8,892 results match your criteria connected networks


LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing.

IEEE Trans Neural Netw Learn Syst 2021 May 12;PP. Epub 2021 May 12.

Spiking neural networks (SNNs) based on the leaky integrate and fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the bioplausible neuronal dynamics and simplicity, LIF-SNN benefits from event-driven processing, however, usually face the embarrassment of reduced performance. This may because, in LIF-SNN, the neurons transmit information via spikes. Read More

View Article and Full-Text PDF

Full-length de novo protein structure determination from cryo-EM maps using deep learning.

Bioinformatics 2021 May 12. Epub 2021 May 12.

School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China.

Motivation: Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-EM maps. However, building accurate models for the EM maps at 3-5 Å resolution remains a challenging and time-consuming process. With the rapid growth of deposited EM maps, there is an increasing gap between the maps and reconstructed/modeled 3-dimensional (3D) structures. Read More

View Article and Full-Text PDF

Roost sites of chimney swift () form large-scale spatial networks.

Ecol Evol 2021 May 20;11(9):3820-3829. Epub 2021 Mar 20.

University of New Brunswick Fredericton NB Canada.

Several biodiversity-centered metrics exist to quantify the importance of landscape and habitat features for conservation efforts. However, for species whose habitat use is not quantified by these metrics, such as those in urban areas, we need a method to best identify features for targeted conservation efforts. We investigated the use of social network analysis (SNA) to identify and quantify these critical habitat features. Read More

View Article and Full-Text PDF

Network geometry and market instability.

R Soc Open Sci 2021 Feb 24;8(2):201734. Epub 2021 Feb 24.

School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India.

The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Read More

View Article and Full-Text PDF
February 2021

Stable leaders pave the way for cooperation under time-dependent exploration rates.

R Soc Open Sci 2021 Feb 3;8(2):200910. Epub 2021 Feb 3.

INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, 2744-016 Porto Salvo, Portugal.

The exploration of different behaviours is part of the adaptation repertoire of individuals to new environments. Here, we explore how the evolution of cooperative behaviour is affected by the interplay between exploration dynamics and social learning, in particular when individuals engage on prisoner's dilemma along the edges of a social network. We show that when the population undergoes a transition from strong to weak exploration rates a decline in the overall levels of cooperation is observed. Read More

View Article and Full-Text PDF
February 2021

Imaging and Quantification of Intact Neuronal Dendrites via CLARITY Tissue Clearing.

J Vis Exp 2021 Apr 20(170). Epub 2021 Apr 20.

Department of Genetics and Genomics, Baylor College of Medicine; Department of Neuroscience, Baylor College of Medicine.

Brain activity, the electrochemical signals passed between neurons, is determined by the connectivity patterns of neuronal networks, and from the morphology of processes and substructures within these neurons. As such, much of what is known about brain function has arisen alongside developments in imaging technologies that allow further insight into how neurons are organized and connected in the brain. Improvements in tissue clearing have allowed for high-resolution imaging of thick brain slices, facilitating morphological reconstruction and analyses of neuronal substructures, such as dendritic arbors and spines. Read More

View Article and Full-Text PDF

Inter-provincial sectoral embodied CO net-transfer analysis in China based on hypothetical extraction method and complex network analysis.

Sci Total Environ 2021 Apr 21;786:147211. Epub 2021 Apr 21.

School of Economics and Management, China University of Geosciences, Beijing 100083, China; Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources of the People's Republic of China, Beijing 100083, China.

To address the CO emissions issue, China promised to increase its nationally determined contributions, trying to reach a CO emissions peak by 2030. For optimizing emission reduction policies, it is important to clarify the CO linkage structure and transfer characteristics. Previous research mainly focused on the calculation and comparison of CO linkage at the national level or the regional level and lacked inter-provincial sector-sector transfer analysis. Read More

View Article and Full-Text PDF

Temporal stability of swine movement networks in the U.S.

Prev Vet Med 2021 May 3;191:105369. Epub 2021 May 3.

Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, MN, 55108, USA.

As a consequence of multi-site pig production practiced in North America, frequent and widespread animal movements create extensive networks of interaction between farms. Social network analysis (SNA) has been used to understand disease transmission risks within these complex and dynamic production ecosystems and is particularly relevant for designing risk-based surveillance and control strategies targeting highly connected farms. However, inferences from SNA and the effectiveness of targeted strategies may be influenced by temporal changes in network structure. Read More

View Article and Full-Text PDF

Hierarchical Density-Aware Dehazing Network.

IEEE Trans Cybern 2021 May 7;PP. Epub 2021 May 7.

