages, Pyomo's modeling objects are embedded within Python, a full- featured high-level programming language that contains a rich set of supporting libraries.

A dynamical pattern recognition model of gamma activity in auditory cortex

PubMed Central

Zavaglia, M.; Canolty, R.T.; Schofield, T.M.; Leff, A.P.; Ursino, M.; Knight, R.T.; Penny, W.D.

2012-01-01

This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75–150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain. PMID:22327049

Color image definition evaluation method based on deep learning method

NASA Astrophysics Data System (ADS)

Liu, Di; Li, YingChun

2018-01-01

In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

Classification of ground glass opacity lesion characteristic based on texture feature using lung CT image

NASA Astrophysics Data System (ADS)

Sebatubun, M. M.; Haryawan, C.; Windarta, B.

2018-03-01

Lung cancer causes a high mortality rate in the world than any other cancers. That can be minimised if the symptoms and cancer cells have been detected early. One of the techniques used to detect lung cancer is by computed tomography (CT) scan. CT scan images have been used in this study to identify one of the lesion characteristics named ground glass opacity (GGO). It has been used to determine the level of malignancy of the lesion. There were three phases in identifying GGO: image cropping, feature extraction using grey level co-occurrence matrices (GLCM) and classification using Naïve Bayes Classifier. In order to improve the classification results, the most significant feature was sought by feature selection using gain ratio evaluation. Based on the results obtained, the most significant features could be identified by using feature selection method used in this research. The accuracy rate increased from 83.33% to 91.67%, the sensitivity from 82.35% to 94.11% and the specificity from 84.21% to 89.47%.

Influencing Attitudes Toward Near and Distant Objects

PubMed Central

Fujita, Kentaro; Eyal, Tal; Chaiken, Shelly; Trope, Yaacov; Liberman, Nira

2008-01-01

It is argued that the temporal distance of attitude objects systematically changes how the object is mentally represented, and thus influences the strength of particular persuasive appeals. Three experiments tested the hypothesis that people preferentially attend to arguments that highlight primary, abstract (high-level) vs. incidental, concrete (low-level) features when attitude objects are temporally distant vs. near. Results suggested that when attitude objects are temporally distant vs. near, arguments emphasizing primary vs. secondary features (Study 1), desirability vs. feasibility features (Study 2), and general classes vs. specific cases are more persuasive (Study 3). The relation of construal theory to dual process theories of persuasion and persuasion phenomena, such as personal relevance effects and functional matching effects, are discussed. PMID:19884971

Aesthetic quality inference for online fashion shopping

NASA Astrophysics Data System (ADS)

Chen, Ming; Allebach, Jan

2014-03-01

On-line fashion communities in which participants post photos of personal fashion items for viewing and possible purchase by others are becoming increasingly popular. Generally, these photos are taken by individuals who have no training in photography with low-cost mobile phone cameras. It is desired that photos of the products have high aesthetic quality to improve the users' online shopping experience. In this work, we design features for aesthetic quality inference in the context of online fashion shopping. Psychophysical experiments are conducted to construct a database of the photos' aesthetic evaluation, specifically for photos from an online fashion shopping website. We then extract both generic low-level features and high-level image attributes to represent the aesthetic quality. Using a support vector machine framework, we train a predictor of the aesthetic quality rating based on the feature vector. Experimental results validate the efficacy of our approach. Metadata such as the product type are also used to further improve the result.

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

PubMed Central

Bao, Wei; Rao, Yulei

2017-01-01

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance. PMID:28708865

Influence of culture medium growth variables on Ganoderma lucidum exopolysaccharides structural features.

PubMed

Fraga, Irene; Coutinho, João; Bezerra, Rui M; Dias, Albino A; Marques, Guilhermina; Nunes, Fernando M

2014-10-13

In this work the effect of carbon and nitrogen levels and initial pH of the wheat extract culture medium of submerged culture of Ganoderma lucidum on the amount, purity and structural features of exopolysaccharides (EPS) were studied. A low peptone level (1.65 g L(-1)) favored mycelium biomass, EPS purity, but a higher supply of peptone (4.80 g L(-1)) is needed for maximum EPS production. The carbohydrate composition of the EPS and structural features also changed significantly according to the different growing conditions, being observed significant differences in the (1 → 3)/(1 → 4)-Glcp ratio and also on the branching degree of EPS. As the biological activities of EPS are highly dependent on the polysaccharide structural features, this variability can have implications on the EPS biological activities, but can also be used advantageously to produce tailor made polysaccharides with specific applications. Copyright © 2014 Elsevier Ltd. All rights reserved.

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

High dynamic range vision sensor for automotive applications

NASA Astrophysics Data System (ADS)

Grenet, Eric; Gyger, Steve; Heim, Pascal; Heitger, Friedrich; Kaess, Francois; Nussbaum, Pascal; Ruedi, Pierre-Francois

2005-02-01

A 128 x 128 pixels, 120 dB vision sensor extracting at the pixel level the contrast magnitude and direction of local image features is used to implement a lane tracking system. The contrast representation (relative change of illumination) delivered by the sensor is independent of the illumination level. Together with the high dynamic range of the sensor, it ensures a very stable image feature representation even with high spatial and temporal inhomogeneities of the illumination. Dispatching off chip image feature is done according to the contrast magnitude, prioritizing features with high contrast magnitude. This allows to reduce drastically the amount of data transmitted out of the chip, hence the processing power required for subsequent processing stages. To compensate for the low fill factor (9%) of the sensor, micro-lenses have been deposited which increase the sensitivity by a factor of 5, corresponding to an equivalent of 2000 ASA. An algorithm exploiting the contrast representation output by the vision sensor has been developed to estimate the position of a vehicle relative to the road markings. The algorithm first detects the road markings based on the contrast direction map. Then, it performs quadratic fits on selected kernel of 3 by 3 pixels to achieve sub-pixel accuracy on the estimation of the lane marking positions. The resulting precision on the estimation of the vehicle lateral position is 1 cm. The algorithm performs efficiently under a wide variety of environmental conditions, including night and rainy conditions.

A systematic exploration of the micro-blog feature space for teens stress detection.

PubMed

Zhao, Liang; Li, Qi; Xue, Yuanyuan; Jia, Jia; Feng, Ling

2016-01-01

In the modern stressful society, growing teenagers experience severe stress from different aspects from school to friends, from self-cognition to inter-personal relationship, which negatively influences their smooth and healthy development. Being timely and accurately aware of teenagers psychological stress and providing effective measures to help immature teenagers to cope with stress are highly valuable to both teenagers and human society. Previous work demonstrates the feasibility to sense teenagers' stress from their tweeting contents and context on the open social media platform-micro-blog. However, a tweet is still too short for teens to express their stressful status in a comprehensive way. Considering the topic continuity from the tweeting content to the follow-up comments and responses between the teenager and his/her friends, we combine the content of comments and responses under the tweet to supplement the tweet content. Also, such friends' caring comments like "what happened?", "Don't worry!", "Cheer up!", etc. provide hints to teenager's stressful status. Hence, in this paper, we propose to systematically explore the micro-blog feature space, comprised of four kinds of features [tweeting content features (FW), posting features (FP), interaction features (FI), and comment-response features (FC) between teenagers and friends] for teenager' stress category and stress level detection. We extract and analyze these feature values and their impacts on teens stress detection. We evaluate the framework through a real user study of 36 high school students aged 17. Different classifiers are employed to detect potential stress categories and corresponding stress levels. Experimental results show that all the features in the feature space positively affect stress detection, and linguistic negative emotion, proportion of negative sentences, friends' caring comments and teen's reply rate play more significant roles than the rest features. Micro-blog platform provides easy and effective channel to detect teenagers' psychological stress. Involving comments and responses under the tweet supplement the detection and improves the detection accuracy of 16.8 %.

The role of learning environment on high school chemistry students' motivation and self-regulatory processes

NASA Astrophysics Data System (ADS)

Judd, Jeffrey S.

Changes to the global workforce and technological advancements require graduating high school students to be more autonomous, self-directed, and critical in their thinking. To reflect societal changes, current educational reform has focused on developing more problem-based, collaborative, and student-centered classrooms to promote effective self-regulatory learning strategies, with the goal of helping students adapt to future learning situations and become life-long learners. This study identifies key features that may characterize these "powerful learning environments", which I term "high self-regulating learning environments" for ease of discussion, and examine the environment's role on students' motivation and self-regulatory processes. Using direct observation, surveys, and formal and informal interviews, I identified perceptions, motivations, and self-regulatory strategies of 67 students in my high school chemistry classes as they completed academic tasks in both high and low self-regulating learning environments. With social cognitive theory as a theoretical framework, I then examined how students' beliefs and processes changed after they moved from low to a high self-regulating learning environment. Analyses revealed that key features such as task meaning, utility, complexity, and control appeared to play a role in promoting positive changes in students' motivation and self-regulation. As embedded cases, I also included four students identified as high self-regulating, and four students identified as low self-regulating to examine whether the key features of high and low self-regulating learning environments played a similar role in both groups. Analysis of findings indicates that key features did play a significant role in promoting positive changes in both groups, with high self-regulating students' motivation and self-regulatory strategies generally remaining higher than the low self-regulating students; this was the case in both environments. Findings suggest that classroom learning environments and instruction can be modified using variations of these key features to promote specific or various levels of motivation and self-regulatory skill. In this way, educators may tailor their lessons or design their classrooms to better match and develop students' current level of motivation and self-regulation in order to maximize engagement in an academic task.

Postcards from America.

ERIC Educational Resources Information Center

Mullin, Penn

"Postcards from America" is the publisher's name for a set of five high-interest low-level novellas written on a second-grade reading level and featuring a multicultural mix of adolescent characters (African-American, Mexican-American, Asian-American, and Caucasian). For recreational reading, the novellas in this set offer a mix of…

Borderline features are associated with inaccurate trait self-estimations.

PubMed

Morey, Leslie C

2014-01-01

Many treatments for Borderline Personality Disorder (BPD) are based upon the hypothesis that gross distortion in perceptions and attributions related to self and others represent a core mechanism for the enduring difficulties displayed by such patients. However, available experimental evidence of such distortions provides equivocal results, with some studies suggesting that BPD is related to inaccuracy in such perceptions and others indicative of enhanced accuracy in some judgments. The current study uses a novel methodology to explore whether individuals with BPD features are less accurate in estimating their levels of universal personality characteristics as compared to community norms. One hundred and four students received course instruction on the Five Factor Model of personality, and then were asked to estimate their levels of these five traits relative to community norms. They then completed the NEO-Five Factor Inventory and the Personality Assessment Inventory-Borderline Features scale (PAI-BOR). Accuracy of estimates was calculated by computing squared differences between self-estimated trait levels and norm-referenced standardized scores in the NEO-FFI. There was a moderately strong relationship between PAI-BOR score and inaccuracy of trait level estimates. In particular, high BOR individuals dramatically overestimated their levels of Agreeableness and Conscientiousness, estimating themselves to be slightly above average on each of these characteristics but actually scoring well below average on both. The accuracy of estimates of levels of Neuroticism were unrelated to BOR scores, despite the fact that BOR scores were highly correlated with Neuroticism. These findings support the hypothesis that a key feature of BPD involves marked perceptual distortions of various aspects of self in relationship to others. However, the results also indicate that this is not a global perceptual deficit, as high BOR scorers accurately estimated that their emotional responsiveness was well above average. However, such individuals appear to have limited insight into their relative disadvantages in the capacity for cooperative relationships, or their limited ability to approach life in a planful and non-impulsive manner. Such results suggest important targets for treatments addressing problems in self-other representations.

Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data.

PubMed

Zhang, Chuncheng; Song, Sutao; Wen, Xiaotong; Yao, Li; Long, Zhiying

2015-04-30

Feature selection plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI-based decoding due to the "few samples and large features" nature of functional magnetic resonance imaging (fMRI) data. Recently, several sparse representation methods have been applied to the voxel selection of fMRI data. Despite the low computational efficiency of the sparse representation methods, they still displayed promise for applications that select features from fMRI data. In this study, we proposed the Laplacian smoothed L0 norm (LSL0) approach for feature selection of fMRI data. Based on the fast sparse decomposition using smoothed L0 norm (SL0) (Mohimani, 2007), the LSL0 method used the Laplacian function to approximate the L0 norm of sources. Results of the simulated and real fMRI data demonstrated the feasibility and robustness of LSL0 for the sparse source estimation and feature selection. Simulated results indicated that LSL0 produced more accurate source estimation than SL0 at high noise levels. The classification accuracy using voxels that were selected by LSL0 was higher than that by SL0 in both simulated and real fMRI experiment. Moreover, both LSL0 and SL0 showed higher classification accuracy and required less time than ICA and t-test for the fMRI decoding. LSL0 outperformed SL0 in sparse source estimation at high noise level and in feature selection. Moreover, LSL0 and SL0 showed better performance than ICA and t-test for feature selection. Copyright © 2015 Elsevier B.V. All rights reserved.

Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

PubMed

Horikawa, Tomoyasu; Kamitani, Yukiyasu

2017-01-01

Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

Single season changes in resting state network power and the connectivity between regions distinguish head impact exposure level in high school and youth football players

NASA Astrophysics Data System (ADS)

Murugesan, Gowtham; Saghafi, Behrouz; Davenport, Elizabeth; Wagner, Ben; Urban, Jillian; Kelley, Mireille; Jones, Derek; Powers, Alex; Whitlow, Christopher; Stitzel, Joel; Maldjian, Joseph; Montillo, Albert

2018-02-01

The effect of repetitive sub-concussive head impact exposure in contact sports like American football on brain health is poorly understood, especially in the understudied populations of youth and high school players. These players, aged 9-18 years old may be particularly susceptible to impact exposure as their brains are undergoing rapid maturation. This study helps fill the void by quantifying the association between head impact exposure and functional connectivity, an important aspect of brain health measurable via resting-state fMRI (rs-fMRI). The contributions of this paper are three fold. First, the data from two separate studies (youth and high school) are combined to form a high-powered analysis with 60 players. These players experience head acceleration within overlapping impact exposure making their combination particularly appropriate. Second, multiple features are extracted from rs-fMRI and tested for their association with impact exposure. One type of feature is the power spectral density decomposition of intrinsic, spatially distributed networks extracted via independent components analysis (ICA). Another feature type is the functional connectivity between brain regions known often associated with mild traumatic brain injury (mTBI). Third, multiple supervised machine learning algorithms are evaluated for their stability and predictive accuracy in a low bias, nested cross-validation modeling framework. Each classifier predicts whether a player sustained low or high levels of head impact exposure. The nested cross validation reveals similarly high classification performance across the feature types, and the Support Vector, Extremely randomized trees, and Gradboost classifiers achieve F1-score up to 75%.

Hypnosis and belief: A review of hypnotic delusions.

PubMed

Connors, Michael H

2015-11-01

Hypnosis can create temporary, but highly compelling alterations in belief. As such, it can be used to model many aspects of clinical delusions in the laboratory. This approach allows researchers to recreate features of delusions on demand and examine underlying processes with a high level of experimental control. This paper reviews studies that have used hypnosis to model delusions in this way. First, the paper reviews studies that have focused on reproducing the surface features of delusions, such as their high levels of subjective conviction and strong resistance to counter-evidence. Second, the paper reviews studies that have focused on modelling underlying processes of delusions, including anomalous experiences or cognitive deficits that underpin specific delusional beliefs. Finally, the paper evaluates this body of research as a whole. The paper discusses advantages and limitations of using hypnotic models to study delusions and suggests some directions for future research. Copyright © 2015 Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Textural features of 18F-fluorodeoxyglucose positron emission tomography scanning in diagnosing aortic prosthetic graft infection.

PubMed

Saleem, Ben R; Beukinga, Roelof J; Boellaard, Ronald; Glaudemans, Andor W J M; Reijnen, Michel M P J; Zeebregts, Clark J; Slart, Riemer H J A

2017-05-01

The clinical problem in suspected aortoiliac graft infection (AGI) is to obtain proof of infection. Although 18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography scanning (PET) has been suggested to play a pivotal role, an evidence-based interpretation is lacking. The objective of this retrospective study was to examine the feasibility and utility of 18 F-FDG uptake heterogeneity characterized by textural features to diagnose AGI. Thirty patients with a history of aortic graft reconstruction who underwent 18 F-FDG PET/CT scanning were included. Sixteen patients were suspected to have an AGI (group I). AGI was considered proven only in the case of a positive bacterial culture. Positive cultures were found in 10 of the 16 patients (group Ia), and in the other six patients, cultures remained negative (group Ib). A control group was formed of 14 patients undergoing 18 F-FDG PET for other reasons (group II). PET images were assessed using conventional maximal standardized uptake value (SUVmax), tissue-to-background ratio (TBR), and visual grading scale (VGS). Additionally, 64 different 18 F-FDG PET based textural features were applied to characterize 18 F-FDG uptake heterogeneity. To select candidate predictors, univariable logistic regression analysis was performed (α = 0.16). The accuracy was satisfactory in case of an AUC > 0.8. The feature selection process yielded the textural features named variance (AUC = 0.88), high grey level zone emphasis (AUC = 0.87), small zone low grey level emphasis (AUC = 0.80), and small zone high grey level emphasis (AUC = 0.81) most optimal for distinguishing between groups I and II. SUVmax, TBR, and VGS were also able to distinguish between these groups with AUCs of 0.87, 0.78, and 0.90, respectively. The textural feature named short run high grey level emphasis was able to distinguish group Ia from Ib (AUC = 0.83), while for the same task the TBR and VGS were not found to be predictive. SUVmax was found predictive in distinguishing these groups, but showed an unsatisfactory accuracy (AUC = 0.75). Textural analysis to characterize 18 F-FDG uptake heterogeneity is feasible and shows promising results in diagnosing AGI, but requires additional external validation and refinement before it can be implemented in the clinical decision-making process.

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.

PubMed

Bühnemann, Claudia; Li, Simon; Yu, Haiyue; Branford White, Harriet; Schäfer, Karl L; Llombart-Bosch, Antonio; Machado, Isidro; Picci, Piero; Hogendoorn, Pancras C W; Athanasou, Nicholas A; Noble, J Alison; Hassan, A Bassim

2014-01-01

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.

Examining the Effects of Text Genre and Structure on Fourth-and Fifth-Grade Students' High-Level Comprehension as Evidenced in Small-Group Discussions

ERIC Educational Resources Information Center

Li, Mengyi; Murphy, P. Karen; Firetto, Carla M.

2014-01-01

Although there is a rich literature on the role of text genre and structure on students' literal comprehension, more research is needed regarding the role of these text features on students' high-level comprehension as evidenced in their small-group discussions. As such, the present study examined the effects of text genre (i.e., narrative and…

Cigarette characteristic and emission variations across high-, middle- and low-income countries.

PubMed

O'Connor, R J; Wilkins, K J; Caruso, R V; Cummings, K M; Kozlowski, L T

2010-12-01

The public health burden of tobacco use is shifting to the developing world, and the tobacco industry may apply some of its successful marketing tactics, such as allaying health concerns with product modifications. This study used standard smoking machine tests to examine the extent to which the industry is introducing engineering features that reduce tar and nicotine to cigarettes sold in middle- and low-income countries. Multicountry observational study. Cigarettes from 10 different countries were purchased in 2005 and 2007 with low-, middle- and high-income countries identified using the World Bank's per capita gross national income metric. Physical measurements of each brand were tested, and tobacco moisture and weight, paper porosity, filter ventilation and pressure drop were analysed. Tar, nicotine and carbon monoxide emission levels were determined for each brand using International Organization for Standardization and Canadian Intensive methods. Statistical analyses were performed using Statistical Package for the Social Sciences. Among cigarette brands with filters, more brands were ventilated in high-income countries compared with middle- and low-income countries [χ(2)(4)=25.92, P<0.001]. Low-income brands differed from high- and middle-income brands in engineering features such as filter density, ventilation and paper porosity, while tobacco weight and density measures separated the middle- and high-income groups. Smoke emissions differed across income groups, but these differences were largely negated when one accounted for design features. This study showed that as a country's income level increases, cigarettes become more highly engineered and the emissions levels decrease. In order to reduce the burden of tobacco-related disease and further effective product regulation, health officials must understand cigarette design and function within and between countries. Copyright © 2010 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Cigarette characteristic and emission variations across high-, middle- and low-income countries

PubMed Central

O’Connor, R.J.; Wilkins, K.J.; Caruso, R.V.; Cummings, K.M.; Kozlowski, L.T.

2010-01-01

SUMMARY Objectives The public health burden of tobacco use is shifting to the developing world, and the tobacco industry may apply some of its successful marketing tactics, such as allaying health concerns with product modifications. This study used standard smoking machine tests to examine the extent to which the industry is introducing engineering features that reduce tar and nicotine to cigarettes sold in middle- and low-income countries. Study design Multicountry observational study. Methods Cigarettes from 10 different countries were purchased in 2005 and 2007 with low-, middle- and high-income countries identified using the World Bank’s per-capita gross national income metric. Physical measurements of each brand were tested, and tobacco moisture and weight, paper porosity, filter ventilation and pressure drop were analysed. Tar, nicotine and carbon monoxide emission levels were determined for each brand using International Organization for Standardization and Canadian Intensive methods. Statistical analyses were performed using Statistical Package for the Social Sciences. Results Among cigarette brands with filters, more brands were ventilated in high-income countries compared with middle- and low-income countries [χ2(4)=25.92, P<0.001]. Low-income brands differed from high- and middle-income brands in engineering features such as filter density, ventilation and paper porosity, while tobacco weight and density measures separated the middle- and high-income groups. Smoke emissions differed across income groups, but these differences were largely negated when one accounted for design features. Conclusions This study showed that as a country’s income level increases, cigarettes become more highly engineered and the emissions levels decrease. In order to reduce the burden of tobacco-related disease and further effective product regulation, health officials must understand cigarette design and function within and between countries. PMID:21030055

Evaluation of the FIR Example using Xilinx Vivado High-Level Synthesis Compiler

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

Jin, Zheming; Finkel, Hal; Yoshii, Kazutomo

Compared to central processing units (CPUs) and graphics processing units (GPUs), field programmable gate arrays (FPGAs) have major advantages in reconfigurability and performance achieved per watt. This development flow has been augmented with high-level synthesis (HLS) flow that can convert programs written in a high-level programming language to Hardware Description Language (HDL). Using high-level programming languages such as C, C++, and OpenCL for FPGA-based development could allow software developers, who have little FPGA knowledge, to take advantage of the FPGA-based application acceleration. This improves developer productivity and makes the FPGA-based acceleration accessible to hardware and software developers. Xilinx Vivado HLSmore » compiler is a high-level synthesis tool that enables C, C++ and System C specification to be directly targeted into Xilinx FPGAs without the need to create RTL manually. The white paper [1] published recently by Xilinx uses a finite impulse response (FIR) example to demonstrate the variable-precision features in the Vivado HLS compiler and the resource and power benefits of converting floating point to fixed point for a design. To get a better understanding of variable-precision features in terms of resource usage and performance, this report presents the experimental results of evaluating the FIR example using Vivado HLS 2017.1 and a Kintex Ultrascale FPGA. In addition, we evaluated the half-precision floating-point data type against the double-precision and single-precision data type and present the detailed results.« less

Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.

PubMed

Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

2016-12-01

This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Benchmark Problems for Space Mission Formation Flying

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell; Leitner, Jesse A.; Folta, David C.; Burns, Richard

2003-01-01

To provide a high-level focus to distributed space system flight dynamics and control research, several benchmark problems are suggested for space mission formation flying. The problems cover formation flying in low altitude, near-circular Earth orbit, high altitude, highly elliptical Earth orbits, and large amplitude lissajous trajectories about co-linear libration points of the Sun-Earth/Moon system. These problems are not specific to any current or proposed mission, but instead are intended to capture high-level features that would be generic to many similar missions that are of interest to various agencies.

Texture analysis of apparent diffusion coefficient maps for treatment response assessment in prostate cancer bone metastases-A pilot study.

PubMed

Reischauer, Carolin; Patzwahl, René; Koh, Dow-Mu; Froehlich, Johannes M; Gutzeit, Andreas

2018-04-01

To evaluate whole-lesion volumetric texture analysis of apparent diffusion coefficient (ADC) maps for assessing treatment response in prostate cancer bone metastases. Texture analysis is performed in 12 treatment-naïve patients with 34 metastases before treatment and at one, two, and three months after the initiation of androgen deprivation therapy. Four first-order and 19 second-order statistical texture features are computed on the ADC maps in each lesion at every time point. Repeatability, inter-patient variability, and changes in the feature values under therapy are investigated. Spearman rank's correlation coefficients are calculated across time to demonstrate the relationship between the texture features and the serum prostate specific antigen (PSA) levels. With few exceptions, the texture features exhibited moderate to high precision. At the same time, Friedman's tests revealed that all first-order and second-order statistical texture features changed significantly in response to therapy. Thereby, the majority of texture features showed significant changes in their values at all post-treatment time points relative to baseline. Bivariate analysis detected significant correlations between the great majority of texture features and the serum PSA levels. Thereby, three first-order and six second-order statistical features showed strong correlations with the serum PSA levels across time. The findings in the present work indicate that whole-tumor volumetric texture analysis may be utilized for response assessment in prostate cancer bone metastases. The approach may be used as a complementary measure for treatment monitoring in conjunction with averaged ADC values. Copyright © 2018 Elsevier B.V. All rights reserved.

TU-F-CAMPUS-J-05: Effect of Uncorrelated Noise Texture On Computed Tomography Quantitative Image Features

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

Oliver, J; Budzevich, M; Moros, E

Purpose: To investigate the relationship between quantitative image features (i.e. radiomics) and statistical fluctuations (i.e. electronic noise) in clinical Computed Tomography (CT) using the standardized American College of Radiology (ACR) CT accreditation phantom and patient images. Methods: Three levels of uncorrelated Gaussian noise were added to CT images of phantom and patients (20) acquired in static mode and respiratory tracking mode. We calculated the noise-power spectrum (NPS) of the original CT images of the phantom, and of the phantom images with added Gaussian noise with means of 50, 80, and 120 HU. Concurrently, on patient images (original and noise-added images),more » image features were calculated: 14 shape, 19 intensity (1st order statistics from intensity volume histograms), 18 GLCM features (2nd order statistics from grey level co-occurrence matrices) and 11 RLM features (2nd order statistics from run-length matrices). These features provide the underlying structural information of the images. GLCM (size 128x128) was calculated with a step size of 1 voxel in 13 directions and averaged. RLM feature calculation was performed in 13 directions with grey levels binning into 128 levels. Results: Adding the electronic noise to the images modified the quality of the NPS, shifting the noise from mostly correlated to mostly uncorrelated voxels. The dramatic increase in noise texture did not affect image structure/contours significantly for patient images. However, it did affect the image features and textures significantly as demonstrated by GLCM differences. Conclusion: Image features are sensitive to acquisition factors (simulated by adding uncorrelated Gaussian noise). We speculate that image features will be more difficult to detect in the presence of electronic noise (an uncorrelated noise contributor) or, for that matter, any other highly correlated image noise. This work focuses on the effect of electronic, uncorrelated, noise and future work shall examine the influence of changes in quantum noise on the features. J. Oliver was supported by NSF FGLSAMP BD award HRD #1139850 and the McKnight Doctoral Fellowship.« less

Cyclic development of igneous features and their relationship to high-temperature hydrothermal features in the Henderson porphyry molybdenum deposit, Colorado

USGS Publications Warehouse

Carten, R.B.; Geraghty, E.P.; Walker, B.M.

1988-01-01

The Henderson porphyry molybdenum deposit was formed by the superposition of coupled alteration and mineralization events, of varying intensity and size, that were associated with each of at least 11 intrusions. Deposition of molybdenite was accompanied by time-equivalent silicic and potassic alteration. High-temperature alteration and mineralization are spatially and temporally linked to the crystallization of compositionally zoned magma in the apex of stocks. Differences in hydrothermal features associated with each intrusion (e.g., mass of ore, orientation and type of veins, density of veins, and intensity of alteration) correlate with differences in primary igneous features (e.g., composition, texture, morphology, and size). The systematic relations between hydrothermal and magmatic features suggest that primary magma compositions, including volatile contents, largely control the geometry, volume, level of emplacement, and mechanisms of crystallization of stocks. These elements in turn govern the orientations and densities of fractures, which ultimately determine the distribution patterns of hydrothermal alteration and mineralization. -from Authors

Prosocial Behavior: Long-Term Trajectories and Psychosocial Outcomes.

PubMed

Flynn, Elinor; Ehrenreich, Samuel E; Beron, Kurt J; Underwood, Marion K

2015-08-01

This study investigated developmental trajectories for prosocial behavior for a sample followed from age 10 - 18 and examined possible adjustment outcomes associated with membership in different trajectory groups. Participants were 136 boys and 148 girls, their teachers, and their parents (19.4% African American, 2.4% Asian, 51.9% Caucasian, 19.5% Hispanic, and 5.8% other). Teachers rated children's prosocial behavior yearly in grades 4 - 12. At the end of the 12 th grade year, teachers, parents, and participants reported externalizing behaviors and participants reported internalizing symptoms, narcissism, and features of borderline personality disorder. Results suggested that prosocial behavior remained stable from middle childhood through late adolescence. Group-based mixture modeling revealed three prosocial trajectory groups: low (18.7%), medium (52.8%), and high (29.6%). Membership in the high prosocial trajectory group predicted lower levels of externalizing behavior as compared to the low prosocial trajectory group, and for girls, lower levels of internalizing symptoms. Membership in the medium prosocial trajectory group also predicted being lower on externalizing behaviors. Membership in the high prosocial trajectory group predicted lower levels of borderline personality features for girls only.

A metabolomic study on high-risk stroke patients determines low levels of serum lysine metabolites: a retrospective cohort study.

PubMed

Lee, Yeseung; Khan, Adnan; Hong, Seri; Jee, Sun Ha; Park, Youngja H

2017-05-30

Identifying changes in serum metabolites during cerebral ischemia is an important approach for early diagnosis of thrombotic stroke. Herein, we highlight novel biomarkers for early diagnosis of patients at high risk of thrombotic stroke using high resolution metabolomics (HRM). In this retrospective cohort study, serum samples obtained from patients at risk of thrombotic stroke (n  =  62) and non-risk individuals (n  =  348) were tested using HRM, coupled with LC-MS/MS, to discriminate between metabolic profiles of control and stroke risk patients. Multivariate analysis and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed to determine the top 5% metabolites within 95% group identities, followed by filtering with p-value <0.05 and annotating significant metabolites using a Metlin database. Mapping identified features from Kyoto Encyclopedia of Genes and Genomes (KEGG) and Mummichog resulted in 341 significant features based on OPLS-DA with p-value <0.05. Among these 341 features, nine discriminated the thrombotic stroke risk group from the control group: low levels of N 6 -acetyl-l-lysine, 5-aminopentanoate, cadaverine, 2-oxoglutarate, nicotinamide, l-valine, S-(2-methylpropionyl)-dihydrolipoamide-E and ubiquinone, and elevated levels of homocysteine sulfinic acid. Further analysis showed that these metabolite biomarkers are specifically related to stroke occurrence, and unrelated to other factors such as diabetes or smoking. Lower levels of lysine catabolites in thrombotic stroke risk patients, as compared to the control, supports targeting these compounds as novel biomarkers for early and non-invasive detection of a thrombotic stroke.

Development of a novel mouse model of hepatocellular carcinoma with nonalcoholic steatohepatitis using a high-fat, choline-deficient diet and intraperitoneal injection of diethylnitrosamine.

PubMed

Kishida, Norihiro; Matsuda, Sachiko; Itano, Osamu; Shinoda, Masahiro; Kitago, Minoru; Yagi, Hiroshi; Abe, Yuta; Hibi, Taizo; Masugi, Yohei; Aiura, Koichi; Sakamoto, Michiie; Kitagawa, Yuko

2016-06-13

The incidence of hepatocellular carcinoma with nonalcoholic steatohepatitis is increasing, and its clinicopathological features are well established. Several animal models of nonalcoholic steatohepatitis have been developed to facilitate its study; however, few fully recapitulate all its clinical features, which include insulin resistance, inflammation, fibrosis, and carcinogenesis. Moreover, these models require a relatively long time to produce hepatocellular carcinoma reliably. The aim of this study was to develop a mouse model of hepatocellular carcinoma with nonalcoholic steatohepatitis that develops quickly and reflects all clinically relevant features. Three-week-old C57BL/6J male mice were fed either a standard diet (MF) or a choline-deficient, high-fat diet (HFCD). The mice in the MF + diethylnitrosamine (DEN) and HFCD + DEN groups received a one-time intraperitoneal injection of DEN at the start of the respective feeding protocols. The mice in the HFCD and HFCD + DEN groups developed obesity early in the experiment and insulin resistance after 12 weeks. Triglyceride levels peaked at 8 weeks for all four groups and decreased thereafter. Alanine aminotransferase levels increased every 4 weeks, with the HFCD and HFCD + DEN groups showing remarkably high levels; the HFCD + DEN group presented the highest incidence of nonalcoholic steatohepatitis. The levels of fibrosis and steatosis varied, but they tended to increase every 4 weeks in the HFCD and HFCD + DEN groups. Computed tomography scans indicated that all the HFCD + DEN mice developed hepatic tumors from 20 weeks, some of which were glutamine synthetase-positive. The nonalcoholic steatohepatitis-hepatocellular carcinoma model we describe here is simple to establish, results in rapid tumor formation, and recapitulates most of the key features of nonalcoholic steatohepatitis. It could therefore facilitate further studies of the development, oncogenic potential, diagnosis, and treatment of this condition.

In search of a unifying theory of complex brain evolution.

PubMed

Krubitzer, Leah

2009-03-01

The neocortex is the part of the brain that is involved in perception, cognition, and volitional motor control. In mammals it is a highly dynamic structure that has been dramatically altered in different lineages, and these alterations account for the remarkable variations in behavior that species exhibit. When we consider how this structure changes and becomes more complex in some mammals such as humans, we must also consider how the alterations that occur at macro levels of organization, such as the level of the individual and social system, as well as micro levels of organization, such as the level of neurons, synapses and molecules, impact the neocortex. It is also important to consider the constraints imposed on the evolution of the neocortex. Observations of highly conserved features of cortical organization that all mammals share, as well as the convergent evolution of similar features of organization, indicate that the constraints imposed on the neocortex are pervasive and restrict the avenues along which evolution can proceed. Although both genes and the laws of physics place formidable constraints on the evolution of all animals, humans have evolved a number of mechanisms that allow them to loosen these constraints and often alter the course of their own evolution. While this cortical plasticity is a defining feature of mammalian neocortex, it appears to be exaggerated in humans and could be considered a unique derivation of our species.

Reflectance confocal microscopy and features of melanocytic lesions: an internet-based study of the reproducibility of terminology.

PubMed

Pellacani, Giovanni; Vinceti, Marco; Bassoli, Sara; Braun, Ralph; Gonzalez, Salvador; Guitera, Pascale; Longo, Caterina; Marghoob, Ashfaq A; Menzies, Scott W; Puig, Susana; Scope, Alon; Seidenari, Stefania; Malvehy, Josep

2009-10-01

To test the interobserver and intraobserver reproducibility of the standard terminology for description and diagnosis of melanocytic lesions in in vivo confocal microscopy. A dedicated Web platform was developed to train the participants and to allow independent distant evaluations of confocal images via the Internet. Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy. The study population was composed of 15 melanomas, 30 nevi, and 5 Spitz/Reed nevi. Six expert centers were invited to participate at the study. Intervention Evaluation of 36 features in 345 confocal microscopic images from melanocytic lesions. Interobserved and intraobserved agreement, by calculating the Cohen kappa statistics measure for each descriptor. High overall levels of reproducibility were shown for most of the evaluated features. In both the training and test sets there was a parallel trend of decreasing kappa values as deeper anatomic skin levels were evaluated. All of the features, except 1, used for melanoma diagnosis, including roundish pagetoid cells, nonedged papillae, atypical cells in basal layer, cerebriform clusters, and nucleated cells infiltrating dermal papillae, showed high overall levels of reproducibility. However, less-than-ideal reproducibility was obtained for some descriptors, such as grainy appearance of the epidermis, junctional thickening, mild atypia in basal layer, plump bright cells, small bright cells, and reticulated fibers in the dermis. Conclusion The standard consensus confocal terminology useful for the evaluation of melanocytic lesions was reproducibly recognized by independent observers.

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence.

PubMed

Qazi, Emad-Ul-Haq; Hussain, Muhammad; Aboalsamh, Hatim; Malik, Aamir Saeed; Amin, Hafeez Ullah; Bamatraf, Saeed

2016-01-01

Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0-1.875 Hz (delta low) and 1.875-3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

PubMed Central

Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

2017-01-01

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

Small numbers are sensed directly, high numbers constructed from size and density.

PubMed

Zimmermann, Eckart

2018-04-01

Two theories compete to explain how we estimate the numerosity of visual object sets. The first suggests that the apparent numerosity is derived from an analysis of more low-level features like size and density of the set. The second theory suggests that numbers are sensed directly. Consistent with the latter claim is the existence of neurons in parietal cortex which are specialized for processing the numerosity of elements in the visual scene. However, recent evidence suggests that only low numbers can be sensed directly whereas the perception of high numbers is supported by the analysis of low-level features. Processing of low and high numbers, being located at different levels of the neural hierarchy should involve different receptive field sizes. Here, I tested this idea with visual adaptation. I measured the spatial spread of number adaptation for low and high numerosities. A focused adaptation spread of high numerosities suggested the involvement of early neural levels where receptive fields are comparably small and the broad spread for low numerosities was consistent with processing of number neurons which have larger receptive fields. These results provide evidence for the claim that different mechanism exist generating the perception of visual numerosity. Whereas low numbers are sensed directly as a primary visual attribute, the estimation of high numbers however likely depends on the area size over which the objects are spread. Copyright © 2017 Elsevier B.V. All rights reserved.

Plasma plasminogen activator inhibitor-1 levels and nonalcoholic fatty liver in individuals with features of metabolic syndrome.

PubMed

de Larrañaga, Gabriela; Wingeyer, Silvia Perés; Graffigna, Mabel; Belli, Susana; Bendezú, Karla; Alvarez, Silvia; Levalle, Oscar; Fainboim, Hugo

2008-07-01

Fatty liver represents the liver component of metabolic syndrome and may be involved in plasminogen activator inhibitor-1 (PAI-1) synthesis. We studied plasma PAI-1 levels and relationships with risk factors for metabolic syndrome, including fatty liver, in 170 patients. Liver ultrasound scan was performed on all patients, and a liver biopsy was performed on those patients with chronically elevated transaminase levels. Plasma PAI-1 levels correlated significantly (P < .05) with body mass index, degree of steatosis, insulin resistance, insulin level, waist circumference, triglycerides, and high-density lipoprotein (HDL) -cholesterol. However, only body mass index (beta = .455) and HDL-cholesterol (beta = .293) remained predictors of PAI-1 levels. Liver biopsy revealed a significant correlation (P < .05) between insulin resistance (r = 0.381) or insulin level (r = 0.519) and liver fibrosis. In patients presenting features of metabolic syndrome, plasma PAI-1 levels were mainly conditioned by the whole-body fat content.

An evaluation of the suitability of ERTS data for the purposes of petroleum exploration

NASA Technical Reports Server (NTRS)

Collins, R. J., Jr. (Principal Investigator); Mccown, F. P.; Stonis, L. P.; Petzel, G.

1973-01-01

The author has identified the following significant results. ERTS-1 imagery seems to be good to excellent for reconnaissance level investigations of large sedimentary basins such as the Anadarko Basin. Many lithologic boundaries, and geomorphic features, and linear features inferred to be indicative of geologic structure are visible in the imagery. This imagery in conjunction with high altitude photography seems to be useful as a tool for intermediate level geologic exploration. Several types of crudely circular anomalous features, such as geomorphic/structural anomalies, hazy areas and tonal anomalies, are identifiable in the imagery. There seems to be a strong correlation between the geomorphic/structural and hazy anomalies and known structurally controlled oil and gas fields. The features recognizable on ERTS-1 imagery and their ease of recognition vary from area to area even in imagery acquired at the same time under essentially uniform atmospheric conditions. Repeated coverage is exceedingly valuable in geologic applications. One time complete coverage even for the various seasons does not reveal all the features that ERTS-1 can reveal.

