Results 211 to 220 of about 11,648,449 (278)

Fractional Variational Framework for Systems With Higher‐Order Elements. Part I: Lagrangian Construction and Equations of Motion

open access: yesAdvanced Electronic Materials, EarlyView.
This work introduces a physics‐consistent fractional variational framework for modeling systems with higher‐order elements. The developed methodology successfully incorporates energy dissipation, nonlinear dynamics, and memory effects into a unified Lagrangian formulation.
Dalibor Biolek   +4 more
wiley   +1 more source

MicroRNA‐941 Acts as a Novel Prognostic Predictor and Oncogenic Driver in Colorectal Cancer

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
Upregulation of miR‐941 was a promising diagnostic and prognostic biomarker for CRC patients. Inhibition of miR‐941 significantly suppressed CRC cellular events and triggered ferroptosis. The oncogenic functions of miR‐941 were mediated through targeting TRPS1.
Hong Li   +5 more
wiley   +1 more source

Metastasis Pattern‐Specific Biomarkers and Therapeutic Targets in Gastric Cancer: From Surgical Specimens to Clinical Translation

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
Gastric cancer metastasis should be considered according to its anatomical route, because lymphatic, peritoneal, and hematogenous dissemination involve partly distinct molecular mechanisms. This review summarizes metastasis pattern‐associated and pattern‐specific molecules identified using surgical specimens, evaluates their evidence and translational ...
Mitsuro Kanda
wiley   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

open access: yesAdvanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser   +6 more
wiley   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

open access: yesAdvanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu   +11 more
wiley   +1 more source

Autonomous AI‐Driven Design for Skin Product Formulations

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang   +5 more
wiley   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

open access: yesAdvanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu   +3 more
wiley   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

open access: yesAdvanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta   +3 more
wiley   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

open access: yesAdvanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha   +2 more
wiley   +1 more source

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