Adiponectin protects against paraquat-induced lung injury by attenuating oxidative/nitrative stress.

The specific mechanisms underlying paraquat (PQ)-induced lung injury remain unknown, which limits understanding of its cytotoxic potential. Although oxidative stress has been established as an important mechanism underlying PQ toxicity, multiple antioxidants have proven ineffective in attenuating the deleterious effects of PQ. Adiponectin, which shows anti-oxidative and antinitrative effects, may have the potential to reduce PQ-mediated injury. The present study determined the protective action of globular domain adiponectin (gAd) on PQ-induced lung injury, and attempted to elucidate the underlying mechanism or mechanisms of action. BALB/c mice were administered PQ, with and without 12 or 36 h of gAd pre-treatment. The pulmonary oxidative/nitrative status was assessed by measuring pulmonary O2(•-), superoxide dismutase (SOD), malondialdehyde (MDA), nitric oxide (NO) and 8-hydroxy-2-dydeoxy guanosine (8-OHdG) production, and blood 3-Nitrotyrosine (3-NT). At a dose of 20 mg/kg, PQ markedly increased O2(•-), SOD, MDA, NO and 8-OHdG production 3 h post-administration, but did not significantly increase 3-NT levels until 12 h. gAd inhibited these changes in a dose-dependent manner, via transient activation of MDA, followed by attenuation of MDA formation from 6 h onwards. Histological analysis demonstrated that gAd decreased interstitial edema and inflammatory cell infiltration. These results suggest that gAd protects against PQ-induced lung injury by mitigating oxidative/nitrative stress. Furthermore, gAd may be a potential therapeutic agent for PQ-induced lung injury, and further pharmacological studies are therefore warranted

Improving heavy Dirac neutrino prospects at future hadron colliders using machine learning

In this work, by using the machine learning methods, we study the sensitivities of heavy pseudo-Dirac neutrino $N$ in the inverse seesaw at the high-energy hadron colliders. The production process for the signal is $pp \to \ell N \to 3 \ell + E_T^{\rm miss}$, while the dominant background is $p p \to W Z \to 3 \ell + E_T^{\rm miss}$. We use either the Multi-Layer Perceptron or the Boosted Decision Tree with Gradient Boosting to analyse the kinematic observables and optimize the discrimination of background and signal events. It is found that the reconstructed $Z$ boson mass and heavy neutrino mass from the charged leptons and missing transverse energy play crucial roles in separating the signal from backgrounds. The prospects of heavy-light neutrino mixing $|V_{\ell N}|^2$ (with $\ell = e,\,\mu$) are estimated by using machine learning at the hadron colliders with $\sqrt{s}=14$ TeV, 27 TeV, and 100 TeV, and it is found that $|V_{\ell N}|^2$ can be improved up to ${\cal O} (10^{-6})$ for heavy neutrino mass $m_N = 100$ GeV and ${\cal O} (10^{-4})$ for $m_N = 1$ TeV.Comment: 33 pages, 14 figures, 4 tables, more details and more references added, version published in JHE

LocMoE: A Low-Overhead MoE for Large Language Model Training

The Mixtures-of-Experts (MoE) model is a widespread distributed and integrated learning method for large language models (LLM), which is favored due to its ability to sparsify and expand models efficiently. However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity. Load imbalance may result from existing routing policies that consistently tend to select certain experts. The frequent inter-node communication in the All-to-All procedure also significantly prolongs the training time. To alleviate the above performance problems, we propose a novel routing strategy that combines load balance and locality by converting partial inter-node communication to that of intra-node. Notably, we elucidate that there is a minimum threshold for expert capacity, calculated through the maximal angular deviation between the gating weights of the experts and the assigned tokens. We port these modifications on the PanGu-Sigma model based on the MindSpore framework with multi-level routing and conduct experiments on Ascend clusters. The experiment results demonstrate that the proposed LocMoE reduces training time per epoch by 12.68% to 22.24% compared to classical routers, such as hash router and switch router, without impacting the model accuracy.Comment: 1. Update the font size of all figures. 2. Update the name of the proposed layer Grouped Average Pooling (GrAP). 3. Change the order of the Section Contribution Statemen

Association studies of several cholesterol-related genes (ABCA1, CETP and LIPC) with serum lipids and risk of Alzheimer’s disease

