Results 91 to 100 of about 3,165,100 (264)
Partial Least Squares Methods for Non-Metric Data [PDF]
Partial Least Squares (PLS) methods embrace a suite of data analysis techniques based on algorithms belonging to PLS family. These algorithms consist in various extensions of the Nonlinear estimation by Iterative PArtial Least Squares (NIPALS) algorithm,
Russolillo, Giorgio
core +1 more source
Heterotropic regulation and negative homotropic cooperativity
We identified a structural module common to some proteins that couple negative cooperativity with heterotropic regulation, two features that rarely coexist. These proteins are ring‐like and present an ordered asymmetry whereby noncontacting subunits are symmetric, and their tertiary structure differs from that of contacting subunits.
Veronica Morea +5 more
wiley +1 more source
Partial Least Squares Regression for Binary Data
Classical Partial Least Squares Regression (PLSR) models were developed primarily for continuous data, allowing dimensionality reduction while preserving relationships between predictors and responses. However, their application to binary data is limited.
Laura Vicente-Gonzalez +2 more
doaj +1 more source
Least-Squares Solution of Linear Differential Equations
This study shows how to obtain least-squares solutions to initial value problems (IVPs), boundary value problems (BVPs), and multi-value problems (MVPs) for nonhomogeneous linear differential equations (DEs) with nonconstant coefficients of any order ...
Daniele Mortari
doaj +1 more source
GelMA‐based 3D spheroids recapitulate transcriptomic and functional hallmarks of myeloid sarcoma
GelMA 5% hydrogels support the formation of myeloid leukemia spheroids that recapitulate MS‐specific features, including G1 arrest, apoptosis, and ECM‐driven transcriptomic reprogramming. The 3D model mimicked soft‐tissue‐like stiffness and oxygen conditions, and transcriptomic convergence with primary MS samples confirmed its utility as a preclinical ...
Nicolas Germain +11 more
wiley +1 more source
Least-Squares Means: The R Package lsmeans
Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects of factors, and for testing linear contrasts among predictions. The lsmeans package (Lenth 2016)
Russell V. Lenth
doaj +1 more source
Local polynomial fitting in a least squares sense is a common technique in scattered data approximation. However, some known error bounds indicate that the error may become rather large for certain configurations of the given data points. In this paper, the author proves that these bounds are indeed realistic and proposes a modified technique, called ...
openaire +3 more sources
Comparative assessment of crystallographic and cryo‐EM models in the Protein Data Bank
Raw data obtained by X‐ray crystallography or cryo‐EM result in experimental maps, ultimately fitted by atomic models. Although the physical principles are different, the final results can be viewed, compared, and evaluated in the same way. With cryogenic electron microscopy (cryo‐EM) on track to surpass X‐ray crystallography as the preferred method ...
Alexander Wlodawer +7 more
wiley +1 more source
A minimal cellulosome‐like system in Cellulosilyticum lentocellum
Cellulose‐degrading bacteria typically use cellulosomes, large multi‐enzyme complexes on a scaffold protein. In Cellulosilyticum lentocellum, we characterise a far smaller arrangement, a single scaffold bound to one cellulase through a single cohesin‐dockerin interaction.
John Allan +2 more
wiley +1 more source
MARK4 enhances stress granule formation under oxidative stress and increases tau accumulation
MARK4 (red dots) localizes to stress granules (orange dots) and promotes their formation under oxidative stress by modulating TIA1 (blue dots). MARK4 and TIA1 synergistically increase tau (purple) accumulation, and the reduction of the TIA1 ortholog suppresses neurodegeneration in a fly model.
Sho Nakajima +8 more
wiley +1 more source

