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https://www.readbyqxmd.com/read/29228510/left-behind-and-left-out-the-impact-of-the-school-environment-on-young-people-with-continence-problems
#1
Katie Whale, Helen Cramer, Carol Joinson
OBJECTIVES: To explore the impact of the secondary school environment on young people with continence problems. DESIGN: In-depth qualitative semi-structured interviews. METHODS: We interviewed 20 young people aged 11-19 years (11 female and nine male) with continence problems (daytime wetting, bedwetting, and/or soiling). Interviews were conducted by Skype (n = 11) and telephone (n = 9). Transcripts were analysed using inductive thematic analysis...
December 11, 2017: British Journal of Health Psychology
https://www.readbyqxmd.com/read/29226437/on-effective-graphic-communication-of-health-inequality-considerations-for-health-policy-researchers
#2
Yukiko Asada, Hannah Abel, Chris Skedgel, Grace Warner
Policy Points: Effective graphs can be a powerful tool in communicating health inequality. The choice of graphs is often based on preferences and familiarity rather than science. According to the literature on graph perception, effective graphs allow human brains to decode visual cues easily. Dot charts are easier to decode than bar charts, and thus they are more effective. Dot charts are a flexible and versatile way to display information about health inequality. Consistent with the health risk communication literature, the captions accompanying health inequality graphs should provide a numerical, explicitly calculated description of health inequality, expressed in absolute and relative terms, from carefully thought-out perspectives...
December 2017: Milbank Quarterly
https://www.readbyqxmd.com/read/29220313/how-does-the-low-rank-matrix-decomposition-help-internal-and-external-learnings-for-super-resolution
#3
Shuang Wang, Bo Yue, Xuefeng Liang, Licheng Jiao
Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem. To address this issue, we analyze the attributes of two methodologies and find two observations of their recovered details: 1) they are complementary in both feature space and image plane and 2) they distribute sparsely in the spatial space. These inspire us to propose a low-rank solution which effectively integrates two learning methods and then achieves a superior result. To fit this solution, the internal learning method and the external learning method are tailored to produce multiple preliminary results...
March 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29220307/from-winner-takes-all-to-winners-share-all-exploiting-the-information-capacity-in-temporal-codes
#4
Melika Payvand, Luke Theogarajan
In this letter, we have implemented and compared two neural coding algorithms in the networks of spiking neurons: Winner-takes-all (WTA) and winners-share-all (WSA). Winners-Share-All exploits the code space provided by the temporal code by training a different combination of [Formula: see text] out of [Formula: see text] neurons to fire together in response to different patterns, while WTA uses a one-hot-coding to respond to distinguished patterns. Using WSA, the maximum value of [Formula: see text] in order to maximize information capacity using [Formula: see text] output neurons was theoretically determined and utilized...
December 8, 2017: Neural Computation
https://www.readbyqxmd.com/read/29219084/an-improved-bayesian-network-method-for-reconstructing-gene-regulatory-network-based-on-candidate-auto-selection
#5
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/29218877/automated-disease-cohort-selection-using-word-embeddings-from-electronic-health-records
#6
Benjamin S Glicksberg, Riccardo Miotto, Kipp W Johnson, Khader Shameer, Li Li, Rong Chen, Joel T Dudley
Accurate and robust cohort definition is critical to biomedical discovery using Electronic Health Records (EHR). Similar to prospective study designs, high quality EHR-based research requires rigorous selection criteria to designate case/control status particular to each disease. Electronic phenotyping algorithms, which are manually built and validated per disease, have been successful in filling this need. However, these approaches are time-consuming, leading to only a relatively small amount of algorithms for diseases developed...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218871/extracting-a-biologically-relevant-latent-space-from-cancer-transcriptomes-with-variational-autoencoders
#7
Gregory P Way, Casey S Greene
The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumor's response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218869/chemical-reaction-vector-embeddings-towards-predicting-drug-metabolism-in-the-human-gut-microbiome
#8
Emily K Mallory, Ambika Acharya, Stefano E Rensi, Peter J Turnbaugh, Roselie A Bright, Russ B Altman
Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29216413/quantum-machine-learning-in-chemical-compound-space
#9
Anatole von Lilienfeld
Rather than numerically solving the computationally demanding equations of quantum or statistical mechanics, machine learning methods can infer approximate solutions, interpolating previously acquired property data sets of molecules and materials. The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.
December 7, 2017: Angewandte Chemie
https://www.readbyqxmd.com/read/29215876/deep-learning-of-atomically-resolved-scanning-transmission-electron-microscopy-images-chemical-identification-and-tracking-local-transformations
#10
Maxim Ziatdinov, Ondrej Dyck, Artem Maksov, Xufan Li, Xiahan Sang, Kai Xiao, Raymond R Unocic, Rama Vasudevan, Stephen Jesse, Sergei V Kalinin
Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This progress has been accompanied by an exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent...
December 7, 2017: ACS Nano
https://www.readbyqxmd.com/read/29212468/a-comparison-of-graph-and-kernel-based-omics-data-integration-algorithms-for-classifying-complex-traits
#11
Kang K Yan, Hongyu Zhao, Herbert Pang
BACKGROUND: High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking...
December 6, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29209058/learning-the-value-of-information-and-reward-over-time-when-solving-exploration-exploitation-problems
#12
Irene Cogliati Dezza, Angela J Yu, Axel Cleeremans, William Alexander
To flexibly adapt to the demands of their environment, animals are constantly exposed to the conflict resulting from having to choose between predictably rewarding familiar options (exploitation) and risky novel options, the value of which essentially consists of obtaining new information about the space of possible rewards (exploration). Despite extensive research, the mechanisms that subtend the manner in which animals solve this exploitation-exploration dilemma are still poorly understood. Here, we investigate human decision-making in a gambling task in which the informational value of each trial and the reward potential were separately manipulated...