Abstract Objectives Accumulating evidence suggested that dysregulation of cholesterol homeostasis might be a major etiologic factor in initiating and promoting neurodegeneration in Alzheimer’s disease (AD). ATP-binding cassette transporter A1 (ABCA1), hepatic lipase (HL, coding genes named LIPC) and cholesteryl ester transfer protein (CETP) are important components of high-density lipoprotein (HDL) metabolism and reverse cholesterol transport (RCT) implicated in atherosclerosis and neurodegenerative diseases. In the present study, we will investigate the possible association of several common polymorphisms (ABCA1R219K, CETPTaqIB and LIPC-250 G/A) with susceptibility to AD and plasma lipid levels. Methods Case–control study of 208 Han Chinese (104 AD patients and 104 non-demented controls) from Changsha area in Hunan Province was performed using the PCR-RFLP analysis. Cognitive decline was assessed using Mini Mental State Examination (MMSE) as a standardized method. Additionally, fasting lipid profile and the cognitive testing scores including Wechsler Memory Scale (WMS) and Wisconsin Card Sorting Test (WCST) were recorded. Results and conclusions We found significant differences among the genotype distributions of these three genes in AD patients when compared with controls. But after adjusting other factors, multivariate logistic regression analysis showed only ABCA1R219K (B = −0.903, P = 0.005, OR = 0.405, 95%CI:0.217-0.758) and LIPC-250 G/A variants(B = −0.905, P = 0.018, OR = 0.405, 95%CI:0.191-0.858) were associated with decreased AD risk. There were significantly higher levels of high-density lipoprotein cholesterol (HDL-C) and apolipoproteinA-I in the carriers of KK genotype and K allele (P &lt; 0.05), and B2B2 genotype of CETP Taq1B showed significant association with higher HDL-C levels than other genotypes (F = 5.598, P = 0.004), while -250 G/A polymorphisms had no significant effect on HDL-C. In total population, subjects carrying ABCA1219K allele or LIPC-250A allele obtained higher MMSE or WMS scores than non-carriers, however, no significant association was observed in AD group or controls. Therefore, this preliminary study showed that the gene variants of ABCA1R219K and LIPC-250 G/A might influence AD susceptibility in South Chinese Han population, but the polymorphism of CETPTaq1B didn't show any association in despite of being a significant determinant of HDL-C. </jats:sec

A comparative metabolomics analysis of domestic yak (Bos grunniens) milk with human breast milk

Yaks are tough animals living in Tibet’s hypoxic stress environment. However, the metabolite composition of yak milk and its role in hypoxic stress tolerance remains largely unexplored. The similarities and differences between yak and human milk in hypoxic stress tolerance are also unclear. This study explored yak colostrum (YC) and yak mature milk (YMM) using GC–MS, and 354 metabolites were identified in yak milk. A comparative metabolomic analysis of yak and human milk metabolites showed that over 70% of metabolites were species-specific. Yak milk relies mainly on essential amino acids- arginine and essential branched-chain amino acids (BCAAs): L-isoleucine, L-leucine, and L-valine tolerate hypoxic stress. To slow hypoxic stress, human breast milk relies primarily on the neuroprotective effects of non-essential amino acids or derivates, such as citrulline, sarcosine, and creatine. In addition, metabolites related to hypoxic stress were significantly enriched in YC than in YMM. These results reveal the unique metabolite composition of yak and human milk and provide practical information for applying yak and human milk to hypoxic stress tolerance

Optimising hollow-structured silicon nanoparticles for lithium-ion batteries

Silicon has been proven to be one of the most promising anode materials for the next generation of lithium-ion batteries for application in batteries, the Si anode should have high capacity and must be industrially scalable. In this study, we designed and synthesised a hollow structure to meet these requirements. All the processes were carried out without special equipment. The Si nanoparticles that are commercially available were used as the core sealed inside a TiO2 shell, with rationally designed void space between the particles and shell. The Si@TiO2 were characterised using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). The optimised hollow-structured silicon nanoparticles, when used as the anode in a lithium-ion battery, exhibited a high reversible specific capacity over 630 mAhg−1, much higher than the 370 mAhg−1 from the commercial graphite anodes. This excellent electrochemical property of the nanoparticles could be attributed to their optimised phase and unique hollow nanostructure