Unsupervised universal steganalyzer for high-dimensional steganalytic features

NASA Astrophysics Data System (ADS)

Hou, Xiaodan; Zhang, Tao

2016-11-01

The research in developing steganalytic features has been highly successful. These features are extremely powerful when applied to supervised binary classification problems. However, they are incompatible with unsupervised universal steganalysis because the unsupervised method cannot distinguish embedding distortion from varying levels of noises caused by cover variation. This study attempts to alleviate the problem by introducing similarity retrieval of image statistical properties (SRISP), with the specific aim of mitigating the effect of cover variation on the existing steganalytic features. First, cover images with some statistical properties similar to those of a given test image are searched from a retrieval cover database to establish an aided sample set. Then, unsupervised outlier detection is performed on a test set composed of the given test image and its aided sample set to determine the type (cover or stego) of the given test image. Our proposed framework, called SRISP-aided unsupervised outlier detection, requires no training. Thus, it does not suffer from model mismatch mess. Compared with prior unsupervised outlier detectors that do not consider SRISP, the proposed framework not only retains the universality but also exhibits superior performance when applied to high-dimensional steganalytic features.

Using High-Dimensional Image Models to Perform Highly Undetectable Steganography

NASA Astrophysics Data System (ADS)

Pevný, Tomáš; Filler, Tomáš; Bas, Patrick

This paper presents a complete methodology for designing practical and highly-undetectable stegosystems for real digital media. The main design principle is to minimize a suitably-defined distortion by means of efficient coding algorithm. The distortion is defined as a weighted difference of extended state-of-the-art feature vectors already used in steganalysis. This allows us to "preserve" the model used by steganalyst and thus be undetectable even for large payloads. This framework can be efficiently implemented even when the dimensionality of the feature set used by the embedder is larger than 107. The high dimensional model is necessary to avoid known security weaknesses. Although high-dimensional models might be problem in steganalysis, we explain, why they are acceptable in steganography. As an example, we introduce HUGO, a new embedding algorithm for spatial-domain digital images and we contrast its performance with LSB matching. On the BOWS2 image database and in contrast with LSB matching, HUGO allows the embedder to hide 7× longer message with the same level of security level.

Seeing the Forest when Entry Is Unlikely: Probability and the Mental Representation of Events

ERIC Educational Resources Information Center

Wakslak, Cheryl J.; Trope, Yaacov; Liberman, Nira; Alony, Rotem

2006-01-01

Conceptualizing probability as psychological distance, the authors draw on construal level theory (Y. Trope & N. Liberman, 2003) to propose that decreasing an event's probability leads individuals to represent the event by its central, abstract, general features (high-level construal) rather than by its peripheral, concrete, specific features…

Teaching Robotics Software with the Open Hardware Mobile Manipulator

ERIC Educational Resources Information Center

Vona, M.; Shekar, N. H.

2013-01-01

The "open hardware mobile manipulator" (OHMM) is a new open platform with a unique combination of features for teaching robotics software and algorithms. On-board low- and high-level processors support real-time embedded programming and motor control, as well as higher-level coding with contemporary libraries. Full hardware designs and…

Implementing An Image Understanding System Architecture Using Pipe

NASA Astrophysics Data System (ADS)

Luck, Randall L.

1988-03-01

This paper will describe PIPE and how it can be used to implement an image understanding system. Image understanding is the process of developing a description of an image in order to make decisions about its contents. The tasks of image understanding are generally split into low level vision and high level vision. Low level vision is performed by PIPE -a high performance parallel processor with an architecture specifically designed for processing video images at up to 60 fields per second. High level vision is performed by one of several types of serial or parallel computers - depending on the application. An additional processor called ISMAP performs the conversion from iconic image space to symbolic feature space. ISMAP plugs into one of PIPE's slots and is memory mapped into the high level processor. Thus it forms the high speed link between the low and high level vision processors. The mechanisms for bottom-up, data driven processing and top-down, model driven processing are discussed.

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

PubMed

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

High performance organic integrated device with ultraviolet photodetective and electroluminescent properties consisting of a charge-transfer-featured naphthalimide derivative

NASA Astrophysics Data System (ADS)

Wang, Hanyu; Zhou, Jie; Wang, Xu; Lu, Zhiyun; Yu, Junsheng

2014-08-01

A high performance organic integrated device (OID) with ultraviolet photodetective and electroluminescent (EL) properties was fabricated by using a charge-transfer-featured naphthalimide derivative of 6-{3,5-bis-[9-(4-t-butylphenyl)-9H-carbazol-3-yl]-phenoxy}-2-(4-t-butylphenyl)-benzo[de]isoquinoline-1,3-dione (CzPhONI) as the active layer. The results showed that the OID had a high detectivity of 1.5 × 1011 Jones at -3 V under the UV-350 nm illumination with an intensity of 0.6 mW/cm2, and yielded an exciplex EL light emission with a maximum brightness of 1437 cd/m2. Based on the energy band diagram, both the charge transfer feature of CzPhONI and matched energy level alignment were responsible for the dual ultraviolet photodetective and EL functions of OID.

Metal artifact reduction using a patch-based reconstruction for digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Borges, Lucas R.; Bakic, Predrag R.; Maidment, Andrew D. A.; Vieira, Marcelo A. C.

2017-03-01

Digital breast tomosynthesis (DBT) is rapidly emerging as the main clinical tool for breast cancer screening. Although several reconstruction methods for DBT are described by the literature, one common issue is the interplane artifacts caused by out-of-focus features. For breasts containing highly attenuating features, such as surgical clips and large calcifications, the artifacts are even more apparent and can limit the detection and characterization of lesions by the radiologist. In this work, we propose a novel method of combining backprojected data into tomographic slices using a patch-based approach, commonly used in denoising. Preliminary tests were performed on a geometry phantom and on an anthropomorphic phantom containing metal inserts. The reconstructed images were compared to a commercial reconstruction solution. Qualitative assessment of the reconstructed images provides evidence that the proposed method reduces artifacts while maintaining low noise levels. Objective assessment supports the visual findings. The artifact spread function shows that the proposed method is capable of suppressing artifacts generated by highly attenuating features. The signal difference to noise ratio shows that the noise levels of the proposed and commercial methods are comparable, even though the commercial method applies post-processing filtering steps, which were not implemented on the proposed method. Thus, the proposed method can produce tomosynthesis reconstructions with reduced artifacts and low noise levels.

Pathological narcissism and acute anxiety symptoms after trauma: a study of Israeli civilians exposed to war.

PubMed

Besser, Avi; Zeigler-Hill, Virgil; Pincus, Aaron L; Neria, Yuval

2013-01-01

Diathesis-stress models of posttraumatic stress disorder (PTSD) hypothesize that exposure to trauma may interact with individual differences in the development of PTSD. Previous studies have not assessed immediate responses to a proximate stressor, but the current "natural laboratory" study was designed to empirically test the role that individual differences in pathological narcissism may play in the development of acute anxiety symptoms among civilians facing rocket and missile fire. We assessed demographic features, trauma exposure severity, narcissistic personality features, and acute anxiety symptoms (PTSD and General Anxiety Disorder [GAD]) among 342 Israeli female adults during the November 2012 eruption of violence in the Middle East. Results demonstrate an association between exposure severity and acute anxiety symptoms (both PTSD and GAD) for individuals with high levels of pathological narcissism but not for those with low levels of pathological narcissism. These results suggest that individuals with narcissistic personality features are at high risk for the development of acute anxiety symptoms following exposure to uncontrollable and life-threatening mass trauma. The findings underscore the role of intra-personal resources in the immediate psychological aftermath of war by highlighting the increased risk associated with narcissistic personality features. Theoretical and clinical implications of the findings are discussed.

Remembering Complex Objects in Visual Working Memory: Do Capacity Limits Restrict Objects or Features?

PubMed Central

Hardman, Kyle; Cowan, Nelson

2014-01-01

Visual working memory stores stimuli from our environment as representations that can be accessed by high-level control processes. This study addresses a longstanding debate in the literature about whether storage limits in visual working memory include a limit to the complexity of discrete items. We examined the issue with a number of change-detection experiments that used complex stimuli which possessed multiple features per stimulus item. We manipulated the number of relevant features of the stimulus objects in order to vary feature load. In all of our experiments, we found that increased feature load led to a reduction in change-detection accuracy. However, we found that feature load alone could not account for the results, but that a consideration of the number of relevant objects was also required. This study supports capacity limits for both feature and object storage in visual working memory. PMID:25089739

Search for ultraviolet emission lines from a hot gaseous halo in the edge-on galaxy NGC 4244

NASA Technical Reports Server (NTRS)

Deharveng, J.-M.; Joubert, M.; Bixler, J.; Bowyer, S.; Malina, R.

1986-01-01

Short and long wavelength IUE spectra of the halo region in the edge-on galaxy NGC 4244 are analyzed in order to identify evidence of line emission at the level of 0.000001 ergs per cu cm sr/s. Features are found at 1245 A and 1402 A, having peaks four times greater than the rms intensity fluctuations of nearby spectra. The spectral features are identified with semi-forbidden N V, semi-forbidden S IV at 1240 A, and Si IV and semi-forbidden O IV multiplets at 1400 A, respectively. The appearance of high-peak features and the lack of astrophysically important lines in the sample are evidence of a gas near T = 10 exp 5.2 and emission measure (EM) equal to about 0.000001 pc. However, the case for the existence of such a gas is weakened due to the existence of two other similarly sized features with no identifiable astrophysical origin and the extremely faint nature of the candidate features. The assumed upper limit for the line intensities in NGC 4244 leads to the conclusion that at T less than 100,000 K any emitting gas is either highly clumped or has a p/k value of less than 1000 per cu cm K. It is suggested that if the observed low level features in the short wavelength spectrum are real, the temperature and emission measures allow for a single component gas in the halo of NGC 4244, and are in agreement with those derived by Paresce et al. (1983).

Assessment of rural soundscapes with high-speed train noise.

PubMed

Lee, Pyoung Jik; Hong, Joo Young; Jeon, Jin Yong

2014-06-01

In the present study, rural soundscapes with high-speed train noise were assessed through laboratory experiments. A total of ten sites with varying landscape metrics were chosen for audio-visual recording. The acoustical characteristics of the high-speed train noise were analyzed using various noise level indices. Landscape metrics such as the percentage of natural features (NF) and Shannon's diversity index (SHDI) were adopted to evaluate the landscape features of the ten sites. Laboratory experiments were then performed with 20 well-trained listeners to investigate the perception of high-speed train noise in rural areas. The experiments consisted of three parts: 1) visual-only condition, 2) audio-only condition, and 3) combined audio-visual condition. The results showed that subjects' preference for visual images was significantly related to NF, the number of land types, and the A-weighted equivalent sound pressure level (LAeq). In addition, the visual images significantly influenced the noise annoyance, and LAeq and NF were the dominant factors affecting the annoyance from high-speed train noise in the combined audio-visual condition. In addition, Zwicker's loudness (N) was highly correlated with the annoyance from high-speed train noise in both the audio-only and audio-visual conditions. © 2013.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

PubMed

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

Meltwater channel scars and the extent of Mid-Pleistocene glaciation in central Pennsylvania

NASA Astrophysics Data System (ADS)

Marsh, Ben

2017-10-01

High-resolution digital topographic data permit morphological analyses of glacial processes in detail that was previously infeasible. High-level glaciofluvial erosional scars in central Pennsylvania, identified and delimited using LiDAR data, define the approximate ice depth during a pre-Wisconsin advance, > 770,000 BP, on a landscape unaffected by Wisconsin glaciation. Distinctive scars on the prows of anticlinal ridges at 175-350 m above the valley floor locate the levels of subice meltwater channels. A two-component planar GIS model of the ice surface is derived using these features and intersected with a digital model of contemporary topography to create a glacial limit map. The map is compared to published maps, demonstrating the limits of conventional sediment-based mapping. Additional distinctive meltwater features that were cut during deglaciation are modeled in a similar fashion.

Toward an objective indexing system for ADHD-screening using children's activity monitoring.

PubMed

Kam, Hye Jin; Choi, Jong Pil; Park, Rae Woong

2008-11-06

Signs of ADHD are discernible in specific situations, and usually assessed according to subjective impressions. We performed a preliminary comparative study from children's activity at a natural classroom environment with 3-axis accelerator for a feasible objective index. From a total of 157 children (7-9 yrs) and clinically diagnosed 24 children out of them, variances in 1-min epoch mean activity had shown significant differences among the subgroups: (1) ADHD=.0194, Other Diseases=.0080, Normal=.0009; (2) ADHD=.0194, non-ADHD=.0057(p<.01, respectively). There were also significant differences in high-level activity (>1.6G) features among subgroups with the same order (p<.01, respectively). ADHD patients exhibited more dispersed activities and higher high-level activity ratio than normal. Activity features can be useful to build an objective indexing system for screening ADHD patients.

[Association of serum decoy receptor 3 protein level with the clinicopathologic features of bladder transitional cell carcinoma].

PubMed

Wang, Dong; Wang, Jian; Chen, Guojun

2013-12-01

To investigate the association of serum levels of decoy receptor 3(DcR3) protein and the clinicopathologic features of bladder transitional cell carcinoma. Enzyme-linked immunosorbent assay was used to examine the serum levels of DcR3 in patients with bladder transitional cell carcinoma for analysis of its association with the patients' age, gender, clinical stages and pathological classification. The patients with bladder transitional cell carcinoma showed a significantly elevated serum level of DcR3 (183.43 ∓78.45 pg/m1) compared with the normal level (116.65∓97.43 pg/m1, P<0.05). The serum level of DcR3 in the patients showed close correlations with the TNM stage and pathological classification of the tumor (P<0.05) but not with the patients' age or gender (P>0.05). In patients with bladder transitional cell carcinoma, a high serum level of DcR3 suggests a higher malignancy of the tumor.

Fusion of shallow and deep features for classification of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

2018-02-01

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

Six-State Quantum Key Distribution Using Photons with Orbital Angular Momentum

NASA Astrophysics Data System (ADS)

Li, Jun-Lin; Wang, Chuan

2010-11-01

A new implementation of high-dimensional quantum key distribution (QKD) protocol is discussed. Using three mutual unbiased bases, we present a d-level six-state QKD protocol that exploits the orbital angular momentum with the spatial mode of the light beam. The protocol shows that the feature of a high capacity since keys are encoded using photon modes in d-level Hilbert space. The devices for state preparation and measurement are also discussed. This protocol has high security and the alignment of shared reference frames is not needed between sender and receiver.

High-Level Spectroscopy, Quantum Chemistry, and Catalysis: Not just a Passing Fad.

PubMed

Neese, Frank

2017-09-04

Quantum chemistry can be used as a powerful link between theory and experiment for studying reactions in all areas of catalysis. The key feature of this approach is the combination of quantum chemistry with a range of high-level spectroscopic methods. This allows for conclusions to be reached that neither theory nor experiment would have been able to obtain in isolation. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

IBM Application System/400 as the foundation of the Mayo Clinic/IBM PACS project

NASA Astrophysics Data System (ADS)

Rothman, Melvyn L.; Morin, Richard L.; Persons, Kenneth R.; Gibbons, Patricia S.

1990-08-01

An IBM Application System/400 (AS/400) anchors the Mayo Clinic/IBM joint development PACS project. This paper highlights some of the AS/400's features and the resulting benefits which make it a strong foundation for a medical image archival and review system. Among the AS/400's key features are: 1. A high-level machine architecture 2. Object orientation 3. Relational data base and other functions integrated into the system's architecture 4. High-function interfaces to IBM Personal Computers and IBM Personal System/2s' (pS/2TM).

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

PubMed Central

Chen, Bin; Wang, Yanan; Yan, Zhaoli

2018-01-01

Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method. PMID:29382144

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil.

PubMed

Chen, Bin; Wang, Yanan; Yan, Zhaoli

2018-01-29

Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method.

Concurrent Associations of Physical Activity and Screen-Based Sedentary Behavior on Obesity Among US Adolescents: A Latent Class Analysis.

PubMed

Kim, Youngdeok; Barreira, Tiago V; Kang, Minsoo

2016-01-01

Independent associations of physical activity (PA) and sedentary behavior (SB) with obesity are well documented. However, little is known about the combined associations of these behaviors with obesity in adolescents. The present study examines the prevalence of concurrent levels of PA and SB, and their associations with obesity among US adolescents. Data from a total of 12 081 adolescents who participated in the Youth Risk Behaviors Survey during 2012-2013 were analyzed. A latent class analysis was performed to identify latent subgroups with varying combined levels of subjectively measured PA and screen-based SB. Follow-up analysis examined the changes in the likelihood of being obese as determined by the Center for Disease Control and Prevention Growth Chart between latent subgroups. Four latent subgroups with varying combined levels of PA and SB were identified across gender. The likelihood of being obese was significantly greater for the subgroups featuring either or both Low PA or High SB when compared with High PA/Low SB across genders (odds ratio [OR] ranges, 2.1-2.7 for males and 9.6-23.5 for females). Low PA/High SB showed the greater likelihood of being obese compared to subgroups featuring either or both High PA and Low SB (OR ranges, 2.2-23.5) for female adolescents only. The findings imply that promoting sufficient levels of PA while reducing SB should be encouraged in order to reduce obesity risk among adolescents, particularly for males. The risk of obesity for female adolescents can be reduced by engaging in either high levels of PA or low levels of SB.

EEG-based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

PubMed Central

Wade, Joshua W.; Key, Alexandra P.; Warren, Zachary E.; Sarkar, Nilanjan

2017-01-01

objective To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist’s rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins (Δf = 2 Hz) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention. PMID:28422647

Cold climate deglaciation prior to termination 2 implied by new evidence for high sea-levels at 132 KA

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

Johnson, R.G.

1992-01-01

Radioisotope dating of corals from reefs and beaches suggests a high sea stand just prior to termination 2. Lack of precision in the ages, stratigraphic uncertainties, and possible diagenetic alterations in the corals have prevented a widespread acceptance of this sea stand. These disadvantages can be avoided by an approach that uses differential uplift measurements to determine the duration of the interval of generally high sea-levels. The last interglacial terrace on Barbados has features indicating two intervals of constant sea-level: an older wave-cut at the inshore edge of the terrace, and a younger cut formed near present eustatic sea-level, belowmore » the crest, and just before the earliest Wisconsin glacial buildup. The differential uplift between these two features, measured at five locations having uplift rates between 0.18 and 0.39m/ka, yields a eustatic sea-level differences of 5.4m and a minimal duration of 12.1 [+-] 0.6ka between the two still stands. The assigned age of the younger wave-cut is 120 [+-] 0.5ka, based on sea-level regression due to ice sheet buildup implied by a Little Ice Age analog and rapidly falling Milankovitch summer insolation. The resulting minimal age of the first high sea-stand is 132.1 [+-] 1.1ka, about 7ka before termination 2. This age implies a major early deglaciation caused by a deficit of moisture transported to the great ice sheets, and occurring under relatively cold climate conditions.« less

The Sensory Components of High-Capacity Iconic Memory and Visual Working Memory

PubMed Central

Bradley, Claire; Pearson, Joel

2012-01-01

Early visual memory can be split into two primary components: a high-capacity, short-lived iconic memory followed by a limited-capacity visual working memory that can last many seconds. Whereas a large number of studies have investigated visual working memory for low-level sensory features, much research on iconic memory has used more “high-level” alphanumeric stimuli such as letters or numbers. These two forms of memory are typically examined separately, despite an intrinsic overlap in their characteristics. Here, we used a purely sensory paradigm to examine visual short-term memory for 10 homogeneous items of three different visual features (color, orientation and motion) across a range of durations from 0 to 6 s. We found that the amount of information stored in iconic memory is smaller for motion than for color or orientation. Performance declined exponentially with longer storage durations and reached chance levels after ∼2 s. Further experiments showed that performance for the 10 items at 1 s was contingent on unperturbed attentional resources. In addition, for orientation stimuli, performance was contingent on the location of stimuli in the visual field, especially for short cue delays. Overall, our results suggest a smooth transition between an automatic, high-capacity, feature-specific sensory-iconic memory, and an effortful “lower-capacity” visual working memory. PMID:23055993

Serum CA125: a tumor marker for monitoring response to treatment and follow-up in patients with non-Hodgkin's lymphoma.

PubMed

Zidan, Jamal; Hussein, Osamah; Basher, Walid; Zohar, Shmuel

2004-01-01

Serum CA125 is an important prognostic factor in patients with non-Hodgkin's lymphoma (NHL). Elevation of CA125 level correlates with advanced disease, poor response to treatment, and poor survival rates. The aim of the current study is to evaluate CA125 levels in patients with NHL and to investigate the correlations between high CA125 level and other presenting features. Thirty-eight patients (14 with low-grade and 24 with aggressive histologically proven NHL) were studied prospectively. Serum CA125 assessment was done at diagnosis, during treatment, and at follow-up. The associations between CA125 levels and other presenting features were examined. CA125 levels were elevated in 43% of patients with low-grade NHL and in 46% of patients with aggressive NHL (i.e., 45% of all patients). A higher CA125 level was associated with advanced disease, bone marrow involvement, extranodal involvement, poor performance status, the presence of B symptoms, and high serum lactate dehydrogenase level. Complete responses occurred in 86% of patients with normal CA125 levels and in 59% of patients with elevated CA125 levels. In both low-grade and aggressive NHL, the estimated 5-year overall survival rate was higher in patients with normal CA125 levels than in patients with elevated CA125 levels (88% versus 50% and 70% versus 27%, respectively). High serum CA125 is an important prognostic factor in NHL and correlates with more advanced disease, low response rates, and worse survival. CA125 measurements may be used for staging, monitoring response to treatment, and follow-up of patients with NHL. Copyright AlphaMed Press

Too upset to think: the interplay of borderline personality features, negative emotions, and social problem solving in the laboratory.

PubMed

Dixon-Gordon, Katherine L; Chapman, Alexander L; Lovasz, Nathalie; Walters, Kris

2011-10-01

Borderline personality disorder (BPD) is associated with poor social problem solving and problems with emotion regulation. In this study, the social problem-solving performance of undergraduates with high (n = 26), mid (n = 32), or low (n = 29) levels of BPD features was assessed with the Social Problem-Solving Inventory-Revised and using the means-ends problem-solving procedure before and after a social rejection stressor. The high-BP group, but not the low-BP group, showed a significant reduction in relevant solutions to social problems and more inappropriate solutions following the negative emotion induction. Increases in self-reported negative emotions during the emotion induction mediated the relationship between BP features and reductions in social problem-solving performance. In addition, the high-BP group demonstrated trait deficits in social problem solving on the Social Problem-Solving Inventory-Revised. These findings suggest that future research must examine social problem solving under differing emotional conditions, and that clinical interventions to improve social problem solving among persons with BP features should focus on responses to emotional contexts.

Objective research of auscultation signals in Traditional Chinese Medicine based on wavelet packet energy and support vector machine.

PubMed

Yan, Jianjun; Shen, Xiaojing; Wang, Yiqin; Li, Fufeng; Xia, Chunming; Guo, Rui; Chen, Chunfeng; Shen, Qingwei

2010-01-01

This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.

Regenerative fuel cell systems for space station

NASA Technical Reports Server (NTRS)

Hoberecht, M. A.; Sheibley, D. W.

1985-01-01

Regenerative fuel cell (RFC) systems are the leading energy storage candidates for Space Station. Key design features are the advanced state of technology readiness and high degree of system level design flexibility. Technology readiness was demonstrated through testing at the single cell, cell stack, mechanical ancillary component, subsystem, and breadboard levels. Design flexibility characteristics include independent sizing of power and energy storage portions of the system, integration of common reactants with other space station systems, and a wide range of various maintenance approaches. The design features led to selection of a RFC system as the sole electrochemical energy storage technology option for the space station advanced development program.

[Adoptive parents' satisfaction with the adoption experience and with its impact on family life].

PubMed

Sánchez-Sandoval, Yolanda

2011-11-01

In this study, we discuss the relevance of adoptive families' satisfaction in the assessment of adoption processes. The effects of adoption on a sample group of 272 adoptive families are analyzed. Most families show high levels of satisfaction as to: their decision to adopt, the features of their adopted children and how adoption has affected them as individuals and as a family. Statistical analyses show that these families can have different satisfaction levels depending on certain features of the adoptees, of the adoptive families or of their educational style. Life satisfaction of the adoptees is also related to how their adoptive parents evaluate the adoption.

Line drawing extraction from gray level images by feature integration

NASA Astrophysics Data System (ADS)

Yoo, Hoi J.; Crevier, Daniel; Lepage, Richard; Myler, Harley R.

1994-10-01

We describe procedures that extract line drawings from digitized gray level images, without use of domain knowledge, by modeling preattentive and perceptual organization functions of the human visual system. First, edge points are identified by standard low-level processing, based on the Canny edge operator. Edge points are then linked into single-pixel thick straight- line segments and circular arcs: this operation serves to both filter out isolated and highly irregular segments, and to lump the remaining points into a smaller number of structures for manipulation by later stages of processing. The next stages consist in linking the segments into a set of closed boundaries, which is the system's definition of a line drawing. According to the principles of Gestalt psychology, closure allows us to organize the world by filling in the gaps in a visual stimulation so as to perceive whole objects instead of disjoint parts. To achieve such closure, the system selects particular features or combinations of features by methods akin to those of preattentive processing in humans: features include gaps, pairs of straight or curved parallel lines, L- and T-junctions, pairs of symmetrical lines, and the orientation and length of single lines. These preattentive features are grouped into higher-level structures according to the principles of proximity, similarity, closure, symmetry, and feature conjunction. Achieving closure may require supplying missing segments linking contour concavities. Choices are made between competing structures on the basis of their overall compliance with the principles of closure and symmetry. Results include clean line drawings of curvilinear manufactured objects. The procedures described are part of a system called VITREO (viewpoint-independent 3-D recognition and extraction of objects).

Inventory and analysis of natural vegetation and related resources from space and high altitude photography

NASA Technical Reports Server (NTRS)

Poulton, C. E.; Faulkner, D. P.; Johnson, J. R.; Mouat, D. A.; Schrumpf, B. J.

1971-01-01

A high altitude photomosaic resource map of Site 29 was produced which provided an opportunity to test photo interpretation accuracy of natural vegetation resource features when mapped at a small (1:133,400) scale. Helicopter reconnaissance over 144 previously selected test points revealed a highly adequate level of photo interpretation accuracy. In general, the reasons for errors could be accounted for. The same photomosaic resource map enabled construction of interpretive land use overlays. Based on features of the landscape, including natural vegetation types, judgements for land use suitability were made and have been presented for two types of potential land use. These two, agriculture and urbanization, represent potential land use conflicts.

Analysis and classification of commercial ham slice images using directional fractal dimension features.

PubMed

Mendoza, Fernando; Valous, Nektarios A; Allen, Paul; Kenny, Tony A; Ward, Paddy; Sun, Da-Wen

2009-02-01

This paper presents a novel and non-destructive approach to the appearance characterization and classification of commercial pork, turkey and chicken ham slices. Ham slice images were modelled using directional fractal (DF(0°;45°;90°;135°)) dimensions and a minimum distance classifier was adopted to perform the classification task. Also, the role of different colour spaces and the resolution level of the images on DF analysis were investigated. This approach was applied to 480 wafer thin ham slices from four types of hams (120 slices per type): i.e., pork (cooked and smoked), turkey (smoked) and chicken (roasted). DF features were extracted from digitalized intensity images in greyscale, and R, G, B, L(∗), a(∗), b(∗), H, S, and V colour components for three image resolution levels (100%, 50%, and 25%). Simulation results show that in spite of the complexity and high variability in colour and texture appearance, the modelling of ham slice images with DF dimensions allows the capture of differentiating textural features between the four commercial ham types. Independent DF features entail better discrimination than that using the average of four directions. However, DF dimensions reveal a high sensitivity to colour channel, orientation and image resolution for the fractal analysis. The classification accuracy using six DF dimension features (a(90°)(∗),a(135°)(∗),H(0°),H(45°),S(0°),H(90°)) was 93.9% for training data and 82.2% for testing data.

Image segmentation by hierarchial agglomeration of polygons using ecological statistics

DOEpatents

Prasad, Lakshman; Swaminarayan, Sriram

2013-04-23

A method for rapid hierarchical image segmentation based on perceptually driven contour completion and scene statistics is disclosed. The method begins with an initial fine-scale segmentation of an image, such as obtained by perceptual completion of partial contours into polygonal regions using region-contour correspondences established by Delaunay triangulation of edge pixels as implemented in VISTA. The resulting polygons are analyzed with respect to their size and color/intensity distributions and the structural properties of their boundaries. Statistical estimates of granularity of size, similarity of color, texture, and saliency of intervening boundaries are computed and formulated into logical (Boolean) predicates. The combined satisfiability of these Boolean predicates by a pair of adjacent polygons at a given segmentation level qualifies them for merging into a larger polygon representing a coarser, larger-scale feature of the pixel image and collectively obtains the next level of polygonal segments in a hierarchy of fine-to-coarse segmentations. The iterative application of this process precipitates textured regions as polygons with highly convolved boundaries and helps distinguish them from objects which typically have more regular boundaries. The method yields a multiscale decomposition of an image into constituent features that enjoy a hierarchical relationship with features at finer and coarser scales. This provides a traversable graph structure from which feature content and context in terms of other features can be derived, aiding in automated image understanding tasks. The method disclosed is highly efficient and can be used to decompose and analyze large images.

The Mechanism of Word Crowding

PubMed Central

Yu, Deyue; Akau, Melanie M. U.; Chung, Susana T. L.

2011-01-01

Word reading speed in peripheral vision is slower when words are in close proximity of other words (Chung, 2004). This word crowding effect could arise as a consequence of interaction of low-level letter features between words, or the interaction between high-level holistic representations of words. We evaluated these two hypotheses by examining how word crowding changes for five configurations of flanking words: the control condition — flanking words were oriented upright; scrambled — letters in each flanking word were scrambled in order; horizontal-flip — each flanking word was the left-right mirror-image of the original; letter-flip — each letter of the flanking word was the left-right mirror-image of the original; and vertical-flip — each flanking word was the up-down mirror-image of the original. The low-level letter feature interaction hypothesis predicts similar word crowding effect for all the different flanker configurations, while the high-level holistic representation hypothesis predicts less word crowding effect for all the alternative flanker conditions, compared with the control condition. We found that oral reading speed for words flanked above and below by other words, measured at 10° eccentricity in the nasal field, showed the same dependence on the vertical separation between the target and its flanking words, for the various flanker configurations. The result was also similar when we rotated the flanking words by 90° to disrupt the periodic vertical pattern, which presumably is the main structure in words. The remarkably similar word crowding effect irrespective of the flanker configurations suggests that word crowding arises as a consequence of interactions of low-level letter features. PMID:22079315

Behavioral oscillation in face priming: Prediction about face identity is updated at a theta-band rhythm.

PubMed

Wang, Yuanye; Luo, Huan

2017-01-01

In order to deal with external world efficiently, the brain constantly generates predictions about incoming sensory inputs, a process known as "predictive coding." Our recent studies, by employing visual priming paradigms in combination with a time-resolved behavioral measurement, reveal that perceptual predictions about simple features (e.g., left or right orientation) return to low sensory areas not continuously but recurrently in a theta-band (3-4Hz) rhythm. However, it remains unknown whether high-level object processing is also mediated by the oscillatory mechanism and if yes at which rhythm the mechanism works. In the present study, we employed a morph-face priming paradigm and the time-resolved behavioral measurements to examine the fine temporal dynamics of face identity priming performance. First, we reveal classical priming effects and a rhythmic trend within the prime-to-probe SOA of 600ms (Experiment 1). Next, we densely sampled the face priming behavioral performances within this SOA range (Experiment 2). Our results demonstrate a significant ~5Hz oscillatory component in the face priming behavioral performances, suggesting that a rhythmic process also coordinates the object-level prediction (i.e., face identity here). In comparison to our previous studies, the results suggest that the rhythm for the high-level object is faster than that for simple features. We propose that the seemingly distinctive priming rhythms might be attributable to that the object-level and simple feature-level predictions return to different stages along the visual pathway (e.g., FFA area for face priming and V1 area for simple feature priming). In summary, the findings support a general theta-band (3-6Hz) temporal organization mechanism in predictive coding, and that such wax-and-waning pattern in predictive coding may aid the brain to be more readily updated for new inputs. © 2017 Elsevier B.V. All rights reserved.

The effect of spatial attention on invisible stimuli.

PubMed

Shin, Kilho; Stolte, Moritz; Chong, Sang Chul

2009-10-01

The influence of selective attention on visual processing is widespread. Recent studies have demonstrated that spatial attention can affect processing of invisible stimuli. However, it has been suggested that this effect is limited to low-level features, such as line orientations. The present experiments investigated whether spatial attention can influence both low-level (contrast threshold) and high-level (gender discrimination) adaptation, using the same method of attentional modulation for both types of stimuli. We found that spatial attention was able to increase the amount of adaptation to low- as well as to high-level invisible stimuli. These results suggest that attention can influence perceptual processes independent of visual awareness.

Observation of Phase-Filling Singularities in the Optical Dielectric Function of Highly Doped n-Type Ge.

PubMed

Xu, Chi; Fernando, Nalin S; Zollner, Stefan; Kouvetakis, John; Menéndez, José

2017-06-30

Phase-filling singularities in the optical response function of highly doped (>10^{19}  cm^{-3}) germanium are theoretically predicted and experimentally confirmed using spectroscopic ellipsometry. Contrary to direct-gap semiconductors, which display the well-known Burstein-Moss phenomenology upon doping, the critical point in the joint density of electronic states associated with the partially filled conduction band in n-Ge corresponds to the so-called E_{1} and E_{1}+Δ_{1} transitions, which are two-dimensional in character. As a result of this reduced dimensionality, there is no edge shift induced by Pauli blocking. Instead, one observes the "original" critical point (shifted only by band gap renormalization) and an additional feature associated with the level occupation discontinuity at the Fermi level. The experimental observation of this feature is made possible by the recent development of low-temperature, in situ doping techniques that allow the fabrication of highly doped films with exceptionally flat doping profiles.

Preparing thermoplastic aromatic polyimides

NASA Technical Reports Server (NTRS)

Bell, V. L.

1973-01-01

Method prepares aromatic polyimides with significantly reduced glass-transition temperatures and without accompanying loss of high-level thermo-oxidative stability which has been typical. This has been made possible by use of diamine monomers with specific stereoisomeric features.

Association of MYCN copy number with clinical features, tumor biology, and outcomes in neuroblastoma: A report from the Children's Oncology Group.

PubMed

Campbell, Kevin; Gastier-Foster, Julie M; Mann, Meegan; Naranjo, Arlene H; Van Ryn, Collin; Bagatell, Rochelle; Matthay, Katherine K; London, Wendy B; Irwin, Meredith S; Shimada, Hiroyuki; Granger, M Meaghan; Hogarty, Michael D; Park, Julie R; DuBois, Steven G

2017-11-01

High-level MYCN amplification (MNA) is associated with poor outcome and unfavorable clinical and biological features in patients with neuroblastoma. To the authors' knowledge, less is known regarding these associations in patients with low-level MYCN copy number increases. In this retrospective study, the authors classified patients has having tumors with MYCN wild-type tumors, MYCN gain (2-4-fold increase in MYCN signal compared with the reference probe), or MNA (>4-fold increase). Tests of trend were used to investigate ordered associations between MYCN copy number category and features of interest. Log-rank tests and Cox models compared event-free survival and overall survival by subgroup. Among 4672 patients, 3694 (79.1%) had MYCN wild-type tumors, 133 (2.8%) had MYCN gain, and 845 (18.1%) had MNA. For each clinical/biological feature, the percentage of patients with an unfavorable feature was lowest in the MYCN wild-type category, intermediate in the MYCN gain category, and highest in the MNA category (P<.0001), except for 11q aberration, for which the highest rates were in the MYCN gain category. Patients with MYCN gain had inferior event-free survival and overall survival compared with those with MYCN wild-type. Among patients with high-risk disease, MYCN gain was associated with the lowest response rate after chemotherapy. Patients with non-stage 4 disease (according to the International Neuroblastoma Staging System) and patients with non-high-risk disease with MYCN gain had a significantly increased risk for death, a finding confirmed on multivariable testing. Increasing MYCN copy number is associated with an increasingly higher rate of unfavorable clinical/biological features, with 11q aberration being an exception. Patients with MYCN gain appear to have inferior outcomes, especially in otherwise more favorable groups. Cancer 2017;123:4224-4235. © 2017 American Cancer Society. © 2017 American Cancer Society.

Nuclear DNA Methylation and Chromatin Condensation Phenotypes Are Distinct Between Normally Proliferating/Aging, Rapidly Growing/Immortal, and Senescent Cells

PubMed Central

Gertych, Arkadiusz; Tajbakhsh, Jian

2013-01-01

This study reports on probing the utility of in situ chromatin texture features such as nuclear DNA methylation and chromatin condensation patterns — visualized by fluorescent staining and evaluated by dedicated three-dimensional (3D) quantitative and high-throughput cell-by-cell image analysis — in assessing the proliferative capacity, i.e. growth behavior of cells: to provide a more dynamic picture of a cell population with potential implications in basic science, cancer diagnostics/prognostics and therapeutic drug development. Two types of primary cells and four different cancer cell lines were propagated and subjected to cell-counting, flow cytometry, confocal imaging, and 3D image analysis at various points in culture. Additionally a subset of primary and cancer cells was accelerated into senescence by oxidative stress. DNA methylation and chromatin condensation levels decreased with declining doubling times when primary cells aged in culture with the lowest levels reached at the stage of proliferative senescence. In comparison, immortal cancer cells with constant but higher doubling times mostly displayed lower and constant levels of the two in situ-derived features. However, stress-induced senescent primary and cancer cells showed similar levels of these features compared with primary cells that had reached natural growth arrest. With regards to global DNA methylation and chromatin condensation levels, aggressively growing cancer cells seem to take an intermediate level between normally proliferating and senescent cells. Thus, normal cells apparently reach cancer-cell equivalent stages of the two parameters at some point in aging, which might challenge phenotypic distinction between these two types of cells. Companion high-resolution molecular profiling could provide information on possible underlying differences that would explain benign versus malign cell growth behaviors. PMID:23562889

Nuclear DNA methylation and chromatin condensation phenotypes are distinct between normally proliferating/aging, rapidly growing/immortal, and senescent cells.

PubMed

Oh, Jin Ho; Gertych, Arkadiusz; Tajbakhsh, Jian

2013-03-01

This study reports on probing the utility of in situ chromatin texture features such as nuclear DNA methylation and chromatin condensation patterns - visualized by fluorescent staining and evaluated by dedicated three-dimensional (3D) quantitative and high-throughput cell-by-cell image analysis - in assessing the proliferative capacity, i.e. growth behavior of cells: to provide a more dynamic picture of a cell population with potential implications in basic science, cancer diagnostics/prognostics and therapeutic drug development. Two types of primary cells and four different cancer cell lines were propagated and subjected to cell-counting, flow cytometry, confocal imaging, and 3D image analysis at various points in culture. Additionally a subset of primary and cancer cells was accelerated into senescence by oxidative stress. DNA methylation and chromatin condensation levels decreased with declining doubling times when primary cells aged in culture with the lowest levels reached at the stage of proliferative senescence. In comparison, immortal cancer cells with constant but higher doubling times mostly displayed lower and constant levels of the two in situ-derived features. However, stress-induced senescent primary and cancer cells showed similar levels of these features compared with primary cells that had reached natural growth arrest. With regards to global DNA methylation and chromatin condensation levels, aggressively growing cancer cells seem to take an intermediate level between normally proliferating and senescent cells. Thus, normal cells apparently reach cancer-cell equivalent stages of the two parameters at some point in aging, which might challenge phenotypic distinction between these two types of cells. Companion high-resolution molecular profiling could provide information on possible underlying differences that would explain benign versus malign cell growth behaviors.

