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Trudy Mooren, Yoke Rabaia, Suzan Mitwalli, Relinde Reiffers, Rosemarijn Koenen, Marguerithe de Man
BACKGROUND: Families with a child who has a disability have extra difficulties, particularly when services are hard to reach or less available. In a collaborative project, the Institute of Community and Public Health, the Palestinian community-based rehabilitation programme, and international non-governmental organisations cooperated to share and develop expertise and knowledge on increasing families' resilience through establishing family groups. This contribution focuses on the use of the Multi-Family Approach (MFA) in a Palestinian context...
February 21, 2018: Lancet
Bente Wold, Maurice B Mittelmark
AIMS: This debate paper traces the development of innovative methods for undertaking health promotion research with a socialecological orientation, with a few examples drawn from 30 years of research on adolescent health promotion research at the University of Bergen. CONCLUSION: We aim to show how the social-ecological model is becoming more evident as a guide to research, using three cases that illustrate progress and potential. The first case is the Norwegian part of the European Network of Health Promoting Schools...
February 2018: Scandinavian Journal of Public Health
Heidi L Tessmer, Kimihito Ito, Ryosuke Omori
To estimate and predict the transmission dynamics of respiratory viruses, the estimation of the basic reproduction number, R 0 , is essential. Recently, approximate Bayesian computation methods have been used as likelihood free methods to estimate epidemiological model parameters, particularly R 0 . In this paper, we explore various machine learning approaches, the multi-layer perceptron, convolutional neural network, and long-short term memory, to learn and estimate the parameters. Further, we compare the accuracy of the estimates and time requirements for machine learning and the approximate Bayesian computation methods on both simulated and real-world epidemiological data from outbreaks of influenza A(H1N1)pdm09, mumps, and measles...
2018: Frontiers in Microbiology
Laurie E Powers, Ann Fullerton, Jessica Schmidt, Sarah Geenen, Molly Oberweiser-Kennedy, JoAnn Dohn, May Nelson, Rosemary Iavanditti, Jennifer Blakeslee
Research clearly documents the serious challenges and poor outcomes experienced by many young people exiting foster care, as well as compounded disparities for the high percentage of youth in care who are identified with disabilities and/or mental health challenges. However, very little research has been conducted to specify or validate effective models for improving the transition trajectories of youth exiting care. Evidence suggests the My Life self-determination enhancement model offers a promising approach for supporting youths' self-determined and positive transition to adulthood...
February 2018: Children and Youth Services Review
Dexter R F Irvine
Perceptual learning, improvement in discriminative ability as a consequence of training, is one of the forms of sensory system plasticity that has driven profound changes in our conceptualization of sensory cortical function. Psychophysical and neurophysiological studies of auditory perceptual learning have indicated that the characteristics of the learning, and by implication the nature of the underlying neural changes, are highly task specific. Some studies in animals have indicated that recruitment of neurons to the population responding to the training stimuli, and hence an increase in the so-called cortical "area of representation" of those stimuli, is the substrate of improved performance, but such changes have not been observed in other studies...
March 12, 2018: Hearing Research
Nils Gessert, Matthias Schlüter, Alexander Schlaefer
Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework...
March 10, 2018: Medical Image Analysis
Julian Betancur, Frederic Commandeur, Mahsaw Motlagh, Tali Sharir, Andrew J Einstein, Sabahat Bokhari, Mathews B Fish, Terrence D Ruddy, Philipp Kaufmann, Albert J Sinusas, Edward J Miller, Timothy M Bateman, Sharmila Dorbala, Marcelo Di Carli, Guido Germano, Yuka Otaki, Balaji K Tamarappoo, Damini Dey, Daniel S Berman, Piotr J Slomka
OBJECTIVES: The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD). BACKGROUND: Deep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI. METHODS: A total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress99m Tc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites, with invasive coronary angiography performed within 6 months of MPI, were studied...
March 12, 2018: JACC. Cardiovascular Imaging
Christopher J Martyniuk
Environmental science has benefited a great deal from omics-based technologies. High-throughput toxicology has defined adverse outcome pathways (AOPs), prioritized chemicals of concern, and identified novel actions of environmental chemicals. While many of these approaches are conducted under rigorous laboratory conditions, a significant challenge has been the interpretation of omics data in "real-world" exposure scenarios. Clarity in the interpretation of these data limits their use in environmental monitoring programs...
March 8, 2018: Environmental Toxicology and Pharmacology
Patrick McAllister, Huiru Zheng, Raymond Bond, Anne Moorhead
Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101...
