Results 71 to 80 of about 8,372,639 (310)
Flow characteristics in flow networks [PDF]
Experiments in Fluids, 1993 A flow network is a system of mutually intersecting holes in a plate or an assembly of plates. The flow at each intersection is characterized by a collision of two flow streams, resulting in complex flow patterns through the downstream holes. In the case of multiple intersections, the flow is periodically disrupted at each succeeding intersection, thus
Lee, C. P. +3 more
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Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
wiley +1 more source
FEBS Letters, EarlyView.Animals must match their growth rate to available nutrients. We show that in Drosophila larvae, the nutrient‐sensing TOR kinase controls growth by regulating levels of TFAM, a key regulator of mitochondrial function, in the adipose tissue. When nutrients are abundant, high TOR activity suppresses TFAM, lowering mitochondrial bioenergetic activity and ...
Shrivani Sriskanthadevan‐Pirahas +4 more
wiley +1 more source
Using deep learning for detecting BotCloud
Tongxin xuebao, 2016 The differences of the basic network flow characteristics between BotCloud and normal cloud services were not obvious, and this led to the inefficiency of the method in BotCloud detection based on network flow characteristics analysis.
Guang KOU +4 more
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INFORMATION FLOWS IN CAUSAL NETWORKS [PDF]
Advances in Complex Systems, 2008 We use a notion of causal independence based on intervention, which is a fundamental concept of the theory of causal networks, to define a measure for the strength of a causal effect. We call this measure "information flow" and compare it with known information flow measures such as transfer entropy.
NIHAT AY, DANIEL POLANI
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Liver organoids: modelling complexity in homeostasis and disease
FEBS Letters, EarlyView.Studying liver in vitro has been challenging because simple 2D cell cultures fail to capture liver's cellular and architectural complexity. To bridge this gap, scientists increasingly use organoids, 3D liver models which better mimic liver composition and function. This review examines recent advances in liver organoid complexity and realism, discusses
Anna M. Dowbaj, Meritxell Huch
wiley +1 more source
A bit-capacity scaling algorithm for the constrained minimal cost network flow problem [PDF]
Network Biology, 2021 A polynomial time algorithm for solving the minimum-cost network flow problem has been proposed in this paper. This algorithm is mainly based on the binary representation of capacities; it solves the minimum-cost flow problem in directed graph of n nodes
Muhammad Tlas
doaj
Research on Flow Sampling Algorithm Based on Counting Bloom Filter [PDF]
Jisuanji gongcheng, 2018 In order to adapt to the high-speed network environment and realize the accurate measurement of network traffic,a network algorithm of flow equal probability sampling based on the combination of Counting Bloom Filter and packet-based flow sampling ...
ZHAI Jinfeng,SUN Libo,LU Kai,LIN Xueyong,QIN Wenhu
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The microbiome in human skin aging
FEBS Letters, EarlyView.Age‐related skin changes encompass the well‐known visible phenotypic alterations, together with microbiome dysbiosis and a series of molecular aging hallmarks. These hallmarks characterize not only a fully stablished aged phenotype but also the skin aging process itself.
Manuel Huerta Arana +3 more
wiley +1 more source
On the Duality Between Network Flows and Network Lasso [PDF]
IEEE Signal Processing Letters, 2020 Many applications generate data with an intrinsic network structure such as time series data, image data or social network data. The network Lasso (nLasso) has been proposed recently as a method for joint clustering and optimization of machine learning models for networked data.
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