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1. Right cot, right place, right time: improving the design and organisation of neonatal care networks ? a computer simulation study

Right cot, right place, right time: improving the design and organisation of neonatal care networks ? a computer simulation study Right cot, right place, right time. Using neonatal care data and computer simulation to improve the design and organisation of neonatal care networks. Journals Library An error occurred retrieving content to display, please try again. >> >> >> Page Not Found Page not found (404) Sorry - the page you requested could not be found. Please choose a page from

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2015 NIHR HTA programme

2. Adaptive Computing Optimization in Software-Defined Network-Based Industrial Internet of Things with Fog Computing (PubMed)

Adaptive Computing Optimization in Software-Defined Network-Based Industrial Internet of Things with Fog Computing In recent years, cloud computing and fog computing have appeared one after the other, as promising technologies for augmenting the computing capability of devices locally. By offloading computational tasks to fog servers or cloud servers, the time for task processing decreases greatly. Thus, to guarantee the Quality of Service (QoS) of smart manufacturing systems, fog servers (...) are deployed at network edge to provide fog computing services. In this paper, we study the following problems in a mixed computing system: (1) which computing mode should be chosen for a task in local computing, fog computing or cloud computing? (2) In the fog computing mode, what is the execution sequence for the tasks cached in a task queue? Thus, to solve the problems above, we design a Software-Defined Network (SDN) framework in a smart factory based on an Industrial Internet of Things (IIoT) system

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2018 Sensors (Basel, Switzerland)

3. Cooperative Computing System for Heavy-Computation and Low-Latency Processing in Wireless Sensor Networks (PubMed)

Cooperative Computing System for Heavy-Computation and Low-Latency Processing in Wireless Sensor Networks Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared (...) that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, HeaLow, a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed HeaLow and carried out the practical implementation on real devices. We confirmed the effectiveness of HeaLow through various experiments using the real devices

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2018 Sensors (Basel, Switzerland)

4. Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. (PubMed)

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification (...) , compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN

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2018 PLoS ONE

5. The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility. (PubMed)

The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility. Computational speech segregation attempts to automatically separate speech from noise. This is challenging in conditions with interfering talkers and low signal-to-noise ratios. Recent approaches have adopted deep neural networks and successfully demonstrated speech intelligibility improvements. A selection of components may (...) predicts the distributions of speech and noise for each frequency channel, to a state-of-the-art deep neural network-based architecture. Another improvement of 13.9 percentage points was obtained by changing the learning objective from the ideal binary mask, in which individual time-frequency units are labeled as either speech- or noise-dominated, to the ideal ratio mask, where the units are assigned a continuous value between zero and one. Therefore, both components play significant roles

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2018 PLoS ONE

6. Signaling networks and the feasibility of computational analysis in gastroenteropancreatic neuroendocrine tumors. (PubMed)

Signaling networks and the feasibility of computational analysis in gastroenteropancreatic neuroendocrine tumors. Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are rare diseases with a prevalence that has been increasing over the past three decades. The unsatisfactory outcome of these diseases has encouraged a great amount of research attention and discussion. Currently, to the best of our knowledge, studies on transcriptome screening and molecular mechanisms of the pathogenesis (...) of GEP-NETs are limited. In this review, we have summarized the signaling network of GEP-NETs and the recent meta-analysis-related studies, and we have discussed the potential research direction for GEP-NETs, especially based on computational analysis.Copyright © 2019 Elsevier Ltd. All rights reserved.

2019 Seminars in cancer biology

7. In-Network Computation of the Optimal Weighting Matrix for Distributed Consensus on Wireless Sensor Networks (PubMed)

In-Network Computation of the Optimal Weighting Matrix for Distributed Consensus on Wireless Sensor Networks In a network, a distributed consensus algorithm is fully characterized by its weighting matrix. Although there exist numerical methods for obtaining the optimal weighting matrix, we have not found an in-network implementation of any of these methods that works for all network topologies. In this paper, we propose an in-network algorithm for finding such an optimal weighting matrix.

