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https://www.readbyqxmd.com/read/28344719/residents-perceptions-of-simulation-as-a-clinical-learning-approach
#1
Catharine M Walsh, Ankit Garg, Stella L Ng, Fenny Goyal, Samir C Grover
BACKGROUND: Simulation is increasingly being integrated into medical education; however, there is little research into trainees' perceptions of this learning modality. We elicited trainees' perceptions of simulation-based learning, to inform how simulation is developed and applied to support training. METHODS: We conducted an instrumental qualitative case study entailing 36 semi-structured one-hour interviews with 12 residents enrolled in an introductory simulation-based course...
February 2017: Canadian Medical Education Journal
https://www.readbyqxmd.com/read/28344110/toward-a-systematic-exploration-of-nano-bio-interactions
#2
REVIEW
Xue Bai, Fang Liu, Yin Liu, Cong Li, Shenqing Wang, Hongyu Zhou, Wenyi Wang, Hao Zhu, Dave Winkler, Bing Yan
Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability...
March 24, 2017: Toxicology and Applied Pharmacology
https://www.readbyqxmd.com/read/28342382/words-cluster-phonetically-beyond-phonotactic-regularities
#3
Isabelle Dautriche, Kyle Mahowald, Edward Gibson, Anne Christophe, Steven T Piantadosi
Recent evidence suggests that cognitive pressures associated with language acquisition and use could affect the organization of the lexicon. On one hand, consistent with noisy channel models of language (e.g., Levy, 2008), the phonological distance between wordforms should be maximized to avoid perceptual confusability (a pressure for dispersion). On the other hand, a lexicon with high phonological regularity would be simpler to learn, remember and produce (e.g., Monaghan et al., 2011) (a pressure for clumpiness)...
March 22, 2017: Cognition
https://www.readbyqxmd.com/read/28342214/real-time-fmri-neurofeedback-in-adolescents-with-attention-deficit-hyperactivity-disorder
#4
Analucia A Alegria, Melanie Wulff, Helen Brinson, Gareth J Barker, Luke J Norman, Daniel Brandeis, Daniel Stahl, Anthony S David, Eric Taylor, Vincent Giampietro, Katya Rubia
Attention Deficit Hyperactivity Disorder (ADHD) is associated with poor self-control, underpinned by inferior fronto-striatal deficits. Real-time functional magnetic resonance neurofeedback (rtfMRI-NF) allows participants to gain self-control over dysregulated brain regions. Despite evidence for beneficial effects of electrophysiological-NF on ADHD symptoms, no study has applied the spatially superior rtfMRI-NF neurotherapy to ADHD. A randomized controlled trial tested the efficacy of rtfMRI-NF of right inferior prefrontal cortex (rIFG), a key region that is compromised in ADHD and upregulated with psychostimulants, on improvement of ADHD symptoms, cognition, and inhibitory fMRI activation...
March 25, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28341594/itraq-based-proteomics-analysis-of-hippocampus-in-spatial-memory-deficiency-rats-induced-by-simulated-microgravity
#5
Tingmei Wang, Hailong Chen, Ke Lv, Guohua Ji, Yongliang Zhang, Yanli Wang, Yinghui Li, Lina Qu
It has been demonstrated that simulated microgravity (SM) may lead to cognitive dysfunction. However, the underlying mechanism remains unclear. In present study, tail-suspension (30°) rat was employed to explore the effects of 28 days of SM on hippocampus-dependent learning and memory capability and the underlying mechanisms. We found that 28-day tail-suspension rats displayed decline of learning and memory ability in Morris water maze (MWM) test. Using iTRAQ-based proteomics analysis, a total of 4774 proteins were quantified in hippocampus...
