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https://www.readbyqxmd.com/read/28938081/sleep-in-insects
#1
Charlotte Helfrich-Förster
Sleep is essential for proper brain function in mammals and insects. During sleep, animals are disconnected from the external world; they show high arousal thresholds and changed brain activity. Sleep deprivation results in a sleep rebound. Research using the fruit fly, Drosophila melanogaster, has helped us understand the genetic and neuronal control of sleep. Genes involved in sleep control code for ion channels, factors influencing neurotransmission and neuromodulation, and proteins involved in the circadian clock...
September 22, 2017: Annual Review of Entomology
https://www.readbyqxmd.com/read/28938072/machine-learning-for-silver-nanoparticle-electron-transfer-property-prediction
#2
Baichuan Sun, Michael Fernandez, Amanda S Barnard
Nanoparticles exhibit diverse structural and morphological features that are often inter-connected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanoparticles is developed for the energy of Fermi level, which can be tuned to improve the transfer of electrons in a variety of applications. By combining different machine learning analytical algorithms, including k-mean, logistic regression and random forest with electronic structure simulations, we find that the degree of twinning (characterised by the fraction of hexagonal closed packed atoms) and the population of {111} facet (characterized by a surface coordination number of 9) are strongly correlated to the Fermi energy of silver nanoparticles...
September 22, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28937298/probing-the-toxicity-of-nanoparticles-a-unified-in-silico-machine-learning-model-based-on-perturbation-theory
#3
Riccardo Concu, Valeria V Kleandrova, Alejandro Speck-Planche, M Natália D S Cordeiro
Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological systems and their ecosystems. Toxicity testing is an essential step for assessing the potential risks of the NPs, but the experimental assays are often very expensive and usually too slow to flag the number of NPs that may cause adverse effects. In silico models centered on quantitative structure-activity/toxicity relationships (QSAR/QSTR) are alternative tools that have become valuable supports to risk assessment, rationalizing the search for safer NPs...
September 22, 2017: Nanotoxicology
https://www.readbyqxmd.com/read/28937281/tracking-by-detection-of-surgical-instruments-in-minimally-invasive-surgery-via-the-convolutional-neural-network-deep-learning-based-method
#4
Zijian Zhao, Sandrine Voros, Ying Weng, Faliang Chang, Ruijian Li
BACKGROUND: Worldwide propagation of minimally invasive surgeries (MIS) is hindered by their drawback of indirect observation and manipulation, while monitoring of surgical instruments moving in the operated body required by surgeons is a challenging problem. Tracking of surgical instruments by vision-based methods is quite lucrative, due to its flexible implementation via software-based control with no need to modify instruments or surgical workflow. METHODS: A MIS instrument is conventionally split into a shaft and end-effector portions, while a 2D/3D tracking-by-detection framework is proposed, which performs the shaft tracking followed by the end-effector one...
September 22, 2017: Computer Assisted Surgery (Abingdon, England)
https://www.readbyqxmd.com/read/28936173/combining-spect-and-quantitative-eeg-analysis-for-the-automated-differential-diagnosis-of-disorders-with-amnestic-symptoms
#5
Yvonne Höller, Arne C Bathke, Andreas Uhl, Nicolas Strobl, Adelheid Lang, Jürgen Bergmann, Raffaele Nardone, Fabio Rossini, Harald Zauner, Margarita Kirschner, Amirhossein Jahanbekam, Eugen Trinka, Wolfgang Staffen
Single photon emission computed tomography (SPECT) and Electroencephalography (EEG) have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity) in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD), 69 patients with depressive cognitive impairment (DCI), 71 patients with amnestic mild cognitive impairment (aMCI), and 41 patients with amnestic subjective cognitive complaints (aSCC)...
