Results 201 to 210 of about 24,130 (303)

Multifaceted mechanisms by which environmental endocrine-disrupting chemicals promote cancer progression: crosstalk among carcinogenesis, immunity, and metabolic reprogramming: narrative mini-review. [PDF]

Front Mol Biosci
Du A, Yang Q, Yu L, Peng Y, Huang T, Yuan Q, Wang W, Wang G.   +7 more
europepmc   +1 more source

Rising Pancreatic Cancer Incidence in Young Japanese Women and Increasing Distal Pancreatectomy in Older Adults: A Nationwide Claims‐Based Analysis From 2016 to 2023

Annals of Gastroenterological Surgery, EarlyView.
This nationwide claims‐based study analyzed recent trends in pancreatic cancer incidence (2016–2021) and surgery (2016–2023) in Japan. The study revealed a rising incidence of pancreatic cancer, notably among young women, and an increasing use of distal pancreatectomy among older adults.
Masamitsu Kido, Ryo Morimura, Yusuke Yamamoto, Tomohiro Arita, Shingo Nakashima, Katsutoshi Shoda, Taisuke Imamura, Hidemasa Kubo, Kengo Yoshii, Iichiroh Onishi, Atsushi Shiozaki   +10 more
wiley   +1 more source

Supporting self-reliance for displaced and host populations: insights from the Self-Reliance Index across 16 countries. [PDF]

BMJ Glob Health
Stark L, Jorgenson Diener K, Leeson K, Singh S, Meerdink N, Seff I.   +5 more
europepmc   +1 more source

LEDA : a blending of imperative and relational programming

This paper describes features of a new strongly typed, compiled programming language, called LEDA. LEDA attempts to combine aspects of both imperative (value-oriented) and logical (relation-oriented) styles of programming.
Budd, Timothy, Oregon State University. Dept. of Computer Science   +1 more
core  

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows. [PDF]

Chem Res Toxicol
Mansouri K, Moreira-Filho JT, S Tieghi R, Kleinstreuer N.   +3 more
europepmc   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

A linear programming model for power system planning with hydrogen integration. [PDF]

Sci Rep
Zaiter I, Sleptchenko A, Mayyas A, Jaradat R, Mezher T.   +4 more
europepmc   +1 more source

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

Advanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj, Ella Paasio, Sayani Majumdar   +2 more
wiley   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source