Special Features of Induction Annealing of Friction Stir Welded Joints of Medium-Alloy Steels

NASA Astrophysics Data System (ADS)

Priymak, E. Yu.; Stepanchukova, A. V.; Bashirova, E. V.; Fot, A. P.; Firsova, N. V.

2018-01-01

Welded joints of medium-alloy steels XJY750 and 40KhN2MA are studied in the initial condition and after different variants of annealing. Special features of the phase transformations occurring in the welded steels are determined. Optimum modes of annealing are recommended for the studied welded joints of drill pipes, which provide a high level of mechanical properties including the case of impact loading.

Inhibitory Competition between Shape Properties in Figure-Ground Perception

ERIC Educational Resources Information Center

Peterson, Mary A.; Skow, Emily

2008-01-01

Theories of figure-ground perception entail inhibitory competition between either low-level units (edge or feature units) or high-level shape properties. Extant computational models instantiate the 1st type of theory. The authors investigated a prediction of the 2nd type of theory: that shape properties suggested on the ground side of an edge are…

Bi-level multi-source learning for heterogeneous block-wise missing data.

PubMed

Xiang, Shuo; Yuan, Lei; Fan, Wei; Wang, Yalin; Thompson, Paul M; Ye, Jieping

2014-11-15

Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified "bi-level" learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches. © 2013 Elsevier Inc. All rights reserved.

Both serum and tissue Galectin-1 levels are associated with adverse clinical features in neuroblastoma.

PubMed

Chen, Kai; Cai, Yuanxia; Zhang, Min; Wu, Zhixiang; Wu, Yeming

2018-05-24

Neuroblastoma is one of the most common pediatric solid tumors. Although the 5-year overall survival rate has increased over the past few decades, high-risk patients still have a poor prognosis due to a lack of biomonitoring therapy. This study was performed to investigate the role of Galectin-1 in neuroblastoma biomonitoring therapy. A tissue microarray containing 37 neuroblastoma tissue samples was used to evaluate the correlation between Galectin-1 expression and clinical features. Blood samples were examined to better understand whether serum Galectin-1 (sGalectin-1) could be used for biomonitoring therapy. Kaplan-Meier analysis and ROC analysis was conducted to distinguish the outcome associated with high or low expression of Galectin-1 in patients with neuroblastoma. Increased Galectin-1 expression was found in neuroblastoma and it was further demonstrated that elevated tissue Galectin-1 expression was related to INSS stage, histology, bone marrow metastasis, and poor survival. sGalectin-1 levels were higher in newly diagnosed patients with neuroblastoma than healthy subjects. Patients with elevated sGalectin-1 through treatment cycles correlated with the poor chemo-responses and tended to have worse outcomes, such as metastasis or stable tumor size, whereas gradually decreasing sGalectin-1 levels correlated with no observed progression in clinical symptoms. Tissue and serum Galectin-1 levels were associated with adverse clinical features in patients with neuroblastoma, and sGalectin-1 could be a potential biomarker for monitoring therapy. © 2018 Wiley Periodicals, Inc.

Seeing the invisible: The scope and limits of unconscious processing in binocular rivalry

PubMed Central

Lin, Zhicheng; He, Sheng

2009-01-01

When an image is presented to one eye and a very different image is presented to the corresponding location of the other eye, they compete for perceptual dominance, such that only one image is visible at a time while the other is suppressed. Called binocular rivalry, this phenomenon and its deviants have been extensively exploited to study the mechanism and neural correlates of consciousness. In this paper, we propose a framework, the unconscious binding hypothesis, to distinguish unconscious and conscious processing. According to this framework, the unconscious mind not only encodes individual features but also temporally binds distributed features to give rise to cortical representation, but unlike conscious binding, such unconscious binding is fragile. Under this framework, we review evidence from psychophysical and neuroimaging studies, which suggests that: (1) for invisible low level features, prolonged exposure to visual pattern and simple translational motion can alter the appearance of subsequent visible features (i.e. adaptation); for invisible high level features, although complex spiral motion cannot produce adaptation, nor can objects/words enhance subsequent processing of related stimuli (i.e. priming), images of objects such as tools can nevertheless activate the dorsal pathway; and (2) although invisible central cues cannot orient attention, invisible erotic pictures in the periphery can nevertheless guide attention, likely through emotional arousal; reciprocally, the processing of invisible information can be modulated by attention at perceptual and neural levels. PMID:18824061

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

PubMed

Shim, Miseon; Hwang, Han-Jeong; Kim, Do-Won; Lee, Seung-Hwan; Im, Chang-Hwan

2016-10-01

Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG features can potentially be applied to the differential diagnosis of schizophrenia, most studies have used only sensor-level EEG features such as ERP peak amplitude and power spectrum for machine learning-based diagnosis of schizophrenia. In this study, we used both sensor-level and source-level features extracted from EEG signals recorded during an auditory oddball task for the classification of patients with schizophrenia and healthy controls. EEG signals were recorded from 34 patients with schizophrenia and 34 healthy controls while each subject was asked to attend to oddball tones. Our results demonstrated higher classification accuracy when source-level features were used together with sensor-level features, compared to when only sensor-level features were used. In addition, the selected sensor-level features were mostly found in the frontal area, and the selected source-level features were mostly extracted from the temporal area, which coincide well with the well-known pathological region of cognitive processing in patients with schizophrenia. Our results suggest that our approach would be a promising tool for the computer-aided diagnosis of schizophrenia. Copyright © 2016 Elsevier B.V. All rights reserved.

How Configural Is the Configural Superiority Effect? A Neuroimaging Investigation of Emergent Features in Visual Cortex

PubMed Central

Fox, Olivia M.; Harel, Assaf; Bennett, Kevin B.

2017-01-01

The perception of a visual stimulus is dependent not only upon local features, but also on the arrangement of those features. When stimulus features are perceptually well organized (e.g., symmetric or parallel), a global configuration with a high degree of salience emerges from the interactions between these features, often referred to as emergent features. Emergent features can be demonstrated in the Configural Superiority Effect (CSE): presenting a stimulus within an organized context relative to its presentation in a disarranged one results in better performance. Prior neuroimaging work on the perception of emergent features regards the CSE as an “all or none” phenomenon, focusing on the contrast between configural and non-configural stimuli. However, it is still not clear how emergent features are processed between these two endpoints. The current study examined the extent to which behavioral and neuroimaging markers of emergent features are responsive to the degree of configurality in visual displays. Subjects were tasked with reporting the anomalous quadrant in a visual search task while being scanned. Degree of configurality was manipulated by incrementally varying the rotational angle of low-level features within the stimulus arrays. Behaviorally, we observed faster response times with increasing levels of configurality. These behavioral changes were accompanied by increases in response magnitude across multiple visual areas in occipito-temporal cortex, primarily early visual cortex and object-selective cortex. Our findings suggest that the neural correlates of emergent features can be observed even in response to stimuli that are not fully configural, and demonstrate that configural information is already present at early stages of the visual hierarchy. PMID:28167924

Prosocial Behavior: Long-Term Trajectories and Psychosocial Outcomes

PubMed Central

Flynn, Elinor; Ehrenreich, Samuel E.; Beron, Kurt J.; Underwood, Marion K.

2015-01-01

This study investigated developmental trajectories for prosocial behavior for a sample followed from age 10 – 18 and examined possible adjustment outcomes associated with membership in different trajectory groups. Participants were 136 boys and 148 girls, their teachers, and their parents (19.4% African American, 2.4% Asian, 51.9% Caucasian, 19.5% Hispanic, and 5.8% other). Teachers rated children’s prosocial behavior yearly in grades 4 – 12. At the end of the 12th grade year, teachers, parents, and participants reported externalizing behaviors and participants reported internalizing symptoms, narcissism, and features of borderline personality disorder. Results suggested that prosocial behavior remained stable from middle childhood through late adolescence. Group-based mixture modeling revealed three prosocial trajectory groups: low (18.7%), medium (52.8%), and high (29.6%). Membership in the high prosocial trajectory group predicted lower levels of externalizing behavior as compared to the low prosocial trajectory group, and for girls, lower levels of internalizing symptoms. Membership in the medium prosocial trajectory group also predicted being lower on externalizing behaviors. Membership in the high prosocial trajectory group predicted lower levels of borderline personality features for girls only. PMID:26236108

Textural analysis of early-phase spatiotemporal changes in contrast enhancement of breast lesions imaged with an ultrafast DCE-MRI protocol.

PubMed

Milenković, Jana; Dalmış, Mehmet Ufuk; Žgajnar, Janez; Platel, Bram

2017-09-01

New ultrafast view-sharing sequences have enabled breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to be performed at high spatial and temporal resolution. The aim of this study is to evaluate the diagnostic potential of textural features that quantify the spatiotemporal changes of the contrast-agent uptake in computer-aided diagnosis of malignant and benign breast lesions imaged with high spatial and temporal resolution DCE-MRI. The proposed approach is based on the textural analysis quantifying the spatial variation of six dynamic features of the early-phase contrast-agent uptake of a lesion's largest cross-sectional area. The textural analysis is performed by means of the second-order gray-level co-occurrence matrix, gray-level run-length matrix and gray-level difference matrix. This yields 35 textural features to quantify the spatial variation of each of the six dynamic features, providing a feature set of 210 features in total. The proposed feature set is evaluated based on receiver operating characteristic (ROC) curve analysis in a cross-validation scheme for random forests (RF) and two support vector machine classifiers, with linear and radial basis function (RBF) kernel. Evaluation is done on a dataset with 154 breast lesions (83 malignant and 71 benign) and compared to a previous approach based on 3D morphological features and the average and standard deviation of the same dynamic features over the entire lesion volume as well as their average for the smaller region of the strongest uptake rate. The area under the ROC curve (AUC) obtained by the proposed approach with the RF classifier was 0.8997, which was significantly higher (P = 0.0198) than the performance achieved by the previous approach (AUC = 0.8704) on the same dataset. Similarly, the proposed approach obtained a significantly higher result for both SVM classifiers with RBF (P = 0.0096) and linear kernel (P = 0.0417) obtaining AUC of 0.8876 and 0.8548, respectively, compared to AUC values of previous approach of 0.8562 and 0.8311, respectively. The proposed approach based on 2D textural features quantifying spatiotemporal changes of the contrast-agent uptake significantly outperforms the previous approach based on 3D morphology and dynamic analysis in differentiating the malignant and benign breast lesions, showing its potential to aid clinical decision making. © 2017 American Association of Physicists in Medicine.

OVERALL CONTROL SYSTEM FOR HIGH FLUX PILE

DOEpatents

Newson, H.W.; Durham, N.C.; Wigner, E.P.; Princeton, N.J.; Epler, E.P.

1961-05-23

A control system is given for a high fiux reactor incorporating an anti- scram control feature whereby a neutron absorbing control rod acts as a fine adjustment while a neutron absorbing shim rod, actuated upon a command received from reactor period and level signals, has substantially greater effect on the neutron level and is moved prior to scram conditions to alter the reactor activity before a scram condition is created. Thus the probability that a scram will have to be initiated is substantially decreased.

Spatial Patterns of Inshore Marine Soundscapes.

PubMed

McWilliam, Jamie

2016-01-01

Passive acoustic monitoring was employed to investigate spatial patterns of soundscapes within a marine reserve. High energy level broadband snaps dominated nearly all habitat soundscapes. Snaps, the principal acoustic feature of soundscapes, were primarily responsible for the observed spatial patterns, and soundscapes appeared to retain a level of compositional and configurational stability. In the presence of high-level broadband snaps, soundscape composition was more influenced by geographic location than habitat type. Future research should focus on investigating the spatial patterns of soundscapes across a wider range of coastal and offshore seascapes containing a variety of distinct ecosystems and habitats.

Managing Patient Trust in Managed Care

PubMed Central

Davies, Huw T.O.; Rundall, Thomas G.

2000-01-01

Patient trust has been identified as an important element in the patient-physician relationship. However, common features of managed care, such as risk-sharing, utilization review, and limitations on benefits, may erode the traditionally high trust that patients have in their physicians. High trust is not always justified; rather, an optimal level of trust arises from the level of interdependence between patients and physicians. This analysis of the interrelationship between patient-physician trust and some of the key facets of managed care has important implications for managed care. A return to high levels of trust may be impracticable, and new strategies for balancing trust-building efforts by caregivers with checking mechanisms accessible to patients are recommended. PMID:11191451

Symptoms, Devices, Prevention, Research | NIH MedlinePlus the Magazine

MedlinePlus

... JavaScript on. Feature: Hearing Loss Symptoms, Devices, Prevention & Research Past Issues / Spring 2015 Table of Contents Anatomy ... hearing loss from dangerously high noise levels. NIH Research to Results Teams of scientists, supported by the ...

Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval.

PubMed

Li, Ke; Pang, Kaiyue; Song, Yi-Zhe; Hospedales, Timothy M; Xiang, Tao; Zhang, Honggang

2017-08-25

We study the problem of fine-grained sketch-based image retrieval. By performing instance-level (rather than category-level) retrieval, it embodies a timely and practical application, particularly with the ubiquitous availability of touchscreens. Three factors contribute to the challenging nature of the problem: (i) free-hand sketches are inherently abstract and iconic, making visual comparisons with photos difficult, (ii) sketches and photos are in two different visual domains, i.e. black and white lines vs. color pixels, and (iii) fine-grained distinctions are especially challenging when executed across domain and abstraction-level. To address these challenges, we propose to bridge the image-sketch gap both at the high-level via parts and attributes, as well as at the low-level, via introducing a new domain alignment method. More specifically, (i) we contribute a dataset with 304 photos and 912 sketches, where each sketch and image is annotated with its semantic parts and associated part-level attributes. With the help of this dataset, we investigate (ii) how strongly-supervised deformable part-based models can be learned that subsequently enable automatic detection of part-level attributes, and provide pose-aligned sketch-image comparisons. To reduce the sketch-image gap when comparing low-level features, we also (iii) propose a novel method for instance-level domain-alignment, that exploits both subspace and instance-level cues to better align the domains. Finally (iv) these are combined in a matching framework integrating aligned low-level features, mid-level geometric structure and high-level semantic attributes. Extensive experiments conducted on our new dataset demonstrate effectiveness of the proposed method.

Central auditory neurons have composite receptive fields.

PubMed

Kozlov, Andrei S; Gentner, Timothy Q

2016-02-02

High-level neurons processing complex, behaviorally relevant signals are sensitive to conjunctions of features. Characterizing the receptive fields of such neurons is difficult with standard statistical tools, however, and the principles governing their organization remain poorly understood. Here, we demonstrate multiple distinct receptive-field features in individual high-level auditory neurons in a songbird, European starling, in response to natural vocal signals (songs). We then show that receptive fields with similar characteristics can be reproduced by an unsupervised neural network trained to represent starling songs with a single learning rule that enforces sparseness and divisive normalization. We conclude that central auditory neurons have composite receptive fields that can arise through a combination of sparseness and normalization in neural circuits. Our results, along with descriptions of random, discontinuous receptive fields in the central olfactory neurons in mammals and insects, suggest general principles of neural computation across sensory systems and animal classes.

Sensory features and repetitive behaviors in children with autism and developmental delays.

PubMed

Boyd, Brian A; Baranek, Grace T; Sideris, John; Poe, Michele D; Watson, Linda R; Patten, Elena; Miller, Heather

2010-04-01

This study combined parent and observational measures to examine the association between aberrant sensory features and restricted, repetitive behaviors in children with autism (N=67) and those with developmental delays (N=42). Confirmatory factor analysis was used to empirically validate three sensory constructs of interest: hyperresponsiveness, hyporesponsiveness, and sensory seeking. Examining the association between the three derived sensory factor scores and scores on the Repetitive Behavior Scales--Revised revealed the co-occurrence of these behaviors in both clinical groups. Specifically, high levels of hyperresponsive behaviors predicted high levels of repetitive behaviors, and the relationship between these variables remained the same controlling for mental age. We primarily found non-significant associations between hyporesponsiveness or sensory seeking and repetitive behaviors, with the exception that sensory seeking was associated with ritualistic/sameness behaviors. These findings suggest that shared neurobiological mechanisms may underlie hyperresponsive sensory symptoms and repetitive behaviors and have implications for diagnostic classification as well as intervention.

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

PubMed Central

Drew, Mark S.

2016-01-01

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction. PMID:28096807

Are polynuclear superhalogens without halogen atoms probable? A high-level ab initio case study on triple-bridged binuclear anions with cyanide ligands

NASA Astrophysics Data System (ADS)

Yin, Bing; Li, Teng; Li, Jin-Feng; Yu, Yang; Li, Jian-Li; Wen, Zhen-Yi; Jiang, Zhen-Yi

2014-03-01

The first theoretical exploration of superhalogen properties of polynuclear structures based on pseudohalogen ligand is reported here via a case study on eight triply-bridged [Mg2(CN)5]- clusters. From our high-level ab initio results, all these clusters are superhalogens due to their high vertical electron detachment energies (VDE), of which the largest value is 8.67 eV at coupled-cluster single double triple (CCSD(T)) level. Although outer valence Green's function results are consistent with CCSD(T) in most cases, it overestimates the VDEs of three anions dramatically by more than 1 eV. Therefore, the combined usage of several theoretical methods is important for the accuracy of purely theoretical prediction of superhalogen properties of new structures. Spatial distribution of the extra electron of high-VDE anions here indicates two features: remarkable aggregation on bridging CN units and non-negligible distribution on every CN unit. These two features lower the potential and kinetic energies of the extra electron respectively and thus lead to high VDE. Besides superhalogen properties, the structures, relative stabilities and thermodynamic stabilities with respect to detachment of CN-1 were also investigated for these anions. The collection of these results indicates that polynuclear structures based on pseudohalogen ligand are promising candidates for new superhalogens with enhanced properties.

Are polynuclear superhalogens without halogen atoms probable? A high-level ab initio case study on triple-bridged binuclear anions with cyanide ligands.

PubMed

Yin, Bing; Li, Teng; Li, Jin-Feng; Yu, Yang; Li, Jian-Li; Wen, Zhen-Yi; Jiang, Zhen-Yi

2014-03-07

The first theoretical exploration of superhalogen properties of polynuclear structures based on pseudohalogen ligand is reported here via a case study on eight triply-bridged [Mg2(CN)5](-) clusters. From our high-level ab initio results, all these clusters are superhalogens due to their high vertical electron detachment energies (VDE), of which the largest value is 8.67 eV at coupled-cluster single double triple (CCSD(T)) level. Although outer valence Green's function results are consistent with CCSD(T) in most cases, it overestimates the VDEs of three anions dramatically by more than 1 eV. Therefore, the combined usage of several theoretical methods is important for the accuracy of purely theoretical prediction of superhalogen properties of new structures. Spatial distribution of the extra electron of high-VDE anions here indicates two features: remarkable aggregation on bridging CN units and non-negligible distribution on every CN unit. These two features lower the potential and kinetic energies of the extra electron respectively and thus lead to high VDE. Besides superhalogen properties, the structures, relative stabilities and thermodynamic stabilities with respect to detachment of CN(-1) were also investigated for these anions. The collection of these results indicates that polynuclear structures based on pseudohalogen ligand are promising candidates for new superhalogens with enhanced properties.

Automated texture-based identification of ovarian cancer in confocal microendoscope images

NASA Astrophysics Data System (ADS)

Srivastava, Saurabh; Rodriguez, Jeffrey J.; Rouse, Andrew R.; Brewer, Molly A.; Gmitro, Arthur F.

2005-03-01

The fluorescence confocal microendoscope provides high-resolution, in-vivo imaging of cellular pathology during optical biopsy. There are indications that the examination of human ovaries with this instrument has diagnostic implications for the early detection of ovarian cancer. The purpose of this study was to develop a computer-aided system to facilitate the identification of ovarian cancer from digital images captured with the confocal microendoscope system. To achieve this goal, we modeled the cellular-level structure present in these images as texture and extracted features based on first-order statistics, spatial gray-level dependence matrices, and spatial-frequency content. Selection of the best features for classification was performed using traditional feature selection techniques including stepwise discriminant analysis, forward sequential search, a non-parametric method, principal component analysis, and a heuristic technique that combines the results of these methods. The best set of features selected was used for classification, and performance of various machine classifiers was compared by analyzing the areas under their receiver operating characteristic curves. The results show that it is possible to automatically identify patients with ovarian cancer based on texture features extracted from confocal microendoscope images and that the machine performance is superior to that of the human observer.

Borderline Personality Disorder and Self-Conscious Affect: Too Much Shame But Not Enough Guilt?

PubMed Central

Peters, Jessica R.; Geiger, Paul J.

2016-01-01

Shame has emerged as a particularly relevant emotion to the maintenance and exacerbation of borderline personality disorder (BPD) features; however, little attention has been paid to the potentially differing effects of other forms of self-conscious affect. While guilt has been demonstrated to have adaptive functions in the social psychology literature, it has not been previously explored whether a lack of socially adaptive guilt might also contribute to BPD-related dysfunction. The present study examined the relationship between BPD features and self-conscious emotions in a sample of undergraduate students (n=839). Increased shame and decreased guilt independently accounted for significant variance in the association between BPD features and anger, hostility, and externalization of blame. Only increased shame significantly mediated the association between BPD features and anger rumination, and only decreased guilt significantly mediated the relationship between BPD features and aggression. These findings suggest BPD and its associated problems with anger and externalizing may be characterized not only by high levels of shame, but also by lower levels of guilt. Clinical implications include the need to differentiate between self-conscious emotions and teach adaptive responses to warranted guilt. PMID:26866901

Borderline personality disorder and self-conscious affect: Too much shame but not enough guilt?

PubMed

Peters, Jessica R; Geiger, Paul J

2016-07-01

Shame has emerged as a particularly relevant emotion to the maintenance and exacerbation of borderline personality disorder (BPD) features; however, little attention has been paid to the potentially differing effects of other forms of self-conscious affect. While guilt has been demonstrated to have adaptive functions in the social psychology literature, it has not been previously explored whether a lack of socially adaptive guilt might also contribute to BPD-related dysfunction. The present study examined the relationship between BPD features and self-conscious emotions in a sample of undergraduate students (n = 839). Increased shame and decreased guilt independently accounted for significant variance in the association between BPD features and anger, hostility, and externalization of blame. Only increased shame significantly mediated the association between BPD features and anger rumination, and only decreased guilt significantly mediated the relationship between BPD features and aggression. These findings suggest BPD and its associated problems with anger and externalizing may be characterized not only by high levels of shame, but also by lower levels of guilt. Clinical implications include the need to differentiate between self-conscious emotions and teach adaptive responses to warranted guilt. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Gaze-independent brain-computer interfaces based on covert attention and feature attention

NASA Astrophysics Data System (ADS)

Treder, M. S.; Schmidt, N. M.; Blankertz, B.

2011-10-01

There is evidence that conventional visual brain-computer interfaces (BCIs) based on event-related potentials cannot be operated efficiently when eye movements are not allowed. To overcome this limitation, the aim of this study was to develop a visual speller that does not require eye movements. Three different variants of a two-stage visual speller based on covert spatial attention and non-spatial feature attention (i.e. attention to colour and form) were tested in an online experiment with 13 healthy participants. All participants achieved highly accurate BCI control. They could select one out of thirty symbols (chance level 3.3%) with mean accuracies of 88%-97% for the different spellers. The best results were obtained for a speller that was operated using non-spatial feature attention only. These results show that, using feature attention, it is possible to realize high-accuracy, fast-paced visual spellers that have a large vocabulary and are independent of eye gaze.

Statistical-techniques-based computer-aided diagnosis (CAD) using texture feature analysis: application in computed tomography (CT) imaging to fatty liver disease

NASA Astrophysics Data System (ADS)

Chung, Woon-Kwan; Park, Hyong-Hu; Im, In-Chul; Lee, Jae-Seung; Goo, Eun-Hoe; Dong, Kyung-Rae

2012-09-01

This paper proposes a computer-aided diagnosis (CAD) system based on texture feature analysis and statistical wavelet transformation technology to diagnose fatty liver disease with computed tomography (CT) imaging. In the target image, a wavelet transformation was performed for each lesion area to set the region of analysis (ROA, window size: 50 × 50 pixels) and define the texture feature of a pixel. Based on the extracted texture feature values, six parameters (average gray level, average contrast, relative smoothness, skewness, uniformity, and entropy) were determined to calculate the recognition rate for a fatty liver. In addition, a multivariate analysis of the variance (MANOVA) method was used to perform a discriminant analysis to verify the significance of the extracted texture feature values and the recognition rate for a fatty liver. According to the results, each texture feature value was significant for a comparison of the recognition rate for a fatty liver ( p < 0.05). Furthermore, the F-value, which was used as a scale for the difference in recognition rates, was highest in the average gray level, relatively high in the skewness and the entropy, and relatively low in the uniformity, the relative smoothness and the average contrast. The recognition rate for a fatty liver had the same scale as that for the F-value, showing 100% (average gray level) at the maximum and 80% (average contrast) at the minimum. Therefore, the recognition rate is believed to be a useful clinical value for the automatic detection and computer-aided diagnosis (CAD) using the texture feature value. Nevertheless, further study on various diseases and singular diseases will be needed in the future.

Exploring spatial-temporal dynamics of fire regime features in mainland Spain

NASA Astrophysics Data System (ADS)

Jiménez-Ruano, Adrián; Rodrigues Mimbrero, Marcos; de la Riva Fernández, Juan

2017-10-01

This paper explores spatial-temporal dynamics in fire regime features, such as fire frequency, burnt area, large fires and natural- and human-caused fires, as an essential part of fire regime characterization. Changes in fire features are analysed at different spatial - regional and provincial/NUTS3 - levels, together with summer and winter temporal scales, using historical fire data from Spain for the period 1974-2013. Temporal shifts in fire features are investigated by means of change point detection procedures - Pettitt test, AMOC (at most one change), PELT (pruned exact linear time) and BinSeg (binary segmentation) - at a regional level to identify changes in the time series of the features. A trend analysis was conducted using the Mann-Kendall and Sen's slope tests at both the regional and NUTS3 level. Finally, we applied a principal component analysis (PCA) and varimax rotation to trend outputs - mainly Sen's slope values - to summarize overall temporal behaviour and to explore potential links in the evolution of fire features. Our results suggest that most fire features show remarkable shifts between the late 1980s and the first half of the 1990s. Mann-Kendall outputs revealed negative trends in the Mediterranean region. Results from Sen's slope suggest high spatial and intra-annual variability across the study area. Fire activity related to human sources seems to be experiencing an overall decrease in the northwestern provinces, particularly pronounced during summer. Similarly, the Hinterland and the Mediterranean coast are gradually becoming less fire affected. Finally, PCA enabled trends to be synthesized into four main components: winter fire frequency (PC1), summer burnt area (PC2), large fires (PC3) and natural fires (PC4).

An Ensemble Method with Integration of Feature Selection and Classifier Selection to Detect the Landslides

NASA Astrophysics Data System (ADS)

Zhongqin, G.; Chen, Y.

2017-12-01

Abstract Quickly identify the spatial distribution of landslides automatically is essential for the prevention, mitigation and assessment of the landslide hazard. It's still a challenging job owing to the complicated characteristics and vague boundary of the landslide areas on the image. The high resolution remote sensing image has multi-scales, complex spatial distribution and abundant features, the object-oriented image classification methods can make full use of the above information and thus effectively detect the landslides after the hazard happened. In this research we present a new semi-supervised workflow, taking advantages of recent object-oriented image analysis and machine learning algorithms to quick locate the different origins of landslides of some areas on the southwest part of China. Besides a sequence of image segmentation, feature selection, object classification and error test, this workflow ensemble the feature selection and classifier selection. The feature this study utilized were normalized difference vegetation index (NDVI) change, textural feature derived from the gray level co-occurrence matrices (GLCM), spectral feature and etc. The improvement of this study shows this algorithm significantly removes some redundant feature and the classifiers get fully used. All these improvements lead to a higher accuracy on the determination of the shape of landslides on the high resolution remote sensing image, in particular the flexibility aimed at different kinds of landslides.

Natural image statistics and low-complexity feature selection.

PubMed

Vasconcelos, Manuela; Vasconcelos, Nuno

2009-02-01

Low-complexity feature selection is analyzed in the context of visual recognition. It is hypothesized that high-order dependences of bandpass features contain little information for discrimination of natural images. This hypothesis is characterized formally by the introduction of the concepts of conjunctive interference and decomposability order of a feature set. Necessary and sufficient conditions for the feasibility of low-complexity feature selection are then derived in terms of these concepts. It is shown that the intrinsic complexity of feature selection is determined by the decomposability order of the feature set and not its dimension. Feature selection algorithms are then derived for all levels of complexity and are shown to be approximated by existing information-theoretic methods, which they consistently outperform. The new algorithms are also used to objectively test the hypothesis of low decomposability order through comparison of classification performance. It is shown that, for image classification, the gain of modeling feature dependencies has strongly diminishing returns: best results are obtained under the assumption of decomposability order 1. This suggests a generic law for bandpass features extracted from natural images: that the effect, on the dependence of any two features, of observing any other feature is constant across image classes.

A unified tensor level set for image segmentation.

PubMed

Wang, Bin; Gao, Xinbo; Tao, Dacheng; Li, Xuelong

2010-06-01

This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt- and pepper-type noise. Second, considering the local geometrical features, e.g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a weighted distance definition, the model possesses the capacity to cope with data varying from scalar to vector, then to high-order tensor. We apply the proposed method to synthetic, medical, and natural images, and the result suggests that the proposed method is superior to the available representative region-based level set method.

Design of a verifiable subset for HAL/S

NASA Technical Reports Server (NTRS)

Browne, J. C.; Good, D. I.; Tripathi, A. R.; Young, W. D.

1979-01-01

An attempt to evaluate the applicability of program verification techniques to the existing programming language, HAL/S is discussed. HAL/S is a general purpose high level language designed to accommodate the software needs of the NASA Space Shuttle project. A diversity of features for scientific computing, concurrent and real-time programming, and error handling are discussed. The criteria by which features were evaluated for inclusion into the verifiable subset are described. Individual features of HAL/S with respect to these criteria are examined and justification for the omission of various features from the subset is provided. Conclusions drawn from the research are presented along with recommendations made for the use of HAL/S with respect to the area of program verification.

Geometric quantification of features in large flow fields.

PubMed

Kendall, Wesley; Huang, Jian; Peterka, Tom

2012-01-01

Interactive exploration of flow features in large-scale 3D unsteady-flow data is one of the most challenging visualization problems today. To comprehensively explore the complex feature spaces in these datasets, a proposed system employs a scalable framework for investigating a multitude of characteristics from traced field lines. This capability supports the examination of various neighborhood-based geometric attributes in concert with other scalar quantities. Such an analysis wasn't previously possible because of the large computational overhead and I/O requirements. The system integrates visual analytics methods by letting users procedurally and interactively describe and extract high-level flow features. An exploration of various phenomena in a large global ocean-modeling simulation demonstrates the approach's generality and expressiveness as well as its efficacy.

Combined epigenetic and differentiation-based treatment inhibits neuroblastoma tumor growth and links HIF2α to tumor suppression

PubMed Central

Westerlund, Isabelle; Shi, Yao; Toskas, Konstantinos; Fell, Stuart M.; Li, Shuijie; Surova, Olga; Södersten, Erik; Kogner, Per; Nyman, Ulrika; Schlisio, Susanne; Holmberg, Johan

2017-01-01

Neuroblastoma is a pediatric cancer characterized by variable outcomes ranging from spontaneous regression to life-threatening progression. High-risk neuroblastoma patients receive myeloablative chemotherapy with hematopoietic stem-cell transplant followed by adjuvant retinoid differentiation treatment. However, the overall survival remains low; hence, there is an urgent need for alternative therapeutic approaches. One feature of high-risk neuroblastoma is the high level of DNA methylation of putative tumor suppressors. Combining the reversibility of DNA methylation with the differentiation-promoting activity of retinoic acid (RA) could provide an alternative strategy to treat high-risk neuroblastoma. Here we show that treatment with the DNA-demethylating drug 5-Aza-deoxycytidine (AZA) restores high-risk neuroblastoma sensitivity to RA. Combined systemic distribution of AZA and RA impedes tumor growth and prolongs survival. Genome-wide analysis of treated tumors reveals that this combined treatment rapidly induces a HIF2α-associated hypoxia-like transcriptional response followed by an increase in neuronal gene expression and a decrease in cell-cycle gene expression. A small-molecule inhibitor of HIF2α activity diminishes the tumor response to AZA+RA treatment, indicating that the increase in HIF2α levels is a key component in tumor response to AZA+RA. The link between increased HIF2α levels and inhibited tumor growth is reflected in large neuroblastoma patient datasets. Therein, high levels of HIF2α, but not HIF1α, significantly correlate with expression of neuronal differentiation genes and better prognosis but negatively correlate with key features of high-risk tumors, such as MYCN amplification. Thus, contrary to previous studies, our findings indicate an unanticipated tumor-suppressive role for HIF2α in neuroblastoma. PMID:28696319

Combined epigenetic and differentiation-based treatment inhibits neuroblastoma tumor growth and links HIF2α to tumor suppression.

PubMed

Westerlund, Isabelle; Shi, Yao; Toskas, Konstantinos; Fell, Stuart M; Li, Shuijie; Surova, Olga; Södersten, Erik; Kogner, Per; Nyman, Ulrika; Schlisio, Susanne; Holmberg, Johan

2017-07-25

Neuroblastoma is a pediatric cancer characterized by variable outcomes ranging from spontaneous regression to life-threatening progression. High-risk neuroblastoma patients receive myeloablative chemotherapy with hematopoietic stem-cell transplant followed by adjuvant retinoid differentiation treatment. However, the overall survival remains low; hence, there is an urgent need for alternative therapeutic approaches. One feature of high-risk neuroblastoma is the high level of DNA methylation of putative tumor suppressors. Combining the reversibility of DNA methylation with the differentiation-promoting activity of retinoic acid (RA) could provide an alternative strategy to treat high-risk neuroblastoma. Here we show that treatment with the DNA-demethylating drug 5-Aza-deoxycytidine (AZA) restores high-risk neuroblastoma sensitivity to RA. Combined systemic distribution of AZA and RA impedes tumor growth and prolongs survival. Genome-wide analysis of treated tumors reveals that this combined treatment rapidly induces a HIF2α-associated hypoxia-like transcriptional response followed by an increase in neuronal gene expression and a decrease in cell-cycle gene expression. A small-molecule inhibitor of HIF2α activity diminishes the tumor response to AZA+RA treatment, indicating that the increase in HIF2α levels is a key component in tumor response to AZA+RA. The link between increased HIF2α levels and inhibited tumor growth is reflected in large neuroblastoma patient datasets. Therein, high levels of HIF2α, but not HIF1α, significantly correlate with expression of neuronal differentiation genes and better prognosis but negatively correlate with key features of high-risk tumors, such as MYCN amplification. Thus, contrary to previous studies, our findings indicate an unanticipated tumor-suppressive role for HIF2α in neuroblastoma.

[Features of neurologic semiotics at chronic obstructive pulmonary disease].

PubMed

Litvinenko, I V; Baranov, V L; Kolcheva, Iu A

2011-01-01

Chronic obstructive pulmonary disease (COPD) is actual pathology, when it forms the mixed hypoxemia. In the conditions of a chronic hypoxemia structures of organism with high level of metabolic processes, namely brain tissues, suffer. Character of defeat of the central nervous system at that pathology is insufficiently studied. In this article we studied and analysed the presence of such changes as depression, anxiety, cognitive impairment and features of neurologic semiotics at COPD in 50 patients.

Single-feature polymorphism discovery in the barley transcriptome

PubMed Central

Rostoks, Nils; Borevitz, Justin O; Hedley, Peter E; Russell, Joanne; Mudie, Sharon; Morris, Jenny; Cardle, Linda; Marshall, David F; Waugh, Robbie

2005-01-01

A probe-level model for analysis of GeneChip gene-expression data is presented which identified more than 10,000 single-feature polymorphisms (SFP) between two barley genotypes. The method has good sensitivity, as 67% of known single-nucleotide polymorphisms (SNP) were called as SFPs. This method is applicable to all oligonucleotide microarray data, accounts for SNP effects in gene-expression data and represents an efficient and versatile approach for highly parallel marker identification in large genomes. PMID:15960806

Geospatial mapping of Antarctic coastal oasis using geographic object-based image analysis and high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Jawak, Shridhar D.; Luis, Alvarinho J.

2016-04-01

An accurate spatial mapping and characterization of land cover features in cryospheric regions is an essential procedure for many geoscientific studies. A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (OBIA) to extract cryospheric geospatial information from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for OBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, east Antarctica. Multilevel segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features with respect to scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify land mass, man-made features, snow/ice, and water bodies. We focus on water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and OBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≍97%. In conclusion, our results suggest that OBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geospatial information.

Classifiers utilized to enhance acoustic based sensors to identify round types of artillery/mortar

NASA Astrophysics Data System (ADS)

Grasing, David; Desai, Sachi; Morcos, Amir

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Artillery/mortar type classification based on detected acoustic transients

NASA Astrophysics Data System (ADS)

Morcos, Amir; Grasing, David; Desai, Sachi

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Artillery/mortar round type classification to increase system situational awareness

NASA Astrophysics Data System (ADS)

Desai, Sachi; Grasing, David; Morcos, Amir; Hohil, Myron

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Comparative study of bacteremias caused by Enterococcus spp. with and without high-level resistance to gentamicin. The Grupo Andaluz para el estudio de las Enfermedades Infecciosas.

PubMed

Caballero-Granado, F J; Cisneros, J M; Luque, R; Torres-Tortosa, M; Gamboa, F; Díez, F; Villanueva, J L; Pérez-Cano, R; Pasquau, J; Merino, D; Menchero, A; Mora, D; López-Ruz, M A; Vergara, A

1998-02-01

A prospective, multicenter study was carried out over a period of 10 months. All patients with clinically significant bacteremia caused by Enterococcus spp. were included. The epidemiological, microbiological, clinical, and prognostic features and the relationship of these features to the presence of high-level resistance to gentamicin (HLRG) were studied. Ninety-three patients with enterococcal bacteremia were included, and 31 of these cases were caused by HLRG (33%). The multivariate analysis selected chronic renal failure, intensive care unit stay, previous use of antimicrobial agents, and Enterococcus faecalis species as the independent risk factors that influenced the development of HLRG. The strains with HLRG showed lower levels of susceptibility to penicillin and ciprofloxacin. Clinical features (except for chronic renal failure) were similar in both groups of patients. HLRG did not influence the prognosis for patients with enterococcal bacteremia in terms of either the crude mortality rate (29% for patients with bacteremia caused by enterococci with HLRG and 28% for patients not infected with strains with HLRG) or the hospital stay after the acquisition of enterococcal bacteremia. Hemodynamic compromise, inappropriate antimicrobial therapy, and mechanical ventilation were revealed in the multivariate analysis to be the independent risk factors for mortality. Prolonged hospitalization was associated with the nosocomial acquisition of bacteremia and polymicrobial infections.

Visual Categorization of Natural Movies by Rats

PubMed Central

Vinken, Kasper; Vermaercke, Ben

2014-01-01

Visual categorization of complex, natural stimuli has been studied for some time in human and nonhuman primates. Recent interest in the rodent as a model for visual perception, including higher-level functional specialization, leads to the question of how rodents would perform on a categorization task using natural stimuli. To answer this question, rats were trained in a two-alternative forced choice task to discriminate movies containing rats from movies containing other objects and from scrambled movies (ordinate-level categorization). Subsequently, transfer to novel, previously unseen stimuli was tested, followed by a series of control probes. The results show that the animals are capable of acquiring a decision rule by abstracting common features from natural movies to generalize categorization to new stimuli. Control probes demonstrate that they did not use single low-level features, such as motion energy or (local) luminance. Significant generalization was even present with stationary snapshots from untrained movies. The variability within and between training and test stimuli, the complexity of natural movies, and the control experiments and analyses all suggest that a more high-level rule based on more complex stimulus features than local luminance-based cues was used to classify the novel stimuli. In conclusion, natural stimuli can be used to probe ordinate-level categorization in rats. PMID:25100598

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

The Logical Basis of Evaluation Order and Pattern-Matching

DTIC Science & Technology

2009-04-17

σm] Km = Λ(p 7→ K(p)m) (·)m = () (K/κ)m = Km (σ1, σ2)m = (σm1 , σm2 ) (κ V )m = κ (V m) where pm[σm] is a bit of syntactic high fructose corn syrup ...we should do this if we are using L as a real programming language, as opposed to just studying its high -level features. When we program in...that the precise collection of patterns doesn’t matter for the high -level properties of the language. Thus there is no cost for defining new types when

Only@JCE Online

NASA Astrophysics Data System (ADS)

Holmes, Jon L.

