Results 231 to 240 of about 563,921 (264)
Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young +7 more
wiley +1 more source
The dFoCC pipeline starts with observed DED and resting‐state coordinates, which are then used to generate a library of triggered states. Correlation analysis of the calculated DED features of each candidate vs observed DED permits quantitative evaluation of candidate structural quality.
Meng Iao Fong +3 more
wiley +1 more source
Acute caffeine treatment protects the developing retina from ischemia‐induced cell death
Caffeine reduces cell death in the developing retina under ischemia (OGD). This effect does not involve BDNF upregulation or antioxidant pathways (NRF2/VEGF). Neuroprotection occurs mainly through adenosine A2A receptor antagonism, decreasing glutamate release and excitotoxicity, highlighting caffeine's potential as an acute neuroprotective agent in ...
Amanda Alves Nascimento +6 more
wiley +1 more source
Pharmacological inhibition of PERK in a DEN‐induced mouse model of liver cancer does not reduce tumor burden but alters cellular stress signaling. Despite blocking PERK activity, downstream stress responses, including CHOP expression, remain active, suggesting compensatory mechanisms within the unfolded protein response that may influence tumor ...
Ada Lerma‐Clavero +5 more
wiley +1 more source
Early‐life exposure to a high‐fat diet altered intact Achilles tendons in rat offspring, making them thinner, stiffer, and molecularly distinct even without injury. These findings suggest that developmental high‐fat diet exposure may impair tendon quality and increase susceptibility to mechanical overload or tendon injury later in life.
Heyong Yin +3 more
wiley +1 more source
KIF26B plays an important role in kidney development. We engineered mice lacking the C‐terminal region of KIF26B and found severe kidney defects, including bilateral renal agenesis, similar to full Kif26b knockout mice. The mutation disrupted nephron progenitor condensation and reduced Gdnf‐Wnt11 signaling, showing that the KIF26B C‐terminal region is ...
Yuta Yamamura +19 more
wiley +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Coupled Principal Component Analysis
IEEE Transactions on Neural Networks, 2004A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the
Möller, Ralf, Könies, Axel
openaire +5 more sources
Directed Principal Component Analysis
Operations Research, 2014We consider a problem involving estimation of a high-dimensional covariance matrix that is the sum of a diagonal matrix and a low-rank matrix, and making a decision based on the resulting estimate. Such problems arise, for example, in portfolio management, where a common approach employs principal component analysis (PCA) to estimate factors used in ...
Yi-Hao Kao, Benjamin Van Roy
openaire +2 more sources
2012
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
openaire +3 more sources
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
openaire +3 more sources
Kernel Principal Component Analysis
1997A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images.
Bernhard Schölkopf +2 more
openaire +3 more sources