The commonly used atmospheric model in image dehazing cannot hold in real cases. Although deep end-to-end networks were presented to solve this problem by disregarding the physical model, the transmission map in the atmospheric model contains significant haze density information, which cannot simply be ignored. In this article, we propose a novel hierarchical density-aware dehazing network, which consists of a the densely connected pyramid encoder, a density generator, and a Laplacian pyramid decoder. Read More

View Article and Full-Text PDF

Graph-structured populations and the Hill-Robertson effect.

R Soc Open Sci 2021 Mar 17;8(3):201831. Epub 2021 Mar 17.

Department of Zoology, University of Otago, PO Box 56, Dunedin 9054, New Zealand.

The Hill-Robertson effect describes how, in a finite panmictic diploid population, selection at one diallelic locus reduces the fixation probability of a selectively favoured allele at a second, linked diallelic locus. Here we investigate the influence of population structure on the Hill-Robertson effect in a population of size . We model population structure as a network by assuming that individuals occupy nodes on a graph connected by edges that link members who can reproduce with each other. Read More

View Article and Full-Text PDF

Collaboration and knowledge generation in an 18-year quality improvement research programme in Australian Indigenous primary healthcare: a coauthorship network analysis.

BMJ Open 2021 May 6;11(5):e045101. Epub 2021 May 6.

School of Public Health, The University of Sydney, Sydney, New South Wales, Australia.

Objectives: Though multidisciplinary research networks support the practice and effectiveness of continuous quality improvement (CQI) programmes, their characteristics and development are poorly understood. In this study, we examine publication outputs from a research network in Australian Indigenous primary healthcare (PHC) to assess to what extent the research network changed over time.

Setting: Australian CQI research network in Indigenous PHC from 2002 to 2019. Read More

View Article and Full-Text PDF

Drug-target interaction prediction using multi-head self-attention and graph attention network.

IEEE/ACM Trans Comput Biol Bioinform 2021 May 6;PP. Epub 2021 May 6.

Identifying drug-target interactions (DTIs) is an important step in the process of new drug discovery and drug repositioning. Accurate predictions for DTIs can improve efficiency in drug discovery and development. Although rapid advances in deep learning technologies have generated various computational methods, it is still appealing to further investigate how to design efficient networks for predicting DTIs. Read More

View Article and Full-Text PDF

Tongue image quality assessment based on a deep convolutional neural network.

BMC Med Inform Decis Mak 2021 May 5;21(1):147. Epub 2021 May 5.

Basic Medical College Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China.

Background: Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Read More

View Article and Full-Text PDF

The psychometric network structure of mental health in eating disorder patients.

Eur Eat Disord Rev 2021 May 5. Epub 2021 May 5.

Department of Psychology, Health and Technology, Centre for eHealth and Wellbeing Research, University of Twente, Enschede, The Netherlands.

Objective: Psychometric network analysis has led to new possibilities to assess the structure and dynamics of psychiatric disorders. The current study focuses on mental health networks in patients with anorexia nervosa, bulimia nervosa, binge eating disorder and other specified eating disorders (EDs).

Method: Network analyses were applied with five mental health domains (emotional, psychological and social well-being, and general and specific psychopathology) among 905 ED patients. Read More

View Article and Full-Text PDF

Three coordination polymers built by quaternary-ammonium-modified isophthalic acid.

Acta Crystallogr C Struct Chem 2021 May 9;77(Pt 5):221-226. Epub 2021 Apr 9.

Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing, Jiangsu Province 211800, People's Republic of China.

Three coordination polymers based on quaternary-ammonium-modified isophthalic acid, namely, catena-poly[[[aqua-μ-bromido-di-μ-hydroxido-methanoldinitratotetracopper(II)]-bis{μ-5-[2-(tripropylazaniumyl)ethoxy]benzene-1,3-dicarboxylato}] nitrate], {[CuBr(CHNO)(NO)(OH)(CHO)(HO)]NO}, 1, poly[μ-bromido-μ-bromido-bromido-μ-hydroxido-{μ-5-[2-(tripropylazaniumyl)ethoxy]benzene-1,3-dicarboxylato}tricopper(II)], [CuBr(CHNO)(OH)], 2, and poly[bromido{μ-5-[2-(tripropylazaniumyl)ethoxy]benzene-1,3-dicarboxylato}zinc(II)], [ZnBr(CHNO)], 3, were obtained by solvothermal reactions. Coordination polymer (CP) 1 contains tetranuclear Cu units, in which the four Cu atoms are linked by two μ-OH groups into a Cu(OH) cluster, which are in turn linked by 5-[2-(tripropylazaniumyl)ethoxy]benzene-1,3-dicarboxylate (cpa) ligands into a chain structure. CP 2 also contains a tetranuclear Cu(OH) cluster and these are linked with CuBr units into chains. Read More

View Article and Full-Text PDF

Cellular and Virtualization Technologies for UAVs: An Experimental Perspective.

