Results 191 to 200 of about 10,624,441 (235)

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

open access: yesAdvanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan   +8 more
wiley   +1 more source

Natural Variation in ZmASB1 Links Tryptophan Metabolism to Root Growth and Drought Tolerance in Maize

open access: yesAdvanced Science, EarlyView.
Natural variation in the maize ZmASB1 promoter regulates tryptophan accumulation, root development, and drought tolerance through a tryptophan–auxin pathway. A 5‐bp InDel alters ZmDOF17 binding and promoter activity, and the favorable allele is enriched during breeding. These findings reveal a cis‐regulatory mechanism linking amino acid metabolism with
Xiaoyi Yang   +17 more
wiley   +1 more source

Gm26550 Modulates Learning and Memory by Increasing IGF1 mRNA Expression and Stability in Nrf2−/− Mice

open access: yesAdvanced Science, EarlyView.
The present study shows that Nrf2 directly binds to the Gm26550 promoter, thereby activating Gm26550 transcription and increasing its expression. Mechanistically, Gm26550 promotes IGF1 expression by functionally antagonizing miR‐26a‐5p‐mediated repression and sequestering the RBP KHSRP, thereby enhancing hippocampal neuronal synaptic plasticity and ...
Hongfang Wang   +12 more
wiley   +1 more source

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

open access: yesAdvanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li   +6 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

open access: yesAdvanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan   +8 more
wiley   +1 more source

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