2001-08-01

The JCE High School ChemEd Learning Information Center (CLIC) and Buyers Guide continue to be updated with each issue of the print Journal. Every month, links to articles of interest to high school teachers are added to CLIC. Links to all new book and media reviews are added to the Buyers Guide. Additions to the Biographical Snapshots of Famous Women and Minority Chemists (March 2001) and the updated WWW Site Review feature (July 2001) have been previously noted in this column. The Conceptual Questions and Challenge Problems feature has a useful, new tool, Chemical Concepts Inventory, that can be used to assess the level of chemistry misconceptions held by students.

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

PubMed Central

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

2012-01-01

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

Measurement of Responsibility: A Critical Evaluation of Level of Work Measurement by Time-Span of Discretion.

ERIC Educational Resources Information Center

Laner, S.; And Others

This report is a critical evaluation based on extended field trials and theoretical analysis of the time-span technique of measuring level of work in organizational hierarchies. It is broadly concluded that the technique does possess many of the desirable features claimed by its originator, but that earlier, less highly structured versions based…

Clinical factors associated with high-risk carotid plaque features as assessed by magnetic resonance imaging in patients with established vascular disease (from the AIM-HIGH Study).

PubMed

Zhao, Xue-Qiao; Hatsukami, Thomas S; Hippe, Daniel S; Sun, Jie; Balu, Niranjan; Isquith, Daniel A; Crouse, John R; Anderson, Todd; Huston, John; Polissar, Nayak; O'Brien, Kevin; Yuan, Chun

2014-11-01

Association between clinical factors and high-risk plaque features, such as, thin or ruptured cap, intraplaque hemorrhage, presence of lipid-rich necrotic core (LRNC), and increased LRNC volume as assessed by magnetic resonance imaging (MRI), was examined in patients with established vascular disease in the Atherothrombosis Intervention in Metabolic Syndrome With Low HDL/High Triglycerides (AIM-HIGH) trial. A total of 214 subjects underwent carotid MRI and had acceptable image quality for assessment of plaque burden, tissue contents, and MRI-modified American Heart Association lesion type by a core laboratory. We found that 77% of subjects had carotid plaques, 52% had lipid-containing plaques, and 11% had advanced American Heart Association type-VI lesions with possible surface defect, intraplaque hemorrhage, or mural thrombus. Type-VI lesions were associated with older age (odds ratio [OR] = 2.6 per 5 years increase, p <0.001). After adjusting for age, these lesions were associated with history of cerebrovascular disease (OR = 4.1, p = 0.01), higher levels of lipoprotein(a) (OR = 2.0 per 1 SD increase, p = 0.02), and larger percent wall volume (PWV [OR = 4.6 per 1 SD increase, p <0.001]) but, were negatively associated with metabolic syndrome (OR = 0.2, p = 0.02). Presence of LRNC was associated with the male gender (OR = 3.2, p = 0.02) and PWV (OR = 3.8 per 1 SD, p <0.001); however, it was negatively associated with diabetes (OR = 0.4, p = 0.02) and high-density lipoprotein cholesterol levels (OR = 0.7 per 1 SD, p = 0.02). Increased percent LRNC was associated with PWV (regression coefficient = 0.36, p <0.001) and negatively associated with ApoA1 levels (regression coefficient = -0.20, p = 0.03). In conclusion, older age, male gender, history of cerebrovascular disease, larger plaque burden, higher lipoprotein(a), and lower high-density lipoprotein cholesterol or ApoA1 level have statistically significant associations with high-risk plaque features. Metabolic syndrome and diabetes showed negative associations in this population. Copyright © 2014 Elsevier Inc. All rights reserved.

Multiscale approach to contour fitting for MR images

NASA Astrophysics Data System (ADS)

Rueckert, Daniel; Burger, Peter

1996-04-01

We present a new multiscale contour fitting process which combines information about the image and the contour of the object at different levels of scale. The algorithm is based on energy minimizing deformable models but avoids some of the problems associated with these models. The segmentation algorithm starts by constructing a linear scale-space of an image through convolution of the original image with a Gaussian kernel at different levels of scale, where the scale corresponds to the standard deviation of the Gaussian kernel. At high levels of scale large scale features of the objects are preserved while small scale features, like object details as well as noise, are suppressed. In order to maximize the accuracy of the segmentation, the contour of the object of interest is then tracked in scale-space from coarse to fine scales. We propose a hybrid multi-temperature simulated annealing optimization to minimize the energy of the deformable model. At high levels of scale the SA optimization is started at high temperatures, enabling the SA optimization to find a global optimal solution. At lower levels of scale the SA optimization is started at lower temperatures (at the lowest level the temperature is close to 0). This enforces a more deterministic behavior of the SA optimization at lower scales and leads to an increasingly local optimization as high energy barriers cannot be crossed. The performance and robustness of the algorithm have been tested on spin-echo MR images of the cardiovascular system. The task was to segment the ascending and descending aorta in 15 datasets of different individuals in order to measure regional aortic compliance. The results show that the algorithm is able to provide more accurate segmentation results than the classic contour fitting process and is at the same time very robust to noise and initialization.

Remembering complex objects in visual working memory: do capacity limits restrict objects or features?

PubMed

Hardman, Kyle O; Cowan, Nelson

2015-03-01

Visual working memory stores stimuli from our environment as representations that can be accessed by high-level control processes. This study addresses a longstanding debate in the literature about whether storage limits in visual working memory include a limit to the complexity of discrete items. We examined the issue with a number of change-detection experiments that used complex stimuli that possessed multiple features per stimulus item. We manipulated the number of relevant features of the stimulus objects in order to vary feature load. In all of our experiments, we found that increased feature load led to a reduction in change-detection accuracy. However, we found that feature load alone could not account for the results but that a consideration of the number of relevant objects was also required. This study supports capacity limits for both feature and object storage in visual working memory. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Associations between park features and adolescent park use for physical activity.

PubMed

Edwards, Nicole; Hooper, Paula; Knuiman, Matthew; Foster, Sarah; Giles-Corti, Billie

2015-02-18

Eighty per cent of adolescents globally do insufficient physical activity. Parks are a popular place for adolescents to be active. However, little is known about which park features are associated with higher levels of park use by adolescents. This study aimed to examine which environmental park features, and combination of features, were correlated with higher levels of park use for physical activity among adolescents. By examining park features in parks used by adolescents for physical activity, this study also aimed to create a park 'attractiveness' score predictive of adolescent park use, and to identify factors that might predict use of their closest park. Adolescents (n = 1304) living in Geraldton, a large rural centre of Western Australia, completed a survey that measured physical activity behaviour, perceptions of park availability and the main park used for physical activity. All parks in the study area (n = 58) were digitized using a Geographic Information System (GIS) and features audited using the Public Open Space Desktop Auditing Tool (POSDAT). Only 27% of participants reported using their closest park for physical activity. Park use was associated with seven features: presence of a skate park, walking paths, barbeques, picnic table, public access toilets, lighting around courts and equipment and number of trees >25. When combined to create an overall attractiveness score, every additional 'attractive' feature present, resulted in a park being nearly three times more likely to be in the high use category. To increase park use for physical activity, urban planners and designers should incorporate park features attractive to adolescents.

An integrated framework for detecting suspicious behaviors in video surveillance

NASA Astrophysics Data System (ADS)

Zin, Thi Thi; Tin, Pyke; Hama, Hiromitsu; Toriu, Takashi

2014-03-01

In this paper, we propose an integrated framework for detecting suspicious behaviors in video surveillance systems which are established in public places such as railway stations, airports, shopping malls and etc. Especially, people loitering in suspicion, unattended objects left behind and exchanging suspicious objects between persons are common security concerns in airports and other transit scenarios. These involve understanding scene/event, analyzing human movements, recognizing controllable objects, and observing the effect of the human movement on those objects. In the proposed framework, multiple background modeling technique, high level motion feature extraction method and embedded Markov chain models are integrated for detecting suspicious behaviors in real time video surveillance systems. Specifically, the proposed framework employs probability based multiple backgrounds modeling technique to detect moving objects. Then the velocity and distance measures are computed as the high level motion features of the interests. By using an integration of the computed features and the first passage time probabilities of the embedded Markov chain, the suspicious behaviors in video surveillance are analyzed for detecting loitering persons, objects left behind and human interactions such as fighting. The proposed framework has been tested by using standard public datasets and our own video surveillance scenarios.

A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging.

PubMed

Zhou, Ning; Cheung, William K; Qiu, Guoping; Xue, Xiangyang

2011-07-01

The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

A closed-loop automatic control system for high-intensity acoustic test systems.

NASA Technical Reports Server (NTRS)

Slusser, R. A.

1973-01-01

Description of an automatic control system for high-intensity acoustic tests in reverberation chambers. Working in 14 one-third-octave bands from 50 to 1000 Hz, the desired sound pressure levels are set into the memory in the control system before the test. The control system then increases the sound pressure level in the reverberation chamber gradually in each of the one-third-octave bands until the level set in the memory is reached. This level is then maintained for the duration of the test. Additional features of the system are overtest protection, the capability of 'holding' the spectrum at any time, and the presence of a total test timer.

A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

NASA Astrophysics Data System (ADS)

Taşkin Kaya, Gülşen

2013-10-01

Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The HDMR method is developed to improve the efficiency of the deducing high dimensional behaviors. The method is formed by a particular organization of low dimensional component functions, in which each function is the contribution of one or more input variables to the output variables.

The emergence of polychronization and feature binding in a spiking neural network model of the primate ventral visual system.

PubMed

Eguchi, Akihiro; Isbister, James B; Ahmad, Nasir; Stringer, Simon

2018-07-01

We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomized Poisson spike trains representing the input training images. The performance is improved by including top-down and lateral synaptic connections, as well as introducing multiple synaptic contacts between each pair of pre- and postsynaptic neurons, with different synaptic contacts having different axonal delays. Spike-timing-dependent plasticity thus allows the model to select the most effective axonal transmission delay between neurons. Furthermore, neurons representing the binding relationship between low-level and high-level visual features emerge through visually guided learning. This begins to provide a way forward to solving the classic feature binding problem in visual neuroscience and leads to a new hypothesis concerning how information about visual features at every spatial scale may be projected upward through successive neuronal layers. We name this hypothetical upward projection of information the "holographic principle." (PsycINFO Database Record (c) 2018 APA, all rights reserved).

High Precision Prediction of Functional Sites in Protein Structures

PubMed Central

Buturovic, Ljubomir; Wong, Mike; Tang, Grace W.; Altman, Russ B.; Petkovic, Dragutin

2014-01-01

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta. PMID:24632601

On the scaling features of high-latitude geomagnetic field fluctuations during a large geomagnetic storm

NASA Astrophysics Data System (ADS)

De Michelis, Paola; Federica Marcucci, Maria; Consolini, Giuseppe

2015-04-01

Recently we have investigated the spatial distribution of the scaling features of short-time scale magnetic field fluctuations using measurements from several ground-based geomagnetic observatories distributed in the northern hemisphere. We have found that the scaling features of fluctuations of the horizontal magnetic field component at time scales below 100 minutes are correlated with the geomagnetic activity level and with changes in the currents flowing in the ionosphere. Here, we present a detailed analysis of the dynamical changes of the magnetic field scaling features as a function of the geomagnetic activity level during the well-known large geomagnetic storm occurred on July, 15, 2000 (the Bastille event). The observed dynamical changes are discussed in relationship with the changes of the overall ionospheric polar convection and potential structure as reconstructed using SuperDARN data. This work is supported by the Italian National Program for Antarctic Research (PNRA) - Research Project 2013/AC3.08 and by the European Community's Seventh Framework Programme ([FP7/2007-2013]) under Grant no. 313038/STORM and

The mechanism of word crowding.

PubMed

Yu, Deyue; Akau, Melanie M U; Chung, Susana T L

2012-01-01

Word reading speed in peripheral vision is slower when words are in close proximity of other words (Chung, 2004). This word crowding effect could arise as a consequence of interaction of low-level letter features between words, or the interaction between high-level holistic representations of words. We evaluated these two hypotheses by examining how word crowding changes for five configurations of flanking words: the control condition - flanking words were oriented upright; scrambled - letters in each flanking word were scrambled in order; horizontal-flip - each flanking word was the left-right mirror-image of the original; letter-flip - each letter of the flanking word was the left-right mirror-image of the original; and vertical-flip - each flanking word was the up-down mirror-image of the original. The low-level letter feature interaction hypothesis predicts similar word crowding effect for all the different flanker configurations, while the high-level holistic representation hypothesis predicts less word crowding effect for all the alternative flanker conditions, compared with the control condition. We found that oral reading speed for words flanked above and below by other words, measured at 10° eccentricity in the nasal field, showed the same dependence on the vertical separation between the target and its flanking words, for the various flanker configurations. The result was also similar when we rotated the flanking words by 90° to disrupt the periodic vertical pattern, which presumably is the main structure in words. The remarkably similar word crowding effect irrespective of the flanker configurations suggests that word crowding arises as a consequence of interactions of low-level letter features. Copyright © 2011 Elsevier Ltd. All rights reserved.

Intelligent trend analysis for a solar thermal energy collector field

NASA Astrophysics Data System (ADS)

Juuso, E. K.

2018-03-01

Solar thermal power plants collect available solar energy in a usable form at a temperature range which is adapted to the irradiation levels and seasonal variations. Solar energy can be collected only when the irradiation is high enough to produce the required temperatures. During the operation, a trade-off of the temperature and the flow is needed to achieve a good level for the collected power. The scaling approach brings temporal analysis to all measurements and features: trend indices are calculated by comparing the averages in the long and short time windows, a weighted sum of the trend index and its derivative detects the trend episodes and severity of the trend is estimated by including also the variable level in the sum. The trend index, trend episodes and especially, the deviation index reveal early evolving changes in the operating conditions, including cloudiness and load disturbances. The solution is highly compact: all variables, features and indices are transformed to the range [-2, 2] and represented in natural language which is important in integrating data-driven solutions with domain expertise. The special situations detected during the test campaigns are explained well.

A Markov game theoretic data fusion approach for cyber situational awareness

NASA Astrophysics Data System (ADS)

Shen, Dan; Chen, Genshe; Cruz, Jose B., Jr.; Haynes, Leonard; Kruger, Martin; Blasch, Erik

2007-04-01

This paper proposes an innovative data-fusion/ data-mining game theoretic situation awareness and impact assessment approach for cyber network defense. Alerts generated by Intrusion Detection Sensors (IDSs) or Intrusion Prevention Sensors (IPSs) are fed into the data refinement (Level 0) and object assessment (L1) data fusion components. High-level situation/threat assessment (L2/L3) data fusion based on Markov game model and Hierarchical Entity Aggregation (HEA) are proposed to refine the primitive prediction generated by adaptive feature/pattern recognition and capture new unknown features. A Markov (Stochastic) game method is used to estimate the belief of each possible cyber attack pattern. Game theory captures the nature of cyber conflicts: determination of the attacking-force strategies is tightly coupled to determination of the defense-force strategies and vice versa. Also, Markov game theory deals with uncertainty and incompleteness of available information. A software tool is developed to demonstrate the performance of the high level information fusion for cyber network defense situation and a simulation example shows the enhanced understating of cyber-network defense.

Efficient random access high resolution region-of-interest (ROI) image retrieval using backward coding of wavelet trees (BCWT)

NASA Astrophysics Data System (ADS)

Corona, Enrique; Nutter, Brian; Mitra, Sunanda; Guo, Jiangling; Karp, Tanja

2008-03-01

Efficient retrieval of high quality Regions-Of-Interest (ROI) from high resolution medical images is essential for reliable interpretation and accurate diagnosis. Random access to high quality ROI from codestreams is becoming an essential feature in many still image compression applications, particularly in viewing diseased areas from large medical images. This feature is easier to implement in block based codecs because of the inherent spatial independency of the code blocks. This independency implies that the decoding order of the blocks is unimportant as long as the position for each is properly identified. In contrast, wavelet-tree based codecs naturally use some interdependency that exploits the decaying spectrum model of the wavelet coefficients. Thus one must keep track of the decoding order from level to level with such codecs. We have developed an innovative multi-rate image subband coding scheme using "Backward Coding of Wavelet Trees (BCWT)" which is fast, memory efficient, and resolution scalable. It offers far less complexity than many other existing codecs including both, wavelet-tree, and block based algorithms. The ROI feature in BCWT is implemented through a transcoder stage that generates a new BCWT codestream containing only the information associated with the user-defined ROI. This paper presents an efficient technique that locates a particular ROI within the BCWT coded domain, and decodes it back to the spatial domain. This technique allows better access and proper identification of pathologies in high resolution images since only a small fraction of the codestream is required to be transmitted and analyzed.

Lip-reading aids word recognition most in moderate noise: a Bayesian explanation using high-dimensional feature space.

PubMed

Ma, Wei Ji; Zhou, Xiang; Ross, Lars A; Foxe, John J; Parra, Lucas C

2009-01-01

Watching a speaker's facial movements can dramatically enhance our ability to comprehend words, especially in noisy environments. From a general doctrine of combining information from different sensory modalities (the principle of inverse effectiveness), one would expect that the visual signals would be most effective at the highest levels of auditory noise. In contrast, we find, in accord with a recent paper, that visual information improves performance more at intermediate levels of auditory noise than at the highest levels, and we show that a novel visual stimulus containing only temporal information does the same. We present a Bayesian model of optimal cue integration that can explain these conflicts. In this model, words are regarded as points in a multidimensional space and word recognition is a probabilistic inference process. When the dimensionality of the feature space is low, the Bayesian model predicts inverse effectiveness; when the dimensionality is high, the enhancement is maximal at intermediate auditory noise levels. When the auditory and visual stimuli differ slightly in high noise, the model makes a counterintuitive prediction: as sound quality increases, the proportion of reported words corresponding to the visual stimulus should first increase and then decrease. We confirm this prediction in a behavioral experiment. We conclude that auditory-visual speech perception obeys the same notion of optimality previously observed only for simple multisensory stimuli.

Comparison of Point Matching Techniques for Road Network Matching

NASA Astrophysics Data System (ADS)

Hackeloeer, A.; Klasing, K.; Krisp, J. M.; Meng, L.

2013-05-01

Map conflation investigates the unique identification of geographical entities across different maps depicting the same geographic region. It involves a matching process which aims to find commonalities between geographic features. A specific subdomain of conflation called Road Network Matching establishes correspondences between road networks of different maps on multiple layers of abstraction, ranging from elementary point locations to high-level structures such as road segments or even subgraphs derived from the induced graph of a road network. The process of identifying points located on different maps by means of geometrical, topological and semantical information is called point matching. This paper provides an overview of various techniques for point matching, which is a fundamental requirement for subsequent matching steps focusing on complex high-level entities in geospatial networks. Common point matching approaches as well as certain combinations of these are described, classified and evaluated. Furthermore, a novel similarity metric called the Exact Angular Index is introduced, which considers both topological and geometrical aspects. The results offer a basis for further research on a bottom-up matching process for complex map features, which must rely upon findings derived from suitable point matching algorithms. In the context of Road Network Matching, reliable point matches provide an immediate starting point for finding matches between line segments describing the geometry and topology of road networks, which may in turn be used for performing a structural high-level matching on the network level.

Forced to remember: when memory is biased by salient information.

PubMed

Santangelo, Valerio

2015-04-15

The last decades have seen a rapid growing in the attempt to understand the key factors involved in the internal memory representation of the external world. Visual salience have been found to provide a major contribution in predicting the probability for an item/object embedded in a complex setting (i.e., a natural scene) to be encoded and then remembered later on. Here I review the existing literature highlighting the impact of perceptual- (based on low-level sensory features) and semantics-related salience (based on high-level knowledge) on short-term memory representation, along with the neural mechanisms underpinning the interplay between these factors. The available evidence reveal that both perceptual- and semantics-related factors affect attention selection mechanisms during the encoding of natural scenes. Biasing internal memory representation, both perceptual and semantics factors increase the probability to remember high- to the detriment of low-saliency items. The available evidence also highlight an interplay between these factors, with a reduced impact of perceptual-related salience in biasing memory representation as a function of the increasing availability of semantics-related salient information. The neural mechanisms underpinning this interplay involve the activation of different portions of the frontoparietal attention control network. Ventral regions support the assignment of selection/encoding priorities based on high-level semantics, while the involvement of dorsal regions reflects priorities assignment based on low-level sensory features. Copyright © 2015 Elsevier B.V. All rights reserved.

Salient region detection by fusing bottom-up and top-down features extracted from a single image.

PubMed

Tian, Huawei; Fang, Yuming; Zhao, Yao; Lin, Weisi; Ni, Rongrong; Zhu, Zhenfeng

2014-10-01

Recently, some global contrast-based salient region detection models have been proposed based on only the low-level feature of color. It is necessary to consider both color and orientation features to overcome their limitations, and thus improve the performance of salient region detection for images with low-contrast in color and high-contrast in orientation. In addition, the existing fusion methods for different feature maps, like the simple averaging method and the selective method, are not effective sufficiently. To overcome these limitations of existing salient region detection models, we propose a novel salient region model based on the bottom-up and top-down mechanisms: the color contrast and orientation contrast are adopted to calculate the bottom-up feature maps, while the top-down cue of depth-from-focus from the same single image is used to guide the generation of final salient regions, since depth-from-focus reflects the photographer's preference and knowledge of the task. A more general and effective fusion method is designed to combine the bottom-up feature maps. According to the degree-of-scattering and eccentricities of feature maps, the proposed fusion method can assign adaptive weights to different feature maps to reflect the confidence level of each feature map. The depth-from-focus of the image as a significant top-down feature for visual attention in the image is used to guide the salient regions during the fusion process; with its aid, the proposed fusion method can filter out the background and highlight salient regions for the image. Experimental results show that the proposed model outperforms the state-of-the-art models on three public available data sets.

[Spectral features analysis of Pinus massoniana with pest of Dendrolimus punctatus Walker and levels detection].

PubMed

Xu, Zhang-Hua; Liu, Jian; Yu, Kun-Yong; Gong, Cong-Hong; Xie, Wan-Jun; Tang, Meng-Ya; Lai, Ri-Wen; Li, Zeng-Lu

2013-02-01

Taking 51 field measured hyperspectral data with different pest levels in Yanping, Fujian Province as objects, the spectral reflectance and first derivative features of 4 levels of healthy, mild, moderate and severe insect pest were analyzed. On the basis of 7 detecting parameters construction, the pest level detecting models were built. The results showed that (1) the spectral reflectance of Pinus massoniana with pests were significantly lower than that of healthy state, and the higher the pest level, the lower the reflectance; (2) with the increase in pest level, the spectral reflectance curves' "green peak" and "red valley" of Pinus massoniana gradually disappeared, and the red edge was leveleds (3) the pest led to spectral "green peak" red shift, red edge position blue shift, but the changes in "red valley" and near-infrared position were complicated; (4) CARI, RES, REA and REDVI were highly relevant to pest levels, and the correlations between REP, RERVI, RENDVI and pest level were weak; (5) the multiple linear regression model with the variables of the 7 detection parameters could effectively detect the pest levels of Dendrolimus punctatus Walker, with both the estimation rate and accuracy above 0.85.

Loss of the Cyclin-Dependent Kinase Inhibitor 1 in the Context of Brachyury-Mediated Phenotypic Plasticity Drives Tumor Resistance to Immune Attack.

PubMed

Hamilton, Duane H; McCampbell, Kristen K; Palena, Claudia

2018-01-01

The acquisition of mesenchymal features by carcinoma cells is now recognized as a driver of metastasis and tumor resistance to a range of anticancer therapeutics, including chemotherapy, radiation, and certain small-molecule targeted therapies. With the recent successful implementation of immunotherapies for the treatment of various types of cancer, there is growing interest in understanding whether an immunological approach could be effective at eradicating carcinoma cells bearing mesenchymal features. Recent studies, however, demonstrated that carcinoma cells that have acquired mesenchymal features may also exhibit decreased susceptibility to lysis mediated by immune effector cells, including antigen-specific CD8 + T cells, innate natural killer (NK), and lymphokine-activated killer (LAK) cells. Here, we investigated the mechanism involved in the immune resistance of carcinoma cells that express very high levels of the transcription factor brachyury, a molecule previously shown to drive the acquisition of mesenchymal features by carcinoma cells. Our results demonstrate that very high levels of brachyury expression drive the loss of the cyclin-dependent kinase inhibitor 1 (p21CIP1, p21), an event that results in decreased tumor susceptibility to immune-mediated lysis. We show here that reconstitution of p21 expression markedly increases the lysis of brachyury-high tumor cells mediated by antigen-specific CD8 + T cells, NK, and LAK cells, TNF-related apoptosis-inducing ligand, and chemotherapy. Several reports have now demonstrated a role for p21 loss in cancer as an inducer of the epithelial-mesenchymal transition. The results from the present study situate p21 as a central player in many of the aspects of the phenomenon of brachyury-mediated mesenchymalization of carcinomas, including resistance to chemotherapy and immune-mediated cytotoxicity. We also demonstrate here that the defects in tumor cell death described in association with very high levels of brachyury could be alleviated via the use of a WEE1 inhibitor. Several vaccine platforms targeting brachyury have been developed and are undergoing clinical evaluation. These studies provide further rationale for the use of WEE1 inhibition in combination with brachyury-based immunotherapeutic approaches.

Loss of the Cyclin-Dependent Kinase Inhibitor 1 in the Context of Brachyury-Mediated Phenotypic Plasticity Drives Tumor Resistance to Immune Attack

PubMed Central

Hamilton, Duane H.; McCampbell, Kristen K.; Palena, Claudia

2018-01-01

The acquisition of mesenchymal features by carcinoma cells is now recognized as a driver of metastasis and tumor resistance to a range of anticancer therapeutics, including chemotherapy, radiation, and certain small-molecule targeted therapies. With the recent successful implementation of immunotherapies for the treatment of various types of cancer, there is growing interest in understanding whether an immunological approach could be effective at eradicating carcinoma cells bearing mesenchymal features. Recent studies, however, demonstrated that carcinoma cells that have acquired mesenchymal features may also exhibit decreased susceptibility to lysis mediated by immune effector cells, including antigen-specific CD8+ T cells, innate natural killer (NK), and lymphokine-activated killer (LAK) cells. Here, we investigated the mechanism involved in the immune resistance of carcinoma cells that express very high levels of the transcription factor brachyury, a molecule previously shown to drive the acquisition of mesenchymal features by carcinoma cells. Our results demonstrate that very high levels of brachyury expression drive the loss of the cyclin-dependent kinase inhibitor 1 (p21CIP1, p21), an event that results in decreased tumor susceptibility to immune-mediated lysis. We show here that reconstitution of p21 expression markedly increases the lysis of brachyury-high tumor cells mediated by antigen-specific CD8+ T cells, NK, and LAK cells, TNF-related apoptosis-inducing ligand, and chemotherapy. Several reports have now demonstrated a role for p21 loss in cancer as an inducer of the epithelial–mesenchymal transition. The results from the present study situate p21 as a central player in many of the aspects of the phenomenon of brachyury-mediated mesenchymalization of carcinomas, including resistance to chemotherapy and immune-mediated cytotoxicity. We also demonstrate here that the defects in tumor cell death described in association with very high levels of brachyury could be alleviated via the use of a WEE1 inhibitor. Several vaccine platforms targeting brachyury have been developed and are undergoing clinical evaluation. These studies provide further rationale for the use of WEE1 inhibition in combination with brachyury-based immunotherapeutic approaches. PMID:29774202

Estimating cognitive workload using wavelet entropy-based features during an arithmetic task.

PubMed

Zarjam, Pega; Epps, Julien; Chen, Fang; Lovell, Nigel H

2013-12-01

Electroencephalography (EEG) has shown promise as an indicator of cognitive workload; however, precise workload estimation is an ongoing research challenge. In this investigation, seven levels of workload were induced using an arithmetic task, and the entropy of wavelet coefficients extracted from EEG signals is shown to distinguish all seven levels. For a subject-independent multi-channel classification scheme, the entropy features achieved high accuracy, up to 98% for channels from the frontal lobes, in the delta frequency band. This suggests that a smaller number of EEG channels in only one frequency band can be deployed for an effective EEG-based workload classification system. Together with analysis based on phase locking between channels, these results consistently suggest increased synchronization of neural responses for higher load levels. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nearshore morphology and late Quaternary geologic framework of the northern Monterey Bay Marine Sanctuary, California

USGS Publications Warehouse

Anima, R.J.; Eittreim, S.L.; Edwards, B.D.; Stevenson, A.J.

2002-01-01

A combination of side-scanning sonar and high-resolution seismic reflection data image seafloor bedrock exposures and erosional features across the nearshore shelf. Sediment-filled troughs incise the inner shelf rock exposures and tie directly to modern coastal streams. The resulting bedrock geometry can be related to its resistance to erosion. Comparison of the depth of the transgressive erosional surface to recently developed sea level curves suggests a period of slow sea level rise during the early stages of post-interglacial marine transgression. The slow rise of sea level suggests an erosional episode that limited the preservation of buried paleo-channels beyond 70 m water depth. Seafloor features suggest that localized faulting in the area may have influenced the morphology of bedrock exposures and the coastline. ?? 2002 Elsevier Science B.V. All rights reserved.

Phenological features for winter rapeseed identification in Ukraine using satellite data

NASA Astrophysics Data System (ADS)

Kravchenko, Oleksiy

2014-05-01

Winter rapeseed is one of the major oilseed crops in Ukraine that is characterized by high profitability and often grown with violations of the crop rotation requirements leading to soil degradation. Therefore, rapeseed identification using satellite data is a promising direction for operational estimation of the crop acreage and rotation control. Crop acreage of rapeseed is about 0.5-3% of total area of Ukraine, which poses a major problem for identification using satellite data [1]. While winter rapeseed could be classified using biomass features observed during autumn vegetation, these features are quite unstable due to field to field differences in planting dates as well as spatial and temporal heterogeneity in soil moisture availability. Due to this fact autumn biomass level features could be used only locally (at NUTS-3 level) and are not suitable for large-scale country wide crop identification. We propose to use crop parameters at flowering phenological stage for crop identification and present a method for parameters estimation using time-series of moderate resolution data. Rapeseed flowering could be observed as a bell-shaped peak in red reflectance time series. However the duration of the flowering period that is observable by satellite data is about only two weeks, which is quite short period taking into account inevitable cloud coverage issues. Thus we need daily time series to resolve the flowering peak and due to this we are limited to moderate resolution data. We used daily atmospherically corrected MODIS data coming from Terra and Aqua satellites within 90-160 DOY period to perform features calculations. Empirical BRDF correction is used to minimize angular effects. We used Gaussian Processes Regression (GPR) for temporal interpolation to minimize errors due to residual could coverage, atmospheric correction and a mixed pixel problems. We estimate 12 parameters for each time series. They are red and near-infrared (NIR) reflectance and the timing at four stages: before and after the flowering, at the peak flowering and at the maximum NIR level. We used Support Vector Machine for data classification. The most relevant feature for classification is flowering peak timing followed by flowering peak magnitude. The dependency of the peak time on the latitude as a sole feature could be used to reject 90% of non-rapeseed pixels that is greatly reduces the imbalance of the classification problem. To assess the accuracy of our approach we performed a stratified area frame sampling survey in Odessa region (NUTS-2 level) in 2013. The omission error is about 12.6% while commission error is higher at the level of 22%. This fact is explained by high viewing angle composition criterion that is used in our approach to mitigate high cloud coverage problem. However the errors are quite stable spatially and could be easily corrected by regression technique. To do this we performed area estimation for Odessa region using regression estimator and obtained good area estimation accuracy with 4.6% error (1σ). [1] Gallego, F.J., et al., Efficiency assessment of using satellite data for crop area estimation in Ukraine. Int. J. Appl. Earth Observ. Geoinf. (2014), http://dx.doi.org/10.1016/j.jag.2013.12.013

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

Persistent maritime surveillance using multi-sensor feature association and classification

NASA Astrophysics Data System (ADS)

van den Broek, Sebastiaan P.; Schwering, Piet B. W.; Liem, Kwan D.; Schleijpen, Ric

2012-06-01

In maritime operational scenarios, such as smuggling, piracy, or terrorist threats, it is not only relevant who or what an observed object is, but also where it is now and in the past in relation to other (geographical) objects. In situation and impact assessment, this information is used to determine whether an object is a threat. Single platform (ship, harbor) or single sensor information will not provide all this information. The work presented in this paper focuses on the sensor and object levels that provide a description of currently observed objects to situation assessment. For use of information of objects at higher information levels, it is necessary to have not only a good description of observed objects at this moment, but also from its past. Therefore, currently observed objects have to be linked to previous occurrences. Kinematic features, as used in tracking, are of limited use, as uncertainties over longer time intervals are so large that no unique associations can be made. Features extracted from different sensors (e.g., ESM, EO/IR) can be used for both association and classification. Features and classifications are used to associate current objects to previous object descriptions, allowing objects to be described better, and provide position history. In this paper a description of a high level architecture in which such a multi-sensor association is used is described. Results of an assessment of the usability of several features from ESM (from spectrum), EO and IR (shape, contour, keypoints) data for association and classification are shown.

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

PubMed

Brosch, Tom; Tang, Lisa Y W; Youngjin Yoo; Li, David K B; Traboulsee, Anthony; Tam, Roger

2016-05-01

We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

Attention to Color Sharpens Neural Population Tuning via Feedback Processing in the Human Visual Cortex Hierarchy.

PubMed

Bartsch, Mandy V; Loewe, Kristian; Merkel, Christian; Heinze, Hans-Jochen; Schoenfeld, Mircea A; Tsotsos, John K; Hopf, Jens-Max

2017-10-25

Attention can facilitate the selection of elementary object features such as color, orientation, or motion. This is referred to as feature-based attention and it is commonly attributed to a modulation of the gain and tuning of feature-selective units in visual cortex. Although gain mechanisms are well characterized, little is known about the cortical processes underlying the sharpening of feature selectivity. Here, we show with high-resolution magnetoencephalography in human observers (men and women) that sharpened selectivity for a particular color arises from feedback processing in the human visual cortex hierarchy. To assess color selectivity, we analyze the response to a color probe that varies in color distance from an attended color target. We find that attention causes an initial gain enhancement in anterior ventral extrastriate cortex that is coarsely selective for the target color and transitions within ∼100 ms into a sharper tuned profile in more posterior ventral occipital cortex. We conclude that attention sharpens selectivity over time by attenuating the response at lower levels of the cortical hierarchy to color values neighboring the target in color space. These observations support computational models proposing that attention tunes feature selectivity in visual cortex through backward-propagating attenuation of units less tuned to the target. SIGNIFICANCE STATEMENT Whether searching for your car, a particular item of clothing, or just obeying traffic lights, in everyday life, we must select items based on color. But how does attention allow us to select a specific color? Here, we use high spatiotemporal resolution neuromagnetic recordings to examine how color selectivity emerges in the human brain. We find that color selectivity evolves as a coarse to fine process from higher to lower levels within the visual cortex hierarchy. Our observations support computational models proposing that feature selectivity increases over time by attenuating the responses of less-selective cells in lower-level brain areas. These data emphasize that color perception involves multiple areas across a hierarchy of regions, interacting with each other in a complex, recursive manner. Copyright © 2017 the authors 0270-6474/17/3710346-12$15.00/0.

Status, upgrades, and advances of RTS2: the open source astronomical observatory manager

NASA Astrophysics Data System (ADS)

Kubánek, Petr

2016-07-01

RTS2 is an open source observatory control system. Being developed from early 2000, it continue to receive new features in last two years. RTS2 is a modulat, network-based distributed control system, featuring telescope drivers with advanced tracking and pointing capabilities, fast camera drivers and high level modules for "business logic" of the observatory, connected to a SQL database. Running on all continents of the planet, it accumulated a lot to control parts or full observatory setups.

A Different Kind of Language: Prolog, Programming in Logic.

ERIC Educational Resources Information Center

Cabrol, D.

1986-01-01

Prolog is one of the most successful "very high level languages." Describes this programming language (a product of artificial intelligence research) and attempts to show how it functions by using some short examples to illustrate its essential features. (JN)

[Clinico-psychological features of patients with favorable outcomes of slowly-progressive juvenile schizophrenia].

PubMed

Tsutsul'kovskaia, M Ia; Bil'zho, A G; Kritskaia, V P; Meleshko, T K

1986-01-01

A follow-up study of patients with favourable outcomes of juvenile slowly progressing schizophrenia at the level of clinical cure (n = 84) revealed a number of clinical characteristics in the pattern of personality changes which correlated with a high level of the patients' social and occupational adaptation. The authors also determined external factors contributing to the achievement and stabilization of the "clinical cure" status. An experimental and psychological examination of these patients revealed finer mechanisms contributing to their social adaptation. These are high motivation of activities, compliance with social norms, a high level of voluntary regulation of activity and self-regulation, as well as the ability to overcome autistic trends in situations of interpersonal activities and cooperation.

Temperament features in adolescents with ego-syntonic or ego-dystonic obsessive-compulsive symptoms.

PubMed

Marchesi, Carlo; Ampollini, Paolo; DePanfilis, Chiara; Maggini, Carlo

2008-09-01

The present study evaluated whether different patterns of temperament may predict a different threshold of acceptability of obsessive-compulsive (OC) symptoms in adolescents. OC symptomatology was detected with the Leyton Obsessional Inventory-Child Version (LOI-CV) and temperament was assessed using the tridimensional personality questionnaire in 2,775 high-school students. According to the LOI-CV scores, the adolescents were classified as high interference (interfering, ego-dystonic symptoms) (HI), supernormal (noninterfering, ego-syntonic symptoms) (Sn) and controls (C) HI were 119 (4.3%), Sn 85 (3.1%) and C 2,571 (92.6%). The best predictor of belonging to HI or Sn groups was the temperament configuration of high Harm Avoidance (HA) and high Persistence (P). The feature that mainly distinguishes the two symptomatic groups were Novelty Seeking (NS) levels. Our data suggest that people characterized by pessimistic worry in anticipation of future problems, passive avoidant behaviour, rapid fatigability (high HA) and irresoluteness, ambitiousness, perseverance, perfectionism, enduring feelings of frustration (high P) might develop OC symptoms. Whether OC symptoms become ego-syntonic or ego-dystonic seems to mainly depend on NS levels: low NS might protect people (with the prevention of "exploratory and active behaviours" that may elicit loss of control on symptoms) from the development of interfering OC symptoms.

Classification of LIDAR Data for Generating a High-Precision Roadway Map

NASA Astrophysics Data System (ADS)

Jeong, J.; Lee, I.

2016-06-01

Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.

Exploration of complex visual feature spaces for object perception

PubMed Central

Leeds, Daniel D.; Pyles, John A.; Tarr, Michael J.

