Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

NASA Astrophysics Data System (ADS)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

3D face recognition based on multiple keypoint descriptors and sparse representation.

PubMed

Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

2014-01-01

Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.

3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation

PubMed Central

Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

2014-01-01

Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm. PMID:24940876

Ear recognition from one sample per person.

PubMed

Chen, Long; Mu, Zhichun; Zhang, Baoqing; Zhang, Yi

2015-01-01

Biometrics has the advantages of efficiency and convenience in identity authentication. As one of the most promising biometric-based methods, ear recognition has received broad attention and research. Previous studies have achieved remarkable performance with multiple samples per person (MSPP) in the gallery. However, most conventional methods are insufficient when there is only one sample per person (OSPP) available in the gallery. To solve the OSPP problem by maximizing the use of a single sample, this paper proposes a hybrid multi-keypoint descriptor sparse representation-based classification (MKD-SRC) ear recognition approach based on 2D and 3D information. Because most 3D sensors capture 3D data accessorizing the corresponding 2D data, it is sensible to use both types of information. First, the ear region is extracted from the profile. Second, keypoints are detected and described for both the 2D texture image and 3D range image. Then, the hybrid MKD-SRC algorithm is used to complete the recognition with only OSPP in the gallery. Experimental results on a benchmark dataset have demonstrated the feasibility and effectiveness of the proposed method in resolving the OSPP problem. A Rank-one recognition rate of 96.4% is achieved for a gallery of 415 subjects, and the time involved in the computation is satisfactory compared to conventional methods.

Ensemble Sparse Classification of Alzheimer’s Disease

PubMed Central

Liu, Manhua; Zhang, Daoqiang; Shen, Dinggang

2012-01-01

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. PMID:22270352

Collaborative sparse priors for multi-view ATR

NASA Astrophysics Data System (ADS)

Li, Xuelu; Monga, Vishal

2018-04-01

Recent work has seen a surge of sparse representation based classification (SRC) methods applied to automatic target recognition problems. While traditional SRC approaches used l0 or l1 norm to quantify sparsity, spike and slab priors have established themselves as the gold standard for providing general tunable sparse structures on vectors. In this work, we employ collaborative spike and slab priors that can be applied to matrices to encourage sparsity for the problem of multi-view ATR. That is, target images captured from multiple views are expanded in terms of a training dictionary multiplied with a coefficient matrix. Ideally, for a test image set comprising of multiple views of a target, coefficients corresponding to its identifying class are expected to be active, while others should be zero, i.e. the coefficient matrix is naturally sparse. We develop a new approach to solve the optimization problem that estimates the sparse coefficient matrix jointly with the sparsity inducing parameters in the collaborative prior. ATR problems are investigated on the mid-wave infrared (MWIR) database made available by the US Army Night Vision and Electronic Sensors Directorate, which has a rich collection of views. Experimental results show that the proposed joint prior and coefficient estimation method (JPCEM) can: 1.) enable improved accuracy when multiple views vs. a single one are invoked, and 2.) outperform state of the art alternatives particularly when training imagery is limited.

Vietnamese Document Representation and Classification

NASA Astrophysics Data System (ADS)

Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

Longitudinal Brain Magnetic Resonance Imaging CO2 Stress Testing in Individual Adolescent Sports-Related Concussion Patients: A Pilot Study.

PubMed

Mutch, W Alan C; Ellis, Michael J; Ryner, Lawrence N; Morissette, Marc P; Pries, Philip J; Dufault, Brenden; Essig, Marco; Mikulis, David J; Duffin, James; Fisher, Joseph A

2016-01-01

Advanced neuroimaging studies in concussion have been limited to detecting group differences between concussion patients and healthy controls. In this small pilot study, we used brain magnetic resonance imaging (MRI) CO2 stress testing to longitudinally assess cerebrovascular responsiveness (CVR) in individual sports-related concussion (SRC) patients. Six SRC patients (three males and three females; mean age = 15.7, range = 15-17 years) underwent longitudinal brain MRI CO2 stress testing using blood oxygen level-dependent (BOLD) MRI and model-based prospective end-tidal CO2 targeting under isoxic conditions. First-level and second-level comparisons were undertaken using statistical parametric mapping (SPM) to score the scans and compare them to an atlas of 24 healthy control subjects. All tests were well tolerated and without any serious adverse events. Anatomical MRI was normal in all study participants. The CO2 stimulus was consistent between the SRC patients and control subjects and within SRC patients across the longitudinal study. Individual SRC patients demonstrated both quantitative and qualitative patient-specific alterations in CVR (p < 0.005) that correlated strongly with clinical findings, and that persisted beyond clinical recovery. Standardized brain MRI CO2 stress testing is capable of providing a longitudinal assessment of CVR in individual SRC patients. Consequently, larger prospective studies are needed to examine the utility of brain MRI CO2 stress testing as a clinical tool to help guide the evaluation, classification, and longitudinal management of SRC patients.

Recent Developments in the Classification, Evaluation, Pathophysiology, and Management of Scleroderma Renal Crisis.

PubMed

Ghossein, Cybele; Varga, John; Fenves, Andrew Z

2016-01-01

Scleroderma renal crisis (SRC) is an uncommon complication of systemic sclerosis. Despite the advent of angiotensin-converting inhibitor therapy, SRC remains a life-threatening complication. Recent studies have contributed to a better understanding of SRC, but much remains unknown regarding its pathophysiology, risk factors, and optimal management. Genetic studies provide evidence that immune dysregulation might be a contributing factor, providing hope that further research in this direction might illuminate pathogenesis and provide novel predictors for this complication.

Integrating conventional and inverse representation for face recognition.

PubMed

Xu, Yong; Li, Xuelong; Yang, Jian; Lai, Zhihui; Zhang, David

2014-10-01

Representation-based classification methods are all constructed on the basis of the conventional representation, which first expresses the test sample as a linear combination of the training samples and then exploits the deviation between the test sample and the expression result of every class to perform classification. However, this deviation does not always well reflect the difference between the test sample and each class. With this paper, we propose a novel representation-based classification method for face recognition. This method integrates conventional and the inverse representation-based classification for better recognizing the face. It first produces conventional representation of the test sample, i.e., uses a linear combination of the training samples to represent the test sample. Then it obtains the inverse representation, i.e., provides an approximation representation of each training sample of a subject by exploiting the test sample and training samples of the other subjects. Finally, the proposed method exploits the conventional and inverse representation to generate two kinds of scores of the test sample with respect to each class and combines them to recognize the face. The paper shows the theoretical foundation and rationale of the proposed method. Moreover, this paper for the first time shows that a basic nature of the human face, i.e., the symmetry of the face can be exploited to generate new training and test samples. As these new samples really reflect some possible appearance of the face, the use of them will enable us to obtain higher accuracy. The experiments show that the proposed conventional and inverse representation-based linear regression classification (CIRLRC), an improvement to linear regression classification (LRC), can obtain very high accuracy and greatly outperforms the naive LRC and other state-of-the-art conventional representation based face recognition methods. The accuracy of CIRLRC can be 10% greater than that of LRC.

The DTW-based representation space for seismic pattern classification

NASA Astrophysics Data System (ADS)

Orozco-Alzate, Mauricio; Castro-Cabrera, Paola Alexandra; Bicego, Manuele; Londoño-Bonilla, John Makario

2015-12-01

Distinguishing among the different seismic volcanic patterns is still one of the most important and labor-intensive tasks for volcano monitoring. This task could be lightened and made free from subjective bias by using automatic classification techniques. In this context, a core but often overlooked issue is the choice of an appropriate representation of the data to be classified. Recently, it has been suggested that using a relative representation (i.e. proximities, namely dissimilarities on pairs of objects) instead of an absolute one (i.e. features, namely measurements on single objects) is advantageous to exploit the relational information contained in the dissimilarities to derive highly discriminant vector spaces, where any classifier can be used. According to that motivation, this paper investigates the suitability of a dynamic time warping (DTW) dissimilarity-based vector representation for the classification of seismic patterns. Results show the usefulness of such a representation in the seismic pattern classification scenario, including analyses of potential benefits from recent advances in the dissimilarity-based paradigm such as the proper selection of representation sets and the combination of different dissimilarity representations that might be available for the same data.

Drug related webpages classification using images and text information based on multi-kernel learning

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Xiao, Liping; Zheng, Wenjuan

2015-12-01

In this paper, multi-kernel learning(MKL) is used for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and valid images are chosen by the FOCARSS algorithm. Second, text based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Last, text and images representation are fused with a few methods. Experimental results demonstrate that the classification accuracy of MKL is higher than those of all other fusion methods in decision level and feature level, and much higher than the accuracy of single-modal classification.

Classification of forensic autopsy reports through conceptual graph-based document representation model.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2018-06-01

Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD. Copyright © 2018 Elsevier Inc. All rights reserved.

Dissimilarity representations in lung parenchyma classification

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; de Bruijne, Marleen

2009-02-01

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

A Review on Human Activity Recognition Using Vision-Based Method.

PubMed

Zhang, Shugang; Wei, Zhiqiang; Nie, Jie; Huang, Lei; Wang, Shuang; Li, Zhen

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

A Review on Human Activity Recognition Using Vision-Based Method

PubMed Central

Nie, Jie

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research. PMID:29065585

Video based object representation and classification using multiple covariance matrices.

PubMed

Zhang, Yurong; Liu, Quan

2017-01-01

Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.

Gaussian Discriminant Analysis for Optimal Delineation of Mild Cognitive Impairment in Alzheimer's Disease.

PubMed

Fang, Chen; Li, Chunfei; Cabrerizo, Mercedes; Barreto, Armando; Andrian, Jean; Rishe, Naphtali; Loewenstein, David; Duara, Ranjan; Adjouadi, Malek

2018-04-12

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

PubMed

Shi, Jun; Liu, Xiao; Li, Yan; Zhang, Qi; Li, Yingjie; Ying, Shihui

2015-10-30

Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation. Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search. The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively. The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR. The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals. Copyright © 2015 Elsevier B.V. All rights reserved.

Sparsity-Based Representation for Classification Algorithms and Comparison Results for Transient Acoustic Signals

DTIC Science & Technology

2016-05-01

large but correlated noise and signal interference (i.e., low -rank interference). Another contribution is the implementation of deep learning...representation, low rank, deep learning 52 Tung-Duong Tran-Luu 301-394-3082Unclassified Unclassified Unclassified UU ii Approved for public release; distribution...Classification of Acoustic Transients 6 3.2 Joint Sparse Representation with Low -Rank Interference 7 3.3 Simultaneous Group-and-Joint Sparse Representation

Weighted Discriminative Dictionary Learning based on Low-rank Representation

NASA Astrophysics Data System (ADS)

Chang, Heyou; Zheng, Hao

2017-01-01

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.

Internal representations for face detection: an application of noise-based image classification to BOLD responses.

PubMed

Nestor, Adrian; Vettel, Jean M; Tarr, Michael J

2013-11-01

What basic visual structures underlie human face detection and how can we extract such structures directly from the amplitude of neural responses elicited by face processing? Here, we address these issues by investigating an extension of noise-based image classification to BOLD responses recorded in high-level visual areas. First, we assess the applicability of this classification method to such data and, second, we explore its results in connection with the neural processing of faces. To this end, we construct luminance templates from white noise fields based on the response of face-selective areas in the human ventral cortex. Using behaviorally and neurally-derived classification images, our results reveal a family of simple but robust image structures subserving face representation and detection. Thus, we confirm the role played by classical face selective regions in face detection and we help clarify the representational basis of this perceptual function. From a theory standpoint, our findings support the idea of simple but highly diagnostic neurally-coded features for face detection. At the same time, from a methodological perspective, our work demonstrates the ability of noise-based image classification in conjunction with fMRI to help uncover the structure of high-level perceptual representations. Copyright © 2012 Wiley Periodicals, Inc.

Mentalizing about emotion and its relationship to empathy.

PubMed

Hooker, Christine I; Verosky, Sara C; Germine, Laura T; Knight, Robert T; D'Esposito, Mark

2008-09-01

Mentalizing involves the ability to predict someone else's behavior based on their belief state. More advanced mentalizing skills involve integrating knowledge about beliefs with knowledge about the emotional impact of those beliefs. Recent research indicates that advanced mentalizing skills may be related to the capacity to empathize with others. However, it is not clear what aspect of mentalizing is most related to empathy. In this study, we used a novel, advanced mentalizing task to identify neural mechanisms involved in predicting a future emotional response based on a belief state. Subjects viewed social scenes in which one character had a False Belief and one character had a True Belief. In the primary condition, subjects were asked to predict what emotion the False Belief Character would feel if they had a full understanding about the situation. We found that neural regions related to both mentalizing and emotion were involved when predicting a future emotional response, including the superior temporal sulcus, medial prefrontal cortex, temporal poles, somatosensory related cortices (SRC), inferior frontal gyrus and thalamus. In addition, greater neural activity in primarily emotion-related regions, including right SRC and bilateral thalamus, when predicting emotional response was significantly correlated with more self-reported empathy. The findings suggest that predicting emotional response involves generating and using internal affective representations and that greater use of these affective representations when trying to understand the emotional experience of others is related to more empathy.

Spectral-spatial hyperspectral image classification using super-pixel-based spatial pyramid representation

NASA Astrophysics Data System (ADS)

Fan, Jiayuan; Tan, Hui Li; Toomik, Maria; Lu, Shijian

2016-10-01

Spatial pyramid matching has demonstrated its power for image recognition task by pooling features from spatially increasingly fine sub-regions. Motivated by the concept of feature pooling at multiple pyramid levels, we propose a novel spectral-spatial hyperspectral image classification approach using superpixel-based spatial pyramid representation. This technique first generates multiple superpixel maps by decreasing the superpixel number gradually along with the increased spatial regions for labelled samples. By using every superpixel map, sparse representation of pixels within every spatial region is then computed through local max pooling. Finally, features learned from training samples are aggregated and trained by a support vector machine (SVM) classifier. The proposed spectral-spatial hyperspectral image classification technique has been evaluated on two public hyperspectral datasets, including the Indian Pines image containing 16 different agricultural scene categories with a 20m resolution acquired by AVIRIS and the University of Pavia image containing 9 land-use categories with a 1.3m spatial resolution acquired by the ROSIS-03 sensor. Experimental results show significantly improved performance compared with the state-of-the-art works. The major contributions of this proposed technique include (1) a new spectral-spatial classification approach to generate feature representation for hyperspectral image, (2) a complementary yet effective feature pooling approach, i.e. the superpixel-based spatial pyramid representation that is used for the spatial correlation study, (3) evaluation on two public hyperspectral image datasets with superior image classification performance.

Functional Interactions Between c-Src and HER1 Potentiate Neoplastic Transformation: Implications for the Etiology of Human Breast Cancer

DTIC Science & Technology

2000-07-01

receptor 120 16. PRICE CODE 17. SECURITY CLASSIFICATION 18 . SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT OF REPORT OF...THIS PAGE OF ABSTRACT Unclassified Unclassified Unclassified Unlimited NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) Prescribed by ANSI Std. Z39- 18 ... 18 -26 Appended Manuscripts 3 INTRODUCTION Recent work in our laboratory has established the importance of a

Lessons Learned from Data on Women's Careers in Physics

NASA Astrophysics Data System (ADS)

Ivie, Rachel

2010-03-01

It is well known that the participation of women in physics decreases at every step along the academic ladder. However, the exact points at which this loss occurs are less well understood. In this talk, I will present data on women in physics collected by the Statistical Research Center (SRC) of the American Institute of Physics. I will compare these data to data recently published in a National Research Council (NRC) Report, Gender Differences at Critical Transitions in the Careers of Science, Engineering, and Mathematics Faculty. This report includes data on gender differences in: number of applications for faculty positions in physics, number of interviews, number of hires, tenure and promotion rates, salaries, and start-up packages. Taken together, the SRC and the NRC data can inform the physics community about specific areas that should be addressed to increase the representation of women in physics faculty positions. SRC data on minority women in physics also will be presented.

A Wavelet Polarization Decomposition Net Model for Polarimetric SAR Image Classification

NASA Astrophysics Data System (ADS)

He, Chu; Ou, Dan; Yang, Teng; Wu, Kun; Liao, Mingsheng; Chen, Erxue

2014-11-01

In this paper, a deep model based on wavelet texture has been proposed for Polarimetric Synthetic Aperture Radar (PolSAR) image classification inspired by recent successful deep learning method. Our model is supposed to learn powerful and informative representations to improve the generalization ability for the complex scene classification tasks. Given the influence of speckle noise in Polarimetric SAR image, wavelet polarization decomposition is applied first to obtain basic and discriminative texture features which are then embedded into a Deep Neural Network (DNN) in order to compose multi-layer higher representations. We demonstrate that the model can produce a powerful representation which can capture some untraceable information from Polarimetric SAR images and show a promising achievement in comparison with other traditional SAR image classification methods for the SAR image dataset.

Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions.

PubMed

Acosta-Mesa, Héctor-Gabriel; Rechy-Ramírez, Fernando; Mezura-Montes, Efrén; Cruz-Ramírez, Nicandro; Hernández Jiménez, Rodolfo

2014-06-01

In this work, we present a novel application of time series discretization using evolutionary programming for the classification of precancerous cervical lesions. The approach optimizes the number of intervals in which the length and amplitude of the time series should be compressed, preserving the important information for classification purposes. Using evolutionary programming, the search for a good discretization scheme is guided by a cost function which considers three criteria: the entropy regarding the classification, the complexity measured as the number of different strings needed to represent the complete data set, and the compression rate assessed as the length of the discrete representation. This discretization approach is evaluated using a time series data based on temporal patterns observed during a classical test used in cervical cancer detection; the classification accuracy reached by our method is compared with the well-known times series discretization algorithm SAX and the dimensionality reduction method PCA. Statistical analysis of the classification accuracy shows that the discrete representation is as efficient as the complete raw representation for the present application, reducing the dimensionality of the time series length by 97%. This representation is also very competitive in terms of classification accuracy when compared with similar approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

Towards the use of similarity distances to music genre classification: A comparative study.

PubMed

Goienetxea, Izaro; Martínez-Otzeta, José María; Sierra, Basilio; Mendialdua, Iñigo

2018-01-01

Music genre classification is a challenging research concept, for which open questions remain regarding classification approach, music piece representation, distances between/within genres, and so on. In this paper an investigation on the classification of generated music pieces is performed, based on the idea that grouping close related known pieces in different sets -or clusters- and then generating in an automatic way a new song which is somehow "inspired" in each set, the new song would be more likely to be classified as belonging to the set which inspired it, based on the same distance used to separate the clusters. Different music pieces representations and distances among pieces are used; obtained results are promising, and indicate the appropriateness of the used approach even in a such a subjective area as music genre classification is.

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

PubMed

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Towards the use of similarity distances to music genre classification: A comparative study

PubMed Central

Martínez-Otzeta, José María; Sierra, Basilio; Mendialdua, Iñigo

2018-01-01

Music genre classification is a challenging research concept, for which open questions remain regarding classification approach, music piece representation, distances between/within genres, and so on. In this paper an investigation on the classification of generated music pieces is performed, based on the idea that grouping close related known pieces in different sets –or clusters– and then generating in an automatic way a new song which is somehow “inspired” in each set, the new song would be more likely to be classified as belonging to the set which inspired it, based on the same distance used to separate the clusters. Different music pieces representations and distances among pieces are used; obtained results are promising, and indicate the appropriateness of the used approach even in a such a subjective area as music genre classification is. PMID:29444160

An application to pulmonary emphysema classification based on model of texton learning by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

2012-03-01

We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

Classification of multiple sclerosis lesions using adaptive dictionary learning.

PubMed

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

The impact of category structure and training methodology on learning and generalizing within-category representations.

PubMed

Ell, Shawn W; Smith, David B; Peralta, Gabriela; Hélie, Sébastien

2017-08-01

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.

Two-tier tissue decomposition for histopathological image representation and classification.

PubMed

Gultekin, Tunc; Koyuncu, Can Fahrettin; Sokmensuer, Cenk; Gunduz-Demir, Cigdem

2015-01-01

In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.

Age and gender classification in the wild with unsupervised feature learning

NASA Astrophysics Data System (ADS)

Wan, Lihong; Huo, Hong; Fang, Tao

2017-03-01

Inspired by unsupervised feature learning (UFL) within the self-taught learning framework, we propose a method based on UFL, convolution representation, and part-based dimensionality reduction to handle facial age and gender classification, which are two challenging problems under unconstrained circumstances. First, UFL is introduced to learn selective receptive fields (filters) automatically by applying whitening transformation and spherical k-means on random patches collected from unlabeled data. The learning process is fast and has no hyperparameters to tune. Then, the input image is convolved with these filters to obtain filtering responses on which local contrast normalization is applied. Average pooling and feature concatenation are then used to form global face representation. Finally, linear discriminant analysis with part-based strategy is presented to reduce the dimensions of the global representation and to improve classification performances further. Experiments on three challenging databases, namely, Labeled faces in the wild, Gallagher group photos, and Adience, demonstrate the effectiveness of the proposed method relative to that of state-of-the-art approaches.

A novel collaborative representation and SCAD based classification method for fibrosis and inflammatory activity analysis of chronic hepatitis C

NASA Astrophysics Data System (ADS)

Cai, Jiaxin; Chen, Tingting; Li, Yan; Zhu, Nenghui; Qiu, Xuan

2018-03-01

In order to analysis the fibrosis stage and inflammatory activity grade of chronic hepatitis C, a novel classification method based on collaborative representation (CR) with smoothly clipped absolute deviation penalty (SCAD) penalty term, called CR-SCAD classifier, is proposed for pattern recognition. After that, an auto-grading system based on CR-SCAD classifier is introduced for the prediction of fibrosis stage and inflammatory activity grade of chronic hepatitis C. The proposed method has been tested on 123 clinical cases of chronic hepatitis C based on serological indexes. Experimental results show that the performance of the proposed method outperforms the state-of-the-art baselines for the classification of fibrosis stage and inflammatory activity grade of chronic hepatitis C.

Biomedical literature classification using encyclopedic knowledge: a Wikipedia-based bag-of-concepts approach.

PubMed

Mouriño García, Marcos Antonio; Pérez Rodríguez, Roberto; Anido Rifón, Luis E

2015-01-01

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria-that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text-thus suffering from synonymy and polysemy-and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge-concretely Wikipedia-in order to create bag-of-concepts (BoC) representations of documents, understanding concept as "unit of meaning", and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus.

Biomedical literature classification using encyclopedic knowledge: a Wikipedia-based bag-of-concepts approach

PubMed Central

Pérez Rodríguez, Roberto; Anido Rifón, Luis E.

2015-01-01

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria—that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text—thus suffering from synonymy and polysemy—and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge—concretely Wikipedia—in order to create bag-of-concepts (BoC) representations of documents, understanding concept as “unit of meaning”, and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus. PMID:26468436

Locality-preserving sparse representation-based classification in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Gao, Lianru; Yu, Haoyang; Zhang, Bing; Li, Qingting

2016-10-01

This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification.

General tensor discriminant analysis and gabor features for gait recognition.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2007-10-01

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.

Molecular cancer classification using a meta-sample-based regularized robust coding method.

PubMed

Wang, Shu-Lin; Sun, Liuchao; Fang, Jianwen

2014-01-01

Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data. In this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Extensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.

The Development of Categorization: Effects of Classification and Inference Training on Category Representation

PubMed Central

Deng, Wei (Sophia); Sloutsky, Vladimir M.

2015-01-01

Does category representation change in the course of development? And if so, how and why? The current study attempted to answer these questions by examining category learning and category representation. In Experiment 1, 4-year-olds, 6-year-olds, and adults were trained with either a classification task or an inference task and their categorization performance and memory for items were tested. Adults and 6-year-olds exhibited an important asymmetry: they relied on a single deterministic feature during classification training, but not during inference training. In contrast, regardless of the training condition, 4-year-olds relied on multiple probabilistic features. In Experiment 2, 4-year-olds were presented with classification training and their attention was explicitly directed to the deterministic feature. Under this condition, their categorization performance was similar to that of older participants in Experiment 1, yet their memory performance pointed to a similarity-based representation, which was similar to that of 4-year-olds in Experiment 1. These results are discussed in relation to theories of categorization and the role of selective attention in the development of category learning. PMID:25602938

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

PubMed

Fiannaca, Antonino; La Rosa, Massimo; Rizzo, Riccardo; Urso, Alfonso

2015-07-01

In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed. In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database". The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%. Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments. Copyright © 2015 Elsevier B.V. All rights reserved.

Src binds cortactin through an SH2 domain cystine-mediated linkage.

PubMed

Evans, Jason V; Ammer, Amanda G; Jett, John E; Bolcato, Chris A; Breaux, Jason C; Martin, Karen H; Culp, Mark V; Gannett, Peter M; Weed, Scott A

2012-12-15

Tyrosine-kinase-based signal transduction mediated by modular protein domains is critical for cellular function. The Src homology (SH)2 domain is an important conductor of intracellular signaling that binds to phosphorylated tyrosines on acceptor proteins, producing molecular complexes responsible for signal relay. Cortactin is a cytoskeletal protein and tyrosine kinase substrate that regulates actin-based motility through interactions with SH2-domain-containing proteins. The Src kinase SH2 domain mediates cortactin binding and tyrosine phosphorylation, but how Src interacts with cortactin is unknown. Here we demonstrate that Src binds cortactin through cystine bonding between Src C185 in the SH2 domain within the phosphotyrosine binding pocket and cortactin C112/246 in the cortactin repeats domain, independent of tyrosine phosphorylation. Interaction studies show that the presence of reducing agents ablates Src-cortactin binding, eliminates cortactin phosphorylation by Src, and prevents Src SH2 domain binding to cortactin. Tandem MS/MS sequencing demonstrates cystine bond formation between Src C185 and cortactin C112/246. Mutational studies indicate that an intact cystine binding interface is required for Src-mediated cortactin phosphorylation, cell migration, and pre-invadopodia formation. Our results identify a novel phosphotyrosine-independent binding mode between the Src SH2 domain and cortactin. Besides Src, one quarter of all SH2 domains contain cysteines at or near the analogous Src C185 position. This provides a potential alternative mechanism to tyrosine phosphorylation for cysteine-containing SH2 domains to bind cognate ligands that may be widespread in propagating signals regulating diverse cellular functions.

Src binds cortactin through an SH2 domain cystine-mediated linkage

PubMed Central

Evans, Jason V.; Ammer, Amanda G.; Jett, John E.; Bolcato, Chris A.; Breaux, Jason C.; Martin, Karen H.; Culp, Mark V.; Gannett, Peter M.; Weed, Scott A.

2012-01-01

Summary Tyrosine-kinase-based signal transduction mediated by modular protein domains is critical for cellular function. The Src homology (SH)2 domain is an important conductor of intracellular signaling that binds to phosphorylated tyrosines on acceptor proteins, producing molecular complexes responsible for signal relay. Cortactin is a cytoskeletal protein and tyrosine kinase substrate that regulates actin-based motility through interactions with SH2-domain-containing proteins. The Src kinase SH2 domain mediates cortactin binding and tyrosine phosphorylation, but how Src interacts with cortactin is unknown. Here we demonstrate that Src binds cortactin through cystine bonding between Src C185 in the SH2 domain within the phosphotyrosine binding pocket and cortactin C112/246 in the cortactin repeats domain, independent of tyrosine phosphorylation. Interaction studies show that the presence of reducing agents ablates Src-cortactin binding, eliminates cortactin phosphorylation by Src, and prevents Src SH2 domain binding to cortactin. Tandem MS/MS sequencing demonstrates cystine bond formation between Src C185 and cortactin C112/246. Mutational studies indicate that an intact cystine binding interface is required for Src-mediated cortactin phosphorylation, cell migration, and pre-invadopodia formation. Our results identify a novel phosphotyrosine-independent binding mode between the Src SH2 domain and cortactin. Besides Src, one quarter of all SH2 domains contain cysteines at or near the analogous Src C185 position. This provides a potential alternative mechanism to tyrosine phosphorylation for cysteine-containing SH2 domains to bind cognate ligands that may be widespread in propagating signals regulating diverse cellular functions. PMID:23097045

Classification of Chemical Substances Using Particulate Representations of Matter: An Analysis of Student Thinking

ERIC Educational Resources Information Center

Stains, Marilyne; Talanquer, Vicente

2007-01-01

We applied a mixed-method research design to investigate the patterns of reasoning used by novice undergraduate chemistry students to classify chemical substances as elements, compounds, or mixtures based on their particulate representations. We were interested in the identification of the representational features that students use to build a…

Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning

PubMed Central

Wang, Zhengxia; Zhu, Xiaofeng; Adeli, Ehsan; Zhu, Yingying; Nie, Feiping; Munsell, Brent

2018-01-01

Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores and labels) from the training data to the testing data in the label domain. However, this type of graph is exclusively learned in the feature domain, and primarily due to outliers in the observed features, may not be optimal for label propagation in the label domain. To address this limitation, a progressive GTL (pGTL) method is proposed that gradually finds an intrinsic data representation that more accurately aligns imaging features with the phenotype data. In general, optimal feature-to-phenotype alignment is achieved using an iterative approach that: (1) refines inter-subject relationships observed in the feature domain by using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined inter-subject relationships, and (3) verifies the intrinsic data representation on the training data to guarantee an optimal classification when applied to testing data. Additionally, the iterative approach is extended to multi-modal imaging data to further improve pGTL classification accuracy. Using Alzheimer’s disease and Parkinson’s disease study data, the classification accuracy of the proposed pGTL method is compared to several state-of-the-art classification methods, and the results show pGTL can more accurately identify subjects, even at different progression stages, in these two study data sets. PMID:28551556

Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth

PubMed Central

Just, Marcel Adam; Pan, Lisa; Cherkassky, Vladimir L.; McMakin, Dana; Cha, Christine; Nock, Matthew K.; Brent, David

2017-01-01

The clinical assessment of suicidal risk would be significantly complemented by a biologically-based measure that assesses alterations in the neural representations of concepts related to death and life in people who engage in suicidal ideation. This study used machine-learning algorithms (Gaussian Naïve Bayes) to identify such individuals (17 suicidal ideators vs 17 controls) with high (91%) accuracy, based on their altered fMRI neural signatures of death and life-related concepts. The most discriminating concepts were death, cruelty, trouble, carefree, good, and praise. A similar classification accurately (94%) discriminated 9 suicidal ideators who had made a suicide attempt from 8 who had not. Moreover, a major facet of the concept alterations was the evoked emotion, whose neural signature served as an alternative basis for accurate (85%) group classification. The study establishes a biological, neurocognitive basis for altered concept representations in participants with suicidal ideation, which enables highly accurate group membership classification. PMID:29367952

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

PubMed

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-08-16

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

PubMed Central

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-01-01

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

A partial list of southern clusters of galaxies

NASA Technical Reports Server (NTRS)

Quintana, H.; White, R. A.

1990-01-01

An inspection of 34 SRC/ESO J southern sky fields is the basis of the present list of clusters of galaxies and their approximate classifications in terms of cluster concentration, defined independently of richness and shape-symmetry. Where possible, an estimate of the cluster morphological population is provided. The Bautz-Morgan classification was applied using a strict comparison with clusters on the Palomar Sky Survey. Magnitudes were estimated on the basis of galaxies with photoelectric or photographic magnitudes.

c-Src activity is differentially required by cancer cell motility modes.

PubMed

Logue, Jeremy S; Cartagena-Rivera, Alexander X; Chadwick, Richard S

2018-04-01

Cancer cell migration requires that cells respond and adapt to their surroundings. In the absence of extracellular matrix cues, cancer cells will undergo a mesenchymal to ameboid transition, whereas a highly confining space will trigger a switch to "leader bleb-based" migration. To identify oncogenic signaling pathways mediating these transitions, we undertook a targeted screen using clinically useful inhibitors. Elevated Src activity was found to change actin and focal adhesion dynamics, whereas inhibiting Src triggered focal adhesion disassembly and blebbing. On non-adherent substrates and in collagen matrices, amoeboid-like, blebbing cells having high Src activity formed protrusions of the plasma membrane. To evaluate the role of Src in confined cells, we use a novel approach that places cells under a slab of polydimethylsiloxane (PDMS), which is held at a defined height. Using this method, we find that leader bleb-based migration is resistant to Src inhibition. High Src activity was found to markedly change the architecture of cortical actomyosin, reduce cell mechanical properties, and the percentage of cells that undergo leader bleb-based migration. Thus, Src is a signal transducer that can potently influence transitions between migration modes with implications for the rational development of metastasis inhibitors.

Model-based object classification using unification grammars and abstract representations

NASA Astrophysics Data System (ADS)

Liburdy, Kathleen A.; Schalkoff, Robert J.

1993-04-01

The design and implementation of a high level computer vision system which performs object classification is described. General object labelling and functional analysis require models of classes which display a wide range of geometric variations. A large representational gap exists between abstract criteria such as `graspable' and current geometric image descriptions. The vision system developed and described in this work addresses this problem and implements solutions based on a fusion of semantics, unification, and formal language theory. Object models are represented using unification grammars, which provide a framework for the integration of structure and semantics. A methodology for the derivation of symbolic image descriptions capable of interacting with the grammar-based models is described and implemented. A unification-based parser developed for this system achieves object classification by determining if the symbolic image description can be unified with the abstract criteria of an object model. Future research directions are indicated.

miR-137 Targets p160 Steroid Receptor Coactivators SRC1, SRC2, and SRC3 and Inhibits Cell Proliferation

PubMed Central

Eedunuri, Vijay Kumar; Rajapakshe, Kimal; Fiskus, Warren; Geng, Chuandong; Chew, Sue Anne; Foley, Christopher; Shah, Shrijal S.; Shou, John; Mohamed, Junaith S.; O'Malley, Bert W.

2015-01-01

The p160 family of steroid receptor coactivators (SRCs) are pleiotropic transcription factor coactivators and “master regulators” of gene expression that promote cancer cell proliferation, survival, metabolism, migration, invasion, and metastasis. Cancers with high p160 SRC expression exhibit poor clinical outcomes and resistance to therapy, highlighting the SRCs as critical oncogenic drivers and, thus, therapeutic targets. microRNAs are important epigenetic regulators of protein expression. To examine the regulation of p160 SRCs by microRNAs, we used and combined 4 prediction algorithms to identify microRNAs that could target SRC1, SRC2, and SRC3 expression. For validation of these predictions, we assessed p160 SRC protein expression and cell viability after transfection of corresponding microRNA mimetics in breast cancer, uveal melanoma, and prostate cancer (PC) cell lines. Transfection of selected microRNA mimetics into breast cancer, uveal melanoma, and PC cells depleted SRC protein expression levels and exerted potent antiproliferative activity in these cell types. In particular, microRNA-137 (miR-137) depleted expression of SRC1, SRC2, and very potently, SRC3. The latter effect can be attributed to the presence of 3 miR-137 recognition sequences within the SRC3 3′-untranslated region. Using reverse phase protein array analysis, we identified a network of proteins, in addition to SRC3, that were modulated by miR-137 in PC cells. We also found that miR-137 and its host gene are epigenetically silenced in human cancer specimens and cell lines. These results support the development and testing of microRNA-based therapies (in particular based on restoring miR-137 levels) for targeting the oncogenic family of p160 SRCs in cancer. PMID:26066330

3D shape representation with spatial probabilistic distribution of intrinsic shape keypoints

NASA Astrophysics Data System (ADS)

Ghorpade, Vijaya K.; Checchin, Paul; Malaterre, Laurent; Trassoudaine, Laurent

2017-12-01

The accelerated advancement in modeling, digitizing, and visualizing techniques for 3D shapes has led to an increasing amount of 3D models creation and usage, thanks to the 3D sensors which are readily available and easy to utilize. As a result, determining the similarity between 3D shapes has become consequential and is a fundamental task in shape-based recognition, retrieval, clustering, and classification. Several decades of research in Content-Based Information Retrieval (CBIR) has resulted in diverse techniques for 2D and 3D shape or object classification/retrieval and many benchmark data sets. In this article, a novel technique for 3D shape representation and object classification has been proposed based on analyses of spatial, geometric distributions of 3D keypoints. These distributions capture the intrinsic geometric structure of 3D objects. The result of the approach is a probability distribution function (PDF) produced from spatial disposition of 3D keypoints, keypoints which are stable on object surface and invariant to pose changes. Each class/instance of an object can be uniquely represented by a PDF. This shape representation is robust yet with a simple idea, easy to implement but fast enough to compute. Both Euclidean and topological space on object's surface are considered to build the PDFs. Topology-based geodesic distances between keypoints exploit the non-planar surface properties of the object. The performance of the novel shape signature is tested with object classification accuracy. The classification efficacy of the new shape analysis method is evaluated on a new dataset acquired with a Time-of-Flight camera, and also, a comparative evaluation on a standard benchmark dataset with state-of-the-art methods is performed. Experimental results demonstrate superior classification performance of the new approach on RGB-D dataset and depth data.

The development of categorization: effects of classification and inference training on category representation.

PubMed

Deng, Wei Sophia; Sloutsky, Vladimir M

2015-03-01

Does category representation change in the course of development? And if so, how and why? The current study attempted to answer these questions by examining category learning and category representation. In Experiment 1, 4-year-olds, 6-year-olds, and adults were trained with either a classification task or an inference task and their categorization performance and memory for items were tested. Adults and 6-year-olds exhibited an important asymmetry: they relied on a single deterministic feature during classification training, but not during inference training. In contrast, regardless of the training condition, 4-year-olds relied on multiple probabilistic features. In Experiment 2, 4-year-olds were presented with classification training and their attention was explicitly directed to the deterministic feature. Under this condition, their categorization performance was similar to that of older participants in Experiment 1, yet their memory performance pointed to a similarity-based representation, which was similar to that of 4-year-olds in Experiment 1. These results are discussed in relation to theories of categorization and the role of selective attention in the development of category learning. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Modeling of geogenic radon in Switzerland based on ordered logistic regression.

PubMed

Kropat, Georg; Bochud, François; Murith, Christophe; Palacios Gruson, Martha; Baechler, Sébastien

2017-01-01

The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression. Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil. The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability. Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data.

PubMed

Li, Yachun; Charalampaki, Patra; Liu, Yong; Yang, Guang-Zhong; Giannarou, Stamatia

2018-06-13

Probe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changes in the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residual cancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation of endomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology. Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissue characterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted intervention procedures. The aim of this work is to propose a deep learning-based framework for brain tissue characterisation for context aware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE data is presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimate the probability of each tumour class. The video classification accuracy is compared for different network architectures and data representation and video segmentation methods. We demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningioma brain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classification framework over state-of-the-art feature-based methods. The use of video data further improves the classification performance, achieving accuracy equal to 99.49%. This work demonstrates that deep learning can provide an efficient representation of pCLE data and accurately classify Glioblastoma and Meningioma tumours. The performance evaluation analysis shows the potential clinical value of the technique.

Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.

Three 3D graphical representations of DNA primary sequences based on the classifications of DNA bases and their applications.

PubMed

Xie, Guosen; Mo, Zhongxi

2011-01-21

In this article, we introduce three 3D graphical representations of DNA primary sequences, which we call RY-curve, MK-curve and SW-curve, based on three classifications of the DNA bases. The advantages of our representations are that (i) these 3D curves are strictly non-degenerate and there is no loss of information when transferring a DNA sequence to its mathematical representation and (ii) the coordinates of every node on these 3D curves have clear biological implication. Two applications of these 3D curves are presented: (a) a simple formula is derived to calculate the content of the four bases (A, G, C and T) from the coordinates of nodes on the curves; and (b) a 12-component characteristic vector is constructed to compare similarity among DNA sequences from different species based on the geometrical centers of the 3D curves. As examples, we examine similarity among the coding sequences of the first exon of beta-globin gene from eleven species and validate similarity of cDNA sequences of beta-globin gene from eight species. Copyright © 2010 Elsevier Ltd. All rights reserved.

Identifying bilingual semantic neural representations across languages

PubMed Central

Buchweitz, Augusto; Shinkareva, Svetlana V.; Mason, Robert A.; Mitchell, Tom M.; Just, Marcel Adam

2015-01-01

The goal of the study was to identify the neural representation of a noun's meaning in one language based on the neural representation of that same noun in another language. Machine learning methods were used to train classifiers to identify which individual noun bilingual participants were thinking about in one language based solely on their brain activation in the other language. The study shows reliable (p < .05) pattern-based classification accuracies for the classification of brain activity for nouns across languages. It also shows that the stable voxels used to classify the brain activation were located in areas associated with encoding information about semantic dimensions of the words in the study. The identification of the semantic trace of individual nouns from the pattern of cortical activity demonstrates the existence of a multi-voxel pattern of activation across the cortex for a single noun common to both languages in bilinguals. PMID:21978845

76 FR 57763 - Alaska Region's Subsistence Resource Commission (SRC) Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-16

...) program. SUMMARY: The Gates of the Arctic National Park SRC will meet to develop and continue work on NPS... changed based on inclement weather or exceptional circumstances. Gates of the Arctic National Park SRC Meeting Dates and Location: The Gates of the Arctic National Park SRC will meet at Sophie Station Hotel...

Structure-based design of an osteoclast-selective, nonpeptide Src homology 2 inhibitor with in vivo antiresorptive activity

PubMed Central

Shakespeare, William; Yang, Michael; Bohacek, Regine; Cerasoli, Franklin; Stebbins, Karin; Sundaramoorthi, Raji; Azimioara, Mihai; Vu, Chi; Pradeepan, Selvi; Metcalf, Chester; Haraldson, Chad; Merry, Taylor; Dalgarno, David; Narula, Surinder; Hatada, Marcos; Lu, Xiaode; van Schravendijk, Marie Rose; Adams, Susan; Violette, Shelia; Smith, Jeremy; Guan, Wei; Bartlett, Catherine; Herson, Jay; Iuliucci, John; Weigele, Manfred; Sawyer, Tomi

2000-01-01

Targeted disruption of the pp60src (Src) gene has implicated this tyrosine kinase in osteoclast-mediated bone resorption and as a therapeutic target for the treatment of osteoporosis and other bone-related diseases. Herein we describe the discovery of a nonpeptide inhibitor (AP22408) of Src that demonstrates in vivo antiresorptive activity. Based on a cocrystal structure of the noncatalytic Src homology 2 (SH2) domain of Src complexed with citrate [in the phosphotyrosine (pTyr) binding pocket], we designed 3′,4′-diphosphonophenylalanine (Dpp) as a pTyr mimic. In addition to its design to bind Src SH2, the Dpp moiety exhibits bone-targeting properties that confer osteoclast selectivity, hence minimizing possible undesired effects on other cells that have Src-dependent activities. The chemical structure AP22408 also illustrates a bicyclic template to replace the post-pTyr sequence of cognate Src SH2 phosphopeptides such as Ac-pTyr-Glu-Glu-Ile (1). An x-ray structure of AP22408 complexed with Lck (S164C) SH2 confirmed molecular interactions of both the Dpp and bicyclic template of AP22408 as predicted from molecular modeling. Relative to the cognate phosphopeptide, AP22408 exhibits significantly increased Src SH2 binding affinity (IC50 = 0.30 μM for AP22408 and 5.5 μM for 1). Furthermore, AP22408 inhibits rabbit osteoclast-mediated resorption of dentine in a cellular assay, exhibits bone-targeting properties based on a hydroxyapatite adsorption assay, and demonstrates in vivo antiresorptive activity in a parathyroid hormone-induced rat model. PMID:10944210

Medical image classification based on multi-scale non-negative sparse coding.

PubMed

Zhang, Ruijie; Shen, Jian; Wei, Fushan; Li, Xiong; Sangaiah, Arun Kumar

2017-11-01

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Featureless classification of light curves

NASA Astrophysics Data System (ADS)

Kügler, S. D.; Gianniotis, N.; Polsterer, K. L.

2015-08-01

In the era of rapidly increasing amounts of time series data, classification of variable objects has become the main objective of time-domain astronomy. Classification of irregularly sampled time series is particularly difficult because the data cannot be represented naturally as a vector which can be directly fed into a classifier. In the literature, various statistical features serve as vector representations. In this work, we represent time series by a density model. The density model captures all the information available, including measurement errors. Hence, we view this model as a generalization to the static features which directly can be derived, e.g. as moments from the density. Similarity between each pair of time series is quantified by the distance between their respective models. Classification is performed on the obtained distance matrix. In the numerical experiments, we use data from the OGLE (Optical Gravitational Lensing Experiment) and ASAS (All Sky Automated Survey) surveys and demonstrate that the proposed representation performs up to par with the best currently used feature-based approaches. The density representation preserves all static information present in the observational data, in contrast to a less-complete description by features. The density representation is an upper boundary in terms of information made available to the classifier. Consequently, the predictive power of the proposed classification depends on the choice of similarity measure and classifier, only. Due to its principled nature, we advocate that this new approach of representing time series has potential in tasks beyond classification, e.g. unsupervised learning.

Clinical predictors of vestibulo-ocular dysfunction in pediatric sports-related concussion.

PubMed

Ellis, Michael J; Cordingley, Dean M; Vis, Sara; Reimer, Karen M; Leiter, Jeff; Russell, Kelly

2017-01-01

OBJECTIVE There were 2 objectives of this study. The first objective was to identify clinical variables associated with vestibulo-ocular dysfunction (VOD) detected at initial consultation among pediatric patients with acute sports-related concussion (SRC) and postconcussion syndrome (PCS). The second objective was to reexamine the prevalence of VOD in this clinical cohort and evaluate the effect of VOD on length of recovery and the development of PCS. METHODS A retrospective review was conducted for all patients with acute SRC and PCS who were evaluated at a pediatric multidisciplinary concussion program from September 2013 to May 2015. Acute SRS was defined as presenting < 30 days postinjury, and PCS was defined according to the International Classification of Diseases, 10th Revision criteria and included being symptomatic 30 days or longer postinjury. The initial assessment included clinical history and physical examination performed by 1 neurosurgeon. Patients were assessed for VOD, defined as the presence of more than 1 subjective vestibular and oculomotor complaint (dizziness, diplopia, blurred vision, etc.) and more than 1 objective physical examination finding (abnormal near point of convergence, smooth pursuits, saccades, or vestibulo-ocular reflex testing). Poisson regression analysis was used to identify factors that increased the risk of VOD at initial presentation and the development of PCS. RESULTS Three hundred ninety-nine children, including 306 patients with acute SRC and 93 with PCS, were included. Of these patients, 30.1% of those with acute SRC (65.0% male, mean age 13.9 years) and 43.0% of those with PCS (41.9% male, mean age 15.4 years) met the criteria for VOD at initial consultation. Independent predictors of VOD at initial consultation included female sex, preinjury history of depression, posttraumatic amnesia, and presence of dizziness, blurred vision, or difficulty focusing at the time of injury. Independent predictors of PCS among patients with acute SRC included the presence of VOD at initial consultation, preinjury history of depression, and posttraumatic amnesia at the time of injury. CONCLUSIONS This study identified important potential risk factors for the development of VOD following pediatric SRC. These results provide confirmatory evidence that VOD at initial consultation is associated with prolonged recovery and is an independent predictor for the development of PCS. Future studies examining clinical prediction rules in pediatric concussion should include VOD. Additional research is needed to elucidate the natural history of VOD following SRC and establish evidence-based indications for targeted vestibular rehabilitation.

Tensorial dynamic time warping with articulation index representation for efficient audio-template learning.

PubMed

Le, Long N; Jones, Douglas L

2018-03-01

Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples.

Visual Tracking Based on Extreme Learning Machine and Sparse Representation

PubMed Central

Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen

2015-01-01

The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. PMID:26506359

Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

PubMed

Zhou, Pan; Lin, Zhouchen; Zhang, Chao

2016-05-01

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

An efficient classification method based on principal component and sparse representation.

PubMed

Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

2016-01-01

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

Evolution of cellular automata with memory: The Density Classification Task.

PubMed

Stone, Christopher; Bull, Larry

2009-08-01

The Density Classification Task is a well known test problem for two-state discrete dynamical systems. For many years researchers have used a variety of evolutionary computation approaches to evolve solutions to this problem. In this paper, we investigate the evolvability of solutions when the underlying Cellular Automaton is augmented with a type of memory based on the Least Mean Square algorithm. To obtain high performance solutions using a simple non-hybrid genetic algorithm, we design a novel representation based on the ternary representation used for Learning Classifier Systems. The new representation is found able to produce superior performance to the bit string traditionally used for representing Cellular automata. Moreover, memory is shown to improve evolvability of solutions and appropriate memory settings are able to be evolved as a component part of these solutions.

An advanced method for classifying atmospheric circulation types based on prototypes connectivity graph

NASA Astrophysics Data System (ADS)

Zagouras, Athanassios; Argiriou, Athanassios A.; Flocas, Helena A.; Economou, George; Fotopoulos, Spiros

2012-11-01

Classification of weather maps at various isobaric levels as a methodological tool is used in several problems related to meteorology, climatology, atmospheric pollution and to other fields for many years. Initially the classification was performed manually. The criteria used by the person performing the classification are features of isobars or isopleths of geopotential height, depending on the type of maps to be classified. Although manual classifications integrate the perceptual experience and other unquantifiable qualities of the meteorology specialists involved, these are typically subjective and time consuming. Furthermore, during the last years different approaches of automated methods for atmospheric circulation classification have been proposed, which present automated and so-called objective classifications. In this paper a new method of atmospheric circulation classification of isobaric maps is presented. The method is based on graph theory. It starts with an intelligent prototype selection using an over-partitioning mode of fuzzy c-means (FCM) algorithm, proceeds to a graph formulation for the entire dataset and produces the clusters based on the contemporary dominant sets clustering method. Graph theory is a novel mathematical approach, allowing a more efficient representation of spatially correlated data, compared to the classical Euclidian space representation approaches, used in conventional classification methods. The method has been applied to the classification of 850 hPa atmospheric circulation over the Eastern Mediterranean. The evaluation of the automated methods is performed by statistical indexes; results indicate that the classification is adequately comparable with other state-of-the-art automated map classification methods, for a variable number of clusters.

Learning object-to-class kernels for scene classification.

PubMed

Zhang, Lei; Zhen, Xiantong; Shao, Ling

2014-08-01

High-level image representations have drawn increasing attention in visual recognition, e.g., scene classification, since the invention of the object bank. The object bank represents an image as a response map of a large number of pretrained object detectors and has achieved superior performance for visual recognition. In this paper, based on the object bank representation, we propose the object-to-class (O2C) distances to model scene images. In particular, four variants of O2C distances are presented, and with the O2C distances, we can represent the images using the object bank by lower-dimensional but more discriminative spaces, called distance spaces, which are spanned by the O2C distances. Due to the explicit computation of O2C distances based on the object bank, the obtained representations can possess more semantic meanings. To combine the discriminant ability of the O2C distances to all scene classes, we further propose to kernalize the distance representation for the final classification. We have conducted extensive experiments on four benchmark data sets, UIUC-Sports, Scene-15, MIT Indoor, and Caltech-101, which demonstrate that the proposed approaches can significantly improve the original object bank approach and achieve the state-of-the-art performance.

Lack of Csk-mediated negative regulation in a unicellular SRC kinase.

PubMed

Schultheiss, Kira P; Suga, Hiroshi; Ruiz-Trillo, Iñaki; Miller, W Todd

2012-10-16

Phosphotyrosine-based signaling plays a vital role in cellular communication in multicellular organisms. Unexpectedly, unicellular choanoflagellates (the closest phylogenetic group to metazoans) possess numbers of tyrosine kinases that are comparable to those in complex metazoans. Here, we have characterized tyrosine kinases from the filasterean Capsaspora owczarzaki, a unicellular protist representing the sister group to choanoflagellates and metazoans. Two Src-like tyrosine kinases have been identified in C. owczarzaki (CoSrc1 and CoSrc2), both of which have the arrangement of SH3, SH2, and catalytic domains seen in mammalian Src kinases. In Capsaspora cells, CoSrc1 and CoSrc2 localize to punctate structures in filopodia that may represent primordial focal adhesions. We have cloned, expressed, and purified both enzymes. CoSrc1 and CoSrc2 are active tyrosine kinases. Mammalian Src kinases are normally regulated in a reciprocal fashion by autophosphorylation in the activation loop (which increases activity) and by Csk-mediated phosphorylation of the C-terminal tail (which inhibits activity). Similar to mammalian Src kinases, the enzymatic activities of CoSrc1 and CoSrc2 are increased by autophosphorylation in the activation loop. We have identified a Csk-like kinase (CoCsk) in the genome of C. owczarzaki. We cloned, expressed, and purified CoCsk and found that it has no measurable tyrosine kinase activity. Furthermore, CoCsk does not phosphorylate or regulate CoSrc1 or CoSrc2 in cells or in vitro, and CoSrc1 and CoSrc2 are active in Capsaspora cell lysates. Thus, the function of Csk as a negative regulator of Src family kinases appears to have arisen with the emergence of metazoans.

A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification

PubMed Central

Xie, Jin; Zhang, Lei; You, Jane; Zhang, David; Qu, Xiaofeng

2012-01-01

Human hand back skin texture (HBST) is often consistent for a person and distinctive from person to person. In this paper, we study the HBST pattern recognition problem with applications to personal identification and gender classification. A specially designed system is developed to capture HBST images, and an HBST image database was established, which consists of 1,920 images from 80 persons (160 hands). An efficient texton learning based method is then presented to classify the HBST patterns. First, textons are learned in the space of filter bank responses from a set of training images using the l1 -minimization based sparse representation (SR) technique. Then, under the SR framework, we represent the feature vector at each pixel over the learned dictionary to construct a representation coefficient histogram. Finally, the coefficient histogram is used as skin texture feature for classification. Experiments on personal identification and gender classification are performed by using the established HBST database. The results show that HBST can be used to assist human identification and gender classification. PMID:23012512

Fine-grained visual marine vessel classification for coastal surveillance and defense applications

NASA Astrophysics Data System (ADS)

Solmaz, Berkan; Gundogdu, Erhan; Karaman, Kaan; Yücesoy, Veysel; Koç, Aykut

2017-10-01

The need for capabilities of automated visual content analysis has substantially increased due to presence of large number of images captured by surveillance cameras. With a focus on development of practical methods for extracting effective visual data representations, deep neural network based representations have received great attention due to their success in visual categorization of generic images. For fine-grained image categorization, a closely related yet a more challenging research problem compared to generic image categorization due to high visual similarities within subgroups, diverse applications were developed such as classifying images of vehicles, birds, food and plants. Here, we propose the use of deep neural network based representations for categorizing and identifying marine vessels for defense and security applications. First, we gather a large number of marine vessel images via online sources grouping them into four coarse categories; naval, civil, commercial and service vessels. Next, we subgroup naval vessels into fine categories such as corvettes, frigates and submarines. For distinguishing images, we extract state-of-the-art deep visual representations and train support-vector-machines. Furthermore, we fine tune deep representations for marine vessel images. Experiments address two scenarios, classification and verification of naval marine vessels. Classification experiment aims coarse categorization, as well as learning models of fine categories. Verification experiment embroils identification of specific naval vessels by revealing if a pair of images belongs to identical marine vessels by the help of learnt deep representations. Obtaining promising performance, we believe these presented capabilities would be essential components of future coastal and on-board surveillance systems.

ELINT Signal Processing Using Choi-Williams Distribution on Reconfigurable Computers for Detection and Classification of LPI Emitters

DTIC Science & Technology

2008-03-01

WVD Wigner - Ville Distribution xiv THIS PAGE INTENTIONALLY LEFT BLANK xv ACKNOWLEDGMENTS Many thanks to David Caliga of SRC Computer for his...11 2. Wigner - Ville Distribution .................................................................11 3. Choi-Williams... Ville Distribution ...................................12 Table 3. C Code Output for Wigner - Ville Distribution

Removal of BCG artifacts using a non-Kirchhoffian overcomplete representation.

PubMed

Dyrholm, Mads; Goldman, Robin; Sajda, Paul; Brown, Truman R

2009-02-01

We present a nonlinear unmixing approach for extracting the ballistocardiogram (BCG) from EEG recorded in an MR scanner during simultaneous acquisition of functional MRI (fMRI). First, an overcomplete basis is identified in the EEG based on a custom multipath EEG electrode cap. Next, the overcomplete basis is used to infer non-Kirchhoffian latent variables that are not consistent with a conservative electric field. Neural activity is strictly Kirchhoffian while the BCG artifact is not, and the representation can hence be used to remove the artifacts from the data in a way that does not attenuate the neural signals needed for optimal single-trial classification performance. We compare our method to more standard methods for BCG removal, namely independent component analysis and optimal basis sets, by looking at single-trial classification performance for an auditory oddball experiment. We show that our overcomplete representation method for removing BCG artifacts results in better single-trial classification performance compared to the conventional approaches, indicating that the derived neural activity in this representation retains the complex information in the trial-to-trial variability.

Improved protein surface comparison and application to low-resolution protein structure data.

PubMed

Sael, Lee; Kihara, Daisuke

2010-12-14

Recent advancements of experimental techniques for determining protein tertiary structures raise significant challenges for protein bioinformatics. With the number of known structures of unknown function expanding at a rapid pace, an urgent task is to provide reliable clues to their biological function on a large scale. Conventional approaches for structure comparison are not suitable for a real-time database search due to their slow speed. Moreover, a new challenge has arisen from recent techniques such as electron microscopy (EM), which provide low-resolution structure data. Previously, we have introduced a method for protein surface shape representation using the 3D Zernike descriptors (3DZDs). The 3DZD enables fast structure database searches, taking advantage of its rotation invariance and compact representation. The search results of protein surface represented with the 3DZD has showngood agreement with the existing structure classifications, but some discrepancies were also observed. The three new surface representations of backbone atoms, originally devised all-atom-surface representation, and the combination of all-atom surface with the backbone representation are examined. All representations are encoded with the 3DZD. Also, we have investigated the applicability of the 3DZD for searching protein EM density maps of varying resolutions. The surface representations are evaluated on structure retrieval using two existing classifications, SCOP and the CE-based classification. Overall, the 3DZDs representing backbone atoms show better retrieval performance than the original all-atom surface representation. The performance further improved when the two representations are combined. Moreover, we observed that the 3DZD is also powerful in comparing low-resolution structures obtained by electron microscopy.

Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

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

Andonov, Rumen; Djidjev, Hristo Nikolov; Klau, Gunnar W.

In this paper, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifiesmore » up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Finally, our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.« less

Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

DOE PAGES

Andonov, Rumen; Djidjev, Hristo Nikolov; Klau, Gunnar W.; ...

2015-10-09

In this paper, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifiesmore » up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Finally, our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.« less

Two-dimensional shape classification using generalized Fourier representation and neural networks

NASA Astrophysics Data System (ADS)

Chodorowski, Artur; Gustavsson, Tomas; Mattsson, Ulf

2000-04-01

A shape-based classification method is developed based upon the Generalized Fourier Representation (GFR). GFR can be regarded as an extension of traditional polar Fourier descriptors, suitable for description of closed objects, both convex and concave, with or without holes. Explicit relations of GFR coefficients to regular moments, moment invariants and affine moment invariants are given in the paper. The dual linear relation between GFR coefficients and regular moments was used to compare shape features derive from GFR descriptors and Hu's moment invariants. the GFR was then applied to a clinical problem within oral medicine and used to represent the contours of the lesions in the oral cavity. The lesions studied were leukoplakia and different forms of lichenoid reactions. Shape features were extracted from GFR coefficients in order to classify potentially cancerous oral lesions. Alternative classifiers were investigated based on a multilayer perceptron with different architectures and extensions. The overall classification accuracy for recognition of potentially cancerous oral lesions when using neural network classifier was 85%, while the classification between leukoplakia and reticular lichenoid reactions gave 96% (5-fold cross-validated) recognition rate.

Iris Image Classification Based on Hierarchical Visual Codebook.

