Fucoidan Supplementation Relieved Kidney Injury and Modulated Intestinal Homeostasis in Hyperuricemia Mice.

Hyperuricemia is a metabolic disease characterized by an excessively increased level of uric acid (UA) in the blood, with an increasing prevalence and often associated with kidney damage. Gut microbiota and endotoxins of gut origin are key mediators in the gut-kidney axis that can cause renal impairment. The study was to reveal the protective effects of fucoidan on renal injury caused by hyperuricemia.

Self-adaptive label discovery and multi-view fusion for complementary label learning.

Unlike traditional supervised classification, complementary label learning (CLL) operates under a weak supervision framework, where each sample is annotated by excluding several incorrect labels, known as complementary labels (CLs). Despite reducing the labeling burden, CLL always suffers a decline in performance due to the weakened supervised information. To overcome such limitations, in this study, a multi-view fusion and self-adaptive label discovery based CLL method (MVSLDCLL) is proposed.

Decoupled graph knowledge distillation: A general logits-based method for learning MLPs on graphs.

While Graph Neural Networks (GNNs) have demonstrated their effectiveness in processing non-Euclidean structured data, the neighborhood fetching of GNNs is time-consuming and computationally intensive, making them difficult to deploy in low-latency industrial applications. To address the issue, a feasible solution is graph knowledge distillation (KD), which can learn high-performance student Multi-layer Perceptrons (MLPs) to replace GNNs by mimicking the superior output of teacher GNNs. However, state-of-the-art graph knowledge distillation methods are mainly based on distilling deep features from intermediate hidden layers, this leads to the significance of logit layer distillation being greatly overlooked.

Probiotics combined with atorvastatin administration in the treatment of hyperlipidemia: A randomized, double-blind, placebo-controlled clinical trial.

Background: Hyperlipidemia is a common feature of chronic diseases. The aim of this work was designed to assess the role of probiotics (Lactobacillus casei Zhang, Bifidobactetium animalis subsp. lactis V9, and Lactobacillus plantarum P-8) in the treatment of hyperlipidemia.

[Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network].

Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study.

Generalized robust loss functions for machine learning.

Loss function is a critical component of machine learning. Some robust loss functions are proposed to mitigate the adverse effects caused by noise. However, they still face many challenges.

ComCo: Complementary supervised contrastive learning for complementary label learning.

Complementary label learning (CLL) is an important problem that aims to reduce the cost of obtaining large-scale accurate datasets by only allowing each training sample to be equipped with labels the sample does not belong. Despite its promise, CLL remains a challenging task. Previous methods have proposed new loss functions or introduced deep learning-based models to CLL, but they mostly overlook the semantic information that may be implicit in the complementary labels.

Causal multi-label learning for image classification.

In this paper, we investigate the problem of causal image classification with multi-label learning. As multi-label learning involves a diversity of supervision signals, it is considered a challenging issue to solve. Previous approaches have attempted to improve performance by identifying label-related image areas or exploiting the co-occurrence of labels.

A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition.

Support vector machines (SVMs) are powerful statistical learning tools, but their application to large datasets can cause time-consuming training complexity. To address this issue, various instance selection (IS) approaches have been proposed, which choose a small fraction of critical instances and screen out others before training. However, existing methods have not been able to balance accuracy and efficiency well.

Fucoidan from Alleviates Hyperuricemia-Induced Renal Fibrosis through Inhibiting the JAK2/STAT3 Signaling Pathway.

Fucoidan is a native sulfated polysaccharide mainly isolated from brown seaweed, with diverse pharmacological activities, such as anti-inflammatory and antifibrosis. Hyperuricemia (HUA) is a common metabolic disease worldwide and mainly causes hyperuricemic nephropathy, including chronic kidney disease and end-stage renal fibrosis. The present study investigated the protective function of fucoidan in renal fibrosis and its pharmacological mechanism.

SMOC2 plays a role in heart failure via regulating TGF-β1/Smad3 pathway-mediated autophagy.

Heart failure (HF) is a major global cause of morbidity and mortality. This study aimed to elucidate the role of secreted protein acidic and rich in cysteine-related modular calcium-binding protein 2 (SMOC2) in HF development and its underlying mechanism. Using a rat HF model, SMOC2 expression was examined and then knocked down via transfection to assess its impact on cardiac function and damage.

Linear-exponential loss incorporated deep learning for imbalanced classification.

The class imbalance issue is a pretty common and enduring topic all the time. When encountering unbalanced data distribution, conventional methods are prone to classify minority samples as majority ones, which may cause severe consequences in reality. It is crucial yet challenging to cope with such problems.

CSAST: Content self-supervised and style contrastive learning for arbitrary style transfer.