2014-01-01

The mid- and high-level visual properties supporting object perception in the ventral visual pathway are poorly understood. In the absence of well-specified theory, many groups have adopted a data-driven approach in which they progressively interrogate neural units to establish each unit's selectivity. Such methods are challenging in that they require search through a wide space of feature models and stimuli using a limited number of samples. To more rapidly identify higher-level features underlying human cortical object perception, we implemented a novel functional magnetic resonance imaging method in which visual stimuli are selected in real-time based on BOLD responses to recently shown stimuli. This work was inspired by earlier primate physiology work, in which neural selectivity for mid-level features in IT was characterized using a simple parametric approach (Hung et al., 2012). To extend such work to human neuroimaging, we used natural and synthetic object stimuli embedded in feature spaces constructed on the basis of the complex visual properties of the objects themselves. During fMRI scanning, we employed a real-time search method to control continuous stimulus selection within each image space. This search was designed to maximize neural responses across a pre-determined 1 cm3 brain region within ventral cortex. To assess the value of this method for understanding object encoding, we examined both the behavior of the method itself and the complex visual properties the method identified as reliably activating selected brain regions. We observed: (1) Regions selective for both holistic and component object features and for a variety of surface properties; (2) Object stimulus pairs near one another in feature space that produce responses at the opposite extremes of the measured activity range. Together, these results suggest that real-time fMRI methods may yield more widely informative measures of selectivity within the broad classes of visual features associated with cortical object representation. PMID:25309408

A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data.

PubMed

Baur, Brittany; Bozdag, Serdar

2016-01-01

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes.

Key Elements for Judging the Quality of a Risk Assessment

PubMed Central

Fenner-Crisp, Penelope A.; Dellarco, Vicki L.

2016-01-01

Background: Many reports have been published that contain recommendations for improving the quality, transparency, and usefulness of decision making for risk assessments prepared by agencies of the U.S. federal government. A substantial measure of consensus has emerged regarding the characteristics that high-quality assessments should possess. Objective: The goal was to summarize the key characteristics of a high-quality assessment as identified in the consensus-building process and to integrate them into a guide for use by decision makers, risk assessors, peer reviewers and other interested stakeholders to determine if an assessment meets the criteria for high quality. Discussion: Most of the features cited in the guide are applicable to any type of assessment, whether it encompasses one, two, or all four phases of the risk-assessment paradigm; whether it is qualitative or quantitative; and whether it is screening level or highly sophisticated and complex. Other features are tailored to specific elements of an assessment. Just as agencies at all levels of government are responsible for determining the effectiveness of their programs, so too should they determine the effectiveness of their assessments used in support of their regulatory decisions. Furthermore, if a nongovernmental entity wishes to have its assessments considered in the governmental regulatory decision-making process, then these assessments should be judged in the same rigorous manner and be held to similar standards. Conclusions: The key characteristics of a high-quality assessment can be summarized and integrated into a guide for judging whether an assessment possesses the desired features of high quality, transparency, and usefulness. Citation: Fenner-Crisp PA, Dellarco VL. 2016. Key elements for judging the quality of a risk assessment. Environ Health Perspect 124:1127–1135; http://dx.doi.org/10.1289/ehp.1510483 PMID:26862984

A robust method for estimating motorbike count based on visual information learning

NASA Astrophysics Data System (ADS)

Huynh, Kien C.; Thai, Dung N.; Le, Sach T.; Thoai, Nam; Hamamoto, Kazuhiko

2015-03-01

Estimating the number of vehicles in traffic videos is an important and challenging task in traffic surveillance, especially with a high level of occlusions between vehicles, e.g.,in crowded urban area with people and/or motorbikes. In such the condition, the problem of separating individual vehicles from foreground silhouettes often requires complicated computation [1][2][3]. Thus, the counting problem is gradually shifted into drawing statistical inferences of target objects density from their shape [4], local features [5], etc. Those researches indicate a correlation between local features and the number of target objects. However, they are inadequate to construct an accurate model for vehicles density estimation. In this paper, we present a reliable method that is robust to illumination changes and partial affine transformations. It can achieve high accuracy in case of occlusions. Firstly, local features are extracted from images of the scene using Speed-Up Robust Features (SURF) method. For each image, a global feature vector is computed using a Bag-of-Words model which is constructed from the local features above. Finally, a mapping between the extracted global feature vectors and their labels (the number of motorbikes) is learned. That mapping provides us a strong prediction model for estimating the number of motorbikes in new images. The experimental results show that our proposed method can achieve a better accuracy in comparison to others.

Characterizing the Nature of Students' Feature Noticing-and-Using with Respect to Mathematical Symbols across Different Levels of Algebra Exposure

ERIC Educational Resources Information Center

Sullivan, Patrick

2013-01-01

The purpose of this study is to examine the nature of what students notice about symbols and use as they solve unfamiliar algebra problems based on familiar algebra concepts and involving symbolic inscriptions. The researcher conducted a study of students at three levels of algebra exposure: (a) students enrolled in a high school pre-calculus…

Thin-film filament-based solar cells and modules

NASA Astrophysics Data System (ADS)

Tuttle, J. R.; Cole, E. D.; Berens, T. A.; Alleman, J.; Keane, J.

1997-04-01

This concept paper describes a patented, novel photovoltaic (PV) technology that is capable of achieving near-term commercialization and profitability based upon design features that maximize product performance while minimizing initial and future manufacturing costs. DayStar Technologies plans to exploit these features and introduce a product to the market based upon these differential positions. The technology combines the demonstrated performance and reliability of existing thin-film PV product with a cell and module geometry that cuts material usage by a factor of 5, and enhances performance and manufacturability relative to standard flat-plate designs. The target product introduction price is 1.50/Watt-peak (Wp). This is approximately one-half the cost of the presently available PV product. Additional features include: increased efficiency through low-level concentration, no scribe or grid loss, simple series interconnect, high voltage, light weight, high-throughput manufacturing, large area immediate demonstration, flexibility, modularity.

Testosterone-mediated sex differences in the face shape during adolescence: subjective impressions and objective features.

PubMed

Marečková, Klára; Weinbrand, Zohar; Chakravarty, M Mallar; Lawrence, Claire; Aleong, Rosanne; Leonard, Gabriel; Perron, Michel; Pike, G Bruce; Richer, Louis; Veillette, Suzanne; Pausova, Zdenka; Paus, Tomáš

2011-11-01

Sex identification of a face is essential for social cognition. Still, perceptual cues indicating the sex of a face, and mechanisms underlying their development, remain poorly understood. Previously, our group described objective age- and sex-related differences in faces of healthy male and female adolescents (12-18 years of age), as derived from magnetic resonance images (MRIs) of the adolescents' heads. In this study, we presented these adolescent faces to 60 female raters to determine which facial features most reliably predicted subjective sex identification. Identification accuracy correlated highly with specific MRI-derived facial features (e.g. broader forehead, chin, jaw, and nose). Facial features that most reliably cued male identity were associated with plasma levels of testosterone (above and beyond age). Perceptible sex differences in face shape are thus associated with specific facial features whose emergence may be, in part, driven by testosterone. Copyright © 2011 Elsevier Inc. All rights reserved.

The clinical implications of high levels of autism spectrum disorder features in anorexia nervosa: a pilot study.

PubMed

Huke, Vanessa; Turk, Jeremy; Saeidi, Saeideh; Kent, Andrew; Morgan, John F

2014-03-01

This study examined autism spectrum disorder (ASD) features in relation to treatment completion and eating disorder psychopathology in anorexia nervosa (AN). Thirty-two adult women were recruited from specialist eating disorder services. Features of ASD and disordered eating were measured. Premature termination of treatment was recorded to explore whether ASD traits had impact on early discharge. A healthy control group was also recruited to investigate ASD traits between clinical and nonclinical samples. Significant differences were found between the AN group and the healthy control group in obsessive-compulsive disorder traits, depression and anxiety and ASD traits, with significant differences between groups in Social Skill and Attention Switching. The AN group reported no significant relationship between disordered eating severity and ASD traits. No significant effect was found between ASD features and treatment completion. Raw data on premature termination of treatment, despite no statistic impact, showed that seven out of the eight participants with high features of ASD completed treatment as planned compared with 50% of those with low ASD traits. Unexpectedly, this suggests enhanced treatment adherence in ASD. Copyright © 2013 John Wiley & Sons, Ltd and Eating Disorders Association.

Interpersonal Subtypes Within Social Anxiety: The Identification of Distinct Social Features.

PubMed

Cooper, Danielle; Anderson, Timothy

2017-10-05

Although social anxiety disorder is defined by anxiety-related symptoms, little research has focused on the interpersonal features of social anxiety. Prior studies (Cain, Pincus, & Grosse Holtforth, 2010; Kachin, Newman, & Pincus, 2001) identified distinct subgroups of socially anxious individuals' interpersonal circumplex problems that were blends of agency and communion, and yet inconsistencies remain. We predicted 2 distinct interpersonal subtypes would exist for individuals with high social anxiety, and that these social anxiety subtypes would differ on empathetic concern, paranoia, received peer victimization, perspective taking, and emotional suppression. From a sample of 175 undergraduate participants, 51 participants with high social anxiety were selected as above a clinical cutoff on the social phobia scale. Cluster analyses identified 2 interpersonal subtypes of socially anxious individuals: low hostility-high submissiveness (Cluster 1) and high hostility-high submissiveness (Cluster 2). Cluster 1 reported higher levels of empathetic concern, lower paranoia, less peer victimization, and lower emotional suppression compared to Cluster 2. There were no differences between subtypes on perspective taking or cognitive reappraisal. Findings are consistent with an interpersonal conceptualization of social anxiety, and provide evidence of distinct social features between these subtypes. Findings have implications for the etiology, classification, and treatment of social anxiety.

A universal deep learning approach for modeling the flow of patients under different severities.

PubMed

Jiang, Shancheng; Chin, Kwai-Sang; Tsui, Kwok L

2018-02-01

The Accident and Emergency Department (A&ED) is the frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources have failed to keep up with continuously increasing demand in recent years, which leads to overcrowding in A&ED. Knowing the fluctuation of patient arrival volume in advance is a significant premise to relieve this pressure. Based on this motivation, the objective of this study is to explore an integrated framework with high accuracy for predicting A&ED patient flow under different triage levels, by combining a novel feature selection process with deep neural networks. Administrative data is collected from an actual A&ED and categorized into five groups based on different triage levels. A genetic algorithm (GA)-based feature selection algorithm is improved and implemented as a pre-processing step for this time-series prediction problem, in order to explore key features affecting patient flow. In our improved GA, a fitness-based crossover is proposed to maintain the joint information of multiple features during iterative process, instead of traditional point-based crossover. Deep neural networks (DNN) is employed as the prediction model to utilize their universal adaptability and high flexibility. In the model-training process, the learning algorithm is well-configured based on a parallel stochastic gradient descent algorithm. Two effective regularization strategies are integrated in one DNN framework to avoid overfitting. All introduced hyper-parameters are optimized efficiently by grid-search in one pass. As for feature selection, our improved GA-based feature selection algorithm has outperformed a typical GA and four state-of-the-art feature selection algorithms (mRMR, SAFS, VIFR, and CFR). As for the prediction accuracy of proposed integrated framework, compared with other frequently used statistical models (GLM, seasonal-ARIMA, ARIMAX, and ANN) and modern machine models (SVM-RBF, SVM-linear, RF, and R-LASSO), the proposed integrated "DNN-I-GA" framework achieves higher prediction accuracy on both MAPE and RMSE metrics in pairwise comparisons. The contribution of our study is two-fold. Theoretically, the traditional GA-based feature selection process is improved to have less hyper-parameters and higher efficiency, and the joint information of multiple features is maintained by fitness-based crossover operator. The universal property of DNN is further enhanced by merging different regularization strategies. Practically, features selected by our improved GA can be used to acquire an underlying relationship between patient flows and input features. Predictive values are significant indicators of patients' demand and can be used by A&ED managers to make resource planning and allocation. High accuracy achieved by the present framework in different cases enhances the reliability of downstream decision makings. Copyright © 2017 Elsevier B.V. All rights reserved.

Hydrogen Embrittlement of Automotive Advanced High-Strength Steels

NASA Astrophysics Data System (ADS)

Lovicu, Gianfranco; Bottazzi, Mauro; D'Aiuto, Fabio; De Sanctis, Massimo; Dimatteo, Antonella; Santus, Ciro; Valentini, Renzo

2012-11-01

Advanced high-strength steels (AHSS) have a better combination between strength and ductility than conventional HSS, and higher crash resistances are obtained in concomitance with weight reduction of car structural components. These steels have been developed in the last few decades, and their use is rapidly increasing. Notwithstanding, some of their important features have to be still understood and studied in order to completely characterize their service behavior. In particular, the high mechanical resistance of AHSS makes hydrogen-related problems a great concern for this steel grade. This article investigates the hydrogen embrittlement (HE) of four AHSS steels. The behavior of one transformation induced plasticity (TRIP), two martensitic with different strength levels, and one hot-stamping steels has been studied using slow strain rate tensile (SSRT) tests on electrochemically hydrogenated notched samples. The embrittlement susceptibility of these AHSS steels has been correlated mainly to their strength level and to their microstructural features. Finally, the hydrogen critical concentrations for HE, established by SSRT tests, have been compared to hydrogen contents absorbed during the painting process of a body in white (BIW) structure, experimentally determined during a real cycle in an industrial plant.

The shortest Th-Th distance from a new type of quadruple bond.

PubMed

Hu, Han-Shi; Kaltsoyannis, Nikolas

2017-02-15

Compounds featuring unsupported metal-metal bonds between actinide elements remain highly sought after yet confined experimentally to inert gas matrix studies. Notwithstanding this paucity, actinide-actinide bonding has been the subject of extensive computational research. In this contribution, high level quantum chemical calculations at both the scalar and spin-orbit levels are used to probe the Th-Th bonding in a range of zero valent systems of general formula LThThL. Several of these compounds have very short Th-Th bonds arising from a new type of Th-Th quadruple bond with a previously unreported electronic configuration featuring two unpaired electrons in 6d-based δ bonding orbitals. H 3 AsThThAsH 3 is found to have the shortest Th-Th bond yet reported (2.590 Å). The Th 2 unit is a highly sensitive probe of ligand electron donor/acceptor ability; we can tune the Th-Th bond from quadruple to triple, double and single by judicious choice of the L group, up to 2.888 Å for singly-bonded ONThThNO.

Potentiometric surface of the Upper Floridan aquifer in the Ichetucknee springshed and vicinity, northern Florida, September 2003

USGS Publications Warehouse

Sepulveda, A. Alejandro; Katz, Brian G.; Mahon, Gary L.

2006-01-01

The Upper Floridan aquifer is a highly permeable unit of carbonate rock extending beneath most of Florida and parts of southern Alabama, Georgia, and South Carolina. The high permeability is due in a large part to the widening of fractures that developed over time and the formation of conduits within the aquifer through dissolution of the limestone. This process has also produced numerous karst features such as springs, sinking streams, and sinkholes in northern Florida. These dissolution features, whether expressed at the surface or not, greatly influence the direction of ground-water flow in the Ichetucknee springshed adjacent to the Ichetucknee River. Ground water generally flows southwestward in the springshed and discharges to the Ichetucknee or Santa Fe Rivers, or to the springs along those rivers. This map depicts the September 9-10, 2003, potentiometric surface of the Upper Floridan aquifer based on 94 water-level measurements made by the Suwannee River Water Management District. Ground-water levels in this watershed fluctuate in response to precipitation and due to the high degree of interconnection between the surface-water system and the aquifer.

Serum levels of interleukin-9 correlate with negative prognostic factors in extranodal NK/T-cell lymphoma.

PubMed

Zhang, Jing; Wang, Wei-da; Geng, Qi-Rong; Wang, Liang; Chen, Xiao-Qin; Liu, Cheng-Cheng; Lv, Yue

2014-01-01

Interleukin-9 (IL-9) is more functionally diverse than previously expected, especially with regards to lymphomagenesis. However, the relationship between IL-9 and the clinicopathological features of extranodal NK/T-cell lymphoma is less well established. Patients with this lymphoma in Sun Yat-Sen University Cancer Center between January 2003 and March 2013 were systematically reviewed in an intention-to-treat analysis. Baseline serum IL-9 levels were determined using sandwich enzyme-linked immunosorbent assays. A total of seventy-four patients were enrolled in this study. The mean concentration of serum IL-9 for all patients was 6.48 pg/mL (range: 1.38-51.87 pg/mL). Age, B symptoms and local lymph node involvement were found to be related to high serum IL-9 levels. Patients with low IL-9 levels tended to have higher rates of complete remission. Notably, the median progression-free survival (PFS) and overall survival (OS) were longer in the low IL-9 level group than in the high IL-9 level group (PFS: 68.7 months vs. 28.3 months, P<0.001; OS: 86 months vs. 42.8 months, P = 0.001). Multivariate analysis revealed independent prognostic factors for PFS. Similarly, high IL-9 levels (P = 0.003) and old age (P = 0.007) were independently predictive of shorter OS. Serum IL-9 is closely related to several clinical features, such as age, B symptoms and local lymph node involvement. It can also be a significant independent prognostic factor for extranodal NK/T-cell lymphoma, which suggests a role for IL-9 in the pathogenesis of this disease and offers new insight into potential therapeutic strategies.

Burnout in the working population: relations to psychosocial work factors.

PubMed

Lindblom, Karin M; Linton, Steven J; Fedeli, Cecilia; Bryngelsson, Ing-Liss

2006-01-01

This study investigated levels of burnout in the general population irrespective of occupation and relations between burnout and psychosocial work factors. A cross-sectional survey featuring sleep problems, psychological distress, burnout (Maslach Burnout Inventory-General Survey), and psychosocial factors at work, was mailed to a random sample of 3,000 participants, aged 20-60. Response rate was 61%. A high level (18%), a low level (19%), and an intermediate group (63%) for burnout were constructed. The high level group was associated with those who were > 50 years old, women, those experiencing psychological distress, and those with a poor psychosocial work climate. The analyses on variables significant in previous analyses showed that the high level group was strongly related to high demands, low control, lack of social support, and disagreeing about values at the workplace even when accounting for age, gender, and psychological distress. We conclude that psychosocial work factors are important in association to burnout regardless of occupation.

DeltaPhage—a novel helper phage for high-valence pIX phagemid display

PubMed Central

Nilssen, Nicolay R.; Frigstad, Terje; Pollmann, Sylvie; Roos, Norbert; Bogen, Bjarne; Sandlie, Inger; Løset, Geir Å.

2012-01-01

Phage display has been instrumental in discovery of novel binding peptides and folded domains for the past two decades. We recently reported a novel pIX phagemid display system that is characterized by a strong preference for phagemid packaging combined with low display levels, two key features that support highly efficient affinity selection. However, high diversity in selected repertoires are intimately coupled to high display levels during initial selection rounds. To incorporate this additional feature into the pIX display system, we have developed a novel helper phage termed DeltaPhage that allows for high-valence display on pIX. This was obtained by inserting two amber mutations close to the pIX start codon, but after the pVII translational stop, conditionally inactivating the helper phage encoded pIX. Until now, the general notion has been that display on pIX is dependent on wild-type complementation, making high-valence display unachievable. However, we found that DeltaPhage does facilitate high-valence pIX display when used with a non-suppressor host. Here, we report a side-by-side comparison with pIII display, and we find that this novel helper phage complements existing pIX phagemid display systems to allow both low and high-valence display, making pIX display a complete and efficient alternative to existing pIII phagemid display systems. PMID:22539265

DeltaPhage--a novel helper phage for high-valence pIX phagemid display.

PubMed

Nilssen, Nicolay R; Frigstad, Terje; Pollmann, Sylvie; Roos, Norbert; Bogen, Bjarne; Sandlie, Inger; Løset, Geir Å

2012-09-01

Phage display has been instrumental in discovery of novel binding peptides and folded domains for the past two decades. We recently reported a novel pIX phagemid display system that is characterized by a strong preference for phagemid packaging combined with low display levels, two key features that support highly efficient affinity selection. However, high diversity in selected repertoires are intimately coupled to high display levels during initial selection rounds. To incorporate this additional feature into the pIX display system, we have developed a novel helper phage termed DeltaPhage that allows for high-valence display on pIX. This was obtained by inserting two amber mutations close to the pIX start codon, but after the pVII translational stop, conditionally inactivating the helper phage encoded pIX. Until now, the general notion has been that display on pIX is dependent on wild-type complementation, making high-valence display unachievable. However, we found that DeltaPhage does facilitate high-valence pIX display when used with a non-suppressor host. Here, we report a side-by-side comparison with pIII display, and we find that this novel helper phage complements existing pIX phagemid display systems to allow both low and high-valence display, making pIX display a complete and efficient alternative to existing pIII phagemid display systems.

Gas barrier properties of bio-inspired Laponite-LC polymer hybrid films.

PubMed

Tritschler, Ulrich; Zlotnikov, Igor; Fratzl, Peter; Schlaad, Helmut; Grüner, Simon; Cölfen, Helmut

2016-05-26

Bio-inspired Laponite (clay)-liquid crystal (LC) polymer composite materials with high clay fractions (>80%) and a high level of orientation of the clay platelets, i.e. with structural features similar to the ones found in natural nacre, have been shown to exhibit a promising behavior in the context of reduced oxygen transmission. Key characteristics of these bio-inspired composite materials are their high inorganic content, high level of exfoliation and orientation of the clay platelets, and the use of a LC polymer forming the organic matrix in between the Laponite particles. Each single feature may be beneficial to increase the materials gas barrier property rendering this composite a promising system with advantageous barrier capacities. In this detailed study, Laponite/LC polymer composite coatings with different clay loadings were investigated regarding their oxygen transmission rate. The obtained gas barrier performance was linked to the quality, respective Laponite content and the underlying composite micro- and nanostructure of the coatings. Most efficient oxygen barrier properties were observed for composite coatings with 83% Laponite loading that exhibit a structure similar to sheet-like nacre. Further on, advantageous mechanical properties of these Laponite/LC polymer composites reported previously give rise to a multifunctional composite system.

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

PubMed

Zhai, Binxu; Chen, Jianguo

2018-04-18

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

Influences of High-Level Features, Gaze, and Scene Transitions on the Reliability of BOLD Responses to Natural Movie Stimuli

PubMed Central

Lu, Kun-Han; Hung, Shao-Chin; Wen, Haiguang; Marussich, Lauren; Liu, Zhongming

2016-01-01

Complex, sustained, dynamic, and naturalistic visual stimulation can evoke distributed brain activities that are highly reproducible within and across individuals. However, the precise origins of such reproducible responses remain incompletely understood. Here, we employed concurrent functional magnetic resonance imaging (fMRI) and eye tracking to investigate the experimental and behavioral factors that influence fMRI activity and its intra- and inter-subject reproducibility during repeated movie stimuli. We found that widely distributed and highly reproducible fMRI responses were attributed primarily to the high-level natural content in the movie. In the absence of such natural content, low-level visual features alone in a spatiotemporally scrambled control stimulus evoked significantly reduced degree and extent of reproducible responses, which were mostly confined to the primary visual cortex (V1). We also found that the varying gaze behavior affected the cortical response at the peripheral part of V1 and in the oculomotor network, with minor effects on the response reproducibility over the extrastriate visual areas. Lastly, scene transitions in the movie stimulus due to film editing partly caused the reproducible fMRI responses at widespread cortical areas, especially along the ventral visual pathway. Therefore, the naturalistic nature of a movie stimulus is necessary for driving highly reliable visual activations. In a movie-stimulation paradigm, scene transitions and individuals’ gaze behavior should be taken as potential confounding factors in order to properly interpret cortical activity that supports natural vision. PMID:27564573

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.

Unsupervised segmentation of lungs from chest radiographs

NASA Astrophysics Data System (ADS)

Ghosh, Payel; Antani, Sameer K.; Long, L. Rodney; Thoma, George R.

2012-03-01

This paper describes our preliminary investigations for deriving and characterizing coarse-level textural regions present in the lung field on chest radiographs using unsupervised grow-cut (UGC), a cellular automaton based unsupervised segmentation technique. The segmentation has been performed on a publicly available data set of chest radiographs. The algorithm is useful for this application because it automatically converges to a natural segmentation of the image from random seed points using low-level image features such as pixel intensity values and texture features. Our goal is to develop a portable screening system for early detection of lung diseases for use in remote areas in developing countries. This involves developing automated algorithms for screening x-rays as normal/abnormal with a high degree of sensitivity, and identifying lung disease patterns on chest x-rays. Automatically deriving and quantitatively characterizing abnormal regions present in the lung field is the first step toward this goal. Therefore, region-based features such as geometrical and pixel-value measurements were derived from the segmented lung fields. In the future, feature selection and classification will be performed to identify pathological conditions such as pulmonary tuberculosis on chest radiographs. Shape-based features will also be incorporated to account for occlusions of the lung field and by other anatomical structures such as the heart and diaphragm.

Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm

PubMed Central

Li, Hao; Lu, Jing; Shi, Guohua; Zhang, Yudong

2010-01-01

With the use of adaptive optics (AO), high-resolution microscopic imaging of living human retina in the single cell level has been achieved. In an adaptive optics confocal scanning laser ophthalmoscope (AOSLO) system, with a small field size (about 1 degree, 280 μm), the motion of the eye severely affects the stabilization of the real-time video images and results in significant distortions of the retina images. In this paper, Scale-Invariant Feature Transform (SIFT) is used to abstract stable point features from the retina images. Kanade-Lucas-Tomasi(KLT) algorithm is applied to track the features. With the tracked features, the image distortion in each frame is removed by the second-order polynomial transformation, and 10 successive frames are co-added to enhance the image quality. Features of special interest in an image can also be selected manually and tracked by KLT. A point on a cone is selected manually, and the cone is tracked from frame to frame. PMID:21258443

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

NASA Astrophysics Data System (ADS)

Kim, Deok-Hwan

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

Bacterial chemoreceptors: high-performance signaling in networked arrays.

PubMed

Hazelbauer, Gerald L; Falke, Joseph J; Parkinson, John S

2008-01-01

Chemoreceptors are crucial components in the bacterial sensory systems that mediate chemotaxis. Chemotactic responses exhibit exquisite sensitivity, extensive dynamic range and precise adaptation. The mechanisms that mediate these high-performance functions involve not only actions of individual proteins but also interactions among clusters of components, localized in extensive patches of thousands of molecules. Recently, these patches have been imaged in native cells, important features of chemoreceptor structure and on-off switching have been identified, and new insights have been gained into the structural basis and functional consequences of higher order interactions among sensory components. These new data suggest multiple levels of molecular interactions, each of which contribute specific functional features and together create a sophisticated signaling device.

Clinical and biological features of multiple myeloma involving the gastrointestinal system.

PubMed

Talamo, Giampaolo; Cavallo, Federica; Zangari, Maurizio; Barlogie, Bart; Lee, Choon-Kee; Pineda-Roman, Mauricio; Kiwan, Elias; Krishna, Somashekar; Tricot, Guido

2006-07-01

We report 24 cases of multiple myeloma (MM) with involvement of the gastrointestinal (GI) system. We found a strong association with high A lactate dehydrogenase levels, plasmablastic morphology, and A unfavorable karyotype. GI involvement at the time of initial diagnosis was much rarer than later in the course of the disease. The A median survival after diagnosis of GI involvement was 7 months. Among 13 patients treated with stem cell transplantation, the response rate was 92%, and median progression-free survival was 4 months. We conclude that MM involving the GI system is associated with adverse biological features and with short-lasting remissions, even after A high-dose chemotherapy.

Bacterial chemoreceptors: high-performance signaling in networked arrays

PubMed Central

Hazelbauer, Gerald L.; Falke, Joseph J.; Parkinson, John S.

2010-01-01

Chemoreceptors are crucial components in the bacterial sensory systems that mediate chemotaxis. Chemotactic responses exhibit exquisite sensitivity, extensive dynamic range and precise adaptation. The mechanisms that mediate these high-performance functions involve not only actions of individual proteins but also interactions among clusters of components, localized in extensive patches of thousands of molecules. Recently, these patches have been imaged in native cells, important features of chemoreceptor structure and on–off switching have been identified, and new insights have been gained into the structural basis and functional consequences of higher order interactions among sensory components. These new data suggest multiple levels of molecular interactions, each of which contribute specific functional features and together create a sophisticated signaling device. PMID:18165013

Geospatial analysis based on GIS integrated with LADAR.

PubMed

Fetterman, Matt R; Freking, Robert; Fernandez-Cull, Christy; Hinkle, Christopher W; Myne, Anu; Relyea, Steven; Winslow, Jim

2013-10-07

In this work, we describe multi-layered analyses of a high-resolution broad-area LADAR data set in support of expeditionary activities. High-level features are extracted from the LADAR data, such as the presence and location of buildings and cars, and then these features are used to populate a GIS (geographic information system) tool. We also apply line-of-sight (LOS) analysis to develop a path-planning module. Finally, visualization is addressed and enhanced with a gesture-based control system that allows the user to navigate through the enhanced data set in a virtual immersive experience. This work has operational applications including military, security, disaster relief, and task-based robotic path planning.

Detecting Circular RNAs by RNA Fluorescence In Situ Hybridization.

PubMed

Zirkel, Anne; Papantonis, Argyris

2018-01-01

Fluorescence in situ hybridization (FISH) coupled to high-resolution microscopy is a powerful method for analyzing the subcellular localization of RNA. However, the detection of circular RNAs (circRNAs) using microscopy is challenging because the only feature of a circRNA that can be used for the probe design is its junction. Circular RNAs are expressed at varying levels, and for their efficient monitoring by FISH, background fluorescence levels need to be kept low. Here, we describe a FISH protocol coupled to high-precision localizations using a single fluorescently labeled probe spanning the circRNA junction; this allows circRNA detection in mammalian cells with high signal-to-noise ratios.

IGF-1 contributes to the expansion of melanoma-initiating cells through an epithelial-mesenchymal transition process.

PubMed

Le Coz, Vincent; Zhu, Chaobin; Devocelle, Aurore; Vazquez, Aimé; Boucheix, Claude; Azzi, Sandy; Gallerne, Cindy; Eid, Pierre; Lecourt, Séverine; Giron-Michel, Julien

2016-12-13

Melanoma is a particularly virulent human cancer, due to its resistance to conventional treatments and high frequency of metastasis. Melanomas contain a fraction of cells, the melanoma-initiating cells (MICs), responsible for tumor propagation and relapse. Identification of the molecular pathways supporting MICs is, therefore, vital for the development of targeted treatments. One factor produced by melanoma cells and their microenvironment, insulin-like growth factor-1 (IGF- 1), is linked to epithelial-mesenchymal transition (EMT) and stemness features in several cancers.We evaluated the effect of IGF-1 on the phenotype and chemoresistance of B16-F10 cells. IGF-1 inhibition in these cells prevented malignant cell proliferation, migration and invasion, and lung colony formation in immunodeficient mice. IGF-1 downregulation also markedly inhibited EMT, with low levels of ZEB1 and mesenchymal markers (N-cadherin, CD44, CD29, CD105) associated with high levels of E-cadherin and MITF, the major regulator of melanocyte differentiation. IGF-1 inhibition greatly reduced stemness features, including the expression of key stem markers (SOX2, Oct-3/4, CD24 and CD133), and the functional characteristics of MICs (melanosphere formation, aldehyde dehydrogenase activity, side population). These features were associated with a high degree of sensitivity to mitoxantrone treatment.In this study, we deciphered new connections between IGF-1 and stemness features and identified IGF-1 as instrumental for maintaining the MIC phenotype. The IGF1/IGF1-R nexus could be targeted for the development of more efficient anti-melanoma treatments. Blocking the IGF-1 pathway would improve the immune response, decrease the metastatic potential of tumor cells and sensitize melanoma cells to conventional treatments.

Target-Oriented High-Resolution SAR Image Formation via Semantic Information Guided Regularizations

NASA Astrophysics Data System (ADS)

Hou, Biao; Wen, Zaidao; Jiao, Licheng; Wu, Qian

2018-04-01

Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its remarkable performance to generate a feature enhanced high resolution image, in which a sparsity-inducing regularizer is involved by exploiting the sparsity priors of some visual features in the underlying image. However, since the simple prior of low level features are insufficient to describe different semantic contents in the image, this type of regularizer will be incapable of distinguishing between the target of interest and unconcerned background clutters. As a consequence, the features belonging to the target and clutters are simultaneously affected in the generated image without concerning their underlying semantic labels. To address this problem, we propose a novel semantic information guided framework for target oriented SAR image formation, which aims at enhancing the interested target scatters while suppressing the background clutters. Firstly, we develop a new semantics-specific regularizer for image formation by exploiting the statistical properties of different semantic categories in a target scene SAR image. In order to infer the semantic label for each pixel in an unsupervised way, we moreover induce a novel high-level prior-driven regularizer and some semantic causal rules from the prior knowledge. Finally, our regularized framework for image formation is further derived as a simple iteratively reweighted $\\ell_1$ minimization problem which can be conveniently solved by many off-the-shelf solvers. Experimental results demonstrate the effectiveness and superiority of our framework for SAR image formation in terms of target enhancement and clutters suppression, compared with the state of the arts. Additionally, the proposed framework opens a new direction of devoting some machine learning strategies to image formation, which can benefit the subsequent decision making tasks.

Image segmentation via foreground and background semantic descriptors

NASA Astrophysics Data System (ADS)

Yuan, Ding; Qiang, Jingjing; Yin, Jihao

2017-09-01

In the field of image processing, it has been a challenging task to obtain a complete foreground that is not uniform in color or texture. Unlike other methods, which segment the image by only using low-level features, we present a segmentation framework, in which high-level visual features, such as semantic information, are used. First, the initial semantic labels were obtained by using the nonparametric method. Then, a subset of the training images, with a similar foreground to the input image, was selected. Consequently, the semantic labels could be further refined according to the subset. Finally, the input image was segmented by integrating the object affinity and refined semantic labels. State-of-the-art performance was achieved in experiments with the challenging MSRC 21 dataset.

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.

Analogues to features and processes of a high-level radioactive waste repository proposed for Yucca Mountain, Nevada

USGS Publications Warehouse

Simmons, Ardyth M.; Stuckless, John S.; with a Foreword by Abraham Van Luik, U.S. Department of Energy

2010-01-01

Natural analogues are defined for this report as naturally occurring or anthropogenic systems in which processes similar to those expected to occur in a nuclear waste repository are thought to have taken place over time periods of decades to millennia and on spatial scales as much as tens of kilometers. Analogues provide an important temporal and spatial dimension that cannot be tested by laboratory or field-scale experiments. Analogues provide one of the multiple lines of evidence intended to increase confidence in the safe geologic disposal of high-level radioactive waste. Although the work in this report was completed specifically for Yucca Mountain, Nevada, as the proposed geologic repository for high-level radioactive waste under the U.S. Nuclear Waste Policy Act, the applicability of the science, analyses, and interpretations is not limited to a specific site. Natural and anthropogenic analogues have provided and can continue to provide value in understanding features and processes of importance across a wide variety of topics in addressing the challenges of geologic isolation of radioactive waste and also as a contribution to scientific investigations unrelated to waste disposal. Isolation of radioactive waste at a mined geologic repository would be through a combination of natural features and engineered barriers. In this report we examine analogues to many of the various components of the Yucca Mountain system, including the preservation of materials in unsaturated environments, flow of water through unsaturated volcanic tuff, seepage into repository drifts, repository drift stability, stability and alteration of waste forms and components of the engineered barrier system, and transport of radionuclides through unsaturated and saturated rock zones.

Ready or not? School preparedness for California's new personal beliefs exemption law.

PubMed

Wheeler, Marissa; Buttenheim, Alison M

2014-05-07

This paper describes elementary school officials' awareness of and preparedness for the implementation of California's new exemption law that went into effect on January 1, 2014. The new law prescribes stricter requirements for claiming a personal beliefs exemption from mandated school-entry immunizations. We used cross-sectional data collected from a stratified random sample of 315 schools with low, middle, and high rates of personal beliefs exemptions. We described schools' awareness and specific knowledge of the new legislation and tested for differences across school types. We additionally tested for associations between outcome variables and school and respondent characteristics using ordered logit and negative binomial regression. Finally, we described schools' plans and needs for implementing the new legislation. Elementary school staff reported an overall low level of awareness and knowledge about the new legislation and could identify few of its features. We observed, however, that across the exemption-level strata, respondents from high-PBE schools reported significantly higher awareness, knowledge and feature identification compared to respondents from low-PBE schools. Multivariate analyses revealed only one significant association with awareness, knowledge and identification: respondent role. Support staff roles were associated with lower odds of having high self-rated awareness or knowledge compared to health workers, as well as with a reduced log count of features identified. Though most school officials were able to identify a communication plan, schools were still in need of resources and support for successful implementation, in particular, the need for information on the new law. Schools need additional information and support from state and local agencies in order to successfully implement and enforce California's new school immunization law. In particular, our results suggest the need to ensure information on the new law reaches all levels of school staff. Copyright © 2014 Elsevier Ltd. All rights reserved.

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J E; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-08-03

Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.

Comparing supervised learning techniques on the task of physical activity recognition.

PubMed

Dalton, A; OLaighin, G

2013-01-01

The objective of this study was to compare the performance of base-level and meta-level classifiers on the task of physical activity recognition. Five wireless kinematic sensors were attached to each subject (n = 25) while they completed a range of basic physical activities in a controlled laboratory setting. Subjects were then asked to carry out similar self-annotated physical activities in a random order and in an unsupervised environment. A combination of time-domain and frequency-domain features were extracted from the sensor data including the first four central moments, zero-crossing rate, average magnitude, sensor cross-correlation, sensor auto-correlation, spectral entropy and dominant frequency components. A reduced feature set was generated using a wrapper subset evaluation technique with a linear forward search and this feature set was employed for classifier comparison. The meta-level classifier AdaBoostM1 with C4.5 Graft as its base-level classifier achieved an overall accuracy of 95%. Equal sized datasets of subject independent data and subject dependent data were used to train this classifier and high recognition rates could be achieved without the need for user specific training. Furthermore, it was found that an accuracy of 88% could be achieved using data from the ankle and wrist sensors only.

Visual categorization of natural movies by rats.

PubMed

Vinken, Kasper; Vermaercke, Ben; Op de Beeck, Hans P

2014-08-06

Visual categorization of complex, natural stimuli has been studied for some time in human and nonhuman primates. Recent interest in the rodent as a model for visual perception, including higher-level functional specialization, leads to the question of how rodents would perform on a categorization task using natural stimuli. To answer this question, rats were trained in a two-alternative forced choice task to discriminate movies containing rats from movies containing other objects and from scrambled movies (ordinate-level categorization). Subsequently, transfer to novel, previously unseen stimuli was tested, followed by a series of control probes. The results show that the animals are capable of acquiring a decision rule by abstracting common features from natural movies to generalize categorization to new stimuli. Control probes demonstrate that they did not use single low-level features, such as motion energy or (local) luminance. Significant generalization was even present with stationary snapshots from untrained movies. The variability within and between training and test stimuli, the complexity of natural movies, and the control experiments and analyses all suggest that a more high-level rule based on more complex stimulus features than local luminance-based cues was used to classify the novel stimuli. In conclusion, natural stimuli can be used to probe ordinate-level categorization in rats. Copyright © 2014 the authors 0270-6474/14/3410645-14$15.00/0.

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

PubMed

Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip

2015-11-01

Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bi-level Multi-Source Learning for Heterogeneous Block-wise Missing Data

PubMed Central

Xiang, Shuo; Yuan, Lei; Fan, Wei; Wang, Yalin; Thompson, Paul M.; Ye, Jieping

2013-01-01

Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer’s Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified “bi-level” learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches. PMID:23988272

Contextually guided very-high-resolution imagery classification with semantic segments

NASA Astrophysics Data System (ADS)

Zhao, Wenzhi; Du, Shihong; Wang, Qiao; Emery, William J.

2017-10-01

Contextual information, revealing relationships and dependencies between image objects, is one of the most important information for the successful interpretation of very-high-resolution (VHR) remote sensing imagery. Over the last decade, geographic object-based image analysis (GEOBIA) technique has been widely used to first divide images into homogeneous parts, and then to assign semantic labels according to the properties of image segments. However, due to the complexity and heterogeneity of VHR images, segments without semantic labels (i.e., semantic-free segments) generated with low-level features often fail to represent geographic entities (such as building roofs usually be partitioned into chimney/antenna/shadow parts). As a result, it is hard to capture contextual information across geographic entities when using semantic-free segments. In contrast to low-level features, "deep" features can be used to build robust segments with accurate labels (i.e., semantic segments) in order to represent geographic entities at higher levels. Based on these semantic segments, semantic graphs can be constructed to capture contextual information in VHR images. In this paper, semantic segments were first explored with convolutional neural networks (CNN) and a conditional random field (CRF) model was then applied to model the contextual information between semantic segments. Experimental results on two challenging VHR datasets (i.e., the Vaihingen and Beijing scenes) indicate that the proposed method is an improvement over existing image classification techniques in classification performance (overall accuracy ranges from 82% to 96%).

