Results 101 to 110 of about 4,901,361 (218)

Stratifications and foliations in phase portraits of gene network models. [PDF]

open access: yesVavilovskii Zhurnal Genet Selektsii, 2022
Golubyatnikov VP   +3 more
europepmc   +1 more source

Synergistic Spin‐Polarization and Single‐Atom Engineering in Magnetic Heterojunctions for Efficient Solar Water Splitting

open access: yesAdvanced Science, EarlyView.
High‐throughput screening led to the identification of 67 Z‐scheme heterojunctions (comprising 2D magnetic transition metal halides and non‐magnetic transition metal chalcogenides). For CrI3/MoTe2 and CrI3/WTe2, electronic structure analysis demonstrated that synergistic crystallographic point group and built‐in electric field effects generate a ...
Hongyang Ren   +8 more
wiley   +1 more source

Radiocarbon signatures of carbon phases exported by Swiss rivers in the Anthropocene. [PDF]

open access: yesPhilos Trans A Math Phys Eng Sci, 2023
Rhyner TMY   +10 more
europepmc   +1 more source

The Mind From Within: Visceral Roots of Human Cognition

open access: yesAdvanced Science, EarlyView.
The physiological activity of visceral organs, such as the heart, the lungs, and the gut, is surprisingly linked to many sophisticated mental operations, such as remembering the past, being aware of ourselves, making choices, and forging social bonds.
Alessandro Monti   +1 more
wiley   +1 more source

Dynamic Regulation of Endogenous Transcription Factor Hubs at Single‐Molecule Resolution

open access: yesAdvanced Science, EarlyView.
This study combines single‐molecule microscopy and genome editing to characterize the dynamic behaviors of endogenous oncofusion transcription factor EWS::FLI1 in Ewing sarcoma cells. EWS::FLI1 forms neomorphic hubs that dynamically assemble and dissolve. The hubs are regulated during mitosis, by RNA, and by specific chemicals.
Shawn Yoshida   +4 more
wiley   +1 more source

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

open access: yesAdvanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen   +7 more
wiley   +1 more source

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