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2017 Sensors (Basel, Switzerland)

8. Computational Molecular Networks and Network Pharmacology (PubMed)

Computational Molecular Networks and Network Pharmacology 29250548 2018 10 02 2018 10 04 2314-6141 2017 2017 BioMed research international Biomed Res Int Computational Molecular Networks and Network Pharmacology. 7573904 10.1155/2017/7573904 Ning Kang K 0000-0003-3325-5387 Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China. Zhao Xinming X Institute of Science (...) and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA. eng Editorial 2017 11 08 United States Biomed Res Int 101600173 IM Computational Biology Humans Metabolic Networks and Pathways Pharmacology 2017 10 09 2017 10 11 2017 12 19 6 0 2017 12 19 6 0 2018 10 3 6 0 ppublish 29250548 10.1155/2017/7573904 PMC5698785

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2017 BioMed research international

9. Right cot, right place, right time: improving the design and organisation of neonatal care networks ? a computer simulation study

Right cot, right place, right time: improving the design and organisation of neonatal care networks ? a computer simulation study Right cot, right place, right time: improving the design and organisation of neonatal care networks – a computer simulation study Right cot, right place, right time: improving the design and organisation of neonatal care networks – a computer simulation study Allen M, Spencer A, Gibson A, Matthews J, Allwood A, Prosser S, Pitt M Record Status This is a bibliographic (...) record of a published health technology assessment from a member of INAHTA. No evaluation of the quality of this assessment has been made for the HTA database. Citation Allen M, Spencer A, Gibson A, Matthews J, Allwood A, Prosser S, Pitt M. Right cot, right place, right time: improving the design and organisation of neonatal care networks – a computer simulation study. Health Services and Delivery Research 2015; 3(20) Authors' objectives To develop a computer model that could mimic the performance

2015 Health Technology Assessment (HTA) Database.

10. Computational analysis of network activity and spatial reach of sharp wave-ripples. (PubMed)

Computational analysis of network activity and spatial reach of sharp wave-ripples. Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex (...) . To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses from neuronal populations monitored by conventional microelectrodes. In this work, we investigate spatiotemporal characteristics of SPW-Rs and how microelectrode size and distance influence SPW-R recordings using a biophysical model of hippocampus. We also explore contributions from neuronal spikes and synaptic potentials to SPW-Rs based on two different types of network activity. Our study suggests

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2017 PLoS ONE

11. Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network. (PubMed)

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network. Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural (...) sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples

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2017 PLoS ONE

12. A neural network based computational model to predict the output power of different types of photovoltaic cells. (PubMed)

A neural network based computational model to predict the output power of different types of photovoltaic cells. In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order

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2017 PLoS ONE

13. A new computational strategy for identifying essential proteins based on network topological properties and biological information. (PubMed)

A new computational strategy for identifying essential proteins based on network topological properties and biological information. Essential proteins are the proteins that are indispensable to the survival and development of an organism. Deleting a single essential protein will cause lethality or infertility. Identifying and analysing essential proteins are key to understanding the molecular mechanisms of living cells. There are two types of methods for predicting essential proteins (...) several topological properties of the protein-protein interaction (PPI) network. Second, we propose new methods for measuring orthologous information and subcellular localization and a new computational strategy that uses a random forest prediction model to obtain a probability score for the proteins being essential. Finally, we conduct experiments on four different Saccharomyces cerevisiae datasets. The experimental results demonstrate that our strategy for identifying essential proteins outperforms

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2017 PLoS ONE

14. Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis. (PubMed)

Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis. To evaluate tacrolimus as therapeutic option for diabetic nephropathy (DN) based on molecular profile and network-based molecular model comparisons.We generated molecular models representing pathophysiological mechanisms of DN and tacrolimus mechanism of action (MoA) based on literature derived data and transcriptomics datasets. Shared enriched molecular pathways were identified based

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2017 PLoS ONE

15. Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network (PubMed)

Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network (CNN). Our

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2018 Diagnostics

16. Expanding Brain–Computer Interfaces for Controlling Epilepsy Networks: Novel Thalamic Responsive Neurostimulation in Refractory Epilepsy (PubMed)

