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Learning networks

Zuzana Novak, Mary Aglipay, Nick Barrowman, Keith O Yeates, Miriam H Beauchamp, Jocelyn Gravel, Stephen B Freedman, Isabelle Gagnon, Gerard Gioia, Kathy Boutis, Emma Burns, Andrée-Anne Ledoux, Martin H Osmond, Roger L Zemek
Importance: Persistent postconcussion symptoms (PPCS) pose long-term challenges and can negatively affect patients' health-related quality of life (HRQoL). To date, no large comprehensive study has addressed the association between PPCS and HRQoL. Objectives: To determine the association between HRQoL and PPCS at 4 weeks after concussion and assess the degree of impairment of HRQoL in the subsequent 12 weeks. Design, Setting, and Participants: In a prospective, multicenter cohort study (Predicting Persistent Postconcussive Problems in Pediatrics [5P]) from August 14, 2013, to September 30, 2014, children aged 5 to 18 years who presented to the emergency department within 48 hours after head injury and were considered to have an acute concussion were enrolled across 9 pediatric emergency departments within the Pediatric Emergency Research Canada Network...
October 24, 2016: JAMA Pediatrics
Cristian Axenie, Christoph Richter, Jörg Conradt
Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a complex scene. In this work we propose a novel sensory processing architecture, inspired by the distributed macro-architecture of the mammalian cortex. The underlying computation is performed by a network of computational maps, each representing a different sensory quantity...
October 20, 2016: Sensors
Qichao Zhang, Dongbin Zhao, Ding Wang
In this paper, the robust control problem for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-based control method. First, the robust control problem is transformed into a corresponding optimal control problem with an augmented control and an appropriate cost function. Under the event-based mechanism, we prove that the solution of the optimal control problem can asymptotically stabilize the uncertain system with an adaptive triggering condition. That is, the designed event-based controller is robust to the original uncertain system...
October 18, 2016: IEEE Transactions on Neural Networks and Learning Systems
Sherie Ma, Craig M Smith, Anna Blasiak, Andrew L Gundlach
Relaxin-3 is a member of a superfamily of structurally-related peptides that includes relaxin and insulin-like peptide hormones. Soon after the discovery of the relaxin-3 gene, relaxin-3 was identified as an abundant neuropeptide in brain with a distinctive topographical distribution within a small number of GABA neuron populations that is well conserved across species. Relaxin-3 is thought to exert its biological actions through a single class-A GPCR - relaxin-family peptide receptor 3 (RXFP3). Class-A comprises GPCRs for relaxin-3 and insulin-like peptide-5 and other peptides such as orexin and the monoamine transmitters...
October 23, 2016: British Journal of Pharmacology
Ningyu Zhang, Huajun Chen, Jiaoyan Chen, Xi Chen
With the design and development of smart cities, opportunities as well as challenges arise at the moment. For this purpose, lots of data need to be obtained. Nevertheless, circumstances vary in different cities due to the variant infrastructures and populations, which leads to the data sparsity. In this paper, we propose a transfer learning method for urban waterlogging disaster analysis, which provides the basis for traffic management agencies to generate proactive traffic operation strategies in order to alleviate congestion...
2016: Computational Intelligence and Neuroscience
Sanna Rönkä, Anu Katainen
BACKGROUND: The non-medical use of prescription drugs is a growing phenomenon associated with increasing health-related harms. However, little is known about the drivers of this process among illicit drug users. Our aim is to show how the qualities of pharmaceutical drugs, pharmaceutical related knowledge, online communities sharing this knowledge and medical professionals mediate and transform the consumption behaviour related to pharmaceutical drugs. METHODS: The data consist of discussion threads from an online drug use forum...
October 18, 2016: International Journal on Drug Policy
Lars Marstaller, Hana Burianová, David C Reutens
Safety learning describes the ability to learn that certain cues predict the absence of a dangerous or threatening event. Although incidental observations of activity within the default mode network (DMN) during the processing of safety cues have been reported previously, there is as yet no evidence demonstrating that the DMN plays a functional rather than a corollary role in safety learning. Using functional magnetic resonance imaging and a Pavlovian fear conditioning and extinction paradigm, we investigated the neural correlates of danger and safety learning...
October 21, 2016: Human Brain Mapping
Vivian M Nguyen, Nathan Young, Steven J Cooke
Scholars across all disciplines have long been interested in how knowledge moves within and beyond their community of peers. In conservation and natural resource management, however, we are lagging behind. Rapid environmental changes and calls for sustainable management practices mean that we urgently need to be using the best knowledge possible in forming decisions, policies, and practices to protect biodiversity and sustainably manage vulnerable natural resources. While the conservation literature on knowledge exchange (KE) and knowledge mobilization (KMb) has grown in recent years, much of it is based on context-specific case studies...