SAR target recognition and posture estimation using spatial pyramid pooling within CNN

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-01-01

Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What's more, the results inspire us the application of deep learning on SAR target posture description.

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

People, clothing, music, and arousal as contextual retrieval cues in verbal memory.

PubMed

Standing, Lionel G; Bobbitt, Kristin E; Boisvert, Kathryn L; Dayholos, Kathy N; Gagnon, Anne M

2008-10-01

Four experiments (N = 164) on context-dependent memory were performed to explore the effects on verbal memory of incidental cues during the test session which replicated specific features of the learning session. These features involved (1) bystanders, (2) the clothing of the experimenter, (3) background music, and (4) the arousal level of the subject. Social contextual cues (bystanders or experimenter clothing) improved verbal recall or recognition. However, recall decreased when the contextual cue was a different stimulus taken from the same conceptual category (piano music by Chopin) that was heard during learning. Memory was unaffected by congruent internal cues, produced by the same physiological arousal level (low, moderate, or high heart rate) during the learning and test sessions. However, recall increased with the level of arousal across the three congruent conditions. The results emphasize the effectiveness as retrieval cues of stimuli which are socially salient, concrete, and external.

A methodological approach for using high-level Petri Nets to model the immune system response.

PubMed

Pennisi, Marzio; Cavalieri, Salvatore; Motta, Santo; Pappalardo, Francesco

2016-12-22

Mathematical and computational models showed to be a very important support tool for the comprehension of the immune system response against pathogens. Models and simulations allowed to study the immune system behavior, to test biological hypotheses about diseases and infection dynamics, and to improve and optimize novel and existing drugs and vaccines. Continuous models, mainly based on differential equations, usually allow to qualitatively study the system but lack in description; conversely discrete models, such as agent based models and cellular automata, permit to describe in detail entities properties at the cost of losing most qualitative analyses. Petri Nets (PN) are a graphical modeling tool developed to model concurrency and synchronization in distributed systems. Their use has become increasingly marked also thanks to the introduction in the years of many features and extensions which lead to the born of "high level" PN. We propose a novel methodological approach that is based on high level PN, and in particular on Colored Petri Nets (CPN), that can be used to model the immune system response at the cellular scale. To demonstrate the potentiality of the approach we provide a simple model of the humoral immune system response that is able of reproducing some of the most complex well-known features of the adaptive response like memory and specificity features. The methodology we present has advantages of both the two classical approaches based on continuous and discrete models, since it allows to gain good level of granularity in the description of cells behavior without losing the possibility of having a qualitative analysis. Furthermore, the presented methodology based on CPN allows the adoption of the same graphical modeling technique well known to life scientists that use PN for the modeling of signaling pathways. Finally, such an approach may open the floodgates to the realization of multi scale models that integrate both signaling pathways (intra cellular) models and cellular (population) models built upon the same technique and software.

Activity Structures and the Unfolding of Problem-Solving Actions in High-School Chemistry Classrooms

NASA Astrophysics Data System (ADS)

Criswell, Brett A.; Rushton, Greg T.

2014-02-01

In this paper, we argue for a more systematic approach for studying the relationship between classroom practices and scientific practices—an approach that will likely better support the systemic reforms being promoted in the Next Generation Science Standards in the USA and similar efforts in other countries. One component of that approach is looking at how the nature of the activity structure may influence the relative alignment between classroom and scientific practices. To that end, we build on previously published research related to the practices utilized by five high-school chemistry teachers as they enacted problem-solving activities in which students were likely to generate proposals that were not aligned with normative scientific understandings. In that prior work, our analysis had emphasized micro-level features of the talk interactions and how they related to the way students' ideas were explored; in the current paper, the analysis zooms out to consider the macro-level nature of the enactments associated with the activity structure of each lesson examined. Our data show that there were two general patterns to the activity structure across the 14 lessons scrutinized, and that each pattern had associated with it a constellation of features that impinged on the way the problem space was navigated. A key finding is that both activity structures (the expansive and the open) had features that aligned with scientific practices espoused in the Next Generation Science Standards—and both had features that were not aligned with those practices. We discuss the nature of these two structures, evidence of the relationship of each structure to key features of how the lessons unfolded, and the implications of these findings for both future research and the training of teachers.

The Built Environment and Cognitive Disorders: Results From the Cognitive Function and Ageing Study II.

PubMed

Wu, Yu-Tzu; Prina, A Matthew; Jones, Andy; Matthews, Fiona E; Brayne, Carol

2017-07-01

Built environment features have been related to behavior modification and might stimulate cognitive activity with a potential impact on cognitive health in later life. This study investigated cross-sectional associations between features of land use and cognitive impairment and dementia, and also explored urban and rural differences in these associations. Postcodes of the 7,505 community-based participants (aged ≥65 years) in the Cognitive Function and Ageing Study II (collected in 2008-2011) were linked to environmental data from government statistics. Multilevel logistic regression investigated associations between cognitive impairment (defined as Mini-Mental State Examination score ≤25) and dementia (Geriatric Mental Status and Automatic Geriatric Examination for Computer-Assisted Taxonomy organicity level ≥3) and land use features, including natural environment availability and land use mix, fitting interaction terms with three rural/urban categories. Data were analyzed in 2015. Associations between features of land use and cognitive impairment were not linear. After adjusting for individual-level factors and area deprivation, living in areas with high land use mix was associated with a nearly 30% decreased odds of cognitive impairment (OR=0.72, 95% CI=0.58, 0.89). This was similar, yet non-significant, for dementia (OR=0.70, 95% CI=0.46, 1.06). In conurbations, living in areas with high natural environment availability was associated with 30% reduced odds of cognitive impairment (OR=0.70, 95% CI=0.50, 0.97). Non-linear associations between features of land use and cognitive impairment were confirmed in this new cohort of older people in England. Both lack of and overload of environmental stimulation may be detrimental to cognition in later life. Copyright © 2017 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Multiple kernel based feature and decision level fusion of iECO individuals for explosive hazard detection in FLIR imagery

NASA Astrophysics Data System (ADS)

Price, Stanton R.; Murray, Bryce; Hu, Lequn; Anderson, Derek T.; Havens, Timothy C.; Luke, Robert H.; Keller, James M.

2016-05-01

A serious threat to civilians and soldiers is buried and above ground explosive hazards. The automatic detection of such threats is highly desired. Many methods exist for explosive hazard detection, e.g., hand-held based sensors, downward and forward looking vehicle mounted platforms, etc. In addition, multiple sensors are used to tackle this extreme problem, such as radar and infrared (IR) imagery. In this article, we explore the utility of feature and decision level fusion of learned features for forward looking explosive hazard detection in IR imagery. Specifically, we investigate different ways to fuse learned iECO features pre and post multiple kernel (MK) support vector machine (SVM) based classification. Three MK strategies are explored; fixed rule, heuristics and optimization-based. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths and times of day. Specifically, the results reveal two interesting things. First, the different MK strategies appear to indicate that the different iECO individuals are all more-or-less important and there is not a dominant feature. This is reinforcing as our hypothesis was that iECO provides different ways to approach target detection. Last, we observe that while optimization-based MK is mathematically appealing, i.e., it connects the learning of the fusion to the underlying classification problem we are trying to solve, it appears to be highly susceptible to over fitting and simpler, e.g., fixed rule and heuristics approaches help us realize more generalizable iECO solutions.

Predicting Relapse in Patients With Medulloblastoma by Integrating Evidence From Clinical and Genomic Features

PubMed Central

Tamayo, Pablo; Cho, Yoon-Jae; Tsherniak, Aviad; Greulich, Heidi; Ambrogio, Lauren; Schouten-van Meeteren, Netteke; Zhou, Tianni; Buxton, Allen; Kool, Marcel; Meyerson, Matthew; Pomeroy, Scott L.; Mesirov, Jill P.

2011-01-01

Purpose Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis. Patients and Methods A Bayesian cumulative log-odds model of outcome was developed from a training cohort of 96 children treated for medulloblastoma, starting with the evidence provided by clinical features of metastasis and histology (model A) and incrementally adding the evidence from gene-expression–derived features representing disease subtype–independent (model B) and disease subtype–dependent (model C) pathways, and finally high-level copy-number genomic abnormalities (model D). The models were validated on an independent test cohort (n = 78). Results On an independent multi-institutional test data set, models A to D attain an area under receiver operating characteristic (au-ROC) curve of 0.73 (95% CI, 0.60 to 0.84), 0.75 (95% CI, 0.64 to 0.86), 0.80 (95% CI, 0.70 to 0.90), and 0.78 (95% CI, 0.68 to 0.88), respectively, for predicting relapse versus no relapse. Conclusion The proposed models C and D outperform the current clinical classification schema (au-ROC, 0.68), our previously published eight-gene outcome signature (au-ROC, 0.71), and several new schemas recently proposed in the literature for medulloblastoma risk stratification. PMID:21357789

Borderline personality features and implicit shame-prone self-concept in middle childhood and early adolescence.

PubMed

Hawes, David J; Helyer, Rebekah; Herlianto, Eugene C; Willing, Jonah

2013-01-01

This study tested if children and adolescents with high levels of borderline personality features (BPF) exhibit the same shame-prone self-concept previously found to characterize adults with borderline personality disorder (Rüsch et al., 2007 ). Self-concept was indexed using the Implicit Association Test, in a community sample of children/adolescents aged 10 to 14 years (48% female; M age = 12.04 years). Common domains of child and adolescent psychopathology and core components of BPF were assessed using self-reports on the Strengths and Difficulties Questionnaire and the Borderline Personality Features Scale for Children. The identity problems component of BPF was found to significantly predict implicit levels of shame-prone self-concept, but only among girls. This effect was independent of the key dimensions of child and adolescent psychopathology that overlap with BPF-including features hyperactivity/inattention, disruptive behavior problems, and anxiety/depression-none of which were associated with shame-prone self-concept at the bivariate level or otherwise. The current findings provide preliminary evidence that self-schemas related to shame are uniquely associated with a core component of BPF in middle childhood and early adolescence and suggest that this correlate may apply uniquely to female individuals. These findings point to the identity problems component of BPF as a priority for future clinical and developmental research into mechanisms associated with BPF across childhood and adolescence.

Deep learning decision fusion for the classification of urban remote sensing data

NASA Astrophysics Data System (ADS)

Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

2018-01-01

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2001-01-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2000-12-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

A supervised learning approach for Crohn's disease detection using higher-order image statistics and a novel shape asymmetry measure.

PubMed

Mahapatra, Dwarikanath; Schueffler, Peter; Tielbeek, Jeroen A W; Buhmann, Joachim M; Vos, Franciscus M

2013-10-01

Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time-consuming and invasive while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive procedure over colonoscopy. Current MRI approaches assess rate of contrast enhancement and bowel wall thickness, and rely on extensive manual segmentation for accurate analysis. We propose a supervised learning method for the identification and localization of regions in abdominal magnetic resonance images that have been affected by CD. Low-level features like intensity and texture are used with shape asymmetry information to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy-based shape asymmetry method and higher-order statistics like skewness and kurtosis. Multi-scale feature extraction renders the method robust. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods.

Paucity of CD4+CCR5+ T cells is a typical feature of natural SIV hosts

PubMed Central

Pandrea, Ivona; Apetrei, Cristian; Gordon, Shari; Barbercheck, Joseph; Dufour, Jason; Bohm, Rudolf; Sumpter, Beth; Roques, Pierre; Marx, Preston A.; Hirsch, Vanessa M.; Kaur, Amitinder; Lackner, Andrew A.; Veazey, Ronald S.; Silvestri, Guido

2007-01-01

In contrast to lentiviral infections of humans and macaques, simian immunodeficiency virus (SIV) infection of natural hosts is nonpathogenic despite high levels of viral replication. However, the mechanisms underlying this absence of disease are unknown. Here we report that natural hosts for SIV infection express remarkably low levels of CCR5 on CD4+ T cells isolated from blood, lymph nodes, and mucosal tissues. Given that this immunologic feature is found in 5 different species of natural SIV hosts (sooty mangabeys, African green monkeys, mandrills, sun-tailed monkeys, and chimpanzees) but is absent in 5 nonnatural/recent hosts (humans, rhesus, pigtail, cynomolgus macaques, and baboons), it may represent a key feature of the coevolution between the virus and its natural hosts that led to a nonpathogenic infection. Beneficial effects of low CCR5 expression on CD4+ T cells may include the reduction of target cells for viral replication and a decreased homing of activated CD4+ T cells to inflamed tissue. PMID:17003371

77 FR 27768 - Greybull Valley Irrigation District; Notice of Preliminary Permit Application Accepted for Filing...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-11

... following new features: (1) A powerhouse containing two turbine/generator units rated at 2.5 megawatts each... total storage capacity of 33,169 acre-feet at a normal high water level elevation of 4,953 feet mean sea...

The levels of perceptual processing and the neural correlates of increasing subjective visibility.

PubMed

Binder, Marek; Gociewicz, Krzysztof; Windey, Bert; Koculak, Marcin; Finc, Karolina; Nikadon, Jan; Derda, Monika; Cleeremans, Axel

2017-10-01

According to the levels-of-processing hypothesis, transitions from unconscious to conscious perception may depend on stimulus processing level, with more gradual changes for low-level stimuli and more dichotomous changes for high-level stimuli. In an event-related fMRI study we explored this hypothesis using a visual backward masking procedure. Task requirements manipulated level of processing. Participants reported the magnitude of the target digit in the high-level task, its color in the low-level task, and rated subjective visibility of stimuli using the Perceptual Awareness Scale. Intermediate stimulus visibility was reported more frequently in the low-level task, confirming prior behavioral results. Visible targets recruited insulo-fronto-parietal regions in both tasks. Task effects were observed in visual areas, with higher activity in the low-level task across all visibility levels. Thus, the influence of level of processing on conscious perception may be mediated by attentional modulation of activity in regions representing features of consciously experienced stimuli. Copyright © 2017 Elsevier Inc. All rights reserved.

Influence of magnetism and correlation on the spectral properties of doped Mott insulators

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

Wang, Yao; Moritz, Brian; Chen, Cheng-Chien

Unraveling the nature of the doping-induced transition between a Mott insulator and a weakly correlated metal is crucial to understanding novel emergent phases in strongly correlated materials. Here, for this purpose, we study the evolution of spectral properties upon doping Mott insulating states by utilizing the cluster perturbation theory on the Hubbard and t – J -like models. Specifically, a quasifree dispersion crossing the Fermi level develops with small doping, and it eventually evolves into the most dominant feature at high doping levels. Although this dispersion is related to the free-electron hopping, our study shows that this spectral feature is,more » in fact, influenced inherently by both electron-electron correlation and spin-exchange interaction: the correlation destroys coherence, while the coupling between spin and mobile charge restores it in the photoemission spectrum. Due to the persistent impact of correlations and spin physics, the onset of gaps or the high-energy anomaly in the spectral functions can be expected in doped Mott insulators.« less

Influence of magnetism and correlation on the spectral properties of doped Mott insulators

DOE PAGES

Wang, Yao; Moritz, Brian; Chen, Cheng-Chien; ...

2018-03-01

Unraveling the nature of the doping-induced transition between a Mott insulator and a weakly correlated metal is crucial to understanding novel emergent phases in strongly correlated materials. Here, for this purpose, we study the evolution of spectral properties upon doping Mott insulating states by utilizing the cluster perturbation theory on the Hubbard and t – J -like models. Specifically, a quasifree dispersion crossing the Fermi level develops with small doping, and it eventually evolves into the most dominant feature at high doping levels. Although this dispersion is related to the free-electron hopping, our study shows that this spectral feature is,more » in fact, influenced inherently by both electron-electron correlation and spin-exchange interaction: the correlation destroys coherence, while the coupling between spin and mobile charge restores it in the photoemission spectrum. Due to the persistent impact of correlations and spin physics, the onset of gaps or the high-energy anomaly in the spectral functions can be expected in doped Mott insulators.« less

a Novel Framework for Remote Sensing Image Scene Classification

NASA Astrophysics Data System (ADS)

Jiang, S.; Zhao, H.; Wu, W.; Tan, Q.

2018-04-01

High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57 % and 83.35 % respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.

Embedded Ultrasonics for SHM of Space Applications

DTIC Science & Technology

2012-07-30

information on material properties and other forms of damage such as cracks, structural fatigue and/or impact events. This synergistic aspect of the embedded...larger the phase shift. However, high excitation levels could contribute to sensor fatigue and levels in a range 15 to 20 (110 to 130 volts) are...joints each featuring three bolts. Piezoelectric wafers ( PZT ) with UNF electrodes were bonded to the isogrid panels using 3M 2216 epoxy

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

NASA Technical Reports Server (NTRS)

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

1970-01-01

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

Multispectral Palmprint Recognition Using a Quaternion Matrix

PubMed Central

Xu, Xingpeng; Guo, Zhenhua; Song, Changjiang; Li, Yafeng

2012-01-01

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%. PMID:22666049

Multispectral palmprint recognition using a quaternion matrix.

PubMed

Xu, Xingpeng; Guo, Zhenhua; Song, Changjiang; Li, Yafeng

2012-01-01

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.

Recurrent landsliding of a high bank at Dunaszekcső, Hungary: Geodetic deformation monitoring and finite element modeling

NASA Astrophysics Data System (ADS)

Bányai, László; Mentes, Gyula; Újvári, Gábor; Kovács, Miklós; Czap, Zoltán; Gribovszki, Katalin; Papp, Gábor

2014-04-01

Five years of geodetic monitoring data at Dunaszekcső, Hungary, are processed to evaluate recurrent landsliding, which is a characteristic geomorphological process affecting the high banks of the Middle Danube valley in Hungary. The integrated geodetic observations provide accurate three dimensional coordinate time series, and these data are used to calculate the kinematic features of point movements and the rigid body behavior of point blocks. Additional datasets include borehole tiltmeter data and hydrological recordings of the Danube and soil water wells. These data, together with two dimensional final element analyses, are utilized to gain a better understanding of the physical, soil mechanical background and stability features of the high bank. Here we indicate that the main trigger of movements is changing groundwater levels, whose effect is an order of magnitude higher than that of river water level changes. Varying displacement rates of the sliding blocks are interpreted as having been caused by basal pore water pressure changes originating from shear zone volume changes, floods of the River Danube through later seepage and rain infiltration. Both data and modeling point to the complex nature of bank sliding at Dunaszekcső. Some features imply that the movements are rotational, some reveal slumping. By contrast, all available observational and modeling data point to the retrogressive development of the high bank at Dunaszekcső. Regarding mitigation, the detailed analysis of three basic parameters (the direction of displacement vectors, tilting, and the acceleration component of the kinematic function) is suggested because these parameters indicate the zone where the largest lateral displacements can be expected and point to the advent of the rapid landsliding phase that affects high banks along the River Danube.

Education Level Is a Strong Prognosticator in the Subgroup Aged More Than 50 Years Regardless of the Molecular Subtype of Breast Cancer: A Study Based on the Nationwide Korean Breast Cancer Registry Database.

PubMed

Hwang, Ki-Tae; Noh, Woochul; Cho, Se-Heon; Yu, Jonghan; Park, Min Ho; Jeong, Joon; Lee, Hyouk Jin; Kim, Jongjin; Oh, Sohee; Kim, Young A

2017-10-01

This study investigated the role of the education level (EL) as a prognostic factor for breast cancer and analyzed the relationship between the EL and various confounding factors. The data for 64,129 primary breast cancer patients from the Korean Breast Cancer Registry were analyzed. The EL was classified into two groups according to the education period; the high EL group (≥ 12 years) and low EL group (< 12 years). Survival analyses were performed with respect to the overall survival between the two groups. A high EL conferred a superior prognosis compared to a low EL in the subgroup aged > 50 years (hazard ratio, 0.626; 95% confidence interval [CI], 0.577 to 0.678) but not in the subgroup aged ≤ 50 years (hazard ratio, 0.941; 95% CI, 0.865 to 1.024). The EL was a significant independent factor in the subgroup aged > 50 years according to multivariate analyses. The high EL group showed more favorable clinicopathologic features and a higher proportion of patients in this group received lumpectomy, radiation therapy, and endocrine therapy. In the high EL group, a higher proportion of patients received chemotherapy in the subgroups with unfavorable clinicopathologic features. The EL was a significant prognosticator across all molecular subtypes of breast cancer. The EL is a strong independent prognostic factor for breast cancer in the subgroup aged > 50 years regardless of the molecular subtype, but not in the subgroup aged ≤ 50 years. Favorable clinicopathologic features and active treatments can explain the main causality of the superior prognosis in the high EL group.

Education Level Is a Strong Prognosticator in the Subgroup Aged More Than 50 Years Regardless of the Molecular Subtype of Breast Cancer: A Study Based on the Nationwide Korean Breast Cancer Registry Database

PubMed Central

Hwang, Ki-Tae; Noh, Woochul; Cho, Se-Heon; Yu, Jonghan; Park, Min Ho; Jeong, Joon; Lee, Hyouk Jin; Kim, Jongjin; Oh, Sohee; Kim, Young A

2017-01-01

Purpose This study investigated the role of the education level (EL) as a prognostic factor for breast cancer and analyzed the relationship between the EL and various confounding factors. Materials and Methods The data for 64,129 primary breast cancer patients from the Korean Breast Cancer Registry were analyzed. The EL was classified into two groups according to the education period; the high EL group (≥ 12 years) and low EL group (< 12 years). Survival analyses were performed with respect to the overall survival between the two groups. Results A high EL conferred a superior prognosis compared to a low EL in the subgroup aged > 50 years (hazard ratio, 0.626; 95% confidence interval [CI], 0.577 to 0.678) but not in the subgroup aged ≤ 50 years (hazard ratio, 0.941; 95% CI, 0.865 to 1.024). The EL was a significant independent factor in the subgroup aged > 50 years according to multivariate analyses. The high EL group showed more favorable clinicopathologic features and a higher proportion of patients in this group received lumpectomy, radiation therapy, and endocrine therapy. In the high EL group, a higher proportion of patients received chemotherapy in the subgroups with unfavorable clinicopathologic features. The EL was a significant prognosticator across all molecular subtypes of breast cancer. Conclusion The EL is a strong independent prognostic factor for breast cancer in the subgroup aged > 50 years regardless of the molecular subtype, but not in the subgroup aged ≤ 50 years. Favorable clinicopathologic features and active treatments can explain the main causality of the superior prognosis in the high EL group. PMID:28161933

Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images.

PubMed

Xu, Jun; Xiang, Lei; Liu, Qingshan; Gilmore, Hannah; Wu, Jianzhong; Tang, Jinghai; Madabhushi, Anant

2016-01-01

Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of "Deep Learning" strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 × 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.

Histogram-based adaptive gray level scaling for texture feature classification of colorectal polyps

NASA Astrophysics Data System (ADS)

Pomeroy, Marc; Lu, Hongbing; Pickhardt, Perry J.; Liang, Zhengrong

2018-02-01

Texture features have played an ever increasing role in computer aided detection (CADe) and diagnosis (CADx) methods since their inception. Texture features are often used as a method of false positive reduction for CADe packages, especially for detecting colorectal polyps and distinguishing them from falsely tagged residual stool and healthy colon wall folds. While texture features have shown great success there, the performance of texture features for CADx have lagged behind primarily because of the more similar features among different polyps types. In this paper, we present an adaptive gray level scaling and compare it to the conventional equal-spacing of gray level bins. We use a dataset taken from computed tomography colonography patients, with 392 polyp regions of interest (ROIs) identified and have a confirmed diagnosis through pathology. Using the histogram information from the entire ROI dataset, we generate the gray level bins such that each bin contains roughly the same number of voxels Each image ROI is the scaled down to two different numbers of gray levels, using both an equal spacing of Hounsfield units for each bin, and our adaptive method. We compute a set of texture features from the scaled images including 30 gray level co-occurrence matrix (GLCM) features and 11 gray level run length matrix (GLRLM) features. Using a random forest classifier to distinguish between hyperplastic polyps and all others (adenomas and adenocarcinomas), we find that the adaptive gray level scaling can improve performance based on the area under the receiver operating characteristic curve by up to 4.6%.

Intelligent data analysis to interpret major risk factors for diabetic patients with and without ischemic stroke in a small population

PubMed Central

Gürgen, Fikret; Gürgen, Nurgül

2003-01-01

This study proposes an intelligent data analysis approach to investigate and interpret the distinctive factors of diabetes mellitus patients with and without ischemic (non-embolic type) stroke in a small population. The database consists of a total of 16 features collected from 44 diabetic patients. Features include age, gender, duration of diabetes, cholesterol, high density lipoprotein, triglyceride levels, neuropathy, nephropathy, retinopathy, peripheral vascular disease, myocardial infarction rate, glucose level, medication and blood pressure. Metric and non-metric features are distinguished. First, the mean and covariance of the data are estimated and the correlated components are observed. Second, major components are extracted by principal component analysis. Finally, as common examples of local and global classification approach, a k-nearest neighbor and a high-degree polynomial classifier such as multilayer perceptron are employed for classification with all the components and major components case. Macrovascular changes emerged as the principal distinctive factors of ischemic-stroke in diabetes mellitus. Microvascular changes were generally ineffective discriminators. Recommendations were made according to the rules of evidence-based medicine. Briefly, this case study, based on a small population, supports theories of stroke in diabetes mellitus patients and also concludes that the use of intelligent data analysis improves personalized preventive intervention. PMID:12685939

Automated Interpretation of Subcellular Patterns in Fluorescence Microscope Images for Location Proteomics

PubMed Central

Chen, Xiang; Velliste, Meel; Murphy, Robert F.

2010-01-01

Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein’s subcellular location is essential to a complete understanding of its functions. Currently subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during and after the onset of various diseases. PMID:16752421

Digital transmitter for data bus communications system

NASA Technical Reports Server (NTRS)

Proch, G. E. (Inventor)

1975-01-01

An improved digital transmitter for transmitting serial pulse code modulation (pcm) data at high bit rates over a transmission line is disclosed. When not transmitting, the transmitter features a high output impedance which prevents the transmitter from loading the transmission line. The pcm input is supplied to a logic control circuit which produces two discrete logic level signals which are supplied to an amplifier. The amplifier, which is transformer coupled to the output isolation circuitry, converts the discrete logic level signals to two high current level, ground isolated signals in the secondary windings of the coupling transformer. The latter signals are employed as inputs to the isolation circuitry which includes two series transistor pairs operating into a hybrid transformer functioning to isolate the transmitter circuitry from the transmission line.

The influence of attention levels on psychophysiological responses.

PubMed

Chang, Yu-Chieh; Huang, Shwu-Lih

2012-10-01

This study aimed to examine which brain oscillatory activities and peripheral physiological measures were influenced by attention levels. A new experimental procedure was designed. Participants were asked to count the number of target events while viewing eight moving white circles. An event occurred when two of the circles changed from white to red or blue. In the low-attention task, similar to a feature search, the target events were defined by color only. In the high-attention task, similar to a conjunction search, the target events were defined by both color and size. In the control task, participants were asked to passively watch the series of events while remembering a number. Based on Feature Integration Theory, our high-attention task would demand more attentional investment than the low-attention task. Given the identical visual stimuli and requirement of keeping a number in working memory for all three tasks, the changes in brain oscillatory activities can be attributed to attention level rather than to perceptual content or memory processes. Peripheral measures such as heart rate, heart rate variability (HRV), respiration rate, eye blinks, and skin conductance level were also evaluated. In comparing the high-attention task with the low-attention task, theta synchronization at the Fz, Cz, and Pz electrodes as a group, alpha2 desynchronization at the Fz, Cz, Pz, and Oz electrodes as a group, and a decrease in the low-frequency component and ratio measure of HRV were evident. These measures are considered to be promising indices for discriminating between attention levels. Copyright © 2012 Elsevier B.V. All rights reserved.

Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer.

PubMed

Garcia-Vicente, Ana María; Molina, David; Pérez-Beteta, Julián; Amo-Salas, Mariano; Martínez-González, Alicia; Bueno, Gloria; Tello-Galán, María Jesús; Soriano-Castrejón, Ángel

2017-12-01

To study the influence of dual time point 18F-FDG PET/CT in textural features and SUV-based variables and their relation among them. Fifty-six patients with locally advanced breast cancer (LABC) were prospectively included. All of them underwent a standard 18F-FDG PET/CT (PET-1) and a delayed acquisition (PET-2). After segmentation, SUV variables (SUVmax, SUVmean, and SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained. Eighteen three-dimensional (3D) textural measures were computed including: run-length matrices (RLM) features, co-occurrence matrices (CM) features, and energies. Differences between all PET-derived variables obtained in PET-1 and PET-2 were studied. Significant differences were found between the SUV-based parameters and MTV obtained in the dual time point PET/CT, with higher values of SUV-based variables and lower MTV in the PET-2 with respect to the PET-1. In relation with the textural parameters obtained in dual time point acquisition, significant differences were found for the short run emphasis, low gray-level run emphasis, short run high gray-level emphasis, run percentage, long run emphasis, gray-level non-uniformity, homogeneity, and dissimilarity. Textural variables showed relations with MTV and TLG. Significant differences of textural features were found in dual time point 18F-FDG PET/CT. Thus, a dynamic behavior of metabolic characteristics should be expected, with higher heterogeneity in delayed PET acquisition compared with the standard PET. A greater heterogeneity was found in bigger tumors.

Feature level fusion for enhanced geological mapping of ophiolile complex using ASTER and Landsat TM data

NASA Astrophysics Data System (ADS)

Pournamdari, M.; Hashim, M.

2014-02-01

Chromite ore deposit occurrence is related to ophiolite complexes as a part of the oceanic crust and provides a good opportunity for lithological mapping using remote sensing data. The main contribution of this paper is a novel approaches to discriminate different rock units associated with ophiolite complex using the Feature Level Fusion technique on ASTER and Landsat TM satellite data at regional scale. In addition this study has applied spectral transform approaches, consisting of Spectral Angle Mapper (SAM) to distinguish the concentration of high-potential areas of chromite and also for determining the boundary between different rock units. Results indicated both approaches show superior outputs compared to other methods and can produce a geological map for ophiolite complex rock units in the arid and the semi-arid region. The novel technique including feature level fusion and Spectral Angle Mapper (SAM) discriminated ophiolitic rock units and produced detailed geological maps of the study area. As a case study, Sikhoran ophiolite complex located in SE, Iran has been selected for image processing techniques. In conclusion, a suitable approach for lithological mapping of ophiolite complexes is demonstrated, this technique contributes meaningfully towards economic geology in terms of identifying new prospects.

Sex differences in face gender recognition: an event-related potential study.

PubMed

Sun, Yueting; Gao, Xiaochao; Han, Shihui

2010-04-23

Multiple level neurocognitive processes are involved in face processing in humans. The present study examined whether the early face processing such as structural encoding is modulated by task demands that manipulate attention to perceptual or social features of faces and such an effect, if any, is different between men and women. Event-related brain potentials were recorded from male and female adults while they identified a low-level perceptual feature of faces (i.e., face orientation) and a high-level social feature of faces (i.e., gender). We found that task demands that required the processing of face orientations or face gender resulted in modulations of both the early occipital/temporal negativity (N170) and the late central/parietal positivity (P3). The N170 amplitude was smaller in the gender relative to the orientation identification task whereas the P3 amplitude was larger in the gender identification task relative to the orientation identification task. In addition, these effects were much stronger in women than in men. Our findings suggest that attention to social information in faces such as gender modulates both the early encoding of facial structures and late evaluative process of faces to a greater degree in women than in men.

Interplay of ICP and IXP over the Internet with power-law features

NASA Astrophysics Data System (ADS)

Fan, Zhongyan; Tang, Wallace Kit-Sang

The Internet is the largest artificial network consisting of billions of IP devices, managed by tens of thousands of autonomous systems (ASes). Due to its importance, the Internet has received much attention and its topological features, mainly in AS-level, have been widely explored from the complex network perspective. However, most of the previous studies assume a homogeneous model in which nodes are indistinguishable in nature. It may be good for a general study of topological structure, but unfortunately it fails to reflect the functionality. The Internet ecology is in fact heterogeneous and highly complex. It consists of various elements such as Internet Exchange Points (IXPs), Internet Content Providers (ICPs), and normal Autonomous System (ASes), realizing different roles in the Internet. In this paper, we propose level-structured network models for investigating how ICP performs under the AS-topology with power-law features and how IXP enhances its performance from a complex network perspective. Based on real data, our results reveal that the power-law nature of the Internet facilitates content delivery not only in efficiency but also in path redundancy. Moreover, the proposed multi-level framework is able to clearly illustrate the significant benefits gained by ICP from IXP peerings.

A DC Transformer

NASA Technical Reports Server (NTRS)

Youngquist, Robert C.; Ihlefeld, Curtis M.; Starr, Stanley O.

2013-01-01

A component level dc transformer is described in which no alternating currents or voltages are present. It operates by combining features of a homopolar motor and a homopolar generator, both de devices, such that the output voltage of a de power supply can be stepped up (or down) with a corresponding step down (or up) in current. The basic theory for this device is developed, performance predictions are made, and the results from a small prototype are presented. Based on demonstrated technology in the literature, this de transformer should be scalable to low megawatt levels, but it is more suited to high current than high voltage applications. Significant development would be required before it could achieve the kilovolt levels needed for de power transmission.

Pre-trained D-CNN models for detecting complex events in unconstrained videos

NASA Astrophysics Data System (ADS)

Robinson, Joseph P.; Fu, Yun

2016-05-01

Rapid event detection faces an emergent need to process large videos collections; whether surveillance videos or unconstrained web videos, the ability to automatically recognize high-level, complex events is a challenging task. Motivated by pre-existing methods being complex, computationally demanding, and often non-replicable, we designed a simple system that is quick, effective and carries minimal overhead in terms of memory and storage. Our system is clearly described, modular in nature, replicable on any Desktop, and demonstrated with extensive experiments, backed by insightful analysis on different Convolutional Neural Networks (CNNs), as stand-alone and fused with others. With a large corpus of unconstrained, real-world video data, we examine the usefulness of different CNN models as features extractors for modeling high-level events, i.e., pre-trained CNNs that differ in architectures, training data, and number of outputs. For each CNN, we use 1-fps from all training exemplar to train one-vs-rest SVMs for each event. To represent videos, frame-level features were fused using a variety of techniques. The best being to max-pool between predetermined shot boundaries, then average-pool to form the final video-level descriptor. Through extensive analysis, several insights were found on using pre-trained CNNs as off-the-shelf feature extractors for the task of event detection. Fusing SVMs of different CNNs revealed some interesting facts, finding some combinations to be complimentary. It was concluded that no single CNN works best for all events, as some events are more object-driven while others are more scene-based. Our top performance resulted from learning event-dependent weights for different CNNs.

Automatic classification of tissue malignancy for breast carcinoma diagnosis.

PubMed

Fondón, Irene; Sarmiento, Auxiliadora; García, Ana Isabel; Silvestre, María; Eloy, Catarina; Polónia, António; Aguiar, Paulo

2018-05-01

Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images. We provide four malignancy levels as the output of the system: normal, benign, in situ and invasive. The method is based on the calculation of three sets of features related to nuclei, colour regions and textures considering local characteristics and global image properties. By taking advantage of well-established image processing techniques, we build a feature vector for each image that serves as an input to an SVM (Support Vector Machine) classifier with a quadratic kernel. The method has been rigorously evaluated, first with a 5-fold cross-validation within an initial set of 120 images, second with an external set of 30 different images and third with images with artefacts included. Accuracy levels range from 75.8% when the 5-fold cross-validation was performed to 75% with the external set of new images and 61.11% when the extremely difficult images were added to the classification experiment. The experimental results indicate that the proposed method is capable of distinguishing between four malignancy levels with high accuracy. Our results are close to those obtained with recent deep learning-based methods. Moreover, it performs better than other state-of-the-art methods based on feature extraction, and it can help improve the CAD of breast cancer. Copyright © 2018 Elsevier Ltd. All rights reserved.

Preoperative easily misdiagnosed telangiectatic osteosarcoma: clinical-radiologic-pathologic correlations.

PubMed

Gao, Zhen-Hua; Yin, Jun-Qiang; Liu, Da-Wei; Meng, Quan-Fei; Li, Jia-Ping

2013-12-11

To describe the clinical, imaging, and pathologic characteristics and diagnostic methods of telangiectatic osteosarcoma (TOS) for improving the diagnostic level. The authors retrospectively reviewed patient demographics, serum alkaline phosphatase (AKP) levels, preoperative biopsy pathologic reports, pathologic materials, imaging findings, and treatment outcomes from 26 patients with TOS. Patient images from radiography (26 cases) and magnetic resonance (MR) imaging (22 cases) were evaluated by 3 authors in consensus for intrinsic characteristics. There were 15 male and 11 female patients in the study, with an age of 9-32 years (mean age 15.9 years). Eighteen of 26 patients died of lung metastases within 5 years of follow-up. The distal femur was affected more commonly (14 cases, 53.8%). Regarding serum AKP, normal (8 cases) or mildly elevated (18 cases) levels were found before preoperative chemotherapy. Radiographs showed geographic bone lysis without sclerotic margin (26 cases), cortical destruction (26 cases), periosteal new bone formation (24 cases), soft-tissue mass (23 cases), and matrix mineralization (4 cases). The aggressive radiographic features of TOS simulated the appearance of conventional high-grade intramedullary osteosarcoma, though different from aneurysmal bone cyst. MR images demonstrated multiple big (16 cases) or small (6 cases) cystic spaces, fluid-fluid levels (14 cases), soft-tissue mass (22 cases), and thick peripheral and septal enhancement (22 cases). Nine of 26 cases were misdiagnosed as aneurysmal bone cysts by preoperative core-needle biopsy, owing to the absence of viable high-grade sarcomatous cells in the small tissue samples. The aggressive growth pattern with occasional matrix mineralization, and multiple big or small fluid-filled cavities with thick peripheral, septal, and nodular tissue surrounding the fluid-filled cavities are characteristic imaging features of TOS, and these features are helpful in making the correct preoperative diagnosis of TOS.

Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

PubMed Central

Hu, Shiqiang; Zhang, Huanlong; Luo, Lingkun

2014-01-01

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance. PMID:25105164

Extensions of algebraic image operators: An approach to model-based vision

NASA Technical Reports Server (NTRS)

Lerner, Bao-Ting; Morelli, Michael V.

1990-01-01

Researchers extend their previous research on a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets. Addition and multiplication are defined for the set of all grey-level images, which can then be described as polynomials of two variables. Utilizing this new algebraic structure, researchers devised an innovative, efficient edge detection scheme. An accurate method for deriving gradient component information from this edge detector is presented. Based upon this new edge detection system researchers developed a robust method for linear feature extraction by combining the techniques of a Hough transform and a line follower. The major advantage of this feature extractor is its general, object-independent nature. Target attributes, such as line segment lengths, intersections, angles of intersection, and endpoints are derived by the feature extraction algorithm and employed during model matching. The algebraic operators are global operations which are easily reconfigured to operate on any size or shape region. This provides a natural platform from which to pursue dynamic scene analysis. A method for optimizing the linear feature extractor which capitalizes on the spatially reconfiguration nature of the edge detector/gradient component operator is discussed.

Intracranial EEG fluctuates over months after implanting electrodes in human brain

NASA Astrophysics Data System (ADS)

Ung, Hoameng; Baldassano, Steven N.; Bink, Hank; Krieger, Abba M.; Williams, Shawniqua; Vitale, Flavia; Wu, Chengyuan; Freestone, Dean; Nurse, Ewan; Leyde, Kent; Davis, Kathryn A.; Cook, Mark; Litt, Brian

2017-10-01

Objective. Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. Approach. Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient’s recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. Main results. A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. Significance. These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in patients, depending upon the application, may require extended monitoring.

