Results 71 to 80 of about 3,973,078 (288)
Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source
IEEE AccessMicro-expression (ME) is spontaneous, rapid, and subtle facial mechanism that can reveal the concealed emotions. However, the short duration, low motion intensity, and small dataset of MEs make the extraction and learning of features from ME samples more
Peiyun Xue +3 more
doaj +1 more source
An isoform of 14‐3‐3 protein regulates transbilayer lipid movement at the plasma membrane
FEBS Letters, EarlyView.Loss of 14‐3‐3ζ in CHO cells confers resistance to exogenous phosphatidylserine (PS) and impairs endocytosis‐independent inward flip‐flop of fluorescent PS at the plasma membrane. RNAi‐mediated knockdown reproduces this defect, while no additive effect is seen in ATP11C‐deficient cells.
Akiko Yamaji‐Hasegawa +3 more
wiley +1 more source
Frontiers in Earth ScienceSlope displacement interval prediction quantifies uncertainty in future deformation and supports slope monitoring and early warning. This study extends the point-prediction FA-VAG backbone previously reported by Gan et al.
YingChao Dai +6 more
doaj +1 more source
Ensemble Feature Selection Method with Fast Transfer Model [PDF]
Jisuanji kexue yu tansuoCompared with the traditional ensemble feature selection methods, the recently-developed ensemble feature selection with block-regularized [m×2] cross-validation (EFSBCV) not only has a variance of the estimator smaller than that of random [m×2] cross ...
NING Baobin, WANG Shitong
doaj +1 more source
Towards Understanding Transfer Learning Algorithms Using Meta Transfer Features [PDF]
, 2020 Transfer learning, which aims to reuse knowledge in different domains, has achieved great success in many scenarios via minimizing domain discrepancy and enhancing feature discriminability. However, there are seldom practical determination methods for measuring the transferability among domains.
Xin-Chun Li +5 more
openaire +2 more sources
FEBS Letters, EarlyView.Plasma membranes contain dynamic nanoscale domains that organize lipids and receptors. Because viruses operate at similar scales, this architecture shapes early infection steps, including attachment, receptor engagement, and entry. Using influenza A virus and HIV‐1 as examples, we highlight how receptor nanoclusters, multivalent glycan interactions ...
Jan Schlegel, Christian Sieben
wiley +1 more source
IEEE AccessIn this research, we address the sophisticated task of reconstructing the semantic elements within complex scenes, a critical endeavor for a broad range of artificial intelligence (AI) applications.
Zhendong Du, Yu Yu
doaj +1 more source
Biophysical approaches for studying viral entry
FEBS Letters, EarlyView.Viruses infect all living organisms and have been responsible for major epidemics and pandemics. Their ongoing evolutionary battle with host defenses creates a constant need for improved tools to study viral behavior. Advancing methods to probe viral attachment, fusion, and genome release deepen our understanding of how infections begin and support the
Inbar Yosibash, Raya Sorkin
wiley +1 more source
Modular feature selection using relative importance factors [PDF]
, 2004 Feature selection plays an important role in finding relevant or irrelevant features in classification. Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems.
Li, P, Guan, SU, Zhu, F
core +1 more source