Results 81 to 90 of about 9,974,561 (338)

Mechanistic basis for inhibition of the extended‐spectrum β‐lactamase GES‐1 by enmetazobactam and tazobactam

open access: yesFEBS Letters, EarlyView.
Antimicrobial resistance (AMR) is of huge importance, resulting in over 1 million deaths each year. Here, we describe how a new drug, enmetazobactam, designed to help fight resistant bacterial diseases, inhibits a key enzyme (GES‐1) responsible for AMR. Our data show it is a more potent inhibitor than the related tazobactam, with high‐level computation
Michael Beer   +10 more
wiley   +1 more source

Energy and Spectral Efficiency Analysis for UAV-to-UAV Communication in Dynamic Networks for Smart Cities

open access: yesSmart Cities
Unmanned Aerial Vehicles (UAVs) are integral to the development of smart city infrastructures, enabling essential services such as real-time surveillance, urban traffic regulation, and cooperative environmental monitoring.
Mfonobong Uko   +4 more
doaj   +1 more source

PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks

open access: yesJournal of Statistical Software, 2020
Many real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world ...
Thong Pham   +2 more
doaj   +1 more source

Revealing the hidden structure of dynamic ecological networks [PDF]

open access: yesRoyal Society Open Science, 2017
In ecology, recent technological advances and long-term data studies now provide longitudinal interaction data (e.g. between individuals or species). Most often, time is the parameter along which interactions evolve but any other one-dimensional gradient
Vincent Miele, Catherine Matias
doaj   +1 more source

Decoding network dynamics

open access: yesIndustrial Marketing Management, 2012
AbstractBusiness networks are fluid, yet decoding network dynamics provides a number of methodological challenges. This research illustrates how, by using a technology-bundled business net, the temporal fluidity of the network boundary and the associated processes and events that affect this can be understood.
Chou, Hsin Hui, Zolkiewski, Judy
openaire   +2 more sources

Diffusion Dynamics on Multiplex Networks [PDF]

open access: yesPhysical Review Letters, 2013
6 Pages including supplemental material.
Sergio Gómez   +7 more
openaire   +8 more sources

Mapping the evolution of mitochondrial complex I through structural variation

open access: yesFEBS Letters, EarlyView.
Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin   +2 more
wiley   +1 more source

Developing a method for modeling and monitoring of dynamic networks using latent variables

open access: yesInternational Journal of Industrial Engineering and Production Research, 2021
Statistical monitoring of dynamic networks is a major topic of interest in complex social systems. Many researches have been conducted on modeling and monitoring dynamic social networks. This article proposes a new methodology for modeling and monitoring
Fatemeh Elhambakhsh   +1 more
doaj  

DeepEye: Link prediction in dynamic networks based on non-negative matrix factorization

open access: yesBig Data Mining and Analytics, 2018
A Non-negative Matrix Factorization (NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction
N. Ibrahim   +5 more
semanticscholar   +1 more source

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs [PDF]

open access: yesAAAI Conference on Artificial Intelligence, 2019
Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs.
A. Pareja   +7 more
semanticscholar   +1 more source

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