October 21, 2016: Conservation Biology: the Journal of the Society for Conservation Biology
Mutlu Mete, Unal Sakoglu, Jeffrey S Spence, Michael D Devous, Thomas S Harris, Bryon Adinoff
BACKGROUND: Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented...
October 6, 2016: BMC Bioinformatics
Eve Valera, Aaron Kucyi
Traumatic brain injury (TBI) in women experiencing intimate-partner violence (IPV) is common, and IPV afflicts 30 % of women worldwide. However, the neurobiology and related sequelae of these TBIs have never been systematically examined. Consequently, TBI treatments are typically absent and IPV interventions are inadequate. There has been a call for a comprehensive assessment of IPV-related TBIs and their relationship to aspects of women's cognitive and neural functioning. In response, we examined brain-network organization associated with TBI and its cognitive effects using clinical interviews and neuropsychological measures as well as structural and functional Magnetic Resonance Imaging (fMRI) in women experiencing IPV-related TBI...
October 20, 2016: Brain Imaging and Behavior
Erich Kummerfeld, Joseph Ramsey
Many scientific research programs aim to learn the causal structure of real world phenomena. This learning problem is made more difficult when the target of study cannot be directly observed. One strategy commonly used by social scientists is to create measurable "indicator" variables that covary with the latent variables of interest. Before leveraging the indicator variables to learn about the latent variables, however, one needs a measurement model of the causal relations between the indicators and their corresponding latents...
2016: KDD: Proceedings
Eric Smart, Adeeta Aulakh, Carolyn McDougall, Patty Rigby, Gillian King
PURPOSE: Identify strategies youth perceive will optimize their engagement in goal pursuit in life skills and transition programs using an engagement framework involving affective, cognitive, and behavioral components. METHODS: A qualitative descriptive design was used. Two semi-structured interviews were conducted with seven youth. The first was informed by a prior observation session, and the second occurred after the program ended and explored youths' perceptions of whether and how their engagement changed...
October 21, 2016: Disability and Rehabilitation
Hasseeb Azzawi, Jingyu Hou, Yong Xiang, Russul Alanni
Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models...
October 2016: IET Systems Biology
Yoonsik Shim, Andrew Philippides, Kevin Staras, Phil Husbands
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events...
October 2016: PLoS Computational Biology
Eric N Beck, Quincy J Almeida
BACKGROUND: Parkinson's disease (PD) impairs control of well-learned movements. Movement control improvements are found when individuals complete tasks while focusing attention externally on manipulating an object, which is argued to occur due to automatic processing associated with well-learned movements. Focusing attention internally (on movements of ones' limbs) is believed to involve conscious control networks, and hinders movement performance. Previous work has found that an external focus of attention improved postural stability in individuals with PD (compared to internal), but this was when patients were taking dopamine medication, which modulates basal ganglia functioning responsible for well-learned movements...
October 6, 2016: Physical Therapy
Hans Lehrach
Every human is unique. We differ in our genomes, environment, behavior, disease history, and past and current medical treatment-a complex catalog of differences that often leads to variations in the way each of us responds to a particular therapy. We argue here that true personalization of drug therapies will rely on "virtual patient" models based on a detailed characterization of the individual patient by molecular, imaging, and sensor techniques. The models will be based, wherever possible, on the molecular mechanisms of disease processes and drug action but can also expand to hybrid models including statistics/machine learning/artificial intelligence-based elements trained on available data to address therapeutic areas or therapies for which insufficient information on mechanisms is available...
September 2016: Dialogues in Clinical Neuroscience
Salma Jamal, Sukriti Goyal, Asheesh Shanker, Abhinav Grover
BACKGROUND: Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics...
October 18, 2016: BMC Genomics
Peter A Lewis, Naomi F Tutticci, Clint Douglas, Genevieve Gray, Yvonne Osborne, Katie Evans, Catherine M Nielson
The professional development of nurse academics has been high on the agenda in many of the Asia-Pacific's developing countries including Vietnam. In collaboration with the Vietnamese Nurses Association, an Australian university designed and delivered a distance learning programme (DLP). The DLP sought to build academic capacity with a specific focus on the skills required to develop, implement and deliver a new national nursing curriculum. This paper will describe the design and delivery of the DLP as well as report on programme evaluation survey findings...
October 8, 2016: Nurse Education in Practice
Peng Jiang, Zhixin Hu, Jun Liu, Shanen Yu, Feng Wu
Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process...
October 13, 2016: Sensors
Yun Lin, Chao Wang, Jiaxing Wang, Zheng Dou
Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks...
October 12, 2016: Sensors
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