February 17, 2018: Computers in Biology and Medicine
Piotr Klukowski, Michal Augoff, Maciej Zieba, Maciej Drwal, Adam Gonczarek, Michal J Walczak
Motivation: Automated selection of signals in protein NMR spectra, known as peak picking, has been studied for over 20 years, nevertheless existing peak picking methods are still largely deficient. Accurate and precise automated peak picking would accelerate the structure calculation, and analysis of dynamics and interactions of macromolecules. Recent advancement in handling big data, together with an outburst of machine learning techniques, offer an opportunity to tackle the peak picking problem substantially faster than manual picking and on par with human accuracy...
March 14, 2018: Bioinformatics
Felix Weissenberger, Marcelo Matheus Gauy, Johannes Lengler, Florian Meier, Angelika Steger
In computational neuroscience, synaptic plasticity rules are often formulated in terms of firing rates. The predominant description of in vivo neuronal activity, however, is the instantaneous rate (or spiking probability). In this article we resolve this discrepancy by showing that fluctuations of the membrane potential carry enough information to permit a precise estimate of the instantaneous rate in balanced networks. As a consequence, we find that rate based plasticity rules are not restricted to neuronal activity that is stable for hundreds of milliseconds to seconds, but can be carried over to situations in which it changes every few milliseconds...
March 15, 2018: Scientific Reports
Dezső Ribli, Anna Horváth, Zsuzsa Unger, Péter Pollner, István Csabai
In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions...
March 15, 2018: Scientific Reports
James P Roach, Aleksandra Pidde, Eitan Katz, Jiaxing Wu, Nicolette Ognjanovski, Sara J Aton, Michal R Zochowski
Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations' spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections...
March 15, 2018: Proceedings of the National Academy of Sciences of the United States of America
Laura B Tucker, Alexander G Velosky, Joseph T McCabe
Acquired traumatic brain injury (TBI) is frequently accompanied by persistent cognitive symptoms, including executive function disruptions and memory deficits. The Morris Water Maze (MWM) is the most widely-employed laboratory behavioral test for assessing cognitive deficits in rodents after experimental TBI. Numerous protocols exist for performing the test, which has shown great robustness in detecting learning and memory deficits in rodents after infliction of TBI. We review applications of the MWM for the study of cognitive deficits following TBI in pre-clinical studies, describing multiple ways in which the test can be employed to examine specific aspects of learning and memory...
March 12, 2018: Neuroscience and Biobehavioral Reviews
Shuchao Pang, Mehmet A Orgun, Zhezhou Yu
BACKGROUND AND OBJECTIVES: The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images...
May 2018: Computer Methods and Programs in Biomedicine
Eli Gibson, Wenqi Li, Carole Sudre, Lucas Fidon, Dzhoshkun I Shakir, Guotai Wang, Zach Eaton-Rosen, Robert Gray, Tom Doel, Yipeng Hu, Tom Whyntie, Parashkev Nachev, Marc Modat, Dean C Barratt, Sébastien Ourselin, M Jorge Cardoso, Tom Vercauteren
BACKGROUND AND OBJECTIVES: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups...
May 2018: Computer Methods and Programs in Biomedicine
Yoonjin Nah, Na-Young Shin, Sehjung Yi, Seung-Koo Lee, Sanghoon Han
Numerous studies have suggested that postpartum women show a decline in cognitive abilities. However, to date, no study has investigated the presence of qualitative alterations in recognition memory processes in postpartum women that may lead to a decline in cognitive ability. To address this issue, we employed the Remember/Know procedure and functional magnetic resonance imaging (fMRI). Behavioral results demonstrated that compared with the matched control (CTRL) group, the postpartum (PP) group endorsed "Remember" less and "Know" more to old items...
March 12, 2018: Neurobiology of Learning and Memory
Hythem Sidky, Jonathan K Whitmer
Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes...
March 14, 2018: Journal of Chemical Physics
Shiri Lev-Ari
We learn language from our social environment, but the more sources we have, the less informative each source is, and therefore, the less weight we ascribe its input. According to this principle, people with larger social networks should give less weight to new incoming information, and should therefore be less susceptible to the influence of new speakers. This paper tests this prediction, and shows that speakers with smaller social networks indeed have more malleable linguistic representations. In particular, they are more likely to adjust their lexical boundary following exposure to a new speaker...
March 12, 2018: Cognition
Akihiro Suzuki, Takashi Morie, Hakaru Tamukoh
This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weights are shared between the synapses of an input and a hidden layer, and between the synapses of a hidden and an output layer. This architecture utilizes less of the limited resources of an FPGA than an architecture which does not share the synapse weights, and reduces the amount of synapse modules used by half...
2018: PloS One
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