Results 181 to 190 of about 245,870 (292)

LLMs as Representation Generators for Gradient‐Free Scientific Optimization: A Case Study in Battery Fast Charging

Advanced Science, EarlyView.
Large language models serve as representation generators for gradient‐free scientific optimization. Two complementary approaches produce executable battery‐charging policies either directly or through LLM‐designed neural representations coupled to Bayesian optimization.
Ge Lei, Ferran Brosa Planella, Sterling G. Baird, Samuel J. Cooper   +3 more
wiley   +1 more source

Genomic and epidemiologic evidence for attenuated virulence in <i>Salmonella</i> Montevideo associated with cattle and beef products in the United States. [PDF]

Front Microbiol
Chen R, Yang L, Wiedmann M, Orsi RH.
europepmc   +1 more source

A Generalizable Multimodal Model for Treatment‐Stratified Risk and Survival Assessment under Real‐World Constraints: A Multi‐Center Study of Colorectal Cancer

Advanced Science, EarlyView.
DAIMS is a multimodal model for treatment stratified risk and survival assessment in colorectal cancer. Training draws on pathology, genomics, and clinical reports, yet inference requires only whole slide images, and adaptation to new cohorts proceeds without target survival outcomes.
Chuangjie Cao, Zheyi Ji, Chang He, Jiying Wang, Xiaoming Luo, Junyi Liu, Xiaohu Jing, Fang Yan, Yirong Chen, Linhao Qu, Sen Yang, Le Lu, Yuming Jiang, Xiyue Wang, Chengyun Dou, Junhan Zhao   +15 more
wiley   +1 more source

Unveiling the anti-obesity potential of Kemuning (Murraya paniculata): A network pharmacology approach. [PDF]

PLoS One
Fatriani R, Pratiwi FAK, Annisa A, Septaningsih DA, Aziz SA, Miladiyah I, Kusumastuti SA, Nasution MAF, Ramadhan D, Kusuma WA.   +9 more
europepmc   +1 more source

Mechanically Regulated Secretion: How Physical Forces Instruct the Secretory Pathway and Remodel the Secretome

Advanced Science, EarlyView.
Mechanical cues, including ECM stiffness, stretch, compression, shear stress, and traction forces, remodel trafficking across the secretory pathway, from the ER and Golgi to endolysosomal compartments and the plasma membrane. The resulting secretome reshapes ECM composition and tissue mechanics, establishing feedback loops that support homeostasis or ...
Domenico Russo, Seetharaman Parashuraman, Maria Luigia Maresca, Angela Francesca Dinoi, Alessia Calcagnì, Riccardo Rizzo   +5 more
wiley   +1 more source

Massive deciphering of the tissue nucleic acid-binding proteome via affinity chromatography integrated with data-independent acquisition-based proteomics. [PDF]

Nucleic Acids Res
Wang H, Zhu J, Li S, Zhang L, Guo M, Zhao J, Zhang Y, Cao C, Hu S, Xiao H.   +9 more
europepmc   +1 more source

Stochastic‐MTJ Sampler Arrays for In‐Array Monte–Carlo Estimation

Advanced Electronic Materials, EarlyView.
A low‐energy‐barrier magnetic tunnel junction array is operated as a probability‐domain sampler: each cell's random switching, programmed through a shared digital‐to‐analog converter, makes the per‐column multiply–accumulate an unbiased Monte–Carlo expectation estimator that returns both a mean and a calibrated uncertainty.
Ran Zhang, Nellie Laleni, Sina Ranjbar, Andreas Tsiougkos, Nandakishor Yadav, Vasilis Pavlidis, Thomas Kämpfe   +6 more
wiley   +1 more source

The global genomic landscape of hypervirulent <i>Klebsiella pneumoniae</i> from 1932 to 2021. [PDF]

mLife
Jiang X, Li S, Li C, Yin Z, Chen F, Hu L, Lu T, Liu X, Wang Y, Ma G, Wang X, Chen F, Zhou D.   +12 more
europepmc   +1 more source

Electrical Properties and Interfacial Strain in Thermally Oxidized 4H‐SiC MOS Structures Under Different Post‐Oxidation Annealing

Advanced Electronic Materials, EarlyView.
This work investigates the electrical properties and interfacial strain in thermally oxidized 4H‐SiC MOS structures under different post‐oxidation annealing. Nitrogen annealing simultaneously reduces interface state density and increases interfacial tensile strain, thus being accompanied by the formation of an interfacial dipole.
Fabrizio Roccaforte, Salvatore Ethan Panasci, Valerio Votadoro, Salvatore Di Franco, Marilena Vivona, Simone Milazzo, Marco Zignale, Filippo Giannazzo, Béla Pécz, Patrick Fiorenza   +9 more
wiley   +1 more source

Koina: Democratizing machine learning for proteomics research. [PDF]

Nat Commun
Lautenbacher L, Yang KL, Kockmann T, Panse C, Gabriel W, Bold D, Kahl E, Chambers M, MacLean BX, Li K, Yu F, Searle BC, Wilburn DB, Shahneh MRZ, Hong Y, Tang H, Wang M, Gabriels R, Bouwmeester R, Devreese R, Angelis J, Sabidó E, Schmidt TK, Nesvizhskii AI, Wilhelm M.   +24 more
europepmc   +1 more source