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Immune Predictors of Radiotherapy Outcomes in Cervical Cancer
Advanced Science, EarlyView.This study reveals dynamic immune remodeling in cervical cancer following radiotherapy. Single‐cell analysis identifies the C3/C3AR1 axis as a central mediator of epithelial–myeloid crosstalk, whose inhibition reduces treatment efficacy in mice. Guided by these insights, the eight‐feature machine‐learning model: Cervical Cancer Radiotherapy Immune ...
Linghao Wang +8 more
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Lactylation‐Driven YTHDC1 Alleviates MASLD by Suppressing PTPN22‐Mediated Dephosphorylation of NLRP3
Advanced Science, EarlyView.In MASLD, YTHDC1 undergoes increased lactylation and ubiquitination, reducing its expression. AARS1 mediates lactylation at lysine 565, while disrupted binding to LDHA further promotes lactylation, suppressing YTHDC1. This downregulation enhances PTPN22 mRNA stability, leading to NLRP3 dephosphorylation and activation, which exacerbates inflammation ...
Feng Zhang +16 more
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Alkyltriphenylphosphonium Binding to Cardiolipin Triggers Oncosis in Cancer Cells
Advanced Science, EarlyView.Alkyltriphenylphosphonium, exemplified by TPP+‐C14, preferentially accumulates in mitochondria and selectively binds to cardiolipin, a key phospholipid of the inner mitochondrial membrane, causing loss of mitochondrial membrane potential, severe cellular ATP depletion, and calcium imbalance.
Jin Li +8 more
wiley +1 more source
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Canonical Correlation Analysis
2012Canonical correlation analysis (CCA) is one of the most important tools of multivariate statistical analysis. Its extension to the functional context is not trivial, and in many ways illustrates the differences between multivariate and functional data. One of the most influential contributions has been made by Leurgans et al.
Lajos Horváth, Piotr Kokoszka
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Canonical Correlation Analysis
2017This chapter covers classical and robust canonical correlation analysis (CCA). Let \(\varvec{x}\) be the \(p \times 1\) vector of predictors, and partition \({\varvec{x}} = ({\varvec{w}}^T, {\varvec{y}}^T)^T\) where \({\varvec{w}}\) is \(m \times 1\) and \({\varvec{y}}\) is \(q \times 1\) with \(m = p-q \le q\) and \(m, q \ge 1\). If \(m = 1\) and \(q =
Jacob Benesty, Israel Cohen
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Canonical Correlation Analysis
2003Complex multivariate data structures are better understood by studying low-dimensional projections. For a joint study of two data sets, we may ask what type of low-dimensional projection helps in finding possible joint structures for the two samples.
Wolfgang Karl Härdle, Léopold Simar
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Canonical Correlation Analysis
2019In many applications, one wants to associate one kind of data with another. For example, every data item could be a video sequence together with its sound track. You might want to use this data to learn to associate sounds with video, so you can predict a sound for a new, silent, video.
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Canonical Correlation Analysis
2009In canonical correlation analysis, the objective is to relate a set of dependent or criterion variables to another set of independent or predictor variables. In order to do that, we find a scalar, defined as a linear combination of the dependent variables, as well as a scalar defined as a linear combination of the independent variables.
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