March 21, 2017: Journal of Proteomics
https://www.readbyqxmd.com/read/28336938/a-pso-based-multi-objective-multi-label-feature-selection-method-in-classification
#6
Yong Zhang, Dun-Wei Gong, Xiao-Yan Sun, Yi-Nan Guo
Feature selection is an important data preprocessing technique in multi-label classification. Although a large number of studies have been proposed to tackle feature selection problem, there are a few cases for multi-label data. This paper studies a multi-label feature selection algorithm using an improved multi-objective particle swarm optimization (PSO), with the purpose of searching for a Pareto set of non-dominated solutions (feature subsets). Two new operators are employed to improve the performance of the proposed PSO-based algorithm...
March 23, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28333955/producing-or-reproducing-reasoning-socratic-dialog-is-very-effective-but-only-for-a-few
#7
Andrea Paula Goldin, Olivia Pedroncini, Mariano Sigman
Successful communication between a teacher and a student is at the core of pedagogy. A well known example of a pedagogical dialog is 'Meno', a socratic lesson of geometry in which a student learns (or 'discovers') how to double the area of a given square 'in essence, a demonstration of Pythagoras' theorem. In previous studies we found that after engaging in the dialog participants can be divided in two kinds: those who can only apply a rule to solve the problem presented in the dialog and those who can go beyond and generalize that knowledge to solve any square problems...
2017: PloS One
https://www.readbyqxmd.com/read/28333637/multi-scale-multi-feature-context-modeling-for-scene-recognition-in-the-semantic-manifold
#8
Xinhang Song, Shuqiang Jiang, Luis Herranz
Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, etc). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own cooccurrence patterns that are consistent across the images in that category...
March 22, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28333584/unsupervised-2d-dimensionality-reduction-with-adaptive-structure-learning
#9
Xiaowei Zhao, Feiping Nie, Sen Wang, Jun Guo, Pengfei Xu, Xiaojiang Chen
In recent years, unsupervised two-dimensional (2D) dimensionality reduction methods for unlabeled large-scale data have made progress. However, performance of these degrades when the learning of similarity matrix is at the beginning of the dimensionality reduction process. A similarity matrix is used to reveal the underlying geometry structure of data in unsupervised dimensionality reduction methods. Because of noise data, it is difficult to learn the optimal similarity matrix. In this letter, we propose a new dimensionality reduction model for 2D image matrices: unsupervised 2D dimensionality reduction with adaptive structure learning (DRASL)...
March 23, 2017: Neural Computation
https://www.readbyqxmd.com/read/28331847/learning-parsimonious-classification-rules-from-gene-expression-data-using-bayesian-networks-with-local-structure
#10
Jonathan Lyle Lustgarten, Jeya Balaji Balasubramanian, Shyam Visweswaran, Vanathi Gopalakrishnan
The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules...
March 2017: Data (Basel)
https://www.readbyqxmd.com/read/28331639/emerging-synaptic-molecules-as-candidates-in-the-etiology-of-neurological-disorders
#11
REVIEW
Viviana I Torres, Daniela Vallejo, Nibaldo C Inestrosa
Synapses are complex structures that allow communication between neurons in the central nervous system. Studies conducted in vertebrate and invertebrate models have contributed to the knowledge of the function of synaptic proteins. The functional synapse requires numerous protein complexes with specialized functions that are regulated in space and time to allow synaptic plasticity. However, their interplay during neuronal development, learning, and memory is poorly understood. Accumulating evidence links synapse proteins to neurodevelopmental, neuropsychiatric, and neurodegenerative diseases...
2017: Neural Plasticity
https://www.readbyqxmd.com/read/28331463/a-gibbs-sampler-for-learning-dags
#12
Robert J B Goudie, Sach Mukherjee
We propose a Gibbs sampler for structure learning in directed acyclic graph (DAG) models. The standard Markov chain Monte Carlo algorithms used for learning DAGs are random-walk Metropolis-Hastings samplers. These samplers are guaranteed to converge asymptotically but often mix slowly when exploring the large graph spaces that arise in structure learning. In each step, the sampler we propose draws entire sets of parents for multiple nodes from the appropriate conditional distribution. This provides an efficient way to make large moves in graph space, permitting faster mixing whilst retaining asymptotic guarantees of convergence...