PubMed

Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

2014-06-01

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

Roles of Raft-Anchored Adaptor Cbp/PAG1 in Spatial Regulation of c-Src Kinase

PubMed Central

Oneyama, Chitose; Suzuki, Takashi; Okada, Masato

2014-01-01

The tyrosine kinase c-Src is upregulated in numerous human cancers, implying a role for c-Src in cancer progression. Previously, we have shown that sequestration of activated c-Src into lipid rafts via a transmembrane adaptor, Cbp/PAG1, efficiently suppresses c-Src-induced cell transformation in Csk-deficient cells, suggesting that the transforming activity of c-Src is spatially regulated via Cbp in lipid rafts. To dissect the molecular mechanisms of the Cbp-mediated regulation of c-Src, a combined analysis was performed that included mathematical modeling and in vitro experiments in a c-Src- or Cbp-inducible system. c-Src activity was first determined as a function of c-Src or Cbp levels, using focal adhesion kinase (FAK) as a crucial c-Src substrate. Based on these experimental data, two mathematical models were constructed, the sequestration model and the ternary model. The computational analysis showed that both models supported our proposal that raft localization of Cbp is crucial for the suppression of c-Src function, but the ternary model, which includes a ternary complex consisting of Cbp, c-Src, and FAK, also predicted that c-Src function is dependent on the lipid-raft volume. Experimental analysis revealed that c-Src activity is elevated when lipid rafts are disrupted and the ternary complex forms in non-raft membranes, indicating that the ternary model accurately represents the system. Moreover, the ternary model predicted that, if Cbp enhances the interaction between c-Src and FAK, Cbp could promote c-Src function when lipid rafts are disrupted. These findings underscore the crucial role of lipid rafts in the Cbp-mediated negative regulation of c-Src-transforming activity, and explain the positive role of Cbp in c-Src regulation under particular conditions where lipid rafts are perturbed. PMID:24675741

Multi-voxel patterns of visual category representation during episodic encoding are predictive of subsequent memory

PubMed Central

Kuhl, Brice A.; Rissman, Jesse; Wagner, Anthony D.

2012-01-01

Successful encoding of episodic memories is thought to depend on contributions from prefrontal and temporal lobe structures. Neural processes that contribute to successful encoding have been extensively explored through univariate analyses of neuroimaging data that compare mean activity levels elicited during the encoding of events that are subsequently remembered vs. those subsequently forgotten. Here, we applied pattern classification to fMRI data to assess the degree to which distributed patterns of activity within prefrontal and temporal lobe structures elicited during the encoding of word-image pairs were diagnostic of the visual category (Face or Scene) of the encoded image. We then assessed whether representation of category information was predictive of subsequent memory. Classification analyses indicated that temporal lobe structures contained information robustly diagnostic of visual category. Information in prefrontal cortex was less diagnostic of visual category, but was nonetheless associated with highly reliable classifier-based evidence for category representation. Critically, trials associated with greater classifier-based estimates of category representation in temporal and prefrontal regions were associated with a higher probability of subsequent remembering. Finally, consideration of trial-by-trial variance in classifier-based measures of category representation revealed positive correlations between prefrontal and temporal lobe representations, with the strength of these correlations varying as a function of the category of image being encoded. Together, these results indicate that multi-voxel representations of encoded information can provide unique insights into how visual experiences are transformed into episodic memories. PMID:21925190

The Radon cumulative distribution transform and its application to image classification

PubMed Central

Kolouri, Soheil; Park, Se Rim; Rohde, Gustavo K.

2016-01-01

Invertible image representation methods (transforms) are routinely employed as low-level image processing operations based on which feature extraction and recognition algorithms are developed. Most transforms in current use (e.g. Fourier, Wavelet, etc.) are linear transforms, and, by themselves, are unable to substantially simplify the representation of image classes for classification. Here we describe a nonlinear, invertible, low-level image processing transform based on combining the well known Radon transform for image data, and the 1D Cumulative Distribution Transform proposed earlier. We describe a few of the properties of this new transform, and with both theoretical and experimental results show that it can often render certain problems linearly separable in transform space. PMID:26685245

Plant species classification using flower images—A comparative study of local feature representations

PubMed Central

Seeland, Marco; Rzanny, Michael; Alaqraa, Nedal; Wäldchen, Jana; Mäder, Patrick

2017-01-01

Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification. PMID:28234999

Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407

Novel methodologies for spectral classification of exon and intron sequences

NASA Astrophysics Data System (ADS)

Kwan, Hon Keung; Kwan, Benjamin Y. M.; Kwan, Jennifer Y. Y.

2012-12-01

Digital processing of a nucleotide sequence requires it to be mapped to a numerical sequence in which the choice of nucleotide to numeric mapping affects how well its biological properties can be preserved and reflected from nucleotide domain to numerical domain. Digital spectral analysis of nucleotide sequences unfolds a period-3 power spectral value which is more prominent in an exon sequence as compared to that of an intron sequence. The success of a period-3 based exon and intron classification depends on the choice of a threshold value. The main purposes of this article are to introduce novel codes for 1-sequence numerical representations for spectral analysis and compare them to existing codes to determine appropriate representation, and to introduce novel thresholding methods for more accurate period-3 based exon and intron classification of an unknown sequence. The main findings of this study are summarized as follows: Among sixteen 1-sequence numerical representations, the K-Quaternary Code I offers an attractive performance. A windowed 1-sequence numerical representation (with window length of 9, 15, and 24 bases) offers a possible speed gain over non-windowed 4-sequence Voss representation which increases as sequence length increases. A winner threshold value (chosen from the best among two defined threshold values and one other threshold value) offers a top precision for classifying an unknown sequence of specified fixed lengths. An interpolated winner threshold value applicable to an unknown and arbitrary length sequence can be estimated from the winner threshold values of fixed length sequences with a comparable performance. In general, precision increases as sequence length increases. The study contributes an effective spectral analysis of nucleotide sequences to better reveal embedded properties, and has potential applications in improved genome annotation.

Wavelet images and Chou's pseudo amino acid composition for protein classification.

PubMed

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

2012-08-01

The last decade has seen an explosion in the collection of protein data. To actualize the potential offered by this wealth of data, it is important to develop machine systems capable of classifying and extracting features from proteins. Reliable machine systems for protein classification offer many benefits, including the promise of finding novel drugs and vaccines. In developing our system, we analyze and compare several feature extraction methods used in protein classification that are based on the calculation of texture descriptors starting from a wavelet representation of the protein. We then feed these texture-based representations of the protein into an Adaboost ensemble of neural network or a support vector machine classifier. In addition, we perform experiments that combine our feature extraction methods with a standard method that is based on the Chou's pseudo amino acid composition. Using several datasets, we show that our best approach outperforms standard methods. The Matlab code of the proposed protein descriptors is available at http://bias.csr.unibo.it/nanni/wave.rar .

Improved protein surface comparison and application to low-resolution protein structure data

PubMed Central

2010-01-01

Background Recent advancements of experimental techniques for determining protein tertiary structures raise significant challenges for protein bioinformatics. With the number of known structures of unknown function expanding at a rapid pace, an urgent task is to provide reliable clues to their biological function on a large scale. Conventional approaches for structure comparison are not suitable for a real-time database search due to their slow speed. Moreover, a new challenge has arisen from recent techniques such as electron microscopy (EM), which provide low-resolution structure data. Previously, we have introduced a method for protein surface shape representation using the 3D Zernike descriptors (3DZDs). The 3DZD enables fast structure database searches, taking advantage of its rotation invariance and compact representation. The search results of protein surface represented with the 3DZD has showngood agreement with the existing structure classifications, but some discrepancies were also observed. Results The three new surface representations of backbone atoms, originally devised all-atom-surface representation, and the combination of all-atom surface with the backbone representation are examined. All representations are encoded with the 3DZD. Also, we have investigated the applicability of the 3DZD for searching protein EM density maps of varying resolutions. The surface representations are evaluated on structure retrieval using two existing classifications, SCOP and the CE-based classification. Conclusions Overall, the 3DZDs representing backbone atoms show better retrieval performance than the original all-atom surface representation. The performance further improved when the two representations are combined. Moreover, we observed that the 3DZD is also powerful in comparing low-resolution structures obtained by electron microscopy. PMID:21172052

Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

NASA Astrophysics Data System (ADS)

Ahmed, H. O. A.; Wong, M. L. D.; Nandi, A. K.

2018-01-01

Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques.

Annular convective-radiative fins with a step change in thickness, and temperature-dependent thermal conductivity and heat transfer coefficient

NASA Astrophysics Data System (ADS)

Barforoush, M. S. M.; Saedodin, S.

2018-01-01

This article investigates the thermal performance of convective-radiative annular fins with a step reduction in local cross section (SRC). The thermal conductivity of the fin's material is assumed to be a linear function of temperature, and heat transfer coefficient is assumed to be a power-law function of surface temperature. Moreover, nonzero convection and radiation sink temperatures are included in the mathematical model of the energy equation. The well-known differential transformation method (DTM) is used to derive the analytical solution. An exact analytical solution for a special case is derived to prove the validity of the obtained results from the DTM. The model provided here is a more realistic representation of SRC annular fins in actual engineering practices. Effects of many parameters such as conduction-convection parameters, conduction-radiation parameter and sink temperature, and also some parameters which deal with step fins such as thickness parameter and dimensionless parameter describing the position of junction in the fin on the temperature distribution of both thin and thick sections of the fin are investigated. It is believed that the obtained results will facilitate the design and performance evaluation of SRC annular fins.

An integration of minimum local feature representation methods to recognize large variation of foods

NASA Astrophysics Data System (ADS)

Razali, Mohd Norhisham bin; Manshor, Noridayu; Halin, Alfian Abdul; Mustapha, Norwati; Yaakob, Razali

2017-10-01

Local invariant features have shown to be successful in describing object appearances for image classification tasks. Such features are robust towards occlusion and clutter and are also invariant against scale and orientation changes. This makes them suitable for classification tasks with little inter-class similarity and large intra-class difference. In this paper, we propose an integrated representation of the Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors, using late fusion strategy. The proposed representation is used for food recognition from a dataset of food images with complex appearance variations. The Bag of Features (BOF) approach is employed to enhance the discriminative ability of the local features. Firstly, the individual local features are extracted to construct two kinds of visual vocabularies, representing SURF and SIFT. The visual vocabularies are then concatenated and fed into a Linear Support Vector Machine (SVM) to classify the respective food categories. Experimental results demonstrate impressive overall recognition at 82.38% classification accuracy based on the challenging UEC-Food100 dataset.

Parental Representations and Attachment Security in Young Israeli Mothers' Bird's Nest Drawings

ERIC Educational Resources Information Center

Goldner, Limor; Golan, Yifat

2016-01-01

The Bird's Nest Drawing (BND; Kaiser, 1996) is an art-based technique developed to assess attachment security. In an attempt to expand the BND's validity, the authors explored the possible associations between parental representations and the BND's dimensions and attachment classifications in a sample of 80 young Israeli mothers. Positive…

TNM-O: ontology support for staging of malignant tumours.

PubMed

Boeker, Martin; França, Fábio; Bronsert, Peter; Schulz, Stefan

2016-11-14

Objectives of this work are to (1) present an ontological framework for the TNM classification system, (2) exemplify this framework by an ontology for colon and rectum tumours, and (3) evaluate this ontology by assigning TNM classes to real world pathology data. The TNM ontology uses the Foundational Model of Anatomy for anatomical entities and BioTopLite 2 as a domain top-level ontology. General rules for the TNM classification system and the specific TNM classification for colorectal tumours were axiomatised in description logic. Case-based information was collected from tumour documentation practice in the Comprehensive Cancer Centre of a large university hospital. Based on the ontology, a module was developed that classifies pathology data. TNM was represented as an information artefact, which consists of single representational units. Corresponding to every representational unit, tumours and tumour aggregates were defined. Tumour aggregates consist of the primary tumour and, if existing, of infiltrated regional lymph nodes and distant metastases. TNM codes depend on the location and certain qualities of the primary tumour (T), the infiltrated regional lymph nodes (N) and the existence of distant metastases (M). Tumour data from clinical and pathological documentation were successfully classified with the ontology. A first version of the TNM Ontology represents the TNM system for the description of the anatomical extent of malignant tumours. The present work demonstrates its representational power and completeness as well as its applicability for classification of instance data.

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

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

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

Polarimetric SAR image classification based on discriminative dictionary learning model

NASA Astrophysics Data System (ADS)

Sang, Cheng Wei; Sun, Hong

2018-03-01

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

Extracting Information from Electronic Medical Records to Identify the Obesity Status of a Patient Based on Comorbidities and Bodyweight Measures.

PubMed

Figueroa, Rosa L; Flores, Christopher A

2016-08-01

Obesity is a chronic disease with an increasing impact on the world's population. In this work, we present a method of identifying obesity automatically using text mining techniques and information related to body weight measures and obesity comorbidities. We used a dataset of 3015 de-identified medical records that contain labels for two classification problems. The first classification problem distinguishes between obesity, overweight, normal weight, and underweight. The second classification problem differentiates between obesity types: super obesity, morbid obesity, severe obesity and moderate obesity. We used a Bag of Words approach to represent the records together with unigram and bigram representations of the features. We implemented two approaches: a hierarchical method and a nonhierarchical one. We used Support Vector Machine and Naïve Bayes together with ten-fold cross validation to evaluate and compare performances. Our results indicate that the hierarchical approach does not work as well as the nonhierarchical one. In general, our results show that Support Vector Machine obtains better performances than Naïve Bayes for both classification problems. We also observed that bigram representation improves performance compared with unigram representation.

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

Src inhibitor reduces permeability without disturbing vascularization and prevents bone destruction in steroid-associated osteonecrotic lesions in rabbits.

PubMed

He, Yi-Xin; Liu, Jin; Guo, Baosheng; Wang, Yi-Xiang; Pan, Xiaohua; Li, Defang; Tang, Tao; Chen, Yang; Peng, Songlin; Bian, Zhaoxiang; Liang, Zicai; Zhang, Bao-Ting; Lu, Aiping; Zhang, Ge

2015-03-09

To examine the therapeutic effect of Src inhibitor on the VEGF mediating vascular hyperpermeability and bone destruction within steroid-associated osteonecrotic lesions in rabbits. Rabbits with high risk for progress to destructive repair in steroid-associated osteonecrosis were selected according to our published protocol. The selected rabbits were systemically administrated with either Anti-VEGF antibody (Anti-VEGF Group) or Src inhibitor (Src-Inhibition Group) or VEGF (VEGF-Supplement Group) or a combination of VEGF and Src inhibitor (Supplement &Inhibition Group) or control vehicle (Control Group) for 4 weeks. At 0, 2 and 4 weeks after administration, in vivo dynamic MRI, micro-CT based-angiography, histomorphometry and immunoblotting were employed to evaluate the vascular and skeletal events in different groups. The incidence of the destructive repair in the Anti-VEGF Group, Src-Inhibition Group and Supplement &Inhibition Group was all significantly lower than that in the Control Group. The angiogenesis was promoted in VEGF-Supplement Group, Src-Inhibition Group and Supplement &Inhibition Group, while the hyperpermeability was inhibited in Anti-VEGF Group, Src-Inhibition Group and Supplement &Inhibition Group. The trabecular structure was improved in Src-Inhibition Group and Supplement &Inhibition Group. Src inhibitor could reduce permeability without disturbing vascularization and prevent destructive repair in steroid-associated osteonecrosis.

Effect of wheat flour characteristics on sponge cake quality.

PubMed

Moiraghi, Malena; de la Hera, Esther; Pérez, Gabriela T; Gómez, Manuel

2013-02-01

To select the flour parameters that relate strongly to cake-making performance, in this study the relationship between sponge cake quality, solvent retention capacity (SRC) profile and flour physicochemical characteristics was investigated using 38 soft wheat samples of different origins. Particle size average, protein, damaged starch, water-soluble pentosans, total pentosans, SRC and pasting properties were analysed. Sponge cake volume and crumb texture were measured to evaluate cake quality. Cluster analysis was applied to assess differences in flour quality parameters among wheat lines based on the SRC profile. Cluster 1 showed significantly higher sponge cake volume and crumb softness, finer particle size and lower SRC sucrose, SRC carbonate, SRC water, damaged starch and protein content. Particle size, damaged starch, protein, thickening capacity and SRC parameters correlated negatively with sponge cake volume, while total pentosans and pasting temperature showed the opposite effect. The negative correlations between cake volume and SRC parameters along with the cluster analysis results indicated that flours with smaller particle size, lower absorption capacity and higher pasting temperature had better cake-making performance. Some simple analyses, such as SRC, particle size distribution and pasting properties, may help to choose flours suitable for cake making. Copyright © 2012 Society of Chemical Industry.

Deep feature representation with stacked sparse auto-encoder and convolutional neural network for hyperspectral imaging-based detection of cucumber defects

USDA-ARS?s Scientific Manuscript database

It is challenging to achieve rapid and accurate processing of large amounts of hyperspectral image data. This research was aimed to develop a novel classification method by employing deep feature representation with the stacked sparse auto-encoder (SSAE) and the SSAE combined with convolutional neur...

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.

Dynamic activation of Src induced by low-power laser irradiation in living cells mediated by reactive oxygen species

NASA Astrophysics Data System (ADS)

Zhang, Juntao; Gao, Xuejuan; Xing, Da; Liu, Lei

2007-11-01

Low-power laser irradiation (LPLI) leads to photochemical reaction and then activates intracellular several signaling pathway. Reactive oxygen species (ROS) are considered to be the primary messengers produced by LPLI. Here, we studied the signaling pathway mediated by ROS upon the stimulation of LPLI. Src tyrosine kinases are well-known targets of ROS and can be activated by oxidative events. Using a Src reporter based on fluorescence resonance energy transfer (FRET) technique, we visualized the dynamic Src activation in Hela cells immediately after LPLI. Moreover, Src activity was enhanced by increasing the duration of LPLI. In addition, our results suggested that ROS were key mediators of Src activation, as ROS scavenger, vitamin C decreased and exogenous H IIO II increased the activity of Src. Meanwhile, Gö6983 loading did not block the effect of LPLI. CCK-8 experiments proved that cell vitality was prominently improved by LPLI with all the doses we applied in our experiments ranging from 3 to 25J/cm2. The results indicated that LPLI/ROS/Src pathway may be involved in the LPLI biostimulation effects.

A Discovery Strategy for Selective Inhibitors of c-Src in Complex with the Focal Adhesion Kinase SH3/SH2-binding Region.

PubMed

Moroco, Jamie A; Baumgartner, Matthew P; Rust, Heather L; Choi, Hwan Geun; Hur, Wooyoung; Gray, Nathanael S; Camacho, Carlos J; Smithgall, Thomas E

2015-08-01

The c-Src tyrosine kinase co-operates with the focal adhesion kinase to regulate cell adhesion and motility. Focal adhesion kinase engages the regulatory SH3 and SH2 domains of c-Src, resulting in localized kinase activation that contributes to tumor cell metastasis. Using assay conditions where c-Src kinase activity required binding to a tyrosine phosphopeptide based on the focal adhesion kinase SH3-SH2 docking sequence, we screened a kinase-biased library for selective inhibitors of the Src/focal adhesion kinase peptide complex versus c-Src alone. This approach identified an aminopyrimidinyl carbamate compound, WH-4-124-2, with nanomolar inhibitory potency and fivefold selectivity for c-Src when bound to the phospho-focal adhesion kinase peptide. Molecular docking studies indicate that WH-4-124-2 may preferentially inhibit the 'DFG-out' conformation of the kinase active site. These findings suggest that interaction of c-Src with focal adhesion kinase induces a unique kinase domain conformation amenable to selective inhibition. © 2014 John Wiley & Sons A/S.

Coupling ontology driven semantic representation with multilingual natural language generation for tuning international terminologies.

PubMed

Rassinoux, Anne-Marie; Baud, Robert H; Rodrigues, Jean-Marie; Lovis, Christian; Geissbühler, Antoine

2007-01-01

The importance of clinical communication between providers, consumers and others, as well as the requisite for computer interoperability, strengthens the need for sharing common accepted terminologies. Under the directives of the World Health Organization (WHO), an approach is currently being conducted in Australia to adopt a standardized terminology for medical procedures that is intended to become an international reference. In order to achieve such a standard, a collaborative approach is adopted, in line with the successful experiment conducted for the development of the new French coding system CCAM. Different coding centres are involved in setting up a semantic representation of each term using a formal ontological structure expressed through a logic-based representation language. From this language-independent representation, multilingual natural language generation (NLG) is performed to produce noun phrases in various languages that are further compared for consistency with the original terms. Outcomes are presented for the assessment of the International Classification of Health Interventions (ICHI) and its translation into Portuguese. The initial results clearly emphasize the feasibility and cost-effectiveness of the proposed method for handling both a different classification and an additional language. NLG tools, based on ontology driven semantic representation, facilitate the discovery of ambiguous and inconsistent terms, and, as such, should be promoted for establishing coherent international terminologies.

Discrimination of Dynamic Tactile Contact by Temporally Precise Event Sensing in Spiking Neuromorphic Networks

PubMed Central

Lee, Wang Wei; Kukreja, Sunil L.; Thakor, Nitish V.

2017-01-01

This paper presents a neuromorphic tactile encoding methodology that utilizes a temporally precise event-based representation of sensory signals. We introduce a novel concept where touch signals are characterized as patterns of millisecond precise binary events to denote pressure changes. This approach is amenable to a sparse signal representation and enables the extraction of relevant features from thousands of sensing elements with sub-millisecond temporal precision. We also proposed measures adopted from computational neuroscience to study the information content within the spiking representations of artificial tactile signals. Implemented on a state-of-the-art 4096 element tactile sensor array with 5.2 kHz sampling frequency, we demonstrate the classification of transient impact events while utilizing 20 times less communication bandwidth compared to frame based representations. Spiking sensor responses to a large library of contact conditions were also synthesized using finite element simulations, illustrating an 8-fold improvement in information content and a 4-fold reduction in classification latency when millisecond-precise temporal structures are available. Our research represents a significant advance, demonstrating that a neuromorphic spatiotemporal representation of touch is well suited to rapid identification of critical contact events, making it suitable for dynamic tactile sensing in robotic and prosthetic applications. PMID:28197065

Differential Requirements for Src-Family Kinases in SYK or ZAP70-Mediated SLP-76 Phosphorylation in Lymphocytes

PubMed Central

Fasbender, Frank; Claus, Maren; Wingert, Sabine; Sandusky, Mina; Watzl, Carsten

2017-01-01

In a synthetic biology approach using Schneider (S2) cells, we show that SLP-76 is directly phosphorylated at tyrosines Y113 and Y128 by SYK in the presence of ITAM-containing adapters such as CD3ζ, DAP12, or FcεRγ. This phosphorylation was dependent on at least one functional ITAM and a functional SH2 domain within SYK. Inhibition of Src-kinases by inhibitors PP1 and PP2 did not reduce SLP-76 phosphorylation in S2 cells, suggesting an ITAM and SYK dependent, but Src-kinase independent signaling pathway. This direct ITAM/SYK/SLP-76 signaling pathway therefore differs from previously described ITAM signaling. However, the SYK-family kinase ZAP70 required the additional co-expression of the Src-family kinases Fyn or Lck to efficiently phosphorylate SLP-76 in S2 cells. This difference in Src-family kinase dependency of SYK versus ZAP70-mediated ITAM-based signaling was further demonstrated in human lymphocytes. ITAM signaling in ZAP70-expressing T cells was dependent on the activity of Src-family kinases. In contrast, Src-family kinases were partially dispensable for ITAM signaling in SYK-expressing B cells or in natural killer cells, which express SYK and ZAP70. This demonstrates that SYK can signal using a Src-kinase independent ITAM-based signaling pathway, which may be involved in calibrating the threshold for lymphocyte activation. PMID:28736554

Differential Requirements for Src-Family Kinases in SYK or ZAP70-Mediated SLP-76 Phosphorylation in Lymphocytes.

PubMed

Fasbender, Frank; Claus, Maren; Wingert, Sabine; Sandusky, Mina; Watzl, Carsten

2017-01-01

In a synthetic biology approach using Schneider (S2) cells, we show that SLP-76 is directly phosphorylated at tyrosines Y113 and Y128 by SYK in the presence of ITAM-containing adapters such as CD3ζ, DAP12, or FcεRγ. This phosphorylation was dependent on at least one functional ITAM and a functional SH2 domain within SYK. Inhibition of Src-kinases by inhibitors PP1 and PP2 did not reduce SLP-76 phosphorylation in S2 cells, suggesting an ITAM and SYK dependent, but Src-kinase independent signaling pathway. This direct ITAM/SYK/SLP-76 signaling pathway therefore differs from previously described ITAM signaling. However, the SYK-family kinase ZAP70 required the additional co-expression of the Src-family kinases Fyn or Lck to efficiently phosphorylate SLP-76 in S2 cells. This difference in Src-family kinase dependency of SYK versus ZAP70-mediated ITAM-based signaling was further demonstrated in human lymphocytes. ITAM signaling in ZAP70-expressing T cells was dependent on the activity of Src-family kinases. In contrast, Src-family kinases were partially dispensable for ITAM signaling in SYK-expressing B cells or in natural killer cells, which express SYK and ZAP70. This demonstrates that SYK can signal using a Src-kinase independent ITAM-based signaling pathway, which may be involved in calibrating the threshold for lymphocyte activation.

Multiclass fMRI data decoding and visualization using supervised self-organizing maps.

PubMed

Hausfeld, Lars; Valente, Giancarlo; Formisano, Elia

2014-08-01

When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches). Copyright © 2014. Published by Elsevier Inc.

Classification-based reasoning

NASA Technical Reports Server (NTRS)

Gomez, Fernando; Segami, Carlos

1991-01-01

A representation formalism for N-ary relations, quantification, and definition of concepts is described. Three types of conditions are associated with the concepts: (1) necessary and sufficient properties, (2) contingent properties, and (3) necessary properties. Also explained is how complex chains of inferences can be accomplished by representing existentially quantified sentences, and concepts denoted by restrictive relative clauses as classification hierarchies. The representation structures that make possible the inferences are explained first, followed by the reasoning algorithms that draw the inferences from the knowledge structures. All the ideas explained have been implemented and are part of the information retrieval component of a program called Snowy. An appendix contains a brief session with the program.

Three-dimensional quantitative structure-activity relationship studies on c-Src inhibitors based on different docking methods.

PubMed

Bairy, Santhosh Kumar; Suneel Kumar, B V S; Bhalla, Joseph Uday Tej; Pramod, A B; Ravikumar, Muttineni

2009-04-01

c-Src kinase play an important role in cell growth and differentiation and its inhibitors can be useful for the treatment of various diseases, including cancer, osteoporosis, and metastatic bone disease. Three dimensional quantitative structure-activity relationship (3D-QSAR) studies were carried out on quinazolin derivatives inhibiting c-Src kinase. Molecular field analysis (MFA) models with four different alignment techniques, namely, GLIDE, GOLD, LIGANDFIT and Least squares based methods were developed. glide based MFA model showed better results (Leave one out cross validation correlation coefficient r(2)(cv) = 0.923 and non-cross validation correlation coefficient r(2)= 0.958) when compared with other models. These results help us to understand the nature of descriptors required for activity of these compounds and thereby provide guidelines to design novel and potent c-Src kinase inhibitors.

Segmentation of Hyperacute Cerebral Infarcts Based on Sparse Representation of Diffusion Weighted Imaging.

PubMed

Zhang, Xiaodong; Jing, Shasha; Gao, Peiyi; Xue, Jing; Su, Lu; Li, Weiping; Ren, Lijie; Hu, Qingmao

2016-01-01

Segmentation of infarcts at hyperacute stage is challenging as they exhibit substantial variability which may even be hard for experts to delineate manually. In this paper, a sparse representation based classification method is explored. For each patient, four volumetric data items including three volumes of diffusion weighted imaging and a computed asymmetry map are employed to extract patch features which are then fed to dictionary learning and classification based on sparse representation. Elastic net is adopted to replace the traditional L 0 -norm/ L 1 -norm constraints on sparse representation to stabilize sparse code. To decrease computation cost and to reduce false positives, regions-of-interest are determined to confine candidate infarct voxels. The proposed method has been validated on 98 consecutive patients recruited within 6 hours from onset. It is shown that the proposed method could handle well infarcts with intensity variability and ill-defined edges to yield significantly higher Dice coefficient (0.755 ± 0.118) than the other two methods and their enhanced versions by confining their segmentations within the regions-of-interest (average Dice coefficient less than 0.610). The proposed method could provide a potential tool to quantify infarcts from diffusion weighted imaging at hyperacute stage with accuracy and speed to assist the decision making especially for thrombolytic therapy.

Data-driven heterogeneity in mathematical learning disabilities based on the triple code model.

PubMed

Peake, Christian; Jiménez, Juan E; Rodríguez, Cristina

2017-12-01

Many classifications of heterogeneity in mathematical learning disabilities (MLD) have been proposed over the past four decades, however no empirical research has been conducted until recently, and none of the classifications are derived from Triple Code Model (TCM) postulates. The TCM proposes MLD as a heterogeneous disorder, with two distinguishable profiles: a representational subtype and a verbal subtype. A sample of elementary school 3rd to 6th graders was divided into two age cohorts (3rd - 4th grades, and 5th - 6th grades). Using data-driven strategies, based on the cognitive classification variables predicted by the TCM, our sample of children with MLD clustered as expected: a group with representational deficits and a group with number-fact retrieval deficits. In the younger group, a spatial subtype also emerged, while in both cohorts a non-specific cluster was produced whose profile could not be explained by this theoretical approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Politics of Student Housing: Student Activism and Representation in the Determination of the User-Price of a Public-Private Partnership Residence on a Public University Campus in South Africa

ERIC Educational Resources Information Center

Mugume, Taabo; Luescher, Thierry M.

2015-01-01

South African universities have been facing a critical shortage in the provision of student housing for several years now, and the establishment of public--private partnerships (PPPs) is seen as part of the solution to address the shortage (Rensburg, 2011). This article investigates the effectiveness of the Students' Representative Council (SRC)…

Delineation and geometric modeling of road networks

NASA Astrophysics Data System (ADS)

Poullis, Charalambos; You, Suya

In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.

Retrieval and classification of food images.

PubMed

Farinella, Giovanni Maria; Allegra, Dario; Moltisanti, Marco; Stanco, Filippo; Battiato, Sebastiano

2016-10-01

Automatic food understanding from images is an interesting challenge with applications in different domains. In particular, food intake monitoring is becoming more and more important because of the key role that it plays in health and market economies. In this paper, we address the study of food image processing from the perspective of Computer Vision. As first contribution we present a survey of the studies in the context of food image processing from the early attempts to the current state-of-the-art methods. Since retrieval and classification engines able to work on food images are required to build automatic systems for diet monitoring (e.g., to be embedded in wearable cameras), we focus our attention on the aspect of the representation of the food images because it plays a fundamental role in the understanding engines. The food retrieval and classification is a challenging task since the food presents high variableness and an intrinsic deformability. To properly study the peculiarities of different image representations we propose the UNICT-FD1200 dataset. It was composed of 4754 food images of 1200 distinct dishes acquired during real meals. Each food plate is acquired multiple times and the overall dataset presents both geometric and photometric variabilities. The images of the dataset have been manually labeled considering 8 categories: Appetizer, Main Course, Second Course, Single Course, Side Dish, Dessert, Breakfast, Fruit. We have performed tests employing different representations of the state-of-the-art to assess the related performances on the UNICT-FD1200 dataset. Finally, we propose a new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification. Copyright © 2016 Elsevier Ltd. All rights reserved.

Steroid Receptor Coactivator 3 Contributes to Host Defense against Enteric Bacteria by Recruiting Neutrophils via Upregulation of CXCL2 Expression.

PubMed

Chen, Wenbo; Lu, Xuqiang; Chen, Yuan; Li, Ming; Mo, Pingli; Tong, Zhangwei; Wang, Wei; Wan, Wei; Su, Guoqiang; Xu, Jianming; Yu, Chundong

2017-02-15

Steroid receptor coactivator 3 (SRC-3) is a transcriptional coactivator that interacts with nuclear receptors and some other transcription factors to enhance their effects on target gene transcription. We reported previously that SRC-3-deficient (SRC-3 -/- ) mice are extremely susceptible to Escherichia coli-induced septic peritonitis as a result of uncontrolled inflammation and a defect in bacterial clearance. In this study, we observed significant upregulation of SRC-3 in colonic epithelial cells in response to Citrobacter rodentium infection. Based on these findings, we hypothesized that SRC-3 is involved in host defense against attaching and effacing bacterial infection. We compared the responses of SRC-3 -/- and wild-type mice to intestinal C. rodentium infection. We found that SRC-3 -/- mice exhibited delayed clearance of C. rodentium and more severe tissue pathology after oral infection with C. rodentium compared with wild-type mice. SRC-3 -/- mice expressed normal antimicrobial peptides in the colons but exhibited delayed recruitment of neutrophils into the colonic mucosa. Accordingly, SRC-3 -/- mice showed a delayed induction of CXCL2 and CXCL5 in colonic epithelial cells, which are responsible for neutrophil recruitment. At the molecular level, we found that SRC-3 can activate the NF-κB signaling pathway to promote CXCL2 expression at the transcriptional level. Collectively, we show that SRC-3 contributes to host defense against enteric bacteria, at least in part via upregulating CXCL2 expression to recruit neutrophils. Copyright © 2017 by The American Association of Immunologists, Inc.

The Development of Categorization: Effects of Classification and Inference Training on Category Representation

ERIC Educational Resources Information Center

Deng, Wei; Sloutsky, Vladimir M.

2015-01-01

Does category representation change in the course of development? And if so, how and why? The current study attempted to answer these questions by examining category learning and category representation. In Experiment 1, 4-year-olds, 6-year-olds, and adults were trained with either a classification task or an inference task and their…

Formal Representations of Eligibility Criteria: A Literature Review

PubMed Central

Weng, Chunhua; Tu, Samson W.; Sim, Ida; Richesson, Rachel

2010-01-01

Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representations that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of them (expression language, codification of eligibility concepts, and patient data modeling), to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward sharable knowledge representation of eligibility criteria. PMID:20034594

Cooperative Atmosphere-Surface Exchange Study-1999.

NASA Astrophysics Data System (ADS)

Moeng, Chin-Hoh; Poulos, Gregory S.; Lemone, Margaret A.

2003-10-01

Surface-station, radiosonde, and Doppler minisodar data from the Cooperative Atmosphere-Surface Exchange Study-1997 (CASES-97) field project, collected in a 60-km-wide array in the lower Walnut River watershed (terrain variation 150 m) southeast of Wichita, Kansas, are used to study the relationship of the change of the 2-m potential temperature 2m with station elevation ze, 2m/ze ,ze to the ambient wind and thermal stratification /z ,z during fair-weather nights. As in many previous studies, predawn 2m varies linearly with ze, and ,ze ,z over a depth h that represents the maximum elevation range of the stations. Departures from the linear 2m-elevation relationship (,ze line) are related to vegetation (cool for vegetation, warm for bare ground), local terrain (drainage flows from nearby hills, although a causal relationship is not established), and the formation of a cold pool at lower elevations on some days.The near-surface flow and its evolution are functions of the Froude number Fr = S/(Nh), where S is the mean wind speed from the surface to h, and N is the corresponding Brunt-Väisälä frequency. The near-surface wind is coupled to the ambient flow for Fr = 3.3, based on where the straight line relating ,ze to ln Fr intersects the ln Fr axis. Under these conditions, 2m is constant horizontally even though ,z > 0, suggesting that near-surface air moves up- and downslope dry adiabatically. However, 2m cools (or warms) everywhere at the same rate. The lowest Froude numbers are associated with drainage flows, while intermediate values characterize regimes with intermediate behavior. The evolution of 2m horizontal variability σ through the night is also a function of the predawn Froude number. For the nights with the lowest Fr, the σ maximum occurs in the last 1-3 h before sunrise. For nights with Fr 3.3 (,ze 0) and for intermediate values, σ peaks 2-3 h after sunset. The standard deviations relative to the ,ze line reach their lowest values in the last hours of darkness. Thus, it is not surprising that the relationships of ,ze to Fr and ,z based on data through the night show more scatter, and ,ze 0.5,z in contrast to the predawn relationship. However, ,ze 0 for ln Fr = 3.7, a value similar to that just before sunrise.A heuristic Lagrangian parcel model is used to explain the horizontal uniformity of time-evolving 2m when the surface flow is coupled with the ambient wind, as well as both the linear variation of 2m with elevation and the time required to reach maximum values of σ under drainage-flow conditions.

Comparison of Marine Spatial Planning Methods in Madagascar Demonstrates Value of Alternative Approaches

PubMed Central

Allnutt, Thomas F.; McClanahan, Timothy R.; Andréfouët, Serge; Baker, Merrill; Lagabrielle, Erwann; McClennen, Caleb; Rakotomanjaka, Andry J. M.; Tianarisoa, Tantely F.; Watson, Reg; Kremen, Claire

2012-01-01

The Government of Madagascar plans to increase marine protected area coverage by over one million hectares. To assist this process, we compare four methods for marine spatial planning of Madagascar's west coast. Input data for each method was drawn from the same variables: fishing pressure, exposure to climate change, and biodiversity (habitats, species distributions, biological richness, and biodiversity value). The first method compares visual color classifications of primary variables, the second uses binary combinations of these variables to produce a categorical classification of management actions, the third is a target-based optimization using Marxan, and the fourth is conservation ranking with Zonation. We present results from each method, and compare the latter three approaches for spatial coverage, biodiversity representation, fishing cost and persistence probability. All results included large areas in the north, central, and southern parts of western Madagascar. Achieving 30% representation targets with Marxan required twice the fish catch loss than the categorical method. The categorical classification and Zonation do not consider targets for conservation features. However, when we reduced Marxan targets to 16.3%, matching the representation level of the “strict protection” class of the categorical result, the methods show similar catch losses. The management category portfolio has complete coverage, and presents several management recommendations including strict protection. Zonation produces rapid conservation rankings across large, diverse datasets. Marxan is useful for identifying strict protected areas that meet representation targets, and minimize exposure probabilities for conservation features at low economic cost. We show that methods based on Zonation and a simple combination of variables can produce results comparable to Marxan for species representation and catch losses, demonstrating the value of comparing alternative approaches during initial stages of the planning process. Choosing an appropriate approach ultimately depends on scientific and political factors including representation targets, likelihood of adoption, and persistence goals. PMID:22359534

Comparison of marine spatial planning methods in Madagascar demonstrates value of alternative approaches.

PubMed

Allnutt, Thomas F; McClanahan, Timothy R; Andréfouët, Serge; Baker, Merrill; Lagabrielle, Erwann; McClennen, Caleb; Rakotomanjaka, Andry J M; Tianarisoa, Tantely F; Watson, Reg; Kremen, Claire

2012-01-01

The Government of Madagascar plans to increase marine protected area coverage by over one million hectares. To assist this process, we compare four methods for marine spatial planning of Madagascar's west coast. Input data for each method was drawn from the same variables: fishing pressure, exposure to climate change, and biodiversity (habitats, species distributions, biological richness, and biodiversity value). The first method compares visual color classifications of primary variables, the second uses binary combinations of these variables to produce a categorical classification of management actions, the third is a target-based optimization using Marxan, and the fourth is conservation ranking with Zonation. We present results from each method, and compare the latter three approaches for spatial coverage, biodiversity representation, fishing cost and persistence probability. All results included large areas in the north, central, and southern parts of western Madagascar. Achieving 30% representation targets with Marxan required twice the fish catch loss than the categorical method. The categorical classification and Zonation do not consider targets for conservation features. However, when we reduced Marxan targets to 16.3%, matching the representation level of the "strict protection" class of the categorical result, the methods show similar catch losses. The management category portfolio has complete coverage, and presents several management recommendations including strict protection. Zonation produces rapid conservation rankings across large, diverse datasets. Marxan is useful for identifying strict protected areas that meet representation targets, and minimize exposure probabilities for conservation features at low economic cost. We show that methods based on Zonation and a simple combination of variables can produce results comparable to Marxan for species representation and catch losses, demonstrating the value of comparing alternative approaches during initial stages of the planning process. Choosing an appropriate approach ultimately depends on scientific and political factors including representation targets, likelihood of adoption, and persistence goals.

Pulmonary emphysema classification based on an improved texton learning model by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Fujita, Hiroshi

2013-03-01

In this paper, we present a texture classification method based on texton learned via sparse representation (SR) with new feature histogram maps in the classification of emphysema. First, an overcomplete dictionary of textons is learned via KSVD learning on every class image patches in the training dataset. In this stage, high-pass filter is introduced to exclude patches in smooth area to speed up the dictionary learning process. Second, 3D joint-SR coefficients and intensity histograms of the test images are used for characterizing regions of interest (ROIs) instead of conventional feature histograms constructed from SR coefficients of the test images over the dictionary. Classification is then performed using a classifier with distance as a histogram dissimilarity measure. Four hundreds and seventy annotated ROIs extracted from 14 test subjects, including 6 paraseptal emphysema (PSE) subjects, 5 centrilobular emphysema (CLE) subjects and 3 panlobular emphysema (PLE) subjects, are used to evaluate the effectiveness and robustness of the proposed method. The proposed method is tested on 167 PSE, 240 CLE and 63 PLE ROIs consisting of mild, moderate and severe pulmonary emphysema. The accuracy of the proposed system is around 74%, 88% and 89% for PSE, CLE and PLE, respectively.

A Review of Major Nursing Vocabularies and the Extent to Which They Have the Characteristics Required for Implementation in Computer-based Systems

PubMed Central

Henry, Suzanne Bakken; Warren, Judith J.; Lange, Linda; Button, Patricia

1998-01-01

Building on the work of previous authors, the Computer-based Patient Record Institute (CPRI) Work Group on Codes and Structures has described features of a classification scheme for implementation within a computer-based patient record. The authors of the current study reviewed the evaluation literature related to six major nursing vocabularies (the North American Nursing Diagnosis Association Taxonomy 1, the Nursing Interventions Classification, the Nursing Outcomes Classification, the Home Health Care Classification, the Omaha System, and the International Classification for Nursing Practice) to determine the extent to which the vocabularies include the CPRI features. None of the vocabularies met all criteria. The Omaha System, Home Health Care Classification, and International Classification for Nursing Practice each included five features. Criteria not fully met by any systems were clear and non-redundant representation of concepts, administrative cross-references, syntax and grammar, synonyms, uncertainty, context-free identifiers, and language independence. PMID:9670127

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

NASA Technical Reports Server (NTRS)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Classification of chemical substances, reactions, and interactions: The effect of expertise

NASA Astrophysics Data System (ADS)

Stains, Marilyne Nicole Olivia

2007-12-01

This project explored the strategies that undergraduate and graduate chemistry students engaged in when solving classification tasks involving microscopic (particulate) representations of chemical substances and microscopic and symbolic representations of different chemical reactions. We were specifically interested in characterizing the basic features to which students pay attention while classifying, identifying the patterns of reasoning that they follow, and comparing the performance of students with different levels of preparation in the discipline. In general, our results suggest that advanced levels of expertise in chemical classification do not necessarily evolve in a linear and continuous way with academic training. Novice students had a tendency to reduce the cognitive demand of the task and rely on common-sense reasoning; they had difficulties differentiating concepts (conceptual undifferentiation) and based their classification decisions on only one variable (reduction). These ways of thinking lead them to consider extraneous features, pay more attention to explicit or surface features than implicit features and to overlook important and relevant features. However, unfamiliar levels of representations (microscopic level) seemed to trigger deeper and more meaningful thinking processes. On the other hand, expert students classified entities using a specific set of rules that they applied throughout the classification tasks. They considered a larger variety of implicit features and the unfamiliarity with the microscopic level of representation did not affect their reasoning processes. Consequently, novices created numerous small groups, few of them being chemically meaningful, while experts created few but large chemically meaningful groups. Novices also had difficulties correctly classifying entities in chemically meaningful groups. Finally, expert chemists in our study used classification schemes that are not necessarily traditionally taught in classroom chemistry (e.g. the structure of substances is more relevant to them than their composition when classifying substances as compounds or elements). This result suggests that practice in the field may develop different types of knowledge framework than those usually presented in chemistry textbooks.

Multimodal Sparse Coding for Event Detection

DTIC Science & Technology

2015-10-13

classification tasks based on single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities...The shared representa- tions are applied to multimedia event detection (MED) and evaluated in compar- ison to unimodal counterparts, as well as other...and video tracks from the same multimedia clip, we can force the two modalities to share a similar sparse representation whose benefit includes robust

Hierarchical classification method and its application in shape representation

NASA Astrophysics Data System (ADS)

Ireton, M. A.; Oakley, John P.; Xydeas, Costas S.

1992-04-01

In this paper we describe a technique for performing shaped-based content retrieval of images from a large database. In order to be able to formulate such user-generated queries about visual objects, we have developed an hierarchical classification technique. This hierarchical classification technique enables similarity matching between objects, with the position in the hierarchy signifying the level of generality to be used in the query. The classification technique is unsupervised, robust, and general; it can be applied to any suitable parameter set. To establish the potential of this classifier for aiding visual querying, we have applied it to the classification of the 2-D outlines of leaves.

Entanglement classification with matrix product states

NASA Astrophysics Data System (ADS)

Sanz, M.; Egusquiza, I. L.; di Candia, R.; Saberi, H.; Lamata, L.; Solano, E.

2016-07-01

We propose an entanglement classification for symmetric quantum states based on their diagonal matrix-product-state (MPS) representation. The proposed classification, which preserves the stochastic local operation assisted with classical communication (SLOCC) criterion, relates entanglement families to the interaction length of Hamiltonians. In this manner, we establish a connection between entanglement classification and condensed matter models from a quantum information perspective. Moreover, we introduce a scalable nesting property for the proposed entanglement classification, in which the families for N parties carry over to the N + 1 case. Finally, using techniques from algebraic geometry, we prove that the minimal nontrivial interaction length n for any symmetric state is bounded by .

Text categorization of biomedical data sets using graph kernels and a controlled vocabulary.

PubMed

Bleik, Said; Mishra, Meenakshi; Huan, Jun; Song, Min

2013-01-01

Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.

Identification of the SRC pyrimidine-binding protein (SPy) as hnRNP K: implications in the regulation of SRC1A transcription

PubMed Central

Ritchie, Shawn A.; Pasha, Mohammed K.; Batten, Danielle J. P.; Sharma, Rajendra K.; Olson, Douglas J. H.; Ross, Andrew R. S.; Bonham, Keith

2003-01-01

The human SRC gene encodes pp60c–src, a non-receptor tyrosine kinase involved in numerous signaling pathways. Activation or overexpression of c-Src has also been linked to a number of important human cancers. Transcription of the SRC gene is complex and regulated by two closely linked but highly dissimilar promoters, each associated with its own distinct non-coding exon. In many tissues SRC expression is regulated by the housekeeping-like SRC1A promoter. In addition to other regulatory elements, three substantial polypurine:polypyrimidine (TC) tracts within this promoter are required for full transcriptional activity. Previously, we described an unusual factor called SRC pyrimidine-binding protein (SPy) that could bind to two of these TC tracts in their double-stranded form, but was also capable of interacting with higher affinity to all three pyrimidine tracts in their single-stranded form. Mutations in the TC tracts, which abolished the ability of SPy to interact with its double-stranded DNA target, significantly reduced SRC1A promoter activity, especially in concert with mutations in critical Sp1 binding sites. Here we expand upon our characterization of this interesting factor and describe the purification of SPy from human SW620 colon cancer cells using a DNA affinity-based approach. Subsequent in-gel tryptic digestion of purified SPy followed by MALDI-TOF mass spectrometric analysis identified SPy as heterogeneous nuclear ribonucleoprotein K (hnRNP K), a known nucleic-acid binding protein implicated in various aspects of gene expression including transcription. These data provide new insights into the double- and single-stranded DNA-binding specificity, as well as functional properties of hnRNP K, and suggest that hnRNP K is a critical component of SRC1A transcriptional processes. PMID:12595559

Character recognition using a neural network model with fuzzy representation

NASA Technical Reports Server (NTRS)

Tavakoli, Nassrin; Seniw, David

1992-01-01

The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

A robust probabilistic collaborative representation based classification for multimodal biometrics

NASA Astrophysics Data System (ADS)

Zhang, Jing; Liu, Huanxi; Ding, Derui; Xiao, Jianli

2018-04-01

Most of the traditional biometric recognition systems perform recognition with a single biometric indicator. These systems have suffered noisy data, interclass variations, unacceptable error rates, forged identity, and so on. Due to these inherent problems, it is not valid that many researchers attempt to enhance the performance of unimodal biometric systems with single features. Thus, multimodal biometrics is investigated to reduce some of these defects. This paper proposes a new multimodal biometric recognition approach by fused faces and fingerprints. For more recognizable features, the proposed method extracts block local binary pattern features for all modalities, and then combines them into a single framework. For better classification, it employs the robust probabilistic collaborative representation based classifier to recognize individuals. Experimental results indicate that the proposed method has improved the recognition accuracy compared to the unimodal biometrics.

Mapping the Natchez Trace Parkway

USGS Publications Warehouse

Rangoonwala, Amina; Bannister, Terri; Ramsey, Elijah W.

2011-01-01

Based on a National Park Service (NPS) landcover classification, a landcover map of the 715-km (444-mile) NPS Natchez Trace Parkway (hereafter referred to as the "Parkway") was created. The NPS landcover classification followed National Vegetation Classification (NVC) protocols. The landcover map, which extended the initial landcover classification to the entire Parkway, was based on color-infrared photography converted to 1-m raster-based digital orthophoto quarter quadrangles, according to U.S. Geological Survey mapping standards. Our goal was to include as many alliance classes as possible in the Parkway landcover map. To reach this goal while maintaining a consistent and quantifiable map product throughout the Parkway extent, a mapping strategy was implemented based on the migration of class-based spectral textural signatures and the congruent progressive refinement of those class signatures along the Parkway. Progressive refinement provided consistent mapping by evaluating the spectral textural distinctiveness of the alliance-association classes, and where necessary, introducing new map classes along the Parkway. By following this mapping strategy, the use of raster-based image processing and geographic information system analyses for the map production provided a quantitative and reproducible product. Although field-site classification data were severely limited, the combination of spectral migration of class membership along the Parkway and the progressive classification strategy produced an organization of alliances that was internally highly consistent. The organization resulted from the natural patterns or alignments of spectral variance and the determination of those spectral patterns that were compositionally similar in the dominant species as NVC alliances. Overall, the mapped landcovers represented the existent spectral textural patterns that defined and encompassed the complex variety of compositional alliances and associations of the Parkway. Based on that mapped representation, forests dominate the Parkway landscape. Grass is the second largest Parkway land cover, followed by scrub-shrub and shrubland classes and pine plantations. The map provides a good representation of the landcover patterns and their changes over the extent of the Parkway, south to north.

LBMD : a layer-based mesh data structure tailored for generic API infrastructures.

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

Ebeida, Mohamed S.; Knupp, Patrick Michael

2010-11-01

A new mesh data structure is introduced for the purpose of mesh processing in Application Programming Interface (API) infrastructures. This data structure utilizes a reduced mesh representation to increase its ability to handle significantly larger meshes compared to full mesh representation. In spite of the reduced representation, each mesh entity (vertex, edge, face, and region) is represented using a unique handle, with no extra storage cost, which is a crucial requirement in most API libraries. The concept of mesh layers makes the data structure more flexible for mesh generation and mesh modification operations. This flexibility can have a favorable impactmore » in solver based queries of finite volume and multigrid methods. The capabilities of LBMD make it even more attractive for parallel implementations using Message Passing Interface (MPI) or Graphics Processing Units (GPUs). The data structure is associated with a new classification method to relate mesh entities to their corresponding geometrical entities. The classification technique stores the related information at the node level without introducing any ambiguities. Several examples are presented to illustrate the strength of this new data structure.« less

Efficient alignment-free DNA barcode analytics.

PubMed

Kuksa, Pavel; Pavlovic, Vladimir

2009-11-10

In this work we consider barcode DNA analysis problems and address them using alternative, alignment-free methods and representations which model sequences as collections of short sequence fragments (features). The methods use fixed-length representations (spectrum) for barcode sequences to measure similarities or dissimilarities between sequences coming from the same or different species. The spectrum-based representation not only allows for accurate and computationally efficient species classification, but also opens possibility for accurate clustering analysis of putative species barcodes and identification of critical within-barcode loci distinguishing barcodes of different sample groups. New alignment-free methods provide highly accurate and fast DNA barcode-based identification and classification of species with substantial improvements in accuracy and speed over state-of-the-art barcode analysis methods. We evaluate our methods on problems of species classification and identification using barcodes, important and relevant analytical tasks in many practical applications (adverse species movement monitoring, sampling surveys for unknown or pathogenic species identification, biodiversity assessment, etc.) On several benchmark barcode datasets, including ACG, Astraptes, Hesperiidae, Fish larvae, and Birds of North America, proposed alignment-free methods considerably improve prediction accuracy compared to prior results. We also observe significant running time improvements over the state-of-the-art methods. Our results show that newly developed alignment-free methods for DNA barcoding can efficiently and with high accuracy identify specimens by examining only few barcode features, resulting in increased scalability and interpretability of current computational approaches to barcoding.

Decomposition and extraction: a new framework for visual classification.

PubMed

Fang, Yuqiang; Chen, Qiang; Sun, Lin; Dai, Bin; Yan, Shuicheng

2014-08-01

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

Bag of Lines (BoL) for Improved Aerial Scene Representation

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

Sridharan, Harini; Cheriyadat, Anil M.

2014-09-22

Feature representation is a key step in automated visual content interpretation. In this letter, we present a robust feature representation technique, referred to as bag of lines (BoL), for high-resolution aerial scenes. The proposed technique involves extracting and compactly representing low-level line primitives from the scene. The compact scene representation is generated by counting the different types of lines representing various linear structures in the scene. Through extensive experiments, we show that the proposed scene representation is invariant to scale changes and scene conditions and can discriminate urban scene categories accurately. We compare the BoL representation with the popular scalemore » invariant feature transform (SIFT) and Gabor wavelets for their classification and clustering performance on an aerial scene database consisting of images acquired by sensors with different spatial resolutions. The proposed BoL representation outperforms the SIFT- and Gabor-based representations.« less

Exploiting salient semantic analysis for information retrieval

NASA Astrophysics Data System (ADS)

Luo, Jing; Meng, Bo; Quan, Changqin; Tu, Xinhui

2016-11-01

Recently, many Wikipedia-based methods have been proposed to improve the performance of different natural language processing (NLP) tasks, such as semantic relatedness computation, text classification and information retrieval. Among these methods, salient semantic analysis (SSA) has been proven to be an effective way to generate conceptual representation for words or documents. However, its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use SSA to improve the information retrieval performance, and propose a SSA-based retrieval method under the language model framework. First, SSA model is adopted to build conceptual representations for documents and queries. Then, these conceptual representations and the bag-of-words (BOW) representations can be used in combination to estimate the language models of queries and documents. The proposed method is evaluated on several standard text retrieval conference (TREC) collections. Experiment results on standard TREC collections show the proposed models consistently outperform the existing Wikipedia-based retrieval methods.

Spatiotemporal dynamics of similarity-based neural representations of facial identity.

PubMed

Vida, Mark D; Nestor, Adrian; Plaut, David C; Behrmann, Marlene

2017-01-10

Humans' remarkable ability to quickly and accurately discriminate among thousands of highly similar complex objects demands rapid and precise neural computations. To elucidate the process by which this is achieved, we used magnetoencephalography to measure spatiotemporal patterns of neural activity with high temporal resolution during visual discrimination among a large and carefully controlled set of faces. We also compared these neural data to lower level "image-based" and higher level "identity-based" model-based representations of our stimuli and to behavioral similarity judgments of our stimuli. Between ∼50 and 400 ms after stimulus onset, face-selective sources in right lateral occipital cortex and right fusiform gyrus and sources in a control region (left V1) yielded successful classification of facial identity. In all regions, early responses were more similar to the image-based representation than to the identity-based representation. In the face-selective regions only, responses were more similar to the identity-based representation at several time points after 200 ms. Behavioral responses were more similar to the identity-based representation than to the image-based representation, and their structure was predicted by responses in the face-selective regions. These results provide a temporally precise description of the transformation from low- to high-level representations of facial identity in human face-selective cortex and demonstrate that face-selective cortical regions represent multiple distinct types of information about face identity at different times over the first 500 ms after stimulus onset. These results have important implications for understanding the rapid emergence of fine-grained, high-level representations of object identity, a computation essential to human visual expertise.

Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

PubMed

Miller, Vonda H; Jansen, Ben H

2008-12-01

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

Automatic classification of sleep stages based on the time-frequency image of EEG signals.

PubMed

Bajaj, Varun; Pachori, Ram Bilas

2013-12-01

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

The Role of the Rab Coupling Protein in ErbB2-Driven Mammary Tumorigenesis and Metastasis

DTIC Science & Technology

2015-10-01

Epithelial Mesenchymal Transition , Cell junctions , Cell Proliferation 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF...Epithelial Mesenchymal Transition , Cell junctions and Cell Proliferation. 6 ACCOMPLISHMENTS The PI is reminded that the recipient organization is...oncogene. The potent transforming potential of ErbB2 in the mammary epithelium is thought to be due to its capacity to couple with a number of Src

A computational theory for the classification of natural biosonar targets based on a spike code.

PubMed

Müller, Rolf

2003-08-01

A computational theory for the classification of natural biosonar targets is developed based on the properties of an example stimulus ensemble. An extensive set of echoes (84 800) from four different foliages was transcribed into a spike code using a parsimonious model (linear filtering, half-wave rectification, thresholding). The spike code is assumed to consist of time differences (interspike intervals) between threshold crossings. Among the elementary interspike intervals flanked by exceedances of adjacent thresholds, a few intervals triggered by disjoint half-cycles of the carrier oscillation stand out in terms of resolvability, visibility across resolution scales and a simple stochastic structure (uncorrelatedness). They are therefore argued to be a stochastic analogue to edges in vision. A three-dimensional feature vector representing these interspike intervals sustained a reliable target classification performance (0.06% classification error) in a sequential probability ratio test, which models sequential processing of echo trains by biological sonar systems. The dimensions of the representation are the first moments of duration and amplitude location of these interspike intervals as well as their number. All three quantities are readily reconciled with known principles of neural signal representation, since they correspond to the centre of gravity of excitation on a neural map and the total amount of excitation.

Mid-level image representations for real-time heart view plane classification of echocardiograms.

PubMed

Penatti, Otávio A B; Werneck, Rafael de O; de Almeida, Waldir R; Stein, Bernardo V; Pazinato, Daniel V; Mendes Júnior, Pedro R; Torres, Ricardo da S; Rocha, Anderson

2015-11-01

In this paper, we explore mid-level image representations for real-time heart view plane classification of 2D echocardiogram ultrasound images. The proposed representations rely on bags of visual words, successfully used by the computer vision community in visual recognition problems. An important element of the proposed representations is the image sampling with large regions, drastically reducing the execution time of the image characterization procedure. Throughout an extensive set of experiments, we evaluate the proposed approach against different image descriptors for classifying four heart view planes. The results show that our approach is effective and efficient for the target problem, making it suitable for use in real-time setups. The proposed representations are also robust to different image transformations, e.g., downsampling, noise filtering, and different machine learning classifiers, keeping classification accuracy above 90%. Feature extraction can be performed in 30 fps or 60 fps in some cases. This paper also includes an in-depth review of the literature in the area of automatic echocardiogram view classification giving the reader a through comprehension of this field of study. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

PubMed Central

Mack, Michael L.; Love, Bradley C.; Preston, Alison R.

2016-01-01

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal. PMID:27803320

Social representations of biosecurity in nursing: occupational health and preventive care.

PubMed

Sousa, Álvaro Francisco Lopes de; Queiroz, Artur Acelino Francisco Luz Nunes; Oliveira, Layze Braz de; Moura, Maria Eliete Batista; Batista, Odinéa Maria Amorim; Andrade, Denise de

2016-01-01

to understand the biosecurity social representations by primary care nursing professionals and analyze how they articulate with quality of care. exploratory and qualitative research based on social representation theory. The study participants were 36 nursing workers from primary health care in a state capital in the Northeast region of Brazil. The data were analyzed by descending hierarchical classification. five classes were obtained: occupational accidents suffered by professionals; occupational exposure to biological agents; biosecurity management in primary health care; the importance of personal protective equipment; and infection control and biosecurity. the different positions taken by the professionals seem to be based on a field of social representations related to the concept of biosecurity, namely exposure to accidents and risks to which they are exposed. However, occupational accidents are reported as inherent to the practice.