Spatial Heterogeneity as a Genetic Mixing Mechanism in Highly Philopatric Colonial Seabirds

PubMed Central

Cristofari, Robin; Trucchi, Emiliano; Whittington, Jason D.; Vigetta, Stéphanie; Gachot-Neveu, Hélène; Stenseth, Nils Christian; Le Maho, Yvon; Le Bohec, Céline

2015-01-01

How genetic diversity is maintained in philopatric colonial systems remains unclear, and understanding the dynamic balance of philopatry and dispersal at all spatial scales is essential to the study of the evolution of coloniality. In the King penguin, Aptenodytes patagonicus, return rates of post-fledging chicks to their natal sub-colony are remarkably high. Empirical studies have shown that adults return year after year to their previous breeding territories within a radius of a few meters. Yet, little reliable data are available on intra- and inter-colonial dispersal in this species. Here, we present the first fine-scale study of the genetic structure in a king penguin colony in the Crozet Archipelago. Samples were collected from individual chicks and analysed at 8 microsatellite loci. Precise geolocation data of hatching sites and selective pressures associated with habitat features were recorded for all sampling locations. We found that despite strong natal and breeding site fidelity, king penguins retain a high degree of panmixia and genetic diversity. Yet, genetic structure appears markedly heterogeneous across the colony, with higher-than-expected inbreeding levels, and local inbreeding and relatedness hotspots that overlap predicted higher-quality nesting locations. This points towards heterogeneous population structure at the sub-colony level, in which fine-scale environmental features drive local philopatric behaviour, while lower-quality patches may act as genetic mixing mechanisms at the colony level. These findings show how a lack of global genetic structuring can emerge from small-scale heterogeneity in ecological parameters, as opposed to the classical model of homogeneous dispersal. Our results also emphasize the importance of sampling design for estimation of population parameters in colonial seabirds, as at high spatial resolution, basic genetic features are shown to be location-dependent. Finally, this study stresses the importance of understanding intra-colonial dispersal and genetic mixing mechanisms in order to better estimate species-wide gene flows and population dynamics. PMID:25680103

Spatial heterogeneity as a genetic mixing mechanism in highly philopatric colonial seabirds.

PubMed

Cristofari, Robin; Trucchi, Emiliano; Whittington, Jason D; Vigetta, Stéphanie; Gachot-Neveu, Hélène; Stenseth, Nils Christian; Le Maho, Yvon; Le Bohec, Céline

2015-01-01

How genetic diversity is maintained in philopatric colonial systems remains unclear, and understanding the dynamic balance of philopatry and dispersal at all spatial scales is essential to the study of the evolution of coloniality. In the King penguin, Aptenodytes patagonicus, return rates of post-fledging chicks to their natal sub-colony are remarkably high. Empirical studies have shown that adults return year after year to their previous breeding territories within a radius of a few meters. Yet, little reliable data are available on intra- and inter-colonial dispersal in this species. Here, we present the first fine-scale study of the genetic structure in a king penguin colony in the Crozet Archipelago. Samples were collected from individual chicks and analysed at 8 microsatellite loci. Precise geolocation data of hatching sites and selective pressures associated with habitat features were recorded for all sampling locations. We found that despite strong natal and breeding site fidelity, king penguins retain a high degree of panmixia and genetic diversity. Yet, genetic structure appears markedly heterogeneous across the colony, with higher-than-expected inbreeding levels, and local inbreeding and relatedness hotspots that overlap predicted higher-quality nesting locations. This points towards heterogeneous population structure at the sub-colony level, in which fine-scale environmental features drive local philopatric behaviour, while lower-quality patches may act as genetic mixing mechanisms at the colony level. These findings show how a lack of global genetic structuring can emerge from small-scale heterogeneity in ecological parameters, as opposed to the classical model of homogeneous dispersal. Our results also emphasize the importance of sampling design for estimation of population parameters in colonial seabirds, as at high spatial resolution, basic genetic features are shown to be location-dependent. Finally, this study stresses the importance of understanding intra-colonial dispersal and genetic mixing mechanisms in order to better estimate species-wide gene flows and population dynamics.

The signature of positive selection at randomly chosen loci.

PubMed

Przeworski, Molly

2002-03-01

In Drosophila and humans, there are accumulating examples of loci with a significant excess of high-frequency-derived alleles or high levels of linkage disequilibrium, relative to a neutral model of a random-mating population of constant size. These are features expected after a recent selective sweep. Their prevalence suggests that positive directional selection may be widespread in both species. However, as I show here, these features do not persist long after the sweep ends: The high-frequency alleles drift to fixation and no longer contribute to polymorphism, while linkage disequilibrium is broken down by recombination. As a result, loci chosen without independent evidence of recent selection are not expected to exhibit either of these features, even if they have been affected by numerous sweeps in their genealogical history. How then can we explain the patterns in the data? One possibility is population structure, with unequal sampling from different subpopulations. Alternatively, positive selection may not operate as is commonly modeled. In particular, the rate of fixation of advantageous mutations may have increased in the recent past.

Spatial Relationships of Auroral Particle Acceleration Relative to High Latitude Plasma Boundaries

NASA Technical Reports Server (NTRS)

Ghielmetti, Arthur G.

1997-01-01

This final report describes the activities under NASA contract to Lockheed Missiles and Space Company. It covers the period from 10-1-94 to 12-31-97. The objective of this investigation is to identify and characterize the spatial relationships of auroral particle acceleration features relative to the characteristic transition features in the surrounding polar ionospheric plasmas. Due to the reduced funding level approved for this contract, the original scope of the proposed work was readjusted with the focus placed on examining spatial relationships with respect to particle structures.

[Specific features of 2-methyl hydroxybenzene and 3-methyl hydroxybenzene distribution in the organism of warm-blooded animals].

PubMed

Shormanov, B K; Grishenko, V K; Astashkina, A P; Elizarova, M K

2013-01-01

The present work was designed to study the specific features of 2-methyl hydroxybezene and 3-methyl hydroxybenzene distribution after intragastric administration of these toxicants to warm-blooded animals (rats). They were detected in the unmetabolized form in the internal organs and blood of the animals. The levels of 2-methyl hydroxybezene were especially high in the stomach and blood whereas the maximum content of 3-methyl hydroxybenzene was found in brain, blood, small intestines of the poisoned rats.

Cognitive Load Measurement in a Virtual Reality-based Driving System for Autism Intervention

PubMed Central

Zhang, Lian; Wade, Joshua; Bian, Dayi; Fan, Jing; Swanson, Amy; Weitlauf, Amy; Warren, Zachary; Sarkar, Nilanjan

2016-01-01

Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes—feature level fusion, decision level fusion and hybrid level fusion—were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization. PMID:28966730

Cognitive Load Measurement in a Virtual Reality-based Driving System for Autism Intervention.

PubMed

Zhang, Lian; Wade, Joshua; Bian, Dayi; Fan, Jing; Swanson, Amy; Weitlauf, Amy; Warren, Zachary; Sarkar, Nilanjan

2017-01-01

Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes-feature level fusion, decision level fusion and hybrid level fusion-were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization.

Lack of mutations in the leptin receptor gene in severely obese children.

PubMed

Dias, Natasha Favoretto; Fernandes, Ariana Ester; Melo, Maria Edna de; Reinhardt, Heidi Lui; Cercato, Cintia; Villares, Sandra Mara Ferreira; Halpern, Alfredo; Mancini, Marcio C

2012-04-01

To analyze the LEPR gene in obese children and to investigate the associations between molecular findings and anthropometric and metabolic features. Thirty-two patients were evaluated regarding anthropometric characteristics, blood pressure, heart rate, serum glucose, insulin, leptin levels, and lipid profile. The molecular study consisted of the amplification and automatic sequencing of the coding region of LEPR in order to investigate new mutations. We identified a high prevalence of metabolic disorders: impaired fasting glucose in 12.5% of the patients, elevated HOMA-IR in 85.7%, low HDL-cholesterol levels in 46.9%, high triglyceride levels in 40.6%, and hypertension in 58.6% of the patients. The molecular study identified 6 already described allelic variants: rs1137100 (exon-2), rs1137101 (exon-4), rs1805134 (exon-7), rs8179183 (exon-12), rs1805096 (exon-18), and the deletion/insertion of the pentanucleotide CTTTA at 3'untranslated region. The frequency of alleles observed in this cohort is similar to that described in the literature, and was not correlated with any clinical feature. The molecular findings in the analysis of the LEPR did not seem to be implicated in the etiology of obesity in these patients.

SGSLR Testing Facility at GGAO

NASA Technical Reports Server (NTRS)

Hoffman, Evan

2016-01-01

This document describes the SGSLR Test Facility at Goddards Geophysical and Astronomical Observatory (NASA Goddard area 200) and its features are described at a high level for users. This is the facility that the Contractor will be required to use for the Testing and Verification of all SGSLR systems.

Bayesian Analogy with Relational Transformations

ERIC Educational Resources Information Center

Lu, Hongjing; Chen, Dawn; Holyoak, Keith J.

2012-01-01

How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy…

Representations of an Osmosis Problem.

ERIC Educational Resources Information Center

Zuckerman, June Trop

1998-01-01

Explores whether students with several years of high school science are able to represent an osmosis problem correctly. The study problem features a typical osmotic system with students expected to make a graph to show how the solution level in the stem of the funnel changes over time. (DDR)

47 CFR 15.403 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Network. (r) Transmit Power Control (TPC). A feature that enables a U-NII device to dynamically switch... control level. Power must be summed across all antennas and antenna elements. The average must not include... modulation techniques and provide a wide array of high data rate mobile and fixed communications for...

47 CFR 15.403 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Network. (r) Transmit Power Control (TPC). A feature that enables a U-NII device to dynamically switch... control level. Power must be summed across all antennas and antenna elements. The average must not include... modulation techniques and provide a wide array of high data rate mobile and fixed communications for...

10 CFR 60.142 - Design testing.

Code of Federal Regulations, 2010 CFR

2010-01-01

... construction, a program for in situ testing of such features as borehole and shaft seals, backfill, and the... 10 Energy 2 2010-01-01 2010-01-01 false Design testing. 60.142 Section 60.142 Energy NUCLEAR REGULATORY COMMISSION (CONTINUED) DISPOSAL OF HIGH-LEVEL RADIOACTIVE WASTES IN GEOLOGIC REPOSITORIES...

10 CFR 60.142 - Design testing.

Code of Federal Regulations, 2013 CFR

2013-01-01

... construction, a program for in situ testing of such features as borehole and shaft seals, backfill, and the... 10 Energy 2 2013-01-01 2013-01-01 false Design testing. 60.142 Section 60.142 Energy NUCLEAR REGULATORY COMMISSION (CONTINUED) DISPOSAL OF HIGH-LEVEL RADIOACTIVE WASTES IN GEOLOGIC REPOSITORIES...

10 CFR 60.142 - Design testing.

Code of Federal Regulations, 2012 CFR

2012-01-01

... construction, a program for in situ testing of such features as borehole and shaft seals, backfill, and the... 10 Energy 2 2012-01-01 2012-01-01 false Design testing. 60.142 Section 60.142 Energy NUCLEAR REGULATORY COMMISSION (CONTINUED) DISPOSAL OF HIGH-LEVEL RADIOACTIVE WASTES IN GEOLOGIC REPOSITORIES...

10 CFR 60.142 - Design testing.

Code of Federal Regulations, 2014 CFR

2014-01-01

... construction, a program for in situ testing of such features as borehole and shaft seals, backfill, and the... 10 Energy 2 2014-01-01 2014-01-01 false Design testing. 60.142 Section 60.142 Energy NUCLEAR REGULATORY COMMISSION (CONTINUED) DISPOSAL OF HIGH-LEVEL RADIOACTIVE WASTES IN GEOLOGIC REPOSITORIES...

10 CFR 60.142 - Design testing.

Code of Federal Regulations, 2011 CFR

2011-01-01

... construction, a program for in situ testing of such features as borehole and shaft seals, backfill, and the... 10 Energy 2 2011-01-01 2011-01-01 false Design testing. 60.142 Section 60.142 Energy NUCLEAR REGULATORY COMMISSION (CONTINUED) DISPOSAL OF HIGH-LEVEL RADIOACTIVE WASTES IN GEOLOGIC REPOSITORIES...

It's 1984 and Robots Are in the Classroom.

ERIC Educational Resources Information Center

Howe, Samuel F.

1984-01-01

Describes the features of TOPO, HERO, RB5X, and Tasman Turtle, personal robots used in elementary and secondary schools and colleges to introduce concepts of artificial intelligence, advanced high school and college computer science, and elementary level programming. Mechanical arms are also briefly mentioned. (MBR)

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

The Cladistic Basis for the Phylogenetic Diversity (PD) Measure Links Evolutionary Features to Environmental Gradients and Supports Broad Applications of Microbial Ecology’s “Phylogenetic Beta Diversity” Framework

PubMed Central

Faith, Daniel P.; Lozupone, Catherine A.; Nipperess, David; Knight, Rob

2009-01-01

The PD measure of phylogenetic diversity interprets branch lengths cladistically to make inferences about feature diversity. PD calculations extend conventional species-level ecological indices to the features level. The “phylogenetic beta diversity” framework developed by microbial ecologists calculates PD-dissimilarities between community localities. Interpretation of these PD-dissimilarities at the feature level explains the framework’s success in producing ordinations revealing environmental gradients. An example gradients space using PD-dissimilarities illustrates how evolutionary features form unimodal response patterns to gradients. This features model supports new application of existing species-level methods that are robust to unimodal responses, plus novel applications relating to climate change, commercial products discovery, and community assembly. PMID:20087461

Programming Language Software For Graphics Applications

NASA Technical Reports Server (NTRS)

Beckman, Brian C.

1993-01-01

New approach reduces repetitive development of features common to different applications. High-level programming language and interactive environment with access to graphical hardware and software created by adding graphical commands and other constructs to standardized, general-purpose programming language, "Scheme". Designed for use in developing other software incorporating interactive computer-graphics capabilities into application programs. Provides alternative to programming entire applications in C or FORTRAN, specifically ameliorating design and implementation of complex control and data structures typifying applications with interactive graphics. Enables experimental programming and rapid development of prototype software, and yields high-level programs serving as executable versions of software-design documentation.

''Towards a High-Performance and Robust Implementation of MPI-IO on Top of GPFS''

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

Prost, J.P.; Tremann, R.; Blackwore, R.

2000-01-11

MPI-IO/GPFS is a prototype implementation of the I/O chapter of the Message Passing Interface (MPI) 2 standard. It uses the IBM General Parallel File System (GPFS), with prototyped extensions, as the underlying file system. this paper describes the features of this prototype which support its high performance and robustness. The use of hints at the file system level and at the MPI-IO level allows tailoring the use of the file system to the application needs. Error handling in collective operations provides robust error reporting and deadlock prevention in case of returning errors.

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

Stewart, Gary

The primary objective of this project was to demonstrate the feasibility and reliability of utilizing high-temperature superconducting (HTS) materials in a Transmission Level Superconducting Fault Current Limiter (SFCL) application. During the project, the type of high-temperature superconducting material used evolved from 1 st generation (1G) BSCCO-2212 melt cast bulk high-temperature superconductors to 2 nd generation (2G) YBCO-based high-temperature superconducting tape. The SFCL employed SuperPower's “Matrix” technology, that offers modular features to enable scale up to transmission voltage levels. The SFCL consists of individual modules that contain elements and parallel inductors that assist in carrying the current during the fault. Amore » number of these modules are arranged in an m x n array to form the current-limiting matrix.« less

Probing features in inflaton potential and reionization history with future CMB space observations

NASA Astrophysics Data System (ADS)

Hazra, Dhiraj Kumar; Paoletti, Daniela; Ballardini, Mario; Finelli, Fabio; Shafieloo, Arman; Smoot, George F.; Starobinsky, Alexei A.

2018-02-01

We consider the prospects of probing features in the primordial power spectrum with future Cosmic Microwave Background (CMB) polarization measurements. In the scope of the inflationary scenario, such features in the spectrum can be produced by local non-smooth pieces in an inflaton potential (smooth and quasi-flat in general) which in turn may originate from fast phase transitions during inflation in other quantum fields interacting with the inflaton. They can fit some outliers in the CMB temperature power spectrum which are unaddressed within the standard inflationary ΛCDM model. We consider Wiggly Whipped Inflation (WWI) as a theoretical framework leading to improvements in the fit to the Planck 2015 temperature and polarization data in comparison with the standard inflationary models, although not at a statistically significant level. We show that some type of features in the potential within the WWI models, leading to oscillations in the primordial power spectrum that extend to intermediate and small scales can be constrained with high confidence (at 3σ or higher confidence level) by an instrument as the Cosmic ORigins Explorer (CORE). In order to investigate the possible confusion between inflationary features and footprints from the reionization era, we consider an extended reionization history with monotonic increase of free electrons with decrease in redshift. We discuss the present constraints on this model of extended reionization and future predictions with CORE. We also project, to what extent, this extended reionization can create confusion in identifying inflationary features in the data.

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

PubMed Central

Lee, Seong-Whan

2014-01-01

Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis. PMID:24363140

Exploring Specialized STEM High Schools: Three Dissertation Studies Examining Commonalities and Differences Across Six Case Studies

NASA Astrophysics Data System (ADS)

Tofel-Grehl, Colby

This dissertation is comprised of three independently conducted analyses of a larger investigation into the practices and features of specialized STEM high schools. While educators and policy makers advocate the development of many new specialized STEM high schools, little is known about the unique features and practices of these schools. The results of these manuscripts add to the literature exploring the promise of specialized STEM schools. Manuscript 1¹ is a qualitative investigation of the common features of STEM schools across multiple school model types. Schools were found to possess common cultural and academic features regardless of model type. Manuscript 2² builds on the findings of manuscript 1. With no meaningful differences found attributable to model type, the researchers used grounded theory to explore the relationships between observed differences among programs as related to the intensity of the STEM experience offered at schools. Schools were found to fall into two categories, high STEM intensity (HSI) and low STEM intensity (LSI), based on five major traits. Manuscript 3³ examines the commonalities and differences in classroom discourse and teachers' questioning techniques in STEM schools. It explicates these discursive practices in order to explore instructional practices across schools. It also examines factors that may influence classroom discourse such as discipline, level of teacher education, and course status as required or elective. Collectively, this research furthers the agenda of better understanding the potential advantages of specialized STEM high schools for preparing a future scientific workforce. ¹Tofel-Grehl, C., Callahan, C., & Gubbins, E. (2012). STEM high school communities: Common and differing features. Manuscript in preparation. ²Tofel-Grehl, C., Callahan, C., & Gubbins, E. (2012). Variations in the intensity of specialized science, technology, engineering, and mathematics (STEM) high schools. Manuscript in preparation. ³Tofel-Grehl, C., Callahan, C., & Gubbins, E. (2012). Comparative analyses of discourse in specialized STEM school classes. Manuscript in preparation.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

PubMed Central

Kurtz, Camille; Depeursinge, Adrien; Napel, Sandy; Beaulieu, Christopher F.; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies. PMID:25036769

"If It Feels Right, Do It": Intuitive Decision Making in a Sample of High-Level Sport Coaches.

PubMed

Collins, Dave; Collins, Loel; Carson, Howie J

2016-01-01

Comprehensive understanding and application of decision making is important for the professional practice and status of sports coaches. Accordingly, building on a strong work base exploring the use of professional judgment and decision making (PJDM) in sport, we report a preliminary investigation into uses of intuition by high-level coaches. Two contrasting groups of high-level coaches from adventure sports (n = 10) and rugby union (n = 8), were interviewed on their experiences of using intuitive and deliberative decision making styles, the source of these skills, and the interaction between the two. Participants reported similarly high levels of usage to other professions. Interaction between the two styles was apparent to varying degrees, while the role of experience was seen as an important precursor to greater intuitive practice and employment. Initially intuitive then deliberate decision making was a particular feature, offering participants an immediate check on the accuracy and validity of the decision. Integration of these data with the extant literature and implications for practice are discussed.

Rapid subsidence in damaging sinkholes: Measurement by high-precision leveling and the role of salt dissolution

NASA Astrophysics Data System (ADS)

Desir, G.; Gutiérrez, F.; Merino, J.; Carbonel, D.; Benito-Calvo, A.; Guerrero, J.; Fabregat, I.

2018-02-01

Investigations dealing with subsidence monitoring in active sinkholes are very scarce, especially when compared with other ground instability phenomena like landslides. This is largely related to the catastrophic behaviour that typifies most sinkholes in carbonate karst areas. Active subsidence in five sinkholes up to ca. 500 m across has been quantitatively characterised by means of high-precision differential leveling. The sinkholes occur on poorly indurated alluvium underlain by salt-bearing evaporites and cause severe damage on various human structures. The leveling data have provided accurate information on multiple features of the subsidence phenomena with practical implications: (1) precise location of the vaguely-defined edges of the subsidence zones and their spatial relationships with surveyed surface deformation features; (2) spatial deformation patterns and relative contribution of subsidence mechanisms (sagging versus collapse); (3) accurate subsidence rates and their spatial variability with maximum and mean vertical displacement rates ranging from 1.0 to 11.8 cm/yr and 1.9 to 26.1 cm/yr, respectively; (4) identification of sinkholes that experience continuous subsidence at constant rates or with significant temporal changes; and (5) rates of volumetric surface changes as an approximation to rates of dissolution-induced volumetric depletion in the subsurface, reaching as much as 10,900 m3/yr in the largest sinkhole. The high subsidence rates as well as the annual volumetric changes are attributed to rapid dissolution of high-solubility salts.

[A case of GH and TSH secreting pituitary macroadenoma].

PubMed

Gołkowski, Filip; Buziak-Bereza, Monika; Stefańska, Agnieszka; Trofimiuk, Małgorzata; Pantofliński, Jacek; Huszno, Bohdan; Czepko, Ryszard; Adamek, Dariusz

2006-01-01

A case of GH and TSH secreting pituitary macroadenoma is reported. A 45-year-old female presented clinical features of acromegaly (the abnormal growth of the hands and feet, with lower jaw protrusion), diabetes mellitus, hypertension, nodular goiter and hyperthyroidism of unclear origin. NMR pituitary imaging revealed intra and extrasellar tumor. The laboratory examinations showed very high plasma levels of GH and IGF-1 and normal level of TSH coexisting with high plasma levels of free thyroid hormones. Pharmacological pretreatment with somatostatin analogues caused the substantial reduction of GH and TSH plasma levels. Histological and immunohistochemical examination of the tissue obtained at transsphenoidal surgery showed GH and TSH secreting adenoma. The laboratory examinations after surgery showed normal GH and IGF-1 plasma levels and reduced insulin requirement, what indicates radical operation. The very low plasma levels of TSH and free thyroid hormones after surgery and immunohistochemical examination suggest central hyperthyroidism due to TSH secreting pituitary tumor (thyrotropinoma).

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

NASA Astrophysics Data System (ADS)

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

2011-03-01

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

Friendship and Emotion Control in Pre-Adolescents With or Without Hearing Loss.

PubMed

Rieffe, Carolien; Broekhof, Evelien; Eichengreen, Adva; Kouwenberg, Maartje; Veiga, Guida; da Silva, Brenda M S; van der Laan, Anneke; Frijns, Johan H M

2018-05-04

Emotional functioning plays a crucial role in the social development of children and adolescents. We examined the extent to which emotion control was related to the quality of friendships in pre-adolescents with and without hearing loss. We tested 350 pre-adolescents (75 deaf/hard of hearing in mainstream education (DHHm), 48 deaf/hard of hearing in special education (DHHs), and 227 hearing) through self-report. Outcomes confirmed a positive association between emotion control and positive friendships for all groups, with one notable exception: more approach strategies for emotion regulation were associated with more negative friendship features in the DHHs group. In addition, the DHHm group demonstrated high levels of emotion control, while their levels of positive friendship features were still lower compared to the hearing group.

Automating the generation of finite element dynamical cores with Firedrake

NASA Astrophysics Data System (ADS)

Ham, David; Mitchell, Lawrence; Homolya, Miklós; Luporini, Fabio; Gibson, Thomas; Kelly, Paul; Cotter, Colin; Lange, Michael; Kramer, Stephan; Shipton, Jemma; Yamazaki, Hiroe; Paganini, Alberto; Kärnä, Tuomas

2017-04-01

The development of a dynamical core is an increasingly complex software engineering undertaking. As the equations become more complete, the discretisations more sophisticated and the hardware acquires ever more fine-grained parallelism and deeper memory hierarchies, the problem of building, testing and modifying dynamical cores becomes increasingly complex. Here we present Firedrake, a code generation system for the finite element method with specialist features designed to support the creation of geoscientific models. Using Firedrake, the dynamical core developer writes the partial differential equations in weak form in a high level mathematical notation. Appropriate function spaces are chosen and time stepping loops written at the same high level. When the programme is run, Firedrake generates high performance C code for the resulting numerics which are executed in parallel. Models in Firedrake typically take a tiny fraction of the lines of code required by traditional hand-coding techniques. They support more sophisticated numerics than are easily achieved by hand, and the resulting code is frequently higher performance. Critically, debugging, modifying and extending a model written in Firedrake is vastly easier than by traditional methods due to the small, highly mathematical code base. Firedrake supports a wide range of key features for dynamical core creation: A vast range of discretisations, including both continuous and discontinuous spaces and mimetic (C-grid-like) elements which optimally represent force balances in geophysical flows. High aspect ratio layered meshes suitable for ocean and atmosphere domains. Curved elements for high accuracy representations of the sphere. Support for non-finite element operators, such as parametrisations. Access to PETSc, a world-leading library of programmable linear and nonlinear solvers. High performance adjoint models generated automatically by symbolically reasoning about the forward model. This poster will present the key features of the Firedrake system, as well as those of Gusto, an atmospheric dynamical core, and Thetis, a coastal ocean model, both of which are written in Firedrake.

Association of blood lead level with neurological features in 972 children affected by an acute severe lead poisoning outbreak in Zamfara State, northern Nigeria.

PubMed

Greig, Jane; Thurtle, Natalie; Cooney, Lauren; Ariti, Cono; Ahmed, Abdulkadir Ola; Ashagre, Teshome; Ayela, Anthony; Chukwumalu, Kingsley; Criado-Perez, Alison; Gómez-Restrepo, Camilo; Meredith, Caitlin; Neri, Antonio; Stellmach, Darryl; Sani-Gwarzo, Nasir; Nasidi, Abdulsalami; Shanks, Leslie; Dargan, Paul I

2014-01-01

In 2010, Médecins Sans Frontières (MSF) investigated reports of high mortality in young children in Zamfara State, Nigeria, leading to confirmation of villages with widespread acute severe lead poisoning. In a retrospective analysis, we aimed to determine venous blood lead level (VBLL) thresholds and risk factors for encephalopathy using MSF programmatic data from the first year of the outbreak response. We included children aged ≤5 years with VBLL ≥45 µg/dL before any chelation and recorded neurological status. Odds ratios (OR) for neurological features were estimated; the final model was adjusted for age and baseline VBLL, using random effects for village of residence. 972 children met inclusion criteria: 885 (91%) had no neurological features; 34 (4%) had severe features; 47 (5%) had reported recent seizures; and six (1%) had other neurological abnormalities. The geometric mean VBLLs for all groups with neurological features were >100 µg/dL vs 65.9 µg/dL for those without neurological features. The adjusted OR for neurological features increased with increasing VBLL: from 2.75, 95%CI 1.27-5.98 (80-99.9 µg/dL) to 22.95, 95%CI 10.54-49.96 (≥120 µg/dL). Neurological features were associated with younger age (OR 4.77 [95% CI 2.50-9.11] for 1-<2 years and 2.69 [95%CI 1.15-6.26] for 2-<3 years, both vs 3-5 years). Severe neurological features were seen at VBLL <105 µg/dL only in those with malaria. Increasing VBLL (from ≥80 µg/dL) and age 1-<3 years were strongly associated with neurological features; in those tested for malaria, a positive test was also strongly associated. These factors will help clinicians managing children with lead poisoning in prioritising therapy and developing chelation protocols.

Self-balancing feature of Lithium-Sulfur batteries

NASA Astrophysics Data System (ADS)

Knap, Vaclav; Stroe, Daniel-Ioan; Christensen, Andreas E.; Propp, Karsten; Fotouhi, Abbas; Auger, Daniel J.; Schaltz, Erik; Teodorescu, Remus

2017-12-01

The Li-S batteries are a prospective battery technology, which despite to its currently remaining drawbacks offers useable performance and interesting features. The polysulfide shuttle mechanism, a characteristic phenomenon for the Li-S batteries, causes a significant self-discharge at higher state-of-charge (SOC) levels, which leads to the energy dissipation of cells with higher charge. In an operation of series-connected Li-S cells, the shuttle mechanism results into a self-balancing effect which is studied here. A model for prediction of the self-balancing effect is proposed in this work and it is validated by experiments. Our results confirm the self-balancing feature of Li-S cells and illustrate their dependence on various conditions such as temperature, charging limits and idling time at high SOC.

Hypofractionated Radiation Therapy in Treating Participants With Prostate Cancer High-Risk Features Following Radical Prostatectomy

ClinicalTrials.gov

2018-06-25

Prostate Adenocarcinoma; PSA Level Less Than Two; Stage IIB Prostate Cancer AJCC v8; Stage III Prostate Cancer AJCC v8; Stage IIIA Prostate Cancer AJCC v8; Stage IIIB Prostate Cancer AJCC v8; Stage IIIC Prostate Cancer AJCC v8

Locker Room Talk.

ERIC Educational Resources Information Center

Fickes, Michael

1999-01-01

Examines the trends in college and university sports and recreation center locker rooms as envisioned by a specialist. Features of the modern locker room and the different levels of locker room design are explained. Final comments discuss whether college and university facility managers are inclined to move to high-end locker rooms. (GR)

Managing Tensions in a Service-Learning Programme: Some Reflections

ERIC Educational Resources Information Center

Maistry, S. M.; Ramdhani, J.

2010-01-01

Service learning as a strategy for raising awareness amongst university students of their responsibilities to the community is rapidly gaining currency in higher education institutions in South Africa. High levels of unemployment and striking economic inequality have been an unfortunate feature of South African society for several decades. Since…

Panoramic Night Vision Goggle Testing For Diagnosis and Repair

DTIC Science & Technology

2000-01-01

Visual Acuity Visual Acuity [ Marasco & Task, 1999] measures how well a human observer can see high contrast targets at specified light levels through...grid through the PNVG in-board and out-board channels simultaneously and comparing the defects to the size of grid features ( Marasco & Task, 1999). The

Knowledge and Awareness of Tuberculosis among Pre-University Students in Trinidad.

ERIC Educational Resources Information Center

Orrett, Fitzroy A.; Shurland, Simone M.

2001-01-01

Surveyed pre-university high school students in Trinidad regarding their awareness and knowledge of tuberculosis. Results indicated that while most students had heard of tuberculosis, knowledge levels were generally poor regarding the major presenting features of tuberculosis, vaccination and treatment, and the impact of overcrowded living…

Geospatial Data Science Publications | Geospatial Data Science | NREL

Science.gov Websites

research in these publications. Featured Publications U.S. Renewable Energy Technical Potentials: A GIS -Based Analysis, NREL Technical Report (2012) 2016 Offshore Wind Energy Resource Assessment for the -Temperature Geothermal Resources of the United States, 40th GRC Annual Meeting (2016) High-Level Overview of

Designing Culturally Responsive Organized After-School Activities

ERIC Educational Resources Information Center

Simpkins, Sandra D.; Riggs, Nathaniel R.; Ngo, Bic; Vest Ettekal, Andrea; Okamoto, Dina

2017-01-01

Organized after-school activities promote positive youth development across a range of outcomes. To be most effective, organized activities need to meet high-quality standards. The eight features of quality developed by the National Research Council's Committee on Community-Level Programs for Youth have helped guide the field in this regard.…

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter Je; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-11-01

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.

Learning and Recognition of Clothing Genres From Full-Body Images.

PubMed

Hidayati, Shintami C; You, Chuang-Wen; Cheng, Wen-Huang; Hua, Kai-Lung

2018-05-01

According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body shots crawled from popular online stores was built. Recognition results show that our proposed algorithms achieved promising overall precision, recall, and -score of 88.76%, 88.53%, and 88.64% for recognizing upperwear genres, and 88.21%, 88.17%, and 88.19% for recognizing lowerwear genres, respectively. The effectiveness of each style element and its visual features on recognizing clothing genres was demonstrated through a set of experiments involving different sets of style elements or features. In summary, our experimental results demonstrate the effectiveness of the proposed method in clothing genre recognition.

Large stationary wave features appearing repeatedly at the cloud top of Venus

NASA Astrophysics Data System (ADS)

Kouyama, Toru; Imamura, Takeshi; Taguchi, Makoto; Fukuhara, Tetsuya; Sato, Takao M.; Hashimoto, George L.; Futaguchi, Masahiko; Takamura, Mao; Yamada, Takeru; Satoh, Takehiko; Nakamura, Masato; Akatsuki Science Team

2017-10-01

At the first observation sequence after Akatsuki’s Venus orbiter re-insertion (VOI-R) on December 7, 2015, Akatsuki revealed an existence of a large-scale “bow-shaped” feature staying at almost same geographic location (above Aphrodite Terra) at the cloud top level with the Longwave Infrared Camera (LIR) and Ultra Violet Imager (UVI). It expanded ~10,000 km from south to north and bended to downstream side of the super-rotation of Venus. A numerical calculation in Fukuhara et al. (2017) suggested that a gravity wave generated in the lower atmosphere can propagate upward to the cloud top and reproduce the observed bow-shape structure. Because the wave can transport momentum to the upper atmosphere which possibly decelerates the super-rotation, it is an interesting topic whether the stationary wave event is regular or just an occasional event. For more than three Venus years, or four Venus solar days, Akatsuki has observed huge stationary wave features in LIR images again and again since the VOI-R. It has been confirmed that four high-altitude regions, east and west part of Aphrodite Terra, Atra Regio, and Beta Regio, accompany with the large stationary features. All four regions are located in lower latitudes (< 30°), while no clear stationary feature has been confirmed above Maxwell Mountain, which is the highest mountain but located at a high latitude (60°), indicating geographical and latitudinal dependencies of the generation of the stationary waves. Akatsuki also reveals the stationary features can be considered as "daily" phenomena in Venus atmosphere. At every timing when the four high-altitude regions were passing afternoon region of Venus, huge stationary waves became clearer. On the other hand, when the high mountains were located around mid-night and morning, stationary features were much weaker than that in afternoon, or cannot be confirmed, indicating strong local time dependency of the appearance. Since lower latitude has more incident solar flux and afternoon area experiences longer solar heating than morning area, the geographical and the local time dependencies indicate that interaction between mountains and solar heating or solar fixed atmospheric structure may cause the large-scale features.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

PubMed

Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E

2016-08-01

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

Memory for a single object has differently variable precisions for relevant and irrelevant features.

PubMed

Swan, Garrett; Collins, John; Wyble, Brad

2016-01-01

Working memory is a limited resource. To further characterize its limitations, it is vital to understand exactly what is encoded about a visual object beyond the "relevant" features probed in a particular task. We measured the memory quality of a task-irrelevant feature of an attended object by coupling a delayed estimation task with a surprise test. Participants were presented with a single colored arrow and were asked to retrieve just its color for the first half of the experiment before unexpectedly being asked to report its direction. Mixture modeling of the data revealed that participants had highly variable precision on the surprise test, indicating a coarse-grained memory for the irrelevant feature. Following the surprise test, all participants could precisely recall the arrow's direction; however, this improvement in direction memory came at a cost in precision for color memory even though only a single object was being remembered. We attribute these findings to varying levels of attention to different features during memory encoding.

The Four-Year Investigation of Physical and Physiological Features of Students in a Physical Education and Sports Department

ERIC Educational Resources Information Center

Ocak, Yucel

2016-01-01

Problem Statement: Student candidates who want to be a Physical Education Teacher in Turkey should take special ability exams of Physical Education and Sports Schools. In this exam, it is required to have a high physical capability apart from a high level of special branch skills. For this reason, the students who pass and start their education at…

Importance of conduction electron correlation in a Kondo lattice, Ce₂CoSi₃.

PubMed

Patil, Swapnil; Pandey, Sudhir K; Medicherla, V R R; Singh, R S; Bindu, R; Sampathkumaran, E V; Maiti, Kalobaran

2010-06-30

Kondo systems are usually described by the interaction of the correlation induced local moments with the highly itinerant conduction electrons. Here, we study the role of electron correlations among conduction electrons in the electronic structure of a Kondo lattice compound, Ce₂CoSi₃, using high resolution photoemission spectroscopy and ab initio band structure calculations, where Co 3d electrons contribute in the conduction band. High energy resolution employed in the measurements helped to reveal the signatures of Ce 4f states derived Kondo resonance features at the Fermi level and the dominance of Co 3d contributions at higher binding energies in the conduction band. The lineshape of the experimental Co 3d band is found to be significantly different from that obtained from the band structure calculations within the local density approximations, LDA. Consideration of electron-electron Coulomb repulsion, U, among Co 3d electrons within the LDA + U method leads to a better representation of experimental results. The signature of an electron correlation induced satellite feature is also observed in the Co 2p core level spectrum. These results clearly demonstrate the importance of the electron correlation among conduction electrons in deriving the microscopic description of such Kondo systems.

Dynamic Encoding of Speech Sequence Probability in Human Temporal Cortex

PubMed Central

Leonard, Matthew K.; Bouchard, Kristofer E.; Tang, Claire

2015-01-01

Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning. PMID:25948269

Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach.

PubMed

O'Toole, John M; Boylan, Geraldine B; Lloyd, Rhodri O; Goulding, Robert M; Vanhatalo, Sampsa; Stevenson, Nathan J

2017-07-01

To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features. Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features. Area under the receiver operator characteristic (AUC) and Cohen's kappa (κ) evaluated performance within a cross-validation procedure. The proposed channel-independent method improves AUC by 4-5% over existing methods (p < 0.001, n=36), with median (95% confidence interval) AUC of 0.989 (0.973-0.997) and sensitivity-specificity of 95.8-94.4%. Agreement rates between the detector and experts' annotations, κ=0.72 (0.36-0.83) and κ=0.65 (0.32-0.81), are comparable to inter-rater agreement, κ=0.60 (0.21-0.74). Automating the visual identification of bursts in preterm EEG is achievable with a high level of accuracy. Multiple features, combined using a data-driven approach, improves on existing single-feature methods. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Random variations in the ultraviolet spectrum of Beta Lyrae

NASA Technical Reports Server (NTRS)

Bless, R. C.; Eaton, J. A.; Meade, M. R.

1977-01-01

Spectrophotometric scans of Beta Lyrae over the wavelength range from 1100 to 3700 A are analyzed which were obtained at different times with different resolutions by the OAO 2 satellite and from the ground. A model atmosphere with normal H and He abundances, an electron temperature of 11,000 K, and log g of 3.0 is found to fit the visual region of the spectrum well but to be a poor representation in the Balmer continuum. It is shown that a large complex emission feature dominates the spectrum from about 1700 to 2200 A, that there is a very pronounced strengthening of the spectrum just shortward of the 1550-A C IV feature at phase 0.69, and that the overall level of the spectrum shortward of 1400 A is quite high in comparison with the broad emission feature. A model is discussed in which the light from a disk-shaped secondary is highly concentrated toward the polar regions.

Classification of simulated and actual NOAA-6 AVHRR data for hydrologic land-surface feature definition. [Advanced Very High Resolution Radiometer

NASA Technical Reports Server (NTRS)

Ormsby, J. P.