April 2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/28327091/knn-mdr-a-learning-approach-for-improving-interactions-mapping-performances-in-genome-wide-association-studies
#13
Sinan Abo Alchamlat, Frédéric Farnir
BACKGROUND: Finding epistatic interactions in large association studies like genome-wide association studies (GWAS) with the nowadays-available large volume of genomic data is a challenging and largely unsolved issue. Few previous studies could handle genome-wide data due to the intractable difficulties met in searching a combinatorial explosive search space and statistically evaluating epistatic interactions given a limited number of samples. Our work is a contribution to this field...
March 21, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28323824/local-structure-preserving-sparse-coding-for-infrared-target-recognition
#14
Jing Han, Jiang Yue, Yi Zhang, Lianfa Bai
Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images...
2017: PloS One
https://www.readbyqxmd.com/read/28323684/shifting-spatial-neglect-with-repeated-line-bisections-possible-role-of-lateralized-attentional-fatigue
#15
Mary Woods, John B Williamson, Keith D White, Charles G Maitland, Kenneth M Heilman
BACKGROUND AND OBJECTIVE: Many patients who have signs of neglect immediately after a right hemisphere stroke remain disabled even when they improve on tests of neglect. Few patients are tested for attentional persistence and fatigue despite their importance in many instrumental activities. To investigate whether stimulus repetition might alter the allocation of attention, we repeatedly tested a patient 16 weeks after she developed hemispatial neglect from a right hemisphere stroke. METHODS: During each of three testing sessions given in 1 day, we asked the patient to bisect 90 lines of two lengths, presented in 30-trial blocks in three locations: left, center, and right of her midsagittal plane, partially counterbalanced across sessions...
March 2017: Cognitive and Behavioral Neurology: Official Journal of the Society for Behavioral and Cognitive Neurology
https://www.readbyqxmd.com/read/28320440/ecological-niche-modeling-of-rabies-in-the-changing-arctic-of-alaska
#16
Falk Huettmann, Emily Elizabeth Magnuson, Karsten Hueffer
BACKGROUND: Rabies is a disease of global significance including in the circumpolar Arctic. In Alaska enzootic rabies persist in northern and western coastal areas. Only sporadic cases have occurred in areas outside of the regions considered enzootic for the virus, such as the interior of the state and urbanized regions. RESULTS: Here we examine the distribution of diagnosed rabies cases in Alaska, explicit in space and time. We use a geographic information system (GIS), 20 environmental data layers and provide a quantitative non-parsimonious estimate of the predicted ecological niche, based on data mining, machine learning and open access data...
March 20, 2017: Acta Veterinaria Scandinavica
https://www.readbyqxmd.com/read/28316616/a-novel-graph-constructor-for-semisupervised-discriminant-analysis-combined-low-rank-and-k-nearest-neighbor-graph
#17
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/28316615/a-robust-shape-reconstruction-method-for-facial-feature-point-detection
#18
Shuqiu Tan, Dongyi Chen, Chenggang Guo, Zhiqi Huang
Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28316614/image-classification-using-biomimetic-pattern-recognition-with-convolutional-neural-networks-features
#19
Liangji Zhou, Qingwu Li, Guanying Huo, Yan Zhou
As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28316586/spatial-and-temporal-high-processing-of-visual-and-auditory-stimuli-in-cervical-dystonia
#20
Gaetana Chillemi, Alessandro Calamuneri, Francesca Morgante, Carmen Terranova, Vincenzo Rizzo, Paolo Girlanda, Maria Felice Ghilardi, Angelo Quartarone
OBJECTIVE: Investigation of spatial and temporal cognitive processing in idiopathic cervical dystonia (CD) by means of specific tasks based on perception in time and space domains of visual and auditory stimuli. BACKGROUND: Previous psychophysiological studies have investigated temporal and spatial characteristics of neural processing of sensory stimuli (mainly somatosensorial and visual), whereas the definition of such processing at higher cognitive level has not been sufficiently addressed...
2017: Frontiers in Neurology
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