Sparse Representation Based Classification with Structure Preserving Dimension Reduction

DTIC Science & Technology

2014-03-13

dictionary learning [39] used stochastic approximations to update dictionary with a large data set. Laplacian score dictionary ( LSD ) [58], which is based on...vol. 4. 2003. p. 864–7. 47. Shaw B, Jebara T. Structure preserving embedding. In: The 26th annual international conference on machine learning, ICML

International Classification of Functioning, Disability and Health Core Sets for cerebral palsy, autism spectrum disorder, and attention-deficit-hyperactivity disorder.

PubMed

Schiariti, Verónica; Mahdi, Soheil; Bölte, Sven

2018-05-30

Capturing functional information is crucial in childhood disability. The International Classification of Functioning, Disability and Health (ICF) Core Sets promote assessments of functional abilities and disabilities in clinical practice regarding circumscribed diagnoses. However, the specificity of ICF Core Sets for childhood-onset disabilities has been doubted. This study aimed to identify content commonalities and differences among the ICF Core Sets for cerebral palsy (CP), and the newly developed Core Sets for autism spectrum disorder (ASD) and attention-deficit-hyperactivity disorder (ADHD). The categories within each Core Set were aggregated at the ICF component and chapter levels. Content comparison was conducted using descriptive analyses. The activities and participation component of the ICF was the most covered across all Core Sets. Main differences included representation of ICF components and coverage of ICF chapters within each component. CP included all ICF components, while ADHD and ASD predominantly focused on activities and participation. Environmental factors were highly represented in the ADHD Core Sets (40.5%) compared to the ASD (28%) and CP (27%) Core Sets. International Classification of Functioning, Disability and Health Core Sets for CP, ASD, and ADHD capture both common but also unique functional information, showing the importance of creating condition-specific, ICF-based tools to build functional profiles of individuals with childhood-onset disabilities. The International Classification of Functioning, Disability and Health (ICF) Core Sets for cerebral palsy (CP), autism spectrum disorder (ASD), and attention-deficit-hyperactivity disorder (ADHD) include unique functional information. The ICF-based tools for CP, ASD, and ADHD differ in terms of representation and coverage of ICF components and ICF chapters. Representation of environmental factors uniquely influences functioning and disability across ICF Core Sets for CP, ASD and ADHD. © 2018 Mac Keith Press.

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

Cho, Nancy L., E-mail: nlcho@partners.org; Lin, Chi-Iou; Du, Jinyan

Highlights: Black-Right-Pointing-Pointer Kinome profiling is a novel technique for identifying activated kinases in human cancers. Black-Right-Pointing-Pointer Src activity is increased in invasive thyroid cancers. Black-Right-Pointing-Pointer Inhibition of Src activity decreased proliferation and invasion in vitro. Black-Right-Pointing-Pointer Further investigation of Src targeted therapies in thyroid cancer is warranted. -- Abstract: Background: Novel therapies are needed for the treatment of invasive thyroid cancers. Aberrant activation of tyrosine kinases plays an important role in thyroid oncogenesis. Because current targeted therapies are biased toward a small subset of tyrosine kinases, we conducted a study to reveal novel therapeutic targets for thyroid cancer using amore » bead-based, high-throughput system. Methods: Thyroid tumors and matched normal tissues were harvested from twenty-six patients in the operating room. Protein lysates were analyzed using the Luminex immunosandwich, a bead-based kinase phosphorylation assay. Data was analyzed using GenePattern 3.0 software and clustered according to histology, demographic factors, and tumor status regarding capsular invasion, size, lymphovascular invasion, and extrathyroidal extension. Survival and invasion assays were performed to determine the effect of Src inhibition in papillary thyroid cancer (PTC) cells. Results: Tyrosine kinome profiling demonstrated upregulation of nine tyrosine kinases in tumors relative to matched normal thyroid tissue: EGFR, PTK6, BTK, HCK, ABL1, TNK1, GRB2, ERK, and SRC. Supervised clustering of well-differentiated tumors by histology, gender, age, or size did not reveal significant differences in tyrosine kinase activity. However, supervised clustering by the presence of invasive disease showed increased Src activity in invasive tumors relative to non-invasive tumors (60% v. 0%, p < 0.05). In vitro, we found that Src inhibition in PTC cells decreased cell invasion and proliferation. Conclusion: Global kinome analysis enables the discovery of novel targets for thyroid cancer therapy. Further investigation of Src targeted therapy for advanced thyroid cancer is warranted.« less

Employing wavelet-based texture features in ammunition classification

NASA Astrophysics Data System (ADS)

Borzino, Ángelo M. C. R.; Maher, Robert C.; Apolinário, José A.; de Campos, Marcello L. R.

2017-05-01

Pattern recognition, a branch of machine learning, involves classification of information in images, sounds, and other digital representations. This paper uses pattern recognition to identify which kind of ammunition was used when a bullet was fired based on a carefully constructed set of gunshot sound recordings. To do this task, we show that texture features obtained from the wavelet transform of a component of the gunshot signal, treated as an image, and quantized in gray levels, are good ammunition discriminators. We test the technique with eight different calibers and achieve a classification rate better than 95%. We also compare the performance of the proposed method with results obtained by standard temporal and spectrographic techniques

Examining Recovery Trajectories Following Sport-related Concussion Using a Multi-Modal Clinical Assessment Approach

PubMed Central

Henry, Luke C.; Elbin, RJ; Collins, Michael W.; Marchetti, Gregory; Kontos, Anthony P.

2016-01-01

Background Previous research estimates that the majority of athletes with sport-related concussion (SRC) will recover between 7–10 days following injury. This short, temporal window of recovery is predominately based on symptom resolution and cognitive improvement, and does not accurately reflect recent advances to the clinical assessment model. Objective To characterize SRC recovery at 1-week post-injury time intervals on symptom, neurocognitive, and vestibular-oculomotor outcomes, and examine gender differences on SRC recovery time. Methods A prospective, repeated measures design was used to examine the temporal resolution of neurocognitive, symptom, and vestibular-oculomotor impairment in 66 subjects (16.5 ± 1.9 years, range 14–23, 64% male) with SRC. Results Recovery time across all outcomes was between 21–28 days post SRC for most athletes. Symptoms demonstrated the greatest improvement in the first 2 weeks, while neurocognitive impairment lingered across various domains up to 28 days post SRC. Vestibular-oculomotor decrements also resolved between one to three weeks post injury. There were no gender differences in neurocognitive recovery. Males were more likely to be asymptomatic by the fourth week and reported less vestibular-oculomotor impairment than females at weeks 1 and 2. Conclusion When utilizing the recommended “comprehensive” approach for concussion assessment, recovery time for SRC is approximately three to four weeks, which is longer than the commonly reported 7–14 days. Sports medicine clinicians should use a variety of complementing assessment tools to capture the heterogeneity of SRC. PMID:26445375

Examining Recovery Trajectories After Sport-Related Concussion With a Multimodal Clinical Assessment Approach.

PubMed

Henry, Luke C; Elbin, R J; Collins, Michael W; Marchetti, Gregory; Kontos, Anthony P

2016-02-01

Previous research estimates that the majority of athletes with sport-related concussion (SRC) will recover between 7 and 10 days after injury. This short temporal window of recovery is based predominately on symptom resolution and cognitive improvement and does not accurately reflect recent advances in the clinical assessment model. To characterize SRC recovery at 1-week postinjury time intervals on symptom, neurocognitive, and vestibular-oculomotor outcomes and to examine sex differences in SRC recovery time. A prospective, repeated-measures design was used to examine the temporal resolution of neurocognitive, symptom, and vestibular-oculomotor impairment in 66 subjects (age, 16.5 ± 1.9 years; range, 14-23 years; 64% male) with SRC. Recovery time across all outcomes was between 21 and 28 days after SRC for most athletes. Symptoms demonstrated the greatest improvement in the first 2 weeks, although neurocognitive impairment lingered across various domains up to 28 days after SRC. Vestibular-oculomotor decrements also resolved between 1 and 3 weeks after injury. There were no sex differences in neurocognitive recovery. Male subjects were more likely to be asymptomatic by the fourth week and reported less vestibular-oculomotor impairment than female subjects at weeks 1 and 2. When the recommended "comprehensive" approach is used for concussion assessment, recovery time for SRC is approximately 3 to 4 weeks, which is longer than the commonly reported 7 to 14 days. Sports medicine clinicians should use a variety of complementing assessment tools to capture the heterogeneity of SRC.

Molecular signaling in live cells studied by FRET

NASA Astrophysics Data System (ADS)

Chien, Shu; Wang, Yingxiao

2011-11-01

Genetically encoded biosensors based on fluorescence resonance energy transfer (FRET) enables visualization of signaling events in live cells with high spatiotemporal resolution. We have used FRET to assess temporal and spatial characteristics for signaling molecules, including tyrosine kinases Src and FAK, small GTPase Rac, calcium, and a membrane-bound matrix metalloproteinase MT1-MMP. Activations of Src and Rac by platelet derived growth factor (PDGF) led to distinct subcellular patterns during cell migration on micropatterned surface, and these two enzymes interact with each other to form a feedback loop with differential regulations at different subcellular locations. We have developed FRET biosensors to monitor FAK activities at rafts vs. non-raft regions of plasma membrane in live cells. In response to cell adhesion on matrix proteins or stimulation by PDGF, the raft-targeting FAK biosensor showed a stronger FRET response than that at non-rafts. The FAK activation at rafts induced by PDGF is mediated by Src. In contrast, the FAK activation at rafts induced by adhesion is independent of Src activity, but rather is essential for Src activation. Thus, Src is upstream to FAK in response to chemical stimulation (PDGF), but FAK is upstream to Src in response to mechanical stimulation (adhesion). A novel biosensor has been developed to dynamically visualize the activity of membrane type-1-matrix metalloproteinase (MT1-MMP), which proteolytically remodels the extracellular matrix. Epidermal growth factor (EGF) directed active MT1-MMP to the leading edge of migrating live cancer cells with local accumulation of EGF receptor via a process dependent on an intact cytoskeletal network. In summary, FRET-based biosensors enable the elucidation of molecular processes and hierarchies underlying spatiotemporal regulation of biological and pathological processes, thus advancing our knowledge on how cells perceive mechanical/chemical cues in space and time to coordinate molecular/cellular functions.

Molecular signaling in live cells studied by FRET

NASA Astrophysics Data System (ADS)

Chien, Shu; Wang, Yingxiao

2012-03-01

Genetically encoded biosensors based on fluorescence resonance energy transfer (FRET) enables visualization of signaling events in live cells with high spatiotemporal resolution. We have used FRET to assess temporal and spatial characteristics for signaling molecules, including tyrosine kinases Src and FAK, small GTPase Rac, calcium, and a membrane-bound matrix metalloproteinase MT1-MMP. Activations of Src and Rac by platelet derived growth factor (PDGF) led to distinct subcellular patterns during cell migration on micropatterned surface, and these two enzymes interact with each other to form a feedback loop with differential regulations at different subcellular locations. We have developed FRET biosensors to monitor FAK activities at rafts vs. non-raft regions of plasma membrane in live cells. In response to cell adhesion on matrix proteins or stimulation by PDGF, the raft-targeting FAK biosensor showed a stronger FRET response than that at non-rafts. The FAK activation at rafts induced by PDGF is mediated by Src. In contrast, the FAK activation at rafts induced by adhesion is independent of Src activity, but rather is essential for Src activation. Thus, Src is upstream to FAK in response to chemical stimulation (PDGF), but FAK is upstream to Src in response to mechanical stimulation (adhesion). A novel biosensor has been developed to dynamically visualize the activity of membrane type-1-matrix metalloproteinase (MT1-MMP), which proteolytically remodels the extracellular matrix. Epidermal growth factor (EGF) directed active MT1-MMP to the leading edge of migrating live cancer cells with local accumulation of EGF receptor via a process dependent on an intact cytoskeletal network. In summary, FRET-based biosensors enable the elucidation of molecular processes and hierarchies underlying spatiotemporal regulation of biological and pathological processes, thus advancing our knowledge on how cells perceive mechanical/chemical cues in space and time to coordinate molecular/cellular functions.

Water use of a multigenotype poplar short-rotation coppice from tree to stand scale.

PubMed

Bloemen, Jasper; Fichot, Régis; Horemans, Joanna A; Broeckx, Laura S; Verlinden, Melanie S; Zenone, Terenzio; Ceulemans, Reinhart

2017-02-01

Short-rotation coppice (SRC) has great potential for supplying biomass-based heat and energy, but little is known about SRC's ecological footprint, particularly its impact on the water cycle. To this end, we quantified the water use of a commercial scale poplar ( Populus ) SRC plantation in East Flanders (Belgium) at tree and stand level, focusing primarily on the transpiration component. First, we used the AquaCrop model and eddy covariance flux data to analyse the different components of the stand-level water balance for one entire growing season. Transpiration represented 59% of evapotranspiration (ET) at stand scale over the whole year. Measured ET and modelled ET were lower as compared to the ET of reference grassland, suggesting that the SRC only used a limited amount of water. Secondly, we compared leaf area scaled and sapwood area scaled sap flow ( F s ) measurements on individual plants vs. stand scale eddy covariance flux data during a 39-day intensive field campaign in late summer 2011. Daily stem diameter variation (∆ D ) was monitored simultaneously with F s to understand water use strategies for three poplar genotypes. Canopy transpiration based on sapwood area or leaf area scaling was 43.5 and 50.3 mm, respectively, and accounted for 74%, respectively, 86%, of total ecosystem ET measured during the intensive field campaign. Besides differences in growth, the significant intergenotypic differences in daily ∆ D (due to stem shrinkage and swelling) suggested different water use strategies among the three genotypes which were confirmed by the sap flow measurements. Future studies on the prediction of SRC water use, or efforts to enhance the biomass yield of SRC genotypes, should consider intergenotypic differences in transpiration water losses at tree level as well as the SRC water balance at stand level.

Protein classification using modified n-grams and skip-grams.

PubMed

Islam, S M Ashiqul; Heil, Benjamin J; Kearney, Christopher Michel; Baker, Erich J

2018-05-01

Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. erich_baker@baylor.edu. Supplementary data are available at Bioinformatics online.

Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

2001-03-01

This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

Efficient alignment-free DNA barcode analytics

PubMed Central

Kuksa, Pavel; Pavlovic, Vladimir

2009-01-01

Background In this work we consider barcode DNA analysis problems and address them using alternative, alignment-free methods and representations which model sequences as collections of short sequence fragments (features). The methods use fixed-length representations (spectrum) for barcode sequences to measure similarities or dissimilarities between sequences coming from the same or different species. The spectrum-based representation not only allows for accurate and computationally efficient species classification, but also opens possibility for accurate clustering analysis of putative species barcodes and identification of critical within-barcode loci distinguishing barcodes of different sample groups. Results New alignment-free methods provide highly accurate and fast DNA barcode-based identification and classification of species with substantial improvements in accuracy and speed over state-of-the-art barcode analysis methods. We evaluate our methods on problems of species classification and identification using barcodes, important and relevant analytical tasks in many practical applications (adverse species movement monitoring, sampling surveys for unknown or pathogenic species identification, biodiversity assessment, etc.) On several benchmark barcode datasets, including ACG, Astraptes, Hesperiidae, Fish larvae, and Birds of North America, proposed alignment-free methods considerably improve prediction accuracy compared to prior results. We also observe significant running time improvements over the state-of-the-art methods. Conclusion Our results show that newly developed alignment-free methods for DNA barcoding can efficiently and with high accuracy identify specimens by examining only few barcode features, resulting in increased scalability and interpretability of current computational approaches to barcoding. PMID:19900305

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

PubMed

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

2012-02-01

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

Phosphorylation of TOPK at Y74, Y272 by Src increases the stability of TOPK and promotes tumorigenesis of colon

PubMed Central

Wang, Zhe; Yan, Wei; Sun, Huimin; Xue, Peipei; Fan, Xiaoming; Zeng, Xiaoyu; Chen, Juan; Shao, Chen; Zhu, Feng

2016-01-01

T-LAK cell-originated protein kinase (TOPK), a serine/threonine protein kinase, is highly expressed in a variety of tumors and associated with a poor prognosis of human malignancies. However, the activation mechanism of TOPK is still unrevealed. Herein, first we found that Src directly bound with and phosphorylated TOPK at Y74 and Y272 in vitro. Anti-phospho-TOPK at Y74 was prepared, the endogenous phosphorylation of TOPK at Y74 was detected in colon cancer cells, and the phosphorylation was inhibited in cells expressing low levels of Src. Subsequently, we stably transfected Y74 and Y272 double mutated TOPK (TOPK-FF) into JB6 or SW480 cells, and observed that both the anchorage-independent growth ability and tumorigenesis of TOPK-FF cells were suppressed compared with those of wild type TOPK (TOPK-WT) ex vivo and in vivo. The phosphorylation level of TOPK substrate, Histone H3 at Ser10 also decreased dramatically ex vivo or in vivo. Moreover, we showed that Src could inhibit the ubiquitination of TOPK. Transiently expressed TOPK-WT was more stable than TOPK-FF in pause and chase experiment. Endogenous TOPK was more stable in Src wild type (Src+/+) MEFs than in Src knockout (Src−/−). Taken together, our results indicate that Src is a novel upstream kinase of TOPK. The phosphorylation of TOPK at Y74 and Y272 by Src increases the stability and activity of TOPK, and promotes the tumorigenesis of colon cancer. It may provide opportunities for TOPK based prognosis and targeted therapy for colon cancer patients. PMID:27016416

Explanatory characteristics for nutrient concentrations and loads in the Sava River Catchment and cross-regionally

NASA Astrophysics Data System (ADS)

Levi, L.; Cvetkovic, V.; Destouni, G.

2015-12-01

This study compiles estimates of waterborne nutrient concentrations and loads in the Sava River Catchment (SRC). Based on this compilation, we investigate hotspots of nutrient inputs and retention along the river, as well as concentration and load correlations with river discharge and various human drivers of excess nutrient inputs to the SRC. For cross-regional assessment and possible generalization, we also compare corresponding results between the SRC and the Baltic Sea Drainage Basin (BSDB). In the SRC, one small incremental subcatchment, which is located just downstream of Zagreb and has the highest population density among the SRC subcatchments, is identified as a major hotspot for net loading (input minus retention) of both total nitrogen (TN) and total phosphorus (TP) to the river and through it to downstream areas of the SRC. The other SRC subcatchments exhibit relatively similar characteristics with smaller net nutrient loading. The annual loads of both TN and TP along the Sava River exhibit dominant temporal variability with considerably higher correlation with annual river discharge (R2 = 0.51 and 0.28, respectively) than that of annual average nutrient concentrations (R2 = 0.0 versus discharge for both TN and TP). Nutrient concentrations exhibit instead dominant spatial variability with relatively high correlation with population density among the SRC subcatchments (R2=0.43-0.64). These SRC correlation characteristics compare well with corresponding ones for the BSDB, even though the two regions are quite different in their hydroclimatic, agricultural and wastewater treatment conditions. Such cross-regional consistency in dominant variability type and explanatory catchment characteristics may be a useful generalization basis, worthy of further investigation, for at least first-order estimation of nutrient concentration and load conditions in less data-rich regions.

Towards automated human gait disease classification using phase space representation of intrinsic mode functions

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Patra, Sayantani; Pratiher, Souvik

2017-06-01

A novel analytical methodology for segregating healthy and neurological disorders from gait patterns is proposed by employing a set of oscillating components called intrinsic mode functions (IMF's). These IMF's are generated by the Empirical Mode Decomposition of the gait time series and the Hilbert transformed analytic signal representation forms the complex plane trace of the elliptical shaped analytic IMFs. The area measure and the relative change in the centroid position of the polygon formed by the Convex Hull of these analytic IMF's are taken as the discriminative features. Classification accuracy of 79.31% with Ensemble learning based Adaboost classifier validates the adequacy of the proposed methodology for a computer aided diagnostic (CAD) system for gait pattern identification. Also, the efficacy of several potential biomarkers like Bandwidth of Amplitude Modulation and Frequency Modulation IMF's and it's Mean Frequency from the Fourier-Bessel expansion from each of these analytic IMF's has been discussed for its potency in diagnosis of gait pattern identification and classification.

Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

NASA Astrophysics Data System (ADS)

Yan, Dan; Bai, Lianfa; Zhang, Yi; Han, Jing

2018-02-01

For the problems of missing details and performance of the colorization based on sparse representation, we propose a conceptual model framework for colorizing gray-scale images, and then a multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement (CEMDC) is proposed based on this framework. The algorithm can achieve a natural colorized effect for a gray-scale image, and it is consistent with the human vision. First, the algorithm establishes a multi-sparse dictionary classification colorization model. Then, to improve the accuracy rate of the classification, the corresponding local constraint algorithm is proposed. Finally, we propose a detail enhancement based on Laplacian Pyramid, which is effective in solving the problem of missing details and improving the speed of image colorization. In addition, the algorithm not only realizes the colorization of the visual gray-scale image, but also can be applied to the other areas, such as color transfer between color images, colorizing gray fusion images, and infrared images.

Clustering-based Feature Learning on Variable Stars

NASA Astrophysics Data System (ADS)

Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

2016-04-01

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

What is the definition of sports-related concussion: a systematic review.

PubMed

McCrory, Paul; Feddermann-Demont, Nina; Dvořák, Jiří; Cassidy, J David; McIntosh, Andrew; Vos, Pieter E; Echemendia, Ruben J; Meeuwisse, Willem; Tarnutzer, Alexander A

2017-06-01

Various definitions for concussion have been proposed, each having its strengths and weaknesses. We reviewed and compared current definitions and identified criteria necessary for an operational definition of sports-related concussion (SRC) in preparation of the 5th Concussion Consensus Conference (Berlin, Germany). We also assessed the role of biomechanical studies in informing an operational definition of SRC. This is a systematic literature review. Data sources include MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, Cochrane Central Register of Clinical Trials and SPORT Discus (accessed 14 September 2016). Eligibility criteria were studies reporting (clinical) criteria for diagnosing SRC and studies containing SRC impact data. Out of 1601 articles screened, 36 studies were included (2.2%), 14 reported on criteria for SRC definitions and 22 on biomechanical aspects of concussions. Six different operational definitions focusing on clinical findings and their dynamics were identified. Biomechanical studies were obtained almost exclusively on American football players. Angular and linear head accelerations linked to clinically confirmed concussions demonstrated considerable individual variation. SRC is a traumatic brain injury that is defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces with several common features that help define its nature. Limitations identified include that the current criteria for diagnosing SRC are clinically oriented and that there is no gold/standard to assess their diagnostic properties. A future, more valid definition of SRC would better identify concussed players by demonstrating high predictive positive/negative values. Currently, the use of helmet-based systems to study the biomechanics of SRC is limited to few collision sports. New approaches need to be developed to provide objective markers for SRC. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

An embedded system for face classification in infrared video using sparse representation

NASA Astrophysics Data System (ADS)

Saavedra M., Antonio; Pezoa, Jorge E.; Zarkesh-Ha, Payman; Figueroa, Miguel

2017-09-01

We propose a platform for robust face recognition in Infrared (IR) images using Compressive Sensing (CS). In line with CS theory, the classification problem is solved using a sparse representation framework, where test images are modeled by means of a linear combination of the training set. Because the training set constitutes an over-complete dictionary, we identify new images by finding their sparsest representation based on the training set, using standard l1-minimization algorithms. Unlike conventional face-recognition algorithms, we feature extraction is performed using random projections with a precomputed binary matrix, as proposed in the CS literature. This random sampling reduces the effects of noise and occlusions such as facial hair, eyeglasses, and disguises, which are notoriously challenging in IR images. Thus, the performance of our framework is robust to these noise and occlusion factors, achieving an average accuracy of approximately 90% when the UCHThermalFace database is used for training and testing purposes. We implemented our framework on a high-performance embedded digital system, where the computation of the sparse representation of IR images was performed by a dedicated hardware using a deeply pipelined architecture on an Field-Programmable Gate Array (FPGA).

Bag-of-visual-ngrams for histopathology image classification

NASA Astrophysics Data System (ADS)

López-Monroy, A. Pastor; Montes-y-Gómez, Manuel; Escalante, Hugo Jair; Cruz-Roa, Angel; González, Fabio A.

2013-11-01

This paper describes an extension of the Bag-of-Visual-Words (BoVW) representation for image categorization (IC) of histophatology images. This representation is one of the most used approaches in several high-level computer vision tasks. However, the BoVW representation has an important limitation: the disregarding of spatial information among visual words. This information may be useful to capture discriminative visual-patterns in specific computer vision tasks. In order to overcome this problem we propose the use of visual n-grams. N-grams based-representations are very popular in the field of natural language processing (NLP), in particular within text mining and information retrieval. We propose building a codebook of n-grams and then representing images by histograms of visual n-grams. We evaluate our proposal in the challenging task of classifying histopathology images. The novelty of our proposal lies in the fact that we use n-grams as attributes for a classification model (together with visual-words, i.e., 1-grams). This is common practice within NLP, although, to the best of our knowledge, this idea has not been explored yet within computer vision. We report experimental results in a database of histopathology images where our proposed method outperforms the traditional BoVWs formulation.

Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions.

PubMed

Najibi, Seyed Morteza; Maadooliat, Mehdi; Zhou, Lan; Huang, Jianhua Z; Gao, Xin

2017-01-01

Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

Effects of visual cue and response assignment on spatial stimulus coding in stimulus-response compatibility.

PubMed

Nishimura, Akio; Yokosawa, Kazuhiko

2012-01-01

Tlauka and McKenna ( 2000 ) reported a reversal of the traditional stimulus-response compatibility (SRC) effect (faster responding to a stimulus presented on the same side than to one on the opposite side) when the stimulus appearing on one side of a display is a member of a superordinate unit that is largely on the opposite side. We investigated the effects of a visual cue that explicitly shows a superordinate unit, and of assignment of multiple stimuli within each superordinate unit to one response, on the SRC effect based on superordinate unit position. Three experiments revealed that stimulus-response assignment is critical, while the visual cue plays a minor role, in eliciting the SRC effect based on the superordinate unit position. Findings suggest bidirectional interaction between perception and action and simultaneous spatial stimulus coding according to multiple frames of reference, with contribution of each coding to the SRC effect flexibly varying with task situations.

Gait recognition based on Gabor wavelets and modified gait energy image for human identification

NASA Astrophysics Data System (ADS)

Huang, Deng-Yuan; Lin, Ta-Wei; Hu, Wu-Chih; Cheng, Chih-Hsiang

2013-10-01

This paper proposes a method for recognizing human identity using gait features based on Gabor wavelets and modified gait energy images (GEIs). Identity recognition by gait generally involves gait representation, extraction, and classification. In this work, a modified GEI convolved with an ensemble of Gabor wavelets is proposed as a gait feature. Principal component analysis is then used to project the Gabor-wavelet-based gait features into a lower-dimension feature space for subsequent classification. Finally, support vector machine classifiers based on a radial basis function kernel are trained and utilized to recognize human identity. The major contributions of this paper are as follows: (1) the consideration of the shadow effect to yield a more complete segmentation of gait silhouettes; (2) the utilization of motion estimation to track people when walkers overlap; and (3) the derivation of modified GEIs to extract more useful gait information. Extensive performance evaluation shows a great improvement of recognition accuracy due to the use of shadow removal, motion estimation, and gait representation using the modified GEIs and Gabor wavelets.

Unsupervised feature learning for autonomous rock image classification

NASA Astrophysics Data System (ADS)

Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

2017-09-01

Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

Artificial intelligence systems based on texture descriptors for vaccine development.

PubMed

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

2011-02-01

The aim of this work is to analyze and compare several feature extraction methods for peptide classification that are based on the calculation of texture descriptors starting from a matrix representation of the peptide. This texture-based representation of the peptide is then used to train a support vector machine classifier. In our experiments, the best results are obtained using local binary patterns variants and the discrete cosine transform with selected coefficients. These results are better than those previously reported that employed texture descriptors for peptide representation. In addition, we perform experiments that combine standard approaches based on amino acid sequence. The experimental section reports several tests performed on a vaccine dataset for the prediction of peptides that bind human leukocyte antigens and on a human immunodeficiency virus (HIV-1). Experimental results confirm the usefulness of our novel descriptors. The matlab implementation of our approaches is available at http://bias.csr.unibo.it/nanni/TexturePeptide.zip.

Representation learning via Dual-Autoencoder for recommendation.

PubMed

Zhuang, Fuzhen; Zhang, Zhiqiang; Qian, Mingda; Shi, Chuan; Xie, Xing; He, Qing

2017-06-01

Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

PubMed

Peng, Yong; Lu, Bao-Liang; Wang, Suhang

2015-05-01

Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches. Copyright © 2015 Elsevier Ltd. All rights reserved.

Extraction of texture features with a multiresolution neural network

NASA Astrophysics Data System (ADS)

Lepage, Richard; Laurendeau, Denis; Gagnon, Roger A.

1992-09-01

Texture is an important surface characteristic. Many industrial materials such as wood, textile, or paper are best characterized by their texture. Detection of defaults occurring on such materials or classification for quality control anD matching can be carried out through careful texture analysis. A system for the classification of pieces of wood used in the furniture industry is proposed. This paper is concerned with a neural network implementation of the features extraction and classification components of the proposed system. Texture appears differently depending at which spatial scale it is observed. A complete description of a texture thus implies an analysis at several spatial scales. We propose a compact pyramidal representation of the input image for multiresolution analysis. The feature extraction system is implemented on a multilayer artificial neural network. Each level of the pyramid, which is a representation of the input image at a given spatial resolution scale, is mapped into a layer of the neural network. A full resolution texture image is input at the base of the pyramid and a representation of the texture image at multiple resolutions is generated by the feedforward pyramid structure of the neural network. The receptive field of each neuron at a given pyramid level is preprogrammed as a discrete Gaussian low-pass filter. Meaningful characteristics of the textured image must be extracted if a good resolving power of the classifier must be achieved. Local dominant orientation is the principal feature which is extracted from the textured image. Local edge orientation is computed with a Sobel mask at four orientation angles (multiple of (pi) /4). The resulting intrinsic image, that is, the local dominant orientation image, is fed to the texture classification neural network. The classification network is a three-layer feedforward back-propagation neural network.

4-Hydroxynonenal activates Src through a non-canonical pathway that involves EGFR/PTP1B

PubMed Central

Zhang, Hongqiao; Forman, Henry Jay

2015-01-01

Src, a non-receptor protein tyrosine kinase involved in many biological processes, can be activated through both redox-dependent and independent mechanisms. 4-Hydroxy-2-nonenal (HNE) is a lipid peroxidation product that is increased in pathophysiological conditions associated with Src activation. This study examined how HNE activates human c-Src. In the canonical pathway Src activation is initiated by dephosphorylation of pTyr530 followed by conformational change that causes Src auto-phosphorylation at Tyr419 and its activation. HNE increased Src activation in both dose- and time-dependent manner, while it also increased Src phosphorylation at Tyr530 (pTyr530 Src), suggesting that HNE activated Src via a non-canonical mechanism. Protein tyrosine phosphatase 1B inhibitor (539741), at concentrations that increased basal pTyr530 Src, also increased basal Src activity and significantly reduced HNE-mediated Src activation. The EGFR inhibitor, AG1478, and EGFR silencing, abrogated HNE-mediated EGFR activation and inhibited basal and HNE-induced Src activity. In addition, AG1478 also eliminated the increase of basal Src activation by a PTP1B inhibitor. Taken together these data suggest that HNE can activate Src partly through a non-canonical pathway involving activation of EGFR and inhibition of PTP1B. PMID:26453921

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.

PubMed

Lu, Na; Li, Tengfei; Ren, Xiaodong; Miao, Hongyu

2017-06-01

Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by electroencephalography (EEG) as nonstationary time series of low signal-to-noise ratio. Although a variety of methods have been previously developed to learn EEG signal features, the deep learning idea has rarely been explored to generate new representation of EEG features and achieve further performance improvement for motor imagery classification. In this study, a novel deep learning scheme based on restricted Boltzmann machine (RBM) is proposed. Specifically, frequency domain representations of EEG signals obtained via fast Fourier transform (FFT) and wavelet package decomposition (WPD) are obtained to train three RBMs. These RBMs are then stacked up with an extra output layer to form a four-layer neural network, which is named the frequential deep belief network (FDBN). The output layer employs the softmax regression to accomplish the classification task. Also, the conjugate gradient method and backpropagation are used to fine tune the FDBN. Extensive and systematic experiments have been performed on public benchmark datasets, and the results show that the performance improvement of FDBN over other selected state-of-the-art methods is statistically significant. Also, several findings that may be of significant interest to the BCI community are presented in this article.

Spatiotemporal dynamics of similarity-based neural representations of facial identity

PubMed Central

Vida, Mark D.; Nestor, Adrian; Plaut, David C.; Behrmann, Marlene

2017-01-01

Humans’ remarkable ability to quickly and accurately discriminate among thousands of highly similar complex objects demands rapid and precise neural computations. To elucidate the process by which this is achieved, we used magnetoencephalography to measure spatiotemporal patterns of neural activity with high temporal resolution during visual discrimination among a large and carefully controlled set of faces. We also compared these neural data to lower level “image-based” and higher level “identity-based” model-based representations of our stimuli and to behavioral similarity judgments of our stimuli. Between ∼50 and 400 ms after stimulus onset, face-selective sources in right lateral occipital cortex and right fusiform gyrus and sources in a control region (left V1) yielded successful classification of facial identity. In all regions, early responses were more similar to the image-based representation than to the identity-based representation. In the face-selective regions only, responses were more similar to the identity-based representation at several time points after 200 ms. Behavioral responses were more similar to the identity-based representation than to the image-based representation, and their structure was predicted by responses in the face-selective regions. These results provide a temporally precise description of the transformation from low- to high-level representations of facial identity in human face-selective cortex and demonstrate that face-selective cortical regions represent multiple distinct types of information about face identity at different times over the first 500 ms after stimulus onset. These results have important implications for understanding the rapid emergence of fine-grained, high-level representations of object identity, a computation essential to human visual expertise. PMID:28028220

An Efficient, Lossless Database for Storing and Transmitting Medical Images

NASA Technical Reports Server (NTRS)

Fenstermacher, Marc J.

1998-01-01

This research aimed in creating new compression methods based on the central idea of Set Redundancy Compression (SRC). Set Redundancy refers to the common information that exists in a set of similar images. SRC compression methods take advantage of this common information and can achieve improved compression of similar images by reducing their Set Redundancy. The current research resulted in the development of three new lossless SRC compression methods: MARS (Median-Aided Region Sorting), MAZE (Max-Aided Zero Elimination) and MaxGBA (Max-Guided Bit Allocation).

Structural classification of proteins using texture descriptors extracted from the cellular automata image.

PubMed

Kavianpour, Hamidreza; Vasighi, Mahdi

2017-02-01

Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.

The evaluative imaging of mental models - Visual representations of complexity

NASA Technical Reports Server (NTRS)

Dede, Christopher

1989-01-01

The paper deals with some design issues involved in building a system that could visually represent the semantic structures of training materials and their underlying mental models. In particular, hypermedia-based semantic networks that instantiate classification problem solving strategies are thought to be a useful formalism for such representations; the complexity of these web structures can be best managed through visual depictions. It is also noted that a useful approach to implement in these hypermedia models would be some metrics of conceptual distance.

Low-dimensional representations of the three component loop braid group

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

Bruillard, Paul; Chang, Liang; Hong, Seung-Moon

2015-11-01

Motivated by physical and topological applications, we study representations of the group LB3 o motions of 3 unlinked oriented circles in R3. Our point of view is to regard the three strand braid group B3 as a subgroup of LB3 and study the problem of extending B3 representations. We introduce the notion of a standard extension and characterize B3 represenations admiting such an extension. In particular we show, using a classification result of Tuba and Wenzl, that every irreducible B3 representation of dimension at most 5 has a (standard) extension. We show that this result is sharp by exhibiting anmore » irreducible 6-dimensional B3 representation that has no extension (standard or otherwise). We obtain complete classifications of (1) irreducible 2-dimensional LB3 representations (2) extensions of irreducible B3 representations and (3) irreducible LB3 representations whose restriction to B3 has abelian image.« less

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

Tokola, Ryan A; Mikkilineni, Aravind K; Boehnen, Chris Bensing

Despite being increasingly easy to acquire, 3D data is rarely used for face-based biometrics applications beyond identification. Recent work in image-based demographic biometrics has enjoyed much success, but these approaches suffer from the well-known limitations of 2D representations, particularly variations in illumination, texture, and pose, as well as a fundamental inability to describe 3D shape. This paper shows that simple 3D shape features in a face-based coordinate system are capable of representing many biometric attributes without problem-specific models or specialized domain knowledge. The same feature vector achieves impressive results for problems as diverse as age estimation, gender classification, and racemore » classification.« less

Expert identification of visual primitives used by CNNs during mammogram classification

NASA Astrophysics Data System (ADS)

Wu, Jimmy; Peck, Diondra; Hsieh, Scott; Dialani, Vandana; Lehman, Constance D.; Zhou, Bolei; Syrgkanis, Vasilis; Mackey, Lester; Patterson, Genevieve

2018-02-01

This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms. We propose an expert-in-the-loop inter- pretation method to label the behavior of internal units in convolutional neural networks (CNNs). Expert radiologists identify that the visual patterns detected by the units are correlated with meaningful medical phenomena such as mass tissue and calcificated vessels. We demonstrate that several trained CNN models are able to produce explanatory descriptions to support the final classification decisions. We view this as an important first step toward interpreting the internal representations of medical classification CNNs and explaining their predictions.

The spatiotemporal pattern of Src activation at lipid rafts revealed by diffusion-corrected FRET imaging.

PubMed

Lu, Shaoying; Ouyang, Mingxing; Seong, Jihye; Zhang, Jin; Chien, Shu; Wang, Yingxiao

2008-07-25

Genetically encoded biosensors based on fluorescence resonance energy transfer (FRET) have been widely applied to visualize the molecular activity in live cells with high spatiotemporal resolution. However, the rapid diffusion of biosensor proteins hinders a precise reconstruction of the actual molecular activation map. Based on fluorescence recovery after photobleaching (FRAP) experiments, we have developed a finite element (FE) method to analyze, simulate, and subtract the diffusion effect of mobile biosensors. This method has been applied to analyze the mobility of Src FRET biosensors engineered to reside at different subcompartments in live cells. The results indicate that the Src biosensor located in the cytoplasm moves 4-8 folds faster (0.93+/-0.06 microm(2)/sec) than those anchored on different compartments in plasma membrane (at lipid raft: 0.11+/-0.01 microm(2)/sec and outside: 0.18+/-0.02 microm(2)/sec). The mobility of biosensor at lipid rafts is slower than that outside of lipid rafts and is dominated by two-dimensional diffusion. When this diffusion effect was subtracted from the FRET ratio images, high Src activity at lipid rafts was observed at clustered regions proximal to the cell periphery, which remained relatively stationary upon epidermal growth factor (EGF) stimulation. This result suggests that EGF induced a Src activation at lipid rafts with well-coordinated spatiotemporal patterns. Our FE-based method also provides an integrated platform of image analysis for studying molecular mobility and reconstructing the spatiotemporal activation maps of signaling molecules in live cells.

v-Src-driven transformation is due to chromosome abnormalities but not Src-mediated growth signaling.

PubMed

Honda, Takuya; Morii, Mariko; Nakayama, Yuji; Suzuki, Ko; Yamaguchi, Noritaka; Yamaguchi, Naoto

2018-01-18

v-Src is the first identified oncogene product and has a strong tyrosine kinase activity. Much of the literature indicates that v-Src expression induces anchorage-independent and infinite cell proliferation through continuous stimulation of growth signaling by v-Src activity. Although all of v-Src-expressing cells are supposed to form transformed colonies, low frequencies of v-Src-induced colony formation have been observed so far. Using cells that exhibit high expression efficiencies of inducible v-Src, we show that v-Src expression causes cell-cycle arrest through p21 up-regulation despite ERK activation. v-Src expression also induces chromosome abnormalities and unexpected suppression of v-Src expression, leading to p21 down-regulation and ERK inactivation. Importantly, among v-Src-suppressed cells, only a limited number of cells gain the ability to re-proliferate and form transformed colonies. Our findings provide the first evidence that v-Src-driven transformation is attributed to chromosome abnormalities, but not continuous stimulation of growth signaling, possibly through stochastic genetic alterations.

Quantitative assessment of fat infiltration in the rotator cuff muscles using water-fat MRI.

PubMed

Nardo, Lorenzo; Karampinos, Dimitrios C; Lansdown, Drew A; Carballido-Gamio, Julio; Lee, Sonia; Maroldi, Roberto; Ma, C Benjamin; Link, Thomas M; Krug, Roland

2014-05-01

To evaluate a chemical shift-based fat quantification technique in the rotator cuff muscles in comparison with the semiquantitative Goutallier fat infiltration classification (GC) and to assess their relationship with clinical parameters. The shoulders of 57 patients were imaged using a 3T MR scanner. The rotator cuff muscles were assessed for fat infiltration using GC by two radiologists and an orthopedic surgeon. Sequences included oblique-sagittal T1-, T2-, and proton density-weighted fast spin echo, and six-echo gradient echo. The iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) was used to measure fat fraction. Pain and range of motion of the shoulder were recorded. Fat fraction values were significantly correlated with GC grades (P < 0.0001, κ >0.9) showing consistent increase with GC grades (grade = 0, 0%-5.59%; grade = 1, 1.1%-9.70%; grade = 2, 6.44%-14.86%; grade = 3, 15.25%-17.77%; grade = 4, 19.85%-29.63%). A significant correlation between fat infiltration of the subscapularis muscle quantified with IDEAL versus 1) deficit in internal rotation (Spearman Rank Correlation Coefficient [SRC] = 0.39, 95% confidence interval [CI] 0.13-0.60, P < 0.01) and 2) pain (SRC coefficient = 0.313, 95% CI 0.049-0.536, P = 0.02) was found but was not seen between the clinical parameters and GC grades. Additionally, only quantitative fat infiltration measures of the supraspinatus muscle were significantly correlated with a deficit in abduction (SRC coefficient = 0.45, 95% CI 0.20-0.60, P < 0.01). An accurate and highly reproducible fat quantification in the rotator cuff muscles using water-fat magnetic resonance imaging (MRI) techniques is possible and significantly correlates with shoulder pain and range of motion. Copyright © 2013 Wiley Periodicals, Inc.

Interactive Classification Technology

NASA Technical Reports Server (NTRS)

deBessonet, Cary

2000-01-01

The investigators upgraded a knowledge representation language called SL (Symbolic Language) and an automated reasoning system called SMS (Symbolic Manipulation System) to enable the more effective use of the technologies in automated reasoning and interactive classification systems. The overall goals of the project were: 1) the enhancement of the representation language SL to accommodate a wider range of meaning; 2) the development of a default inference scheme to operate over SL notation as it is encoded; and 3) the development of an interpreter for SL that would handle representations of some basic cognitive acts and perspectives.

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.

PubMed

Wang, Shunfang; Liu, Shuhui

2015-12-19

An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

PubMed Central

Wang, Shunfang; Liu, Shuhui

2015-01-01

An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one. PMID:26703574

Learning semantic histopathological representation for basal cell carcinoma classification

NASA Astrophysics Data System (ADS)

Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

2013-03-01

Diagnosis of a histopathology glass slide is a complex process that involves accurate recognition of several structures, their function in the tissue and their relation with other structures. The way in which the pathologist represents the image content and the relations between those objects yields a better and accurate diagnoses. Therefore, an appropriate semantic representation of the image content will be useful in several analysis tasks such as cancer classification, tissue retrieval and histopahological image analysis, among others. Nevertheless, to automatically recognize those structures and extract their inner semantic meaning are still very challenging tasks. In this paper we introduce a new semantic representation that allows to describe histopathological concepts suitable for classification. The approach herein identify local concepts using a dictionary learning approach, i.e., the algorithm learns the most representative atoms from a set of random sampled patches, and then models the spatial relations among them by counting the co-occurrence between atoms, while penalizing the spatial distance. The proposed approach was compared with a bag-of-features representation in a tissue classification task. For this purpose, 240 histological microscopical fields of view, 24 per tissue class, were collected. Those images fed a Support Vector Machine classifier per class, using 120 images as train set and the remaining ones for testing, maintaining the same proportion of each concept in the train and test sets. The obtained classification results, averaged from 100 random partitions of training and test sets, shows that our approach is more sensitive in average than the bag-of-features representation in almost 6%.

Dasatinib is preclinically active against Src-overexpressing human transitional cell carcinoma of the urothelium with activated Src signaling.

PubMed

Levitt, Jonathan M; Yamashita, Hideyuki; Jian, Weiguo; Lerner, Seth P; Sonpavde, Guru

2010-05-01

Dasatinib is an orally administered multitargeted kinase inhibitor that targets Src family tyrosine kinases, Abl, c-Kit, and PDGFR. A preclinical study was conducted to evaluate dasatinib alone or combined with cisplatin for human transitional cell carcinoma (TCC). Expression of Src in a human TCC tissue microarray was evaluated by immunohistochemistry. The activity of dasatinib and/or cisplatin was evaluated in six human TCC cell lines. Western blot was done to assess Src and phosphorylated-Src (p-Src) expression. The activity of dasatinib alone and in combination with cisplatin was determined in murine subcutaneous xenografts. Sixty-two percent to 75% of human TCC expressed Src. Dasatinib displayed significant antiproliferative activity at nanomolar concentrations against two human TCC cell lines (RT4 and Hu456) that exhibited high Src and p-Src expression and were cisplatin-resistant. RT4 cells were the most sensitive and displayed the highest level of Src pathway activation (p-Src/Src ratio). Dasatinib downregulated p-Src in either sensitive or resistant cells. TCC cells that were sensitive to cisplatin (5637 and TCC-SUP) were highly resistant to dasatinib and exhibited low Src expression. Dasatinib showed antitumor activity in RT4 murine xenografts, and the combination of dasatinib and cisplatin was significantly more active than placebo. Combination dasatinib plus cisplatin significantly inhibited proliferation and promoted apoptosis in vivo. In conclusion, dasatinib displayed significant preclinical antitumor activity against Src-overexpressing human TCC with active Src signaling and was highly active in combination with cisplatin in vivo. Further clinical development might be warranted in selected human subjects.

Hyperspectral Image Classification via Kernel Sparse Representation

DTIC Science & Technology

2013-01-01

classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model , where...joint sparsity model , where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training...hyperspectral imagery, joint spar- sity model , kernel methods, sparse representation. I. INTRODUCTION HYPERSPECTRAL imaging sensors capture images

Aldosterone rapidly activates Src kinase in M-1 cells involving the mineralocorticoid receptor and HSP84.

PubMed

Braun, Sabine; Lösel, Ralf; Wehling, Martin; Boldyreff, Brigitte

2004-07-16

We investigated the effect of aldosterone on Src kinase. In the kidney cell line, M-1 aldosterone leads to a >2-fold transient activation of Src kinase seen as early as 2 min after aldosterone administration. Maximal Src kinase activation was measured at an aldosterone concentration of 1 nM. In parallel to activation, autophosphorylation at Tyr-416 of Src kinase increased. Src kinase activation was blocked by spironolactone. Aldosterone led to increased association of Src with HSP84. Furthermore, rapamycin blocked aldosterone-induced Src activation. We conclude that Src activation by aldosterone is mediated through the mineralocorticoid receptor and HSP84.

Progressive sparse representation-based classification using local discrete cosine transform evaluation for image recognition

NASA Astrophysics Data System (ADS)

Song, Xiaoning; Feng, Zhen-Hua; Hu, Guosheng; Yang, Xibei; Yang, Jingyu; Qi, Yunsong

2015-09-01

This paper proposes a progressive sparse representation-based classification algorithm using local discrete cosine transform (DCT) evaluation to perform face recognition. Specifically, the sum of the contributions of all training samples of each subject is first taken as the contribution of this subject, then the redundant subject with the smallest contribution to the test sample is iteratively eliminated. Second, the progressive method aims at representing the test sample as a linear combination of all the remaining training samples, by which the representation capability of each training sample is exploited to determine the optimal "nearest neighbors" for the test sample. Third, the transformed DCT evaluation is constructed to measure the similarity between the test sample and each local training sample using cosine distance metrics in the DCT domain. The final goal of the proposed method is to determine an optimal weighted sum of nearest neighbors that are obtained under the local correlative degree evaluation, which is approximately equal to the test sample, and we can use this weighted linear combination to perform robust classification. Experimental results conducted on the ORL database of faces (created by the Olivetti Research Laboratory in Cambridge), the FERET face database (managed by the Defense Advanced Research Projects Agency and the National Institute of Standards and Technology), AR face database (created by Aleix Martinez and Robert Benavente in the Computer Vision Center at U.A.B), and USPS handwritten digit database (gathered at the Center of Excellence in Document Analysis and Recognition at SUNY Buffalo) demonstrate the effectiveness of the proposed method.

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

PubMed Central

Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

2016-01-01

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions. PMID:28154470

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.

PubMed

Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

2016-05-26

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

A cell-penetrating peptide based on the interaction between c-Src and connexin43 reverses glioma stem cell phenotype

PubMed Central

Gangoso, E; Thirant, C; Chneiweiss, H; Medina, J M; Tabernero, A

2014-01-01

Connexin43 (Cx43), the main gap junction channel-forming protein in astrocytes, is downregulated in malignant gliomas. These tumors are composed of a heterogeneous population of cells that include many with stem-cell-like properties, called glioma stem cells (GSCs), which are highly tumorigenic and lack Cx43 expression. Interestingly, restoring Cx43 reverses GSC phenotype and consequently reduces their tumorigenicity. In this study, we investigated the mechanism by which Cx43 exerts its antitumorigenic effects on GSCs. We have focused on the tyrosine kinase c-Src, which interacts with the intracellular carboxy tail of Cx43. We found that Cx43 regulates c-Src activity and proliferation in human GSCs expanded in adherent culture. Thus, restoring Cx43 in GSCs inhibited c-Src activity, which in turn promoted the downregulation of the inhibitor of differentiation Id1. Id1 sustains stem cell phenotype as it controls the expression of Sox2, responsible for stem cell self-renewal, and promotes cadherin switching, which has been associated to epithelial–mesenchymal transition. Our results show that both the ectopic expression of Cx43 and the inhibition of c-Src reduced Id1, Sox2 expression and promoted the switch from N- to E-cadherin, suggesting that Cx43, by inhibiting c-Src, downregulates Id1 with the subsequent changes in stem cell phenotype. On the basis of this mechanism, we found that a cell-penetrating peptide, containing the region of Cx43 that interacts with c-Src, mimics the effect of Cx43 on GSC phenotype, confirming the relevance of the interaction between Cx43 and c-Src in the regulation of the malignant phenotype and pinpointing this interaction as a promising therapeutic target. PMID:24457967

Mammographic mass classification based on possibility theory

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2017-03-01

Shape and margin features are very important for differentiating between benign and malignant masses in mammographic images. In fact, benign masses are usually round and oval and have smooth contours. However, malignant tumors have generally irregular shape and appear lobulated or speculated in margins. This knowledge suffers from imprecision and ambiguity. Therefore, this paper deals with the problem of mass classification by using shape and margin features while taking into account the uncertainty linked to the degree of truth of the available information and the imprecision related to its content. Thus, in this work, we proposed a novel mass classification approach which provides a possibility based representation of the extracted shape features and builds a possibility knowledge basis in order to evaluate the possibility degree of malignancy and benignity for each mass. For experimentation, the MIAS database was used and the classification results show the great performance of our approach in spite of using simple features.

Rough set classification based on quantum logic

NASA Astrophysics Data System (ADS)

Hassan, Yasser F.

2017-11-01

By combining the advantages of quantum computing and soft computing, the paper shows that rough sets can be used with quantum logic for classification and recognition systems. We suggest the new definition of rough set theory as quantum logic theory. Rough approximations are essential elements in rough set theory, the quantum rough set model for set-valued data directly construct set approximation based on a kind of quantum similarity relation which is presented here. Theoretical analyses demonstrate that the new model for quantum rough sets has new type of decision rule with less redundancy which can be used to give accurate classification using principles of quantum superposition and non-linear quantum relations. To our knowledge, this is the first attempt aiming to define rough sets in representation of a quantum rather than logic or sets. The experiments on data-sets have demonstrated that the proposed model is more accuracy than the traditional rough sets in terms of finding optimal classifications.

Generating virtual training samples for sparse representation of face images and face recognition

NASA Astrophysics Data System (ADS)

Du, Yong; Wang, Yu

2016-03-01

There are many challenges in face recognition. In real-world scenes, images of the same face vary with changing illuminations, different expressions and poses, multiform ornaments, or even altered mental status. Limited available training samples cannot convey these possible changes in the training phase sufficiently, and this has become one of the restrictions to improve the face recognition accuracy. In this article, we view the multiplication of two images of the face as a virtual face image to expand the training set and devise a representation-based method to perform face recognition. The generated virtual samples really reflect some possible appearance and pose variations of the face. By multiplying a training sample with another sample from the same subject, we can strengthen the facial contour feature and greatly suppress the noise. Thus, more human essential information is retained. Also, uncertainty of the training data is simultaneously reduced with the increase of the training samples, which is beneficial for the training phase. The devised representation-based classifier uses both the original and new generated samples to perform the classification. In the classification phase, we first determine K nearest training samples for the current test sample by calculating the Euclidean distances between the test sample and training samples. Then, a linear combination of these selected training samples is used to represent the test sample, and the representation result is used to classify the test sample. The experimental results show that the proposed method outperforms some state-of-the-art face recognition methods.

Learning and transfer of category knowledge in an indirect categorization task.

PubMed

Helie, Sebastien; Ashby, F Gregory

2012-05-01

Knowledge representations acquired during category learning experiments are 'tuned' to the task goal. A useful paradigm to study category representations is indirect category learning. In the present article, we propose a new indirect categorization task called the "same"-"different" categorization task. The same-different categorization task is a regular same-different task, but the question asked to the participants is about the stimulus category membership instead of stimulus identity. Experiment 1 explores the possibility of indirectly learning rule-based and information-integration category structures using the new paradigm. The results suggest that there is little learning about the category structures resulting from an indirect categorization task unless the categories can be separated by a one-dimensional rule. Experiment 2 explores whether a category representation learned indirectly can be used in a direct classification task (and vice versa). The results suggest that previous categorical knowledge acquired during a direct classification task can be expressed in the same-different categorization task only when the categories can be separated by a rule that is easily verbalized. Implications of these results for categorization research are discussed.

SH2 Ligand-Like Effects of Second Cytosolic Domain of Na/K-ATPase α1 Subunit on Src Kinase.

PubMed

Banerjee, Moumita; Duan, Qiming; Xie, Zijian

2015-01-01

Our previous studies have suggested that the α1 Na/K-ATPase interacts with Src to form a receptor complex. In vitro binding assays indicate an interaction between second cytosolic domain (CD2) of Na/K-ATPase α1 subunit and Src SH2 domain. Since SH2 domain targets Src to specific signaling complexes, we expressed CD2 as a cytosolic protein and studied whether it could act as a Src SH2 ligand in LLC-PK1 cells. Co-immunoprecipitation analyses indicated a direct binding of CD2 to Src, consistent with the in vitro binding data. Functionally, CD2 expression increased basal Src activity, suggesting a Src SH2 ligand-like property of CD2. Consistently, we found that CD2 expression attenuated several signaling pathways where Src plays an important role. For instance, although it increased surface expression of Na/K-ATPase, it decreased ouabain-induced activation of Src and ERK by blocking the formation of Na/K-ATPase/Src complex. Moreover, it also attenuated cell attachment-induced activation of Src/FAK. Consequently, CD2 delayed cell spreading, and inhibited cell proliferation. Furthermore, these effects appear to be Src-specific because CD2 expression had no effect on EGF-induced activation of EGF receptor and ERK. Hence, the new findings indicate the importance of Na/K-ATPase/Src interaction in ouabain-induced signal transduction, and support the proposition that the CD2 peptide may be utilized as a Src SH2 ligand capable of blocking Src-dependent signaling pathways via a different mechanism from a general Src kinase inhibitor.

"Interactive Classification Technology"

NASA Technical Reports Server (NTRS)

deBessonet, Cary

1999-01-01

The investigators are upgrading a knowledge representation language called SL (Symbolic Language) and an automated reasoning system called SMS (Symbolic Manipulation System) to enable the technologies to be used in automated reasoning and interactive classification systems. The overall goals of the project are: a) the enhancement of the representation language SL to accommodate multiple perspectives and a wider range of meaning; b) the development of a sufficient set of operators to enable the interpreter of SL to handle representations of basic cognitive acts; and c) the development of a default inference scheme to operate over SL notation as it is encoded. As to particular goals the first-year work plan focused on inferencing and.representation issues, including: 1) the development of higher level cognitive/ classification functions and conceptual models for use in inferencing and decision making; 2) the specification of a more detailed scheme of defaults and the enrichment of SL notation to accommodate the scheme; and 3) the adoption of additional perspectives for inferencing.

Identification and functional characterization of an Src homology domain 3 domain-binding site on Cbl.

PubMed

Sanjay, Archana; Miyazaki, Tsuyoshi; Itzstein, Cecile; Purev, Enkhtsetseg; Horne, William C; Baron, Roland

2006-12-01

Cbl is an adaptor protein and ubiquitin ligase that binds and is phosphorylated by the nonreceptor tyrosine kinase Src. We previously showed that the primary interaction between Src and Cbl is mediated by the Src homology domain 3 (SH3) of Src binding to proline-rich sequences of Cbl. The peptide Cbl RDLPPPPPPDRP(540-551), which corresponds to residues 540-551 of Cbl, inhibited the binding of a GST-Src SH3 fusion protein to Cbl, whereas RDLAPPAPPPDR(540-551) did not, suggesting that Src binds to this site on Cbl in a class I orientation. Mutating prolines 543-548 reduced Src binding to the Cbl 479-636 fragment significantly more than mutating the prolines in the PPVPPR(494-499) motif, which was previously reported to bind Src SH3. Mutating Cbl prolines 543-548 to alanines substantially reduced Src binding to Cbl, Src-induced phosphorylation of Cbl, and the inhibition of Src kinase activity by Cbl. Expressing the mutated Cbl in osteoclasts induced a moderate reduction in bone-resorbing activity and increased amounts of Src protein. In contrast, disabling the tyrosine kinase-binding domain of full-length Cbl by mutating glycine 306 to glutamic acid, and thereby preventing the previously described binding of the tyrosine kinase-binding domain to the Src phosphotyrosine 416, had no effect on Cbl phosphorylation, the inhibition of Src activity by full-length Cbl, or bone resorption. These data indicate that the Cbl RDLPPPP(540-546) sequence is a functionally important binding site for Src.

Vessel classification in overhead satellite imagery using weighted "bag of visual words"

NASA Astrophysics Data System (ADS)

Parameswaran, Shibin; Rainey, Katie

2015-05-01

Vessel type classification in maritime imagery is a challenging problem and has applications to many military and surveillance applications. The ability to classify a vessel correctly varies significantly depending on its appearance which in turn is affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The difficulty in classifying vessels also varies among different ship types as some types of vessels show more within-class variation than others. In our previous work, we showed that the bag of visual words" (V-BoW) was an effective feature representation for this classification task in the maritime domain. The V-BoW feature representation is analogous to the bag of words" (BoW) representation used in information retrieval (IR) application in text or natural language processing (NLP) domain. It has been shown in the textual IR applications that the performance of the BoW feature representation can be improved significantly by applying appropriate term-weighting such as log term frequency, inverse document frequency etc. Given the close correspondence between textual BoW (T-BoW) and V-BoW feature representations, we propose to apply several well-known term weighting schemes from the text IR domain on V-BoW feature representation to increase its ability to discriminate between ship types.

An SH2 domain-based tyrosine kinase assay using biotin ligase modified with a terbium(III) complex.

