Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance

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Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

Background & Aims: Current therapies for the treatment of alcohol-related liver disease (ALD) have proven largely ineffective. Patients relapse and the disease progresses even after liver transplantation. Altered epigenetic mechanisms are characteristic of ...

The gut microbiome produces a plethora of metabolites that influence host metabolism. In a recent article published in Cell, Nie and colleagues reveal that 3-succinylated cholic acid, a microbe-derived bile acid, protects against metabolic dysfunction-asso ...

In this issue of Cell, Nie and co-authors report that the microbe-derived bile acid (BA) 3-succinylated cholic acid protects against the progression of metabolic dysfunction-associated liver disease. Intriguingly, its protective mechanism does not involve ...

Background Excessive inflammation, hemolysis, and accumulation of labile heme play an essential role in the pathophysiology of multi-organ dysfunction syndrome (MODS) in sepsis. Alpha1-antitrypsin (AAT), an acute phase protein with heme binding capacity, i ...

Water is a crucial resource for all forms of life, yet it is becoming increasingly scarce. A significant portion of water loss in urban and industrial areas is attributed to leaks. Addressing this issue is critical for enhancing efficiency, sustainability, ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Utilization of small molecules as passivation materials for perovskite solar cells (PSCs) has gained significant attention recently, with hundreds of small molecules demonstrating passivation effects. In this study, a high-accuracy machine learning model i ...

Whereas pulse-echo ultrasound imaging relied on focused acoustic waves since its inception, the last two decades have seen the development of techniques based on unfocused waves, including ultrafast ultrasound imaging. In large part due to the emergence of ...

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

Poisoning attacks compromise the training data utilized to train machine learning (ML) models, diminishing their overall performance, manipulating predictions on specific test samples, and implanting backdoors. This article thoughtfully explores these atta ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance

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A comparative analysis of gene expression profiling by statistical and machine learning approaches

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

Anti-miR-873-5p improves alcohol-related liver disease by enhancing hepatic deacetylation via SIRT1

Protecting liver health with microbial-derived succinylated bile acids

A microbial-derived succinylated bile acid to safeguard liver health

Alpha1-antitrypsin improves survival in murine abdominal sepsis model by decreasing inflammation and sequestration of free heme

A two-phase approach for leak detection and localization in water distribution systems using wavelet decomposition and machine learning

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells

Inverse Problems in Pulse-Echo Ultrasound: Exploiting Coherence for Image Enhancement and Quantitative Imaging

Boosting likelihood learning with event reweighting

Topics in statistical physics of high-dimensional machine learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Machine Learning Security Against Data Poisoning: Are We There Yet?

Partial discharge localization in power transformer tanks using machine learning methods

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification