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https://www.readbyqxmd.com/read/29231876/consistently-sampled-correlation-filters-with-space-anisotropic-regularization-for-visual-tracking
#1
Guokai Shi, Tingfa Xu, Jie Guo, Jiqiang Luo, Yuankun Li
Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion...
December 12, 2017: Sensors
https://www.readbyqxmd.com/read/29230320/factors-promoting-and-inhibiting-sustained-impact-of-a-mental-health-task-shifting-program-for-hiv-providers-in-ethiopia
#2
D Jerene, M Biru, A Teklu, T Rehman, A Ruff, L Wissow
Background: Task-shifting mental health into general medical care requires more than brief provider training. Generalists need long-term support to master new skills and changes to work context are required to sustain change in the face of competing priorities. We examined program and context factors promoting sustainability of a mental health task-shifting training for hospital-based HIV providers in Ethiopia. Methods: Convergent mixed-methods quasi-experimental study...
2017: Global Mental Health
https://www.readbyqxmd.com/read/29229843/machine-learning-shows-association-between-genetic-variability-in-pparg-and-cerebral-connectivity-in-preterm-infants
#3
Michelle L Krishnan, Zi Wang, Paul Aljabar, Gareth Ball, Ghazala Mirza, Alka Saxena, Serena J Counsell, Joseph V Hajnal, Giovanni Montana, A David Edwards
Preterm infants show abnormal structural and functional brain development, and have a high risk of long-term neurocognitive problems. The molecular and cellular mechanisms involved are poorly understood, but novel methods now make it possible to address them by examining the relationship between common genetic variability and brain endophenotype. We addressed the hypothesis that variability in the Peroxisome Proliferator Activated Receptor (PPAR) pathway would be related to brain development. We employed machine learning in an unsupervised, unbiased, combined analysis of whole-brain diffusion tractography together with genomewide, single-nucleotide polymorphism (SNP)-based genotypes from a cohort of 272 preterm infants, using Sparse Reduced Rank Regression (sRRR) and correcting for ethnicity and age at birth and imaging...
December 11, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29228509/c-learning-a-new-classification-framework-to-estimate-optimal-dynamic-treatment-regimes
#4
Baqun Zhang, Min Zhang
A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error...
December 11, 2017: Biometrics
https://www.readbyqxmd.com/read/29226234/implementation-of-a-clinical-reasoning-course-in-the-internal-medicine-trimester-of-the-final-year-of-undergraduate-medical-training-and-its-effect-on-students-case-presentation-and-differential-diagnostic-skills
#5
Sigrid Harendza, Ingo Krenz, Andreas Klinge, Ulrike Wendt, Matthias Janneck
Background: Clinical reasoning, comprising the processes of clinical thinking, which form the basis of medical decisions, constitutes a central competence in the clinical routine on which diagnostic and therapeutic steps are based. In medical curricula in Germany, clinical reasoning is currently taught explicitly only to a small extend. Therefore, the aim of this project was to develop and implement a clinical reasoning course in the final year of undergraduate medical training. Project description: A clinical reasoning course with six learning units and 18 learning objectives was developed, which was taught by two to four instructors on the basis of 32 paper cases from the clinical practice of the instructors...
2017: GMS Journal for Medical Education
https://www.readbyqxmd.com/read/29226228/seminar-program-for-postgraduate-specialty-training-in-general-practice-proposal-for-a-5-year-thematic-catalogue
#6
Susanne Sommer, Erika Baum, Julia Magez, Jean-Francois Chenot, Gesine Weckmann, Jost Steinhäuser, Susanne Heim, Dagmar Schneider, Stephan Fuchs
Introduction: In different German regions, seminar programs have been conducted for General practice residents. In each region, selection and teaching of learning content is conducted in a different manner. So far, no structured, standardized curriculum has been implemented nationwide. We have investigated, if the development of a common 5-year program of learning topics is conceivable between the different university departments of General practice in Germany. Method: The seminar program working group of the DEGAM (German College of General Practitioners and Family Physicians) has conducted an online survey based on information gathered via preliminary telephone conference (n=7; physicians with postgraduate teaching experience) among all German university departments of General Practice and two non-university teaching institutions, identified via the internet...
