Results 131 to 140 of about 31,167 (257)

Large Language Model‐Informed Dual‐Track AI Framework for the Synergistic Design of Crack‐Free and High‐Strength Superalloys

open access: yesAdvanced Science, EarlyView.
This paper illustrates a knowledge‐augmented dual‐track AI framework for advanced superalloy design. First, Large Language Models translate metallurgical heuristics into explicit rules to rapidly prune a vast compositional search space. Subsequently, LLM‐distilled priors safely guide a reinforcement learning agent during autonomous process optimization,
Jian Yao   +9 more
wiley   +1 more source

Nanopore‐Based Protein Deceleration and Sensing Using Graphene/Si3N4 Dual Membrane Cavity

open access: yesAdvanced Science, EarlyView.
A graphene/Si3N4 dual membrane cavity system achieves multi‐stage protein deceleration through spatial geometry. Multi‐level current traces provide high‐resolution spatial localization and early warning signals. ABSTRACT Sequencing of protein with nanopores has emerged as a powerful tool offering rapid readout, high accuracy, low cost, and portability.
Yubin Cao, Junzhou He, Wei Si
wiley   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

open access: yesAdvanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio   +6 more
wiley   +1 more source

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell–Cell Interaction Inference

open access: yesAdvanced Science, EarlyView.
SPADE integrates spatial transcriptomics with single‐cell RNA sequencing by using cell–cell communications (CCC) as a guide for spatial mapping. It improves cell‐type localization, enhances sparse gene‐expression signals, and reveals CCC programs at single‐spot resolution.
Xinyi Li, Ning Zhang, Zijie Jin
wiley   +1 more source

A Single‐Cell Transcriptomic Atlas of the Ovine Rumen Microbiome Characterizes Lineage‐Specific Metabolic Shifts Associated with Host Heat Tolerance

open access: yesAdvanced Science, EarlyView.
An optimized single‐cell transcriptomic framework profiles over 60 000 cells to map the ovine rumen microbiome, partitioning the ecosystem into seven cross‐species functional clusters. In heat‐resistant hosts, a lineage‐specific metabolic shift in Anaerovibrio lipolyticus toward a highly glycolytic phenotype contributes to a “nutritional sparing ...
Sanbao Zhang   +8 more
wiley   +1 more source

Brain‐Computer Interface Training Fosters Perceptual Skills to Detect Errors

open access: yesAdvanced Science, EarlyView.
Accurate perception of visuomotor errors underpins motor precision and learning, yet conventional behavioral training fails to improve sensitivity to subtle errors. Real‐time EEG‐based brain‐computer interface feedback targeting the error positivity component enhances perceptual learning of small errors.
Deland H. Liu   +4 more
wiley   +1 more source

Tandem Duplication‐Driven Neofunctionalization of UDP‐Glycosyltransferases Shapes the Diversification of Triterpenoid Saponins in the Cucurbitaceae

open access: yesAdvanced Science, EarlyView.
We present a chromosome‐level genome assembly of Siraitia grosvenorii and, through comparative genomics, uncover a conserved UGT73 tandem array driving triterpenoid saponin diversification in Cucurbitaceae. Crystalized SgUGT73AM30 further reveals the regioselectivity mechanism underlying its catalytic activity.
Guangyi Wang   +13 more
wiley   +1 more source

CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning

open access: yesAdvanced Science, EarlyView.
CauFinder combines causal disentanglement modeling and network control to prioritize causal drivers of cell‐state transitions from observational transcriptomic data. The framework separates transition‐relevant signals from spurious associations, nominates intervention targets across biological and disease contexts, and identifies DAAM1 as an actionable
Chengming Zhang   +11 more
wiley   +1 more source

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