December 5, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29207235/a-data-driven-perspective-on-the-hierarchical-assembly-of-molecular-structures
#13
Lorenzo Boninsegna, Ralf Banisch, Cecilia Clementi
Macromolecular systems are composed of a very large number of atomic degrees of freedom. There is strong evidence suggesting that structural changes occurring in large biomolecular systems at long timescale dynamics may be captured by models coarser than atomistic, although a suitable or optimal coarse-graining is a priori unknown. Here we propose a systematic approach to learning a coarse representation of a macromolecule from microscopic simulation data. In particular, the definition of effective coarse variables is achieved by partitioning the degrees of freedom both in the structural (physical) space, and in the conformational space...
December 5, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/29202265/kernel-bayesian-art-and-artmap
#14
Naoki Masuyama, Chu Kiong Loo, Farhan Dawood
Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable...
November 10, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29202135/latest-local-adaptive-and-sequential-training-for-tissue-segmentation-of-isointense-infant-brain-mr-images
#15
Li Wang, Yaozong Gao, Gang Li, Feng Shi, Weili Lin, Dinggang Shen
Accurate segmentation of isointense infant (~6 months of age) brain MRIs is of great importance, however, a very challenging task, due to extremely low tissue contrast caused by ongoing myelination processes. In this work, we propose a novel learning method based on Local AdapTivE and Sequential Training (LATEST) for segmentation. Specifically, random forest technique is employed to train a local classifier (a single decision tree) for each voxel in the common space based on the neighboring training samples from atlases...
2017: Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging: MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers
https://www.readbyqxmd.com/read/29197412/does-motivation-matter-in-upper-limb-rehabilitation-after-stroke-armeosenso-reward-study-protocol-for-a-randomized-controlled-trial
#16
Mario Widmer, Jeremia P Held, Frieder Wittmann, Olivier Lambercy, Kai Lutz, Andreas R Luft
BACKGROUND: Fifty percent of all stroke survivors remain with functional impairments of their upper limb. While there is a need to improve the effectiveness of rehabilitative training, so far no new training approach has proven to be clearly superior to conventional therapy. As training with rewarding feedback has been shown to improve motor learning in humans, it is hypothesized that rehabilitative arm training could be enhanced by rewarding feedback. In this paper, we propose a trial protocol investigating rewards in the form of performance feedback and monetary gains as ways to improve effectiveness of rehabilitative training...
December 2, 2017: Trials
https://www.readbyqxmd.com/read/29192805/social-participation-for-people-with-communication-disability-in-coffee-shops-and-restaurants-is-a-human-right
#17
Clare Carroll, Nicole Guinan, Libby Kinneen, Denise Mulheir, Hannah Loughnane, Orla Joyce, Elaine Higgins, Emma Boyle, Margaret Mullarney, Rena Lyons
Although Article 19 of the Universal Declaration of Human Rights states that "everyone has a right to freedom of opinion and expression", for people with communication disability this may not be a reality. This commentary shares a practical example of how people with communication disabilities together with speech-language pathology (SLP) students, academics and clinical staff co-designed and co-implemented a Communication Awareness Training Programme for catering staff to enable communication access in coffee shops and restaurants...
December 1, 2017: International Journal of Speech-language Pathology
https://www.readbyqxmd.com/read/29192618/how-children-perceive-the-acoustic-environment-of-their-school
#18
Karl Jonas Brännström, Erika Johansson, Daniel Vigertsson, David J Morris, Birgitta Sahlén, Viveka Lyberg-Åhlander
OBJECTIVE: Children's own ratings and opinions on their schools sound environments add important information on noise sources. They can also provide information on how to further improve and optimize children's learning situation in their classrooms. This study reports on the Swedish translation and application of an evidence-based questionnaire that measures how children perceive the acoustic environment of their school. STUDY DESIGN: The Swedish version was made using a back-to-back translation...
March 2017: Noise & Health
https://www.readbyqxmd.com/read/29187831/learning-where-to-look-for-high-value-improves-decision-making-asymmetrically
#19
Jaron T Colas, Joy Lu
Decision making in any brain is imperfect and costly in terms of time and energy. Operating under such constraints, an organism could be in a position to improve performance if an opportunity arose to exploit informative patterns in the environment being searched. Such an improvement of performance could entail both faster and more accurate (i.e., reward-maximizing) decisions. The present study investigated the extent to which human participants could learn to take advantage of immediate patterns in the spatial arrangement of serially presented foods such that a region of space would consistently be associated with greater subjective value...
2017: Frontiers in Psychology
https://www.readbyqxmd.com/read/29187548/estimating-properties-of-the-fast-and-slow-adaptive-processes-during-sensorimotor-adaptation
#20
Scott Tyler Albert, Reza Shadmehr
Experience of a prediction error recruits multiple motor learning processes: some that learn strongly from error but have weak retention, some that learn weakly from error but exhibit strong retention. These processes are not generally observable, but are inferred from their collective influence on behavior. Is there a robust way to uncover the hidden processes? A standard approach is to consider a state-space model where the hidden states change following experience of error, and then fit the model to the measured data by minimizing the squared error between measurement and model prediction...
November 29, 2017: Journal of Neurophysiology
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