Results 201 to 210 of about 617,408 (262)

TOLLIP Inhibits Psoriasis Progression via Suppressing PKM2‐Mediated Glycolysis in Keratinocytes

open access: yesAdvanced Science, EarlyView.
In this study, we identify TOLLIP as a critical regulator of psoriasis pathogenesis through its modulation of glycolytic metabolism. Our findings establish the TOLLIP‐PKM2‐glycolysis axis as a key mechanism linking metabolic reprogramming to psoriasis pathogenesis, and propose TOLLIP as a promising therapeutic target.
Xiuhuan Jiang   +11 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

open access: yesAdvanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray   +3 more
wiley   +1 more source

Who Is the Proxy?

open access: yes, 1989
Krason, Stephen M.
core   +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

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

open access: yesAdvanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu   +8 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

open access: yesAdvanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin   +12 more
wiley   +1 more source

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