PubMed

Sueda, Shinji; Shinboku, Yuki; Kusaba, Takeshi

2013-01-01

Src homology 2 (SH2) domains are modules of approximately 100 amino acids and are known to bind phosphotyrosine-containing sequences with high affinity and specificity. In the present work, we developed an SH2 domain-based assay for Src tyrosine kinase using a unique biotinylation reaction from archaeon Sulfolobus tokodaii. S. tokodaii biotinylation has a unique property that biotin protein ligase (BPL) forms a stable complex with its biotinylated substrate protein (BCCP). Here, an SH2 domain from lymphocyte-specific tyrosine kinase was genetically fused to a truncated BCCP, and the resulting fusion protein was labeled through biotinylation with BPL carrying multiple copies of a luminescent Tb(3+) complex. The labeled SH2 fusion proteins were employed to detect a phosphorylated peptide immobilized on the surface of the microtiter plate, where the phosphorylated peptide was produced by phosphorylation to the substrate peptide by Src tyrosine kinase. Our assay allows for a reliable determination of the activity of Src kinase lower than 10 pg/μL by a simple procedure.

Soil CO2 flux from three ecosystems in tropical peatland of Sarawak, Malaysia

NASA Astrophysics Data System (ADS)

Melling, Lulie; Hatano, Ryusuke; Goh, Kah Joo

2005-02-01

Soil CO2 flux was measured monthly over a year from tropical peatland of Sarawak, Malaysia using a closed-chamber technique. The soil CO2 flux ranged from 100 to 533 mg C m2 h1 for the forest ecosystem, 63 to 245 mg C m2 h1 for the sago and 46 to 335 mg C m2 h1 for the oil palm. Based on principal component analysis (PCA), the environmental variables over all sites could be classified into three components, namely, climate, soil moisture and soil bulk density, which accounted for 86% of the seasonal variability. A regression tree approach showed that CO2 flux in each ecosystem was related to different underlying environmental factors. They were relative humidity for forest, soil temperature at 5 cm for sago and water-filled pore space for oil palm. On an annual basis, the soil CO2 flux was highest in the forest ecosystem with an estimated production of 2.1 kg C m2 yr1 followed by oil palm at 1.5 kg C m2 yr1 and sago at 1.1 kg C m2 yr1. The different dominant controlling factors in CO2 flux among the studied ecosystems suggested that land use affected the exchange of CO2 between tropical peatland and the atmosphere.

Alcohol approach tendencies in heavy drinkers: comparison of effects in a Relevant Stimulus-Response Compatibility task and an approach/avoidance Simon task.

PubMed

Field, Matt; Caren, Rhiane; Fernie, Gordon; De Houwer, Jan

2011-12-01

Several recent studies suggest that alcohol-related cues elicit automatic approach tendencies in heavy drinkers. A variety of tasks have been used to demonstrate these effects, including Relevant Stimulus-Response Compatibility (R-SRC) tasks and variants of Simon tasks. Previous work with normative stimuli suggests that the R-SRC task may be more sensitive than Simon tasks because the activation of approach tendencies may depend on encoding of the stimuli as alcohol-related, which occurs in the R-SRC task but not in Simon tasks. Our aim was to directly compare these tasks for the first time in the context of alcohol use. We administered alcohol versions of an R-SRC task and a Simon task to 62 social drinkers, who were designated as heavy or light drinkers based on a median split of their weekly alcohol consumption. Results indicated that, compared to light drinkers, heavy drinkers were faster to approach, rather than avoid, alcohol-related pictures in the R-SRC task but not in the Simon task. Theoretical implications and methodological issues are discussed.

Toward semantic-based retrieval of visual information: a model-based approach

NASA Astrophysics Data System (ADS)

Park, Youngchoon; Golshani, Forouzan; Panchanathan, Sethuraman

2002-07-01

This paper center around the problem of automated visual content classification. To enable classification based image or visual object retrieval, we propose a new image representation scheme called visual context descriptor (VCD) that is a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region. VCD utilizes the predetermined quality dimensions (i.e., types of features and quantization level) and semantic model templates mined in priori. Not only observed visual cues, but also contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector (e.g., color histogram, Gabor texture, etc.,) into a discrete event (e.g., terms in text). Good-feature to track, rule of thirds, iterative k-means clustering and TSVQ are involved in transformation of feature vectors into unified symbolic representations called visual terms. Similarity-based visual cue frequency estimation is also proposed and used for ensuring the correctness of model learning and matching since sparseness of sample data causes the unstable results of frequency estimation of visual cues. The proposed method naturally allows integration of heterogeneous visual or temporal or spatial cues in a single classification or matching framework, and can be easily integrated into a semantic knowledge base such as thesaurus, and ontology. Robust semantic visual model template creation and object based image retrieval are demonstrated based on the proposed content description scheme.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

PubMed

Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

PubMed Central

Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases. PMID:26571112

Elevated Src family kinase activity stabilizes E-cadherin-based junctions and collective movement of head and neck squamous cell carcinomas

PubMed Central

Veracini, Laurence; Grall, Dominique; Schaub, Sébastien; Divonne, Stéphanie Beghelli-de la Forest; Etienne-Grimaldi, Marie-Christine; Milano, Gérard; Bozec, Alexandre; Babin, Emmanuel; Sudaka, Anne; Thariat, Juliette; Van Obberghen-Schilling, Ellen

2015-01-01

EGF receptor (EGFR) overexpression is thought to drive head and neck carcinogenesis however clinical responses to EGFR-targeting agents have been modest and alternate targets are actively sought to improve results. Src family kinases (SFKs), reported to act downstream of EGFR are among the alternative targets for which increased expression or activity in epithelial tumors is commonly associated to the dissolution of E-cadherin-based junctions and acquisition of a mesenchymal-like phenotype. Robust expression of total and activated Src was observed in advanced stage head and neck tumors (N=60) and in head and neck squamous cell carcinoma lines. In cultured cancer cells Src co-localized with E-cadherin in cell-cell junctions and its phosphorylation on Y419 was both constitutive and independent of EGFR activation. Selective inhibition of SFKs with SU6656 delocalized E-cadherin and disrupted cellular junctions without affecting E-cadherin expression and this effect was phenocopied by knockdown of Src or Yes. These findings reveal an EGFR-independent role for SFKs in the maintenance of intercellular junctions, which likely contributes to the cohesive invasion E-cadherin-positive cells in advanced tumors. Further, they highlight the need for a deeper comprehension of molecular pathways that drive collective cell invasion, in absence of mesenchymal transition, in order to combat tumor spread. PMID:25779657

More than one kind of inference: re-examining what's learned in feature inference and classification.

PubMed

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

p68 Sam is a substrate of the insulin receptor and associates with the SH2 domains of p85 PI3K.

PubMed

Sánchez-Margalet, V; Najib, S

1999-07-23

The 68 kDa Src substrate associated during mitosis is an RNA binding protein with Src homology 2 and 3 domain binding sites. A role for Src associated in mitosis 68 as an adaptor protein in signaling transduction has been proposed in different systems such as T-cell receptors. In the present work, we have sought to assess the possible role of Src associated in mitosis 68 in insulin receptor signaling. We performed in vivo studies in HTC-IR cells and in vitro studies using recombinant Src associated in mitosis 68, purified insulin receptor and fusion proteins containing either the N-terminal or the C-terminal Src homology 2 domain of p85 phosphatidylinositol-3-kinase. We have found that Src associated in mitosis 68 is a substrate of the insulin receptor both in vivo and in vitro. Moreover, tyrosine-phosphorylated Src associated in mitosis 68 was found to associate with p85 phosphatidylinositol-3-kinase in response to insulin, as assessed by co-immunoprecipitation studies. Therefore, Src associated in mitosis 68 may be part of the signaling complexes of insulin receptor along with p85. In vitro studies demonstrate that Src associated in mitosis 68 associates with the Src homology 2 domains of p85 after tyrosine phosphorylation by the activated insulin receptor. Moreover, tyr-phosphorylated Src associated in mitosis 68 binds with a higher affinity to the N-terminal Src homology 2 domain of p85 compared to the C-terminal Src homology 2 domain of p85, suggesting a preferential association of Src associated in mitosis 68 with the N-terminal Src homology 2 domain of p85. This association may be important for the link of the signaling with RNA metabolism.

SGT1 is required in PcINF1/SRC2-1 induced pepper defense response by interacting with SRC2-1

PubMed Central

Liu, Zhi-qin; Liu, Yan-yan; Shi, Lan-ping; Yang, Sheng; Shen, Lei; Yu, Huan-xin; Wang, Rong-zhang; Wen, Jia-yu; Tang, Qian; Hussain, Ansar; Khan, Muhammad Ifnan; Hu, Jiong; Liu, Cai-ling; Zhang, Yang-wen; Cheng, Wei; He, Shui-lin

2016-01-01

PcINF1 was previously found to induce pepper defense response by interacting with SRC2-1, but the underlying mechanism remains uninvestigated. Herein, we describe the involvement of SGT1 in the PcINF1/SRC2-1-induced immunity. SGT1 was observed to be up-regulated by Phytophthora capsici inoculation and synergistically transient overexpression of PcINF1/SRC2-1 in pepper plants. SGT1-silencing compromised HR cell death, blocked H2O2 accumulation, and downregulated HR-associated and hormones-dependent marker genes’ expression triggered by PcINF1/SRC2-1 co-overexpression. The interaction between SRC2-1 and SGT1 was found by the yeast two hybrid system and was further confirmed by bimolecular fluorescence complementation and co-immunoprecipitation analyses. The SGT1/SRC2-1 interaction was enhanced by transient overexpression of PcINF1 and Phytophthora capsici inoculation, and SGT1-silencing attenuated PcINF1/SRC2-1 interaction. Additionally, by modulating subcellular localizations of SRC2-1, SGT1, and the interacting complex of SGT1/SRC2-1, it was revealed that exclusive nuclear targeting of the SGT1/SRC2-1 complex blocks immunity triggered by formation of SGT1/SRC2-1, and a translocation of the SGT1/SRC2-1 complex from the plasma membrane and cytoplasm to the nuclei upon the inoculation of P. capsici. Our data demonstrate that the SGT1/SRC2-1 interaction, and its nucleocytoplasmic partitioning, is involved in pepper’s immunity against P. capsici, thus providing a molecular link between Ca2+ signaling associated SRC2-1 and SGT1-mediated defense signaling. PMID:26898479

Hidden Semi-Markov Models and Their Application

NASA Astrophysics Data System (ADS)

Beyreuther, M.; Wassermann, J.

2008-12-01

In the framework of detection and classification of seismic signals there are several different approaches. Our choice for a more robust detection and classification algorithm is to adopt Hidden Markov Models (HMM), a technique showing major success in speech recognition. HMM provide a powerful tool to describe highly variable time series based on a double stochastic model and therefore allow for a broader class description than e.g. template based pattern matching techniques. Being a fully probabilistic model, HMM directly provide a confidence measure of an estimated classification. Furthermore and in contrast to classic artificial neuronal networks or support vector machines, HMM are incorporating the time dependence explicitly in the models thus providing a adequate representation of the seismic signal. As the majority of detection algorithms, HMM are not based on the time and amplitude dependent seismogram itself but on features estimated from the seismogram which characterize the different classes. Features, or in other words characteristic functions, are e.g. the sonogram bands, instantaneous frequency, instantaneous bandwidth or centroid time. In this study we apply continuous Hidden Semi-Markov Models (HSMM), an extension of continuous HMM. The duration probability of a HMM is an exponentially decaying function of the time, which is not a realistic representation of the duration of an earthquake. In contrast HSMM use Gaussians as duration probabilities, which results in an more adequate model. The HSMM detection and classification system is running online as an EARTHWORM module at the Bavarian Earthquake Service. Here the signals that are to be classified simply differ in epicentral distance. This makes it possible to easily decide whether a classification is correct or wrong and thus allows to better evaluate the advantages and disadvantages of the proposed algorithm. The evaluation is based on several month long continuous data and the results are additionally compared to the previously published discrete HMM, continuous HMM and a classic STA/LTA. The intermediate evaluation results are very promising.

What domains of clinical function should be assessed after sport-related concussion? A systematic review.

PubMed

Feddermann-Demont, Nina; Echemendia, Ruben J; Schneider, Kathryn J; Solomon, Gary S; Hayden, K Alix; Turner, Michael; Dvořák, Jiří; Straumann, Dominik; Tarnutzer, Alexander A

2017-06-01

Sport-related concussion (SRC) is a clinical diagnosis made after a sport-related head trauma. Inconsistency exists regarding appropriate methods for assessing SRC, which focus largely on symptom-scores, neurocognitive functioning and postural stability. Systematic literature review. MEDLINE, EMBASE, PsycINFO, Cochrane-DSR, Cochrane CRCT, CINAHL, SPORTDiscus (accessed July 9, 2016). Original (prospective) studies reporting on postinjury assessment in a clinical setting and evaluation of diagnostic tools within 2 weeks after an SRC. Forty-six studies covering 3284 athletes were included out of 2170 articles. Only the prospective studies were considered for final analysis (n=33; 2416 athletes). Concussion diagnosis was typically made on the sideline by an (certified) athletic trainer (55.0%), mainly on the basis of results from a symptom-based questionnaire. Clinical domains affected included cognitive, vestibular and headache/migraine. Headache, fatigue, difficulty concentrating and dizziness were the symptoms most frequently reported. Neurocognitive testing was used in 30/33 studies (90.9%), whereas balance was assessed in 9/33 studies (27.3%). The overall quality of the studies was considered low. The absence of an objective, gold standard criterion makes the accurate diagnosis of SRC challenging. Current approaches tend to emphasise cognition, symptom assessment and postural stability with less of a focus on other domains of functioning. We propose that the clinical assessment of SRC should be symptom based and interdisciplinary. Whenever possible, the SRC assessment should incorporate neurological, vestibular, ocular motor, visual, neurocognitive, psychological and cervical aspects. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Thyroid function in mice with compound heterozygous and homozygous disruptions of SRC-1 and TIF-2 coactivators: evidence for haploinsufficiency.

PubMed

Weiss, Roy E; Gehin, Martine; Xu, Jianming; Sadow, Peter M; O'Malley, Bert W; Chambon, Pierre; Refetoff, Samuel

2002-04-01

Steroid receptor coactivator (SRC)-1 and transcriptional intermediary factor (TIF)-2 are homologous nuclear receptor coactivators. We have investigated their possible redundancy as thyroid hormone (TH) coactivators by measuring thyroid function in compound SRC-1 and TIF-2 knock out (KO) mice. Whereas SRC-1 KO (SRC-1(-/-)) mice are resistant to TH and SRC-1(+/-) are not, we now demonstrate that TIF-2 KO (TIF-2(-/-)) mice have normal thyroid function. Yet double heterozygous, SRC-1(+/-)/TIF-2(+/-) mice manifested resistance to TH of a similar degree as that in mice completely deficient in SRC-1. KO of both SRC-1 and TIF-2 resulted in marked increases of serum TH and thyrotropin concentrations. This work demonstrates gene dosage effect in nuclear coactivators manifesting as haploinsufficiency and functional redundancy of SRC-1 and TIF-2.

BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.

PubMed

Guo, Yang; Liu, Shuhui; Li, Zhanhuai; Shang, Xuequn

2018-04-11

The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes by using deep learning on high-dimensional and small-scale biology data.

Visual Recognition Software for Binary Classification and Its Application to Spruce Pollen Identification

PubMed Central

Tcheng, David K.; Nayak, Ashwin K.; Fowlkes, Charless C.; Punyasena, Surangi W.

2016-01-01

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based “pollen spotting” model to segment pollen grains from the slide background. We next tested ARLO’s ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems. PMID:26867017

Cbl Associates with Pyk2 and Src to Regulate Src Kinase Activity, αvβ3 Integrin-Mediated Signaling, Cell Adhesion, and Osteoclast Motility

PubMed Central

Sanjay, Archana; Houghton, Adam; Neff, Lynn; DiDomenico, Emilia; Bardelay, Chantal; Antoine, Evelyne; Levy, Joan; Gailit, James; Bowtell, David; Horne, William C.; Baron, Roland

2001-01-01

The signaling events downstream of integrins that regulate cell attachment and motility are only partially understood. Using osteoclasts and transfected 293 cells, we find that a molecular complex comprising Src, Pyk2, and Cbl functions to regulate cell adhesion and motility. The activation of integrin αvβ3 induces the [Ca2+]i-dependent phosphorylation of Pyk2 Y402, its association with Src SH2, Src activation, and the Src SH3-dependent recruitment and phosphorylation of c-Cbl. Furthermore, the PTB domain of Cbl is shown to bind to phosphorylated Tyr-416 in the activation loop of Src, the autophosphorylation site of Src, inhibiting Src kinase activity and integrin-mediated adhesion. Finally, we show that deletion of c Src or c-Cbl leads to a decrease in osteoclast migration. Thus, binding of αvβ3 integrin induces the formation of a Pyk2/Src/Cbl complex in which Cbl is a key regulator of Src kinase activity and of cell adhesion and migration. These findings may explain the osteopetrotic phenotype in the Src−/− mice. PMID:11149930

Emotion Recognition of Weblog Sentences Based on an Ensemble Algorithm of Multi-label Classification and Word Emotions

NASA Astrophysics Data System (ADS)

Li, Ji; Ren, Fuji

Weblogs have greatly changed the communication ways of mankind. Affective analysis of blog posts is found valuable for many applications such as text-to-speech synthesis or computer-assisted recommendation. Traditional emotion recognition in text based on single-label classification can not satisfy higher requirements of affective computing. In this paper, the automatic identification of sentence emotion in weblogs is modeled as a multi-label text categorization task. Experiments are carried out on 12273 blog sentences from the Chinese emotion corpus Ren_CECps with 8-dimension emotion annotation. An ensemble algorithm RAKEL is used to recognize dominant emotions from the writer's perspective. Our emotion feature using detailed intensity representation for word emotions outperforms the other main features such as the word frequency feature and the traditional lexicon-based feature. In order to deal with relatively complex sentences, we integrate grammatical characteristics of punctuations, disjunctive connectives, modification relations and negation into features. It achieves 13.51% and 12.49% increases for Micro-averaged F1 and Macro-averaged F1 respectively compared to the traditional lexicon-based feature. Result shows that multiple-dimension emotion representation with grammatical features can efficiently classify sentence emotion in a multi-label problem.

Design of Cancelable Palmprint Templates Based on Look Up Table

NASA Astrophysics Data System (ADS)

Qiu, Jian; Li, Hengjian; Dong, Jiwen

2018-03-01

A novel cancelable palmprint templates generation scheme is proposed in this paper. Firstly, the Gabor filter and chaotic matrix are used to extract palmprint features. It is then arranged into a row vector and divided into equal size blocks. These blocks are converted to corresponding decimals and mapped to look up tables, forming final cancelable palmprint features based on the selected check bits. Finally, collaborative representation based classification with regularized least square is used for classification. Experimental results on the Hong Kong PolyU Palmprint Database verify that the proposed cancelable templates can achieve very high performance and security levels. Meanwhile, it can also satisfy the needs of real-time applications.

Activation pathway of Src kinase reveals intermediate states as novel targets for drug design

PubMed Central

Shukla, Diwakar; Meng, Yilin; Roux, Benoît; Pande, Vijay S.

2014-01-01

Unregulated activation of Src kinases leads to aberrant signaling, uncontrolled growth, and differentiation of cancerous cells. Reaching a complete mechanistic understanding of large scale conformational transformations underlying the activation of kinases could greatly help in the development of therapeutic drugs for the treatment of these pathologies. In principle, the nature of conformational transition could be modeled in silico via atomistic molecular dynamics simulations, although this is very challenging due to the long activation timescales. Here, we employ a computational paradigm that couples transition pathway techniques and Markov state model-based massively distributed simulations for mapping the conformational landscape of c-src tyrosine kinase. The computations provide the thermodynamics and kinetics of kinase activation for the first time, and help identify key structural intermediates. Furthermore, the presence of a novel allosteric site in an intermediate state of c-src that could be potentially utilized for drug design is predicted. PMID:24584478

Selective Targeting of SH2 Domain–Phosphotyrosine Interactions of Src Family Tyrosine Kinases with Monobodies

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

Kükenshöner, Tim; Schmit, Nadine Eliane; Bouda, Emilie

The binding of Src-homology 2 (SH2) domains to phosphotyrosine (pY) sites is critical for the autoinhibition and substrate recognition of the eight Src family kinases (SFKs). The high sequence conservation of the 120 human SH2 domains poses a significant challenge to selectively perturb the interactions of even the SFK SH2 family against the rest of the SH2 domains. We have developed synthetic binding proteins, termed monobodies, for six of the SFK SH2 domains with nanomolar affinity. Most of these monobodies competed with pY ligand binding and showed strong selectivity for either the SrcA (Yes, Src, Fyn, Fgr) or SrcB subgroupmore » (Lck, Lyn, Blk, Hck). Interactome analysis of intracellularly expressed monobodies revealed that they bind SFKs but no other SH2-containing proteins. Three crystal structures of monobody–SH2 complexes unveiled different and only partly overlapping binding modes, which rationalized the observed selectivity and enabled structure-based mutagenesis to modulate inhibition mode and selectivity. In line with the critical roles of SFK SH2 domains in kinase autoinhibition and T-cell receptor signaling, monobodies binding the Src and Hck SH2 domains selectively activated respective recombinant kinases, whereas an Lck SH2-binding monobody inhibited proximal signaling events downstream of the T-cell receptor complex. Our results show that SFK SH2 domains can be targeted with unprecedented potency and selectivity using monobodies. They are excellent tools for dissecting SFK functions in normal development and signaling and to interfere with aberrant SFK signaling networks in cancer cells.« less

The Activation of c-Src Tyrosine Kinase: Conformational Transition Pathway and Free Energy Landscape.

PubMed

Fajer, Mikolai; Meng, Yilin; Roux, Benoît

2017-04-20

Tyrosine kinases are important cellular signaling allosteric enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Their activity must be tightly controlled, and malfunction can lead to a variety of diseases, particularly cancer. The nonreceptor tyrosine kinase c-Src, a prototypical model system and a representative member of the Src-family, functions as complex multidomain allosteric molecular switches comprising SH2 and SH3 domains modulating the activity of the catalytic domain. The broad picture of self-inhibition of c-Src via the SH2 and SH3 regulatory domains is well characterized from a structural point of view, but a detailed molecular mechanism understanding is nonetheless still lacking. Here, we use advanced computational methods based on all-atom molecular dynamics simulations with explicit solvent to advance our understanding of kinase activation. To elucidate the mechanism of regulation and self-inhibition, we have computed the pathway and the free energy landscapes for the "inactive-to-active" conformational transition of c-Src for different configurations of the SH2 and SH3 domains. Using the isolated c-Src catalytic domain as a baseline for comparison, it is observed that the SH2 and SH3 domains, depending upon their bound orientation, promote either the inactive or active state of the catalytic domain. The regulatory structural information from the SH2-SH3 tandem is allosterically transmitted via the N-terminal linker of the catalytic domain. Analysis of the conformational transition pathways also illustrates the importance of the conserved tryptophan 260 in activating c-Src, and reveals a series of concerted events during the activation process.

Sports-Related Concussion.

PubMed

Laker, Scott R

2015-08-01

Sports-related concussions (SRC) are common in all ages and occur in all sports. The diagnosis based on clinical suspicion after more serious injury is ruled out. Symptoms of concussion are due to a temporary and reversible neurometabolic cascade resulting in blood flow changes, neuronal excitotoxicity, ionic shifts, and mitochondrial changes. Symptoms are nonspecific, and commonly include headache, cognitive complaints, photophobia, and phonophobia. Loss of consciousness is rare in SRC and has limited influence on recovery and prognosis. Imaging has a limited role in the management of concussion and should be used to evaluate for more serious intracranial pathology. Treatment is based on symptoms and an understanding of the typical, rapid (7-10 days) recovery. No athlete should return to play until their symptoms have resolved and they have completed a supervised, step-wise return to play protocol. The article covers the most recent literature on the diagnosis and management of SRC, including evidence-based recommendations and expert-based consensus opinion. The article will also discuss issues regarding medical retirement, legislation, and future concepts in concussion diagnosis and management.

Visual saliency detection based on in-depth analysis of sparse representation

NASA Astrophysics Data System (ADS)

Wang, Xin; Shen, Siqiu; Ning, Chen

2018-03-01

Visual saliency detection has been receiving great attention in recent years since it can facilitate a wide range of applications in computer vision. A variety of saliency models have been proposed based on different assumptions within which saliency detection via sparse representation is one of the newly arisen approaches. However, most existing sparse representation-based saliency detection methods utilize partial characteristics of sparse representation, lacking of in-depth analysis. Thus, they may have limited detection performance. Motivated by this, this paper proposes an algorithm for detecting visual saliency based on in-depth analysis of sparse representation. A number of discriminative dictionaries are first learned with randomly sampled image patches by means of inner product-based dictionary atom classification. Then, the input image is partitioned into many image patches, and these patches are classified into salient and nonsalient ones based on the in-depth analysis of sparse coding coefficients. Afterward, sparse reconstruction errors are calculated for the salient and nonsalient patch sets. By investigating the sparse reconstruction errors, the most salient atoms, which tend to be from the most salient region, are screened out and taken away from the discriminative dictionaries. Finally, an effective method is exploited for saliency map generation with the reduced dictionaries. Comprehensive evaluations on publicly available datasets and comparisons with some state-of-the-art approaches demonstrate the effectiveness of the proposed algorithm.

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation.

PubMed

Adetiba, Emmanuel; Olugbara, Oludayo O

2015-01-01

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square error.

Automatic topic identification of health-related messages in online health community using text classification.

PubMed

Lu, Yingjie

2013-01-01

To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.

Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform.

PubMed

Samiee, Kaveh; Kovács, Petér; Gabbouj, Moncef

2015-02-01

A system for epileptic seizure detection in electroencephalography (EEG) is described in this paper. One of the challenges is to distinguish rhythmic discharges from nonstationary patterns occurring during seizures. The proposed approach is based on an adaptive and localized time-frequency representation of EEG signals by means of rational functions. The corresponding rational discrete short-time Fourier transform (DSTFT) is a novel feature extraction technique for epileptic EEG data. A multilayer perceptron classifier is fed by the coefficients of the rational DSTFT in order to separate seizure epochs from seizure-free epochs. The effectiveness of the proposed method is compared with several state-of-art feature extraction algorithms used in offline epileptic seizure detection. The results of the comparative evaluations show that the proposed method outperforms competing techniques in terms of classification accuracy. In addition, it provides a compact representation of EEG time-series.

Dimensional Representation and Gradient Boosting for Seismic Event Classification

NASA Astrophysics Data System (ADS)

Semmelmayer, F. C.; Kappedal, R. D.; Magana-Zook, S. A.

2017-12-01

In this research, we conducted experiments of representational structures on 5009 seismic signals with the intent of finding a method to classify signals as either an explosion or an earthquake in an automated fashion. We also applied a gradient boosted classifier. While perfect classification was not attained (approximately 88% was our best model), some cases demonstrate that many events can be filtered out as very high probability being explosions or earthquakes, diminishing subject-matter experts'(SME) workload for first stage analysis. It is our hope that these methods can be refined, further increasing the classification probability.

Exploring high dimensional data with Butterfly: a novel classification algorithm based on discrete dynamical systems.

PubMed

Geraci, Joseph; Dharsee, Moyez; Nuin, Paulo; Haslehurst, Alexandria; Koti, Madhuri; Feilotter, Harriet E; Evans, Ken

2014-03-01

We introduce a novel method for visualizing high dimensional data via a discrete dynamical system. This method provides a 2D representation of the relationship between subjects according to a set of variables without geometric projections, transformed axes or principal components. The algorithm exploits a memory-type mechanism inherent in a certain class of discrete dynamical systems collectively referred to as the chaos game that are closely related to iterative function systems. The goal of the algorithm was to create a human readable representation of high dimensional patient data that was capable of detecting unrevealed subclusters of patients from within anticipated classifications. This provides a mechanism to further pursue a more personalized exploration of pathology when used with medical data. For clustering and classification protocols, the dynamical system portion of the algorithm is designed to come after some feature selection filter and before some model evaluation (e.g. clustering accuracy) protocol. In the version given here, a univariate features selection step is performed (in practice more complex feature selection methods are used), a discrete dynamical system is driven by this reduced set of variables (which results in a set of 2D cluster models), these models are evaluated for their accuracy (according to a user-defined binary classification) and finally a visual representation of the top classification models are returned. Thus, in addition to the visualization component, this methodology can be used for both supervised and unsupervised machine learning as the top performing models are returned in the protocol we describe here. Butterfly, the algorithm we introduce and provide working code for, uses a discrete dynamical system to classify high dimensional data and provide a 2D representation of the relationship between subjects. We report results on three datasets (two in the article; one in the appendix) including a public lung cancer dataset that comes along with the included Butterfly R package. In the included R script, a univariate feature selection method is used for the dimension reduction step, but in the future we wish to use a more powerful multivariate feature reduction method based on neural networks (Kriesel, 2007). A script written in R (designed to run on R studio) accompanies this article that implements this algorithm and is available at http://butterflygeraci.codeplex.com/. For details on the R package or for help installing the software refer to the accompanying document, Supporting Material and Appendix.

Multi-Agent Information Classification Using Dynamic Acquaintance Lists.

ERIC Educational Resources Information Center

Mukhopadhyay, Snehasis; Peng, Shengquan; Raje, Rajeev; Palakal, Mathew; Mostafa, Javed

2003-01-01

Discussion of automated information services focuses on information classification and collaborative agents, i.e. intelligent computer programs. Highlights include multi-agent systems; distributed artificial intelligence; thesauri; document representation and classification; agent modeling; acquaintances, or remote agents discovered through…

Phosphopeptide occupancy and photoaffinity cross-linking of the v-Src SH2 domain attenuates tyrosine kinase activity.

PubMed

Garcia, P; Shoelson, S E; Drew, J S; Miller, W T

1994-12-02

Phosphorylation of c-Src at carboxyl-terminal Tyr-527 suppresses tyrosine kinase activity and transforming potential, presumably by facilitating the intramolecular interaction of the C terminus of Src with its SH2 domain. In addition, it has been shown previously that occupancy of the c-Src SH2 domain with a phosphopeptide stimulates c-Src kinase catalytic activity. We have performed analogous studies with v-Src, the transforming protein from Rous sarcoma virus, which has extensive homology with c-Src. v-Src lacks an autoregulatory phosphorylation site, and its kinase domain is constitutively active. Phosphopeptides corresponding to the sequences surrounding c-Src Tyr-527 and a Tyr-Glu-Glu-Ile motif from the hamster polyoma virus middle T antigen inhibit tyrosine kinase activity of baculovirus-expressed v-Src 2- and 4-fold, respectively. To determine the mechanism of this regulation, the Tyr-527 phosphopeptide was substituted with the photoactive amino acid p-benzoylphenylalanine at the adjacent positions (N- and C-terminal) to phosphotyrosine. These peptides photoinactivate the v-Src tyrosine kinase 5-fold in a time- and concentration-dependent manner. Furthermore, the peptides cross-link an isolated Src SH2 domain with similar rates and specificity. These data indicate that occupancy of the v-Src SH2 domain induces a conformational change that is transmitted to the kinase domain and attenuates tyrosine kinase activity.

Knowledge acquisition from natural language for expert systems based on classification problem-solving methods

NASA Technical Reports Server (NTRS)

Gomez, Fernando

1989-01-01

It is shown how certain kinds of domain independent expert systems based on classification problem-solving methods can be constructed directly from natural language descriptions by a human expert. The expert knowledge is not translated into production rules. Rather, it is mapped into conceptual structures which are integrated into long-term memory (LTM). The resulting system is one in which problem-solving, retrieval and memory organization are integrated processes. In other words, the same algorithm and knowledge representation structures are shared by these processes. As a result of this, the system can answer questions, solve problems or reorganize LTM.

Effluviibacter roseus gen. nov., sp. nov., isolated from muddy water, belonging to the family "Flexibacteraceae".

PubMed

Suresh, K; Mayilraj, S; Chakrabarti, T

2006-07-01

A Gram-negative bacterial isolate (designated SRC-1(T)) was isolated from an occasional drainage system and characterized by a polyphasic approach to determine its taxonomic position. Phylogenetic analysis based on 16S rRNA gene sequences affiliated strain SRC-1(T) with the family "Flexibacteraceae" of the phylum Bacteroidetes. It showed greatest sequence similarity to Pontibacter actiniarum KMM 6156(T) (95.5 %) followed by Adhaeribacter aquaticus MBRG1.5(T) (89.0 %) and Hymenobacter roseosalivarius DSM 11622(T) (88.9 %), but it differed from these micro-organisms in many phenotypic characteristics. Strain SRC-1(T) was an obligate aerobe and its cells were non-motile, irregular rods. The major fatty acids included mainly unsaturated and hydroxy fatty acids, including 17 : 1 iso I/anteiso B (36.7 %), 15 : 0 iso (15.8 %) and 17 : 0 iso 3-OH (10.3 %), and the DNA G+C content was 59.5 mol%. From the phenotypic and genotypic analyses it was clear that strain SRC-1(T) was quite different from members other genera in the family '"Flexibacteraceae". Therefore we conclude that strain SRC-1(T) represents a novel genus, for which the name Effluviibacter gen. nov., containing a single species Effluviibacter roseus sp. nov., is proposed. The type species of the genus is Effluviibacter roseus, the type strain of which is strain SRC-1(T) (=MTCC 7260(T)=DSM 17521(T)).

Investigation of Sheath Phenomena in Electronegative Glow Discharges.

DTIC Science & Technology

1985-04-01

NUMBER 22c OFFICE SYMBOL DD FORM 1473, 83 APR )sTI,)N OF I IAN 71lIS rfJBSOLE ’I. UNCLASSIFIED _______ SECURITY CLASSIFICATION OF THIS PAGE _t,[L L; Z...Energy Electron Cross Sections in HCI 199 . .. . ° ° . - LiST OF TABLES TABLE PAGE 1 Summary uf GLOW Code Fesults for He/HCl rNixtures... Cross Src: tiuns in HCI. ......... ... 197 A, i~lX I 4.’ S[LIION I. INTRODUCTION 1. INTRODUCT1O0 AND DEFIr’ITION OF TEI<.S An understanding of the role

Classification and Realizations of Type III Factor Representations of Cuntz-Krieger Algebras Associated with Quasi-Free States

NASA Astrophysics Data System (ADS)

Kawamura, Katsunori

2009-03-01

We completely classify type III factor representations of Cuntz-Krieger algebras associated with quasi-free states up to unitary equivalence. Furthermore, we realize these representations on concrete Hilbert spaces without using GNS construction. Free groups and their type II1 factor representations are used in these realizations.

Immunohistochemical localization of steroid receptor coactivators in chondrosarcoma: an in vivo tissue microarray study.

PubMed

Li, Wei; Fu, Jingshu; Bian, Chen; Zhang, Jiqiang; Xie, Zhao

2014-12-01

Chondrosarcoma is the second most common type of primary bone malignancy following up osteosarcoma, characterized by resistance to conventional chemotherapeutic agents and radiation regimens. The p160 family members steroid receptor coactivator-1 and -3 (SRC-1 and SRC-3) have been implied in the regulation of cancer growth, migration, invasion, metastasis and chemotherapeutic resistance; but we still lack detailed information about the levels of SRCs in chondrosarcoma. In this study, expression of SRC-1 and SRC-3 in chondrosarcoma was examined by immunohistochemistry with tissue microarrays; the four score system (0, 1, 2 and 3) was used to evaluate the staining. The results showed that there were no gender-, site- or age-differences regarding the expression of SRC-1 or SRC-3 (p>0.05); organ (bone or cartilage) -differences were only detected for SRC-1 but not SRC-3 (p<0.05). Significant higher levels of SRC-1 and SRC-3 were detected in MDC and PDC when compared to WDC. Our study clearly demonstrated differentiation-dependant expression of SRC-1 and SRC-3 in chondrosarcoma, may be novel targets for the prognosis and/or treatment of chondrosarcoma, would have opened a new avenue and established foundation for studying chondrosarcoma. Copyright © 2014 Elsevier GmbH. All rights reserved.

Cytokinesis Failure Leading to Chromosome Instability in v-Src-Induced Oncogenesis.

PubMed

Nakayama, Yuji; Soeda, Shuhei; Ikeuchi, Masayoshi; Kakae, Keiko; Yamaguchi, Naoto

2017-04-12

v-Src, an oncogene found in Rous sarcoma virus, is a constitutively active variant of c-Src. Activation of Src is observed frequently in colorectal and breast cancers, and is critical in tumor progression through multiple processes. However, in some experimental conditions, v-Src causes growth suppression and apoptosis. In this review, we highlight recent progress in our understanding of cytokinesis failure and the attenuation of the tetraploidy checkpoint in v-Src-expressing cells. v-Src induces cell cycle changes-such as the accumulation of the 4N cell population-and increases the number of binucleated cells, which is accompanied by an excess number of centrosomes. Time-lapse analysis of v-Src-expressing cells showed that cytokinesis failure is caused by cleavage furrow regression. Microscopic analysis revealed that v-Src induces delocalization of cytokinesis regulators including Aurora B and Mklp1. Tetraploid cell formation is one of the causes of chromosome instability; however, tetraploid cells can be eliminated at the tetraploidy checkpoint. Interestingly, v-Src weakens the tetraploidy checkpoint by inhibiting the nuclear exclusion of the transcription coactivator YAP, which is downstream of the Hippo pathway and its nuclear exclusion is critical in the tetraploidy checkpoint. We also discuss the relationship between v-Src-induced chromosome instability and growth suppression in v-Src-induced oncogenesis.

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

Soeda, Shuhei; Nakayama, Yuji, E-mail: nakayama@mb.kyoto-phu.ac.jp; Department of Biochemistry and Molecular Biology, Kyoto Pharmaceutical University, 5 Nakauchi-cho, Misasagi, Yamashina-ku, Kyoto 607-8414

Src-family tyrosine kinases are aberrantly activated in cancers, and this activation is associated with malignant tumor progression. v-Src, encoded by the v-src transforming gene of the Rous sarcoma virus, is a mutant variant of the cellular proto-oncogene c-Src. Although investigations with temperature sensitive mutants of v-Src have shown that v-Src induces many oncogenic processes, the effects on cell division are unknown. Here, we show that v-Src inhibits cellular proliferation of HCT116, HeLa S3 and NIH3T3 cells. Flow cytometry analysis indicated that inducible expression of v-Src results in an accumulation of 4N cells. Time-lapse analysis revealed that binucleation is induced throughmore » the inhibition of cytokinesis, a final step of cell division. The localization of Mklp1, which is essential for cytokinesis, to the spindle midzone is inhibited in v-Src-expressing cells. Intriguingly, Aurora B, which regulates Mklp1 localization at the midzone, is delocalized from the spindle midzone and the midbody but not from the metaphase chromosomes upon v-Src expression. Mklp2, which is responsible for the relocation of Aurora B from the metaphase chromosomes to the spindle midzone, is also lost from the spindle midzone. These results suggest that v-Src inhibits cytokinesis through the delocalization of Mklp1 and Aurora B from the spindle midzone, resulting in binucleation. -- Highlights: • v-Src inhibits cell proliferation of HCT116, HeLa S3 and NIH3T3 cells. • v-Src induces binucleation together with cytokinesis failure. • v-Src causes delocalization of Mklp1, Aurora B and INCENP from the spindle midzone.« less

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

Category Representation for Classification and Feature Inference

ERIC Educational Resources Information Center

Johansen, Mark K.; Kruschke, John K.

2005-01-01

This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas…

Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors.

PubMed

Tavakoli, Nasrin; Karimi, Maryam; Nejati, Mansour; Karimi, Nader; Reza Soroushmehr, S M; Samavi, Shadrokh; Najarian, Kayvan

2017-07-01

Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.

The effect of training methodology on knowledge representation in categorization.

PubMed

Hélie, Sébastien; Shamloo, Farzin; Ell, Shawn W

2017-01-01

Category representations can be broadly classified as containing within-category information or between-category information. Although such representational differences can have a profound impact on decision-making, relatively little is known about the factors contributing to the development and generalizability of different types of category representations. These issues are addressed by investigating the impact of training methodology and category structures using a traditional empirical approach as well as the novel adaptation of computational modeling techniques from the machine learning literature. Experiment 1 focused on rule-based (RB) category structures thought to promote between-category representations. Participants learned two sets of two categories during training and were subsequently tested on a novel categorization problem using the training categories. Classification training resulted in a bias toward between-category representations whereas concept training resulted in a bias toward within-category representations. Experiment 2 focused on information-integration (II) category structures thought to promote within-category representations. With II structures, there was a bias toward within-category representations regardless of training methodology. Furthermore, in both experiments, computational modeling suggests that only within-category representations could support generalization during the test phase. These data suggest that within-category representations may be dominant and more robust for supporting the reconfiguration of current knowledge to support generalization.

The effect of training methodology on knowledge representation in categorization

PubMed Central

Shamloo, Farzin; Ell, Shawn W.

2017-01-01

Category representations can be broadly classified as containing within–category information or between–category information. Although such representational differences can have a profound impact on decision–making, relatively little is known about the factors contributing to the development and generalizability of different types of category representations. These issues are addressed by investigating the impact of training methodology and category structures using a traditional empirical approach as well as the novel adaptation of computational modeling techniques from the machine learning literature. Experiment 1 focused on rule–based (RB) category structures thought to promote between–category representations. Participants learned two sets of two categories during training and were subsequently tested on a novel categorization problem using the training categories. Classification training resulted in a bias toward between–category representations whereas concept training resulted in a bias toward within–category representations. Experiment 2 focused on information-integration (II) category structures thought to promote within–category representations. With II structures, there was a bias toward within–category representations regardless of training methodology. Furthermore, in both experiments, computational modeling suggests that only within–category representations could support generalization during the test phase. These data suggest that within–category representations may be dominant and more robust for supporting the reconfiguration of current knowledge to support generalization. PMID:28846732

Nck adaptor proteins link Tks5 to invadopodia actin regulation and ECM degradation.

PubMed

Stylli, Stanley S; Stacey, T T I; Verhagen, Anne M; Xu, San San; Pass, Ian; Courtneidge, Sara A; Lock, Peter

2009-08-01

Invadopodia are actin-based projections enriched with proteases, which invasive cancer cells use to degrade the extracellular matrix (ECM). The Phox homology (PX)-Src homology (SH)3 domain adaptor protein Tks5 (also known as SH3PXD2A) cooperates with Src tyrosine kinase to promote invadopodia formation but the underlying pathway is not clear. Here we show that Src phosphorylates Tks5 at Y557, inducing it to associate directly with the SH3-SH2 domain adaptor proteins Nck1 and Nck2 in invadopodia. Tks5 mutants unable to bind Nck show reduced matrix degradation-promoting activity and recruit actin to invadopodia inefficiently. Conversely, Src- and Tks5-driven matrix proteolysis and actin assembly in invadopodia are enhanced by Nck1 or Nck2 overexpression and inhibited by Nck1 depletion. We show that clustering at the plasma membrane of the Tks5 inter-SH3 region containing Y557 triggers phosphorylation at this site, facilitating Nck recruitment and F-actin assembly. These results identify a Src-Tks5-Nck pathway in ECM-degrading invadopodia that shows parallels with pathways linking several mammalian and pathogen-derived proteins to local actin regulation.

The targeted delivery of the c-Src peptide complexed with schizophyllan to macrophages inhibits polymicrobial sepsis and ulcerative colitis in mice.

PubMed

Kim, Ye-Ram; Hwang, Jangsun; Koh, Hyun-Jung; Jang, Kiseok; Lee, Jong-Dae; Choi, Jonghoon; Yang, Chul-Su

2016-05-01

Hyper-inflammatory responses triggered by intracellular reactive oxygen species (ROS) can lead to a variety of diseases, including sepsis and colitis. However, the regulators of this process remain poorly defined. In this study, we demonstrate that c-Src is a negative regulator of cellular ROS generation through its binding to p47phox. This molecule also competitively inhibits the NADPH oxidase complex (NOX) assembly. Furthermore, we developed the schizophyllan (SPG)-c-Src SH3 peptide, which is a β-1,3-glucan conjugated c-Src SH3-derived peptide composed of amino acids 91-108 and 121-140 of c-Src. The SPG-SH3 peptide has a significant therapeutic effect on mouse ROS-mediated inflammatory disease models, cecal-ligation-puncture-induced sepsis, and dextran sodium sulfate-induced colitis. It does so by inhibiting the NOX subunit assembly and proinflammatory mediator production. Therefore, the SPG-SH3 peptide is a potential therapeutic agent for ROS-associated lethal inflammatory diseases. Our findings provide clues for the development of new peptide-base drugs that will target p47phox. Copyright © 2016 Elsevier Ltd. All rights reserved.

A multiple distributed representation method based on neural network for biomedical event extraction.

PubMed

Wang, Anran; Wang, Jian; Lin, Hongfei; Zhang, Jianhai; Yang, Zhihao; Xu, Kan

2017-12-20

Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.

Cross-Classification and Category Representation in Children's Concepts

ERIC Educational Resources Information Center

Nguyen, Simone P.

2007-01-01

Items commonly belong to many categories. Cross-classification is the classification of a single item into more than one category. This research explored 2- to 6-year-old children's use of 2 different category systems for cross-classification: script (e.g., school-time items, birthday party items) and taxonomic (e.g., animals, clothes). The…

Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization.

PubMed

Yuan, Yuan; Lin, Jianzhe; Wang, Qi

2016-12-01

Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.

Hard X-ray spectral investigations of gamma-ray bursts 120521C and 130606A at high-redshift z ˜ 6

NASA Astrophysics Data System (ADS)

Yasuda, T.; Urata, Y.; Enomoto, J.; Tashiro, M. S.

2017-04-01

This study presents a temporal and spectral analysis of the prompt emission of two high-redshift gamma-ray bursts (GRBs), 120521C at z ˜ 6 and 130606A at z ˜ 5.91, using data obtained from the Swift-XRT/BAT and the Suzaku-WAM simultaneously. Based on follow-up XRT observations, the longest durations of the prompt emissions were approximately 80 s (120521C) and 360 s (130606A) in the rest-frames of the two GRBs. These objects are thus categorized as long-duration GRBs; however, the durations are short compared with the predicted duration of GRBs originating from first-generation stars. Because of the wide bandpass of the instruments, covering the ranges 15 keV-5 MeV (BAT-WAM) and 0.3 keV-5.0 MeV (XRT-BAT-WAM), we could successfully determine the νFν peak energies E_peak^src in the rest-frame and isotropic-equivalent radiated energies Eiso; E^src_peak = 682^{+845}_{-207} keV and E_iso = (8. 25^{+2.24}_{-1.96}) × 10^{52} erg for 120521C, and E^src_peak = 1209^{+553}_{-304} keV and E_iso = (2.82^{+0.17}_{-0.71}) × 10^{53} erg for 130606A. These obtained characteristic parameters are in accordance with the well-known relationship between E_peak^src and Eiso (Amati relationship). In addition, we examined the relationships between E_peak^src and the 1-s peak luminosity, Lp, and between E_peak^src and the geometrical corrected radiated energy, Eγ, and confirmed the E_peak^src-Lp (Yonetoku) and E_peak^src-Eγ (Ghirlanda) relationships. The results imply that these high-redshift GRBs at z ˜ 6, which are expected to have radiated during the reionization epoch, have properties similar to those of low-redshift GRBs regarding X-ray prompt emission.

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

PubMed Central

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C. M.

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases. PMID:25692885

Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.

PubMed

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C M

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain's representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.

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.

The research on construction and application of machining process knowledge base

NASA Astrophysics Data System (ADS)

Zhao, Tan; Qiao, Lihong; Qie, Yifan; Guo, Kai

2018-03-01

In order to realize the application of knowledge in machining process design, from the perspective of knowledge in the application of computer aided process planning(CAPP), a hierarchical structure of knowledge classification is established according to the characteristics of mechanical engineering field. The expression of machining process knowledge is structured by means of production rules and the object-oriented methods. Three kinds of knowledge base models are constructed according to the representation of machining process knowledge. In this paper, the definition and classification of machining process knowledge, knowledge model, and the application flow of the process design based on the knowledge base are given, and the main steps of the design decision of the machine tool are carried out as an application by using the knowledge base.

Prospective multi-institutional study evaluating the performance of prostate cancer risk calculators.

PubMed

Nam, Robert K; Kattan, Michael W; Chin, Joseph L; Trachtenberg, John; Singal, Rajiv; Rendon, Ricardo; Klotz, Laurence H; Sugar, Linda; Sherman, Christopher; Izawa, Jonathan; Bell, David; Stanimirovic, Aleksandra; Venkateswaran, Vasundara; Diamandis, Eleftherios P; Yu, Changhong; Loblaw, D Andrew; Narod, Steven A

2011-08-01

Prostate cancer risk calculators incorporate many factors to evaluate an individual's risk for prostate cancer. We validated two common North American-based, prostate cancer risk calculators. We conducted a prospective, multi-institutional study of 2,130 patients who underwent a prostate biopsy for prostate cancer detection from five centers. We evaluated the performance of the Sunnybrook nomogram-based prostate cancer risk calculator (SRC) and the Prostate Cancer Prevention Trial (PCPT) -based risk calculator (PRC) to predict the presence of any cancer and high-grade cancer. We examined discrimination, calibration, and decision curve analysis techniques to evaluate the prediction models. Of the 2,130 patients, 867 men (40.7%) were found to have cancer, and 1,263 (59.3%) did not have cancer. Of the patients with cancer, 403 (46.5%) had a Gleason score of 7 or more. The area under the [concentration-time] curve (AUC) for the SRC was 0.67 (95% CI, 0.65 to 0.69); the AUC for the PRC was 0.61 (95% CI, 0.59 to 0.64). The AUC was higher for predicting aggressive disease from the SRC (0.72; 95% CI, 0.70 to 0.75) compared with that from the PRC (0.67; 95% CI, 0.64 to 0.70). Decision curve analyses showed that the SRC performed better than the PRC for risk thresholds of more than 30% for any cancer and more than 15% for aggressive cancer. The SRC performed better than the PRC, but neither one added clinical benefit for risk thresholds of less than 30%. Further research is needed to improve the AUCs of the risk calculators, particularly for higher-grade cancer.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Calcium-permeable α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid Receptors Trigger Neuronal Nitric-oxide Synthase Activation to Promote Nerve Cell Death in an Src Kinase-dependent Fashion*

PubMed Central

Socodato, Renato; Santiago, Felipe N.; Portugal, Camila C.; Domingues, Ana F.; Santiago, Ana R.; Relvas, João B.; Ambrósio, António F.; Paes-de-Carvalho, Roberto

2012-01-01

In the retina information decoding is dependent on excitatory neurotransmission and is critically modulated by AMPA glutamate receptors. The Src-tyrosine kinase has been implicated in modulating neurotransmission in CNS. Thus, our main goal was to correlate AMPA-mediated excitatory neurotransmission with the modulation of Src activity in retinal neurons. Cultured retinal cells were used to access the effects of AMPA stimulation on nitric oxide (NO) production and Src phosphorylation. 4-Amino-5-methylamino-2′,7′-difluorofluorescein diacetate fluorescence mainly determined NO production, and immunocytochemistry and Western blotting evaluated Src activation. AMPA receptors activation rapidly up-regulated Src phosphorylation at tyrosine 416 (stimulatory site) and down-regulated phosphotyrosine 527 (inhibitory site) in retinal cells, an effect mainly mediated by calcium-permeable AMPA receptors. Interestingly, experiments confirmed that neuronal NOS was activated in response to calcium-permeable AMPA receptor stimulation. Moreover, data suggest NO pathway as a key regulatory signaling in AMPA-induced Src activation in neurons but not in glial cells. The NO donor SNAP (S-nitroso-N-acetyl-dl-penicillamine) and a soluble guanylyl cyclase agonist (YC-1) mimicked AMPA effect in Src Tyr-416 phosphorylation, reinforcing that Src activation is indeed modulated by the NO pathway. Gain and loss-of-function data demonstrated that ERK is a downstream target of AMPA-induced Src activation and NO signaling. Furthermore, AMPA stimulated NO production in organotypic retinal cultures and increased Src activity in the in vivo retina. Additionally, AMPA-induced apoptotic retinal cell death was regulated by both NOS and Src activity. Because Src activity is pivotal in several CNS regions, the data presented herein highlight that Src modulation is a critical step in excitatory retinal cell death. PMID:22992730

Src kinase regulation by phosphorylation and dephosphorylation

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

Roskoski, Robert

2005-05-27

Src and Src-family protein-tyrosine kinases are regulatory proteins that play key roles in cell differentiation, motility, proliferation, and survival. The initially described phosphorylation sites of Src include an activating phosphotyrosine 416 that results from autophosphorylation, and an inhibiting phosphotyrosine 527 that results from phosphorylation by C-terminal Src kinase (Csk) and Csk homologous kinase. Dephosphorylation of phosphotyrosine 527 increases Src kinase activity. Candidate phosphotyrosine 527 phosphatases include cytoplasmic PTP1B, Shp1 and Shp2, and transmembrane enzymes include CD45, PTP{alpha}, PTP{epsilon}, and PTP{lambda}. Dephosphorylation of phosphotyrosine 416 decreases Src kinase activity. Thus far PTP-BL, the mouse homologue of human PTP-BAS, has been shownmore » to dephosphorylate phosphotyrosine 416 in a regulatory fashion. The platelet-derived growth factor receptor protein-tyrosine kinase mediates the phosphorylation of Src Tyr138; this phosphorylation has no direct effect on Src kinase activity. The platelet-derived growth factor receptor and the ErbB2/HER2 growth factor receptor protein-tyrosine kinases mediate the phosphorylation of Src Tyr213 and activation of Src kinase activity. Src kinase is also a substrate for protein-serine/threonine kinases including protein kinase C (Ser12), protein kinase A (Ser17), and CDK1/cdc2 (Thr34, Thr46, and Ser72). Of the three protein-serine/threonine kinases, only phosphorylation by CDK1/cdc2 has been demonstrated to increase Src kinase activity. Although considerable information on the phosphoprotein phosphatases that catalyze the hydrolysis of Src phosphotyrosine 527 is at hand, the nature of the phosphatases that mediate the hydrolysis of phosphotyrosine 138 and 213, and phosphoserine and phosphothreonine residues has not been determined.« less

Calcium-permeable α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors trigger neuronal nitric-oxide synthase activation to promote nerve cell death in an Src kinase-dependent fashion.

PubMed

Socodato, Renato; Santiago, Felipe N; Portugal, Camila C; Domingues, Ana F; Santiago, Ana R; Relvas, João B; Ambrósio, António F; Paes-de-Carvalho, Roberto

2012-11-09

In the retina information decoding is dependent on excitatory neurotransmission and is critically modulated by AMPA glutamate receptors. The Src-tyrosine kinase has been implicated in modulating neurotransmission in CNS. Thus, our main goal was to correlate AMPA-mediated excitatory neurotransmission with the modulation of Src activity in retinal neurons. Cultured retinal cells were used to access the effects of AMPA stimulation on nitric oxide (NO) production and Src phosphorylation. 4-Amino-5-methylamino-2',7'-difluorofluorescein diacetate fluorescence mainly determined NO production, and immunocytochemistry and Western blotting evaluated Src activation. AMPA receptors activation rapidly up-regulated Src phosphorylation at tyrosine 416 (stimulatory site) and down-regulated phosphotyrosine 527 (inhibitory site) in retinal cells, an effect mainly mediated by calcium-permeable AMPA receptors. Interestingly, experiments confirmed that neuronal NOS was activated in response to calcium-permeable AMPA receptor stimulation. Moreover, data suggest NO pathway as a key regulatory signaling in AMPA-induced Src activation in neurons but not in glial cells. The NO donor SNAP (S-nitroso-N-acetyl-DL-penicillamine) and a soluble guanylyl cyclase agonist (YC-1) mimicked AMPA effect in Src Tyr-416 phosphorylation, reinforcing that Src activation is indeed modulated by the NO pathway. Gain and loss-of-function data demonstrated that ERK is a downstream target of AMPA-induced Src activation and NO signaling. Furthermore, AMPA stimulated NO production in organotypic retinal cultures and increased Src activity in the in vivo retina. Additionally, AMPA-induced apoptotic retinal cell death was regulated by both NOS and Src activity. Because Src activity is pivotal in several CNS regions, the data presented herein highlight that Src modulation is a critical step in excitatory retinal cell death.

Differential subcellular membrane recruitment of Src may specify its downstream signalling

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

Diesbach, Philippe de; Medts, Thierry; Carpentier, Sarah

2008-04-15

Most Src family members are diacylated and constitutively associate with membrane 'lipid rafts' that coordinate signalling. Whether the monoacylated Src, frequently hyperactive in carcinomas, also localizes at 'rafts' remains controversial. Using polarized MDCK cells expressing the thermosensitive v-Src/tsLA31 variant, we here addressed how Src tyrosine-kinase activation may impact on its (i) membrane recruitment, in particular to 'lipid rafts'; (ii) subcellular localization; and (iii) signalling. The kinetics of Src-kinase thermoactivation correlated with its recruitment from the cytosol to sedimentable membranes where Src largely resisted solubilisation by non-ionic detergents at 4 deg. C and floated into sucrose density gradients like caveolin-1 andmore » flotillin-2, i.e. 'lipid rafts'. By immunofluorescence, activated Src showed a dual localization, at apical endosomes/macropinosomes and at the apical plasma membrane. The plasma membrane Src pool did not colocalize with caveolin-1 and flotillin-2, but extensively overlapped GM1 labelling by cholera toxin. Severe ({approx} 70%) cholesterol extraction with methyl-{beta}-cyclodextrin (M{beta}CD) did not abolish 'rafts' floatation, but strongly decreased Src association with floating 'rafts' and abolished its localization at the apical plasma membrane. Src activation independently activated first the MAP-kinase - ERK1/2 pathway, then the PI3-kinase - Akt pathway. MAP-kinase - ERK1/2 activation was insensitive to M{beta}CD, which suppressed Akt phosphorylation and apical endocytosis induced by Src, both depending on the PI3-kinase pathway. We therefore suggest that activated Src is recruited at two membrane compartments, allowing differential signalling, first via ERK1/2 at 'non-raft' domains on endosomes, then via PI3-kinase-Akt on a distinct set of 'rafts' at the apical plasma membrane. Whether this model is applicable to c-Src remains to be examined.« less

Continuous robust sound event classification using time-frequency features and deep learning

PubMed Central

Song, Yan; Xiao, Wei; Phan, Huy

2017-01-01

The automatic detection and recognition of sound events by computers is a requirement for a number of emerging sensing and human computer interaction technologies. Recent advances in this field have been achieved by machine learning classifiers working in conjunction with time-frequency feature representations. This combination has achieved excellent accuracy for classification of discrete sounds. The ability to recognise sounds under real-world noisy conditions, called robust sound event classification, is an especially challenging task that has attracted recent research attention. Another aspect of real-word conditions is the classification of continuous, occluded or overlapping sounds, rather than classification of short isolated sound recordings. This paper addresses the classification of noise-corrupted, occluded, overlapped, continuous sound recordings. It first proposes a standard evaluation task for such sounds based upon a common existing method for evaluating isolated sound classification. It then benchmarks several high performing isolated sound classifiers to operate with continuous sound data by incorporating an energy-based event detection front end. Results are reported for each tested system using the new task, to provide the first analysis of their performance for continuous sound event detection. In addition it proposes and evaluates a novel Bayesian-inspired front end for the segmentation and detection of continuous sound recordings prior to classification. PMID:28892478

Continuous robust sound event classification using time-frequency features and deep learning.

PubMed

McLoughlin, Ian; Zhang, Haomin; Xie, Zhipeng; Song, Yan; Xiao, Wei; Phan, Huy

2017-01-01

The automatic detection and recognition of sound events by computers is a requirement for a number of emerging sensing and human computer interaction technologies. Recent advances in this field have been achieved by machine learning classifiers working in conjunction with time-frequency feature representations. This combination has achieved excellent accuracy for classification of discrete sounds. The ability to recognise sounds under real-world noisy conditions, called robust sound event classification, is an especially challenging task that has attracted recent research attention. Another aspect of real-word conditions is the classification of continuous, occluded or overlapping sounds, rather than classification of short isolated sound recordings. This paper addresses the classification of noise-corrupted, occluded, overlapped, continuous sound recordings. It first proposes a standard evaluation task for such sounds based upon a common existing method for evaluating isolated sound classification. It then benchmarks several high performing isolated sound classifiers to operate with continuous sound data by incorporating an energy-based event detection front end. Results are reported for each tested system using the new task, to provide the first analysis of their performance for continuous sound event detection. In addition it proposes and evaluates a novel Bayesian-inspired front end for the segmentation and detection of continuous sound recordings prior to classification.

