Results 131 to 140 of about 166,195 (196)

Artificial intelligence-assisted design of MAGE-A4 ×CD16 T-cell receptor-like natural killer cell engagers. [PDF]

MAbs
Liu Y, Peng H, Fan X, Liu X, Jiao G, Li J, Wang T, Fong SJ, Zhu L, Dai Y, Zhao Q.   +10 more
europepmc   +1 more source

Estimation of Import Demand Functions of Two Key Food and Feed Commodities (Corn and Soybean) for India

Agribusiness, EarlyView.
ABSTRACT The derived demand for the increased demand for meat and dairy products is clearly observed in the fast‐paced growth of the Indian feed industry and its increased demand for agricultural commodities (e.g., corn, soybean, rice, wheat, broken rice, millet, etc.) as feed ingredients.
Dwiti Baruah Thapa, Sanjib Bhuyan, Kishor Goswami   +2 more
wiley   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Toward an Aptamer-Based Biosensor for Cross-Serotype Detection of Botulinum Neurotoxin: Binding Mechanism from a Comprehensive Computational Investigation. [PDF]

ACS Omega
Nguyen LH, Truong LTN, Nguyen PL, Luu-Manh Q, Pham YB, Phung TVB, Nguyen TT.   +6 more
europepmc   +1 more source

LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?

Advanced Intelligent Discovery, EarlyView.
A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler, Manuel Raimann, Marc Münker, Maxim Rosin, Joaquin E. Urrutia Gómez, Christof M. Niemeyer, Markus Reischl, Luca Rettenberger   +7 more
wiley   +1 more source

Phase 1/2 study of IMC-C103C, a T cell receptor bispecific (MAGE-A4×CD3) ImmTAC targeting MAGE-A4-expressing malignancies. [PDF]

J Immunother Cancer
Sweis RF, Melero I, Davar D, Garralda E, Hamid O, Chen HA, Evans TRJ, Moore KN, Sacco JJ, Thistlethwaite F, Williams A, Blumenschein GR, Callahan MK, Johnson ML, Schenk EL, Wustner J, Stanhope S, Tao K, Marshall S, Lopez J.   +19 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Digital Twins for the Design, Interactive Control, and Deployment of Modular, Fiber‐Reinforced Soft Continuum Arms

Advanced Intelligent Systems, EarlyView.
A digital twin framework is presented for modular soft continuum arms built from fiber‐reinforced pneumatic actuators. Cosserat rod models describe individual actuators, while virtual assemblies are formed through serial and parallel networks, preserving actuator‐level architecture and arm deformation.
Seung Hyun Kim, Jiamiao Guo, Arman Tekinalp, Heng‐Sheng Chang, Ugur Akcal, Tixian Wang, Darren Biskup, Benjamin Walt, Girish Chowdhary, Girish Krishnan, Prashant G. Mehta, Mattia Gazzola   +11 more
wiley   +1 more source

Reproducibility of Serum Androgen Concentrations by Liquid Chromatography Mass Spectrometry in Healthy Male and Female Athletes. [PDF]

Drug Test Anal
Handelsman DJ, Bermon S.
europepmc   +1 more source

Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training

Advanced Intelligent Systems, EarlyView.
The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez, Raúl Parada, Jordi Casas‐Roma, Laia Subirats   +3 more
wiley   +1 more source