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https://www.readbyqxmd.com/read/28330163/probing-an-optimal-class-distribution-for-enhancing-prediction-and-feature-characterization-of-plant-virus-encoded-rna-silencing-suppressors
#1
Abhigyan Nath, Karthikeyan Subbiah
To counter the host RNA silencing defense mechanism, many plant viruses encode RNA silencing suppressor proteins. These groups of proteins share very low sequence and structural similarities among them, which consequently hamper their annotation using sequence similarity-based search methods. Alternatively the machine learning-based methods can become a suitable choice, but the optimal performance through machine learning-based methods is being affected by various factors such as class imbalance, incomplete learning, selection of inappropriate features, etc...
June 2016: 3 Biotech
https://www.readbyqxmd.com/read/28328516/anrad-a-neuromorphic-anomaly-detection-framework-for-massive-concurrent-data-streams
#2
Qiuwen Chen, Ryan Luley, Qing Wu, Morgan Bishop, Richard W Linderman, Qinru Qiu
The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic...
March 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#3
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28323747/supporting-a-youth-with-cerebellar-ataxia-into-adolescence
#4
Veronica Meneses, Zurisadai Gonzalez-Castillo, Veronica B Edgar, Marilyn Augustyn
Zoe, a 13-year-old white girl, presents as a new patient to your pediatric clinic with complaints of frequent emesis, anxiety, and learning problems, and previous diagnosis of cerebellar ataxia. Parents accompany Zoe and state, "it is really hard for her to go out, she gets sick and falls easily." She was born full term by vaginal delivery without complications. Given globally delayed milestones, she received early intervention services. Feeding problems began at infancy, including gastroesophageal reflux and aspiration pneumonia...
March 17, 2017: Journal of Developmental and Behavioral Pediatrics: JDBP
https://www.readbyqxmd.com/read/28322513/site-of-metabolism-prediction-based-on-ab-initio-derived-atom-representations
#5
Arndt R Finkelmann, Andreas H Göller, Gisbert Schneider
Machine learning models for site of metabolism (SoM) prediction offer the ability to identify metabolic soft spots in low molecular weight drug molecules at low computational cost and enable data-based reactivity prediction. SoM prediction is an atom classification problem. Successful construction of machine learning models requires atom representations that capture the reactivity-determining features of a potential reaction site. We have developed a descriptor scheme that characterizes an atom's steric and electronic environment and its relative location in the molecular structure...
March 21, 2017: ChemMedChem
https://www.readbyqxmd.com/read/28321248/patch-based-multiple-instance-learning-algorithm-for-object-tracking
#6
Zhenjie Wang, Lijia Wang, Hua Zhang
To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28320846/working-memory-load-strengthens-reward-prediction-errors
#7
Anne G E Collins, Brittany Ciullo, Michael J Frank, David Badre
Reinforcement learning in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we asked how working memory and incremental reinforcement learning processes interact to guide human learning. Working memory load was manipulated by varying the number of stimuli to be learned across blocks...
March 20, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28320667/learning-correspondence-structures-for-person-re-identification
#8
Weiyao Lin, Yang Shen, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, Ke Lu
This paper addresses the problem of handling spatial misalignments due to camera-view changes or humanpose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or humanpose variation in individual images. We further introduce a global constraint-based matching process...
March 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320666/rgbd-salient-object-detection-via-deep-fusion
#9
Liangqiong Qu, Shengfeng He, Jiawei Zhang, Jiandong Tian, Yandong Tang, Qingxiong Yang
Numerous efforts have been made to design various low-level saliency cues for RGBD saliency detection, such as color and depth contrast features as well as background and color compactness priors. However, how these low-level saliency cues interact with each other and how they can be effectively incorporated to generate a master saliency map remain challenging problems. In this paper, we design a new convolutional neural network (CNN) to automatically learn the interaction mechanism for RGBD salient object detection...
March 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320663/iterative-re-constrained-group-sparse-face-recognition-with-adaptive-weights-learning
#10
Jianwei Zheng, Ping Yang, Shengyong Chen, Guojiang Shen, Wanliang Wang
In this paper, we consider the robust face recognition problem via iterative re-constrained group sparse classifier with adaptive weights learning (IRGSC). Specifically, we propose a group sparse representation classification (GSRC) approach in which weighted features and groups are collaboratively adopted to encode more structure information and discriminative information than other regression based methods. In addition, we derive an efficient algorithm to optimize the proposed objective function, and theoretically prove the convergence...
