Results 61 to 70 of about 3,113,449 (308)

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

Method of Multi-Label Visual Emotion Recognition Fusing Fore-Background Features

Applied Sciences
This paper proposes a method for multi-label visual emotion recognition that fuses fore-background features to address the following issues that visual-based multi-label emotion recognition often overlooks: the impacts of the background that the person ...
Yuehua Feng, Ruoyan Wei
doaj   +1 more source

The human gut microbiome across the life course

FEBS Letters, EarlyView.
Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero, Basak Bahcivanci, Antonietta Hayhoe, Animesh Acharjee, Ezgi Özkurt   +4 more
wiley   +1 more source

MSFA: Multi‐stage feature aggregation network for multi‐label image recognition

IET Image Processing
Multi‐label image recognition (MLR) is a significant branch of image classification that aims to assign multiple categorical labels to each input. Previous research has focused on enhancing the learning of category‐related regional features. However, the
Jiale Chen, Feng Xu, Tao Zeng, Xin Li, Shangjing Chen, Jie Yu   +5 more
doaj   +1 more source

Multi-Label Classification with Label Clusters

Knowledge and Information Systems, 2023
Abstract Multi-Label Classification is the task of simultaneously predicting a set of labels for an instance. Typically, two approaches are used: global, which trains a single classifier to deal with all classes at once, and local, which divides the problem into many binary problems.
Elaine Cecília Gatto, Mauri Ferrandin, Ricardo Cerri   +2 more
openaire   +2 more sources

Microbiome−host proteostasis crosstalk—An emerging perspective on mechanisms and interventions toward healthy longevity

FEBS Letters, EarlyView.
Proteostasis and the gut microbiota play a key role in shaping host physiology. Microbiota‐derived metabolites, vitamins, and RNA modulate host proteostasis. Findings from model systems, including C. elegans, indicate microbes can either stabilize or disrupt host proteostasis.
Abhishek Anil Dubey, Maria Ermolaeva
wiley   +1 more source

Video Classification of Cloth Simulations: Deep Learning and Position-Based Dynamics for Stiffness Prediction

Sensors
In virtual reality, augmented reality, or animation, the goal is to represent the movement of deformable objects in the real world as similar as possible in the virtual world.
Makara Mao, Hongly Va, Min Hong
doaj   +1 more source

Multi-label Learning with Label Enhancement [PDF]

2018 IEEE International Conference on Data Mining (ICDM), 2018
The task of multi-label learning is to predict a set of relevant labels for the unseen instance. Traditional multi-label learning algorithms treat each class label as a logical indicator of whether the corresponding label is relevant or irrelevant to the instance, i.e., +1 represents relevant to the instance and -1 represents irrelevant to the instance.
Ruifeng Shao, Ning Xu 0009, Xin Geng 0001   +2 more
openaire   +3 more sources

From mice to humans—divergent strategies for intestinal homeostasis and regeneration

FEBS Letters, EarlyView.
Recent advances such as organoid genome editing, xenotransplantation, imaging, and whole‐genome sequencing have enabled direct studies of human intestinal stem cells (ISCs). These studies reveal species‐specific features, including slower ISC proliferation, distinct injury responses, slower somatic mutation accumulation in humans, and an inverse ...
Keiko Ishikawa, Shinya Sugimoto, Toshiro Sato   +2 more
wiley   +1 more source

Multi label AdaBoost Algorithm Based on Label Correlations

工程科学与技术, 2016
:In order to improve classification performance and exploit label correlations,AdaBoost.MLR algorithm was proposed.Cosine similarity was adopted to capture the complex correlations among labels in AdaBoost.MLR algorithm,a supplementary label matrix was ...
王莉莉, 付忠良
doaj