Results 221 to 230 of about 1,028,782 (297)

Compositional and temporal division of labor modulates mixed sugar fermentation by an engineered yeast consortium. [PDF]

Nat Commun
Shin J, Liao S, Kuanyshev N, Xin Y, Kim C, Lu T, Jin YS.   +6 more
europepmc   +1 more source

Contemporary Regional Disparities in Robotic‐Assisted Rectal Resection in Japan: A Nationwide Population‐Based Study Using the National Database Open Data

Annals of Gastroenterological Surgery, EarlyView.
Robotic‐assisted rectal resection in Japan increased markedly after national insurance reimbursement in 2018, with a concurrent decline in open surgery. Using NDB Open Data, we found substantial inter‐prefectural heterogeneity in surgical volume and robotic utilization that persisted after age and sex standardization (SCR). Urban–rural differences were
Ryo Ohta, Takeshi Yamada, Key Uehara, Hiromichi Sonoda, Akihisa Matsuda, Seiichi Shinji, Yasuyuki Yokoyama, Goro Takahashi, Nobuhiko Taniai, Hiroshi Yoshida   +9 more
wiley   +1 more source

Division of Labor

The Iowa Review, 2015
openaire   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Rrm3 and Pif1 division of labor during replication through leading and lagging strand G-quadruplex. [PDF]

Nucleic Acids Res
Varon M, Dovrat D, Heuzé J, Tsirkas I, Singh SP, Pasero P, Galletto R, Aharoni A.   +7 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

Autonomous Machine Learning‐Based Classification and Arrangement of Submillimeter Objects Using a Capillary Force Gripper

Advanced Intelligent Discovery, EarlyView.
This study presents an automated system integrating a capillary force gripper and machine learning‐based object detection for sorting and placing submillimeter objects. The system achieved stable and simultaneous manipulation of four object types, with an average task time of 86.0 seconds and a positioning error of 157 ± 84 µm, highlighting its ...
Satoshi Ando, Naoto Watanabe, Chihiro Sekine, Shogen Sekiguchi, Ohmi Fuchiwaki   +4 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

A division-of-labor mode contributes to the cardioprotective potential of mesenchymal stem/stromal cells in heart failure post myocardial infarction. [PDF]

Front Immunol
Wang X, Yang C, Ma X, Li X, Qi Y, Bai Z, Xu Y, Ma K, Luo Y, Song J, Jia W, He Z, Liu Z.   +12 more
europepmc   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source