General methodology for simultaneous representation and discrimination of multiple object classes

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

We address a new general method for linear and nonlinear feature extraction for simultaneous representation and classification. We call this approach the maximum representation and discrimination feature (MRDF) method. We develop a novel nonlinear eigenfeature extraction technique to represent data with closed-form solutions and use it to derive a nonlinear MRDF algorithm. Results of the MRDF method on synthetic databases are shown and compared with results from standard Fukunaga-Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem and for classification and pose estimation of two similar objects under 3D aspect angle variations.

Attachment to Mother and Father at Transition to Middle Childhood.

PubMed

Di Folco, Simona; Messina, Serena; Zavattini, Giulio Cesare; Psouni, Elia

2017-01-01

The present study investigated concordance between representations of attachment to mother and attachment to father, and convergence between two narrative-based methods addressing these representations in middle childhood: the Manchester Child Attachment Story Task (MCAST) and the Secure Base Script Test (SBST). One hundred and twenty 6-year-old children were assessed by separate administrations of the MCAST for mother and father, respectively, and results showed concordance of representations of attachment to mother and attachment to father at age 6.5 years. 75 children were additionally tested about 12 months later, with the SBST, which assesses scripted knowledge of secure base (and safe haven), not differentiating between mother and father attachment relationships. Concerning attachment to father, dichotomous classifications (MCAST) and a continuous dimension capturing scripted secure base knowledge (MCAST) converged with secure base scriptedness (SBST), yet we could not show the same pattern of convergence concerning attachment to mother. Results suggest some convergence between the two narrative methods of assessment of secure base script but also highlight complications when using the MCAST for measuring attachment to father in middle childhood.

Differential Prognostic Implications of Gastric Signet Ring Cell Carcinoma

PubMed Central

Chon, Hong Jae; Hyung, Woo Jin; Kim, Chan; Park, Sohee; Kim, Jie-Hyun; Park, Chan Hyuk; Ahn, Joong Bae; Kim, Hyunki; Chung, Hyun Cheol; Rha, Sun Young; Noh, Sung Hoon; Jeung, Hei-Cheul

2017-01-01

Objective: The aim of this study was to analyze the clinicopathologic characteristics and prognosis of signet ring cell carcinoma (SRC) according to disease status (early vs advanced gastric cancer) in gastric cancer patients. Background: The prognostic implication of gastric SRC remains a subject of debate. Methods: A retrospective analysis was performed using the clinical records of 7667 patients including 1646 SRC patients who underwent radical gastrectomy between 2001 and 2010. A further analysis was also performed after dividing patients into three groups according to histologic subtype: SRC, well-to-moderately differentiated (WMD), and poorly differentiated adenocarcinoma. Results: SRC patients have younger age distribution and female predominance compared with other histologic subtypes. Notably, the distribution of T stage of SRC patients was distinct, located in extremes (T1: 66.2% and T4: 20%). Moreover, the prognosis of SRC in early gastric cancer and advanced gastric cancer was contrasting. In early gastric cancer, SRC demonstrated more favorable prognosis than WMD after adjusting for age, sex, and stage. In contrast, SRC in advanced gastric cancer displayed worse prognosis than WMD. As stage increased, survival outcomes of SRC continued to worsen compared with WMD. Conclusions: Although conferring favorable prognosis in early stage, SRC has worse prognostic impact as disease progresses. The longstanding controversy of SRC on prognosis may result from disease status at presentation, which leads to differing prognosis compared with tubular adenocarinoma. PMID:27232252

Variant estrogen receptor-c-Src molecular interdependence and c-Src structural requirements for endothelial NO synthase activation.

PubMed

Li, Lei; Hisamoto, Koji; Kim, Kyung Hee; Haynes, M Page; Bauer, Philip M; Sanjay, Archana; Collinge, Mark; Baron, Roland; Sessa, William C; Bender, Jeffrey R

2007-10-16

Little is known about the tyrosine kinase c-Src's function in the systemic circulation, in particular its role in arterial responses to hormonal stimuli. In human aortic and venous endothelial cells, c-Src is indispensable for 17beta-estradiol (E2)-stimulated phosphatidylinositol 3-kinase/Akt/endothelial NO synthase (eNOS) pathway activation, a possible mechanism in E2-mediated vascular protection. Here we show that c-Src supports basal and E2-stimulated NO production and is required for E2-induced vasorelaxation in murine aortas. Only membrane c-Src is structurally and functionally involved in E2-induced eNOS activation. Independent of c-Src kinase activity, c-Src is associated with an N-terminally truncated estrogen receptor alpha variant (ER46) and eNOS in the plasma membrane through its "open" (substrate-accessible) conformation. In the presence of E2, c-Src kinase is activated by membrane ER46 and in turn phosphorylates ER46 for subsequent ER46 and c-Src membrane recruitment, the assembly of an eNOS-centered membrane macrocomplex, and membrane-initiated eNOS activation. Overall, these results provide insights into a critical role for the tyrosine kinase c-Src in estrogen-stimulated arterial responses, and in membrane-initiated rapid signal transduction, for which obligate complex assembly and localization require the c-Src substrate-accessible structure.

Focal adhesion kinase-dependent focal adhesion recruitment of SH2 domains directs SRC into focal adhesions to regulate cell adhesion and migration

PubMed Central

Wu, Jui-Chung; Chen, Yu-Chen; Kuo, Chih-Ting; Wenshin Yu, Helen; Chen, Yin-Quan; Chiou, Arthur; Kuo, Jean-Cheng

2015-01-01

Directed cell migration requires dynamical control of the protein complex within focal adhesions (FAs) and this control is regulated by signaling events involving tyrosine phosphorylation. We screened the SH2 domains present in tyrosine-specific kinases and phosphatases found within FAs, including SRC, SHP1 and SHP2, and examined whether these enzymes transiently target FAs via their SH2 domains. We found that the SRC_SH2 domain and the SHP2_N-SH2 domain are associated with FAs, but only the SRC_SH2 domain is able to be regulated by focal adhesion kinase (FAK). The FAK-dependent association of the SRC_SH2 domain is necessary and sufficient for SRC FA targeting. When the targeting of SRC into FAs is inhibited, there is significant suppression of SRC-mediated phosphorylation of paxillin and FAK; this results in an inhibition of FA formation and maturation and a reduction in cell migration. This study reveals an association between FAs and the SRC_SH2 domain as well as between FAs and the SHP2_N-SH2 domains. This supports the hypothesis that the FAK-regulated SRC_SH2 domain plays an important role in directing SRC into FAs and that this SRC-mediated FA signaling drives cell migration. PMID:26681405

Focal adhesion kinase-dependent focal adhesion recruitment of SH2 domains directs SRC into focal adhesions to regulate cell adhesion and migration.

PubMed

Wu, Jui-Chung; Chen, Yu-Chen; Kuo, Chih-Ting; Wenshin Yu, Helen; Chen, Yin-Quan; Chiou, Arthur; Kuo, Jean-Cheng

2015-12-18

Directed cell migration requires dynamical control of the protein complex within focal adhesions (FAs) and this control is regulated by signaling events involving tyrosine phosphorylation. We screened the SH2 domains present in tyrosine-specific kinases and phosphatases found within FAs, including SRC, SHP1 and SHP2, and examined whether these enzymes transiently target FAs via their SH2 domains. We found that the SRC_SH2 domain and the SHP2_N-SH2 domain are associated with FAs, but only the SRC_SH2 domain is able to be regulated by focal adhesion kinase (FAK). The FAK-dependent association of the SRC_SH2 domain is necessary and sufficient for SRC FA targeting. When the targeting of SRC into FAs is inhibited, there is significant suppression of SRC-mediated phosphorylation of paxillin and FAK; this results in an inhibition of FA formation and maturation and a reduction in cell migration. This study reveals an association between FAs and the SRC_SH2 domain as well as between FAs and the SHP2_N-SH2 domains. This supports the hypothesis that the FAK-regulated SRC_SH2 domain plays an important role in directing SRC into FAs and that this SRC-mediated FA signaling drives cell migration.

Preliminary study of semi-refined carrageenan (SRC) as secondary gelling agent in natural rubber (NR) latex foam

NASA Astrophysics Data System (ADS)

Norhazariah, S.; Azura, A. R.; Azahari, B.; Sivakumar, R.

2017-12-01

Semi-refined carrageenan (SRC) product is considerably cheaper and easier to produce as a natural polysaccharide, which was utilized in food and other product application. However, the application in latex is limited. The aim of this work is to evaluate the SRC produced from low industrial grade seaweed (LIGS) in the latex foam application. The FTIR spectra showed the SRC produced as kappa type carrageenan with lower sulfur content compared to native LIGS. NR latex foam is produced by using the Dunlop method with some modifications. The effect of SRC loading as a secondary gelling agent in NR latex foam is investigated. The density and morphology of the NR latex foam with the addition of the SRC are analyzed. NR latex foam density increased with SRC loading and peaked at 1.8 phr SRC. The addition of SRC has induced the bigger cell size compared to the cell size of the control NR latex foam, as shown in the optical micrograph. It can be concluded that SRC LIGS could be acted as secondary gelling agent in NR latex foam.

Endosomal-sorting complexes required for transport (ESCRT) pathway-dependent endosomal traffic regulates the localization of active Src at focal adhesions.

PubMed

Tu, Chun; Ortega-Cava, Cesar F; Winograd, Paul; Stanton, Marissa Jo; Reddi, Alagarsamy Lakku; Dodge, Ingrid; Arya, Ranjana; Dimri, Manjari; Clubb, Robert J; Naramura, Mayumi; Wagner, Kay-Uwe; Band, Vimla; Band, Hamid

2010-09-14

Active Src localization at focal adhesions (FAs) is essential for cell migration. How this pool is linked mechanistically to the large pool of Src at late endosomes (LEs)/lysosomes (LY) is not well understood. Here, we used inducible Tsg101 gene deletion, TSG101 knockdown, and dominant-negative VPS4 expression to demonstrate that the localization of activated cellular Src and viral Src at FAs requires the endosomal-sorting complexes required for transport (ESCRT) pathway. Tsg101 deletion also led to impaired Src-dependent activation of STAT3 and focal adhesion kinase and reduced cell migration. Impairment of the ESCRT pathway or Rab7 function led to the accumulation of active Src at aberrant LE/LY compartments followed by its loss. Analyses using fluorescence recovery after photo-bleaching show that dynamic mobility of Src in endosomes is ESCRT pathway-dependent. These results reveal a critical role for an ESCRT pathway-dependent LE/LY trafficking step in Src function by promoting localization of active Src to FAs.

SRC activates TAZ for intestinal tumorigenesis and regeneration.

PubMed

Byun, Mi Ran; Hwang, Jun-Ha; Kim, A Rum; Kim, Kyung Min; Park, Jung Il; Oh, Ho Taek; Hwang, Eun Sook; Hong, Jeong-Ho

2017-12-01

Proto-oncogene tyrosine-protein kinase Src (cSRC) is involved in colorectal cancer (CRC) development and damage-induced intestinal regeneration, although the cellular mechanisms involved are poorly understood. Here, we report that transcriptional coactivator with PDZ binding domain (TAZ) is activated by cSRC, regulating CRC cell proliferation and tumor formation, where cSRC overexpression increases TAZ expression in CRC cells. In contrast, knockdown of cSRC decreases TAZ expression. Additionally, direct phosphorylation of TAZ at Tyr316 by cSRC stimulates nuclear localization and facilitates transcriptional enhancer factor TEF-3 (TEAD4)-mediated transcription. However, a TAZ phosphorylation mutant significantly decreased cell proliferation, wound healing, colony forming, and tumor formation. In a CRC mouse model, Apc Min/+ , activated SRC expression was associated with increased TAZ expression in polyps and TAZ depletion decreased polyp formation. Moreover, intestinal TAZ knockout mice had intestinal regeneration defects following γ-irradiation. Finally, significant correspondence between SRC activation and TAZ overexpression was observed in CRC patients. These results suggest that TAZ is a critical factor for SRC-mediated intestinal tumor formation and regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

Protective equipment and player characteristics associated with the incidence of sport-related concussion in high school football players: a multifactorial prospective study.

PubMed

McGuine, Timothy A; Hetzel, Scott; McCrea, Michael; Brooks, M Alison

2014-10-01

The incidence of sport-related concussion (SRC) in high school football is well documented. However, limited prospective data are available regarding how player characteristics and protective equipment affect the incidence of SRC. To determine whether the type of protective equipment (helmet and mouth guard) and player characteristics affect the incidence of SRC in high school football players. Cohort study; Level of evidence, 2. Certified athletic trainers (ATs) at each high school recorded the type of helmet worn (brand, model, purchase year, and recondition status) by each player as well as information regarding players' demographics, type of mouth guard used, and history of SRC. The ATs also recorded the incidence and days lost from participation for each SRC. Incidence of SRC was compared for various helmets, type of mouth guard, history of SRC, and player demographics. A total of 2081 players (grades 9-12) enrolled during the 2012 and/or 2013 football seasons (2287 player-seasons) and participated in 134,437 football (practice or competition) exposures. Of these players, 206 (9%) sustained a total of 211 SRCs (1.56/1000 exposures). There was no difference in the incidence of SRC (number of helmets, % SRC [95% CI]) for players wearing Riddell (1171, 9.1% [7.6%-11.0%]), Schutt (680, 8.7% [6.7%-11.1%]), or Xenith (436, 9.2% [6.7%-12.4%]) helmets. Helmet age and recondition status did not affect the incidence of SRC. The rate of SRC (hazard ratio [HR]) was higher in players who wore a custom mouth guard (HR = 1.69 [95% CI, 1.20-2.37], P < .001) than in players who wore a generic mouth guard. The rate of SRC was also higher (HR = 1.96 [95% CI, 1.40-2.73], P < .001) in players who had sustained an SRC within the previous 12 months (15.1% of the 259 players [95% CI, 11.0%-20.1%]) than in players without a previous SRC (8.2% of the 2028 players [95% CI, 7.1%-9.5%]). Incidence of SRC was similar regardless of the helmet brand (manufacturer) worn by high school football players. Players who had sustained an SRC within the previous 12 months were more likely to sustain an SRC than were players without a history of SRC. Sports medicine providers who work with high school football players need to realize that factors other than the type of protective equipment worn affect the risk of SRC in high school players. © 2014 The Author(s).

Targeting Src in Mucinous Ovarian Carcinoma

PubMed Central

Matsuo, Koji; Nishimura, Masato; Bottsford-Miller, Justin N.; Huang1, Jie; Komurov, Kakajan; Armaiz-Pena, Guillermo N.; Shahzad, Mian M. K.; Stone, Rebecca L.; Roh, Ju Won; Sanguino, Angela M.; Lu, Chunhua; Im, Dwight D.; Rosenshien, Neil B.; Sakakibara, Atsuko; Nagano, Tadayoshi; Yamasaki, Masato; Enomoto, Takayuki; Kimura, Tadashi; Ram, Prahlad T.; Schmeler, Kathleen M.; Gallick, Gary E.; Wong, Kwong K.; Frumovitz, Michael; Sood, Anil K.

2014-01-01

PURPOSE Mucinous ovarian carcinomas have a distinct clinical pattern compared to other subtypes of ovarian carcinoma. Here, we evaluated (i) stage-specific clinical significance of mucinous ovarian carcinomas in a large cohort and (ii) the functional role of src kinase in pre-clinical models of mucinous ovarian carcinoma. EXPERIMENTAL DESIGN 1302 ovarian cancer patients including 122 (9.4%) cases of mucinous carcinoma were evaluated for survival analyses. Biological effects of src kinase inhibition were tested in a novel orthotopic mucinous ovarian cancer model (RMUG-S-ip2) using dasatinib-based therapy. RESULTS Patients with advanced-stage mucinous ovarian cancer had significantly worse survival compared to those with serous histology: median overall survival, 1.67 versus 3.41 years, p=0.002; and median survival time after recurrence of 0.53 versus 1.66 years, p<0.0001. Among multiple ovarian cancer cell lines, RMUG-S-ip2 mucinous ovarian cancer cells showed the highest src kinase activity. Moreover, oxaliplatin treatment induced phosphorylation of src kinase. This induced activity by oxaliplatin therapy was inhibited by concurrent administration of dasatinib. Targeting src with dasatinib in vivo showed significant anti-tumor effects in the RMUG-S-ip2 model, but not in the serous ovarian carcinoma (SKOV3-TR) model. Combination therapy of oxaliplatin with dasatinib further demonstrated significant effects on reducing cell viability, increasing apoptosis, and in vivo anti-tumor effects in the RMUG-S-ip2 model. CONCLUSIONS Our results suggest that poor survival of women with mucinous ovarian carcinoma is associated with resistance to cytotoxic therapy. Targeting src kinase with combination of dasatinib and oxaliplatin may be an attractive approach in this disease. PMID:21737505

Selective Targeting of SH2 Domain-Phosphotyrosine Interactions of Src Family Tyrosine Kinases with Monobodies.

PubMed

Kükenshöner, Tim; Schmit, Nadine Eliane; Bouda, Emilie; Sha, Fern; Pojer, Florence; Koide, Akiko; Seeliger, Markus; Koide, Shohei; Hantschel, Oliver

2017-05-05

The binding of Src-homology 2 (SH2) domains to phosphotyrosine (pY) sites is critical for the autoinhibition and substrate recognition of the eight Src family kinases (SFKs). The high sequence conservation of the 120 human SH2 domains poses a significant challenge to selectively perturb the interactions of even the SFK SH2 family against the rest of the SH2 domains. We have developed synthetic binding proteins, termed monobodies, for six of the SFK SH2 domains with nanomolar affinity. Most of these monobodies competed with pY ligand binding and showed strong selectivity for either the SrcA (Yes, Src, Fyn, Fgr) or SrcB subgroup (Lck, Lyn, Blk, Hck). Interactome analysis of intracellularly expressed monobodies revealed that they bind SFKs but no other SH2-containing proteins. Three crystal structures of monobody-SH2 complexes unveiled different and only partly overlapping binding modes, which rationalized the observed selectivity and enabled structure-based mutagenesis to modulate inhibition mode and selectivity. In line with the critical roles of SFK SH2 domains in kinase autoinhibition and T-cell receptor signaling, monobodies binding the Src and Hck SH2 domains selectively activated respective recombinant kinases, whereas an Lck SH2-binding monobody inhibited proximal signaling events downstream of the T-cell receptor complex. Our results show that SFK SH2 domains can be targeted with unprecedented potency and selectivity using monobodies. They are excellent tools for dissecting SFK functions in normal development and signaling and to interfere with aberrant SFK signaling networks in cancer cells. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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Science.gov Websites

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A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data.

PubMed

Saa, Jaime F Delgado; Çetin, Müjdat

2012-04-01

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on autoregressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for the classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy.

Sex Differences in High School Athletes' Knowledge of Sport-Related Concussion Symptoms and Reporting Behaviors.

PubMed

Wallace, Jessica; Covassin, Tracey; Beidler, Erica

2017-07-01

  Recent researchers have reported that athletes' knowledge of sport-related concussion (SRC) has increased but that athletes still lack knowledge of all the signs and symptoms of SRC. Understanding the signs and symptoms of SRC and the dangers of playing while symptomatic are critical to reporting behaviors in high school athletes.   To examine sex differences in knowledge of SRC symptoms and reasons for not reporting a suspected SRC to an authoritative figure in high school athletes.   Cross-sectional study.   Survey.   A total of 288 athletes across 7 sports (198 males [68.8%] and 90 females [31.2%]).   A validated knowledge-of-SRC survey consisted of demographic questions, a list of 21 signs and symptoms of SRC, and reasons why athletes would not report their SRC. The independent variable was sex. Athlete knowledge of SRC symptoms was assessed by having participants identify the signs and symptoms of SRC from a list of 21 symptoms. Knowledge scores were calculated by summing the number of correct answers; scores ranged from 0 to 21, with a score closer to 21 representing greater knowledge. Reporting-behavior questions asked athletes to choose reasons why they decided not to report any possible SRC signs and symptoms to an authoritative figure.   A sex difference in total SRC symptom knowledge was found (F 286 = 4.97, P = .03, d = 0.26). Female high school athletes had more total SRC symptom knowledge (mean ± standard deviation = 15.06 ± 2.63; 95% confidence interval = 14.54, 15.57) than males (14.36 ± 2.76; 95% confidence interval = 13.97, 14.74). Chi-square tests identified significant relationships between sex and 8 different reasons for not reporting an SRC.   High school males and females had similar SRC symptom knowledge; however, female athletes were more likely to report their concussive symptoms to an authoritative figure.

PANDORA: keyword-based analysis of protein sets by integration of annotation sources.

PubMed

Kaplan, Noam; Vaaknin, Avishay; Linial, Michal

2003-10-01

Recent advances in high-throughput methods and the application of computational tools for automatic classification of proteins have made it possible to carry out large-scale proteomic analyses. Biological analysis and interpretation of sets of proteins is a time-consuming undertaking carried out manually by experts. We have developed PANDORA (Protein ANnotation Diagram ORiented Analysis), a web-based tool that provides an automatic representation of the biological knowledge associated with any set of proteins. PANDORA uses a unique approach of keyword-based graphical analysis that focuses on detecting subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA currently supports SwissProt keywords, NCBI Taxonomy, InterPro entries and the hierarchical classification terms from ENZYME, SCOP and GO databases. The integrated study of several annotation sources simultaneously allows a representation of biological relations of structure, function, cellular location, taxonomy, domains and motifs. PANDORA is also integrated into the ProtoNet system, thus allowing testing thousands of automatically generated clusters. We illustrate how PANDORA enhances the biological understanding of large, non-uniform sets of proteins originating from experimental and computational sources, without the need for prior biological knowledge on individual proteins.

Myristoylation of Src kinase mediates Src-induced and high-fat diet-accelerated prostate tumor progression in mice.

PubMed

Kim, Sungjin; Yang, Xiangkun; Li, Qianjin; Wu, Meng; Costyn, Leah; Beharry, Zanna; Bartlett, Michael G; Cai, Houjian

2017-11-10

Exogenous fatty acids provide substrates for energy production and biogenesis of the cytoplasmic membrane, but they also enhance cellular signaling during cancer cell proliferation. However, it remains controversial whether dietary fatty acids are correlated with tumor progression. In this study, we demonstrate that increased Src kinase activity is associated with high-fat diet-accelerated progression of prostate tumors and that Src kinases mediate this pathological process. Moreover, in the in vivo prostate regeneration assay, host SCID mice carrying Src(Y529F)-transduced regeneration tissues were fed a low-fat diet or a high-fat diet and treated with vehicle or dasatinib. The high-fat diet not only accelerated Src-induced prostate tumorigenesis in mice but also compromised the inhibitory effect of the anticancer drug dasatinib on Src kinase oncogenic potential in vivo We further show that myristoylation of Src kinase is essential to facilitate Src-induced and high-fat diet-accelerated tumor progression. Mechanistically, metabolism of exogenous myristic acid increased the biosynthesis of myristoyl CoA and myristoylated Src and promoted Src kinase-mediated oncogenic signaling in human cells. Of the fatty acids tested, only exogenous myristic acid contributed to increased intracellular myristoyl CoA levels. Our results suggest that targeting Src kinase myristoylation, which is required for Src kinase association at the cellular membrane, blocks dietary fat-accelerated tumorigenesis in vivo Our findings uncover the molecular basis of how the metabolism of myristic acid stimulates high-fat diet-mediated prostate tumor progression. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Cyr61 as mediator of Src signaling in triple negative breast cancer cells

PubMed Central

Molinari, Agnese; Wagner, Kay-Uwe; Losada, Jesús Pérez; Ciordia, Sergio; Albar, Juan Pablo; Martín-Pérez, Jorge

2015-01-01

SFKs are involved in tumorigenesis and metastasis. Here we analyzed c-Src contribution to initial steps of metastasis by tetracycline-dependent expression of a specific shRNA-c-Src, which suppressed c-Src mRNA and protein levels in metastatic MDA-MB-231 cells. c-Src suppression did not alter cell proliferation or survival, but it significantly reduced anchorage-independent growth. Concomitantly with diminished tyrosine-phosphorylation/activation of Fak, caveolin-1, paxillin and p130CAS, c-Src depletion also inhibited cellular migration, invasion and transendothelial migration. Quantitative proteomic analyses of the secretome showed that Cyr61 levels, which were detected in the exosomal fraction, were diminished upon shRNA-c-Src expression. In contrast, Cyr61 expression was unaltered inside cells. Cyr61 partially colocalized with cis-Golgi gp74 marker and with exosomal marker CD63, but c-Src depletion did not alter their cellular distribution. In SUM159PT cells, transient c-Src suppression also reduced secreted exosomal Cyr61 levels. Furthermore, conditional expression of a c-Src dominant negative mutant (SrcDN, c-Src-K295M/Y527F) in MDA-MB-231 and in SUM159PT diminished secreted Cyr61 as well. Cyr61 transient suppression in MDA-MB-231 inhibited invasion and transendothelial migration. Finally, in both MDA-MB-231 and SUM159PT, a neutralizing Cyr61 antibody restrained migration. Collectively, these results suggest that c-Src regulates secreted proteins, including the exosomal Cyr61, which are involved in modulating the metastatic potential of triple negative breast cancer cells. PMID:25980494

Constrained Low-Rank Learning Using Least Squares-Based Regularization.

PubMed

Li, Ping; Yu, Jun; Wang, Meng; Zhang, Luming; Cai, Deng; Li, Xuelong

2017-12-01

Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization. Most low-rank methods are incapable of capturing low-dimensional subspace for supervised learning tasks, e.g., classification and regression. This paper aims to learn both the discriminant low-rank representation (LRR) and the robust projecting subspace in a supervised manner. To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization. Naturally, the data label structure tends to resemble that of the corresponding low-dimensional representation, which is derived from the robust subspace projection of clean data by low-rank learning. Moreover, the low-dimensional representation of original data can be paired with some informative structure by imposing an appropriate constraint, e.g., Laplacian regularizer. Therefore, we propose a novel constrained LRR method. The objective function is formulated as a constrained nuclear norm minimization problem, which can be solved by the inexact augmented Lagrange multiplier algorithm. Extensive experiments on image classification, human pose estimation, and robust face recovery have confirmed the superiority of our method.

New feature extraction method for classification of agricultural products from x-ray images

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

1999-01-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

The Tyrosine Kinase Activity of c-Src Regulates Actin Dynamics and Organization of Podosomes in Osteoclasts

PubMed Central

Destaing, Olivier; Sanjay, Archana; Itzstein, Cecile; Horne, William C.; Toomre, Derek

2008-01-01

Podosomes are dynamic actin-rich structures composed of a dense F-actin core surrounded by a cloud of more diffuse F-actin. Src performs one or more unique functions in osteoclasts (OCLs), and podosome belts and bone resorption are impaired in the absence of Src. Using Src−/− OCLs, we investigated the specific functions of Src in the organization and dynamics of podosomes. We found that podosome number and the podosome-associated actin cloud were decreased in Src−/− OCLs. Videomicroscopy and fluorescence recovery after photobleaching analysis revealed that the life span of Src−/− podosomes was increased fourfold and that the rate of actin flux in the core was decreased by 40%. Thus, Src regulates the formation, structure, life span, and rate of actin polymerization in podosomes and in the actin cloud. Rescue of Src−/− OCLs with Src mutants showed that both the kinase activity and either the SH2 or the SH3 binding domain are required for Src to restore normal podosome organization and dynamics. Moreover, inhibition of Src family kinase activities in Src−/− OCLs by Src inhibitors or by expressing dominant-negative SrcK295M induced the formation of abnormal podosomes. Thus, Src is an essential regulator of podosome structure, dynamics and organization. PMID:17978100

A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats.

PubMed

Tejedor, Javier; Macias-Guarasa, Javier; Martins, Hugo F; Piote, Daniel; Pastor-Graells, Juan; Martin-Lopez, Sonia; Corredera, Pedro; Gonzalez-Herraez, Miguel

2017-02-12

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.

Connectivity Strength-Weighted Sparse Group Representation-Based Brain Network Construction for MCI Classification

PubMed Central

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-01-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. PMID:28150897

76 FR 21404 - National Park Service Alaska Region's Subsistence Resource Commission (SRC) Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-15

... Resource Commission (SRC) program. SUMMARY: The Gates of the Arctic National Park SRC will meet to develop... to do so. Gates of the Arctic National Park SRC Meeting Date and Location: The Gates of the Arctic... weather or local circumstances. For Further Information on the Gates of the Arctic National Park SRC...

75 FR 51103 - Notice of Public Meetings for the National Park Service (NPS) Alaska Region's Subsistence...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-18

... SRC and Wrangell-St. Elias SRC plan to meet to develop and continue work on National Park Service (NPS... SRC Meeting Date and Location: The Lake Clark National Park SRC meeting will be held on Tuesday... Alaska Regional Office, at (907) 644- 3603. Aniakchak National Monument SRC Meeting Date and Location...

Grounded Classification: Grounded Theory and Faceted Classification.

ERIC Educational Resources Information Center

Star, Susan Leigh

1998-01-01

Compares the qualitative method of grounded theory (GT) with Ranganathan's construction of faceted classifications (FC) in library and information science. Both struggle with a core problem--the representation of vernacular words and processes, empirically discovered, which will, although ethnographically faithful, be powerful beyond the single…

Efficient Fingercode Classification

NASA Astrophysics Data System (ADS)

Sun, Hong-Wei; Law, Kwok-Yan; Gollmann, Dieter; Chung, Siu-Leung; Li, Jian-Bin; Sun, Jia-Guang

In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e. g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

Attachment-based classifications of children's family drawings: psychometric properties and relations with children's adjustment in kindergarten.

PubMed

Pianta, R C; Longmaid, K; Ferguson, J E

1999-06-01

Investigated an attachment-based theoretical framework and classification system, introduced by Kaplan and Main (1986), for interpreting children's family drawings. This study concentrated on the psychometric properties of the system and the relation between drawings classified using this system and teacher ratings of classroom social-emotional and behavioral functioning, controlling for child age, ethnic status, intelligence, and fine motor skills. This nonclinical sample consisted of 200 kindergarten children of diverse racial and socioeconomic status (SES). Limited support for reliability of this classification system was obtained. Kappas for overall classifications of drawings (e.g., secure) exceeded .80 and mean kappa for discrete drawing features (e.g., figures with smiles) was .82. Coders' endorsement of the presence of certain discrete drawing features predicted their overall classification at 82.5% accuracy. Drawing classification was related to teacher ratings of classroom functioning independent of child age, sex, race, SES, intelligence, and fine motor skills (with p values for the multivariate effects ranging from .043-.001). Results are discussed in terms of the psychometric properties of this system for classifying children's representations of family and the limitations of family drawing techniques for young children.

Reticulate classification of mosaic microbial genomes using NeAT website.

PubMed

Lima-Mendez, Gipsi

2012-01-01

The tree of life is the classical representation of the evolutionary relationships between existent species. A tree is appropriate to display the divergence of species through mutation, i.e., by vertical descent. However, lateral gene transfer (LGT) is excluded from such representations. When LGT contribution to genome evolution cannot be neglected (e.g., for prokaryotes and mobile genetic elements), the tree becomes misleading. Networks appear as an intuitive way to represent both vertical and horizontal relationships, while overlapping groups within such graphs are more suitable for their classification. Here, we describe a method to represent both vertical and horizontal relationships. We start with a set of genomes whose coded proteins have been grouped into families based on sequence similarity. Next, all pairs of genomes are compared, counting the number of proteins classified into the same family. From this comparison, we derive a weighted graph where genomes with a significant number of similar proteins are linked. Finally, we apply a two-step clustering of this graph to produce a classification where nodes can be assigned to multiple clusters. The procedure can be performed using the Network Analysis Tools (NeAT) website.

Simultaneous inhibition of aryl hydrocarbon receptor (AhR) and Src abolishes androgen receptor signaling.

PubMed

Ghotbaddini, Maryam; Cisse, Keyana; Carey, Alexis; Powell, Joann B

2017-01-01

Altered c-Src activity has been strongly implicated in the development, growth, progression, and metastasis of human cancers including prostate cancer. Src is known to regulate several biological functions of tumor cells, including proliferation. There are several Src inhibitors under evaluation for clinical effectiveness but have shown little activity in monotherapy trials of solid tumors. Combination studies are being explored by in vitro analysis and in clinical trials. Here we investigate the effect of simultaneous inhibition of the aryl hydrocarbon receptor (AhR) and Src on androgen receptor (AR) signaling in prostate cancer cells. AhR has also been reported to interact with the Src signaling pathway during prostate development. c-Src protein kinase is associated with the AhR complex in the cytosol and upon ligand binding to AhR, c-Src is activated and released from the complex. AhR has also been shown to regulate AR signaling which remains functionally important in the development and progression of prostate cancer. We provide evidence that co-inhibition of AhR and Src abolish AR activity. Evaluation of total protein and cellular fractions revealed decreased pAR expression and AR nuclear localization. Assays utilizing an androgen responsive element (ARE) and qRT-PCR analysis of AR genes revealed decreased AR promoter activity and transcriptional activity in the presence of both AhR and Src inhibitors. Furthermore, co-inhibition of AhR and Src reduced the growth of prostate cancer cells compared to individual treatments. Several studies have revealed that AhR and Src individually inhibit cellular proliferation. However, this study is the first to suggest simultaneous inhibition of AhR and Src to inhibit AR signaling and prostate cancer cell growth.

Differential effects of phosphotyrosine phosphatase expression on hormone-dependent and independent pp60c-src activity.

PubMed

Way, B A; Mooney, R A

1994-10-26

pp60c-src kinase activity can be increased by phosphotyrosine dephosphorylation or growth factor-dependent phosphorylation reactions. Expression of the transmembrane phosphotyrosine phosphatase (PTPase) CD45 has been shown to inhibit growth factor receptor signal transduction (Mooney, RA, Freund, GG, Way, BA and Bordwell, KL (1992) J Biol Chem 267, 23443-23446). Here it is shown that PTPase expression decreased platelet-derived growth factor (PDGF)-dependent activation of pp60c-src but failed to increase hormone independent (basal) pp60c-src activity. PDGF-dependent tyrosine phosphorylation of its receptor was reduced by approximately 60% in cells expressing the PTPase. In contrast, a change in phosphotyrosine content of pp60c-src was not detected in response to PDGF or in PTPase+ cells. PDGF increased the intrinsic tyrosine kinase activity of pp60c-src in both control and PTPase+ cells, but the effect was smaller in PTPase+ cells. In an in vitro assay, hormone-stimulated pp60c-src autophosphorylation from PTPase+ cells was decreased 64 +/- 22%, and substrate phosphorylation by pp60c-src was reduced 54 +/- 16% compared to controls. Hormone-independent pp60c-src kinase activity was unchanged by expression of the PTPase. pp60c-src was, however, an in vitro substrate for CD45, being dephosphorylated at both the regulatory (Tyr527) and kinase domain (Tyr416) residues. In addition, in vitro dephosphorylation by CD45 increased pp60c-src activity. These findings suggest that the PDGF receptor was an in vivo substrate of CD45 but pp60c-src was not. The lack of activation of pp60c-src in the presence of expressed PTPase may demonstrate the importance of compartmentalization and/or accessory proteins to PTPase-substrate interactions.

v-Src-induced nuclear localization of YAP is involved in multipolar spindle formation in tetraploid cells.

PubMed

Kakae, Keiko; Ikeuchi, Masayoshi; Kuga, Takahisa; Saito, Youhei; Yamaguchi, Naoto; Nakayama, Yuji

2017-01-01

The protein-tyrosine kinase, c-Src, is involved in a variety of signaling events, including cell division. We have reported that v-Src, which is a mutant variant of the cellular proto-oncogene, c-Src, causes delocalization of Aurora B kinase, resulting in a furrow regression in cytokinesis and the generation of multinucleated cells. However, the effect of v-Src on mitotic spindle formation is unknown. Here we show that v-Src-expressing HCT116 and NIH3T3 cells undergo abnormal cell division, in which cells separate into more than two cells. Upon v-Src expression, the proportion of multinucleated cells is increased in a time-dependent manner. Flow cytometry analysis revealed that v-Src increases the number of cells having a ≥4N DNA content. Microscopic analysis showed that v-Src induces the formation of multipolar spindles with excess centrosomes. These results suggest that v-Src induces multipolar spindle formation by generating multinucleated cells. Tetraploidy activates the tetraploidy checkpoint, leading to a cell cycle arrest of tetraploid cells at the G1 phase, in which the nuclear exclusion of the transcription co-activator YAP plays a critical role. In multinucleated cells that are induced by cytochalasin B and the Plk1 inhibitor, YAP is excluded from the nucleus. However, v-Src prevents this nuclear exclusion of YAP through a decrease in the phosphorylation of YAP at Ser127 in multinucleated cells. Furthermore, v-Src decreases the expression level of p53, which also plays a critical role in the cell cycle arrest of tetraploid cells. These results suggest that v-Src promotes abnormal spindle formation in at least two ways: generation of multinucleated cells and a weakening of the tetraploidy checkpoint. Copyright © 2016 Elsevier Inc. All rights reserved.

Protective Equipment and Player Characteristics Associated With the Incidence of Sport-Related Concussion in High School Football Players

PubMed Central

McGuine, Timothy A.; Hetzel, Scott; McCrea, Michael; Brooks, M. Alison

2015-01-01

Background The incidence of sport-related concussion (SRC) in high school football is well documented. However, limited prospective data are available regarding how player characteristics and protective equipment affect the incidence of SRC. Purpose To determine whether the type of protective equipment (helmet and mouth guard) and player characteristics affect the incidence of SRC in high school football players. Design Cohort study; Level of evidence, 2. Methods Certified athletic trainers (ATs) at each high school recorded the type of helmet worn (brand, model, purchase year, and recondition status) by each player as well as information regarding players’ demographics, type of mouth guard used, and history of SRC. The ATs also recorded the incidence and days lost from participation for each SRC. Incidence of SRC was compared for various helmets, type of mouth guard, history of SRC, and player demographics. Results A total of 2081 players (grades 9–12) enrolled during the 2012 and/or 2013 football seasons (2287 player-seasons) and participated in 134,437 football (practice or competition) exposures. Of these players, 206 (9%) sustained a total of 211 SRCs (1.56/1000 exposures). There was no difference in the incidence of SRC (number of helmets, % SRC [95% CI]) for players wearing Riddell (1171, 9.1% [7.6%–11.0%]), Schutt (680, 8.7% [6.7%–11.1%]), or Xenith (436, 9.2% [6.7%–12.4%]) helmets. Helmet age and recondition status did not affect the incidence of SRC. The rate of SRC (hazard ratio [HR]) was higher in players who wore a custom mouth guard (HR = 1.69 [95% CI, 1.20–2.37], P <.001) than in players who wore a generic mouth guard. The rate of SRC was also higher (HR = 1.96 [95% CI, 1.40–2.73], P <.001) in players who had sustained an SRC within the previous 12 months (15.1% of the 259 players [95% CI, 11.0%–20.1%]) than in players without a previous SRC (8.2% of the 2028 players [95% CI, 7.1%–9.5%]). Conclusion Incidence of SRC was similar regardless of the helmet brand (manufacturer) worn by high school football players. Players who had sustained an SRC within the previous 12 months were more likely to sustain an SRC than were players without a history of SRC. Sports medicine providers who work with high school football players need to realize that factors other than the type of protective equipment worn affect the risk of SRC in high school players. PMID:25060072

c-Src, Insulin-Like Growth Factor I Receptor, G-Protein-Coupled Receptor Kinases and Focal Adhesion Kinase are Enriched Into Prostate Cancer Cell Exosomes.

PubMed

DeRita, Rachel M; Zerlanko, Brad; Singh, Amrita; Lu, Huimin; Iozzo, Renato V; Benovic, Jeffrey L; Languino, Lucia R

2017-01-01

It is well known that Src tyrosine kinase, insulin-like growth factor 1 receptor (IGF-IR), and focal adhesion kinase (FAK) play important roles in prostate cancer (PrCa) development and progression. Src, which signals through FAK in response to integrin activation, has been implicated in many aspects of tumor biology, such as cell proliferation, metastasis, and angiogenesis. Furthermore, Src signaling is known to crosstalk with IGF-IR, which also promotes angiogenesis. In this study, we demonstrate that c-Src, IGF-IR, and FAK are packaged into exosomes (Exo), c-Src in particular being highly enriched in Exo from the androgen receptor (AR)-positive cell line C4-2B and AR-negative cell lines PC3 and DU145. Furthermore, we show that the active phosphorylated form of Src (Src pY416 ) is co-expressed in Exo with phosphorylated FAK (FAK pY861 ), a known target site of Src, which enhances proliferation and migration. We further demonstrate for the first time exosomal enrichment of G-protein-coupled receptor kinase (GRK) 5 and GRK6, both of which regulate Src and IGF-IR signaling and have been implicated in cancer. Finally, Src pY416 and c-Src are both expressed in Exo isolated from the plasma of prostate tumor-bearing TRAMP mice, and those same mice have higher levels of exosomal c-Src than their wild-type counterparts. In summary, we provide new evidence that active signaling molecules relevant to PrCa are enriched in Exo, and this suggests that the Src signaling network may provide useful biomarkers detectable by liquid biopsy, and may contribute to PrCa progression via Exo. J. Cell. Biochem. 118: 66-73, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Heterogeneity of signal transduction by Na-K-ATPase α-isoforms: role of Src interaction.

PubMed

Yu, Hui; Cui, Xiaoyu; Zhang, Jue; Xie, Joe X; Banerjee, Moumita; Pierre, Sandrine V; Xie, Zijian

2018-02-01

Of the four Na-K-ATPase α-isoforms, the ubiquitous α1 Na-K-ATPase possesses both ion transport and Src-dependent signaling functions. Mechanistically, we have identified two putative pairs of domain interactions between α1 Na-K-ATPase and Src that are critical for α1 signaling function. Our subsequent report that α2 Na-K-ATPase lacks these putative Src-binding sites and fails to carry on Src-dependent signaling further supported our proposed model of direct interaction between α1 Na-K-ATPase and Src but fell short of providing evidence for a causative role. This hypothesis was specifically tested here by introducing key residues of the two putative Src-interacting domains present on α1 but not α2 sequence into the α2 polypeptide, generating stable cell lines expressing this mutant, and comparing its signaling properties to those of α2-expressing cells. The mutant α2 was fully functional as a Na-K-ATPase. In contrast to wild-type α2, the mutant gained α1-like signaling function, capable of Src interaction and regulation. Consistently, the expression of mutant α2 redistributed Src into caveolin-1-enriched fractions and allowed ouabain to activate Src-mediated signaling cascades, unlike wild-type α2 cells. Finally, mutant α2 cells exhibited a growth phenotype similar to that of the α1 cells and proliferated much faster than wild-type α2 cells. These findings reveal the structural requirements for the Na-K-ATPase to function as a Src-dependent receptor and provide strong evidence of isoform-specific Src interaction involving the identified key amino acids. The sequences surrounding the putative Src-binding sites in α2 are highly conserved across species, suggesting that the lack of Src binding may play a physiologically important and isoform-specific role.

H-Ras Modulates N-Methyl-d-aspartate Receptor Function via Inhibition of Src Tyrosine Kinase Activity*

PubMed Central

Thornton, Claire; Yaka, Rami; Dinh, Son; Ron, Dorit

2005-01-01

Tyrosine phosphorylation of the NR2A and NR2B subunits of the N-methyl-d-aspartate (NMDA) receptor by Src protein-tyrosine kinases modulates receptor channel activity and is necessary for the induction of long term potentiation (LTP). Deletion of H-Ras increases both NR2 tyrosine phosphorylation and NMDA receptor-mediated hippocampal LTP. Here we investigated whether H-Ras regulates phosphorylation and function of the NMDA receptor via Src family protein-tyrosine kinases. We identified Src as a novel H-Ras binding partner. H-Ras bound to Src but not Fyn both in vitro and in brain via the Src kinase domain. Cotransfection of H-Ras and Src inhibited Src activity and decreased NR2A tyrosine phosphorylation. Treatment of rat brain slices with Tat-H-Ras depleted NR2A from the synaptic membrane, decreased endogenous Src activity and NR2A phosphorylation, and decreased the magnitude of hip-pocampal LTP. No change was observed for NR2B. We suggest that H-Ras negatively regulates Src phosphorylation of NR2A and retention of NR2A into the synaptic membrane leading to inhibition of NMDA receptor function. This mechanism is specific for Src and NR2A and has implications for studies in which regulation of NMDA receptor-mediated LTP is important, such as synaptic plasticity, learning, and memory and addiction. PMID:12695509

Targeting the yin and the yang: combined inhibition of the tyrosine kinase c-Src and the tyrosine phosphatase SHP-2 disrupts pancreatic cancer signaling and biology in vitro and tumor formation in vivo.

PubMed

Gomes, Evan G; Connelly, Sarah F; Summy, Justin M

2013-07-01

Although c-Src (Src) has emerged as a potential pancreatic cancer target in preclinical studies, Src inhibitors have not demonstrated a significant therapeutic benefit in clinical trials. The objective of these studies was to examine the effects of combining Src inhibition with inhibition of the protein tyrosine phosphatase SHP-2 in pancreatic cancer cells in vitro and in vivo. SHP-2 and Src functions were inhibited by siRNA or small molecule inhibitors. The effects of dual Src/SHP-2 functional inhibition were evaluated by Western blot analysis of downstream signaling pathways; cell biology assays to examine caspase activity, viability, adhesion, migration, and invasion in vitro; and an orthotopic nude mouse model to observe pancreatic tumor formation in vivo. Dual targeting of Src and SHP-2 induces an additive or supra-additive loss of phosphorylation of Akt and ERK-1/2 and corresponding increases in expression of apoptotic markers, relative to targeting either protein individually. Combinatorial inhibition of Src and SHP-2 significantly reduces viability, adhesion, migration, and invasion of pancreatic cancer cells in vitro and tumor formation in vivo, relative to individual Src/SHP-2 inhibition. These data suggest that the antitumor effects of Src inhibition in pancreatic cancer may be enhanced through simultaneous inhibition of SHP-2.

SRC-2-mediated coactivation of anti-tumorigenic target genes suppresses MYC-induced liver cancer

PubMed Central

Zhou, Xiaorong; Comerford, Sarah A.; York, Brian; O’Donnell, Kathryn A.

2017-01-01

Hepatocellular carcinoma (HCC) is the fifth most common solid tumor in the world and the third leading cause of cancer-associated deaths. A Sleeping Beauty-mediated transposon mutagenesis screen previously identified mutations that cooperate with MYC to accelerate liver tumorigenesis. This revealed a tumor suppressor role for Steroid Receptor Coactivator 2/Nuclear Receptor Coactivator 2 (Src-2/Ncoa2) in liver cancer. In contrast, SRC-2 promotes survival and metastasis in prostate cancer cells, suggesting a tissue-specific and context-dependent role for SRC-2 in tumorigenesis. To determine if genetic loss of SRC-2 is sufficient to accelerate MYC-mediated liver tumorigenesis, we bred Src-2-/- mice with a MYC-induced liver tumor model and observed a significant increase in liver tumor burden. RNA sequencing of liver tumors and in vivo chromatin immunoprecipitation assays revealed a set of direct target genes that are bound by SRC-2 and exhibit downregulated expression in Src-2-/- liver tumors. We demonstrate that activation of SHP (Small Heterodimer Partner), DKK4 (Dickkopf-4), and CADM4 (Cell Adhesion Molecule 4) by SRC-2 suppresses tumorigenesis in vitro and in vivo. These studies suggest that SRC-2 may exhibit oncogenic or tumor suppressor activity depending on the target genes and nuclear receptors that are expressed in distinct tissues and illuminate the mechanisms of tumor suppression by SRC-2 in liver. PMID:28273073

Acetylation within the N- and C-Terminal Domains of Src Regulates Distinct Roles of STAT3-Mediated Tumorigenesis.

PubMed

Huang, Chao; Zhang, Zhe; Chen, Lihan; Lee, Hank W; Ayrapetov, Marina K; Zhao, Ting C; Hao, Yimei; Gao, Jinsong; Yang, Chunzhang; Mehta, Gautam U; Zhuang, Zhengping; Zhang, Xiaoren; Hu, Guohong; Chin, Y Eugene

2018-06-01

Posttranslational modifications of mammalian c-Src N-terminal and C-terminal domains regulate distinct functions. Myristoylation of G 2 controls its cell membrane association and phosphorylation of Y419/Y527 controls its activation or inactivation, respectively. We provide evidence that Src-cell membrane association-dissociation and catalytic activation-inactivation are both regulated by acetylation. In EGF-treated cells, CREB binding protein (CBP) acetylates an N-terminal lysine cluster (K5, K7, and K9) of c-Src to promote dissociation from the cell membrane. CBP also acetylates the C-terminal K401, K423, and K427 of c-Src to activate intrinsic kinase activity for STAT3 recruitment and activation. N-terminal domain phosphorylation (Y14, Y45, and Y68) of STAT3 by c-Src activates transcriptionally active dimers of STAT3. Moreover, acetyl-Src translocates into nuclei, where it forms the Src-STAT3 enhanceosome for gene regulation and cancer cell proliferation. Thus, c-Src acetylation in the N-terminal and C-terminal domains play distinct roles in Src activity and regulation. Significance: CBP-mediated acetylation of lysine clusters in both the N-terminal and C-terminal regions of c-Src provides additional levels of control over STAT3 transcriptional activity. Cancer Res; 78(11); 2825-38. ©2018 AACR . ©2018 American Association for Cancer Research.

Functional diversity of Csk, Chk, and Src SH2 domains due to a single residue variation.

PubMed

Ayrapetov, Marina K; Nam, Nguyen Hai; Ye, Guofeng; Kumar, Anil; Parang, Keykavous; Sun, Gongqin

2005-07-08

The C-terminal Src kinase (Csk) family of protein tyrosine kinases contains two members: Csk and Csk homologous kinase (Chk). Both phosphorylate and inactivate Src family kinases. Recent reports suggest that the Src homology (SH) 2 domains of Csk and Chk may bind to different phosphoproteins, which provides a basis for different cellular functions for Csk and Chk. To verify and characterize such a functional divergence, we compared the binding properties of the Csk, Chk, and Src SH2 domains and investigated the structural basis for the functional divergence. First, the study demonstrated striking functional differences between the Csk and Chk SH2 domains and revealed functional similarities between the Chk and Src SH2 domains. Second, structural analysis and mutagenic studies revealed that the functional differences among the three SH2 domains were largely controlled by one residue, Glu127 in Csk, Ile167 in Chk, and Lys200 in Src. Mutating these residues in the Csk or Chk SH2 domain to the Src counterpart resulted in dramatic gain of function similar to Src SH2 domain, whereas mutating Lys200 in Src SH2 domain to Glu (the Csk counterpart) resulted in loss of Src SH2 function. Third, a single point mutation of E127K rendered Csk responsive to activation by a Src SH2 domain ligand. Finally, the optimal phosphopeptide sequence for the Chk SH2 domain was determined. These results provide a compelling explanation for the functional differences between two homologous protein tyrosine kinases and reveal a new structure-function relationship for the SH2 domains.

Classifying Black Hole States with Machine Learning

NASA Astrophysics Data System (ADS)

Huppenkothen, Daniela

2018-01-01

Galactic black hole binaries are known to go through different states with apparent signatures in both X-ray light curves and spectra, leading to important implications for accretion physics as well as our knowledge of General Relativity. Existing frameworks of classification are usually based on human interpretation of low-dimensional representations of the data, and generally only apply to fairly small data sets. Machine learning, in contrast, allows for rapid classification of large, high-dimensional data sets. In this talk, I will report on advances made in classification of states observed in Black Hole X-ray Binaries, focusing on the two sources GRS 1915+105 and Cygnus X-1, and show both the successes and limitations of using machine learning to derive physical constraints on these systems.

Decipher the dynamic coordination between enzymatic activity and structural modulation at focal adhesions in living cells

NASA Astrophysics Data System (ADS)

Lu, Shaoying; Seong, Jihye; Wang, Yi; Chang, Shiou-Chi; Eichorst, John Paul; Ouyang, Mingxing; Li, Julie Y.-S.; Chien, Shu; Wang, Yingxiao

2014-07-01

Focal adhesions (FAs) are dynamic subcellular structures crucial for cell adhesion, migration and differentiation. It remains an enigma how enzymatic activities in these local complexes regulate their structural remodeling in live cells. Utilizing biosensors based on fluorescence resonance energy transfer (FRET), we developed a correlative FRET imaging microscopy (CFIM) approach to quantitatively analyze the subcellular coordination between the enzymatic Src activation and the structural FA disassembly. CFIM reveals that the Src kinase activity only within the microdomain of lipid rafts at the plasma membrane is coupled with FA dynamics. FA disassembly at cell periphery was linearly dependent on this raft-localized Src activity, although cells displayed heterogeneous levels of response to stimulation. Within lipid rafts, the time delay between Src activation and FA disassembly was 1.2 min in cells seeded on low fibronectin concentration ([FN]) and 4.3 min in cells on high [FN]. CFIM further showed that the level of Src-FA coupling, as well as the time delay, was regulated by cell-matrix interactions, as a tight enzyme-structure coupling occurred in FA populations mediated by integrin αvβ3, but not in those by integrin α5β1. Therefore, different FA subpopulations have distinctive regulation mechanisms between their local kinase activity and structural FA dynamics.

Chlorogenic acid inhibits hypoxia-induced pulmonary artery smooth muscle cells proliferation via c-Src and Shc/Grb2/ERK2 signaling pathway.

PubMed

Li, Qun-Yi; Zhu, Ying-Feng; Zhang, Meng; Chen, Li; Zhang, Zhen; Du, Yong-Li; Ren, Guo-Qiang; Tang, Jian-Min; Zhong, Ming-Kang; Shi, Xiao-Jin

2015-03-15

Chlorogenic acid (CGA), abundant in coffee and particular fruits, can modulate hypertension and vascular dysfunction. Hypoxia-induced pulmonary artery smooth muscle cells (PASMCs) proliferation has been tightly linked to vascular remodeling in pulmonary arterial hypertension (PAH). Thus, the present study was designed to investigate the effect of CGA on hypoxia-induced proliferation in cultured rat PASMCs. The data showed that CGA potently inhibited PASMCs proliferation and DNA synthesis induced by hypoxia. These inhibitory effects were associated with G1 cell cycle arrest and down-regulation of cell cycle proteins. Treatment with CGA reduced hypoxia-induced hypoxia inducible factor 1α (HIF-1α) expression and trans-activation. Furthermore, hypoxia-evoked c-Src phosphorylation was inhibited by CGA. In vitro ELISA-based tyrosine kinase assay indicated that CGA was a direct inhibitor of c-Src. Moreover, CGA attenuated physical co-association of c-Src/Shc/Grb2 and ERK2 phosphorylation in PASMCs. These results suggest that CGA inhibits hypoxia-induced proliferation in PASMCs via regulating c-Src-mediated signaling pathway. In vivo investigation showed that chronic CGA treatment inhibits monocrotaline-induced PAH in rats. These findings presented here highlight the possible therapeutic use of CGA in hypoxia-related PAH. Copyright © 2015 Elsevier B.V. All rights reserved.

Robust kernel representation with statistical local features for face recognition.

PubMed

Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

2013-06-01

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa

2018-07-01

Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Designing a training tool for imaging mental models

NASA Technical Reports Server (NTRS)

Dede, Christopher J.; Jayaram, Geetha

1990-01-01

The training process can be conceptualized as the student acquiring an evolutionary sequence of classification-problem solving mental models. For example a physician learns (1) classification systems for patient symptoms, diagnostic procedures, diseases, and therapeutic interventions and (2) interrelationships among these classifications (e.g., how to use diagnostic procedures to collect data about a patient's symptoms in order to identify the disease so that therapeutic measures can be taken. This project developed functional specifications for a computer-based tool, Mental Link, that allows the evaluative imaging of such mental models. The fundamental design approach underlying this representational medium is traversal of virtual cognition space. Typically intangible cognitive entities and links among them are visible as a three-dimensional web that represents a knowledge structure. The tool has a high degree of flexibility and customizability to allow extension to other types of uses, such a front-end to an intelligent tutoring system, knowledge base, hypermedia system, or semantic network.

On Utilizing Optimal and Information Theoretic Syntactic Modeling for Peptide Classification

NASA Astrophysics Data System (ADS)

Aygün, Eser; Oommen, B. John; Cataltepe, Zehra

Syntactic methods in pattern recognition have been used extensively in bioinformatics, and in particular, in the analysis of gene and protein expressions, and in the recognition and classification of bio-sequences. These methods are almost universally distance-based. This paper concerns the use of an Optimal and Information Theoretic (OIT) probabilistic model [11] to achieve peptide classification using the information residing in their syntactic representations. The latter has traditionally been achieved using the edit distances required in the respective peptide comparisons. We advocate that one can model the differences between compared strings as a mutation model consisting of random Substitutions, Insertions and Deletions (SID) obeying the OIT model. Thus, in this paper, we show that the probability measure obtained from the OIT model can be perceived as a sequence similarity metric, using which a Support Vector Machine (SVM)-based peptide classifier, referred to as OIT_SVM, can be devised.

A Web-Based Framework For a Time-Domain Warehouse

NASA Astrophysics Data System (ADS)

Brewer, J. M.; Bloom, J. S.; Kennedy, R.; Starr, D. L.

2009-09-01

The Berkeley Transients Classification Pipeline (TCP) uses a machine-learning classifier to automatically categorize transients from large data torrents and provide automated notification of astronomical events of scientific interest. As part of the training process, we created a large warehouse of light-curve sources with well-labelled classes that serve as priors to the classification engine. This web-based interactive framework, which we are now making public via DotAstro.org (http://dotastro.org/), allows us to ingest time-variable source data in a wide variety of formats and store it in a common internal data model. Data is passed between pipeline modules in a prototype XML representation of time-series format (VOTimeseries), which can also be emitted to collaborators through dotastro.org. After import, the sources can be visualized using Google Sky, light curves can be inspected interactively, and classifications can be manually adjusted.

Convolutional neural networks with balanced batches for facial expressions recognition

NASA Astrophysics Data System (ADS)

Battini Sönmez, Elena; Cangelosi, Angelo

2017-03-01

This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.

Haptic Classification of Common Objects: Knowledge-Driven Exploration.

ERIC Educational Resources Information Center

Lederman, Susan J.; Klatzky, Roberta L.

1990-01-01

Theoretical and empirical issues relating to haptic exploration and the representation of common objects during haptic classification were investigated in 3 experiments involving a total of 112 college students. Results are discussed in terms of a computational model of human haptic object classification with implications for dextrous robot…

Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL

NASA Astrophysics Data System (ADS)

Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.

2017-10-01

Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

Src promotes cutaneous wound healing by regulating MMP-2 through the ERK pathway.

PubMed

Wu, Xue; Yang, Longlong; Zheng, Zhao; Li, Zhenzhen; Shi, Jihong; Li, Yan; Han, Shichao; Gao, Jianxin; Tang, Chaowu; Su, Linlin; Hu, Dahai

2016-03-01

Wound healing is a highly orchestrated, multistep process, and delayed wound healing is a significant symptomatic clinical problem. Keratinocyte migration and re-epithelialization play the most important roles in wound healing, as they determine the rate of wound healing. In our previous study, we found that Src, one of the oldest proto‑oncogenes encoding a membrane-associated, non-receptor protein tyrosine kinase, promotes keratinocyte migration. We therefore hypothesized that Src promotes wound healing through enhanced keratinocyte migration. In order to test this hypothesis, vectors for overexpressing Src and small interfering RNAs (siRNAs) for silencing of Src were used in the present study. We found that the overexpression of Src accelerated keratinocyte migration in vitro and promoted wound healing in vivo without exerting a marked effect on cell proliferation. The extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) signaling pathways play important roles in Src-accelerated keratinocyte migration. Further experiments demonstrated that Src induced the protein expression of matrix metalloproteinase-2 (MMP-2) and decreased the protein expression of E-cadherin. We suggest that ERK signaling is involved in the Src-mediated regulation of MMP-2 expression. The present study provided evidence that Src promotes keratinocyte migration and cutaneous wound healing, in which the regulation of MMP-2 through the ERK pathway plays an important role, and thus we also demonstrated a potential therapeutic role for Src in cutaneous wound healing.

