Results 151 to 160 of about 12,022 (264)

Generating Dynamic Structures Through Physics‐Based Sampling of Predicted Inter‐Residue Geometries

open access: yesAdvanced Science, EarlyView.
While static structure prediction has been revolutionized, modeling protein dynamics remains elusive. trRosettaX2‐Dynamics is presented to address this challenge. This framework leverages a Transformer‐based network to predict inter‐residue geometric constraints, guiding conformation generation via physics‐based iterative sampling. The resulting method
Chenxiao Xiang   +3 more
wiley   +1 more source

Bioenergy Cropping Reduces the Spatiotemporal Scaling of Soil Bacterial Biodiversity

open access: yesAdvanced Science, EarlyView.
Consistent with patterns observed in plant and animal communities, soil bacterial communities exhibit significant species–time–area and phylogenetic–time–area relationships independent of nested structure. Bioenergy cropping significantly reduces the spatiotemporal scaling rates, particularly in sandy loam soils.
Zhencheng Ye   +19 more
wiley   +1 more source

Noise Fingerprints as a Quantitative Order Parameter for Polarization‐ and Defect‐Mediated Switching in Hafnia Ferroelectrics

open access: yesAdvanced Science, EarlyView.
Low‐frequency noise fingerprints in hafnia ferroelectrics provide a quantitative handle to resolve the long‐standing debate between polarization‐mediated and defect‐mediated switching. By tuning oxygen vacancy density via ALD O3 dose time and applying a physically constrained deconvolution, we extract bias‐resolved current fractions for both mechanisms
Ryun‐Han Koo   +8 more
wiley   +1 more source

Encoding Cumulation to Learn Perturbative Nonlinear Oscillatory Dynamics

open access: yesAdvanced Science, EarlyView.
Weak nonlinearities critically shape the long term behavior of oscillatory systems but are difficult to identify from data. A data‐driven framework is introduced to infer governing equations of weakly nonlinear oscillators from sparse and noisy observations.
Teng Ma   +5 more
wiley   +1 more source

Multimodal Cross‐Attentive Graph‐Based Framework for Predicting In Vivo Endocrine Disruptors

open access: yesAdvanced Science, EarlyView.
A multimodal cross‐attentive graph neural network integrates molecular graphs with androgen and estrogen adverse outcome pathway (AOP)–anchored in vitro assay signals to predict in vivo endocrine disruption. By fusing information on Tier‐1 AOP logits with chemical structures, the framework achieves high accuracy and provides assay‐traceable ...
Eder Soares de Almeida Santos   +6 more
wiley   +1 more source

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