Sensors (Basel) 2021 Apr 29;21(9). Epub 2021 Apr 29.

Telefónica I+D, Distrito Telefónica, Ronda de la Comunicación s/n, 28050 Madrid, Spain.

The Unmanned Aircraft System (UAS) ecosystem is exponentially growing in both recreational and professional fields to provide novel services and applications to consumers from multiple engineering fields. However, this technology has only scraped the surface of its potential, especially in those cases that require fast reaction times. Accordingly, the UAS Traffic Management (UTM) project aims at efficiently managing the air traffic for Unmanned Aerial Vehicle (UAV) operations, including those cases where UAVs might be remotely managed from a completely different geographical location. Read More

View Article and Full-Text PDF

Validation of a Low-Cost Pavement Monitoring Inertial-Based System for Urban Road Networks.

Sensors (Basel) 2021 Apr 30;21(9). Epub 2021 Apr 30.

Department of Civil, Constructional and Environmental Engineering, Sapienza University, Via Eudossiana, 18-00184 Rome, Italy.

Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, a novel inertial sensor-based system is proposed using a low-cost inertial measurement unit (IMU) and a global positioning system (GPS) module, which are connected to a Raspberry Pi Zero W board and embedded inside a vehicle to indirectly monitor the road condition. To assess the level of pavement decay, the comfort index defined by the ISO 2631 standard was used. Read More

View Article and Full-Text PDF

Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs.

Proc Natl Acad Sci U S A 2021 May;118(19)

Department of Psychiatry, Carver College of Medicine, Iowa City, IA 52242.

Hubs are highly connected brain regions important for coordinating processing in brain networks. It is unclear, however, which measures of network "hubness" are most useful in identifying brain regions critical to human cognition. We tested how closely two measures of hubness-edge density and participation coefficient, derived from white and gray matter, respectively-were associated with general cognitive impairment after brain damage in two large cohorts of patients with focal brain lesions (N = 402 and 102, respectively) using cognitive tests spanning multiple cognitive domains. Read More

View Article and Full-Text PDF

The Coronavirus Network Explorer: mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function.

BMC Bioinformatics 2021 May 3;22(1):229. Epub 2021 May 3.

Digital Insights, QIAGEN, Redwood City, USA.

Background: Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, and pathways in a large-scale knowledge graph derived from the biomedical literature. Our goal is to explore how SARS-CoV-2 could interfere with various host cell functions, and to identify drug targets amongst the host genes that could potentially be modulated against COVID-19 by repurposing existing drugs. The machine learning model employed here involves gene embeddings that leverage causal gene expression signatures curated from literature. Read More

View Article and Full-Text PDF

Weak biodiversity connectivity in the European network of no-take marine protected areas.

Sci Total Environ 2021 Jun 6;773:145664. Epub 2021 Feb 6.

CCMAR - Centre of Marine Sciences, University of Algarve, 8005-139 Faro, Portugal.

The need for international cooperation in marine resource management and conservation has been reflected in the increasing number of agreements aiming for effective and well-connected networks of Marine Protected Areas (MPAs). However, the extent to which individual MPAs are connected remains mostly unknown. Here, we use a biophysical model tuned with empirical data on species dispersal ecology to predict connectivity of a vast spectrum of biodiversity in the European network of marine reserves (i. Read More

View Article and Full-Text PDF

Integrated regulatory network based on lncRNA-miRNA-mRNA-TF reveals key genes and sub-networks associated with dilated cardiomyopathy.

Comput Biol Chem 2021 Apr 25;92:107500. Epub 2021 Apr 25.

Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamilnadu, India. Electronic address:

Dilated Cardiomyopathy (DCM) is a multifactorial condition often leading to heart failure in many clinical cases. Due to the high number of DCMincidence reported as familial, a gene level network based study was conducted utilizing high throughput next generation sequencing data. We exploited the exome and transcriptome sequencing data in NCBI-SRA database to construct a high confidence scale-free regulatory network consisting of lncRNA, miRNA, mRNA and Transcription Factors (TFs). Read More

View Article and Full-Text PDF

A social network analysis of the organizations focusing on tuberculosis, malaria and pneumonia.

Soc Sci Med 2021 Apr 19;278:113940. Epub 2021 Apr 19.

