Results 41 to 50 of about 280,565 (313)
Singular-value decomposition of solution operators to model evolution equations [PDF]
We consider evolution equations generated by quadratic operators admitting a decomposition in creation-annihilation operators without usual ellipticity-type hypotheses; this class includes hypocoercive model operators.
Aleman, Alexandru, Viola, Joe
core +1 more source
Combining PTEN protein assessment and transcriptomic profiling of prostate tumors, we uncovered a network enriched in senescence and extracellular matrix (ECM) programs associated with PTEN loss and conserved in a mouse model. We show that PTEN‐deficient cells trigger paracrine remodeling of the surrounding stroma and this information could help ...
Ivana Rondon‐Lorefice +16 more
wiley +1 more source
Singular Value Decomposition Wavelength-Multiplexing Ghost Imaging
To enhance imaging quality, singular value decomposition (SVD) has been applied to single-wavelength ghost imaging (GI) or color GI. In this paper, we extend the application of SVD to wavelength-multiplexing ghost imaging (WMGI) for reducing the ...
Yingtao Zhang +3 more
doaj +1 more source
Peroxidasin enables melanoma immune escape by inhibiting natural killer cell cytotoxicity
Peroxidasin (PXDN) is secreted by melanoma cells and binds the NK cell receptor NKG2D, thereby suppressing NK cell activation and cytotoxicity. PXDN depletion restores NKG2D signaling and enables effective NK cell–mediated melanoma killing. These findings identify PXDN as a previously unrecognized immune evasion factor and a potential target to improve
Hsu‐Min Sung +17 more
wiley +1 more source
Singular Value Decomposition [PDF]
While the eigenvalue decomposition \({\bf A} = \bf T{\bf \Lambda}T^{\prime},\) say, concerns only symmetric matrices, the singular value decomposition (SVD) \({\bf A} = \bf U{\bf \Delta}V^{\prime},\) say, concerns any n × m matrix. In this chapter we illustrate the usefulness of the SVD, particularly from the statistical point of view.
Simo Puntanen +2 more
openaire +2 more sources
Singular Value Decomposition and Ligand Binding Analysis
Singular values decomposition (SVD) is one of the most important computations in linear algebra because of its vast application for data analysis. It is particularly useful for resolving problems involving least-squares minimization, the determination of
André Luiz Galo +1 more
doaj +1 more source
Rethinking plastic waste: innovations in enzymatic breakdown of oil‐based polyesters and bioplastics
Plastic pollution remains a critical environmental challenge, and current mechanical and chemical recycling methods are insufficient to achieve a fully circular economy. This review highlights recent breakthroughs in the enzymatic depolymerization of both oil‐derived polyesters and bioplastics, including high‐throughput protein engineering, de novo ...
Elena Rosini +2 more
wiley +1 more source
Enzymatic degradation of biopolymers in amorphous and molten states: mechanisms and applications
This review explains how polymer morphology and thermal state shape enzymatic degradation pathways, comparing amorphous and molten biopolymer structures. By integrating structure–reactivity principles with insights from thermodynamics and enzyme engineering, it highlights mechanisms that enable efficient polymer breakdown.
Anđela Pustak, Aleksandra Maršavelski
wiley +1 more source
Singular Value Decomposition Approaches in A Correspondence Analysis with The Use of R
The aim of a correspondence analysis is the graphical representation of the categories of variables in one frame of reference. This visualization is possible due to the decomposition of the basic matrix with the use of Singular Value Decomposition (SVD).
Brzezińska Justyna
doaj +1 more source
Interpolating compact binary waveforms using the singular value decomposition
Compact binary systems with total masses between tens and hundreds of solar masses will produce gravitational waves during their merger phase that are detectable by second-generation ground-based gravitational-wave detectors.
Chad Hanna +3 more
core +1 more source

