Results 161 to 170 of about 1,039,771 (351)

Mammalian Proteome Profiling Reveals Readers and Antireaders of Strand‐Symmetric and ‐Asymmetric 5‐Hydroxymethylcytosine‐Modifications in DNA

open access: yesAdvanced Science, EarlyView.
ABSTRACT The cytosine (C) modifications 5‐methylcytosine (mC) and 5‐hydroxymethylcytosine (hmC) are central regulatory elements of mammalian genomes. Both marks occur in double‐stranded DNA in either strand‐symmetric or ‐asymmetric fashion, but it is still poorly understood how this symmetry information is selectively read out by the nuclear proteome ...
Lena Engelhard   +8 more
wiley   +1 more source

Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

open access: yesAdvanced Science, EarlyView.
We present Diffusion‐MRI‐based Estimation of Cortical Architecture via Machine Learning (DECAM), a deep‐learning framework for estimating primate brain cortical architecture optimized with best response constraint and cortical label vectors. Trained using macaque brain high‐resolution multi‐shell dMRI and histology data, DECAM generates high‐fidelity ...
Tianjia Zhu   +7 more
wiley   +1 more source

Lateralized costs of divided attention to faces. [PDF]

open access: yesAtten Percept Psychophys
Lee SC, Strother L.
europepmc   +1 more source

CLinNET: An Interpretable and Uncertainty‐Aware Deep Learning Framework for Multi‐Modal Clinical Genomics

open access: yesAdvanced Science, EarlyView.
Identifying disease‐causing genes in neurocognitive disorders remains challenging due to variants of uncertain significance. CLinNET employs dual‐branch neural networks integrating Reactome pathways and Gene Ontology terms to provide pathway‐level interpretability of genomic alterations.
Ivan Bakhshayeshi   +5 more
wiley   +1 more source

The influence of eye and mouth position on judgments of face orientation

open access: gold, 2010
Masayoshi Nagai   +6 more
openalex   +1 more source

A Machine Learning‐Driven Pore‐Scale Network Model Coupling Reaction Kinetics and Interparticle Transport for Catalytic Process Design

open access: yesAdvanced Science, EarlyView.
Designing catalytic processes in porous reactors requires resolving coupled multiscale reaction–transport phenomena. We develop a machine‐learning‐accelerated pore‐scale dual‐network model with kinetics (DNMK), which captures reaction kinetics, pore‐scale transport, and reactor‐level behavior.
Ming‐Liang Qu   +10 more
wiley   +1 more source

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