Results 141 to 150 of about 19,357,515 (242)

Mapping the Social Network Structure and Composition of Black Individuals With Rheumatic and Musculoskeletal Conditions

Arthritis Care &Research, EarlyView.
Objective Our objective was to describe the social networks of Black individuals with rheumatic and musculoskeletal conditions and understand the clustering of health‐related behaviors to inform future community‐based, peer‐led interventions. Methods We used an adapted Personal Network Survey for Clinical Research (PERSNET) to map the personal social ...
Taussia Boadi, Amar Dhand, Leah Santacroce, Neil Pillai, Komel Safdar, Mia T. Chandler, Ariel Childs, Monica Crespo‐Bosque, Gina Curry, Mary Dollear, Alice Eggleston, Daniel Erickson, Dieufort Fleurissaint, Denice Garrett, Gail Granville, Tyler Green, Magdalena Hamielec, Elena Losina, Karen Mancera‐Cuevas, Mary Ann Nelson, Chisa Nosamiefan, Bisola Ojikutu, Tonya S. Roberson, Mary Beth Son, Marie Jacques Toussaint, Michael York, Rosalind Ramsey‐Goldman, Candace H. Feldman   +27 more
wiley   +1 more source

Accelerating Discovery of Leukemia Inhibitors Using AI-Driven Quantitative Structure-Activity Relationship: Algorithm Development and Validation. [PDF]

JMIR AI
Kakraba S, Agyemang EF, Shmookler Reis RJ.   +2 more
europepmc   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Machine Learning-Enhanced Quantitative Structure-Activity Relationship Modeling for DNA Polymerase Inhibitor Discovery: Algorithm Development and Validation. [PDF]

JMIR AI
Kakraba S, Ayyadevara S, Yadem Clement A, Abraham KE, Compadre CM, Shmookler Reis RJ.   +5 more
europepmc   +1 more source

A Quantitative Structure-Activity Relationship for Human Plasma Protein Binding: Prediction, Validation and Applicability Domain. [PDF]

Adv Pharm Bull, 2023
Khaouane A, Ferhat S, Hanini S.
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Quantitative Structure-Activity Relationship Modeling and Molecular Docking Studies of TgCDPK1 Inhibitors in Toxoplasma gondii. [PDF]

Microbiologyopen
Lesani S, Tavalla M, Eslami G, Boozhmehrani MJ.   +3 more
europepmc   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes. [PDF]

Sci Rep, 2022
Hamzehali H, Lotfi S, Ahmadi S, Kumar P.
europepmc   +1 more source

Microstructural Evolution and Vacancy Defect Formation in Mn–Mo–Ni RPV Steel Under Low Cycle Fatigue: Insights From EBSD and PALS

Advanced Engineering Materials, EarlyView.
Low‐cycle fatigue damage in Mn–Mo–Ni reactor pressure vessel steel is examined using a combined electron backscatter diffraction and positron annihilation lifetime spectroscopy approach. The study correlates texture evolution, dislocation substructure development, and vacancy‐type defect formation across uniform, necked, and fracture regions, providing
Apu Sarkar, Dhanadeep Dutta, G. Bharat Reddy   +2 more
wiley   +1 more source