Scuola Superiore Sant'Anna di Pisa- Institute of Management, Piazza dei Martiri della Libertà, 3, 56127, Pisa, Italy. Electronic address:

In this paper,we present an original study on the use of social media data to analyze the structure of the global health networks (GHNs) relative to health organizations targeted to malaria, tuberculosis (TBC) and pneumonia as well as twitter popularity, evaluating the performance of their strategies in response to the arising health threats. We use a machine learning ensemble classifier and social network analysis to discover the Twitter users that represent organizations or groups active for each disease. We have found evidence that the GHN of TBC is the more mature, active and global. Read More

View Article and Full-Text PDF

Small-World Networks and Their Relationship With Hippocampal Glutamine/Glutamate Concentration in Healthy Adults With Varying Genetic Risk for Alzheimer's Disease.

J Magn Reson Imaging 2021 May 3. Epub 2021 May 3.

Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong.

Background: Apolipoprotein E ɛ4 allele (ApoE4) is the most common gene polymorphism related to Alzheimer's disease (AD). Impaired synaptic dysfunction occurs in ApoE4 carriers before any clinical symptoms. It remains unknown whether ApoE4 status affects the hippocampal neuromodulation, which further influences brain network topology. Read More

View Article and Full-Text PDF

A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment.

Front Med (Lausanne) 2021 16;8:626369. Epub 2021 Apr 16.

Department of Dermatology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Numerous studies have attempted to apply artificial intelligence (AI) in the dermatological field, mainly on the classification and segmentation of various dermatoses. However, researches under real clinical settings are scarce. This study was aimed to construct a novel framework based on deep learning trained by a dataset that represented the real clinical environment in a tertiary class hospital in China, for better adaptation of the AI application in clinical practice among Asian patients. Read More

View Article and Full-Text PDF

DenseCNN: A Densely Connected CNN Model for Alzheimer's Disease Classification Based on Hippocampus MRI Data.

AMIA Annu Symp Proc 2020 25;2020:1277-1286. Epub 2021 Jan 25.

Center for Artificial Intelligence for Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.

Alzheimer's Disease (AD) is a common type of dementia, affecting human memory, language ability and behavior. Hippocampus is an important biomarker for AD diagnosis. Previous hippocampus-based biomarker analyses mainly focused on volume, texture and shape of the bilateral hippocampus. Read More

View Article and Full-Text PDF
January 2021

IMCC: A Novel Quantitative Approach Revealing Variation of Global Modular Map and Local Inter-Module Coordination Among Differential Drug's Targeted Cerebral Ischemic Networks.

Front Pharmacol 2021 16;12:637253. Epub 2021 Apr 16.

Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.

Stroke is a common disease characterized by multiple genetic dysfunctions. In this complex disease, detecting the strength of inter-module coordination (genetic community interaction) and subsequent modular rewiring is essential to characterize the reactive biosystematic variation (biosystematic perturbation) brought by multiple-target drugs, whose effects are achieved by hitting on a series of targets (target profile) jointly. Here, a quantitative approach for inter-module coordination and its transition, named as IMCC, was developed. Read More

View Article and Full-Text PDF

Impairment of the neurotrophic signaling hub B-Raf contributes to motoneuron degeneration in spinal muscular atrophy.

Proc Natl Acad Sci U S A 2021 May;118(18)

Institute of Neuroanatomy and Cell Biology, Hannover Medical School, Hannover 30625, Germany.

Spinal muscular atrophy (SMA) is a motoneuron disease caused by deletions of the ( and low SMN protein levels. SMN restoration is the concept behind a number of recently approved drugs which result in impressive yet limited effects. Since SMN has already been enhanced in treated patients, complementary SMN-independent approaches are needed. Read More

View Article and Full-Text PDF

Convolutional neural networks with image representation of amino acid sequences for protein function prediction.

Comput Biol Chem 2021 Apr 24;92:107494. Epub 2021 Apr 24.

Department of Computer Science and Engineering, United International University, Plot-2, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh. Electronic address:

Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approaches are applied in this field to predict the functionality of proteins. Read More

View Article and Full-Text PDF

Cluster Identification Using Modularity Optimization to Uncover Chemical Heterogeneity in Complex Solutions.

J Phys Chem A 2021 Apr 30. Epub 2021 Apr 30.

Department of Chemistry, Washington State University, Pullman, Washington 99164, United States.

Structural heterogeneity is commonly manifested in solutions and liquids that feature competition of different interparticle forces. Identifying and characterizing heterogeneity across different length scales requires multimodal experimental measurement and/or the application of new techniques for the interrogation of atomistic simulation data. Within the latter, the parsing of networks of interparticle interactions (chemical networks) has been demonstrated to be a valuable tool for identifying subensembles of chemical environments. Read More

View Article and Full-Text PDF

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks.

Sensors (Basel) 2021 Apr 26;21(9). Epub 2021 Apr 26.

Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, Macedonia.

Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease's progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. Read More

View Article and Full-Text PDF