Towards an Effective Theory of Reformulation. Part 1; Semantics

NASA Technical Reports Server (NTRS)

Benjamin, D. Paul

1992-01-01

This paper describes an investigation into the structure of representations of sets of actions, utilizing semigroup theory. The goals of this project are twofold: to shed light on the relationship between tasks and representations, leading to a classification of tasks according to the representations they admit; and to develop techniques for automatically transforming representations so as to improve problem-solving performance. A method is demonstrated for automatically generating serial algorithms for representations whose actions form a finite group. This method is then extended to representations whose actions form a finite inverse semigroup.

Regularization strategies for hyperplane classifiers: application to cancer classification with gene expression data.

PubMed

Andries, Erik; Hagstrom, Thomas; Atlas, Susan R; Willman, Cheryl

2007-02-01

Linear discrimination, from the point of view of numerical linear algebra, can be treated as solving an ill-posed system of linear equations. In order to generate a solution that is robust in the presence of noise, these problems require regularization. Here, we examine the ill-posedness involved in the linear discrimination of cancer gene expression data with respect to outcome and tumor subclasses. We show that a filter factor representation, based upon Singular Value Decomposition, yields insight into the numerical ill-posedness of the hyperplane-based separation when applied to gene expression data. We also show that this representation yields useful diagnostic tools for guiding the selection of classifier parameters, thus leading to improved performance.

Cigarette Smoke Activates the Proto-Oncogene c-Src to Promote Airway Inflammation and Lung Tissue Destruction

PubMed Central

Geraghty, Patrick; Hardigan, Andrew

2014-01-01

The diagnosis of chronic obstructive pulmonary disease (COPD) confers a 2-fold increased lung cancer risk even after adjusting for cigarette smoking, suggesting that common pathways are operative in both diseases. Although the role of the tyrosine kinase c-Src is established in lung cancer, less is known about its impact in other lung diseases, such as COPD. This study examined whether c-Src activation by cigarette smoke contributes to the pathogenesis of COPD. Cigarette smoke increased c-Src activity in human small airway epithelial (SAE) cells from healthy donors and in the lungs of exposed mice. Similarly, higher c-Src activation was measured in SAE cells from patients with COPD compared with healthy control subjects. In SAE cells, c-Src silencing or chemical inhibition prevented epidermal growth factor (EGF) receptor signaling in response to cigarette smoke but not EGF stimulation. Further studies showed that cigarette smoke acted through protein kinase C α to trigger c-Src to phosphorylate EGF receptor and thereby to induce mitogen-activated protein kinase responses in these cells. To further investigate the role of c-Src, A/J mice were orally administered the specific Src inhibitor AZD-0530 while they were exposed to cigarette smoke for 2 months. AZD-0530 treatment blocked c-Src activation, decreased macrophage influx, and prevented airspace enlargement in the lungs of cigarette smoke–exposed mice. Moreover, inhibiting Src deterred the cigarette smoke–mediated induction of matrix metalloproteinase-9 and -12 in alveolar macrophages and lung expression of cathepsin K, IL-17, TNF-α, MCP-1, and KC, all key factors in the pathogenesis of COPD. These results indicate that activation of the proto-oncogene c-Src by cigarette smoke promotes processes linked to the development of COPD. PMID:24111605

Feature generation and representations for protein-protein interaction classification.

PubMed

Lan, Man; Tan, Chew Lim; Su, Jian

2009-10-01

Automatic detecting protein-protein interaction (PPI) relevant articles is a crucial step for large-scale biological database curation. The previous work adopted POS tagging, shallow parsing and sentence splitting techniques, but they achieved worse performance than the simple bag-of-words representation. In this paper, we generated and investigated multiple types of feature representations in order to further improve the performance of PPI text classification task. Besides the traditional domain-independent bag-of-words approach and the term weighting methods, we also explored other domain-dependent features, i.e. protein-protein interaction trigger keywords, protein named entities and the advanced ways of incorporating Natural Language Processing (NLP) output. The integration of these multiple features has been evaluated on the BioCreAtIvE II corpus. The experimental results showed that both the advanced way of using NLP output and the integration of bag-of-words and NLP output improved the performance of text classification. Specifically, in comparison with the best performance achieved in the BioCreAtIvE II IAS, the feature-level and classifier-level integration of multiple features improved the performance of classification 2.71% and 3.95%, respectively.

Ableson Kinases Negatively Regulate Invadopodia Function and Invasion in Head and Neck Squamous Cell Carcinoma by Inhibiting an HB-EGF Autocrine Loop

PubMed Central

Hayes, Karen E.; Walk, Elyse L.; Ammer, Amanda Gatesman; Kelley, Laura C.; Martin, Karen H.; Weed, Scott A.

2014-01-01

Head and neck squamous cell carcinoma (HNSCC) has a proclivity for locoregional invasion. HNSCC mediates invasion in part through invadopodia-based proteolysis of the extracellular matrix (ECM). Activation of Src, Erk1/2, Abl and Arg downstream of epidermal growth factor receptor (EGFR) modulates invadopodia activity through phosphorylation of the actin regulatory protein cortactin. In MDA-MB-231 breast cancer cells, Abl and Arg function downstream of Src to phosphorylate cortactin, promoting invadopodia ECM degradation activity and thus assigning a pro-invasive role for Ableson kinases. We report that Abl kinases have an opposite, negative regulatory role in HNSCC where they suppress invadopodia and tumor invasion. Impairment of Abl expression or Abl kinase activity with imatinib mesylate enhanced HNSCC matrix degradation and 3D collagen invasion, functions that were impaired in MDA-MB-231. HNSCC lines with elevated EGFR and Src activation did not contain increased Abl or Arg kinase activity, suggesting Src could bypass Abl/Arg to phosphorylate cortactin and promote invadopodia ECM degradation. Src transformed Abl−/−/Arg−/− fibroblasts produced ECM degrading invadopodia containing pY421 cortactin, indicating that Abl/Arg are dispensable for invadopodia function in this system. Imatinib treated HNSCC cells had increased EGFR, Erk1/2 and Src activation, enhancing cortactin pY421 and pS405/418 required for invadopodia function. Imatinib stimulated shedding of the EGFR ligand heparin-binding EGF-like growth factor (HB-EGF) from HNSCC cells, where soluble HB-EGF enhanced invadopodia ECM degradation in HNSCC but not in MDA-MB-231. HNSCC cells treated with inhibitors of the EGFR invadopodia pathway indicated that EGFR and Src are required for invadopodia function. Collectively our results indicate that Abl kinases negatively regulate HNSCC invasive processes through suppression of an HB-EGF autocrine loop responsible for activating a EGFR-Src-cortactin cascade, in contrast to the invasion promoting functions of Abl kinases in breast and other cancer types. Our results provide mechanistic support for recent failed HNSCC clinical trials utilizing imatinib. PMID:23146907

Tyrosine phosphorylation of LRP6 by Src and Fer inhibits Wnt/β-catenin signalling

PubMed Central

Chen, Qing; Su, Yi; Wesslowski, Janine; Hagemann, Anja I; Ramialison, Mirana; Wittbrodt, Joachim; Scholpp, Steffen; Davidson, Gary

2014-01-01

Low-density lipoprotein receptor-related proteins 5 and 6 (LRP5/6) function as transmembrane receptors to transduce Wnt signals. A key mechanism for signalling is Wnt-induced serine/threonine phosphorylation at conserved PPPSPxS motifs in the LRP6 cytoplasmic domain, which promotes pathway activation. Conserved tyrosine residues are positioned close to all PPPSPxS motifs, which suggests they have a functional significance. Using a cell culture-based cDNA expression screen, we identified the non-receptor tyrosine kinases Src and Fer as novel LRP6 modifiers. Both Src and Fer associate with LRP6 and phosphorylate LRP6 directly. In contrast to the known PPPSPxS Ser/Thr kinases, tyrosine phosphorylation by Src and Fer negatively regulates LRP6-Wnt signalling. Epistatically, they function upstream of β-catenin to inhibit signalling and in agreement with a negative role in regulating LRP6, MEF cells lacking these kinases show enhanced Wnt signalling. Wnt3a treatment of cells enhances tyrosine phosphorylation of endogenous LRP6 and, mechanistically, Src reduces cell surface LRP6 levels and disrupts LRP6 signalosome formation. Interestingly, CK1γ inhibits Fer-induced LRP6 phosphorylation, suggesting a mechanism whereby CK1γ acts to de-represses inhibitory LRP6 tyrosine phosphorylation. We propose that LRP6 tyrosine phosphorylation by Src and Fer serves a negative regulatory function to prevent over-activation of Wnt signalling at the level of the Wnt receptor, LRP6. Subject Categories Membrane & Intracellular Transport; Post-translational Modifications, Proteolysis & Proteomics PMID:25391905

Policies, Procedures, and Practices Regarding Sport-Related Concussion in Community College Athletes.

PubMed

Paddack, Michael; DeWolf, Ryan; Covassin, Tracey; Kontos, Anthony

2016-01-01

College sport organizations and associations endorse concussion-management protocols and policies. To date, little information is available on concussion policies and practices at community college institutions. To assess and describe current practices and policies regarding the assessment, management, and return-to-play criteria for sport-related concussion (SRC) among member institutions of the California Community College Athletic Association (CCCAA). Cross-sectional study. Web-based survey. A total of 55 head athletic trainers (ATs) at CCCAA institutions. Data about policies, procedures, and practices regarding SRC were collected over a 3-week period in March 2012 and analyzed using descriptive statistics, the Fisher exact test, and the Spearman test. Almost half (47%) of ATs stated they had a policy for SRC assessment, management, and return to play at their institution. They reported being in compliance with baseline testing guidelines (25%), management guidelines (34.5%), and return-to-play guidelines (30%). Nearly 31% of ATs described having an SRC policy in place for academic accommodations. Conference attendance was positively correlated with institutional use of academic accommodations after SRC (r = 0.44, P = .01). The number of meetings ATs attended and their use of baseline testing were also positively correlated (r = 0.38, P = .01). At the time of this study, nearly half of CCCAA institutions had concussion policies and 31% had academic-accommodation policies. However, only 18% of ATs at CCCAA institutions were in compliance with all of their concussion policies. Our findings demonstrate improvements in the management of SRCs by ATs at California community colleges compared with previous research but a need for better compliance with SRC policies.

Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Heterogeneous but “Standard” Coding Systems for Adverse Events: Issues in Achieving Interoperability between Apples and Oranges

PubMed Central

Richesson, Rachel L.; Fung, Kin Wah; Krischer, Jeffrey P.

2008-01-01

Monitoring adverse events (AEs) is an important part of clinical research and a crucial target for data standards. The representation of adverse events themselves requires the use of controlled vocabularies with thousands of needed clinical concepts. Several data standards for adverse events currently exist, each with a strong user base. The structure and features of these current adverse event data standards (including terminologies and classifications) are different, so comparisons and evaluations are not straightforward, nor are strategies for their harmonization. Three different data standards - the Medical Dictionary for Regulatory Activities (MedDRA) and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) terminologies, and Common Terminology Criteria for Adverse Events (CTCAE) classification - are explored as candidate representations for AEs. This paper describes the structural features of each coding system, their content and relationship to the Unified Medical Language System (UMLS), and unsettled issues for future interoperability of these standards. PMID:18406213

Adaptor protein GRB2 promotes Src tyrosine kinase activation and podosomal organization by protein-tyrosine phosphatase ϵ in osteoclasts.

PubMed

Levy-Apter, Einat; Finkelshtein, Eynat; Vemulapalli, Vidyasiri; Li, Shawn S-C; Bedford, Mark T; Elson, Ari

2014-12-26

The non-receptor isoform of protein-tyrosine phosphatase ϵ (cyt-PTPe) supports adhesion of bone-resorbing osteoclasts by activating Src downstream of integrins. Loss of cyt-PTPe reduces Src activity in osteoclasts, reduces resorption of mineralized matrix both in vivo and in cell culture, and induces mild osteopetrosis in young female PTPe KO mice. Activation of Src by cyt-PTPe is dependent upon this phosphatase undergoing phosphorylation at its C-terminal Tyr-638 by partially active Src. To understand how cyt-PTPe activates Src, we screened 73 Src homology 2 (SH2) domains for binding to Tyr(P)-638 of cyt-PTPe. The SH2 domain of GRB2 bound Tyr(P)-638 of cyt-PTPe most prominently, whereas the Src SH2 domain did not bind at all, suggesting that GRB2 may link PTPe with downstream molecules. Further studies indicated that GRB2 is required for activation of Src by cyt-PTPe in osteoclast-like cells (OCLs) in culture. Overexpression of GRB2 in OCLs increased activating phosphorylation of Src at Tyr-416 and of cyt-PTPe at Tyr-638; opposite results were obtained when GRB2 expression was reduced by shRNA or by gene inactivation. Phosphorylation of cyt-PTPe at Tyr-683 and its association with GRB2 are integrin-driven processes in OCLs, and cyt-PTPe undergoes autodephosphorylation at Tyr-683, thus limiting Src activation by integrins. Reduced GRB2 expression also reduced the ability of bone marrow precursors to differentiate into OCLs and reduced the fraction of OCLs in which podosomal adhesion structures assume organization typical of active, resorbing cells. We conclude that GRB2 physically links cyt-PTPe with Src and enables cyt-PTPe to activate Src downstream of activated integrins in OCLs. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

A Kinase-Independent Function of c-Src Mediates p130Cas Phosphorylation at the Serine-639 Site in Pressure Overloaded Myocardium.

PubMed

Palanisamy, Arun P; Suryakumar, Geetha; Panneerselvam, Kavin; Willey, Christopher D; Kuppuswamy, Dhandapani

2015-12-01

Early work in pressure overloaded (PO) myocardium shows that integrins mediate focal adhesion complex formation by recruiting the adaptor protein p130Cas (Cas) and nonreceptor tyrosine kinase c-Src. To explore c-Src role in Cas-associated changes during PO, we used a feline right ventricular in vivo PO model and a three-dimensional (3D) collagen-embedded adult cardiomyocyte in vitro model that utilizes a Gly-Arg-Gly-Asp-Ser (RGD) peptide for integrin stimulation. Cas showed slow electrophoretic mobility (band-shifting), recruitment to the cytoskeleton, and tyrosine phosphorylation at 165, 249, and 410 sites in both 48 h PO myocardium and 1 h RGD-stimulated cardiomyocytes. Adenoviral mediated expression of kinase inactive (negative) c-Src mutant with intact scaffold domains (KN-Src) in cardiomyocytes did not block the RGD stimulated changes in Cas. Furthermore, expression of KN-Src or kinase active c-Src mutant with intact scaffold function (A-Src) in two-dimensionally (2D) cultured cardiomyocytes was sufficient to cause Cas band-shifting, although tyrosine phosphorylation required A-Src. These data indicate that c-Src's adaptor function, but not its kinase function, is required for a serine/threonine specific phosphorylation(s) responsible for Cas band-shifting. To explore this possibility, Chinese hamster ovary cells that stably express Cas were infected with either β-gal or KN-Src adenoviruses and used for Cas immunoprecipitation combined with mass spectrometry analysis. In the KN-Src expressing cells, Cas showed phosphorylation at the serine-639 (human numbering) site. A polyclonal antibody raised against phospho-serine-639 detected Cas phosphorylation in 24-48 h PO myocardium. Our studies indicate that c-Src's adaptor function mediates serine-639 phosphorylation of Cas during integrin activation in PO myocardium. © 2015 Wiley Periodicals, Inc.

Role of src-family kinases in hypoxic vasoconstriction of rat pulmonary artery

PubMed Central

Knock, Greg A.; Snetkov, Vladimir A.; Shaifta, Yasin; Drndarski, Svetlana; Ward, Jeremy P.T.; Aaronson, Philip I.

2008-01-01

Aims We investigated the role of src-family kinases (srcFKs) in hypoxic pulmonary vasoconstriction (HPV) and how this relates to Rho-kinase-mediated Ca2+ sensitization and changes in intracellular Ca2+ concentration ([Ca2+]i). Methods and results Intra-pulmonary arteries (IPAs) were obtained from male Wistar rats. HPV was induced in myograph-mounted IPAs. Auto-phosphorylation of srcFKs and phosphorylation of the regulatory subunit of myosin phosphatase (MYPT-1) and myosin light-chain (MLC20) in response to hypoxia were determined by western blotting. Translocation of Rho-kinase and effects of siRNA knockdown of src and fyn were examined in cultured pulmonary artery smooth muscle cells (PASMCs). [Ca2+]i was estimated in Fura-PE3-loaded IPA. HPV was inhibited by two blockers of srcFKs, SU6656 and PP2. Hypoxia enhanced phosphorylation of three srcFK proteins at Tyr-416 (60, 59, and 54 kDa, corresponding to src, fyn, and yes, respectively) and enhanced srcFK-dependent tyrosine phosphorylation of multiple target proteins. Hypoxia caused a complex, time-dependent enhancement of MYPT-1 and MLC20 phosphorylation, both in the absence and presence of pre-constriction. The sustained component of this enhancement was blocked by SU6656 and the Rho-kinase inhibitor Y27632. In PASMCs, hypoxia caused translocation of Rho-kinase from the nucleus to the cytoplasm, and this was prevented by anti-src siRNA and to a lesser extent by anti-fyn siRNA. The biphasic increases in [Ca2+]i that accompany HPV were also inhibited by PP2. Conclusion Hypoxia activates srcFKs and triggers protein tyrosine phosphorylation in IPA. Hypoxia-mediated Rho-kinase activation, Ca2+ sensitization, and [Ca2+]i responses are depressed by srcFK inhibitors and/or siRNA knockdown, suggesting a central role of srcFKs in HPV. PMID:18682436

Divergent modulation of Rho‐kinase and Ca2+ influx pathways by Src family kinases and focal adhesion kinase in airway smooth muscle

PubMed Central

Shaifta, Yasin; Irechukwu, Nneka; Prieto‐Lloret, Jesus; MacKay, Charles E; Marchon, Keisha A; Ward, Jeremy P T

2015-01-01

Background and Purpose The importance of tyrosine kinases in airway smooth muscle (ASM) contraction is not fully understood. The aim of this study was to investigate the role of Src‐family kinases (SrcFK) and focal adhesion kinase (FAK) in GPCR‐mediated ASM contraction and associated signalling events. Experimental Approach Contraction was recorded in intact or α‐toxin permeabilized rat bronchioles. Phosphorylation of SrcFK, FAK, myosin light‐chain‐20 (MLC20) and myosin phosphatase targeting subunit‐1 (MYPT‐1) was evaluated in cultured human ASM cells (hASMC). [Ca2+]i was evaluated in Fura‐2 loaded hASMC. Responses to carbachol (CCh) and bradykinin (BK) and the contribution of SrcFK and FAK to these responses were determined. Key Results Contractile responses in intact bronchioles were inhibited by antagonists of SrcFK, FAK and Rho‐kinase, while after α‐toxin permeabilization, they were sensitive to inhibition of SrcFK and Rho‐kinase, but not FAK. CCh and BK increased phosphorylation of MYPT‐1 and MLC20 and auto‐phosphorylation of SrcFK and FAK. MYPT‐1 phosphorylation was sensitive to inhibition of Rho‐kinase and SrcFK, but not FAK. Contraction induced by SR Ca2+ depletion and equivalent [Ca2+]i responses in hASMC were sensitive to inhibition of both SrcFK and FAK, while depolarization‐induced contraction was sensitive to FAK inhibition only. SrcFK auto‐phosphorylation was partially FAK‐dependent, while FAK auto‐phosphorylation was SrcFK‐independent. Conclusions and Implications SrcFK mediates Ca2+‐sensitization in ASM, while SrcFK and FAK together and individually influence multiple Ca2+ influx pathways. Tyrosine phosphorylation is therefore a key upstream signalling event in ASM contraction and may be a viable target for modulating ASM tone in respiratory disease. PMID:26294392

Mitochondrial events responsible for morphine's cardioprotection against ischemia/reperfusion injury

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

He, Haiyan; Department of Pharmacology, Tianjin Medical University, Tianjin 300070; Huh, Jin

Morphine may induce cardioprotection by targeting mitochondria, but little is known about the exact mitochondrial events that mediate morphine's protection. We aimed to address the role of the mitochondrial Src tyrosine kinase in morphine's protection. Isolated rat hearts were subjected to 30 min ischemia and 2 h of reperfusion. Morphine was given before the onset of ischemia. Infarct size and troponin I release were measured to evaluate cardiac injury. Oxidative stress was evaluated by measuring mitochondrial protein carbonylation and mitochondrial ROS generation. HL-1 cells were subjected to simulated ischemia/reperfusion and LDH release and mitochondrial membrane potential (ΔΨm) were measured. Morphinemore » reduced infarct size as well as cardiac troponin I release which were aborted by the selective Src tyrosine kinase inhibitors PP2 and Src-I1. Morphine also attenuated LDH release and prevented a loss of ΔΨm at reperfusion in a Src tyrosine kinase dependent manner in HL-1 cells. However, morphine failed to reduce LDH release in HL-1 cells transfected with Src siRNA. Morphine increased mitochondrial Src phosphorylation at reperfusion and this was abrogated by PP2. Morphine attenuated mitochondrial protein carbonylation and mitochondrial superoxide generation at reperfusion through Src tyrosine kinase. The inhibitory effect of morphine on the mitochondrial complex I activity was reversed by PP2. These data suggest that morphine induces cardioprotection by preventing mitochondrial oxidative stress through mitochondrial Src tyrosine kinase. Inhibition of mitochondrial complex I at reperfusion by Src tyrosine kinase may account for the prevention of mitochondrial oxidative stress by morphine. - Highlights: • Morphine induced mito-Src phosphorylation and reduced infarct size in rat hearts. • Morphine failed to reduce I/R-induced LDH release in Src-silencing HL-1 cells. • Morphine prevented mitochondria damage caused by I/R through Src. • Morphine reduced mitochondrial ROS generation by inhibiting complex I via Src.« less

Relational versus absolute representation in categorization.

PubMed

Edwards, Darren J; Pothos, Emmanuel M; Perlman, Amotz

2012-01-01

This study explores relational-like and absolute-like representations in categorization. Although there is much evidence that categorization processes can involve information about both the particular physical properties of studied instances and abstract (relational) properties, there has been little work on the factors that lead to one kind of representation as opposed to the other. We tested 370 participants in 6 experiments, in which participants had to classify new items into predefined artificial categories. In 4 experiments, we observed a predominantly relational-like mode of classification, and in 2 experiments we observed a shift toward an absolute-like mode of classification. These results suggest 3 factors that promote a relational-like mode of classification: fewer items per group, more training groups, and the presence of a time delay. Overall, we propose that less information about the distributional properties of a category or weaker memory traces for the category exemplars (induced, e.g., by having smaller categories or a time delay) can encourage relational-like categorization.

Classification of time-series images using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Lin, Daoyu; Fu, Kun; Wang, Yang; Xu, Guangluan; Sun, Xian

2017-11-01

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.

Cross-label Suppression: a Discriminative and Fast Dictionary Learning with Group Regularization.

PubMed

Wang, Xiudong; Gu, Yuantao

2017-05-10

This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose crosslabel suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we don't resort to frequently-used `0-norm or `1- norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets including face recognition, object categorization, scene classification, texture recognition and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.

A Protein Scaffold Coordinates SRC-Mediated JNK Activation in Response to Metabolic Stress.

PubMed

Kant, Shashi; Standen, Claire L; Morel, Caroline; Jung, Dae Young; Kim, Jason K; Swat, Wojciech; Flavell, Richard A; Davis, Roger J

2017-09-19

Obesity is a major risk factor for the development of metabolic syndrome and type 2 diabetes. How obesity contributes to metabolic syndrome is unclear. Free fatty acid (FFA) activation of a non-receptor tyrosine kinase (SRC)-dependent cJun NH 2 -terminal kinase (JNK) signaling pathway is implicated in this process. However, the mechanism that mediates SRC-dependent JNK activation is unclear. Here, we identify a role for the scaffold protein JIP1 in SRC-dependent JNK activation. SRC phosphorylation of JIP1 creates phosphotyrosine interaction motifs that bind the SH2 domains of SRC and the guanine nucleotide exchange factor VAV. These interactions are required for SRC-induced activation of VAV and the subsequent engagement of a JIP1-tethered JNK signaling module. The JIP1 scaffold protein, therefore, plays a dual role in FFA signaling by coordinating upstream SRC functions together with downstream effector signaling by the JNK pathway. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Distribution and associations of intraocular pressure in 7- and 12-year-old Chinese children: The Anyang Childhood Eye Study.

PubMed

Li, Shuning; Li, Shi-Ming; Wang, Xiao-Lei; Kang, Meng-Tian; Liu, Luo-Ru; Li, He; Wei, Shi-Fei; Ran, An-Ran; Zhan, Siyan; Thomas, Ravi; Wang, Ningli

2017-01-01

To report the intraocular pressure (IOP) and its association with myopia and other factors in 7 and 12-year-old Chinese children. All children participating in the Anyang Childhood Eye Study underwent non-contact tonometry as well as measurement of central corneal thickness (CCT), axial length, cycloplegic auto-refraction, blood pressure, height and weight. A questionnaire was used to collect other relevant information. Univariable and multivariable analysis were performed to determine the associations of IOP. A total of 2760 7-year-old children (95.4%) and 2198 12-year-old children (97.0%) were included. The mean IOP was 13.5±3.1 mmHg in the younger cohort and 15.8±3.5 mmHg in older children (P<0.0001). On multivariable analysis, higher IOP in the younger cohort was associated with female gender (standardized regression coefficient [SRC], 0.11, P<0.0001), increasing central corneal thickness (SRC, 0.39, P<0.0001), myopia (SRC, 0.05, P = 0.03), deep anterior chamber (SRC, 0.07, P<0.01), smaller waist (SRC, 0.07, P<0.01) and increasing mean arterial pressure (SRC, 0.13, P<0.0001). In the older cohort, higher IOP was again associated with female gender (SRC, 0.16, P<0.0001), increasing central corneal thickness (SRC, 0.43, P<0.0001), deep anterior chamber (SRC, 0.09, P<0.01), higher body mass index (SRC, 0.07, P = 0.04) and with increasing mean arterial pressure (SRC, 0.09, P = 0.01), age at which reading commenced (SRC, 0.10, P<0.01) and birth method (SRC, 0.09, P = 0.01), but not with myopia (SRC, 0.09, P = 0.20). In Chinese children, higher IOP was associated with female gender, older age, thicker central cornea, deeper anterior chamber and higher mean arterial pressure. Higher body mass index, younger age at commencement of reading and being born of a caesarean section was also associated with higher IOP in adolescence.

Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning.

PubMed

Yang, Yimin; Wu, Q M Jonathan

2016-11-01

The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.

Short-range order clustering in BCC Fe-Mn alloys induced by severe plastic deformation

NASA Astrophysics Data System (ADS)

Shabashov, V. A.; Kozlov, K. A.; Sagaradze, V. V.; Nikolaev, A. L.; Lyashkov, K. A.; Semyonkin, V. A.; Voronin, V. I.

2018-03-01

The effect of severe plastic deformation, namely, high-pressure torsion (HPT) at different temperatures and ball milling (BM) at different time intervals, has been investigated by means of Mössbauer spectroscopy in Fe100-xMnx (x = 4.1, 6.8, 9) alloys. Deformation affects the short-range clustering (SRC) in BCC lattice. Two processes occur: destruction of SRC by moving dislocations and enhancement of the SRC by migration of non-equilibrium defects. Destruction of SRC prevails during HPT at 80-293 K; whereas enhancement of SRC dominates at 473-573 K. BM starts enhancing the SRC formation at as low as 293 K due to local heating at impacts. The efficiency of HPT in terms of enhancing SRC increases with increasing temperature. The authors suppose that at low temperatures, a significant fraction of vacancies are excluded from enhancing SRC because of formation of mobile bi- and tri-vacancies having low efficiency of enhancing SRC as compared to that of mono vacancies. Milling of BCC Fe100-xMnx alloys stabilises the BCC phase with respect to α → γ transition at subsequent isothermal annealing because of a high degree of work hardening and formation of composition inhomogeneity.

SDL: Saliency-Based Dictionary Learning Framework for Image Similarity.

PubMed

Sarkar, Rituparna; Acton, Scott T

2018-02-01

In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios. Classification of histological tissue images for health care analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classification, we propose a saliency guided dictionary learning method and subsequently an image similarity technique for histo-pathological image classification. Salient object detection from images aids in the identification of discriminative image features. We leverage the saliency values for the local image regions to learn a dictionary and respective sparse codes for an image, such that the more salient features are reconstructed with smaller error. The dictionary learned from an image gives a compact representation of the image itself and is capable of representing images with similar content, with comparable sparse codes. We employ this idea to design a similarity measure between a pair of images, where local image features of one image, are encoded with the dictionary learned from the other and vice versa. To effectively utilize the learned dictionary, we take into account the contribution of each dictionary atom in the sparse codes to generate a global image representation for image comparison. The efficacy of the proposed method was evaluated using three tissue data sets that consist of mammalian kidney, lung and spleen tissue, breast cancer, and colon cancer tissue images. From the experiments, we observe that our methods outperform the state of the art with an increase of 14.2% in the average classification accuracy over all data sets.

Reverse correlating love: highly passionate women idealize their partner's facial appearance.

PubMed

Gunaydin, Gul; DeLong, Jordan E

2015-01-01

A defining feature of passionate love is idealization--evaluating romantic partners in an overly favorable light. Although passionate love can be expected to color how favorably individuals represent their partner in their mind, little is known about how passionate love is linked with visual representations of the partner. Using reverse correlation techniques for the first time to study partner representations, the present study investigated whether women who are passionately in love represent their partner's facial appearance more favorably than individuals who are less passionately in love. In a within-participants design, heterosexual women completed two forced-choice classification tasks, one for their romantic partner and one for a male acquaintance, and a measure of passionate love. In each classification task, participants saw two faces superimposed with noise and selected the face that most resembled their partner (or an acquaintance). Classification images for each of high passion and low passion groups were calculated by averaging across noise patterns selected as resembling the partner or the acquaintance and superimposing the averaged noise on an average male face. A separate group of women evaluated the classification images on attractiveness, trustworthiness, and competence. Results showed that women who feel high (vs. low) passionate love toward their partner tend to represent his face as more attractive and trustworthy, even when controlling for familiarity effects using the acquaintance representation. Using an innovative method to study partner representations, these findings extend our understanding of cognitive processes in romantic relationships.

Reverse Correlating Love: Highly Passionate Women Idealize Their Partner’s Facial Appearance

PubMed Central

Gunaydin, Gul; DeLong, Jordan E.

2015-01-01

A defining feature of passionate love is idealization—evaluating romantic partners in an overly favorable light. Although passionate love can be expected to color how favorably individuals represent their partner in their mind, little is known about how passionate love is linked with visual representations of the partner. Using reverse correlation techniques for the first time to study partner representations, the present study investigated whether women who are passionately in love represent their partner’s facial appearance more favorably than individuals who are less passionately in love. In a within-participants design, heterosexual women completed two forced-choice classification tasks, one for their romantic partner and one for a male acquaintance, and a measure of passionate love. In each classification task, participants saw two faces superimposed with noise and selected the face that most resembled their partner (or an acquaintance). Classification images for each of high passion and low passion groups were calculated by averaging across noise patterns selected as resembling the partner or the acquaintance and superimposing the averaged noise on an average male face. A separate group of women evaluated the classification images on attractiveness, trustworthiness, and competence. Results showed that women who feel high (vs. low) passionate love toward their partner tend to represent his face as more attractive and trustworthy, even when controlling for familiarity effects using the acquaintance representation. Using an innovative method to study partner representations, these findings extend our understanding of cognitive processes in romantic relationships. PMID:25806540

Src- and Fyn-dependent apical membrane trafficking events control endothelial lumen formation during vascular tube morphogenesis.

PubMed

Kim, Dae Joong; Norden, Pieter R; Salvador, Jocelynda; Barry, David M; Bowers, Stephanie L K; Cleaver, Ondine; Davis, George E

2017-01-01

Here we examine the question of how endothelial cells (ECs) develop their apical membrane surface domain during lumen and tube formation. We demonstrate marked apical membrane targeting of activated Src kinases to this apical domain during early and late stages of this process. Immunostaining for phosphotyrosine or phospho-Src reveals apical membrane staining in intracellular vacuoles initially. This is then followed by vacuole to vacuole fusion events to generate an apical luminal membrane, which is similarly decorated with activated phospho-Src kinases. Functional blockade of Src kinases completely blocks EC lumen and tube formation, whether this occurs during vasculogenic tube assembly or angiogenic sprouting events. Multiple Src kinases participate in this apical membrane formation process and siRNA suppression of Src, Fyn and Yes, but not Lyn, blocks EC lumen formation. We also demonstrate strong apical targeting of Src-GFP and Fyn-GFP fusion proteins and increasing their expression enhances lumen formation. Finally, we show that Src- and Fyn-associated vacuoles track and fuse along a subapically polarized microtubule cytoskeleton, which is highly acetylated. These vacuoles generate the apical luminal membrane in a stereotypically polarized, perinuclear position. Overall, our study identifies a critical role for Src kinases in creating and decorating the EC apical membrane surface during early and late stages of lumen and tube formation, a central event in the molecular control of vascular morphogenesis.

Association of spiritual/religious coping with depressive symptoms in high- and low-risk pregnant women.

PubMed

Vitorino, Luciano M; Chiaradia, Raíssa; Low, Gail; Cruz, Jonas Preposi; Pargament, Kenneth I; Lucchetti, Alessandra L G; Lucchetti, Giancarlo

2018-02-01

To investigate the role of spiritual/religious coping (SRC) on depressive symptoms in high- and low-risk pregnant women. Spiritual/religious coping is associated with physical and mental health outcomes. However, only few studies investigated the role of these strategies during pregnancy and whether low- and high-risk pregnant women have different coping mechanisms. This study is a cross-sectional comparative study. This study included a total of 160 pregnant women, 80 with low-risk pregnancy and 80 with high-risk pregnancy. The Beck Depression Inventory, the brief SRC scale and a structured questionnaire on sociodemographic and obstetric aspects were used. General linear model regression analysis was used to identify the factors associated with positive and negative SRC strategies in both groups of pregnant women. Positive SRC use was high, whereas negative SRC use was low in both groups. Although we found no difference in SRC strategies between the two groups, negative SRC was associated with depression in women with high-risk pregnancy, but not in those with low-risk pregnancy. Furthermore, positive SRC was not associated with depressive symptoms in both groups. Results showed that only the negative SRC strategies of Brazilian women with high-risk pregnancies were associated with worsened mental health outcomes. Healthcare professionals, obstetricians and nurse midwives should focus on the use of negative SRC strategies in their pregnant patients. © 2017 John Wiley & Sons Ltd.

Joint Feature Selection and Classification for Multilabel Learning.

PubMed

Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

2018-03-01

Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

Identification of a functional interaction between Kv4.3 channels and c-Src tyrosine kinase.

PubMed

Gomes, Pedro; Saito, Tomoaki; Del Corsso, Cris; Alioua, Abderrahmane; Eghbali, Mansoureh; Toro, Ligia; Stefani, Enrico

2008-10-01

Voltage-gated K(+) (Kv) channels are key determinants of cardiac and neuronal excitability. A substantial body of evidence has accumulated in support of a role for Src family tyrosine kinases in the regulation of Kv channels. In this study, we examined the possibility that c-Src tyrosine kinase participates in the modulation of the transient voltage-dependent K(+) channel Kv4.3. Supporting a mechanistic link between Kv4.3 and c-Src, confocal microscopy analysis of HEK293 cells stably transfected with Kv4.3 showed high degree of co-localization of the two proteins at the plasma membrane. Our results further demonstrate an association between Kv4.3 and c-Src by co-immunoprecipitation and GST pull-down assays, this interaction being mediated by the SH2 and SH3 domains of c-Src. Furthermore, we show that Kv4.3 is tyrosine phosphorylated under basal conditions. The functional relevance of the observed interaction between Kv4.3 and c-Src was established in patch-clamp experiments, where application of the Src inhibitor PP2 caused a decrease in Kv4.3 peak current amplitude, but not the inactive structural analogue PP3. Conversely, intracellular application of recombinant c-Src kinase or the protein tyrosine phosphatase inhibitor bpV(phen) increased Kv4.3 peak current amplitude. In conclusion, our findings provide evidence that c-Src-induced Kv4.3 channel activation involves their association in a macromolecular complex and suggest a role for c-Src-Kv4.3 pathway in regulating cardiac and neuronal excitability.

Global Impact of Oncogenic Src on a Phosphotyrosine Proteome

PubMed Central

Luo, Weifeng; Slebos, Robbert J.; Hill, Salisha; Li, Ming; Brábek, Jan; Amanchy, Ramars; Chaerkady, Raghothama; Pandey, Akhilesh; Ham, Amy-Joan L.; Hanks, Steven K.

2008-01-01

Elevated activity of Src, the first characterized protein-tyrosine kinase, is associated with progression of many human cancers, and Src has attracted interest as a therapeutic target. Src is known to act in various receptor signaling systems to impact cell behavior, yet it remains likely that the spectrum of Src protein substrates relevant to cancer is incompletely understood. To better understand the cellular impact of deregulated Src kinase activity, we extensively applied a mass spectrometry shotgun phosphotyrosine (pTyr) proteomics strategy to obtain global pTyr profiles of Src-transformed mouse fibroblasts as well as their nontransformed counterparts. A total of 867 peptides representing 563 distinct pTyr sites on 374 different proteins were identified from the Src-transformed cells, while 514 peptides representing 275 pTyr sites on 167 proteins were identified from nontransformed cells. Distinct characteristics of the two profiles were revealed by spectral counting, indicative of pTyr site relative abundance, and by complementary quantitative analysis using stable isotope labeling with amino acids in cell culture (SILAC). While both pTyr profiles are replete with sites on signaling and adhesion/cytoskeletal regulatory proteins, the Src-transformed profile is more diverse with enrichment in sites on metabolic enzymes and RNA and protein synthesis and processing machinery. Forty-three pTyr sites (32 proteins) are predicted as major biologically relevant Src targets on the basis of frequent identification in both cell populations. This select group, of particular interest as diagnostic biomarkers, includes well-established Src sites on signaling/adhesion/cytoskeletal proteins, but also uncharacterized sites of potential relevance to the transformed cell phenotype. PMID:18563927

Novel Bioluminescent Activatable Reporter for Src Tyrosine Kinase Activity in Living Mice

PubMed Central

Leng, Weibing; Li, Dezhi; Chen, Liang; Xia, Hongwei; Tang, Qiulin; Chen, Baoqin; Gong, Qiyong; Gao, Fabao; Bi, Feng

2016-01-01

Aberrant activation of the Src kinase is implicated in the development of a variety of human malignancies. However, it is almost impossible to monitor Src activity in an in vivo setting with current biochemical techniques. To facilitate the noninvasive investigation of the activity of Src kinase both in vitro and in vivo, we developed a genetically engineered, activatable bioluminescent reporter using split-luciferase complementation. The bioluminescence of this reporter can be used as a surrogate for Src activity in real time. This hybrid luciferase reporter was constructed by sandwiching a Src-dependent conformationally responsive unit (SH2 domain-Srcpep) between the split luciferase fragments. The complementation bioluminescence of this reporter was dependent on the Src activity status. In our study, Src kinase activity in cultured cells and tumor xenografts was monitored quantitatively and dynamically in response to clinical small-molecular kinase inhibitors, dasatinib and saracatinib. This system was also applied for high-throughput screening of Src inhibitors against a kinase inhibitor library in living cells. These results provide unique insights into drug development and pharmacokinetics/phoarmocodynamics of therapeutic drugs targeting Src signaling pathway enabling the optimization of drug administration schedules for maximum benefit. Using both Firefly and Renilla luciferase imaging, we have successfully monitored Src tyrosine kinase activity and Akt serine/threonine kinase activity concurrently in one tumor xenograft. This dual luciferase reporter imaging system will be helpful in exploring the complex signaling networks in vivo. The strategies reported here can also be extended to study and image other important kinases and the cross-talks among them. PMID:26941850

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.

PubMed

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-05-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l 1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A contour-based shape descriptor for biomedical image classification and retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation.

PubMed

Zhang, Y N

2017-01-01

Parkinson's disease (PD) is primarily diagnosed by clinical examinations, such as walking test, handwriting test, and MRI diagnostic. In this paper, we propose a machine learning based PD telediagnosis method for smartphone. Classification of PD using speech records is a challenging task owing to the fact that the classification accuracy is still lower than doctor-level. Here we demonstrate automatic classification of PD using time frequency features, stacked autoencoders (SAE), and K nearest neighbor (KNN) classifier. KNN classifier can produce promising classification results from useful representations which were learned by SAE. Empirical results show that the proposed method achieves better performance with all tested cases across classification tasks, demonstrating machine learning capable of classifying PD with a level of competence comparable to doctor. It concludes that a smartphone can therefore potentially provide low-cost PD diagnostic care. This paper also gives an implementation on browser/server system and reports the running time cost. Both advantages and disadvantages of the proposed telediagnosis system are discussed.

Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation

PubMed Central

2017-01-01

Parkinson's disease (PD) is primarily diagnosed by clinical examinations, such as walking test, handwriting test, and MRI diagnostic. In this paper, we propose a machine learning based PD telediagnosis method for smartphone. Classification of PD using speech records is a challenging task owing to the fact that the classification accuracy is still lower than doctor-level. Here we demonstrate automatic classification of PD using time frequency features, stacked autoencoders (SAE), and K nearest neighbor (KNN) classifier. KNN classifier can produce promising classification results from useful representations which were learned by SAE. Empirical results show that the proposed method achieves better performance with all tested cases across classification tasks, demonstrating machine learning capable of classifying PD with a level of competence comparable to doctor. It concludes that a smartphone can therefore potentially provide low-cost PD diagnostic care. This paper also gives an implementation on browser/server system and reports the running time cost. Both advantages and disadvantages of the proposed telediagnosis system are discussed. PMID:29075547

Cardiac arrhythmia beat classification using DOST and PSO tuned SVM.

PubMed

Raj, Sandeep; Ray, Kailash Chandra; Shankar, Om

2016-11-01

The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias. The proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan-Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM). The proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based assessment scheme respectively to the state-of-art diagnosis. The results reported are further compared to the existing methodologies in literature. The proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmia beats. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder.

PubMed

Igual, Laura; Soliva, Joan Carles; Escalera, Sergio; Gimeno, Roger; Vilarroya, Oscar; Radeva, Petia

2012-12-01

We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods. Copyright © 2012 Elsevier Ltd. All rights reserved.

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Significance of ERa and c-Src Interaction in the Progression of Hormone Independent Breast Cancer

DTIC Science & Technology

2005-12-01

defects in estrogen signaling [1]. Because of global defects in estrogen signaling observed in these c-Src deficient mice, we have recently generated...1998). Interestingly, the region of the kinase domain of ErbB-2 that correlates with c-Src association, referred to as TK2 (Segatto et al., 1991...ductive organs that are dependent on ERa in c-Src- deficient mice. We show that the loss of the c-Src tyrosine kinase correlates with defects in ductal

Crossmodal Congruency Benefits of Tactile and Visual Signalling

DTIC Science & Technology

2013-11-12

modal information format seemed to produce faster and more accurate performance. The question of learning complex tactile communication signals...SECURITY CLASSIFICATION OF: We conducted an experiment in which tactile messages were created based on five common military arm and hand signals. We...compared response times and accuracy rates of novice individuals responding to visual and tactile representations of these messages, which were

High-order statistics of weber local descriptors for image representation.

PubMed

Han, Xian-Hua; Chen, Yen-Wei; Xu, Gang

2015-06-01

Highly discriminant visual features play a key role in different image classification applications. This study aims to realize a method for extracting highly-discriminant features from images by exploring a robust local descriptor inspired by Weber's law. The investigated local descriptor is based on the fact that human perception for distinguishing a pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. Therefore, we firstly transform the original stimulus (the images in our study) into a differential excitation-domain according to Weber's law, and then explore a local patch, called micro-Texton, in the transformed domain as Weber local descriptor (WLD). Furthermore, we propose to employ a parametric probability process to model the Weber local descriptors, and extract the higher-order statistics to the model parameters for image representation. The proposed strategy can adaptively characterize the WLD space using generative probability model, and then learn the parameters for better fitting the training space, which would lead to more discriminant representation for images. In order to validate the efficiency of the proposed strategy, we apply three different image classification applications including texture, food images and HEp-2 cell pattern recognition, which validates that our proposed strategy has advantages over the state-of-the-art approaches.

EG-1 interacts with c-Src and activates its signaling pathway.

PubMed

Lu, Ming; Zhang, Liping; Sartippour, Maryam R; Norris, Andrew J; Brooks, Mai N

2006-10-01

EG-1 is significantly elevated in breast, colorectal, and prostate cancers. Overexpression of EG-1 stimulates cellular proliferation, and targeted inhibition blocks mouse xenograft tumor growth. To further clarify the function of EG-1, we investigated its role in c-Src activation. We observed that EG-1 overexpression results in activation of c-Src, but found no evidence that EG-1 is a direct Src substrate. EG-1 also binds to other members of the Src family. Furthermore, EG-1 shows interaction with multiple other SH3- and WW-containing molecules involved in various signaling pathways. These observations suggest that EG-1 may be involved in signaling pathways including c-Src activation.

Hybrid and Rogue Kinases Encoded in the Genomes of Model Eukaryotes

PubMed Central

Rakshambikai, Ramaswamy; Gnanavel, Mutharasu; Srinivasan, Narayanaswamy

2014-01-01

The highly modular nature of protein kinases generates diverse functional roles mediated by evolutionary events such as domain recombination, insertion and deletion of domains. Usually domain architecture of a kinase is related to the subfamily to which the kinase catalytic domain belongs. However outlier kinases with unusual domain architectures serve in the expansion of the functional space of the protein kinase family. For example, Src kinases are made-up of SH2 and SH3 domains in addition to the kinase catalytic domain. A kinase which lacks these two domains but retains sequence characteristics within the kinase catalytic domain is an outlier that is likely to have modes of regulation different from classical src kinases. This study defines two types of outlier kinases: hybrids and rogues depending on the nature of domain recombination. Hybrid kinases are those where the catalytic kinase domain belongs to a kinase subfamily but the domain architecture is typical of another kinase subfamily. Rogue kinases are those with kinase catalytic domain characteristic of a kinase subfamily but the domain architecture is typical of neither that subfamily nor any other kinase subfamily. This report provides a consolidated set of such hybrid and rogue kinases gleaned from six eukaryotic genomes–S.cerevisiae, D. melanogaster, C.elegans, M.musculus, T.rubripes and H.sapiens–and discusses their functions. The presence of such kinases necessitates a revisiting of the classification scheme of the protein kinase family using full length sequences apart from classical classification using solely the sequences of kinase catalytic domains. The study of these kinases provides a good insight in engineering signalling pathways for a desired output. Lastly, identification of hybrids and rogues in pathogenic protozoa such as P.falciparum sheds light on possible strategies in host-pathogen interactions. PMID:25255313

76 FR 3653 - Alaska Region's Subsistence Resource Commission (SRC) Program; Public Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-20

... subsistence management issues. The NPS SRC program is authorized under Title VIII, Section 808 of the Alaska...: 1. Call to order. 2. SRC Roll Call and Confirmation of Quorum. 3. Welcome and Introductions. 4.... c. Resource Management Program Update. 14. Public and other Agency Comments. 15. SRC Work Session...

OH REACTION KINETICS OF GAS-PHASE A- AND G-HEXACHLOROCYCLOHEXANE AND HEXACHLOROBENZENE. (R825377)

EPA Science Inventory

Rate constants for the gas-phase reactions of the hydroxyl
radical (OH) with - and -hexachlorocyclohexane (-
and 78 FR 51207 - Kobuk Valley National Park Subsistence Resource Commission (SRC) and the Denali National Park SRC...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-20

... DEPARTMENT OF THE INTERIOR National Park Service [NPS-AKR-DENA-KOVA-DTS-13608; PPAKAKROR4; PPMPRLE1Y.LS0000] Kobuk Valley National Park Subsistence Resource Commission (SRC) and the Denali National Park SRC; Meetings AGENCY: National Park Service, Interior. ACTION: Meeting notice. SUMMARY: As...

On soil textural classifications and soil-texture-based estimations

NASA Astrophysics Data System (ADS)

Ángel Martín, Miguel; Pachepsky, Yakov A.; García-Gutiérrez, Carlos; Reyes, Miguel

2018-02-01

The soil texture representation with the standard textural fraction triplet sand-silt-clay is commonly used to estimate soil properties. The objective of this work was to test the hypothesis that other fraction sizes in the triplets may provide a better representation of soil texture for estimating some soil parameters. We estimated the cumulative particle size distribution and bulk density from an entropy-based representation of the textural triplet with experimental data for 6240 soil samples. The results supported the hypothesis. For example, simulated distributions were not significantly different from the original ones in 25 and 85 % of cases when the sand-silt-clay and very coarse+coarse + medium sand - fine + very fine sand - silt+clay were used, respectively. When the same standard and modified triplets were used to estimate the average bulk density, the coefficients of determination were 0.001 and 0.967, respectively. Overall, the textural triplet selection appears to be application and data specific.

Disruption of Src Is Associated with Phenotypes Related to Williams-Beuren Syndrome and Altered Cellular Localization of TFII-I1,2

PubMed Central

Ivakine, Evgueni A.; Lam, Emily; Deurloo, Marielle; Dida, Joana; Zirngibl, Ralph A.

2015-01-01

Abstract Src is a nonreceptor protein tyrosine kinase that is expressed widely throughout the central nervous system and is involved in diverse biological functions. Mice homozygous for a spontaneous mutation in Src (Src thl/thl) exhibited hypersociability and hyperactivity along with impairments in visuospatial, amygdala-dependent, and motor learning as well as an increased startle response to loud tones. The phenotype of Src thl/thl mice showed significant overlap with Williams-Beuren syndrome (WBS), a disorder caused by the deletion of several genes, including General Transcription Factor 2-I (GTF2I). Src phosphorylation regulates the movement of GTF2I protein (TFII-I) between the nucleus, where it is a transcriptional activator, and the cytoplasm, where it regulates trafficking of transient receptor potential cation channel, subfamily C, member 3 (TRPC3) subunits to the plasma membrane. Here, we demonstrate altered cellular localization of both TFII-I and TRPC3 in the Src mutants, suggesting that disruption of Src can phenocopy behavioral phenotypes observed in WBS through its regulation of TFII-I. PMID:26464974

Localized contourlet features in vehicle make and model recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, B. S.

2009-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.

Toward a 30m resolution time series of historical global urban expansion: exploring variation in North American cities

NASA Astrophysics Data System (ADS)

Stuhlmacher, M.; Wang, C.; Georgescu, M.; Tellman, B.; Balling, R.; Clinton, N. E.; Collins, L.; Goldblatt, R.; Hanson, G.

2016-12-01

Global representations of modern day urban land use and land cover (LULC) extent are becoming increasingly prevalent. Yet considerable uncertainties in the representation of built environment extent (i.e. global classifications generated from 250m resolution MODIS imagery or the United States' National Land Cover Database) remain because of the lack of a systematic, globally consistent methodological approach. We aim to increase resolution, accuracy, and improve upon past efforts by establishing a data-driven definition of the urban landscape, based on Landsat 5, 7 & 8 imagery and ancillary data sets. Continuous and discrete machine learning classification algorithms have been developed in Google Earth Engine (GEE), a powerful online cloud-based geospatial storage and parallel-computing platform. Additionally, thousands of ground truth points have been selected from high resolution imagery to fill in the previous lack of accurate data to be used for training and validation. We will present preliminary classification and accuracy assessments for select cities in the United States and Mexico. Our approach has direct implications for development of projected urban growth that is grounded on realistic identification of urbanizing hot-spots, with consequences for local to regional scale climate change, energy demand, water stress, human health, urban-ecological interactions, and efforts used to prioritize adaptation and mitigation strategies to offset large-scale climate change. Future work to apply the built-up detection algorithm globally and yearly is underway in a partnership between GEE, University of California in San Diego, and Arizona State University.

Association of p60c-src with endosomal membranes in mammalian fibroblasts

PubMed Central

1992-01-01

We have examined the subcellular localization of p60c-src in mammalian fibroblasts. Analysis of indirect immunofluorescence by three- dimensional optical sectioning microscopy revealed a granular cytoplasmic staining that co-localized with the microtubule organizing center. Immunofluorescence experiments with antibodies against a number of membrane markers demonstrated a striking co-localization between p60c-src and the cation-dependent mannose-6-phosphate receptor (CI- MPR), a marker that identifies endosomes. Both p60c-src and the CI-MPR were found to cluster at the spindle poles throughout mitosis. In addition, treatment of interphase and mitotic cells with brefeldin A resulted in a clustering of p60c-src and CI-MPR at a peri-centriolar position. Biochemical fractionation of cellular membranes showed that a major proportion of p60c-src co-enriched with endocytic membranes. Treatment of membranes containing HRP to alter their apparent density also altered the density of p60c-src-containing membranes. Similar density shift experiments with total cellular membranes revealed that the majority of membrane-associated p60c-src in the cell is associated with endosomes, while very little is associated with plasma membranes. These results support a role for p60c-src in the regulation of endosomal membranes and protein trafficking. PMID:1378446

Fast protein tertiary structure retrieval based on global surface shape similarity.

PubMed

Sael, Lee; Li, Bin; La, David; Fang, Yi; Ramani, Karthik; Rustamov, Raif; Kihara, Daisuke

2008-09-01

Characterization and identification of similar tertiary structure of proteins provides rich information for investigating function and evolution. The importance of structure similarity searches is increasing as structure databases continue to expand, partly due to the structural genomics projects. A crucial drawback of conventional protein structure comparison methods, which compare structures by their main-chain orientation or the spatial arrangement of secondary structure, is that a database search is too slow to be done in real-time. Here we introduce a global surface shape representation by three-dimensional (3D) Zernike descriptors, which represent a protein structure compactly as a series expansion of 3D functions. With this simplified representation, the search speed against a few thousand structures takes less than a minute. To investigate the agreement between surface representation defined by 3D Zernike descriptor and conventional main-chain based representation, a benchmark was performed against a protein classification generated by the combinatorial extension algorithm. Despite the different representation, 3D Zernike descriptor retrieved proteins of the same conformation defined by combinatorial extension in 89.6% of the cases within the top five closest structures. The real-time protein structure search by 3D Zernike descriptor will open up new possibility of large-scale global and local protein surface shape comparison. 2008 Wiley-Liss, Inc.

Subordinate-level object classification reexamined.

PubMed

Biederman, I; Subramaniam, S; Bar, M; Kalocsai, P; Fiser, J

1999-01-01

The classification of a table as round rather than square, a car as a Mazda rather than a Ford, a drill bit as 3/8-inch rather than 1/4-inch, and a face as Tom have all been regarded as a single process termed "subordinate classification." Despite the common label, the considerable heterogeneity of the perceptual processing required to achieve such classifications requires, minimally, a more detailed taxonomy. Perceptual information relevant to subordinate-level shape classifications can be presumed to vary on continua of (a) the type of distinctive information that is present, nonaccidental or metric, (b) the size of the relevant contours or surfaces, and (c) the similarity of the to-be-discriminated features, such as whether a straight contour has to be distinguished from a contour of low curvature versus high curvature. We consider three, relatively pure cases. Case 1 subordinates may be distinguished by a representation, a geon structural description (GSD), specifying a nonaccidental characterization of an object's large parts and the relations among these parts, such as a round table versus a square table. Case 2 subordinates are also distinguished by GSDs, except that the distinctive GSDs are present at a small scale in a complex object so the location and mapping of the GSDs are contingent on an initial basic-level classification, such as when we use a logo to distinguish various makes of cars. Expertise for Cases 1 and 2 can be easily achieved through specification, often verbal, of the GSDs. Case 3 subordinates, which have furnished much of the grist for theorizing with "view-based" template models, require fine metric discriminations. Cases 1 and 2 account for the overwhelming majority of shape-based basic- and subordinate-level object classifications that people can and do make in their everyday lives. These classifications are typically made quickly, accurately, and with only modest costs of viewpoint changes. Whereas the activation of an array of multiscale, multiorientation filters, presumed to be at the initial stage of all shape processing, may suffice for determining the similarity of the representations mediating recognition among Case 3 subordinate stimuli (and faces), Cases 1 and 2 require that the output of these filters be mapped to classifiers that make explicit the nonaccidental properties, parts, and relations specified by the GSDs.

Coactivator SRC-2–dependent metabolic reprogramming mediates prostate cancer survival and metastasis

PubMed Central

Dasgupta, Subhamoy; Putluri, Nagireddy; Long, Weiwen; Zhang, Bin; Wang, Jianghua; Kaushik, Akash K.; Arnold, James M.; Bhowmik, Salil K.; Stashi, Erin; Brennan, Christine A.; Rajapakshe, Kimal; Coarfa, Cristian; Mitsiades, Nicholas; Ittmann, Michael M.; Chinnaiyan, Arul M.; Sreekumar, Arun; O’Malley, Bert W.

2015-01-01

Metabolic pathway reprogramming is a hallmark of cancer cell growth and survival and supports the anabolic and energetic demands of these rapidly dividing cells. The underlying regulators of the tumor metabolic program are not completely understood; however, these factors have potential as cancer therapy targets. Here, we determined that upregulation of the oncogenic transcriptional coregulator steroid receptor coactivator 2 (SRC-2), also known as NCOA2, drives glutamine-dependent de novo lipogenesis, which supports tumor cell survival and eventual metastasis. SRC-2 was highly elevated in a variety of tumors, especially in prostate cancer, in which SRC-2 was amplified and overexpressed in 37% of the metastatic tumors evaluated. In prostate cancer cells, SRC-2 stimulated reductive carboxylation of α-ketoglutarate to generate citrate via retrograde TCA cycling, promoting lipogenesis and reprogramming of glutamine metabolism. Glutamine-mediated nutrient signaling activated SRC-2 via mTORC1-dependent phosphorylation, which then triggered downstream transcriptional responses by coactivating SREBP-1, which subsequently enhanced lipogenic enzyme expression. Metabolic profiling of human prostate tumors identified a massive increase in the SRC-2–driven metabolic signature in metastatic tumors compared with that seen in localized tumors, further implicating SRC-2 as a prominent metabolic coordinator of cancer metastasis. Moreover, SRC-2 inhibition in murine models severely attenuated the survival, growth, and metastasis of prostate cancer. Together, these results suggest that the SRC-2 pathway has potential as a therapeutic target for prostate cancer. PMID:25664849

A Kinase-Independent Function of c-Src Mediates p130Cas Phosphorylation at the Serine-639 Site in Pressure Overloaded Myocardium

PubMed Central

Palanisamy, Arun P.; Suryakumar, Geetha; Panneerselvam, Kavin; Willey, Christopher D.; Kuppuswamy, Dhandapani

2017-01-01

Early work in pressure overloaded (PO) myocardium shows that integrins mediate focal adhesion complex formation by recruiting the adaptor protein p130Cas (Cas) and nonreceptor tyrosine kinase c-Src. To explore c-Src role in Cas-associated changes during PO, we used a feline right ventricular in vivo PO model and a three-dimensional (3D) collagen-embedded adult cardiomyocyte in vitro model that utilizes a Gly-Arg-Gly-Asp-Ser (RGD) peptide for integrin stimulation. Cas showed slow electrophoretic mobility (band-shifting), recruitment to the cytoskeleton, and tyrosine phosphorylation at 165, 249, and 410 sites in both 48 h PO myocardium and 1 h RGD-stimulated cardiomyocytes. Adenoviral mediated expression of kinase inactive (negative) c-Src mutant with intact scaffold domains (KN-Src) in cardiomyocytes did not block the RGD stimulated changes in Cas. Furthermore, expression of KN-Src or kinase active c-Src mutant with intact scaffold function (A-Src) in two-dimensionally (2D) cultured cardiomyocytes was sufficient to cause Cas band-shifting, although tyrosine phosphorylation required A-Src. These data indicate that c-Src’s adaptor function, but not its kinase function, is required for a serine/threonine specific phosphorylation(s) responsible for Cas band-shifting. To explore this possibility, Chinese hamster ovary cells that stably express Cas were infected with either β-gal or KN-Src adenoviruses and used for Cas immunoprecipitation combined with mass spectrometry analysis. In the KN-Src expressing cells, Cas showed phosphorylation at the serine-639 (human numbering) site. A polyclonal antibody raised against phospho-serine-639 detected Cas phosphorylation in 24–48 h PO myocardium. Our studies indicate that c-Src’s adaptor function mediates serine-639 phosphorylation of Cas during integrin activation in PO myocardium. PMID:25976166

Antiangiogenic and Antitumor Effects of Src Inhibition in Ovarian Carcinoma

PubMed Central

Han, Liz Y.; Landen, Charles N.; Trevino, Jose G.; Halder, Jyotsnabaran; Lin, Yvonne G.; Kamat, Aparna A.; Kim, Tae-Jin; Merritt, William M.; Coleman, Robert L.; Gershenson, David M.; Shakespeare, William C.; Wang, Yihan; Sundaramoorth, Raji; Metcalf, Chester A.; Dalgarno, David C.; Sawyer, Tomi K.; Gallick, Gary E.; Sood, Anil K.