2017: Frontiers in Aging Neuroscience
https://www.readbyqxmd.com/read/28936168/mining-time-resolved-functional-brain-graphs-to-an-eeg-based-chronnectomic-brain-aged-index-cbai
#6
Stavros I Dimitriadis, Christos I Salis
The brain at rest consists of spatially and temporal distributed but functionally connected regions that called intrinsic connectivity networks (ICNs). Resting state electroencephalography (rs-EEG) is a way to characterize brain networks without confounds associated with task EEG such as task difficulty and performance. A novel framework of how to study dynamic functional connectivity under the notion of functional connectivity microstates (FCμstates) and symbolic dynamics is further discussed. Furthermore, we introduced a way to construct a single integrated dynamic functional connectivity graph (IDFCG) that preserves both the strength of the connections between every pair of sensors but also the type of dominant intrinsic coupling modes (DICM)...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28934163/a-parameter-communication-optimization-strategy-for-distributed-machine-learning-in-sensors
#7
Jilin Zhang, Hangdi Tu, Yongjian Ren, Jian Wan, Li Zhou, Mingwei Li, Jue Wang, Lifeng Yu, Chang Zhao, Lei Zhang
In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT)...
September 21, 2017: Sensors
https://www.readbyqxmd.com/read/28932991/automated-reference-free-detection-of-motion-artifacts-in-magnetic-resonance-images
#8
Thomas Küstner, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Bin Yang, Fritz Schick, Sergios Gatidis
OBJECTIVES: Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture. MATERIALS AND METHODS: T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation...
September 20, 2017: Magma
https://www.readbyqxmd.com/read/28932980/understanding-clinical-mammographic-breast-density-assessment-a-deep-learning-perspective
#9
Aly A Mohamed, Yahong Luo, Hong Peng, Rachel C Jankowitz, Shandong Wu
Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view...
September 20, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28932187/linking-network-activity-to-synaptic-plasticity-during-sleep-hypotheses-and-recent-data
#10
REVIEW
Carlos Puentes-Mestril, Sara J Aton
Research findings over the past two decades have supported a link between sleep states and synaptic plasticity. Numerous mechanistic hypotheses have been put forth to explain this relationship. For example, multiple studies have shown structural alterations to synapses (including changes in synaptic volume, spine density, and receptor composition) indicative of synaptic weakening after a period of sleep. Direct measures of neuronal activity and synaptic strength support the idea that a period of sleep can reduce synaptic strength...
2017: Frontiers in Neural Circuits
https://www.readbyqxmd.com/read/28932180/hardware-efficient-on-line-learning-through-pipelined-truncated-error-backpropagation-in-binary-state-networks
#11
Hesham Mostafa, Bruno Pedroni, Sadique Sheik, Gert Cauwenberghs
Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28931480/a-literature-review-of-mentorship-programs-in-academic-nursing
#12
REVIEW
Lorelli Nowell, Jill M Norris, Kelly Mrklas, Deborah E White
BACKGROUND: Nursing education institutions have issued recurring, global calls for mentorship; however, evidence-based program development guidance is scarce. To date, there are no comprehensive syntheses of current mentorship models, objectives, and program components to inform mentorship program development in nursing academia. The purpose of this review is to identify published articles that (1) described models for mentoring programs for academic nurses, and (2) described the objectives and core components of these programs...
September 2017: Journal of Professional Nursing: Official Journal of the American Association of Colleges of Nursing
https://www.readbyqxmd.com/read/28931302/learning-metabolic-brain-networks-in-mci-and-ad-by-robustness-and-leave-one-out-analysis-an-fdg-pet-study
#13
Zhijun Yao, Bin Hu, Xuejiao Chen, Yuanwei Xie, Jürg Gutknecht, Dennis Majoe
This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD...
January 1, 2017: American Journal of Alzheimer's Disease and Other Dementias
https://www.readbyqxmd.com/read/28931014/phonological-memory-in-sign-language-relies-on-the-visuomotor-neural-system-outside-the-left-hemisphere-language-network
#14
Yuji Kanazawa, Kimihiro Nakamura, Toru Ishii, Toshihiko Aso, Hiroshi Yamazaki, Koichi Omori
Sign language is an essential medium for everyday social interaction for deaf people and plays a critical role in verbal learning. In particular, language development in those people should heavily rely on the verbal short-term memory (STM) via sign language. Most previous studies compared neural activations during signed language processing in deaf signers and those during spoken language processing in hearing speakers. For sign language users, it thus remains unclear how visuospatial inputs are converted into the verbal STM operating in the left-hemisphere language network...