Arbitrary artistic style transfer has achieved great success with deep neural networks, but it is still difficult for existing methods to tackle the dilemma of content preservation and style translation due to the inherent content-and-style conflict. In this paper, we introduce content self-supervised learning and style contrastive learning to arbitrary style transfer for improved content preservation and style translation, respectively. The former one is based on the assumption that stylization of a geometrically transformed image is perceptually similar to applying the same transformation to the stylized result of the original image.

End-to-End Multitask Learning With Vision Transformer.

Multitask learning (MTL) is a challenging puzzle, particularly in the realm of computer vision (CV). Setting up vanilla deep MTL requires either hard or soft parameter sharing schemes that employ greedy search to find the optimal network designs. Despite its widespread application, the performance of MTL models is vulnerable to under-constrained parameters.

Exosomes secreted from bone marrow mesenchymal stem cells suppress cardiomyocyte hypertrophy through Hippo-YAP pathway in heart failure.

Mesenchymal stem cells-derived exosomes (MSCs-exosomes) reportedly possess cardioprotective effects. This study investigated the therapeutic potential and mechanisms of MSCs-exosomes on heart failure (HF). H9c2 cells were used to establish a cardiomyocyte hypertrophy model by angiotensin II (Ang II) treatment.

Partial label learning: Taxonomy, analysis and outlook.

Partial label learning (PLL) is an emerging framework in weakly supervised machine learning with broad application prospects. It handles the case in which each training example corresponds to a candidate label set and only one label concealed in the set is the ground-truth label. In this paper, we propose a novel taxonomy framework for PLL including four categories: disambiguation strategy, transformation strategy, theory-oriented strategy and extensions.

Nicotinamide riboside ameliorates high-fructose-induced lipid metabolism disorder in mice improving FGF21 resistance in the liver and white adipose tissue.

Fructose has been reported to acutely elevate the circulating fibroblast growth factor 21 (FGF21) levels, which ultimately causes FGF21 resistance. FGF21 resistance is suggested to result in lipid metabolism disorder. Nicotinamide riboside (NR) can alleviate lipid metabolism disorder in mice.

Protective effect of fucoidan against iron overload and ferroptosis-induced liver injury in rats exposed to alcohol.

This study was aimed to explore the effects of fucoidan on iron overload and ferroptosis-induced liver injury, and the underlying mechanisms in rats exposed to alcohol. Sprague-Dawley rats were used to establish alcoholic liver injury model by intragastric administration with alcohol for 16 weeks. The results showed that fucoidan treatment reversed alcohol-induced increases in reactive oxygen species and malondialdehyde levels, and increased glutathione peroxidase and glutathione levels, thus protecting against liver damage.

Antarctic krill oil ameliorates liver injury in rats exposed to alcohol by regulating bile acids metabolism and gut microbiota.

Bile acids (BAs) metabolism plays an important role in alcohol liver disease through the gut microflora-bile acids-liver axis. Antarctic Krill Oil (AKO) has protective effects on the liver, while whether AKO can protect against liver injury caused by alcohol is unclear. This study investigated the effects of AKO on BAs metabolism and intestinal microbiota in a rat model of alcohol-induced liver disease.

Mapping the risk distribution of sensu lato in China from 1986 to 2020: a geospatial modelling analysis.

Lyme borreliosis, recognized as one of the most important tick-borne diseases worldwide, has been increasing in incidence and spatial extent. Currently, there are few geographic studies about the distribution of Lyme borreliosis risk across China. Here we established a nationwide database that involved sensu lato () detected in humans, vectors, and animals in China.

LLP-GAN: A GAN-Based Algorithm for Learning From Label Proportions.

Learning from label proportions (LLP) is a widespread and important learning paradigm: only the bag-level proportional information of the grouped training instances is available for the classification task, instead of the instance-level labels in the fully supervised scenario. As a result, LLP is a typical weakly supervised learning protocol and commonly exists in privacy protection circumstances due to the sensitivity in label information for real-world applications. In general, it is less laborious and more efficient to collect label proportions as the bag-level supervised information than the instance-level one.

Multi-view Teacher-Student Network.

Multi-view learning aims to fully exploit the view-consistency and view-discrepancy for performance improvement. Knowledge Distillation (KD), characterized by the so-called "Teacher-Student" (T-S) learning framework, can transfer information learned from one model to another. Inspired by knowledge distillation, we propose a Multi-view Teacher-Student Network (MTS-Net), which combines knowledge distillation and multi-view learning into a unified framework.

Automatic diagnosis of COVID-19 infection based on ontology reasoning.

Background: 2019-nCoV has been spreading around the world and becoming a global concern. To prevent further widespread of 2019-nCoV, confirmed and suspected cases of COVID-19 infection are suggested to be kept in quarantine. However, the diagnose of COVID-19 infection is quite time-consuming and labor-intensive.