Results 271 to 280 of about 2,259,407 (341)

Raman‐based label‐free microscopic analysis of the pancreas in living zebrafish larvae

FEBS Open Bio, EarlyView.
Forward stimulated Raman scattering (F‐SRS) and epi coherent anti‐Stokes Raman scattering (E‐CARS) allow label‐free discrimination of distinct subcellular structures in the pancreas of living zebrafish larvae. Given the straightforward applicability, we anticipate broad implementation of Raman microscopy in other organs and across various biomedical ...
Noura Faraj, Eline M. F. de Lange, Klaas A. Sjollema, Ben N. G. Giepmans   +3 more
wiley   +1 more source

Study on Influential Factors of Arable Land Wasting in Yunnan Province Based on Principal Component Analysis and Multiple Linear Regression

, 2015
Tao Wen-xin
openalex   +1 more source

Exploring Predictors of Bioavailability for a Variety Polyphenols Through Caco-2 Cell Model: Insights from Permeability Studies and Principal Component Analysis

Dong-Ho Lee, Jinwoo Kim, I.-K. Na, Kang-Woo Lee, Nayeon Lee, Jinsoo Park, Chau Hoang Bao Nguyen, Kyungsu Kang, Soon‐Mi Shim   +8 more
openalex   +1 more source

Metformin promotes mitochondrial integrity through AMPK‐signaling in Leber's hereditary optic neuropathy

FEBS Open Bio, EarlyView.
Metformin mediates mitochondrial quality control in Leber's hereditary optic neuropathy (LHON) fibroblasts carrying mtDNA mutations. At therapeutic levels, metformin activates AMPK signaling to restore mitochondrial dynamics by promoting fusion and restraining fission, while preserving mitochondrial mass, enhancing autophagy/mitophagy and biogenesis ...
Chatnapa Panusatid, Rapasviranda Soiyangsuk, Maneeluck Tanadjindarat, Chayanon Peerapittayamongkol   +3 more
wiley   +1 more source

A spatial modelling technique using principal component analysis

, 2014
Muhamed Baraković, Peter Rogelj
openalex   +1 more source

Predictors of change in dietary patterns determined by principal component analysis in Australians <55 years

MG Thorpe (15816605), Catherine Milte (13064754), D Crawford (7747316), Sarah McNaughton (13093269)   +3 more
openalex  

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Principal components analysis.

Methods in molecular biology, 2013
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.
Detlef Groth, S. Hartmann, S. Klie, J. Selbig   +3 more
semanticscholar   +4 more sources

Principal Components of Principal Component Analysis

, 2017
M. Stamp
semanticscholar   +2 more sources

Coupled Principal Component Analysis

IEEE Transactions on Neural Networks, 2004
A 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, 2014
We 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 ...
Kao, Yi-Hao, Van Roy, Benjamin
openaire   +2 more sources