Results 201 to 210 of about 120,275 (281)

An efficient <i>in vivo</i>-inducible CRISPR interference system for group A <i>Streptococcus</i> genetic analysis and pathogenesis studies. [PDF]

mBio
Bjånes E, Stream A, Janssen AB, Gibson PS, Bravo AM, Dahesh S, Baker JL, Varble A, Nizet V, Veening J-W.   +9 more
europepmc   +1 more source

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

SARS-CoV-2 Delta variant induces severe damage in the nasal cavity from the first day post-infection in the Syrian hamster model. [PDF]

Virulence
Fusade-Boyer M, Gambino A, Merle-Nguyen L, Saint-Albin A, Ando-Grard O, Jacquelin J, Boujedli Y, Huet H, Ar Gouilh M, Martin-Latil S, Klonjkowski B, Meunier N, Le Poder S.   +12 more
europepmc   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

FOXL2 drives the differentiation of supporting gonadal cells in early ovarian development. [PDF]

Reprod Biol Endocrinol
Danti L, Lundin K, Nedeczey-Ruzsák P, Tuuri T, Tapanainen JS.   +4 more
europepmc   +1 more source

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker, Tamzid Ul Islam, Md. Robiul Islam   +2 more
wiley   +1 more source

Seeing Through the Face: Robust Facial Diagnosis Enhanced by Landmark Detection and Pose Normalization

Advanced Intelligent Systems, EarlyView.
This study proposes a robust, generalizable new approach for facial type diagnosis. Based on landmark detection and pose normalization, a 94.7% diagnostic accuracy is achieved by Combined Heatmap Regression and Coordinate Regression network. This research makes the AI‐generated preliminary diagnosis more interpretable and reducing the impact of ...
Qianyang Xie, Zixian Jiao, Guo Bai, Huimin Zhu, Dahe Zhang, Hui Li, Haixia Lu, Xin Nie, Qing Li, Duohong Zou, Zhiguo Yin, Peilun Li, Chi Yang   +12 more
wiley   +1 more source

The P-loop NTPase RUVBL2 is a conserved clock component across eukaryotes. [PDF]

Nature
Liao M, Liu Y, Xu Z, Fang M, Yu Z, Cui Y, Sun Z, Huo R, Yang J, Huang F, Liu M, Zhou Q, Song X, Han H, Chen S, Xu X, Qin X, He Q, Ju D, Wang T, Thakkar N, Hardin PE, Golden SS, Zhang EE.   +23 more
europepmc   +1 more source

Multiobjective Environmental Cleanup with Autonomous Surface Vehicle Fleets Using Multitask Multiagent Deep Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
This study presents a multitask strategy for plastic cleanup with autonomous surface vehicles, combining exploration and cleaning phases. A two‐headed Deep Q‐Network shared by all agents is traineded via multiobjective reinforcement learning, producing a Pareto front of trade‐offs.
Dame Seck, Samuel Yanes, Manuel Perales, Daniel Gutiérrez, Sergio Toral   +4 more
wiley   +1 more source

The Cord Weekly (April 10, 2002) [PDF]

, 2002

core   +1 more source