2011-01-01

Src, a nonreceptor tyrosine kinase, is a key mediator for multiple signaling pathways that regulate critical cellular functions and is often aberrantly activated in a number of solid tumors, including ovarian carcinoma. The purpose of this study was to determine the role of activated Src inhibition on tumor growth in an orthotopic murine model of ovarian carcinoma. In vitro studies on HeyA8 and SKOV3ip1 cell lines revealed that Src inhibition by the Src-selective inhibitor, AP23846, occurred within 1 hour and responded in a dose-dependent manner. Furthermore, Src inhibition enhanced the cytotoxicity of docetaxel in both chemosensitive and chemoresistant ovarian cancer cell lines, HeyA8 and HeyA8-MDR, respectively. In vivo, Src inhibition by AP23994, an orally bioavailable analogue of AP23846, significantly decreased tumor burden in HeyA8 (P = 0.02), SKOV3ip1 (P = 0.01), as well as HeyA8-MDR (P < 0.03) relative to the untreated controls. However, the greatest effect on tumor reduction was observed in combination therapy with docetaxel (P < 0.001, P = 0.002, and P = 0.01, for the above models, respectively). Proliferating cell nuclear antigen staining showed that Src inhibition alone (P = 0.02) and in combination with docetaxel (P = 0.007) significantly reduced tumor proliferation. In addition, Src inhibition alone and in combination with docetaxel significantly down-regulated tumoral production of vascular endothelial growth factor and interleukin 8, whereas combination therapy decreased the microvessel density (P = 0.02) and significantly affected vascular permeability (P < 0.05). In summary, Src inhibition with AP23994 has potent antiangiogenic effects and significantly reduces tumor burden in preclinical ovarian cancer models. Thus, Src inhibition may be an attractive therapeutic approach for patients with ovarian carcinoma. PMID:16951177

v-Src oncogene product increases sphingosine kinase 1 expression through mRNA stabilization: alteration of AU-rich element-binding proteins.

PubMed

Sobue, S; Murakami, M; Banno, Y; Ito, H; Kimura, A; Gao, S; Furuhata, A; Takagi, A; Kojima, T; Suzuki, M; Nozawa, Y; Murate, T

2008-10-09

Sphingosine kinase 1 (SPHK1) is overexpressed in solid tumors and leukemia. However, the mechanism of SPHK1 overexpression by oncogenes has not been defined. We found that v-Src-transformed NIH3T3 cells showed a high SPHK1 mRNA, SPHK1 protein and SPHK enzyme activity. siRNA of SPHK1 inhibited the growth of v-Src-NIH3T3, suggesting the involvement of SPHK1 in v-Src-induced oncogenesis. v-Src-NIH3T3 showed activations of protein kinase C-alpha, signal transducers and activators of transcription 3 and c-Jun NH(2)-terminal kinase. Their inhibition suppressed SPHK1 expression in v-Src-NIH3T3, whereas their overexpression increased SPHK1 mRNA in NIH3T3. Unexpectedly, the nuclear run-on assay and the promoter analysis using 5'-promoter region of mouse SPHK1 did not show any significant difference between mock- and v-Src-NIH3T3. Furthermore, the half-life of SPHK1 mRNA in mock-NIH3T3 was nearly 15 min, whereas that of v-Src-NIH3T3 was much longer. Examination of two AU-rich region-binding proteins, AUF1 and HuR, that regulate mRNA decay reciprocally, showed decreased total AUF1 protein associated with increased tyrosine-phosphorylated form and increased serine-phosphorylated HuR protein in v-Src-NIH3T3. Modulation of AUF1 and HuR by their overexpression or siRNA revealed that SPHK1 mRNA in v-Src- and mock-NIH3T3 was regulated reciprocally by these factors. Our results showed, for the first time, a novel mechanism of v-Src-induced SPHK1 overexpression.

Platelet-derived growth factor-dependent association of the GTPase-activating protein of Ras and Src.

PubMed Central

Schlesinger, T K; Demali, K A; Johnson, G L; Kazlauskas, A

1999-01-01

Here we report that the platelet-derived growth factor beta receptor (betaPDGFR) is not the only tyrosine kinase able to associate with the GTPase-activating protein of Ras (RasGAP). The interaction of non-betaPDGFR kinase(s) with RasGAP was dependent on stimulation with platelet-derived growth factor (PDGF) and seemed to require tyrosine phosphorylation of RasGAP. Because the tyrosine phosphorylation site of RasGAP is in a sequence context that is favoured by the Src homology 2 ('SH2') domain of Src family members, we tested the possibility that Src was the kinase that associated with RasGAP. Indeed, Src interacted with phosphorylated RasGAP fusion proteins; immunodepletion of Src markedly decreased the recovery of the RasGAP-associated kinase activity. Thus PDGF-dependent tyrosine phosphorylation of RasGAP results in the formation of a complex between RasGAP and Src. To begin to address the relevance of these observations, we focused on the consequences of the interaction of Src and RasGAP. We found that a receptor mutant that did not activate Src was unable to efficiently mediate the tyrosine phosphorylation of phospholipase Cgamma (PLCgamma). Taken together, these observations support the following hypothesis. When RasGAP is recruited to the betaPDGFR, it is phosphorylated and associates with Src. Once bound to RasGAP, Src is no longer able to promote the phosphorylation of PLCgamma. This hypothesis offers a mechanistic explanation for our previously published findings that the recruitment of RasGAP to the betaPDGFR attenuates the tyrosine phosphorylation of PLCgamma. Finally, these findings suggest a novel way in which RasGAP negatively regulates signal relay by the betaPDGFR. PMID:10567236

A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling.

PubMed

Li, Bin-Bin; Wang, Ling

2007-06-01

This paper proposes a hybrid quantum-inspired genetic algorithm (HQGA) for the multiobjective flow shop scheduling problem (FSSP), which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. On the one hand, a quantum-inspired GA (QGA) based on Q-bit representation is applied for exploration in the discrete 0-1 hyperspace by using the updating operator of quantum gate and genetic operators of Q-bit. Moreover, random-key representation is used to convert the Q-bit representation to job permutation for evaluating the objective values of the schedule solution. On the other hand, permutation-based GA (PGA) is applied for both performing exploration in permutation-based scheduling space and stressing exploitation for good schedule solutions. To evaluate solutions in multiobjective sense, a randomly weighted linear-sum function is used in QGA, and a nondominated sorting technique including classification of Pareto fronts and fitness assignment is applied in PGA with regard to both proximity and diversity of solutions. To maintain the diversity of the population, two trimming techniques for population are proposed. The proposed HQGA is tested based on some multiobjective FSSPs. Simulation results and comparisons based on several performance metrics demonstrate the effectiveness of the proposed HQGA.

Registry of the Spanish Network for Systemic Sclerosis

PubMed Central

Simeón-Aznar, C.P.; Fonollosa-Plá, V.; Tolosa-Vilella, Carles; Espinosa-Garriga, G.; Campillo-Grau, M.; Ramos-Casals, M.; García-Hernández, F.J.; Castillo-Palma, M.J.; Sánchez-Román, J.; Callejas-Rubio, J.L.; Ortego-Centeno, N.; Egurbide-Arberas, M.V.; Trapiellla-Martínez, L.; Caminal-Montero, L.; Sáez-Comet, L.; Velilla-Marco, J.; Camps-García, M.T.; de Ramón-Garrido, E.; Esteban-Marcos, E.M.; Pallarés-Ferreres, L.; Navarrete-Navarrete, N.; Vargas-Hitos, J.A.; de la Torre, R. Gómez; Salvador-Cervello, G.; Rios-Blanco, J.J.; Vilardell-Tarrés, M.

2015-01-01

Abstract Systemic sclerosis (SSc) is a rare, multisystem disease showing a large individual variability in disease progression and prognosis. In the present study, we assess survival, causes of death, and risk factors of mortality in a large series of Spanish SSc patients. Consecutive SSc patients fulfilling criteria of the classification by LeRoy were recruited in the survey. Kaplan–Meier and Cox proportional-hazards models were used to analyze survival and to identify predictors of mortality. Among 879 consecutive patients, 138 (15.7%) deaths were registered. Seventy-six out of 138 (55%) deceased patients were due to causes attributed to SSc, and pulmonary hypertension (PH) was the leading cause in 23 (16.6%) patients. Survival rates were 96%, 93%, 83%, and 73% at 5, 10, 20, and 30 years after the first symptom, respectively. Survival rates for diffuse cutaneous SSc (dcSSc) and limited cutaneous SSc were 91%, 86%, 64%, and 39%; and 97%, 95%, 85%, and 81% at 5, 10, 20, and 30 years, respectively (log-rank: 67.63, P < 0.0001). The dcSSc subset, male sex, age at disease onset older than 65 years, digital ulcers, interstitial lung disease (ILD), PH, heart involvement, scleroderma renal crisis (SRC), presence of antitopoisomerase I and absence of anticentromere antibodies, and active capillaroscopic pattern showed reduced survival rate. In a multivariate analysis, older age at disease onset, dcSSc, ILD, PH, and SRC were independent risk factors for mortality. In the present study involving a large cohort of SSc patients, a high prevalence of disease-related causes of death was demonstrated. Older age at disease onset, dcSSc, ILD, PH, and SRC were identified as independent prognostic factors. PMID:26512564

Post-traumatic stress avoidance is attenuated by corticosterone and associated with brain levels of steroid receptor co-activator-1 in rats.

PubMed

Whitaker, Annie M; Farooq, Muhammad A; Edwards, Scott; Gilpin, Nicholas W

2016-01-01

Individuals with post-traumatic stress disorder (PTSD) avoid trauma-related stimuli and exhibit blunted hypothalamic-pituitary-adrenal (HPA) axis activation at the time of stress. Our rodent model of stress mimics the avoidance symptom cluster of PTSD. Rats are classified as "Avoiders" or "Non-Avoiders" post-stress based on the avoidance of a predator-odor paired context. Previously, we found Avoiders exhibit an attenuated HPA stress response to predator odor. We hypothesized that corticosterone administration before stress would reduce the magnitude and incidence of stress-paired context avoidance. Furthermore, we also predicted that Avoiders would exhibit altered expression of glucocorticoid receptor (GR) signaling machinery elements, including steroid receptor co-activator (SRC)-1. Male Wistar rats (n = 16) were pretreated with corticosterone (25 mg/kg) or saline and exposed to predator-odor stress paired with a context and tested for avoidance 24 h later. A second group of corticosterone-naïve rats (n = 24) were stressed (or not), indexed for avoidance 24 h later, and killed 48 h post-odor exposure to measure phosphorylated GR, FKBP51 and SRC-1 levels in the paraventricular nucleus (PVN), central amygdala (CeA) and ventral hippocampus (VH), all brain sites that highly express GRs and regulate HPA function. Corticosterone pretreatment reduced the magnitude and incidence of avoidance. In Avoiders, predator-odor exposure led to lower SRC-1 expression in the PVN and CeA, and higher SRC-1 expression in the VH. SRC-1 expression in PVN, CeA and VH was predicted by prior avoidance behavior. Hence, a blunted HPA stress response may contribute to stress-induced neuroadaptations in central SRC-1 levels and behavioral dysfunction in Avoider rats.

Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries.

PubMed

Han, Bucong; Ma, Xiaohua; Zhao, Ruiying; Zhang, Jingxian; Wei, Xiaona; Liu, Xianghui; Liu, Xin; Zhang, Cunlong; Tan, Chunyan; Jiang, Yuyang; Chen, Yuzong

2012-11-23

Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival. It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. These successes and appearances of drug resistance in some patients have raised significant interest and efforts in discovering new Src inhibitors. Various in-silico methods have been used in some of these efforts. It is desirable to explore additional in-silico methods, particularly those capable of searching large compound libraries at high yields and reduced false-hit rates. We evaluated support vector machines (SVM) as virtual screening tools for searching Src inhibitors from large compound libraries. SVM trained and tested by 1,703 inhibitors and 63,318 putative non-inhibitors correctly identified 93.53%~ 95.01% inhibitors and 99.81%~ 99.90% non-inhibitors in 5-fold cross validation studies. SVM trained by 1,703 inhibitors reported before 2011 and 63,318 putative non-inhibitors correctly identified 70.45% of the 44 inhibitors reported since 2011, and predicted as inhibitors 44,843 (0.33%) of 13.56M PubChem, 1,496 (0.89%) of 168 K MDDR, and 719 (7.73%) of 9,305 MDDR compounds similar to the known inhibitors. SVM showed comparable yield and reduced false hit rates in searching large compound libraries compared to the similarity-based and other machine-learning VS methods developed from the same set of training compounds and molecular descriptors. We tested three virtual hits of the same novel scaffold from in-house chemical libraries not reported as Src inhibitor, one of which showed moderate activity. SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates.

T:G mismatch-specific thymine-DNA glycosylase (TDG) as a coregulator of transcription interacts with SRC1 family members through a novel tyrosine repeat motif

PubMed Central

Lucey, Marie J.; Chen, Dongsheng; Lopez-Garcia, Jorge; Hart, Stephen M.; Phoenix, Fladia; Al-Jehani, Rajai; Alao, John P.; White, Roger; Kindle, Karin B.; Losson, Régine; Chambon, Pierre; Parker, Malcolm G.; Schär, Primo; Heery, David M.; Buluwela, Lakjaya; Ali, Simak

2005-01-01

Gene activation involves protein complexes with diverse enzymatic activities, some of which are involved in chromatin modification. We have shown previously that the base excision repair enzyme thymine DNA glycosylase (TDG) acts as a potent coactivator for estrogen receptor-α. To further understand how TDG acts in this context, we studied its interaction with known coactivators of nuclear receptors. We find that TDG interacts in vitro and in vivo with the p160 coactivator SRC1, with the interaction being mediated by a previously undescribed motif encoding four equally spaced tyrosine residues in TDG, each tyrosine being separated by three amino acids. This is found to interact with two motifs in SRC1 also containing tyrosine residues separated by three amino acids. Site-directed mutagenesis shows that the tyrosines encoded in these motifs are critical for the interaction. The related p160 protein TIF2 does not interact with TDG and has the altered sequence, F-X-X-X-Y, at the equivalent positions relative to SRC1. Substitution of the phenylalanines to tyrosines is sufficient to bring about interaction of TIF2 with TDG. These findings highlight a new protein–protein interaction motif based on Y-X-X-X-Y and provide new insight into the interaction of diverse proteins in coactivator complexes. PMID:16282588

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

Combating resistance to anti-IGFR antibody by targeting the integrin β3-Src pathway.

PubMed

Shin, Dong Hoon; Lee, Hyo-Jong; Min, Hye-Young; Choi, Sun Phil; Lee, Mi-Sook; Lee, Jung Weon; Johnson, Faye M; Mehta, Kapil; Lippman, Scott M; Glisson, Bonnie S; Lee, Ho-Young

2013-10-16

Several phase II/III trials of anti-insulin-like growth factor 1 receptor (IGF-1R) monoclonal antibodies (mAbs) have shown limited efficacy. The mechanisms of resistance to IGF-1R mAb-based therapies and clinically applicable strategies for overcoming drug resistance are still undefined. IGF-1R mAb cixutumumab efficacy, alone or in combination with Src inhibitors, was evaluated in 10 human head and neck squamous cell carcinoma (HNSCC) and six non-small cell lung cancer (NSCLC) cell lines in vitro in two- or three-dimensional culture systems and in vivo in cell line- or patient-derived xenograft tumors in athymic nude mice (n = 6-9 per group). Cixutumumab-induced changes in cell signaling and IGF-1 binding to integrin β3 were determined by Western or ligand blotting, immunoprecipitation, immunofluorescence, and cell adhesion analyses and enzyme-linked immunosorbent assay. Data were analyzed by the two-sided Student t test or one-way analysis of variance. Integrin β3-Src signaling cascade was activated by IGF-1 in HNSCC and NSCLC cells, when IGF-1 binding to IGF-1R was hampered by cixutumumab, resulting in Akt activation and cixutumumab resistance. Targeting integrin β3 or Src enhanced antitumor activity of cixutumumab in multiple cixutumumab-resistant cell lines and patient-derived tumors in vitro and in vivo. Mean tumor volume of mice cotreated with cixutumumab and integrin β3 siRNA was 133.7 mm(3) (95% confidence interval [CI] = 57.6 to 209.8 mm(3)) compared with those treated with cixutumumab (1472.5 mm(3); 95% CI = 1150.7 to 1794.3 mm(3); P < .001) or integrin β3 siRNA (903.2 mm(3); 95% CI = 636.1 to 1170.3 mm(3); P < .001) alone. Increased Src activation through integrin ανβ3 confers considerable resistance against anti-IGF-1R mAb-based therapies in HNSCC and NSCLC cells. Dual targeting of the IGF-1R pathway and collateral integrin β3-Src signaling module may override this resistance.

Combating Resistance to Anti-IGFR Antibody by Targeting the Integrin β3-Src Pathway

PubMed Central

2013-01-01

Background Several phase II/III trials of anti–insulin-like growth factor 1 receptor (IGF-1R) monoclonal antibodies (mAbs) have shown limited efficacy. The mechanisms of resistance to IGF-1R mAb-based therapies and clinically applicable strategies for overcoming drug resistance are still undefined. Methods IGF-1R mAb cixutumumab efficacy, alone or in combination with Src inhibitors, was evaluated in 10 human head and neck squamous cell carcinoma (HNSCC) and six non–small cell lung cancer (NSCLC) cell lines in vitro in two- or three-dimensional culture systems and in vivo in cell line– or patient-derived xenograft tumors in athymic nude mice (n = 6–9 per group). Cixutumumab-induced changes in cell signaling and IGF-1 binding to integrin β3 were determined by Western or ligand blotting, immunoprecipitation, immunofluorescence, and cell adhesion analyses and enzyme-linked immunosorbent assay. Data were analyzed by the two-sided Student t test or one-way analysis of variance. Results Integrin β3–Src signaling cascade was activated by IGF-1 in HNSCC and NSCLC cells, when IGF-1 binding to IGF-1R was hampered by cixutumumab, resulting in Akt activation and cixutumumab resistance. Targeting integrin β3 or Src enhanced antitumor activity of cixutumumab in multiple cixutumumab-resistant cell lines and patient-derived tumors in vitro and in vivo. Mean tumor volume of mice cotreated with cixutumumab and integrin β3 siRNA was 133.7mm3 (95% confidence interval [CI] = 57.6 to 209.8mm3) compared with those treated with cixutumumab (1472.5mm3; 95% CI = 1150.7 to 1794.3mm3; P < .001) or integrin β3 siRNA (903.2mm3; 95% CI = 636.1 to 1170.3mm3; P < .001) alone. Conclusions Increased Src activation through integrin ανβ3 confers considerable resistance against anti–IGF-1R mAb-based therapies in HNSCC and NSCLC cells. Dual targeting of the IGF-1R pathway and collateral integrin β3–Src signaling module may override this resistance. PMID:24092920

Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation.

PubMed

Khalilzadeh, Mohammad Mahdi; Fatemizadeh, Emad; Behnam, Hamid

2013-06-01

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods whose results have been improved. Copyright © 2013 Elsevier Inc. All rights reserved.

Snow mapping and land use studies in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1977-01-01

The author has identified the following significant results. A system was developed for operational snow and land use mapping, based on a supervised classification method using various classification algorithms and representation of the results in maplike form on color film with a photomation system. Land use mapping, under European conditions, was achieved with a stepwise linear discriminant analysis by using additional ratio variables. On fall images, signatures of built-up areas were often not separable from wetlands. Two different methods were tested to correlate the size of settlements and the population with an accuracy for the densely populated Swiss Plateau between +2 or -12%.

Towards automatic music transcription: note extraction based on independent subspace analysis

NASA Astrophysics Data System (ADS)

Wellhausen, Jens; Hoynck, Michael

2005-01-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

Towards automatic music transcription: note extraction based on independent subspace analysis

NASA Astrophysics Data System (ADS)

Wellhausen, Jens; Höynck, Michael

2004-12-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

Self-organized Evaluation of Dynamic Hand Gestures for Sign Language Recognition

NASA Astrophysics Data System (ADS)

Buciu, Ioan; Pitas, Ioannis

Two main theories exist with respect to face encoding and representation in the human visual system (HVS). The first one refers to the dense (holistic) representation of the face, where faces have "holon"-like appearance. The second one claims that a more appropriate face representation is given by a sparse code, where only a small fraction of the neural cells corresponding to face encoding is activated. Theoretical and experimental evidence suggest that the HVS performs face analysis (encoding, storing, face recognition, facial expression recognition) in a structured and hierarchical way, where both representations have their own contribution and goal. According to neuropsychological experiments, it seems that encoding for face recognition, relies on holistic image representation, while a sparse image representation is used for facial expression analysis and classification. From the computer vision perspective, the techniques developed for automatic face and facial expression recognition fall into the same two representation types. Like in Neuroscience, the techniques which perform better for face recognition yield a holistic image representation, while those techniques suitable for facial expression recognition use a sparse or local image representation. The proposed mathematical models of image formation and encoding try to simulate the efficient storing, organization and coding of data in the human cortex. This is equivalent with embedding constraints in the model design regarding dimensionality reduction, redundant information minimization, mutual information minimization, non-negativity constraints, class information, etc. The presented techniques are applied as a feature extraction step followed by a classification method, which also heavily influences the recognition results.

Learning viewpoint invariant object representations using a temporal coherence principle.

PubMed

Einhäuser, Wolfgang; Hipp, Jörg; Eggert, Julian; Körner, Edgar; König, Peter

2005-07-01

Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the "stability" objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells' input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an--also unlabeled--distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformations.

Comparison of binding energies of SrcSH2-phosphotyrosyl peptides with structure-based prediction using surface area based empirical parameterization.

PubMed Central

Henriques, D. A.; Ladbury, J. E.; Jackson, R. M.

2000-01-01

The prediction of binding energies from the three-dimensional (3D) structure of a protein-ligand complex is an important goal of biophysics and structural biology. Here, we critically assess the use of empirical, solvent-accessible surface area-based calculations for the prediction of the binding of Src-SH2 domain with a series of tyrosyl phosphopeptides based on the high-affinity ligand from the hamster middle T antigen (hmT), where the residue in the pY+ 3 position has been changed. Two other peptides based on the C-terminal regulatory site of the Src protein and the platelet-derived growth factor receptor (PDGFR) are also investigated. Here, we take into account the effects of proton linkage on binding, and test five different surface area-based models that include different treatments for the contributions to conformational change and protein solvation. These differences relate to the treatment of conformational flexibility in the peptide ligand and the inclusion of proximal ordered solvent molecules in the surface area calculations. This allowed the calculation of a range of thermodynamic state functions (deltaCp, deltaS, deltaH, and deltaG) directly from structure. Comparison with the experimentally derived data shows little agreement for the interaction of SrcSH2 domain and the range of tyrosyl phosphopeptides. Furthermore, the adoption of the different models to treat conformational change and solvation has a dramatic effect on the calculated thermodynamic functions, making the predicted binding energies highly model dependent. While empirical, solvent-accessible surface area based calculations are becoming widely adopted to interpret thermodynamic data, this study highlights potential problems with application and interpretation of this type of approach. There is undoubtedly some agreement between predicted and experimentally determined thermodynamic parameters: however, the tolerance of this approach is not sufficient to make it ubiquitously applicable. PMID:11106171

Differentiation-induced Colocalization of the KH-type Splicing Regulatory Protein with Polypyrimidine Tract Binding Protein and the c-src Pre-mRNA

PubMed Central

Hall, Megan P.; Huang, Sui; Black, Douglas L.

2004-01-01

We have examined the subcellular localization of the KH-type splicing regulatory protein (KSRP). KSRP is a multidomain RNA-binding protein implicated in a variety of cellular processes, including splicing in the nucleus and mRNA localization in the cytoplasm. We find that KSRP is primarily nuclear with a localization pattern that most closely resembles that of polypyrimidine tract binding protein (PTB). Colocalization experiments of KSRP with PTB in a mouse neuroblastoma cell line determined that both proteins are present in the perinucleolar compartment (PNC), as well as in other nuclear enrichments. In contrast, HeLa cells do not show prominent KSRP staining in the PNC, even though PTB labeling identified the PNC in these cells. Because both PTB and KSRP interact with the c-src transcript to affect N1 exon splicing, we examined the localization of the c-src pre-mRNA by fluorescence in situ hybridization. The src transcript is present in specific foci within the nucleus that are presumably sites of src transcription but are not generally perinucleolar. In normally cultured neuroblastoma cells, these src RNA foci contain PTB, but little KSRP. However, upon induced neuronal differentiation of these cells, KSRP occurs in the same foci with src RNA. PTB localization remains unaffected. This differentiation-induced localization of KSRP with src RNA correlates with an increase in src exon N1 inclusion. These results indicate that PTB and KSRP do indeed interact with the c-src transcript in vivo, and that these associations change with the differentiated state of the cell. PMID:14657238

Inhibition of src family kinases by a combinatorial action of 5'-AMP and small heat shock proteins, identified from the adult heart.

PubMed

Kasi, V S; Kuppuswamy, D

1999-10-01

Src family kinases are implicated in cellular proliferation and transformation. Terminally differentiated myocytes have lost the ability to proliferate, indicating the existence of a down-regulatory mechanism(s) for these mitogenic kinases. Here we show that feline cardiomyocyte lysate contains thermostable components that inhibit c-Src kinase in vitro. This inhibitory activity, present predominantly in heart tissue, involves two components acting combinatorially. After purification by sequential chromatography, one component was identified by mass and nuclear magnetic resonance spectroscopies as 5'-AMP, while the other was identified by peptide sequencing as a small heat shock protein (sHSP). 5'-AMP and to a lesser extent 5'-ADP inhibit c-Src when combined with either HSP-27 or HSP-32. Other HSPs, including alphaB-crystallin, HSP-70, and HSP-90, did not exhibit this effect. The inhibition, observed preferentially on Src family kinases and independent of the Src tyrosine phosphorylation state, occurs via a direct interaction of the c-Src catalytic domain with the inhibitory components. Our study indicates that sHSPs increase the affinity of 5'-AMP for the c-Src ATP binding site, thereby facilitating the inhibition. In vivo, elevation of ATP levels in the cardiomyocytes results in the tyrosine phosphorylation of cellular proteins including c-Src at the activatory site, and this effect is blocked when the 5'-AMP concentration is raised. Thus, this study reveals a novel role for sHSPs and 5'-AMP in the regulation of Src family kinases, presumably for the maintenance of the terminally differentiated state.

Src promotes delta opioid receptor (DOR) desensitization by interfering with receptor recycling.

PubMed

Archer-Lahlou, Elodie; Audet, Nicolas; Amraei, Mohammad Gholi; Huard, Karine; Paquin-Gobeil, Mélanie; Pineyro, Graciela

2009-01-01

Abstract An important limitation in the clinical use of opiates is progressive loss of analgesic efficacy over time. Development of analgesic tolerance is tightly linked to receptor desensitization. In the case of delta opioid receptors (DOR), desensitization is especially swift because receptors are rapidly internalized and are poorly recycled to the membrane. In the present study, we investigated whether Src activity contributed to this sorting pattern and to functional desensitization of DORs. A first series of experiments demonstrated that agonist binding activates Src and destabilizes a constitutive complex formed by the spontaneous association of DORs with the kinase. Src contribution to DOR desensitization was then established by showing that pre-treatment with Src inhibitor PP2 (20 microM; 1 hr) or transfection of a dominant negative Src mutant preserved DOR signalling following sustained exposure to an agonist. This protection was afforded without interfering with endocytosis, but suboptimal internalization interfered with PP2 ability to preserve DOR signalling, suggesting a post-endocytic site of action for the kinase. This assumption was confirmed by demonstrating that Src inhibition by PP2 or its silencing by siRNA increased membrane recovery of internalized DORs and was further corroborated by showing that inhibition of recycling by monensin or dominant negative Rab11 (Rab11S25N) abolished the ability of Src blockers to prevent desensitization. Finally, Src inhibitors accelerated recovery of DOR-Galphal3 coupling after desensitization. Taken together, these results indicate that Src dynamically regulates DOR recycling and by doing so contributes to desensitization of these receptors.

The Endoplasmic Reticulum Is a Reservoir for WAVE/SCAR Regulatory Complex Signaling in the Arabidopsis Leaf1[W][OA

PubMed Central

Zhang, Chunhua; Mallery, Eileen; Reagan, Sara; Boyko, Vitaly P.; Kotchoni, Simeon O.; Szymanski, Daniel B.

2013-01-01

During plant cell morphogenesis, signal transduction and cytoskeletal dynamics interact to locally organize the cytoplasm and define the geometry of cell expansion. The WAVE/SCAR (for WASP family verprolin homologous/suppressor of cyclic AMP receptor) regulatory complex (W/SRC) is an evolutionarily conserved heteromeric protein complex. Within the plant kingdom W/SRC is a broadly used effector that converts Rho-of-Plants (ROP)/Rac small GTPase signals into Actin-Related Protein2/3 and actin-dependent growth responses. Although the components and biochemistry of the W/SRC pathway are well understood, a basic understanding of how cells partition W/SRC into active and inactive pools is lacking. In this paper, we report that the endoplasmic reticulum (ER) is an important organelle for W/SRC regulation. We determined that a large intracellular pool of the core W/SRC subunit NAP1, like the known positive regulator of W/SRC, the DOCK family guanine nucleotide-exchange factor SPIKE1 (SPK1), localizes to the surface of the ER. The ER-associated NAP1 is inactive because it displays little colocalization with the actin network, and ER localization requires neither activating signals from SPK1 nor a physical association with its W/SRC-binding partner, SRA1. Our results indicate that in Arabidopsis (Arabidopsis thaliana) leaf pavement cells and trichomes, the ER is a reservoir for W/SRC signaling and may have a key role in the early steps of W/SRC assembly and/or activation. PMID:23613272

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

PubMed

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

2017-09-22

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

A Ser75-to-Asp phospho-mimicking mutation in Src accelerates ageing-related loss of retinal ganglion cells in mice.

PubMed

Kashiwagi, Kenji; Ito, Sadahiro; Maeda, Shuichiro; Kato, Goro

2017-12-01

Src knockout mice show no detectable abnormalities in central nervous system (CNS) post-mitotic neurons, likely reflecting functional compensation by other Src family kinases. Cdk1- or Cdk5-dependent Ser75 phosphorylation in the amino-terminal Unique domain of Src, which shares no homology with other Src family kinases, regulates the stability of active Src. To clarify the roles of Src Ser75 phosphorylation in CNS neurons, we established two types of mutant mice with mutations in Src: phospho-mimicking Ser75Asp (SD) and non-phosphorylatable Ser75Ala (SA). In ageing SD/SD mice, retinal ganglion cell (RGC) number in whole retinas was significantly lower than that in young SD/SD mice in the absence of inflammation and elevated intraocular pressure, resembling the pathogenesis of progressive optic neuropathy. By contrast, SA/SA mice and wild-type (WT) mice exhibited no age-related RGC loss. The age-related retinal RGC number reduction was greater in the peripheral rather than the mid-peripheral region of the retina in SD/SD mice. Furthermore, Rho-associated kinase activity in whole retinas of ageing SD/SD mice was significantly higher than that in young SD/SD mice. These results suggest that Src regulates RGC survival during ageing in a manner that depends on Ser75 phosphorylation.

Pharmacological inhibition of Src kinase protects against acute kidney injury in a murine model of renal ischemia/reperfusion

PubMed Central

Zhou, Xiaoxu; Liu, Lirong; Masucci, Monica V.; Tang, Jinhua; Li, Xuezhu; Liu, Na; Bayliss, George; Zhao, Ting C.; Zhuang, Shougang

2017-01-01

Activation of Src kinase has been implicated in the pathogenesis of acute brain, liver, and lung injury. However, the role of Src in acute kidney injury (AKI) remains unestablished. To address this, we evaluated the effects of Src inhibition on renal dysfunction and pathological changes in a murine model of AKI induced by ischemia/reperfusion (I/R). I/R injury to the kidney resulted in increased Src phosphorylation at tyrosine 416 (activation). Administration of PP1, a highly selective Src inhibitor, blocked Src phosphorylation, improved renal function and ameliorated renal pathological damage. PP1 treatment also suppressed renal expression of neutrophil gelatinase-associated lipocalin and reduced apoptosis in the injured kidney. Moreover, Src inhibition prevented downregulation of several adherens and tight junction proteins, including E-cadherin, ZO-1, and claudins-1/−4 in the kidney after I/R injury as well as in cultured renal proximal tubular cells following oxidative stress. Finally, PP1 inhibited I/R–induced renal expression of matrix metalloproteinase-2 and -9, phosphorylation of extracellular signal–regulated kinases1/2, signal transducer and activator of transcription-3, and nuclear factor-κB, and the infiltration of macrophages into the kidney. These data indicate that Src is a pivotal mediator of renal epithelial injury and that its inhibition may have a therapeutic potential to treat AKI. PMID:28415724

Clinicopathological Characteristics and Prognostic Value of Signet Ring Cells in Gastric Carcinoma: A Meta-Analysis.

PubMed

Nie, Run-Cong; Yuan, Shu-Qiang; Li, Yuan-Fang; Chen, Yong-Ming; Chen, Xiao-Jiang; Zhu, Bao-Yan; Xu, Li-Pu; Zhou, Zhi-Wei; Chen, Shi; Chen, Ying-Bo

2017-01-01

Background and Objectives: Previous studies of the prognostic value of the signet ring cell (SRC) type have yielded inconsistent results. Therefore, the aim of the present meta-analysis is to explore the clinicopathological characteristics and prognostic value of SRCs. Methods: Relevant articles that compared SRC and non-SRC type in PubMed and Web of Science were comprehensively searched. Then, a meta-analysis was performed. Results: A total of 19 studies including 35947 cases were analyzed. Compared with non-SRC patients, SRC patients tended to be younger (WMD: -3.88, P=0.001) and predominantly female (OR: 1.60, P<0.001). Additionally, SRC patients exhibited less upper third tumor location (OR: 0.62, P<0.001) and less frequent hematogenous metastasis (OR: 0.41, P<0.001). There was no difference in overall survival (OS) between SRC and non-SRC patients in the total population (HR: 1.02, P=0.830). Early gastric cancer with SRCs was associated with better OS (HR: 0.57, P=0.002), while advanced gastric cancer with non-SRCs was associated with a worse prognosis (HR: 1.17, P<0.001). Conclusions: This meta-analysis revealed that SRC tends to affect young females and tends to be located in the middle and lower third of the stomach. Early SRCs are associated with better prognoses, while advanced SRCs are associated with worse prognoses.

Lyn tyrosine kinase promotes silencing of ATM-dependent checkpoint signaling during recovery from DNA double-strand breaks

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

Fukumoto, Yasunori, E-mail: fukumoto@faculty.chiba-u.jp; Kuki, Kazumasa; Morii, Mariko

2014-09-26

Highlights: • Inhibition of Src family kinases decreased γ-H2AX signal. • Inhibition of Src family increased ATM-dependent phosphorylation of Chk2 and Kap1. • shRNA-mediated knockdown of Lyn increased phosphorylation of Kap1 by ATM. • Ectopic expression of Src family kinase suppressed ATM-mediated Kap1 phosphorylation. • Src is involved in upstream signaling for inactivation of ATM signaling. - Abstract: DNA damage activates the DNA damage checkpoint and the DNA repair machinery. After initial activation of DNA damage responses, cells recover to their original states through completion of DNA repair and termination of checkpoint signaling. Currently, little is known about the processmore » by which cells recover from the DNA damage checkpoint, a process called checkpoint recovery. Here, we show that Src family kinases promote inactivation of ataxia telangiectasia mutated (ATM)-dependent checkpoint signaling during recovery from DNA double-strand breaks. Inhibition of Src activity increased ATM-dependent phosphorylation of Chk2 and Kap1. Src inhibition increased ATM signaling both in G2 phase and during asynchronous growth. shRNA knockdown of Lyn increased ATM signaling. Src-dependent nuclear tyrosine phosphorylation suppressed ATM-mediated Kap1 phosphorylation. These results suggest that Src family kinases are involved in upstream signaling that leads to inactivation of the ATM-dependent DNA damage checkpoint.« less

Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM

PubMed Central

Zhao, Zhizhen; Singer, Amit

2014-01-01

We introduce a new rotationally invariant viewing angle classification method for identifying, among a large number of cryo-EM projection images, similar views without prior knowledge of the molecule. Our rotationally invariant features are based on the bispectrum. Each image is denoised and compressed using steerable principal component analysis (PCA) such that rotating an image is equivalent to phase shifting the expansion coefficients. Thus we are able to extend the theory of bispectrum of 1D periodic signals to 2D images. The randomized PCA algorithm is then used to efficiently reduce the dimensionality of the bispectrum coefficients, enabling fast computation of the similarity between any pair of images. The nearest neighbors provide an initial classification of similar viewing angles. In this way, rotational alignment is only performed for images with their nearest neighbors. The initial nearest neighbor classification and alignment are further improved by a new classification method called vector diffusion maps. Our pipeline for viewing angle classification and alignment is experimentally shown to be faster and more accurate than reference-free alignment with rotationally invariant K-means clustering, MSA/MRA 2D classification, and their modern approximations. PMID:24631969

40 CFR Appendix V to Part 86 - The Standard Road Cycle (SRC)

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 19 2010-07-01 2010-07-01 false The Standard Road Cycle (SRC) V... Appendix V to Part 86—The Standard Road Cycle (SRC) 1. The standard road cycle (SRC) is a mileage accumulation cycle that may be used for any vehicle which is covered by the applicability provisions of § 86...

N-terminal deletions in Rous sarcoma virus p60src: effects on tyrosine kinase and biological activities and on recombination in tissue culture with the cellular src gene.

PubMed Central

Cross, F R; Garber, E A; Hanafusa, H

1985-01-01

We have constructed deletions within the region of cloned Rous sarcoma virus DNA coding for the N-terminal 30 kilodaltons of p60src. Infectious virus was recovered after transfection. Deletions of amino acids 15 to 149, 15 to 169, or 149 to 169 attenuated but did not abolish transforming activity, as assayed by focus formation and anchorage-independent growth. These deletions also had only slight effects on the tyrosine kinase activity of the mutant src protein. Deletion of amino acids 169 to 264 or 15 to 264 completely abolished transforming activity, and src kinase activity was reduced at least 10-fold. However, these mutant viruses generated low levels of transforming virus by recombination with the cellular src gene. The results suggest that as well as previously identified functional domains for p60src myristylation and membrane binding (amino acids 1 to 14) and tyrosine kinase activity (amino acids 250 to 526), additional N-terminal sequences (particularly amino acids 82 to 169) can influence the transforming activity of the src protein. Images PMID:2426576

Inhibition of SRC-3 enhances sensitivity of human cancer cells to histone deacetylase inhibitors

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

Zou, Zhengzhi, E-mail: zouzhengzhi@m.scnu.edu.cn; Luo, Xiaoyong; Nie, Peipei

SRC-3 is widely expressed in multiple tumor types and involved in cancer cell proliferation and apoptosis. Histone deacetylase (HDAC) inhibitors are promising antitumor drugs. However, the poor efficacy of HDAC inhibitors in solid tumors has restricted its further clinical application. Here, we reported the novel finding that depletion of SRC-3 enhanced sensitivity of breast and lung cancer cells to HDAC inhibitors (SAHA and romidepsin). In contrast, overexpression of SRC-3 decreased SAHA-induced cancer cell apoptosis. Furthermore, we found that SRC-3 inhibitor bufalin increased cancer cell apoptosis induced by HDAC inhibitors. The combination of bufalin and SAHA was particular efficient in attenuatingmore » AKT activation and reducing Bcl-2 levels. Taken together, these accumulating data might guide development of new breast and lung cancer therapies. - Highlights: • Depletion of SRC-3 enhanced sensitivity of breast and lung cancer cells to HDAC inhibitors. • Overexpression of SRC-3 enhanced cancer cell resistance to HDAC inhibitors. • SRC-3 inhibitor bufalin increased cancer cell apoptosis induced by HDAC inhibitors. • Bufalin synergized with HDAC inhibitor attenuated AKT activation and reduced Bcl-2 levels in human cancer cell.« less

Effect of addition of semi refined carrageenan on mechanical characteristics of gum arabic edible film

NASA Astrophysics Data System (ADS)

Setyorini, D.; Nurcahyani, P. R.

2016-04-01

Currently the seaweed is processed flour and Semi Refined Carraagenan (SRC). However, total production is small, but both of these products have a high value and are used in a wide variety of products such as cosmetics, processed foods, medicines, and edible film. The aim of this study were (1) to determine the effect of SRC on mechanical characteristics of edible film, (2) to determine the best edible film which added by SRC with different concentration. The edible film added by SRC flour which divided into three concentrations of SRC. There are 1.5%; 3%; and 4.5% of SRC, then added 3% glycerol and 0.6% arabic gum. The mechanical properties of the film measured by a universal testing machine Orientec Co. Ltd., while the water vapor permeability measured by the gravimetric method dessicant modified. The experimental design used was completely randomized design with a further test of Duncan. The result show SRC concentration differences affect the elongation breaking point and tensile strength. But not significant effect on the thickness, yield strength and the modulus of elasticity. The best edible film is edible film with the addition of SRC 4.5%.

The Complexity Analysis Tool

DTIC Science & Technology

1988-10-01

overview of the complexity analysis tool ( CAT ), an automated tool which will analyze mission critical computer resources (MCCR) software. CAT is based...84 MAR UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE 19. ABSTRACT: (cont) CAT automates the metric for BASIC (HP-71), ATLAS (EQUATE), Ada (subset...UNIX 5.2). CAT analyzes source code and computes complexity on a module basis. CAT also generates graphic representations of the logic flow paths and

Automated Diagnosis Coding with Combined Text Representations.

PubMed

Berndorfer, Stefan; Henriksson, Aron

2017-01-01

Automated diagnosis coding can be provided efficiently by learning predictive models from historical data; however, discriminating between thousands of codes while allowing a variable number of codes to be assigned is extremely difficult. Here, we explore various text representations and classification models for assigning ICD-9 codes to discharge summaries in MIMIC-III. It is shown that the relative effectiveness of the investigated representations depends on the frequency of the diagnosis code under consideration and that the best performance is obtained by combining models built using different representations.

Video occupant detection and classification

DOEpatents

Krumm, John C.

1999-01-01

A system for determining when it is not safe to arm a vehicle airbag by storing representations of known situations as observed by a camera at a passenger seat; and comparing a representation of a camera output of the current situation to the stored representations to determine the known situation most closely represented by the current situation. In the preferred embodiment, the stored representations include the presence or absence of a person or infant seat in the front passenger seat of an automobile.

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Applying matching pursuit decomposition time-frequency processing to UGS footstep classification

NASA Astrophysics Data System (ADS)

Larsen, Brett W.; Chung, Hugh; Dominguez, Alfonso; Sciacca, Jacob; Kovvali, Narayan; Papandreou-Suppappola, Antonia; Allee, David R.

2013-06-01

The challenge of rapid footstep detection and classification in remote locations has long been an important area of study for defense technology and national security. Also, as the military seeks to create effective and disposable unattended ground sensors (UGS), computational complexity and power consumption have become essential considerations in the development of classification techniques. In response to these issues, a research project at the Flexible Display Center at Arizona State University (ASU) has experimented with footstep classification using the matching pursuit decomposition (MPD) time-frequency analysis method. The MPD provides a parsimonious signal representation by iteratively selecting matched signal components from a pre-determined dictionary. The resulting time-frequency representation of the decomposed signal provides distinctive features for different types of footsteps, including footsteps during walking or running activities. The MPD features were used in a Bayesian classification method to successfully distinguish between the different activities. The computational cost of the iterative MPD algorithm was reduced, without significant loss in performance, using a modified MPD with a dictionary consisting of signals matched to cadence temporal gait patterns obtained from real seismic measurements. The classification results were demonstrated with real data from footsteps under various conditions recorded using a low-cost seismic sensor.

Information Graphic Classification, Decomposition and Alternative Representation

ERIC Educational Resources Information Center

Gao, Jinglun

2012-01-01

This thesis work is mainly focused on two problems related to improving accessibility of information graphics for visually impaired users. The first problem is automated analysis of information graphics for information extraction and the second problem is multi-modal representations for accessibility. Information graphics are graphical…

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.

PubMed

Alsaih, Khaled; Lemaitre, Guillaume; Rastgoo, Mojdeh; Massich, Joan; Sidibé, Désiré; Meriaudeau, Fabrice

2017-06-07

Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers. The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations. Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.

Improved classification of drainage networks using junction angles and secondary tributary lengths

NASA Astrophysics Data System (ADS)

Jung, Kichul; Marpu, Prashanth R.; Ouarda, Taha B. M. J.

2015-06-01

River networks in different regions have distinct characteristics generated by geological processes. These differences enable classification of drainage networks using several measures with many features of the networks. In this study, we propose a new approach that only uses the junction angles with secondary tributary lengths to directly classify different network types. This methodology is based on observations on 50 predefined channel networks. The cumulative distributions of secondary tributary lengths for different ranges of junction angles are used to obtain the descriptive values that are defined using a power-law representation. The averages of the values for the known networks are used to represent the classes, and any unclassified network can be classified based on the similarity of the representative values to those of the known classes. The methodology is applied to 10 networks in the United Arab Emirates and Oman and five networks in the USA, and the results are validated using the classification obtained with other methods.

Prognostic Significance of Signet Ring Gastric Cancer

PubMed Central

Taghavi, Sharven; Jayarajan, Senthil N.; Davey, Adam; Willis, Alliric I.

2012-01-01

Purpose Studies in Asia have questioned the dictum that signet ring cell carcinoma (SRC) has a worse prognosis than other forms of gastric cancer. Our study determined differences in presentation and outcomes between SRC and gastric adenocarcinoma (AC) in the United States. Patients and Methods The National Cancer Institute Surveillance, Epidemiology, and End Results database was reviewed for SRC and AC from 2004 to 2007. Results We reviewed 10,246 cases of patients with gastric cancer, including 2,666 of SRC and 7,580 of AC. SRC presented in younger patients (61.9 v 68.7 years; P < .001) and less often in men (52.7% v 68.7%; P < .001). SRC patients were more frequently black (11.3% v 10.9%), Asian (16.4% v 13.2%), American Indian/Alaska Native (0.9% v 0.8%), or Hispanic (23.3% v 14.0%; P < .001). SRC was more likely to be stage T3-4 (45.8% v 33.3%), have lymph node spread (59.7% v 51.8%), and distant metastases (40.2% v 37.6%; P < .001). SRC was more likely to be found in the lower (30.7% v 24.2%) and middle stomach (30.6% v 20.7%; P < .001). Median survival was not different between the two (AC, 14.0 months v SRC, 13.0 months; P = .073). Multivariable analyses demonstrated SRC was not associated with mortality (hazard ratio [HR], 1.05; 95% CI, 0.96 to 1.11; P = .150). Mortality was associated with age (HR, 1.01; 95% CI, 1.01 to 1.02; P < .001), black race (HR, 1.10; 95% CI, 1.01 to 1.20; P = .026), and tumor grade. Variables associated with lower mortality risk included Asian race (HR, 0.83; 95% CI, 0.77 to 0.91; P < .001) and surgery (HR, 0.37; 95% CI, 0.34 to 0.39; P < .001). Conclusion In the United States, SRC significantly differs from AC in extent of disease at presentation. However, when adjusted for stage, SRC does not portend a worse prognosis. PMID:22927530

Lemongrass essential oil and citral inhibit Src/Stat3 activity and suppress the proliferation/survival of small-cell lung cancer cells, alone or in combination with chemotherapeutic agents.

PubMed

Maruoka, Takayuki; Kitanaka, Akira; Kubota, Yoshitsugu; Yamaoka, Genji; Kameda, Tomohiro; Imataki, Osamu; Dobashi, Hiroaki; Bandoh, Shuji; Kadowaki, Norimitsu; Tanaka, Terukazu

2018-03-13

Small-cell lung cancer (SCLC) is intractable due to its high propensity for relapse. Novel agents are thus needed for SCLC treatment. Lemongrass essential oil (LG-EO) and its major constituent, citral, have been reported to inhibit the proliferation and survival of several types of cancer cells. However, the precise mechanisms through which LG-EO and citral exert their effects on SCLC cells have not been fully elucidated. SCLC cells express Src and have high levels of Src-tyrosine kinase (Src-TK) activity. In most SCLC cell lines, constitutive phosphorylation of Stat3(Y705), which is essential for its activation, has been detected. Src-TK can phosphorylate Stat3(Y705), and activated Stat3 promotes the expression of the anti-apoptotic factors Bcl-xL and Mcl-1. In the present study, LG-EO and citral prevented Src-TK from phosphorylating Stat3(Y705), resulting in decreased Bcl-xL and Mcl-1 expression, in turn suppressing the proliferation/survival of SCLC cells. To confirm these findings, the wild-type-src gene was transfected into the LU135 SCLC cell line (LU135‑wt-src), in which Src and activated phospho-Stat3(Y705) were overexpressed. The suppression of cell proliferation and the induction of apoptosis by treatment with LG-EO or citral were significantly attenuated in the LU135-wt-src cells compared with the control LU135-mock cells. The signal transducer and activator of transcription 3 (Stat3) signaling pathway is also associated with intrinsic drug resistance. LU135-wt-src cells were significantly resistant to conventional chemotherapeutic agents compared with LU135-mock cells. The combined effects of citral and each conventional chemotherapeutic agent on SCLC cells were also evaluated. The combination treatment exerted additive or more prominent effects on LU135-wt-src, LU165 and MN1112 cells, which are relatively chemoresistant SCLC cells. These findings suggest that either LG-EO or citral, alone or in combination with chemotherapeutic agents, may be a novel therapeutic option for SCLC patients.

The role of Src kinase in the biology and pathogenesis of Acanthamoeba castellanii

PubMed Central

2012-01-01

Background Acanthamoeba species are the causative agents of fatal granulomatous encephalitis in humans. Haematogenous spread is thought to be a primary step, followed by blood–brain barrier penetration, in the transmission of Acanthmaoeba into the central nervous system, but the associated molecular mechanisms remain unclear. Here, we evaluated the role of Src, a non-receptor protein tyrosine kinase in the biology and pathogenesis of Acanthamoeba. Methods Amoebistatic and amoebicidal assays were performed by incubating amoeba in the presence of Src kinase-selective inhibitor, PP2 (4-amino-5-(4-chlorophenyl)-7-(t-butyl)pyrazolo[3,4-d]pyrimidine) and its inactive analog, PP3 (4-amino-7-phenylpyrazolo[3,4-d]pyrimidine). Using this inhibitor, the role of Src kinase in A. castellanii interactions with Escherichia coli was determined. Zymographic assays were performed to study effects of Src kinase on extracellular proteolytic activities of A. castellanii. The human brain microvascular endothelial cells were used to determine the effects of Src kinase on A. castellanii adhesion to and cytotoxicity of host cells. Results Inhibition of Src kinase using a specific inhibitor, PP2 (4-amino-5-(4 chlorophenyl)-7-(t-butyl)pyrazolo [3,4-d] pyrimidine) but not its inactive analog, PP3 (4-amino-7-phenylpyrazolo[3,4-d] pyrimidine), had detrimental effects on the growth of A. castellanii (keratitis isolate, belonging to the T4 genotype). Interestingly, inhibition of Src kinase hampered the phagocytic ability of A. castellanii, as measured by the uptake of non-invasive bacteria, but, on the contrary, invasion by pathogenic bacteria was enhanced. Zymographic assays revealed that inhibition of Src kinases reduced extracellular protease activities of A. castellanii. Src kinase inhibition had no significant effect on A. castellanii binding to and cytotoxicity of primary human brain microvascular endothelial cells, which constitute the blood–brain barrier. Conclusions For the first time, these findings demonstrated that Src kinase is involved in A. castellanii proliferation, protease secretions and phagocytic properties. Conversely, invasion of Acanthamoeba by pathogenic bacteria was stimulated by Src kinase inhibition. PMID:22676352

Thin Versus Thick Description: Analyzing Representations of People and Their Life Worlds in the Literature of Communication Sciences and Disorders.

PubMed

Hengst, Julie A; Devanga, Suma; Mosier, Hillary

2015-11-01

Evidence-based practice relies on clinicians to translate research evidence for individual clients. This study, the initial phase of a broader research project, examines the textual resources of such translations by analyzing how people with acquired cognitive-communication disorders (ACCD) and their life worlds have been represented in Communication Sciences and Disorders (CSD) research articles. Using textual analysis, we completed a categorical analysis of 6,059 articles published between 1936 and 2012, coding for genre, population, and any evidence of thick representations of people and their life worlds, and a discourse analysis of representations used in 56 ACCD research articles, identifying thin and thick representations in 4 domains (derived from the International Classification of Functioning, Disability, and Health) and across article sections. The categorical analysis identified a higher percentage of ACCD articles with some evidence of thick representation (30%) compared with all CSD articles (12%) sampled. However, discourse analysis of ACCD research articles found that thick representations were quite limited; 34/56 articles had thin representational profiles, 19/56 had mixed profiles, and 3/56 had thick profiles. These findings document the dominance of thin representations in the CSD literature, which we suggest makes translational work more difficult. How clinicians translate such evidence will be addressed in the next research phase, an interview study of speech-language pathologists.

Evidence for in vivo phosphorylation of the Grb2 SH2-domain binding site on focal adhesion kinase by Src-family protein-tyrosine kinases.

PubMed

Schlaepfer, D D; Hunter, T

1996-10-01

Focal adhesion kinase (FAK) is a nonreceptor protein-tyrosine kinase (PTK) that associates with integrin receptors and participates in extracellular matrix-mediated signal transduction events. We showed previously that the c-Src nonreceptor PTK and the Grb2 SH2/SH3 adaptor protein bound directly to FAK after fibronectin stimulation (D. D. Schlaepfer, S.K. Hanks, T. Hunter, and P. van der Geer, Nature [London] 372:786-791, 1994). Here, we present evidence that c-Src association with FAK is required for Grb2 binding to FAK. Using a tryptic phosphopeptide mapping approach, the in vivo phosphorylation of the Grb2 binding site on FAK (Tyr-925) was detected after fibronectin stimulation of NIH 3T3 cells and was constitutively phosphorylated in v-Src-transformed NIH 3T3 cells. In vitro, c-Src phosphorylated FAK Tyr-925 in a glutathione S-transferase-FAK C-terminal domain fusion protein, whereas FAK did not. Using epitope-tagged FAK constructs, transiently expressed in human 293 cells, we determined the effect of site-directed mutations on c-Src and Grb2 binding to FAK. Mutation of FAK Tyr-925 disrupted Grb2 binding, whereas mutation of the c-Src binding site on FAK (Tyr-397) disrupted both c-Src and Grb2 binding to FAK in vivo. These results support a model whereby Src-family PTKs are recruited to FAK and focal adhesions following integrin-induced autophosphorylation and exposure of FAK Tyr-397. Src-family binding and phosphorylation of FAK at Tyr-925 creates a Grb2 SH2-domain binding site and provides a link to the activation of the Ras signal transduction pathway. In Src-transformed cells, this pathway may be constitutively activated as a result of FAK Tyr-925 phosphorylation in the absence of integrin stimulation.

Focal complex formation in adult cardiomyocytes is accompanied by the activation of beta3 integrin and c-Src.

PubMed

Willey, Christopher D; Balasubramanian, Sundaravadivel; Rodríguez Rosas, María C; Ross, Robert S; Kuppuswamy, Dhandapani

2003-06-01

In pressure-overloaded myocardium, our recent study demonstrated cytoskeletal assembly of c-Src and other signaling proteins which was partially mimicked in vitro using adult feline cardiomyocytes embedded in three-dimensional (3D) collagen matrix and stimulated with an integrin-binding Arg-Gly-Asp (RGD) peptide. In the present study, we improved this model further to activate c-Src and obtain a full assembly of the focal adhesion complex (FAC), and characterized c-Src localization and integrin subtype(s) involved. RGD dose response experiments revealed that c-Src activation occurs subsequent to its cytoskeletal recruitment and is accompanied by p130Cas cytoskeletal binding and focal adhesion kinase (FAK) Tyr925 phosphorylation. When cardiomyocytes expressing hexahistidine-tagged c-Src via adenoviral gene delivery were used for RGD stimulation, the expressed c-Src exhibited relocation: (i) biochemical analysis revealed c-Src movement from the detergent-soluble to the -insoluble cytoskeletal fraction and (ii) confocal microscopic analysis showed c-Src movement from a nuclear/perinuclear to a sarcolemmal region. RGD treatment also caused sarcolemmal co-localization of FAK and vinculin. Characterization of integrin subtypes revealed that beta3, but not beta1, integrin plays a predominant role: (i) expression of cytoplasmic domain of beta1A integrin did not affect the RGD-stimulated FAC formation and (ii) both pressure-overloaded myocardium and RGD-stimulated cardiomyocytes exhibited phosphorylation of beta3 integrin at Tyr773/785 sites but not beta1 integrin at Thr788/789 sites. Together these data indicate that RGD treatment in cardiomyocytes causes beta3 integrin activation and c-Src sarcolemmal localization, that subsequent c-Src activation is accompanied by p130Cas binding and FAK Tyr925 phosphorylation, and that these events might be crucial for growth and remodeling of hypertrophying adult cardiomyocytes.

v-src induction of the TIS10/PGS2 prostaglandin synthase gene is mediated by an ATF/CRE transcription response element.

PubMed

Xie, W; Fletcher, B S; Andersen, R D; Herschman, H R

1994-10-01

We recently reported the cloning of a mitogen-inducible prostaglandin synthase gene, TIS10/PGS2. In addition to growth factors and tumor promoters, the v-src oncogene induces TIS10/PGS2 expression in 3T3 cells. Deletion analysis, using luciferase reporters, identifies a region between -80 and -40 nucleotides 5' of the TIS10/PGS2 transcription start site that mediates pp60v-src induction in 3T3 cells. This region contains the sequence CGTCACGTG, which includes overlapping ATF/CRE (CGTCA) and E-box (CACGTG) sequences. Gel shift-oligonucleotide competition experiments with nuclear extracts from cells stably transfected with a temperature-sensitive v-src gene demonstrate that the CGTCACGTG sequence can bind proteins at both the ATF/CRE and E-box sequences. Dominant-negative CREB and Myc proteins that bind DNA, but do not transactivate, block v-src induction of a luciferase reporter driven by the first 80 nucleotides of the TIS10/PGS2 promoter. Mutational analysis distinguishes which TIS10/PGS2 cis-acting element mediates pp60v-src induction. E-box mutation has no effect on the fold induction in response to pp60v-src. In contrast, ATF/CRE mutation attenuates the pp60v-src response. Antibody supershift and methylation interference experiments demonstrate that CREB and at least one other ATF transcription factor in these extracts bind to the TIS10/PGS2 ATF/CRE element. Expression of a dominant-negative ras gene also blocks TIS10/PGS2 induction by v-src. Our data suggest that Ras mediates pp60v-src activation of an ATF transcription factor, leading to induced TIS10/PGS2 expression via the ATF/CRE element of the TIS10/PGS2 promoter. This is the first description of v-src activation of gene expression via an ATF/CRE element.