2017: GMS Journal for Medical Education
https://www.readbyqxmd.com/read/29226227/online-platform-as-a-tool-to-support-postgraduate-training-in-general-practice-a-case-report
#7
Lorena Dini, Claire Galanski, Susanne Döpfmer, Sabine Gehrke-Beck, Gudrun Bayer, Martin Boeckle, Isabel Micheel, Jasminko Novak, Christoph Heintze
Objective: Physicians in postgraduate training (PPT) in General Practice (GP) typically have very little interaction with their peers, as there is usually only one resident physician working in their respective department or GP office at a given time. Therefore, the online platform KOLEGEA, presented here, aims to support postgraduate training in general practice (PT in GP) in Germany through virtual interaction. Methodology: In 2012, the interdisciplinary research project KOLEGEA set up an online platform that any physicians in PT in GP can use for free after registration with their unitary continuous education number (Einheitliche Fortbildungsnummer, EFN)...
2017: GMS Journal for Medical Education
https://www.readbyqxmd.com/read/29226222/learning-processes-and-learning-problems-in-german-postgraduate-medical-education
#8
Hendrik van den Bussche, Lea Krause-Solberg, Martin Scherer, Stine Ziegler
Objective: In order to evaluate the quality of postgraduate medical education in Germany, we examined how the learning of theoretical and practical competencies is conceptualized and how the learning process takes place in real terms. The training conditions, as perceived by medical residents, are compared with the learning objectives, as stated by the Federal Chamber of Physicians in its regulation on postgraduate education. The analysis is based on the data of the "KarMed" study (abbreviation of "career progression and career breaks among physicians during postgraduate education"), a multicentre cohort study of medical school graduates from seven universities who arre followed until they receive their licenses as specialist physicians...
2017: GMS Journal for Medical Education
https://www.readbyqxmd.com/read/29223507/limitations-in-learning-how-treatment-verifications-fail-and-what-to-do-about-it
#9
Susan Richardson, Bruce Thomadsen
PURPOSE: The purposes of this study were: to provide dialog on why classic incident learning systems have been insufficient for patient safety improvements, discuss failures in treatment verification, and to provide context to the reasons and lessons that can be learned from these failures. METHODS AND MATERIALS: Historically, incident learning in brachytherapy is performed via database mining which might include reading of event reports and incidents followed by incorporating verification procedures to prevent similar incidents...
December 6, 2017: Brachytherapy
https://www.readbyqxmd.com/read/29223012/support-vector-machine-with-dirichlet-feature-mapping
#10
Ali Nedaie, Amir Abbas Najafi
The Support Vector Machine (SVM) is a supervised learning algorithm to analyze data and recognize patterns. The standard SVM suffers from some limitations in nonlinear classification problems. To tackle these limitations, the nonlinear form of the SVM poses a modified machine based on the kernel functions or other nonlinear feature mappings obviating the mentioned imperfection. However, choosing an efficient kernel or feature mapping function is strongly dependent on data structure. Thus, a flexible feature mapping can be confidently applied in different types of data structures without challenging a kernel selection and its tuning...
November 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29222630/-there-s-so-much-to-it-the-ways-physiotherapy-students-and-recent-graduates-experience-practice
#11
S Barradell, T Peseta, S Barrie
Health science courses aim to prepare students for the demands of their chosen profession by learning ways appropriate to that profession and the contexts they will work and live in. Expectations of what students should learn become re-contextualised and translated into entry-level curriculum, with students operating as a connection between what is intended and enacted in curriculum, and required in the real world. Drawing on phenomenology, this paper explores how students understand practice-the collective, purposeful knowing, doing and being of a community-in entry-level physiotherapy programs...
December 8, 2017: Advances in Health Sciences Education: Theory and Practice
https://www.readbyqxmd.com/read/29221459/team-based-learning-tbl-in-the-medical-curriculum-better-than-pbl
#12
Annette Burgess, Jane Bleasel, Inam Haq, Chris Roberts, Roger Garsia, Tomas Robertson, Craig Mellis
BACKGROUND: Internationally, medical schools have long used a variety of approaches to develop hybrid Problem based learning (PBL) curricula. However, Team-based learning (TBL), has gained recent popularity in medical education. TBL maintains the advantages of small group teaching and learning, but in contrast to Problem-based learning (PBL), does not require large numbers of tutors. In 2016, TBL was introduced to Year 1 of the Sydney Medical Program (SMP).This study sought to compare students' perceptions of using TBL in place of PBL...