1982-01-01

An examination of the possibilities of using Landsat data to simulate NOAA-6 Advanced Very High Resolution Radiometer (AVHRR) data on two channels, as well as using actual NOAA-6 imagery, for large-scale hydrological studies is presented. A running average was obtained of 18 consecutive pixels of 1 km resolution taken by the Landsat scanners were scaled up to 8-bit data and investigated for different gray levels. AVHRR data comprising five channels of 10-bit, band-interleaved information covering 10 deg latitude were analyzed and a suitable pixel grid was chosen for comparison with the Landsat data in a supervised classification format, an unsupervised mode, and with ground truth. Landcover delineation was explored by removing snow, water, and cloud features from the cluster analysis, and resulted in less than 10% difference. Low resolution large-scale data was determined useful for characterizing some landcover features if weekly and/or monthly updates are maintained.

Online prediction of organileptic data for snack food using color images

NASA Astrophysics Data System (ADS)

Yu, Honglu; MacGregor, John F.

2004-11-01

In this paper, a study for the prediction of organileptic properties of snack food in real-time using RGB color images is presented. The so-called organileptic properties, which are properties based on texture, taste and sight, are generally measured either by human sensory response or by mechanical devices. Neither of these two methods can be used for on-line feedback control in high-speed production. In this situation, a vision-based soft sensor is very attractive. By taking images of the products, the samples remain untouched and the product properties can be predicted in real time from image data. Four types of organileptic properties are considered in this study: blister level, toast points, taste and peak break force. Wavelet transform are applied on the color images and the averaged absolute value for each filtered image is used as texture feature variable. In order to handle the high correlation among the feature variables, Partial Least Squares (PLS) is used to regress the extracted feature variables against the four response variables.

High-fat diet exposure from pre-pubertal age induces polycystic ovary syndrome (PCOS) in rats.

PubMed

Patel, Roshni; Shah, Gaurang

2018-02-01

Polycystic ovary syndrome (PCOS) is associated with hyperandrogenism, oligo-anovulation, polycystic ovaries and metabolic syndrome. Many researchers reported that PCOS often starts with menarche in adolescents. Presently available animal model focuses on ovarian but not metabolic features of PCOS. Therefore, we hypothesized that high-fat diet feeding to pre-pubertal female rats results in both reproductive and metabolic features of PCOS. Pre-pubertal female rats were divided into two groups: group I received normal pellet diet and group II received high-fat diet (HFD). In the letrozole study, adult female rats were divided into two groups: group I received 1% carboxy methyl cellulose and group II received 1 mg/kg letrozole orally. Oral glucose tolerance test, lipid profile, fasting glucose, insulin, estrus cycle, hormonal profile, ovary weight, luteinizing hormone (LH) receptor and follicle-stimulating hormone receptor expression were measured. Polycystic ovarian morphology was assessed through histopathological changes of ovary. Feeding of HFD gradually increase glucose intolerance and fasting insulin levels. Triglyceride level was higher in HFD study while total cholesterol level was higher in the letrozole study. Alteration in testosterone and estrogen levels was observed in both studies. LH receptor expression was upregulated only in HFD study. Histopathological changes like increase cystic follicle, diminished granulosa cell layer and thickened theca cell layer were observed in letrozole as well as HFD study. High-fat diet initiated at pre-puberty age in rats produces both metabolic disturbances and ovarian changes similar to that observed clinically in PCOS patients. Letrozole on the other hand induces change in ovarian structure and function. © 2018 Society for Reproduction and Fertility.

Input Decimated Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)

2001-01-01

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology.

PubMed

Wu, Shibin; Yu, Shaode; Yang, Yuhan; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

PubMed Central

Wu, Shibin; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII). PMID:24416072

Cloud and surface textural features in polar regions

NASA Technical Reports Server (NTRS)

Welch, Ronald M.; Kuo, Kwo-Sen; Sengupta, Sailes K.

1990-01-01

The study examines the textural signatures of clouds, ice-covered mountains, solid and broken sea ice and floes, and open water. The textural features are computed from sum and difference histogram and gray-level difference vector statistics defined at various pixel displacement distances derived from Landsat multispectral scanner data. Polar cloudiness, snow-covered mountainous regions, solid sea ice, glaciers, and open water have distinguishable texture features. This suggests that textural measures can be successfully applied to the detection of clouds over snow-covered mountains, an ability of considerable importance for the modeling of snow-melt runoff. However, broken stratocumulus cloud decks and thin cirrus over broken sea ice remain difficult to distinguish texturally. It is concluded that even with high spatial resolution imagery, it may not be possible to distinguish broken stratocumulus and thin clouds from sea ice in the marginal ice zone using the visible channel textural features alone.

Practiced musical style shapes auditory skills.

PubMed

Vuust, Peter; Brattico, Elvira; Seppänen, Miia; Näätänen, Risto; Tervaniemi, Mari

2012-04-01

Musicians' processing of sounds depends highly on instrument, performance practice, and level of expertise. Here, we measured the mismatch negativity (MMN), a preattentive brain response, to six types of musical feature change in musicians playing three distinct styles of music (classical, jazz, and rock/pop) and in nonmusicians using a novel, fast, and musical sounding multifeature MMN paradigm. We found MMN to all six deviants, showing that MMN paradigms can be adapted to resemble a musical context. Furthermore, we found that jazz musicians had larger MMN amplitude than all other experimental groups across all sound features, indicating greater overall sensitivity to auditory outliers. Furthermore, we observed a tendency toward shorter latency of the MMN to all feature changes in jazz musicians compared to band musicians. These findings indicate that the characteristics of the style of music played by musicians influence their perceptual skills and the brain processing of sound features embedded in music. © 2012 New York Academy of Sciences.

Decoding memory features from hippocampal spiking activities using sparse classification models.

PubMed

Dong Song; Hampson, Robert E; Robinson, Brian S; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

2016-08-01

To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

Image quality classification for DR screening using deep learning.

PubMed

FengLi Yu; Jing Sun; Annan Li; Jun Cheng; Cheng Wan; Jiang Liu

2017-07-01

The quality of input images significantly affects the outcome of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that only consider simple low-level features such as hand-crafted geometric and structural features, in this paper we propose a novel method for retinal image quality classification (IQC) that performs computational algorithms imitating the working of the human visual system. The proposed algorithm combines unsupervised features from saliency map and supervised features coming from convolutional neural networks (CNN), which are fed to an SVM to automatically detect high quality vs poor quality retinal fundus images. We demonstrate the superior performance of our proposed algorithm on a large retinal fundus image dataset and the method could achieve higher accuracy than other methods. Although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images.

Harm avoidance and persistence are associated with somatoform disorder psychopathology: A study in Taiwan.

PubMed

Huang, Wei-Lieh; Chen, Tzu-Ting; Chen, I-Ming; Chang, Li-Ren; Lin, Yu-Hsuan; Liao, Shih-Cheng; Gau, Susan Shur-Fen

2016-05-15

Whether personality features affect the development of somatoform disorders and their psychopathologies is an important issue. Aim of this study was to resolve this issue by comparing indicators of psychopathology and personality features in subjects with somatoform disorders and healthy controls. This study recruited 148 subjects with somatoform disorders and 146 healthy controls. The severity of psychopathology was measured with the Patient Health Questionnaire-15 (PHQ-15), Health Anxiety Questionnaire (HAQ), Beck Depression Inventory-II (BDI-II), and Beck Anxiety Inventory (BAI). The Tridimensional Personality Questionnaire (TPQ) was used to assess personality features. Demographic data, psychopathology indicators, and TPQ scores were compared between groups. Correlation and multivariate linear regression analysis were used to identify the personality dimensions or demographic variables associated with psychopathology. The somatoform group had lower novelty seeking (NS) and reward dependence (RD) and higher harm avoidance (HA) and severity of psychopathologies. Multiple regression analysis revealed that fatigability, persistence, gender, and education level were predictive of PHQ-15; HA, educational level, persistence, and dependence were predictive of HAQ; HA, persistence, education level, and NS were predictive of BDII-II; and fatigability, education level, persistence, and anticipatory worry were predictive of BAI. The development of somatoform disorders was associated with fatigability, age, residence location, education level, and attachment. The limitations include heterogeneity of the diagnosis, the high proportion of undifferentiated somatoform disorder, and the cross-sectional study design. HA/fatigability, persistence, and education level are associated with each type of psychopathology. Fatigability is a powerful predictor of somatoform disorder development. Copyright © 2016. Published by Elsevier B.V.

Porous Structure Design of Polymeric Membranes for Gas Separation

DOE PAGES

Zhang, Jinshui; Schott, Jennifer Ann; Mahurin, Shannon Mark; ...

2017-04-04

High-performance polymeric membranes for gas separation are of interest for molecular-level separations in industrial-scale chemical, energy and environmental processes. To overcome the inherent trade-off relationship between permeability and selectivity, the creation of permanent microporosity in polymeric matrices is highly desirable because the porous structures can provide a high fractional free volume to facilitate gas transport through the dense layer. In this feature article, recent developments in the formation of porous polymeric membranes and potential strategies for pore structure design are reviewed.

Karst development and speleogenesis, Isla de Mona, Puerto Rico

USGS Publications Warehouse

Frank, E.F.; Mylroie, J.; Troester, J.; Alexander, E.C.; Carew, J.L.

1998-01-01

Isla de Mona consists of a raised table-top Miocene-Pliocene reef platform bounded on three sides by vertical cliffs, up to 80 m high. Hundreds of caves ring the periphery of the island and are preferentially developed in, but not limited to, the Lirio Limestone/Isla de Mona Dolomite contact. These flank margin caves originally formed at sea level and are now exposed at various levels by tectonic uplift of the island (Franbk 1983; Mylroie et al. 1995b). Wall cusps, a characteristic feature of flank margin caves, are ubiquitois features. Comparisons among similar caves formed in the Bahamas and Isla de Mona reveal the same overall morphology throughout the entire range of sizes and complexities. The coincidence of the primary cave development zone with the Lirio Limestone/Isla de Mona Dolomite contact may result from syngenetic speleogenesis and dolomitization rather than preferential dissolution along a lithologic boundary. Tectonic uplift and glacioeustatic sea level fluctuations produced caves at a variety of elevations. Speleothem dissolution took place in many caves under phreatic conditions, evidence these caves were flooded after an initial period of subaerial exposure and speleothem growth. Several features around the perimeter of the island are interpreted to be caves whose roofs were removed by surficial denudation processes. Several large closed depressions and dense pit cave fields are further evidence of surficial karst features. The cliff retreat around the island perimeter since the speleogenesis of the major cave systems is small based upon the distribution of the remnant cave sections.

Objective quantification of perturbations produced with a piecewise PV inversion technique

NASA Astrophysics Data System (ADS)

Fita, L.; Romero, R.; Ramis, C.

2007-11-01

PV inversion techniques have been widely used in numerical studies of severe weather cases. These techniques can be applied as a way to study the sensitivity of the responsible meteorological system to changes in the initial conditions of the simulations. Dynamical effects of a collection of atmospheric features involved in the evolution of the system can be isolated. However, aspects, such as the definition of the atmospheric features or the amount of change in the initial conditions, are largely case-dependent and/or subjectively defined. An objective way to calculate the modification of the initial fields is proposed to alleviate this problem. The perturbations are quantified as the mean absolute variations of the total energy between the original and modified fields, and an unique energy variation value is fixed for all the perturbations derived from different PV anomalies. Thus, PV features of different dimensions and characteristics introduce the same net modification of the initial conditions from an energetic point of view. The devised quantification method is applied to study the high impact weather case of 9-11 November 2001 in the Western Mediterranean basin, when a deep and strong cyclone was formed. On the Balearic Islands 4 people died, and sustained winds of 30 ms-1 and precipitation higher than 200 mm/24 h were recorded. Moreover, 700 people died in Algiers during the first phase of the event. The sensitivities to perturbations in the initial conditions of a deep upper level trough, the anticyclonic system related to the North Atlantic high and the surface thermal anomaly related to the baroclinicity of the environment are determined. Results reveal a high influence of the upper level trough and the surface thermal anomaly and a minor role of the North Atlantic high during the genesis of the cyclone.

Science@NASA: Direct to People!

NASA Technical Reports Server (NTRS)

Koczor, Ronald J.; Adams, Mitzi; Gallagher, Dennis; Whitaker, Ann (Technical Monitor)

2002-01-01

Science@NASA is a science communication effort sponsored by NASA's Marshall Space Flight Center. It is the result of a four year research project between Marshall, the University of Florida College of Journalism and Communications and the internet communications company, Bishop Web Works. The goals of Science@NASA are to inform, inspire, and involve people in the excitement of NASA science by bringing that science directly to them. We stress not only the reporting of the facts of a particular topic, but also the context and importance of the research. Science@NASA involves several levels of activity from academic communications research to production of content for 6 websites, in an integrated process involving all phases of production. A Science Communications Roundtable Process is in place that includes scientists, managers, writers, editors, and Web technical experts. The close connection between the scientists and the writers/editors assures a high level of scientific accuracy in the finished products. The websites each have unique characters and are aimed at different audience segments: 1. http://science.nasa.gov. (SNG) Carries stories featuring various aspects of NASA science activity. The site carries 2 or 3 new stories each week in written and audio formats for science-attentive adults. 2. http://liftoff.msfc.nasa.gov. Features stories from SNG that are recast for a high school level audience. J-Track and J-Pass applets for tracking satellites are our most popular product. 3. http://kids. msfc.nasa.gov. This is the Nursemaids site and is aimed at a middle school audience. The NASAKids Club is a new feature at the site. 4. http://www.thursdaysclassroom.com . This site features lesson plans and classroom activities for educators centered around one of the science stories carried on SNG. 5. http://www.spaceweather.com. This site gives the status of solar activity and its interactions with the Earth's ionosphere and magnetosphere.

The Wisconsin Assessment of the Social and Built Environment (WASABE): a multi-dimensional objective audit instrument for examining neighborhood effects on health.

PubMed

Malecki, Kristen C; Engelman, Corinne D; Peppard, Paul E; Nieto, F Javier; Grabow, Maggie L; Bernardinello, Milena; Bailey, Erin; Bersch, Andrew J; Walsh, Matthew C; Lo, Justin Y; Martinez-Donate, Ana

2014-11-13

Growing evidence suggests that mixed methods approaches to measuring neighborhood effects on health are needed. The Wisconsin Assessment of the Social and Built Environment (WASABE) is an objective audit tool designed as an addition to a statewide household-based health examination survey, the Survey of the Health of Wisconsin (SHOW), to objectively measure participant's neighborhoods. This paper describes the development and implementation of the WASABE and examines the instrument's ability to capture a range of social and built environment features in urban and rural communities. A systematic literature review and formative research were used to create the tool. Inter-rater reliability parameters across items were calculated. Prevalence and density of features were estimated for strata formed according to several sociodemographic and urbanicity factors. The tool is highly reliable with over 81% of 115 derived items having percent agreement above 95%. It captured variance in neighborhood features in for a diverse sample of SHOW participants. Sidewalk density in neighborhoods surrounding households of participants living at less than 100% of the poverty level was 67% (95% confidence interval, 55-80%) compared to 34% (25-44%) for those living at greater than 400% of the poverty level. Walking and biking trails were present in 29% (19-39%) of participant buffer in urban areas compared to only 7% (2-12%) in rural communities. Significant environmental differences were also observed for white versus non-white, high versus low income, and college graduates versus individuals with lower level of education. The WASABE has strong inter-rater reliability and validity properties. It builds on previous work to provide a rigorous and standardized method for systematically gathering objective built and social environmental data in a number of geographic settings. Findings illustrate the complex milieu of built environment features found in participants neighborhoods and have relevance for future research, policy, and community engagement purposes.

Overlapping features of polymyositis and inclusion body myositis in HIV-infected patients

PubMed Central

Lloyd, Thomas E.; Pinal-Fernandez, Iago; Michelle, E. Harlan; Christopher-Stine, Lisa; Pak, Katherine; Sacktor, Ned

2017-01-01

Objective: To characterize patients with myositis with HIV infection. Methods: All HIV-positive patients with myositis seen at the Johns Hopkins Myositis Center from 2003 to 2013 were included in this case series. Muscle biopsy features, weakness pattern, serum creatine kinase (CK) level, and anti–nucleotidase 1A (NT5C1A) status of HIV-positive patients with myositis were assessed. Results: Eleven of 1,562 (0.7%) patients with myositis were HIV-positive. Myositis was the presenting feature of HIV infection in 3 patients. Eight of 11 patients had weakness onset at age 45 years or less. The mean time from the onset of weakness to the diagnosis of myositis was 3.6 years (SD 3.2 years). The mean of the highest measured CK levels was 2,796 IU/L (SD 1,592 IU/L). On muscle biopsy, 9 of 10 (90%) had endomysial inflammation, 7 of 10 (70%) had rimmed vacuoles, and none had perifascicular atrophy. Seven of 11 (64%) patients were anti-NT5C1A-positive. Upon presentation, all had proximal and distal weakness. Five of 6 (83%) patients followed 1 year or longer on immunosuppressive therapy had improved proximal muscle strength. However, each eventually developed weakness primarily affecting wrist flexors, finger flexors, knee extensors, or ankle dorsiflexors. Conclusions: HIV-positive patients with myositis may present with some characteristic polymyositis features including young age at onset, very high CK levels, or proximal weakness that improves with treatment. However, all HIV-positive patients with myositis eventually develop features most consistent with inclusion body myositis, including finger and wrist flexor weakness, rimmed vacuoles on biopsy, or anti-NT5C1A autoantibodies. PMID:28283597

Detection of sub-kilometer craters in high resolution planetary images using shape and texture features

NASA Astrophysics Data System (ADS)

Bandeira, Lourenço; Ding, Wei; Stepinski, Tomasz F.

2012-01-01

Counting craters is a paramount tool of planetary analysis because it provides relative dating of planetary surfaces. Dating surfaces with high spatial resolution requires counting a very large number of small, sub-kilometer size craters. Exhaustive manual surveys of such craters over extensive regions are impractical, sparking interest in designing crater detection algorithms (CDAs). As a part of our effort to design a CDA, which is robust and practical for planetary research analysis, we propose a crater detection approach that utilizes both shape and texture features to identify efficiently sub-kilometer craters in high resolution panchromatic images. First, a mathematical morphology-based shape analysis is used to identify regions in an image that may contain craters; only those regions - crater candidates - are the subject of further processing. Second, image texture features in combination with the boosting ensemble supervised learning algorithm are used to accurately classify previously identified candidates into craters and non-craters. The design of the proposed CDA is described and its performance is evaluated using a high resolution image of Mars for which sub-kilometer craters have been manually identified. The overall detection rate of the proposed CDA is 81%, the branching factor is 0.14, and the overall quality factor is 72%. This performance is a significant improvement over the previous CDA based exclusively on the shape features. The combination of performance level and computational efficiency offered by this CDA makes it attractive for practical application.

Illumination invariant feature point matching for high-resolution planetary remote sensing images

NASA Astrophysics Data System (ADS)

Wu, Bo; Zeng, Hai; Hu, Han

2018-03-01

Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

Blob-level active-passive data fusion for Benthic classification

NASA Astrophysics Data System (ADS)

Park, Joong Yong; Kalluri, Hemanth; Mathur, Abhinav; Ramnath, Vinod; Kim, Minsu; Aitken, Jennifer; Tuell, Grady

2012-06-01

We extend the data fusion pixel level to the more semantically meaningful blob level, using the mean-shift algorithm to form labeled blobs having high similarity in the feature domain, and connectivity in the spatial domain. We have also developed Bhattacharyya Distance (BD) and rule-based classifiers, and have implemented these higher-level data fusion algorithms into the CZMIL Data Processing System. Applying these new algorithms to recent SHOALS and CASI data at Plymouth Harbor, Massachusetts, we achieved improved benthic classification accuracies over those produced with either single sensor, or pixel-level fusion strategies. These results appear to validate the hypothesis that classification accuracy may be generally improved by adopting higher spatial and semantic levels of fusion.

Integration of heterogeneous features for remote sensing scene classification

NASA Astrophysics Data System (ADS)

Wang, Xin; Xiong, Xingnan; Ning, Chen; Shi, Aiye; Lv, Guofang

2018-01-01

Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.

Evidence for metabolic and reproductive phenotypes in mothers of women with polycystic ovary syndrome

PubMed Central

Sam, Susan; Legro, Richard S.; Essah, Paulina A.; Apridonidze, Teimuraz; Dunaif, Andrea

2006-01-01

Dyslipidemia is a feature of polycystic ovary syndrome (PCOS), but its pathogenesis remains controversial. We performed this study of mothers of women with PCOS to test the hypothesis that dyslipidemia is a heritable trait in families of women with PCOS and to investigate the impact of age on reproductive and metabolic phenotypes. Fasting blood was obtained in 215 non-Hispanic white mothers of women with PCOS and 62 control women. The prevalence of metabolic syndrome was compared with that in non-Hispanic white women of comparable age from the National Health and Nutrition Examination Survey III. Mothers had higher total (P < 0.001) and low-density lipoprotein (LDL) cholesterol levels (P = 0.007), whereas high-density lipoprotein and triglyceride levels did not differ compared with control women. The only predictors of LDL levels in mothers were their daughters’ LDL levels (r2 = 0.11, P < 0.001) and their own unbound testosterone levels (r2 = 0.04, P = 0.03). The prevalence of metabolic syndrome was increased in obese (body mass index ≥ 30 kg/m2) mothers compared with obese non-Hispanic white women from the National Health and Nutrition Examination Survey III (P = 0.04). Thirty-one percent of mothers reported a history of menstrual irregularity. These mothers had higher androgen levels, markers of insulin resistance, and LDL levels than mothers with regular menses. LDL levels are increased in mothers of women with PCOS, suggestive of a heritable trait. A history of menstrual irregularity identifies mothers with features of PCOS. Obese mothers have a very high prevalence of metabolic syndrome. These findings suggest that both the reproductive and metabolic abnormalities persist with age in PCOS. PMID:16632599

HIV vaccine trial willingness among injection and non-injection drug users in two urban centres, Barcelona and San Francisco.

PubMed

Etcheverry, M Florencia; Lum, Paula J; Evans, Jennifer L; Sanchez, Emilia; de Lazzari, Elisa; Mendez-Arancibia, Eva; Sierra, Ernesto; Gatell, José M; Page, Kimberly; Joseph, Joan

2011-02-24

Being able to recruit high-risk volunteers who are also willing to consider future participation in vaccine trials are critical features of vaccine preparedness studies. We described data from two cohorts of injection- and non-injection drug users in Barcelona, Spain [Red Cross centre] and in San Francisco, USA, [UFO-VAX study] at high risk of HIV/HCV infection to assess behaviour risk exposure and willingness to participate in future preventive HIV vaccine trials. We successfully identified drug-using populations that would be eligible for future HIV vaccine efficacy trials, based on reported levels of risk during screening and high levels of willingness to participate. In both groups, Red Cross and UFO-VAX respectively, HCV infection was highly prevalent at baseline (41% and 34%), HIV baseline seroprevalence was 4.2% and 1.5%, and high levels of willingness were seen (83% and 78%). Copyright © 2011 Elsevier Ltd. All rights reserved.

SNX -- Starlink Extensions to the NCAR Graphics Utilities

NASA Astrophysics Data System (ADS)

Rees, P. C. T.; Bly, M. J.; Wallace, P. T.

The NCAR graphics suite (SUN/88) consists of a set of subprograms which can be used to produce complete graphs in a variety of formats. The package has been in wide use for some years; the latest version employs the ISO standard GKS interfaces for its low-level plotting, giving it access to all Starlink graphics devices, present and future. The NCAR routines themselves are thoroughly documented, and just a few simple calls will produce graphs of excellent appearance. The package also provides a high level of flexibility, with dozens of different details of the plot independently controllable to give exactly the result required. However, beginners may be daunted by the mass of features offered, and unless they take the extreme step of reading the manual may give up before they realise what the package can do for them. This document describes minor extensions which provide more convenient access to certain features without sacrificing flexibility. The AUTOGRAPH part of the NCAR suite, used in conjunction with the Starlink NCAR extensions and the Starlink low level plotting package SGS (SUN/85), offers an alternative high level system to PGPLOT (SUN/15) for producing graphs of one variable plotted against another. All of the Starlink extensions provided within SNX enhance the power of the facilities provided by AUTOGRAPH and make it more accessible to the beginner.

The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks.

PubMed

Bankson, B B; Hebart, M N; Groen, I I A; Baker, C I

2018-05-17

Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categorical or conceptual representations. Here, we aimed to estimate a lower temporal bound for the emergence of conceptual representations by defining two criteria that characterize such representations: 1) conceptual object representations should generalize across different exemplars of the same object, and 2) these representations should reflect high-level behavioral judgments. To test these criteria, we compared magnetoencephalography (MEG) recordings between two groups of participants (n = 16 per group) exposed to different exemplar images of the same object concepts. Further, we disentangled low-level from high-level MEG responses by estimating the unique and shared contribution of models of behavioral judgments, semantics, and different layers of deep neural networks of visual object processing. We find that 1) both generalization across exemplars as well as generalization of object-related signals across time increase after 150 ms, peaking around 230 ms; 2) representations specific to behavioral judgments emerged rapidly, peaking around 160 ms. Collectively, these results suggest a lower bound for the emergence of conceptual object representations around 150 ms following stimulus onset. Copyright © 2018 Elsevier Inc. All rights reserved.

The KMO allele encoding Arg452 is associated with psychotic features in bipolar disorder type 1, and with increased CSF KYNA level and reduced KMO expression.

PubMed

Lavebratt, C; Olsson, S; Backlund, L; Frisén, L; Sellgren, C; Priebe, L; Nikamo, P; Träskman-Bendz, L; Cichon, S; Vawter, M P; Osby, U; Engberg, G; Landén, M; Erhardt, S; Schalling, M

2014-03-01

The kynurenine pathway metabolite kynurenic acid (KYNA), modulating glutamatergic and cholinergic neurotransmission, is increased in cerebrospinal fluid (CSF) of patients with schizophrenia or bipolar disorder type 1 with psychotic features. KYNA production is critically dependent on kynurenine 3-monooxygenase (KMO). KMO mRNA levels and activity in prefrontal cortex (PFC) are reduced in schizophrenia. We hypothesized that KMO expression in PFC would be reduced in bipolar disorder with psychotic features and that a functional genetic variant of KMO would associate with this disease, CSF KYNA level and KMO expression. KMO mRNA levels were reduced in PFC of bipolar disorder patients with lifetime psychotic features (P=0.005, n=19) or schizophrenia (P=0.02, n=36) compared with nonpsychotic patients and controls. KMO genetic association to psychotic features in bipolar disorder type 1 was studied in 493 patients and 1044 controls from Sweden. The KMO Arg(452) allele was associated with psychotic features during manic episodes (P=0.003). KMO Arg(452) was studied for association to CSF KYNA levels in an independent sample of 55 Swedish patients, and to KMO expression in 717 lymphoblastoid cell lines and 138 hippocampal biopsies. KMO Arg(452) associated with increased levels of CSF KYNA (P=0.03) and reduced lymphoblastoid and hippocampal KMO expression (P≤0.05). Thus, findings from five independent cohorts suggest that genetic variation in KMO influences the risk for psychotic features in mania of bipolar disorder patients. This provides a possible mechanism for the previous findings of elevated CSF KYNA levels in those bipolar patients with lifetime psychotic features and positive association between KYNA levels and number of manic episodes.

National Hydrography Dataset (NHD)

USGS Publications Warehouse

,

2001-01-01

The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000 scale and exists at that scale for the whole country. High resolution NHD adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Like the 1:100,000-scale NHD, high resolution NHD contains reach codes for networked features and isolated lakes, flow direction, names, stream level, and centerline representations for areal water bodies. Reaches are also defined to represent waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria set out by the Federal Geographic Data Committee.

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Shin, Eun Seok; Kim, Sung Min

2018-01-01

The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. The proposed method had high clinical applicability for image-based tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Integrated Cox's model for predicting survival time of glioblastoma multiforme.

PubMed

Ai, Zhibing; Li, Longti; Fu, Rui; Lu, Jing-Min; He, Jing-Dong; Li, Sen

2017-04-01

Glioblastoma multiforme is the most common primary brain tumor and is highly lethal. This study aims to figure out signatures for predicting the survival time of patients with glioblastoma multiforme. Clinical information, messenger RNA expression, microRNA expression, and single-nucleotide polymorphism array data of patients with glioblastoma multiforme were retrieved from The Cancer Genome Atlas. Patients were separated into two groups by using 1 year as a cutoff, and a logistic regression model was used to figure out any variables that can predict whether the patient was able to live longer than 1 year. Furthermore, Cox's model was used to find out features that were correlated with the survival time. Finally, a Cox model integrated the significant clinical variables, messenger RNA expression, microRNA expression, and single-nucleotide polymorphism was built. Although the classification method failed, signatures of clinical features, messenger RNA expression levels, and microRNA expression levels were figured out by using Cox's model. However, no single-nucleotide polymorphisms related to prognosis were found. The selected clinical features were age at initial diagnosis, Karnofsky score, and race, all of which had been suggested to correlate with survival time. Both of the two significant microRNAs, microRNA-221 and microRNA-222, were targeted to p27 Kip1 protein, which implied the important role of p27 Kip1 on the prognosis of glioblastoma multiforme patients. Our results suggested that survival modeling was more suitable than classification to figure out prognostic biomarkers for patients with glioblastoma multiforme. An integrated model containing clinical features, messenger RNA levels, and microRNA expression levels was built, which has the potential to be used in clinics and thus to improve the survival status of glioblastoma multiforme patients.

Clinicopathological and molecular stability and methylation analyses of gastric papillary adenocarcinoma.

PubMed

Uesugi, Noriyuki; Sugai, Tamotsu; Sugimoto, Ryo; Eizuka, Makoto; Fujita, Yasuko; Sato, Ayaka; Osakabe, Mitsumasa; Ishida, Kazuyuki; Koeda, Keisuke; Sasaki, Akira; Matsumoto, Takayuki

2017-10-01

The molecular alterations and pathological features of gastric papillary adenocarcinoma (GPA) remain unknown. We examined GPA samples and compared their molecular and pathological characteristics with those of gastric tubular adenocarcinoma (GTA). Additionally, we identified pathological and molecular features of GPA that vary with microsatellite stability. In the present study, samples from 63 GPA patients and 47 GTA patients were examined using a combination of polymerase chain reaction (PCR)-microsatellite assays and PCR-pyrosequencing in order to detect microsatellite instability (microsatellite instability, MSI; microsatellite stable, MSS), methylation status (low methylation, intermediate methylation and high methylation level), and chromosomal AI in multiple cancer-related loci. Additionally, the expression levels of TP53 and Her2 were evaluated using immunohistochemistry. GTA and GPA are statistically different in their frequency of pathological features, including mucinous, poorly differentiated and invasive micropapillary components. Clear genetic patterns differentiating GPA and GTA could not be identified with a hierarchical cluster analysis, but microsatellite stability was linked with TP53 and Her2 overexpression. Methylation status in GPA was also associated with the development of high microsatellite instability. However, no pathological differences were associated with microsatellite stability. We suggest that although molecular alterations in a subset of GPAs are closely associated with microsatellite stability, they play a minor role in GPA carcinogenesis. Copyright © 2017 Royal College of Pathologists of Australasia. Published by Elsevier B.V. All rights reserved.

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

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

Ma, Guangkai; Gao, Yaozong; Wu, Guorong

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features canmore » be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature.« less

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

PubMed Central

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-01-01

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature. PMID:26843260

Feature-level sentiment analysis by using comparative domain corpora

NASA Astrophysics Data System (ADS)

Quan, Changqin; Ren, Fuji

2016-06-01

Feature-level sentiment analysis (SA) is able to provide more fine-grained SA on certain opinion targets and has a wider range of applications on E-business. This study proposes an approach based on comparative domain corpora for feature-level SA. The proposed approach makes use of word associations for domain-specific feature extraction. First, we assign a similarity score for each candidate feature to denote its similarity extent to a domain. Then we identify domain features based on their similarity scores on different comparative domain corpora. After that, dependency grammar and a general sentiment lexicon are applied to extract and expand feature-oriented opinion words. Lastly, the semantic orientation of a domain-specific feature is determined based on the feature-oriented opinion lexicons. In evaluation, we compare the proposed method with several state-of-the-art methods (including unsupervised and semi-supervised) using a standard product review test collection. The experimental results demonstrate the effectiveness of using comparative domain corpora.

Chaotic Dynamics of Linguistic-Like Processes at the Syntactical and Semantic Levels: in the Pursuit of a Multifractal Attractor

NASA Astrophysics Data System (ADS)

Nicolis, John S.; Katsikas, Anastassis A.

Collective parameters such as the Zipf's law-like statistics, the Transinformation, the Block Entropy and the Markovian character are compared for natural, genetic, musical and artificially generated long texts from generating partitions (alphabets) on homogeneous as well as on multifractal chaotic maps. It appears that minimal requirements for a language at the syntactical level such as memory, selectivity of few keywords and broken symmetry in one dimension (polarity) are more or less met by dynamically iterating simple maps or flows e.g. very simple chaotic hardware. The same selectivity is observed at the semantic level where the aim refers to partitioning a set of enviromental impinging stimuli onto coexisting attractors-categories. Under the regime of pattern recognition and classification, few key features of a pattern or few categories claim the lion's share of the information stored in this pattern and practically, only these key features are persistently scanned by the cognitive processor. A multifractal attractor model can in principle explain this high selectivity, both at the syntactical and the semantic levels.

Development of Digital SLR Camera: PENTAX K-7

NASA Astrophysics Data System (ADS)

Kawauchi, Hiraku

The DSLR "PENTAX K-7" comes with an easy-to-carry, minimal yet functional small form factor, a long inherited identities of the PENTAX brand. Nevertheless for its compact body, this camera has up-to-date enhanced fundamental features such as high-quality viewfinder, enhanced shutter mechanism, extended continuous shooting capabilities, reliable exposure control, and fine-tuned AF systems, as well as strings of newest technologies such as movie recording capability and automatic leveling function. The main focus of this article is to reveal the ideas behind the concept making of this product and its distinguished features.

Design, Simulation, and Preliminary Testing of a 20 Ampere Energy Management System

DTIC Science & Technology

2015-06-01

Vre f 0.5 V 0.58 V Vil 0.8 V 1.1 V Vih 1.9 V 2.25 V An important feature of this power module is the smart shutdown feature [15]. A simpli- fied...protection is removed when the pin voltage reaches the high-logic level Vih [15]. Values for Rshunt , RSD, and CSD had to be selected to implement this over...0.58 V Vil 0.8 V Vih 2.25 V Table 3.3. Truth table for H-bridge IGBTs, from [16]. Logic Input Output Shutdown Pin Lower IGBT Upper IGBT Lower IGBT

Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs

PubMed Central

Gómez-Adorno, Helena; Sidorov, Grigori; Pinto, David; Vilariño, Darnes; Gelbukh, Alexander

2016-01-01

We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution. PMID:27589740

Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions.

PubMed

Contini, Erika W; Wardle, Susan G; Carlson, Thomas A

2017-10-01

Visual object recognition is a complex, dynamic process. Multivariate pattern analysis methods, such as decoding, have begun to reveal how the brain processes complex visual information. Recently, temporal decoding methods for EEG and MEG have offered the potential to evaluate the temporal dynamics of object recognition. Here we review the contribution of M/EEG time-series decoding methods to understanding visual object recognition in the human brain. Consistent with the current understanding of the visual processing hierarchy, low-level visual features dominate decodable object representations early in the time-course, with more abstract representations related to object category emerging later. A key finding is that the time-course of object processing is highly dynamic and rapidly evolving, with limited temporal generalisation of decodable information. Several studies have examined the emergence of object category structure, and we consider to what degree category decoding can be explained by sensitivity to low-level visual features. Finally, we evaluate recent work attempting to link human behaviour to the neural time-course of object processing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Impact of Hearing Aid Technology on Outcomes in Daily Life III: Localization.

PubMed

Johnson, Jani A; Xu, Jingjing; Cox, Robyn M

Compared to basic-feature hearing aids, premium-feature hearing aids have more advanced technologies and sophisticated features. The objective of this study was to explore the difference between premium-feature and basic-feature hearing aids in horizontal sound localization in both laboratory and daily life environments. We hypothesized that premium-feature hearing aids would yield better localization performance than basic-feature hearing aids. Exemplars of premium-feature and basic-feature hearing aids from two major manufacturers were evaluated. Forty-five older adults (mean age 70.3 years) with essentially symmetrical mild to moderate sensorineural hearing loss were bilaterally fitted with each of the four pairs of hearing aids. Each pair of hearing aids was worn during a 4-week field trial and then evaluated using laboratory localization tests and a standardized questionnaire. Laboratory localization tests were conducted in a sound-treated room with a 360°, 24-loudspeaker array. Test stimuli were high frequency and low frequency filtered short sentences. The localization test in quiet was designed to assess the accuracy of front/back localization, while the localization test in noise was designed to assess the accuracy of locating sound sources throughout a 360° azimuth in the horizontal plane. Laboratory data showed that unaided localization was not significantly different from aided localization when all hearing aids were combined. Questionnaire data showed that aided localization was significantly better than unaided localization in everyday situations. Regarding the difference between premium-feature and basic-feature hearing aids, laboratory data showed that, overall, the premium-feature hearing aids yielded more accurate localization than the basic-feature hearing aids when high-frequency stimuli were used, and the listening environment was quiet. Otherwise, the premium-feature and basic-feature hearing aids yielded essentially the same performance in other laboratory tests and in daily life. The findings were consistent for both manufacturers. Laboratory tests for two of six major manufacturers showed that premium-feature hearing aids yielded better localization performance than basic-feature hearing aids in one out of four laboratory conditions. There was no difference between the two feature levels in self-reported everyday localization. Effectiveness research with different hearing aid technologies is necessary, and more research with other manufacturers' products is needed. Furthermore, these results confirm previous observations that research findings in laboratory conditions might not translate to everyday life.

Tunneling spectra for electrons in the lowest Landau level

NASA Astrophysics Data System (ADS)

Burnell, F. J.; Simon, Steven H.

2010-03-01

The recently developed experimental technique of time dependent capacitance spectroscopy [1] allows for measurements of high-resolution tunneling spectra of 2DEGs in the quantum Hall regime, giving a detailed probe of the single particle spectral function (electron addition and subtraction spectra). These experiments show a number of interesting features including Landau level structure, exchange enhanced Zeeman energy, Coulomb gap physics, effects of fractional quantization, as well as several key features that remain to be explained. While there has been some prior theoretical work[2] towards explaining low energy Coulomb gap features of tunneling spectra found in much earlier tunneling experiments [3], the new experiments[1] have uncovered physics outside of the prior theoretical explanations. Building on a number of these prior theoretical works, we investigate theoretically the expected tunneling spectra for electrons in low Landau levels, including the effects of electron spin and coupling to collective modes. [1] O. E. Dial, R.C. Ashoori, L.N. Pfeiffer, and K.W. West, Nature 448, 176-179 (2007) ; O. E. Dial et al, unpublished. [2] I. Aleiner et al, Phys. Rev. Lett 74 3435; (1994) S. R. E. Yang and A. MacDonald PRL 70 4110 (1993); S. He, P.M. Platzman, and B. I. Halperin, PRL 71 777 (1993). [3] J. P. Eisenstein et al, Phy. Rev. Lett. 69, 3804 (1992).

CognitionMaster: an object-based image analysis framework

PubMed Central

2013-01-01

Background Automated image analysis methods are becoming more and more important to extract and quantify image features in microscopy-based biomedical studies and several commercial or open-source tools are available. However, most of the approaches rely on pixel-wise operations, a concept that has limitations when high-level object features and relationships between objects are studied and if user-interactivity on the object-level is desired. Results In this paper we present an open-source software that facilitates the analysis of content features and object relationships by using objects as basic processing unit instead of individual pixels. Our approach enables also users without programming knowledge to compose “analysis pipelines“ that exploit the object-level approach. We demonstrate the design and use of example pipelines for the immunohistochemistry-based cell proliferation quantification in breast cancer and two-photon fluorescence microscopy data about bone-osteoclast interaction, which underline the advantages of the object-based concept. Conclusions We introduce an open source software system that offers object-based image analysis. The object-based concept allows for a straight-forward development of object-related interactive or fully automated image analysis solutions. The presented software may therefore serve as a basis for various applications in the field of digital image analysis. PMID:23445542