Uncertainty management by relaxation of conflicting constraints in production process scheduling

NASA Technical Reports Server (NTRS)

Dorn, Juergen; Slany, Wolfgang; Stary, Christian

1992-01-01

Mathematical-analytical methods as used in Operations Research approaches are often insufficient for scheduling problems. This is due to three reasons: the combinatorial complexity of the search space, conflicting objectives for production optimization, and the uncertainty in the production process. Knowledge-based techniques, especially approximate reasoning and constraint relaxation, are promising ways to overcome these problems. A case study from an industrial CIM environment, namely high-grade steel production, is presented to demonstrate how knowledge-based scheduling with the desired capabilities could work. By using fuzzy set theory, the applied knowledge representation technique covers the uncertainty inherent in the problem domain. Based on this knowledge representation, a classification of jobs according to their importance is defined which is then used for the straightforward generation of a schedule. A control strategy which comprises organizational, spatial, temporal, and chemical constraints is introduced. The strategy supports the dynamic relaxation of conflicting constraints in order to improve tentative schedules.

Fibroblast surface-associated FGF-2 promotes contact-dependent colorectal cancer cell migration and invasion through FGFR-SRC signaling and integrin αvβ5-mediated adhesion

PubMed Central

Knuchel, Sarah; Anderle, Pascale; Werfelli, Patricia; Diamantis, Eva; Rüegg, Curzio

2015-01-01

Carcinoma-associated fibroblasts were reported to promote colorectal cancer (CRC) invasion by secreting motility factors and extracellular matrix processing enzymes. Less is known whether fibroblasts may induce CRC cancer cell motility by contact-dependent mechanisms. To address this question we characterized the interaction between fibroblasts and SW620 and HT29 colorectal cancer cells in 2D and 3D co-culture models in vitro. Here we show that fibroblasts induce contact-dependent cancer cell elongation, motility and invasiveness independently of deposited matrix or secreted factors. These effects depend on fibroblast cell surface-associated fibroblast growth factor (FGF) -2. Inhibition of FGF-2 or FGF receptors (FGFRs) signaling abolishes these effects. FGFRs activate SRC in cancer cells and inhibition or silencing of SRC in cancer cells, but not in fibroblasts, prevents fibroblasts-mediated effects. Using an RGD-based integrin antagonist and function-blocking antibodies we demonstrate that cancer cell adhesion to fibroblasts requires integrin αvβ5. Taken together, these results demonstrate that fibroblasts induce cell-contact-dependent colorectal cancer cell migration and invasion under 2D and 3D conditions in vitro through fibroblast cell surface-associated FGF-2, FGF receptor-mediated SRC activation and αvβ5 integrin-dependent cancer cell adhesion to fibroblasts. The FGF-2-FGFRs-SRC-αvβ5 integrin loop might be explored as candidate therapeutic target to block colorectal cancer invasion. PMID:25973543

Fibroblast surface-associated FGF-2 promotes contact-dependent colorectal cancer cell migration and invasion through FGFR-SRC signaling and integrin αvβ5-mediated adhesion.

PubMed

Knuchel, Sarah; Anderle, Pascale; Werfelli, Patricia; Diamantis, Eva; Rüegg, Curzio

2015-06-10

Carcinoma-associated fibroblasts were reported to promote colorectal cancer (CRC) invasion by secreting motility factors and extracellular matrix processing enzymes. Less is known whether fibroblasts may induce CRC cancer cell motility by contact-dependent mechanisms. To address this question we characterized the interaction between fibroblasts and SW620 and HT29 colorectal cancer cells in 2D and 3D co-culture models in vitro. Here we show that fibroblasts induce contact-dependent cancer cell elongation, motility and invasiveness independently of deposited matrix or secreted factors. These effects depend on fibroblast cell surface-associated fibroblast growth factor (FGF) -2. Inhibition of FGF-2 or FGF receptors (FGFRs) signaling abolishes these effects. FGFRs activate SRC in cancer cells and inhibition or silencing of SRC in cancer cells, but not in fibroblasts, prevents fibroblasts-mediated effects. Using an RGD-based integrin antagonist and function-blocking antibodies we demonstrate that cancer cell adhesion to fibroblasts requires integrin αvβ5. Taken together, these results demonstrate that fibroblasts induce cell-contact-dependent colorectal cancer cell migration and invasion under 2D and 3D conditions in vitro through fibroblast cell surface-associated FGF-2, FGF receptor-mediated SRC activation and αvβ5 integrin-dependent cancer cell adhesion to fibroblasts. The FGF-2-FGFRs-SRC-αvβ5 integrin loop might be explored as candidate therapeutic target to block colorectal cancer invasion.

Inhibition of Src Family Kinases by a Combinatorial Action of 5′-AMP and Small Heat Shock Proteins, Identified from the Adult Heart

PubMed Central

Kasi, Vijaykumar S.; Kuppuswamy, Dhandapani

1999-01-01

Src family kinases are implicated in cellular proliferation and transformation. Terminally differentiated myocytes have lost the ability to proliferate, indicating the existence of a down-regulatory mechanism(s) for these mitogenic kinases. Here we show that feline cardiomyocyte lysate contains thermostable components that inhibit c-Src kinase in vitro. This inhibitory activity, present predominantly in heart tissue, involves two components acting combinatorially. After purification by sequential chromatography, one component was identified by mass and nuclear magnetic resonance spectroscopies as 5′-AMP, while the other was identified by peptide sequencing as a small heat shock protein (sHSP). 5′-AMP and to a lesser extent 5′-ADP inhibit c-Src when combined with either HSP-27 or HSP-32. Other HSPs, including αB-crystallin, HSP-70, and HSP-90, did not exhibit this effect. The inhibition, observed preferentially on Src family kinases and independent of the Src tyrosine phosphorylation state, occurs via a direct interaction of the c-Src catalytic domain with the inhibitory components. Our study indicates that sHSPs increase the affinity of 5′-AMP for the c-Src ATP binding site, thereby facilitating the inhibition. In vivo, elevation of ATP levels in the cardiomyocytes results in the tyrosine phosphorylation of cellular proteins including c-Src at the activatory site, and this effect is blocked when the 5′-AMP concentration is raised. Thus, this study reveals a novel role for sHSPs and 5′-AMP in the regulation of Src family kinases, presumably for the maintenance of the terminally differentiated state. PMID:10490624

Discovery of Diffuse Hard X-Ray Emission from the Vicinity of PSR J1648-4611 with Suzaku

NASA Astrophysics Data System (ADS)

Sakai, Michito; Matsumoto, Hironori; Haba, Yoshito; Kanou, Yasufumi; Miyamoto, Youhei

2013-06-01

We observed the pulsar PSR J1648-4611 with Suzaku. Two X-ray sources, Suzaku J1648-4610 (Src A) and Suzaku J1648-4615 (Src B), were found in the field of view. Src A is coincident with the pulsar PSR J1648-4611, which was also detected by the Fermi Gamma-ray Space Telescope. A hard-band image indicates that Src A is spatially extended. We found point sources in the vicinity of Src A by using a Chandra image of the same region, but the point sources have soft X-ray emission, and cannot explain the hard X-ray emission of Src A. The hard-band spectrum of Src A can be reproduced by a power-law model with a photon index of 2.0+0.9-0.7. The X-ray flux in the 2-10 keV band is 1.4 × 10-13 erg cm-2 s-1. The diffuse emission suggests a pulsar wind nebula around PSR J1648&"8211;4611, but the luminosity of Src A is much larger than that expected from the spin-down luminosity of the pulsar. Parts of the very-high-energy γ-ray emission of HESS J1646-458 may be powered by this pulsar wind nebula driven by PSR J1648-4611. Src B has soft emission, and its X-ray spectrum can be described by a power-law model with a photon index of 3.0+1.4-0.8. The X-ray flux in the 0.4-10 keV band is 6.4 × 10-14 erg s-1 cm-2. No counterpart for Src B has been found in the literature.

Activation of Src kinase by protein-tyrosine phosphatase-PEST in osteoclasts: comparative analysis of the effects of bisphosphonate and protein-tyrosine phosphatase inhibitor on Src activation in vitro.

PubMed

Chellaiah, Meenakshi A; Schaller, Michael D

2009-08-01

PTP-PEST is involved in the regulation of sealing ring formation in osteoclasts. In this article, we have shown a regulatory role for PTP-PEST on dephosphorylation of c-Src at Y527 and phosphorylation at Y418 in the catalytic site. Activation of Src in osteoclasts by over-expression of PTP-PEST resulted in the phosphorylation of cortactin at Y421 and WASP at Y294. Also enhanced as a result, is the interaction of Src, cortactin, and Arp2 with WASP. Moreover, the number of osteoclasts displaying sealing ring and bone resorbing activity was increased in response to PTP-PEST over-expression as compared with control osteoclasts. Cells expressing constitutively active-Src (527YDeltaF) simulate the effects mediated by PTP-PEST. Treatment of osteoclasts with a bisphosphonate alendronate or a potent PTP inhibitor PAO decreased the activity and phosphorylation of Src at Y418 due to reduced dephosphorylation state at Y527. Therefore, Src-mediated phosphorylation of cortactin and WASP as well as the formation of WASP.cortactin.Arp2 complex and sealing ring were reduced in these osteoclasts. Similar effects were observed in osteoclasts treated with an Src inhibitor PP2. We have shown that bisphosphonates could modulate the function of osteoclasts by inhibiting downstream signaling mediated by PTP-PEST/Src, in addition to its effect on the inhibition of the post-translational modification of small GTP-binding proteins such as Rab, Rho, and Rac as shown by others. The promising effects of the inhibitors PP2 and PAO on osteoclast function suggest a therapeutic approach for patients with bone metastases and osteoporosis as an alternative to bisphosphonates.

The Chromatin Assembly Factor Complex 1 (CAF1) and 5-Azacytidine (5-AzaC) Affect Cell Motility in Src-transformed Human Epithelial Cells.

PubMed

Endo, Akinori; Ly, Tony; Pippa, Raffaella; Bensaddek, Dalila; Nicolas, Armel; Lamond, Angus I

2017-01-06

Tumor invasion into surrounding stromal tissue is a hallmark of high grade, metastatic cancers. Oncogenic transformation of human epithelial cells in culture can be triggered by activation of v-Src kinase, resulting in increased cell motility, invasiveness, and tumorigenicity and provides a valuable model for studying how changes in gene expression cause cancer phenotypes. Here, we show that epithelial cells transformed by activated Src show increased levels of DNA methylation and that the methylation inhibitor 5-azacytidine (5-AzaC) potently blocks the increased cell motility and invasiveness induced by Src activation. A proteomic screen for chromatin regulators acting downstream of activated Src identified the replication-dependent histone chaperone CAF1 as an important factor for Src-mediated increased cell motility and invasion. We show that Src causes a 5-AzaC-sensitive decrease in both mRNA and protein levels of the p150 (CHAF1A) and p60 (CHAF1B), subunits of CAF1. Depletion of CAF1 in untransformed epithelial cells using siRNA was sufficient to recapitulate the increased motility and invasive phenotypes characteristic of transformed cells without activation of Src. Maintaining high levels of CAF1 by exogenous expression suppressed the increased cell motility and invasiveness phenotypes when Src was activated. These data identify a critical role of CAF1 in the dysregulation of cell invasion and motility phenotypes seen in transformed cells and also highlight an important role for epigenetic remodeling through DNA methylation for Src-mediated induction of cancer phenotypes. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Scavenger Receptor C Mediates Phagocytosis of White Spot Syndrome Virus and Restricts Virus Proliferation in Shrimp

PubMed Central

Yang, Ming-Chong; Shi, Xiu-Zhen; Yang, Hui-Ting; Sun, Jie-Jie; Xu, Ling; Wang, Xian-Wei; Zhao, Xiao-Fan

2016-01-01

Scavenger receptors are an important class of pattern recognition receptors that play several important roles in host defense against pathogens. The class C scavenger receptors (SRCs) have only been identified in a few invertebrates, and their role in the immune response against viruses is seldom studied. In this study, we firstly identified an SRC from kuruma shrimp, Marsupenaeus japonicus, designated MjSRC, which was significantly upregulated after white spot syndrome virus (WSSV) challenge at the mRNA and protein levels in hemocytes. The quantity of WSSV increased in shrimp after knockdown of MjSRC, compared with the controls. Furthermore, overexpression of MjSRC led to enhanced WSSV elimination via phagocytosis by hemocytes. Pull-down and co-immunoprecipitation assays demonstrated the interaction between MjSRC and the WSSV envelope protein. Electron microscopy observation indicated that the colloidal gold-labeled extracellular domain of MjSRC was located on the outer surface of WSSV. MjSRC formed a trimer and was internalized into the cytoplasm after WSSV challenge, and the internalization was strongly inhibited after knockdown of Mjβ-arrestin2. Further studies found that Mjβ-arrestin2 interacted with the intracellular domain of MjSRC and induced the internalization of WSSV in a clathrin-dependent manner. WSSV were co-localized with lysosomes in hemocytes and the WSSV quantity in shrimp increased after injection of lysosome inhibitor, chloroquine. Collectively, this study demonstrated that MjSRC recognized WSSV via its extracellular domain and invoked hemocyte phagocytosis to restrict WSSV systemic infection. This is the first study to report an SRC as a pattern recognition receptor promoting phagocytosis of a virus. PMID:28027319

Mouse fibroblasts homozygous for c-Src oncogene disruption shows dramatic suppression of expression of the gene encoding osteopontin, and adhesive phosphoprotein implicated in bone differentiation

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

Chackalaparampil, I.; Mukherjee, B.B.; Peri, A.

1994-09-01

Osteopetrosis, affecting mice and humans alike, arises from reduced or impaired bone resorption, causing abnormally dense bone formation. Normal bone differentiation requires continuous resorption and remodeling by osteoclasts which are derived from monocyte/macrophage lineage in the bone marrow. It has been reported that targeted homozygous disruption of c-src proto-oncogene in mice results in the development of osteopetrosis due to impaired bone-resorbing function of osteoclast cells. However, the molecular mechanism(s) which leads to osteoclast dysfunction in c-src deficient (src{sup -/-}) mice remains unclear. Here, we report that in embryonic fibroblasts derived from homozygous Src{sup -/-} mice, the expression of the genemore » coding for osteopontin (OP), a phosphorylated glycoprotein involved in bone differentiation, is drastically repressed. OP gene expression is not, however, affected in the heterozygous (Src{sup +/-}) mutant cells of identical origin, or in the c-src expression and OP production. Moreover, OP expression in c-src-deficient cells could be rescued upon treatment with 12-0-tetradecanoyl phorbol-13-myristate-acetate or okadaic acid. These observations indicate that OP expression is regulated via an src-mediated protein kinase C signaling pathway. Since it is known that OP mediates osteoclast adherence to the bone matrix, a key event in bone differentiation, our data is most significant in that they strongly suggest that drastic inhibition of synthesis of OP prevents osteoclasts in Src{sup -/-} mice from anchoring to the bone matrix. Consequently, this disruption of osteoclast adherence impairs their ability to form bone-resorbing ruffled border, causing osteopetrosis.« less

Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space.

PubMed

Spilka, J; Frecon, J; Leonarduzzi, R; Pustelnik, N; Abry, P; Doret, M

2015-01-01

Intrapartum fetal heart rate (FHR) constitutes a prominent source of information for the assessment of fetal reactions to stress events during delivery. Yet, early detection of fetal acidosis remains a challenging signal processing task. The originality of the present contribution are three-fold: multiscale representations and wavelet leader based multifractal analysis are used to quantify FHR variability ; Supervised classification is achieved by means of Sparse-SVM that aim jointly to achieve optimal detection performance and to select relevant features in a multivariate setting ; Trajectories in the feature space accounting for the evolution along time of features while labor progresses are involved in the construction of indices quantifying fetal health. The classification performance permitted by this combination of tools are quantified on a intrapartum FHR large database (≃ 1250 subjects) collected at a French academic public hospital.

Knowledge-based approaches to the maintenance of a large controlled medical terminology.

PubMed Central

Cimino, J J; Clayton, P D; Hripcsak, G; Johnson, S B

1994-01-01

OBJECTIVE: Develop a knowledge-based representation for a controlled terminology of clinical information to facilitate creation, maintenance, and use of the terminology. DESIGN: The Medical Entities Dictionary (MED) is a semantic network, based on the Unified Medical Language System (UMLS), with a directed acyclic graph to represent multiple hierarchies. Terms from four hospital systems (laboratory, electrocardiography, medical records coding, and pharmacy) were added as nodes in the network. Additional knowledge about terms, added as semantic links, was used to assist in integration, harmonization, and automated classification of disparate terminologies. RESULTS: The MED contains 32,767 terms and is in active clinical use. Automated classification was successfully applied to terms for laboratory specimens, laboratory tests, and medications. One benefit of the approach has been the automated inclusion of medications into multiple pharmacologic and allergenic classes that were not present in the pharmacy system. Another benefit has been the reduction of maintenance efforts by 90%. CONCLUSION: The MED is a hybrid of terminology and knowledge. It provides domain coverage, synonymy, consistency of views, explicit relationships, and multiple classification while preventing redundancy, ambiguity (homonymy) and misclassification. PMID:7719786

A Multifactorial Approach to Sport-Related Concussion Prevention and Education: Application of the Socioecological Framework.

PubMed

Register-Mihalik, Johna; Baugh, Christine; Kroshus, Emily; Y Kerr, Zachary; Valovich McLeod, Tamara C

2017-03-01

To offer an overview of sport-related concussion (SRC) prevention and education strategies in the context of the socioecological framework (SEF). Athletic trainers (ATs) will understand the many factors that interact to influence SRC prevention and the implications of these interactions for effective SRC education. Concussion is a complex injury that is challenging to identify and manage, particularly when athletes fail to disclose symptoms to their health care providers. Education is 1 strategy for increasing disclosure. However, limited information addresses how ATs can integrate the many factors that may influence the effectiveness of SRC education into their specific settings. Public health models provide an example through the SEF, which highlights the interplay among various levels of society and sport that can facilitate SRC prevention strategies, including education. For ATs to develop appropriate SRC prevention strategies, a framework for application is needed. A growing body of information concerning SRC prevention indicates that knowledge alone is insufficient to change concussion-related behaviors. The SEF allows this information to be considered at levels such as policy and societal, community, interpersonal (relationships), and intrapersonal (athlete). The use of such a framework will facilitate more comprehensive SRC prevention efforts that can be applied in all athletic training practice settings. Clinical Applications: Athletic trainers can use this information as they plan SRC prevention strategies in their specific settings. This approach will aid in addressing the layers of complexity that exist when developing a concussion-management policy and plan.

Short-term residential care for dementia patients: predictors for utilization and expected quality from a family caregiver's point of view.

PubMed

Donath, Carolin; Winkler, Angelika; Grässel, Elmar

2009-08-01

Short-term residential care (SRC) has proved to be effective in reducing the burden on family caregivers of dementia patients. Nevertheless, little is known about the factors which influence its usage or the expectations of family caregivers regarding quality. In this paper we address the following questions: (i) which variables of the care situation, the caregivers and their attitudes act as predictors for the utilization of SRC facilities? (ii) What are the views of caregivers about the quality of SRC? The cross-sectional study was carried out as an anonymous written survey of family caregivers of dementia patients in four regions of Germany. With a 20% response it was possible to analyze the quantitative and qualitative data from 404 and 254 family caregivers respectively. Predictors for utilization were evaluated using binary logistic regression analysis. The answers to questions of quality were evaluated using qualitative content analysis. Significant predictors for the utilization of SRC are the assessment of the helpfulness of SRC and the caregiver's knowledge of the accessibility of SRC facilities. Family caregivers who had already used SRC most frequently expressed the wish for "good care" in SRC facilities, followed by a program of suitable activities for dementia patients. In order to increase the rate of utilization, family caregivers must be convinced of the relevant advantages of using SRC facilities. The staff should be trained in caring for dementia patients and appropriate activities should be available.

Steroid receptor coactivator-3 regulates glucose metabolism in bladder cancer cells through coactivation of hypoxia inducible factor 1α.

PubMed

Zhao, Wei; Chang, Cunjie; Cui, Yangyan; Zhao, Xiaozhi; Yang, Jun; Shen, Lan; Zhou, Ji; Hou, Zhibo; Zhang, Zhen; Ye, Changxiao; Hasenmayer, Donald; Perkins, Robert; Huang, Xiaojing; Yao, Xin; Yu, Like; Huang, Ruimin; Zhang, Dianzheng; Guo, Hongqian; Yan, Jun

2014-04-18

Cancer cell proliferation is a metabolically demanding process, requiring high glycolysis, which is known as "Warburg effect," to support anabolic growth. Steroid receptor coactivator-3 (SRC-3), a steroid receptor coactivator, is overexpressed and/or amplified in multiple cancer types, including non-steroid targeted cancers, such as urinary bladder cancer (UBC). However, whether SRC-3 regulates the metabolic reprogramming for cancer cell growth is unknown. Here, we reported that overexpression of SRC-3 accelerated UBC cell growth, accompanied by the increased expression of genes involved in glycolysis. Knockdown of SRC-3 reduced the UBC cell glycolytic rate under hypoxia, decreased tumor growth in nude mice, with reduction of proliferating cell nuclear antigen and lactate dehydrogenase expression levels. We further revealed that SRC-3 could interact with hypoxia inducible factor 1α (HIF1α), which is a key transcription factor required for glycolysis, and coactivate its transcriptional activity. SRC-3 was recruited to the promoters of HIF1α-target genes, such as glut1 and pgk1. The positive correlation of expression levels between SRC-3 and Glut1 proteins was demonstrated in human UBC patient samples. Inhibition of glycolysis through targeting HK2 or LDHA decelerated SRC-3 overexpression-induced cell growth. In summary, overexpression of SRC-3 promoted glycolysis in bladder cancer cells through HIF1α to facilitate tumorigenesis, which may be an intriguing drug target for bladder cancer therapy.

Rhynchophylline Ameliorates Endothelial Dysfunction via Src-PI3K/Akt-eNOS Cascade in the Cultured Intrarenal Arteries of Spontaneous Hypertensive Rats

PubMed Central

Hao, Hui-Feng; Liu, Li-Mei; Pan, Chun-Shui; Wang, Chuan-She; Gao, Yuan-Sheng; Fan, Jing-Yu; Han, Jing-Yan

2017-01-01

Objectives: To examine the protective effect of Rhynchophylline (Rhy) on vascular endothelial function in spontaneous hypertensive rats (SHRs) and the underlying mechanism. Methods: Intrarenal arteries of SHRs and Wistar rats were suspended in myograph for force measurement. Expression and phosphorylation of endothelial nitric oxide (NO) synthase (eNOS), Akt, and Src kinase (Src) were examined by Western blotting. NO production was assayed by ELISA. Results: Rhy time- and concentration-dependently improved endothelium-dependent relaxation in the renal arteries from SHRs, but had no effect on endothelium-independent relaxation in SHR renal arteries. Wortmannin (an inhibitor of phosphatidylinositol 3-kinase) or PP2 (an inhibitor of Src) inhibited the improvement of relaxation in response to acetylcholine by 12 h-incubation with 300 μM Rhy. Western blot analysis revealed that Rhy elevated phosphorylations of eNOS, Akt, and Src in SHR renal arteries. Moreover, wortmannin reversed the increased phosphorylations of Akt and eNOS induced by Rhy, but did not affect the phosphorylation of Src. Furthermore, the enhanced phosphorylations of eNOS, Akt, and Src were blunted by PP2. Importantly, Rhy increased NO production and this effect was blocked by inhibition of Src or PI3K/Akt. Conclusion: The present study provides evidences for the first time that Rhy ameliorates endothelial dysfunction in SHRs through the activation of Src-PI3K/Akt-eNOS signaling pathway. PMID:29187825

Experimental validation of arthroscopic cartilage stiffness measurement using enzymatically degraded cartilage samples

NASA Astrophysics Data System (ADS)

Lyyra, T.; Arokoski, J. P. A.; Oksala, N.; Vihko, A.; Hyttinen, M.; Jurvelin, J. S.; Kiviranta, I.

1999-02-01

In order to evaluate the ability of the arthroscopic indentation instrument, originally developed for the measurement of cartilage stiffness during arthroscopy, to detect cartilage degeneration, we compared changes in the stiffness with the structural and constitutional alterations induced by enzymes on the tissue in vitro. The culturing of osteochondral plugs on Petri dishes was initiated in Minimum Essential Medium with Earle's salts and the baseline stiffness was measured. Then, the experimental specimens were digested using trypsin for 24 h, chondroitinase ABC or purified collagenase (type VII) for 24 h or 48 h ( n = 8-15 per group). The control specimens were incubated in the medium. After the enzyme digestion, the end-point stiffness was measured and the specimens for the microscopic analyses were processed. The proteoglycan (PG) distribution was analysed using quantitative microspectrophotometry and the quantitative evaluation of the collagen network was made using a computer-based polarized light microscopy analysis. Decrease of cartilage stiffness was found after 24 h trypsin (36%) and 48 h chondroitinase ABC (24%) digestion corresponding to a decrease of up to 80% and up to 30% in the PG content respectively. Decrease of the superficial zone collagen content or arrangement (78%, ) after 48 h collagenase digestion also induced a decrease (30%, ) in cartilage stiffness. We conclude that our instrument is capable of detecting early structural and compositional changes related to cartilage degeneration.

Histochemical assessment for osteoblastic activity coupled with dysfunctional osteoclasts in c-src deficient mice.

PubMed

Toray, Hisashi; Hasegawa, Tomoka; Sakagami, Naoko; Tsuchiya, Erika; Kudo, Ai; Zhao, Shen; Moritani, Yasuhito; Abe, Miki; Yoshida, Taiji; Yamamoto, Tomomaya; Yamamoto, Tsuneyuki; Oda, Kimimitsu; Udagawa, Nobuyuki; Luiz de Freitas, Paulo Henrique; Li, Minqi

2017-01-01

Since osteoblastic activities are believed to be coupled with osteoclasts, we have attempted to histologically verify which of the distinct cellular circumstances, the presence of osteoclasts themselves or bone resorption by osteoclasts, is essential for coupled osteoblastic activity, by examining c-fos -/- or c-src -/- mice. Osteopetrotic c-fos deficient (c-fos -/- ) mice have no osteoclasts, while c-src deficient (c-src -/- ) mice, another osteopetrotic model, develop dysfunctional osteoclasts due to a lack of ruffled borders. c-fos -/- mice possessed no tartrate-resistant acid phosphatase (TRAPase)-reactive osteoclasts, and showed very weak tissue nonspecific alkaline phosphatase (TNALPase)-reactive mature osteoblasts. In contrast, c-src -/- mice had many TNALPase-positive osteoblasts and TRAPase-reactive osteoclasts. Interestingly, the parallel layers of TRAPase-reactive/osteopontin-positive cement lines were observed in the superficial region of c-src -/- bone matrix. This indicates the possibility that in c-src -/- mice, osteoblasts were activated to deposit new bone matrices on the surfaces that osteoclasts previously passed along, even without bone resorption. Transmission electron microscopy demonstrated cell-to-cell contacts between mature osteoblasts and neighboring ruffled border-less osteoclasts, and osteoid including many mineralized nodules in c-src -/- mice. Thus, it seems likely that osteoblastic activities would be maintained in the presence of osteoclasts, even if they are dysfunctional.

v-src induces clonal sarcomas and rapid metastasis following transduction with a replication-defective retrovirus

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

Stoker, A.W.; Sieweke, M.H.

1989-12-01

v-src is an effective carcinogen when expressed from Rous sarcoma virus (RSV) in vivo. Whereas RSV tumors require sustained oncogene expression, their growth is largely a balance between viral recruitment of tissues and host immune destruction of infected cells. The authors have therefore examined the tumorigenic potential of v-src in the absence of viral recruitment and viral antigen expression. v-src was introduced with high efficiency into chicken wing web tissues using replication-defective (rd) retroviral vectors. Clonal sarcomas were induced rapidly, and furthermore, v-src potentiated metastatic progression in {approx} 0.1%-1% of tumor clones with unexpectedly short latency. rd vectors proved effectivemore » not only in transducing v-src into tissues but also as insertional markers of tumor clonality. The rd vector present in most primary and metastatic tumors was a highly truncated form of RSV derived by viral transmission of spliced v-src mRNA; this vector should thus avoid viral recruitment and host anti-viral immune reaction through its complete lack of viral structural genes. Under such conditions v-src maintains strong carcinogenicity in vivo when restricted to clonal tumor growth and can confer rapid metastatic potential on a discrete subset of tumor clones.« less

Two Sides of the Same Coin: The Positive and Negative Impact of Spiritual Religious Coping on Quality of Life and Depression in Dialysis Patients.

PubMed

Vitorino, Luciano Magalhães; Soares, Renata de Castro E Santos; Santos, Ana Eliza Oliveira; Lucchetti, Alessandra Lamas Granero; Cruz, Jonas Preposi; Cortez, Paulo José Oliveira; Lucchetti, Giancarlo

2017-08-01

Studies have shown that spiritual/religious beliefs are associated with mental health and health-related quality of life (HRQoL). However, few studies evaluated how spiritual/religious coping (SRC) could affect hemodialysis patients. The present study investigated the role of SRC behaviors on HRQoL and depressive symptoms in hemodialysis patients. This was cross-sectional study with 184 patients. Patients completed the Beck Depression Inventory, Brief SRC Scale, Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), and a Sociodemographic and Health Characterization Questionnaire. From 218 patients, 184 (84.4%) were included (53.8% male with a median age of 55.9 years). Negative SRC, but not positive SRC, was associated with depressive symptoms. Positive SRC presented significant effects in SF-36 pain and physical and social functioning. On the other hand, negative SRC exhibited significant effects in SF-36 role emotional, energy/fatigue, pain, and physical functioning. SRC influences the mental health and HRQoL in Brazilian hemodialysis patients in two distinct ways. If used positively, it may have positive outcomes. However, if used negatively, it may lead to dysfunctional consequences such as greater depressive symptomatology and affect HRQoL. Health professionals must be aware of these "two sides of the same coin."

Representing and organizing information to describe the lived experience of health from a personal factors perspective in the light of the International Classification of Functioning, Disability and Health (ICF): a discussion paper.

PubMed

Geyh, Szilvia; Schwegler, Urban; Peter, Claudio; Müller, Rachel

2018-03-06

To discuss the representation and organization of information describing persons' lived experience of health from a personal factors perspective in the light of the International Classification of Functioning, Disability and Health, using spinal cord injury as a case in point for disability. The scientific literature was reviewed, discussion rounds conducted, and qualitative secondary analyses of data carried out using an iterative inductive-deductive approach. Conceptual considerations are explicated that distinguish the personal factors perspective from other components of the International Classification of Functioning, Disability and Health. A representation structure is developed that organizes health-related concepts describing the internal context of functioning. Concepts are organized as individual facts, subjective experiences, and recurrent patterns of experience and behavior specifying 7 areas and 211 concept groups. The article calls for further scientific debate on the perspective of personal factors in the light of the International Classification of Functioning, Disability and Health. A structure that organizes concepts in relation to a personal factors perspective can enhance the comprehensiveness, transparency and standardization of health information, and contribute to the empowerment of persons with disabilities. Implications for rehabilitation The present study collected data from scientific literature reviews, discussion rounds and qualitative secondary analyses in order to develop a representation and organization of information describing persons' lived experience of health from a personal factors perspective in the light of the International Classification of Functioning, Disability and Health. The following representation structure for health-related information from a personal factors perspective was developed: (i) Individuals facts (i.e., socio-demographical factors, position in the immediate social and physical context, personal history and biography), (ii) subjective experience (i.e., feelings, thoughts and beliefs, motives), and (iii) recurrent patterns of experience (i.e., feelings, thoughts and beliefs) and behavior. With this study, we aim to stimulate further scientific discussion about the personal factors component in the International Classification of Functioning, Disability and Health, including its application and subsequent validation for potential implementation into clinical practice.

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

PubMed Central

Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

2016-01-01

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

A SOFTWARE PACKAGE FOR UNSUPERVISED PATTERN RECOGNITION AND SYNOPTIC REPRESENTATION OF RESULTS: APPLICATION TO VOLCANIC TREMOR DATA OF MT ETNA

NASA Astrophysics Data System (ADS)

Langer, H. K.; Falsaperla, S. M.; Behncke, B.; Messina, A.; Spampinato, S.

2009-12-01

Artificial Intelligence (AI) has found broad applications in volcano observatories worldwide with the aim of reducing volcanic hazard. The need to process larger and larger quantity of data makes indeed AI techniques appealing for monitoring purposes. Tools based on Artificial Neural Networks and Support Vector Machine have proved to be particularly successful in the classification of seismic events and volcanic tremor changes heralding eruptive activity, such as paroxysmal explosions and lava fountaining at Stromboli and Mt Etna, Italy (e.g., Falsaperla et al., 1996; Langer et al., 2009). Moving on from the excellent results obtained from these applications, we present KKAnalysis, a MATLAB based software which combines several unsupervised pattern classification methods, exploiting routines of the SOM Toolbox 2 for MATLAB (http://www.cis.hut.fi/projects/somtoolbox). KKAnalysis is based on Self Organizing Maps (SOM) and clustering methods consisting of K-Means, Fuzzy C-Means, and a scheme based on a metrics accounting for correlation between components of the feature vector. We show examples of applications of this tool to volcanic tremor data recorded at Mt Etna between 2007 and 2009. This time span - during which Strombolian explosions, 7 episodes of lava fountaining and effusive activity occurred - is particularly interesting, as it encompassed different states of volcanic activity (i.e., non-eruptive, eruptive according to different styles) for the unsupervised classifier to identify, highlighting their development in time. Even subtle changes in the signal characteristics allow the unsupervised classifier to recognize features belonging to the different classes and stages of volcanic activity. A convenient color-code representation shows up the temporal development of the different classes of signal, making this method extremely helpful for monitoring purposes and surveillance. Though being developed for volcanic tremor classification, KKAnalysis is generally applicable to any type of physical or chemical pattern, provided that feature vectors are given in numerical form. References: Falsaperla, S., S. Graziani, G. Nunnari, and S. Spampinato (1996). Automatic classification of volcanic earthquakes by using multy-layered neural networks. Natural Hazard, 13, 205-228. Langer, H., S. Falsaperla, M. Masotti, R. Campanini, S. Spampinato, and A. Messina (2008). Synopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data at Mt Etna, Italy. Geophys. J. Int., doi:10.1111/j.1365-246X.2009.04179.x.

Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT

NASA Astrophysics Data System (ADS)

Madokoro, H.; Yamanashi, A.; Sato, K.

2013-08-01

This paper presents an unsupervised scene classification method for actualizing semantic recognition of indoor scenes. Background and foreground features are respectively extracted using Gist and color scale-invariant feature transform (SIFT) as feature representations based on context. We used hue, saturation, and value SIFT (HSV-SIFT) because of its simple algorithm with low calculation costs. Our method creates bags of features for voting visual words created from both feature descriptors to a two-dimensional histogram. Moreover, our method generates labels as candidates of categories for time-series images while maintaining stability and plasticity together. Automatic labeling of category maps can be realized using labels created using adaptive resonance theory (ART) as teaching signals for counter propagation networks (CPNs). We evaluated our method for semantic scene classification using KTH's image database for robot localization (KTH-IDOL), which is popularly used for robot localization and navigation. The mean classification accuracies of Gist, gray SIFT, one class support vector machines (OC-SVM), position-invariant robust features (PIRF), and our method are, respectively, 39.7, 58.0, 56.0, 63.6, and 79.4%. The result of our method is 15.8% higher than that of PIRF. Moreover, we applied our method for fine classification using our original mobile robot. We obtained mean classification accuracy of 83.2% for six zones.

Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods

NASA Astrophysics Data System (ADS)

Vanrolleghem, Peter A.; Mannina, Giorgio; Cosenza, Alida; Neumann, Marc B.

2015-03-01

Sensitivity analysis represents an important step in improving the understanding and use of environmental models. Indeed, by means of global sensitivity analysis (GSA), modellers may identify both important (factor prioritisation) and non-influential (factor fixing) model factors. No general rule has yet been defined for verifying the convergence of the GSA methods. In order to fill this gap this paper presents a convergence analysis of three widely used GSA methods (SRC, Extended FAST and Morris screening) for an urban drainage stormwater quality-quantity model. After the convergence was achieved the results of each method were compared. In particular, a discussion on peculiarities, applicability, and reliability of the three methods is presented. Moreover, a graphical Venn diagram based classification scheme and a precise terminology for better identifying important, interacting and non-influential factors for each method is proposed. In terms of convergence, it was shown that sensitivity indices related to factors of the quantity model achieve convergence faster. Results for the Morris screening method deviated considerably from the other methods. Factors related to the quality model require a much higher number of simulations than the number suggested in literature for achieving convergence with this method. In fact, the results have shown that the term "screening" is improperly used as the method may exclude important factors from further analysis. Moreover, for the presented application the convergence analysis shows more stable sensitivity coefficients for the Extended-FAST method compared to SRC and Morris screening. Substantial agreement in terms of factor fixing was found between the Morris screening and Extended FAST methods. In general, the water quality related factors exhibited more important interactions than factors related to water quantity. Furthermore, in contrast to water quantity model outputs, water quality model outputs were found to be characterised by high non-linearity.

SLAP displays tumour suppressor functions in colorectal cancer via destabilization of the SRC substrate EPHA2

NASA Astrophysics Data System (ADS)

Naudin, Cécile; Sirvent, Audrey; Leroy, Cédric; Larive, Romain; Simon, Valérie; Pannequin, Julie; Bourgaux, Jean-François; Pierre, Josiane; Robert, Bruno; Hollande, Frédéric; Roche, Serge

2014-01-01

The adaptor SLAP is a negative regulator of receptor signalling in immune cells but its role in human cancer is ill defined. Here we report that SLAP is abundantly expressed in healthy epithelial intestine but strongly downregulated in 50% of colorectal cancer. SLAP overexpression suppresses cell tumorigenicity and invasiveness while SLAP silencing enhances these transforming properties. Mechanistically, SLAP controls SRC/EPHA2/AKT signalling via destabilization of the SRC substrate and receptor tyrosine kinase EPHA2. This activity is independent from CBL but requires SLAP SH3 interaction with the ubiquitination factor UBE4A and SLAP SH2 interaction with pTyr594-EPHA2. SRC phosphorylates EPHA2 on Tyr594, thus creating a feedback loop that promotes EPHA2 destruction and thereby self-regulates its transforming potential. SLAP silencing enhances SRC oncogenicity and sensitizes colorectal tumour cells to SRC inhibitors. Collectively, these data establish a tumour-suppressive role for SLAP in colorectal cancer and a mechanism of SRC oncogenic induction through stabilization of its cognate substrates.

Lipid binding by the Unique and SH3 domains of c-Src suggests a new regulatory mechanism

PubMed Central

Pérez, Yolanda; Maffei, Mariano; Igea, Ana; Amata, Irene; Gairí, Margarida; Nebreda, Angel R.; Bernadó, Pau; Pons, Miquel

2013-01-01

c-Src is a non-receptor tyrosine kinase involved in numerous signal transduction pathways. The kinase, SH3 and SH2 domains of c-Src are attached to the membrane-anchoring SH4 domain through the flexible Unique domain. Here we show intra- and intermolecular interactions involving the Unique and SH3 domains suggesting the presence of a previously unrecognized additional regulation layer in c-Src. We have characterized lipid binding by the Unique and SH3 domains, their intramolecular interaction and its allosteric modulation by a SH3-binding peptide or by Calcium-loaded calmodulin binding to the Unique domain. We also show reduced lipid binding following phosphorylation at conserved sites of the Unique domain. Finally, we show that injection of full-length c-Src with mutations that abolish lipid binding by the Unique domain causes a strong in vivo phenotype distinct from that of wild-type c-Src in a Xenopus oocyte model system, confirming the functional role of the Unique domain in c-Src regulation. PMID:23416516

Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

DOEpatents

Moody, Daniela; Wohlberg, Brendt

2018-01-02

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Classification of Chemical Reactions: Stages of Expertise

ERIC Educational Resources Information Center

Stains, Marilyne; Talanquer, Vicente

2008-01-01

In this study we explore the strategies that undergraduate and graduate chemistry students use when engaged in classification tasks involving symbolic and microscopic (particulate) representations of different chemical reactions. We were specifically interested in characterizing the basic features to which students pay attention when classifying…

Seismic event classification system

DOEpatents

Dowla, F.U.; Jarpe, S.P.; Maurer, W.

1994-12-13

In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities. 21 figures.

Nonlinear features for classification and pose estimation of machined parts from single views

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-10-01

A new nonlinear feature extraction method is presented for classification and pose estimation of objects from single views. The feature extraction method is called the maximum representation and discrimination feature (MRDF) method. The nonlinear MRDF transformations to use are obtained in closed form, and offer significant advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We consider MRDFs on image data, provide a new 2-stage nonlinear MRDF solution, and show it specializes to well-known linear and nonlinear image processing transforms under certain conditions. We show the use of MRDF in estimating the class and pose of images of rendered solid CAD models of machine parts from single views using a feature-space trajectory neural network classifier. We show new results with better classification and pose estimation accuracy than are achieved by standard principal component analysis and Fukunaga-Koontz feature extraction methods.

Seismic event classification system

DOEpatents

Dowla, Farid U.; Jarpe, Stephen P.; Maurer, William

1994-01-01

In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.

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.

Automatic Identification of Critical Follow-Up Recommendation Sentences in Radiology Reports

PubMed Central

Yetisgen-Yildiz, Meliha; Gunn, Martin L.; Xia, Fei; Payne, Thomas H.

2011-01-01

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. Using information technology can improve communication and improve patient safety. In this paper, we describe a text processing approach that uses natural language processing (NLP) and supervised text classification methods to automatically identify critical recommendation sentences in radiology reports. To increase the classification performance we enhanced the simple unigram token representation approach with lexical, semantic, knowledge-base, and structural features. We tested different combinations of those features with the Maximum Entropy (MaxEnt) classification algorithm. Classifiers were trained and tested with a gold standard corpus annotated by a domain expert. We applied 5-fold cross validation and our best performing classifier achieved 95.60% precision, 79.82% recall, 87.0% F-score, and 99.59% classification accuracy in identifying the critical recommendation sentences in radiology reports. PMID:22195225

Drug-related webpages classification based on multi-modal local decision fusion

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Su, Xiaojing; Liu, Yanxin

2018-03-01

In this paper, multi-modal local decision fusion is used for drug-related webpages classification. First, meaningful text are extracted through HTML parsing, and effective images are chosen by the FOCARSS algorithm. Second, six SVM classifiers are trained for six kinds of drug-taking instruments, which are represented by PHOG. One SVM classifier is trained for the cannabis, which is represented by the mid-feature of BOW model. For each instance in a webpage, seven SVMs give seven labels for its image, and other seven labels are given by searching the names of drug-taking instruments and cannabis in its related text. Concatenating seven labels of image and seven labels of text, the representation of those instances in webpages are generated. Last, Multi-Instance Learning is used to classify those drugrelated webpages. Experimental results demonstrate that the classification accuracy of multi-instance learning with multi-modal local decision fusion is much higher than those of single-modal classification.

Automatic identification of critical follow-up recommendation sentences in radiology reports.

PubMed

Yetisgen-Yildiz, Meliha; Gunn, Martin L; Xia, Fei; Payne, Thomas H

2011-01-01

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. Using information technology can improve communication and improve patient safety. In this paper, we describe a text processing approach that uses natural language processing (NLP) and supervised text classification methods to automatically identify critical recommendation sentences in radiology reports. To increase the classification performance we enhanced the simple unigram token representation approach with lexical, semantic, knowledge-base, and structural features. We tested different combinations of those features with the Maximum Entropy (MaxEnt) classification algorithm. Classifiers were trained and tested with a gold standard corpus annotated by a domain expert. We applied 5-fold cross validation and our best performing classifier achieved 95.60% precision, 79.82% recall, 87.0% F-score, and 99.59% classification accuracy in identifying the critical recommendation sentences in radiology reports.

A Land System representation for global assessments and land-use modeling.

PubMed

van Asselen, Sanneke; Verburg, Peter H

2012-10-01

Current global scale land-change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land-use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human-environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi-)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land-use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land-change models that include the human drivers of land change are best parameterized at sub-global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions. © 2012 Blackwell Publishing Ltd.

Orthogonal stimulus-response compatibility effects emerge even when the stimulus position is task irrelevant.

PubMed

Nishimura, Akio; Yokosawa, Kazuhiko

2006-06-01

The above-right/below-left mapping advantage with vertical stimuli and horizontal responses is known as the orthogonal stimulus-response compatibility (SRC) effect. We investigated whether the orthogonal SRC effect emerges with irrelevant stimulus dimensions. In Experiment 1, participants responded with a right or left key press to the colour of the stimulus presented above or below the fixation. We observed an above-right/below-left advantage (orthogonal Simon effect). In Experiment 2, we manipulated the polarity in the response dimension by varying the horizontal location of the response set. The orthogonal Simon effect decreased and even reversed as the left response code became more positive. This result provides evidence for the automatic activation of the positive and negative response codes by the corresponding positive and negative stimulus codes. These findings extended the orthogonal SRC effect based on coding asymmetry to an irrelevant stimulus dimension.

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

PubMed

Ortega, Julio; Asensio-Cubero, Javier; Gan, John Q; Ortiz, Andrés

2016-07-15

Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

Age-related differences in the neural basis of the subjective vividness of memories: Evidence from multivoxel pattern classification

PubMed Central

Johnson, Marcia K.; Kuhl, Brice A.; Mitchell, Karen J.; Ankudowich, Elizabeth; Durbin, Kelly A.

2016-01-01

Although older adults often show reduced episodic memory accuracy, their ratings of the subjective vividness of their memories often equal or even exceed those of young adults. Such findings suggest that young and older adults may differentially access and/or weight different kinds of information in making vividness judgments. We examined this idea using multivoxel pattern classification of fMRI data to measure category representations while participants saw and remembered pictures of objects and scenes. Consistent with our hypothesis, there were age-related differences in how category representations related to the subjective sense of vividness. During remembering, older adults’ vividness ratings were more related, relative to young adults’, to category representations in prefrontal cortex. In contrast, young adults’ vividness ratings were more related, relative to older adults, to category representations in parietal cortex. In addition, category representations were more correlated among posterior regions in young than older adults, whereas correlations between PFC and posterior regions did not differ between the two groups. Together, these results are consistent with the idea that young and older adults differentially weight different types of information in assessing subjective vividness of their memories. PMID:25855004

Priming Effects Associated with the Hierarchical Levels of Classification Systems

ERIC Educational Resources Information Center

Loehrlein, Aaron J.

2012-01-01

The act of categorization produces conceptual representations in memory while knowledge organization (KO) systems provide conceptual representations that are used in information storage and retrieval systems. Previous research has explored how KO systems can be designed to resemble the user's internal conceptual structures. However, the more…

University Students' Unions: Changing Functions, a UK and Comparative Perspective

ERIC Educational Resources Information Center

Guan, Lu; Cole, Michael; Worthington, Frank

2016-01-01

In this article, we consider the functions of students' unions (SUs) through a UK case study. First, a functional classification of educational representation; wider representation; delivery of commercial services and faciliating a student community is outlined. Second, we specify a theoretical framework in terms of neo-liberalism and therapeutic…

A general representation for axial-flow fans and turbines

NASA Technical Reports Server (NTRS)

Perl, W; Tucker, M

1945-01-01

A general representation of fan and turbine arrangements on a single classification chart is presented that is made possible by a particular definition of the stage of an axial-flow fan or turbine. Several unconventional fan and turbine arrangements are indicated and the applications of these arrangements are discussed.

Attachment Representation of Institutionalized Children in Japan

ERIC Educational Resources Information Center

Katsurada, Emiko

2007-01-01

This exploratory study represents one of the first attachment investigations of Japanese children who have been institutionalized. Mental representation of attachment was assessed using George and Solomon's (1990, 1996, 2000) Attachment Doll Play Classification System of the Bretherton et al. (1990) doll play story stems. Participants were 32…

A deep learning pipeline for Indian dance style classification

NASA Astrophysics Data System (ADS)

Dewan, Swati; Agarwal, Shubham; Singh, Navjyoti

2018-04-01

In this paper, we address the problem of dance style classification to classify Indian dance or any dance in general. We propose a 3-step deep learning pipeline. First, we extract 14 essential joint locations of the dancer from each video frame, this helps us to derive any body region location within the frame, we use this in the second step which forms the main part of our pipeline. Here, we divide the dancer into regions of important motion in each video frame. We then extract patches centered at these regions. Main discriminative motion is captured in these patches. We stack the features from all such patches of a frame into a single vector and form our hierarchical dance pose descriptor. Finally, in the third step, we build a high level representation of the dance video using the hierarchical descriptors and train it using a Recurrent Neural Network (RNN) for classification. Our novelty also lies in the way we use multiple representations for a single video. This helps us to: (1) Overcome the RNN limitation of learning small sequences over big sequences such as dance; (2) Extract more data from the available dataset for effective deep learning by training multiple representations. Our contributions in this paper are three-folds: (1) We provide a deep learning pipeline for classification of any form of dance; (2) We prove that a segmented representation of a dance video works well with sequence learning techniques for recognition purposes; (3) We extend and refine the ICD dataset and provide a new dataset for evaluation of dance. Our model performs comparable or better in some cases than the state-of-the-art on action recognition benchmarks.

Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography.

PubMed

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

2015-01-01

Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns.

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

PubMed

Li, Wei; Cao, Peng; Zhao, Dazhe; Wang, Junbo

2016-01-01

Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

The imaging performance of the SRC on Mars Express

USGS Publications Warehouse

Oberst, J.; Schwarz, G.; Behnke, T.; Hoffmann, H.; Matz, K.-D.; Flohrer, J.; Hirsch, H.; Roatsch, T.; Scholten, F.; Hauber, E.; Brinkmann, B.; Jaumann, R.; Williams, D.; Kirk, R.; Duxbury, T.; Leu, C.; Neukum, G.

2008-01-01

The Mars Express spacecraft carries the pushbroom scanner high-resolution stereo camera (HRSC) and its added imaging subsystem super resolution channel (SRC). The SRC is equipped with its own optical system and a 1024??1024 framing sensor. SRC produces snapshots with 2.3 m ground pixel size from the nominal spacecraft pericenter height of 250 km, which are typically embedded in the central part of the large HRSC scenes. The salient features of the SRC are its light-weight optics, a reliable CCD detector, and high-speed read-out electronics. The quality and effective visibility of details in the SRC images unfortunately falls short of what has been expected. In cases where thermal balance cannot be reached, artifacts, such as blurring and "ghost features" are observed in the images. In addition, images show large numbers of blemish pixels and are plagued by electronic noise. As a consequence, we have developed various image improving algorithms, which are discussed in this paper. While results are encouraging, further studies of image restoration by dedicated processing appear worthwhile. The SRC has obtained more than 6940 images at the time of writing (1 September 2007), which often show fascinating details in surface morphology. SRC images are highly useful for a variety of applications in planetary geology, for studies of the Mars atmosphere, and for astrometric observations of the Martian satellites. This paper will give a full account of the design philosophy, technical concept, calibration, operation, integration with HRSC, and performance, as well as science accomplishments of the SRC. ?? 2007 Elsevier Ltd. All rights reserved.

Supervised classification of continental shelf sediment off western Donegal, Ireland

NASA Astrophysics Data System (ADS)

Monteys, X.; Craven, K.; McCarron, S. G.

2017-12-01

Managing human impacts on marine ecosystems requires natural regions to be identified and mapped over a range of hierarchically nested scales. In recent years (2000-present) the Irish National Seabed Survey (INSS) and Integrated Mapping for the Sustainable Development of Ireland's Marine Resources programme (INFOMAR) (Geological Survey Ireland and Marine Institute collaborations) has provided unprecedented quantities of high quality data on Ireland's offshore territories. The increasing availability of large, detailed digital representations of these environments requires the application of objective and quantitative analyses. This study presents results of a new approach for sea floor sediment mapping based on an integrated analysis of INFOMAR multibeam bathymetric data (including the derivatives of slope and relative position), backscatter data (including derivatives of angular response analysis) and sediment groundtruthing over the continental shelf, west of Donegal. It applies a Geographic-Object-Based Image Analysis software package to provide a supervised classification of the surface sediment. This approach can provide a statistically robust, high resolution classification of the seafloor. Initial results display a differentiation of sediment classes and a reduction in artefacts from previously applied methodologies. These results indicate a methodology that could be used during physical habitat mapping and classification of marine environments.

Functions in Biological Kind Classification

ERIC Educational Resources Information Center

Lombrozo, Tania; Rehder, Bob

2012-01-01

Biological traits that serve functions, such as a zebra's coloration (for camouflage) or a kangaroo's tail (for balance), seem to have a special role in conceptual representations for biological kinds. In five experiments, we investigate whether and why functional features are privileged in biological kind classification. Experiment 1…

Towards Implementation of a Generalized Architecture for High-Level Quantum Programming Language

NASA Astrophysics Data System (ADS)

Ameen, El-Mahdy M.; Ali, Hesham A.; Salem, Mofreh M.; Badawy, Mahmoud

2017-08-01

This paper investigates a novel architecture to the problem of quantum computer programming. A generalized architecture for a high-level quantum programming language has been proposed. Therefore, the programming evolution from the complicated quantum-based programming to the high-level quantum independent programming will be achieved. The proposed architecture receives the high-level source code and, automatically transforms it into the equivalent quantum representation. This architecture involves two layers which are the programmer layer and the compilation layer. These layers have been implemented in the state of the art of three main stages; pre-classification, classification, and post-classification stages respectively. The basic building block of each stage has been divided into subsequent phases. Each phase has been implemented to perform the required transformations from one representation to another. A verification process was exposed using a case study to investigate the ability of the compiler to perform all transformation processes. Experimental results showed that the efficacy of the proposed compiler achieves a correspondence correlation coefficient about R ≈ 1 between outputs and the targets. Also, an obvious achievement has been utilized with respect to the consumed time in the optimization process compared to other techniques. In the online optimization process, the consumed time has increased exponentially against the amount of accuracy needed. However, in the proposed offline optimization process has increased gradually.

Computer assisted optical biopsy for colorectal polyps

NASA Astrophysics Data System (ADS)

Navarro-Avila, Fernando J.; Saint-Hill-Febles, Yadira; Renner, Janis; Klare, Peter; von Delius, Stefan; Navab, Nassir; Mateus, Diana

2017-03-01

We propose a method for computer-assisted optical biopsy for colorectal polyps, with the final goal of assisting the medical expert during the colonoscopy. In particular, we target the problem of automatic classification of polyp images in two classes: adenomatous vs non-adenoma. Our approach is based on recent advancements in convolutional neural networks (CNN) for image representation. In the paper, we describe and compare four different methodologies to address the binary classification task: a baseline with classical features and a Random Forest classifier, two methods based on features obtained from a pre-trained network, and finally, the end-to-end training of a CNN. With the pre-trained network, we show the feasibility of transferring a feature extraction mechanism trained on millions of natural images, to the task of classifying adenomatous polyps. We then demonstrate further performance improvements when training the CNN for our specific classification task. In our study, 776 polyp images were acquired and histologically analyzed after polyp resection. We report a performance increase of the CNN-based approaches with respect to both, the conventional engineered features and to a state-of-the-art method based on videos and 3D shape features.

Src kinases in chondrosarcoma chemoresistance and migration: dasatinib sensitises to doxorubicin in TP53 mutant cells

PubMed Central

van Oosterwijk, J G; van Ruler, M A J H; Briaire-de Bruijn, I H; Herpers, B; Gelderblom, H; van de Water, B; Bovée, J V M G

2013-01-01

Background: Chondrosarcomas are malignant cartilage-forming tumours of bone. Because of their resistance to conventional chemotherapy and radiotherapy, currently no treatment strategies exist for unresectable and metastatic chondrosarcoma. Previously, PI3K/AKT/GSK3β and Src kinase pathways were shown to be activated in chondrosarcoma cell lines. Our aim was to investigate the role of these kinases in chemoresistance and migration in chondrosarcoma in relation to TP53 mutation status. Methods: We used five conventional and three dedifferentiated chondrosarcoma cell lines and investigated the effect of PI3K/AKT/GSK3β pathway inhibition (enzastaurin) and Src pathway inhibition (dasatinib) in chemoresistance using WST assay and live cell imaging with AnnexinV staining. Immunohistochemistry on tissue microarrays (TMAs) containing 157 cartilaginous tumours was performed for Src family members. Migration assays were performed with the RTCA xCelligence System. Results: Src inhibition was found to overcome chemoresistance, to induce apoptosis and to inhibit migration. Cell lines with TP53 mutations responded better to combination therapy than wild-type cell lines (P=0.002). Tissue microarray immunohistochemistry confirmed active Src (pSrc) signalling, with Fyn being most abundantly expressed (76.1%). Conclusion: These results strongly indicate Src family kinases, in particular Fyn, as a potential target for the treatment of inoperable and metastatic chondrosarcomas, and to sensitise for doxorubicin especially in the presence of TP53 mutations. PMID:23922104

An epitope localized in c-Src negative regulatory domain is a potential marker in early stage of colonic neoplasms.

PubMed

Sakai, T; Kawakatsu, H; Fujita, M; Yano, J; Owada, M K

1998-02-01

In previous work, we established a new monoclonal antibody that specifically recognizes the active form of c-Src tyrosine kinase (Kawakatsu et al, 1996). To determine whether c-Src is active in colorectal tumorigenesis, we examined the expression of an active form of c-Src in human normal mucosa, hyperplastic polyps, adenomas, and adenocarcinomas. The tissue distribution of the active form of c-Src was studied by immunohistochemistry using this antibody, termed Clone 28. Among 66 cases of adenoma tested, 61 (92%) showed positive staining (adenoma with mild atypia, 3 of 3; adenoma with moderate atypia, 38 of 42; adenoma with severe atypia, 20 of 21). In contrast to the frequent and intense staining in adenomas, adenocarcinoma showed weak staining with less frequency in 4 of 16 (25%) cases. The number of specimens with positive staining in well- and moderately differentiated adenocarcinomas was limited to an early stage. The active form of c-Src mainly localized to the nuclear membrane and the perinuclear region. These results provide the first direct evidence that the activation of c-Src appears to be an early event in colonic carcinogenesis in situ. The findings of the present study thus allow us to propose a molecular mechanism involving c-Src activation in the process of malignant transformation of the human colonic neoplastic cells.

Interfacing with USSTRATCOM and UTTR during Stardust Earth Return

NASA Technical Reports Server (NTRS)

Jefferson, David C.; Baird, Darren T.; Cangahuala, Laureano A.; Lewis, George D.

2006-01-01

The Stardust Sample Return Capsule separated from the main spacecraft four hours prior to atmospheric entry. Between this time and the time at which the SRC touched down at the Utah Test and Training Range, two organizations external to JPL were involved in tracking the Sample Return Capsule. Orbit determination for the Stardust spacecraft during deep space cruise, the encounters of asteroid Annefrank and comet Wild 2, and the final approach to Earth used X-band radio metric Doppler and range data obtained through the Deep Space Network. The SRC lacked the electronics needed for coherently transponded radio metric tracking, so the DSN was not able to track the SRC after it separated from the main spacecraft. Although the expected delivery accuracy at atmospheric entry was well within the capability needed to target the SRC to the desired ground location, it was still desirable to obtain direct knowledge of the SRC trajectory in case of anomalies. For this reason U.S. Strategic Command was engaged to track the SRC between separation and atmospheric entry. Once the SRC entered the atmosphere, ground sensors at UTTR were tasked to acquire the descending SRC and maintain track during the descent in order to determine the landing location, to which the ground recovery team was then directed. This paper discusses organizational interfaces, data products, and delivery schedules, and the actual tracking operations are described.