Expanding Brain–Computer Interfaces for Controlling Epilepsy Networks: Novel Thalamic Responsive Neurostimulation in Refractory Epilepsy Seizures have traditionally been considered hypersynchronous excitatory events and epilepsy has been separated into focal and generalized epilepsy based largely on the spatial distribution of brain regions involved at seizure onset. Epilepsy, however, is increasingly recognized as a complex network disorder that may be distributed and dynamic. Responsive (...) neurostimulation (RNS) is a recent technology that utilizes intracranial electroencephalography (EEG) to detect seizures and delivers stimulation to cortical and subcortical brain structures for seizure control. RNS has particular significance in the clinical treatment of medically refractory epilepsy and brain-computer interfaces in epilepsy. Closed loop RNS represents an important step forward to understand and target nodes in the seizure network. The thalamus is a central network node within several

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2018 Frontiers in neuroscience

17. γ-Aminobutyric Acid Type A Receptor Potentiation Inhibits Learning in a Computational Network Model. (PubMed)

γ-Aminobutyric Acid Type A Receptor Potentiation Inhibits Learning in a Computational Network Model. Propofol produces memory impairment at concentrations well below those abolishing consciousness. Episodic memory, mediated by the hippocampus, is most sensitive. Two potentially overlapping scenarios may explain how γ-aminobutyric acid receptor type A (GABAA) potentiation by propofol disrupts episodic memory-the first mediated by shifting the balance from excitation to inhibition while (...) the second involves disruption of rhythmic oscillations. We use a hippocampal network model to explore these scenarios. The basis for these experiments is the proposal that the brain represents memories as groups of anatomically dispersed strongly connected neurons.A neuronal network with connections modified by synaptic plasticity was exposed to patterned stimuli, after which spiking output demonstrated evidence of stimulus-related neuronal group development analogous to memory formation. The effect

2018 Anesthesiology

18. Computer simulations of the signalling network in FLT3 +-acute myeloid leukaemia – indications for an optimal dosage of inhibitors against FLT3 and CDK6 (PubMed)

Computer simulations of the signalling network in FLT3 +-acute myeloid leukaemia – indications for an optimal dosage of inhibitors against FLT3 and CDK6 Mutations in the FMS-like tyrosine kinase 3 (FLT3) are associated with uncontrolled cellular functions that contribute to the development of acute myeloid leukaemia (AML). We performed computer simulations of the FLT3-dependent signalling network in order to study the pathways that are involved in AML development and resistance to targeted

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2018 BMC bioinformatics

19. Computational Protein Design with Deep Learning Neural Networks (PubMed)

Computational Protein Design with Deep Learning Neural Networks Computational protein design has a wide variety of applications. Despite its remarkable success, designing a protein for a given structure and function is still a challenging task. On the other hand, the number of solved protein structures is rapidly increasing while the number of unique protein folds has reached a steady number, suggesting more structural information is being accumulated on each fold. Deep learning neural network (...) is a powerful method to learn such big data set and has shown superior performance in many machine learning fields. In this study, we applied the deep learning neural network approach to computational protein design for predicting the probability of 20 natural amino acids on each residue in a protein. A large set of protein structures was collected and a multi-layer neural network was constructed. A number of structural properties were extracted as input features and the best network achieved an accuracy

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2018 Scientific reports

20. Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network (PubMed)

Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network Introduction Cone beam computed tomography (CBCT) plays an important role in image-guided radiation therapy (IGRT), while having disadvantages of severe shading artifact caused by the reconstruction using scatter contaminated and truncated projections. The purpose of this study is to develop a deep convolutional neural network (DCNN) method for improving CBCT image quality. Methods CBCT (...) and planning computed tomography (pCT) image pairs from 20 prostate cancer patients were selected. Subsequently, each pCT volume was pre-aligned to the corresponding CBCT volume by image registration, thereby leading to registered pCT data (pCTr). Next, a 39-layer DCNN model was trained to learn a direct mapping from the CBCT to the corresponding pCTr images. The trained model was applied to a new CBCT data set to obtain improved CBCT (i-CBCT) images. The resulting i-CBCT images were compared to pCTr using

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2018 Cureus

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