March 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28320267/modulating-effect-of-cytokines-on-mechanisms-of-synaptic-plasticity-in-the-brain
#11
REVIEW
S G Levin, O V Godukhin
After accumulation of data showing that resident brain cells (neurons, astrocytes, and microglia) produce mediators of the immune system, such as cytokines and their receptors under normal physiological conditions, a critical need emerged for investigating the role of these mediators in cognitive processes. The major problem for understanding the functional role of cytokines in the mechanisms of synaptic plasticity, de novo neurogenesis, and learning and memory is the small number of investigated cytokines...
March 2017: Biochemistry. Biokhimii︠a︡
https://www.readbyqxmd.com/read/28319275/mrf-ann-a-machine-learning-approach-for-automated-er-scoring-of-breast-cancer-immunohistochemical-images
#12
T Mungle, S Tewary, D K DAS, I Arun, B Basak, S Agarwal, R Ahmed, S Chatterjee, C Chakraborty
Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells...
March 20, 2017: Journal of Microscopy
https://www.readbyqxmd.com/read/28318904/dual-memory-neural-networks-for-modeling-cognitive-activities-of-humans-via-wearable-sensors
#13
Sang-Woo Lee, Chung-Yeon Lee, Dong-Hyun Kwak, Jung-Woo Ha, Jeonghee Kim, Byoung-Tak Zhang
Wearable devices, such as smart glasses and watches, allow for continuous recording of everyday life in a real world over an extended period of time or lifelong. This possibility helps better understand the cognitive behavior of humans in real life as well as build human-aware intelligent agents for practical purposes. However, modeling the human cognitive activity from wearable-sensor data stream is challenging because learning new information often results in loss of previously acquired information, causing a problem known as catastrophic forgetting...
February 20, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28318505/machine-learning-in-the-prediction-of-cardiac-epicardial-and-mediastinal-fat-volumes
#14
É O Rodrigues, V H A Pinheiro, P Liatsis, A Conci
We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlation, thus alleviating the requirement for manual or automatic segmentation of both fat volumes. Instead, segmenting just one of them suffices, while the volume of the other may be predicted fairly precisely. The correlation coefficient obtained by the Rotation Forest algorithm using MLP Regressor for predicting the mediastinal fat based on the epicardial fat was 0...
February 24, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28316639/feature-extraction-and-classification-of-ehg-between-pregnancy-and-labour-group-using-hilbert-huang-transform-and-extreme-learning-machine
#15
Lili Chen, Yaru Hao
Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM)...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28316616/a-novel-graph-constructor-for-semisupervised-discriminant-analysis-combined-low-rank-and-k-nearest-neighbor-graph
#16
Baokai Zu, Kewen Xia, Yongke Pan, Wenjia Niu
Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28315694/incorporation-of-autopsy-case-based-learning-into-phd-graduate-education-a-novel-approach-to-bridging-the-bench-to-bedside-gap
#17
Erin G Brooks, Joanne M Thornton, Erik A Ranheim, Zsuzsanna Fabry
Given the current rapid expansion of biological knowledge and the challenges of translating that knowledge into clinical practice, finding effective methods of teaching graduate students clinical medicine concepts has become even more critical. The utility of autopsy in medical student and resident education has been well-established. Multiple studies have reported it to be a helpful means of teaching anatomy, pathophysiology, clinical problem-solving skills, and medical diagnostic techniques. While various models of training Ph...
March 15, 2017: Human Pathology
https://www.readbyqxmd.com/read/28315145/educational-intervention-on-undergraduate-cancer-awareness-and-self-directed-learning
#18
Lih-Lian Hwang
Traditional lecture-based learning (LBL) can increase cancer awareness in undergraduates. However, because of the rapidly changing knowledge base in medicine, undergraduates must develop skills required for lifelong self-directed learning (SDL). Problem-based learning (PBL) has been suggested as an SDL approach. This study used a nonequivalent control group with a pretest-posttest design for comparing PBL and LBL for their effectiveness in increasing cancer awareness and SDL among nonmedicine or nonnursing major undergraduates in a health-related general education course...
March 17, 2017: Journal of Cancer Education: the Official Journal of the American Association for Cancer Education
https://www.readbyqxmd.com/read/28314344/problem-solving-in-older-cancer-patients-a-case-study-based-reference-and-learning-resource
#19
(no author information available yet)
No abstract text is available yet for this article.
March 2017: Anticancer Research
https://www.readbyqxmd.com/read/28304359/social-networking-sites-and-addiction-ten-lessons-learned
#20
REVIEW
Daria J Kuss, Mark D Griffiths
Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented...
March 17, 2017: International Journal of Environmental Research and Public Health
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