2017: PloS One
https://www.readbyqxmd.com/read/28930053/a-descriptive-study-of-the-prevalence-and-typology-of-alcohol-related-posts-in-an-online-social-network-for-smoking-cessation
#15
Amy M Cohn, Kang Zhao, Sarah Cha, Xi Wang, Michael S Amato, Jennifer L Pearson, George D Papandonatos, Amanda L Graham
OBJECTIVE: Alcohol use and problem drinking are associated with smoking relapse and poor smoking-cessation success. User-generated content in online social networks for smoking cessation provides an opportunity to understand the challenges and treatment needs of smokers. This study used machine-learning text classification to identify the prevalence, sentiment, and social network correlates of alcohol-related content in the social network of a large online smoking-cessation program, BecomeAnEX...
September 2017: Journal of Studies on Alcohol and Drugs
https://www.readbyqxmd.com/read/28929850/the-emerging-health-leaders-network-experience-reflections-and-lessons-learned-from-a-grassroots-movement
#16
Emily Gruenwoldt, Adrienne Hagen Lyster
The Emerging Health Leaders (EHL) network was established in 2006 to enhance the leadership capacity of early careerists in the health sector in Canada. Ten years later, the development of the next generation of health leaders continues to be a focus for system leaders. Despite the rhetoric, financial investments in leadership development remain stagnant. This article describes the network's experience in supporting the professional development needs of aspiring leaders across Canada. Successes and challenges regarding the development of the network are discussed, as are the results from a recent benchmarking survey, which identify remaining gaps and priorities for aspiring young leaders...
May 2017: Healthcare Management Forum
https://www.readbyqxmd.com/read/28929797/automated-early-detection-of-drops-in-commercial-egg-production-using-neural-networks
#17
I Ramírez-Morales, E Fernández-Blanco, D Rivero, A Pazos
1. The purpose of this work was to support decision making in poultry farms by performing automatic early detection of anomalies in egg production. 2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied. 3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. 4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of cost-sensitive learning...
September 20, 2017: British Poultry Science
https://www.readbyqxmd.com/read/28929121/differential-and-combined-effects-of-physical-activity-profiles-and-prohealth-behaviors-on-diabetes-prevalence-among-blacks-and-whites-in-the-us-population-a-novel-bayesian-belief-network-machine-learning-analysis
#18
Azizi A Seixas, Dwayne A Henclewood, Aisha T Langford, Samy I McFarlane, Ferdinand Zizi, Girardin Jean-Louis
The current study assessed the prevalence of diabetes across four different physical activity lifestyles and infer through machine learning which combinations of physical activity, sleep, stress, and body mass index yield the lowest prevalence of diabetes in Blacks and Whites. Data were extracted from the National Health Interview Survey (NHIS) dataset from 2004-2013 containing demographics, chronic diseases, and sleep duration (N = 288,888). Of the total sample, 9.34% reported diabetes (where the prevalence of diabetes was 12...
2017: Journal of Diabetes Research
https://www.readbyqxmd.com/read/28929098/the-degree-of-one-health-implementation-in-the-west-nile-virus-integrated-surveillance-in-northern-italy-2016
#19
REVIEW
Giulia Paternoster, Laura Tomassone, Marco Tamba, Mario Chiari, Antonio Lavazza, Mauro Piazzi, Anna R Favretto, Giacomo Balduzzi, Alessandra Pautasso, Barbara R Vogler
West Nile virus (WNV) is endemic in the Po valley area, Northern Italy, and within the legal framework of the national plan for the surveillance of human vector-borne diseases, WNV surveillance has over time been implemented. The surveillance plans are based on the transdisciplinary and trans-sectorial collaboration between regional institutions involved in public, animal, and environmental health. This integrated surveillance targets mosquitoes, wild birds, humans, and horses and aims at early detecting the viral circulation and reducing the risk of infection in the human populations...
2017: Frontiers in Public Health
https://www.readbyqxmd.com/read/28928947/puzzles-in-modern-biology-v-why-are-genomes-overwired
#20
Steven A Frank
Many factors affect eukaryotic gene expression. Transcription factors, histone codes, DNA folding, and noncoding RNA modulate expression. Those factors interact in large, broadly connected regulatory control networks. An engineer following classical principles of control theory would design a simpler regulatory network. Why are genomes overwired? Neutrality or enhanced robustness may lead to the accumulation of additional factors that complicate network architecture. Dynamics progresses like a ratchet. New factors get added...
2017: F1000Research
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