Results 111 to 120 of about 37,871 (263)

Essential Updates 2024–2026: Advances in Colorectal Cancer Surgery

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
ABSTRACT Surgical innovation in colorectal cancer is increasingly judged not by technical feasibility alone, but by whether it improves oncological outcomes, preserves function, reduces morbidity, or makes difficult procedures more reproducible. This structured narrative review examines major surgical studies published from January 2024 through July ...
Hiroyasu Kagawa, Yusuke Kinugasa
wiley   +1 more source

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

open access: yesAdvanced Intelligent Discovery, EarlyView.
This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri   +6 more
wiley   +1 more source

The Japanese Language and Literature Association of Daehan

open access: yesThe Japanese Language and Literature Association of Daehan, 2018
openaire   +1 more source

A reappraisal of APOE genetic effects on Alzheimer's disease risk in the Japanese population: a meta-analysis. [PDF]

open access: yesMol Neurodegener
Miyashita A   +10 more
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

open access: yesAdvanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez   +4 more
wiley   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

open access: yesAdvanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi   +3 more
wiley   +1 more source

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