December 8, 2017: BMC Medical Education
https://www.readbyqxmd.com/read/29219084/an-improved-bayesian-network-method-for-reconstructing-gene-regulatory-network-based-on-candidate-auto-selection
#13
Linlin Xing, Maozu Guo, Xiaoyan Liu, Chunyu Wang, Lei Wang, Yin Zhang
BACKGROUND: The reconstruction of gene regulatory network (GRN) from gene expression data can discover regulatory relationships among genes and gain deep insights into the complicated regulation mechanism of life. However, it is still a great challenge in systems biology and bioinformatics. During the past years, numerous computational approaches have been developed for this goal, and Bayesian network (BN) methods draw most of attention among these methods because of its inherent probability characteristics...
November 17, 2017: BMC Genomics
https://www.readbyqxmd.com/read/29219072/a-deep-auto-encoder-model-for-gene-expression-prediction
#14
Rui Xie, Jia Wen, Andrew Quitadamo, Jianlin Cheng, Xinghua Shi
BACKGROUND: Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE)...
November 17, 2017: BMC Genomics
https://www.readbyqxmd.com/read/29219070/predicting-human-splicing-branchpoints-by-combining-sequence-derived-features-and-multi-label-learning-methods
#15
Wen Zhang, Xiaopeng Zhu, Yu Fu, Junko Tsuji, Zhiping Weng
BACKGROUND: Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. RESULTS: Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences...
December 1, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29219068/predicting-enhancers-with-deep-convolutional-neural-networks
#16
Xu Min, Wanwen Zeng, Shengquan Chen, Ning Chen, Ting Chen, Rui Jiang
BACKGROUND: With the rapid development of deep sequencing techniques in the recent years, enhancers have been systematically identified in such projects as FANTOM and ENCODE, forming genome-wide landscapes in a series of human cell lines. Nevertheless, experimental approaches are still costly and time consuming for large scale identification of enhancers across a variety of tissues under different disease status, making computational identification of enhancers indispensable. RESULTS: To facilitate the identification of enhancers, we propose a computational framework, named DeepEnhancer, to distinguish enhancers from background genomic sequences...
December 1, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29218921/machine-learning-and-deep-analytics-for-biocomputing-call-for-better-explainability
#17
Dragutin Petkovic, Lester Kobzik, Christopher Re
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use information explaining why and how the MLDA approach made its decisions. We believe that much greater effort is needed to address the issue of MLDA explainability because of: 1) the ever increasing use and dependence on MLDA in biocomputing including the need for increased adoption by non-MLD experts; 2) the diversity, complexity and scale of biocomputing data and MLDA algorithms; 3) the emerging importance of MLDA-based decisions in patient care, in daily research, as well as in the development of new costly medical procedures and drugs...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218909/reading-between-the-genes-computational-models-to-discover-function-from-noncoding-dna
#18
Yves A Lussier, Joanne Berghout, Francesca Vitali, Kenneth S Ramos, Maricel Kann, Jason H Moore
Noncoding DNA - once called "junk" has revealed itself to be full of function. Technology development has allowed researchers to gather genome-scale data pointing towards complex regulatory regions, expression and function of noncoding RNA genes, and conserved elements. Variation in these regions has been tied to variation in biological function and human disease. This PSB session tackles the problem of handling, analyzing and interpreting the data relating to variation in and interactions between noncoding regions through computational biology...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218570/pure-correlates-of-exploration-and-exploitation-in-the-human-brain
#19
Tommy C Blanchard, Samuel J Gershman
Balancing exploration and exploitation is a fundamental problem in reinforcement learning. Previous neuroimaging studies of the exploration-exploitation dilemma could not completely disentangle these two processes, making it difficult to unambiguously identify their neural signatures. We overcome this problem using a task in which subjects can either observe (pure exploration) or bet (pure exploitation). Insula and dorsal anterior cingulate cortex showed significantly greater activity on observe trials compared to bet trials, suggesting that these regions play a role in driving exploration...
December 7, 2017: Cognitive, Affective & Behavioral Neuroscience
https://www.readbyqxmd.com/read/29214863/impact-of-a-weight-management-intervention-on-eating-competence-importance-of-measurement-interval-in-protocol-design
#20
Barbara Lohse, Jodi Stotts Krall, Tricia Psota, Penny Kris-Etherton
PURPOSE: To examine changes in eating competence (EC) in 12-month weight loss intervention. DESIGN: Randomized, parallel-arm with weight loss phase (baseline to month 4) and weight-maintenance phase (months 4-12). SETTING: Face-to-face in University classrooms, supervised and self-directed fitness sessions at University fitness center, and home. PARTICIPANTS: Premenopausal, mostly college-educated Pennsylvania women, body mass index >25 (n = 101)...
January 1, 2017: American Journal of Health Promotion: AJHP
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