Results 81 to 90 of about 414,528 (301)

Loss of VMP1 Impairs Tight Junction Recycling and Aggravates Intestinal Barrier Dysfunction in Inflammatory Bowel Disease

open access: yesAdvanced Science, EarlyView.
This study identifies vacuole membrane protein 1 (VMP1) as a critical regulator of intestinal epithelial barrier homeostasis. VMP1 facilitates the recruitment of CORO1C to late endosomes, supporting Retromer‐mediated recycling of the tight junction protein Occludin.
Jiawei Zhao   +12 more
wiley   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

open access: yesAdvanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu   +4 more
wiley   +1 more source

Unsupervised end-to-end training with a self-defined target

open access: yesNeuromorphic Computing and Engineering
Designing algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phases of self-supervised and supervised learning are accurate and adaptable to
Dongshu Liu   +4 more
doaj   +1 more source

Investigating Contrastive Pair Learning’s Frontiers in Supervised, Semisupervised, and Self-Supervised Learning

open access: yesJournal of Imaging
In recent years, contrastive learning has been a highly favored method for self-supervised representation learning, which significantly improves the unsupervised training of deep image models. Self-supervised learning is a subset of unsupervised learning
Bihi Sabiri   +3 more
doaj   +1 more source

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

open access: yesRemote Sensing, 2018
When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery ...
Cong Wang   +3 more
doaj   +1 more source

A Circuit of Mechanically Regulated Transcription Factors Balances Regenerative and Fibrotic Memory of Mesenchymal Stromal Cells

open access: yesAdvanced Science, EarlyView.
Producing MSCs on rigid culture substrates induces a scar‐making phenotype, jeapordizing therapeutic success. ‘Tissue‐soft’ surfaces prevent MSC fibrogenesis and preserve regenerative traits. An epigenetic network, driven by HOXA11 and SALL1, maintains ‘soft memory’ by keeping chromatin open in relaxed MSCs, promoting anti‐fibrotic programs.
Fereshteh Sadat Younesi   +7 more
wiley   +1 more source

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

open access: yesAlgorithms
In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into ...
Christos Cholevas   +4 more
doaj   +1 more source

ALKBH3 m1A Demethylase Deficiency Reduces Alzheimer's Amyloid‐β Pathology

open access: yesAdvanced Science, EarlyView.
This study identifies that ALKBH3‐driven m1A demethylation orchestrates Alzheimer's disease progression by disrupting mitochondrial and synaptic homeostasis. This epitranscriptomic mechanism suppresses PINK1‐mediated mitophagy via m1A erasure, leading to mitochondrial dysfunction, oxidative stress, elevated Aβ production, and impaired microglial ...
Yueyang Li   +25 more
wiley   +1 more source

Cell Segmentation Without Annotation by Unsupervised Domain Adaptation Based on Cooperative Self-Learning

open access: yesIEEE Access
Semantic cell segmentation from microscopic images is essential for the quantitative evaluation of cell morphology. Although supervised deep-learning-based models offer accurate segmentation, their performance degrades for unknown cell types.
Shintaro Miyaki   +5 more
doaj   +1 more source

Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES

open access: yesAdvanced Science, EarlyView.
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu   +5 more
wiley   +1 more source

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