Precious but convenient means of prevention and treatment: physiological molecular mechanisms of interaction between exercise and motor factors and Alzheimer’s disease

Disproportionate to the severity of Alzheimer’s disease (AD) and the huge number of patients, the exact treatment and prevention of AD is still being explored. With increasing ageing, the search for means to prevent and treat AD has become a high priority. In the search for AD, it has been suggested that exercise may be one of the more effective and less costly means of preventing and treating AD, and therefore a large part of current research is aimed at exploring the effectiveness of exercise in the prevention and treatment of AD. However, due to the complexity of the specific pathogenesis of AD, there are multiple hypotheses and potential mechanisms for exercise interventions in AD that need to be explored. This review therefore specifically summarises the hypotheses of the interaction between exercise and AD from a molecular perspective, based on the available evidence from animal models or human experiments, and explores them categorised according to the pathologies associated with AD: exercise can activate a number of signalling pathways inhibited by AD (e.g., Wnt and PI3K/Akt signalling pathways) and reactivate the effects of downstream factors regulated by these signalling pathways, thus acting to alleviate autophagic dysfunction, relieve neuroinflammation and mitigate Aβ deposition. In addition, this paper introduces a new approach to regulate the blood-brain barrier, i.e., to restore the stability of the blood-brain barrier, reduce abnormal phosphorylation of tau proteins and reduce neuronal apoptosis. In addition, this paper introduces a new concept.” Motor factors” or “Exerkines”, which act on AD through autocrine, paracrine or endocrine stimulation in response to movement. In this process, we believe there may be great potential for research in three areas: (1) the alleviation of AD through movement in the brain-gut axis (2) the prevention and treatment of AD by movement combined with polyphenols (3) the continued exploration of movement-mediated activation of the Wnt signalling pathway and AD

UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy

In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud.For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60\% on thousands of object instances, which significantly outperforms all baselines, meanwhile showing only a minimal generalization gap.Comment: Accepted to CVPR 202

An asymmetric supercapacitor with excellent cycling performance realized by hierarchical porous NiGa2O4 nanosheets

Rational design of composition and electrochemically favorable structure configuration of electrode materials are highly required to develop high-performance supercapacitors. Here, we report our findings on the design of interconnected NiGa2O4 nanosheets as advanced cathode electrodes for supercapacitors. Rietveld refinement analysis demonstrates that the incorporation of Ga in NiO leads to a larger cubic lattice parameter that promotes faster charge-transfer kinetics, enabling significantly improved electrochemical performance. The NiGa2O4 electrode delivers a specific capacitance of 1508 F g−1 at a current density of 1 A g−1 with the capacitance retention of 63.7% at 20 A g−1, together with excellent cycling stability after 10000 charge–discharge cycles (capacitance retention of 102.4%). An asymmetric supercapacitor device was assembled by using NiGa2O4 and Fe2O3 as cathode and anode electrodes, respectively. The ASC delivers a high energy density of 45.2 Wh kg−1 at a power density of 1600 W kg−1 with exceptional cycling stability (94.3% cell capacitance retention after 10000 cycles). These results suggest that NiGa2O4 can serve as a new class cathode material for advanced electrochemical energy storage applications

Thickened Perirenal Fat Predicts Poor Renal Outcome in Patients with Immunoglobulin A Nephropathy: A Population-Based Retrospective Cohort Study

Introduction: Perirenal fat is a pad that fills the retroperitoneal space outside the kidney, which affects kidney function in various ways. However, the association between perirenal fat and IgA nephropathy (IgAN) has not yet been elucidated. This study aimed to investigate the role of perirenal fat in predicting IgAN progression. Methods: A total of 473 patients with biopsy-proven IgAN and follow-up information were recruited, and perirenal fat thickness (PFT) was measured using color Doppler ultrasonography at renal biopsy. Patients were divided into two groups according to the median PFT: the low-PFT group (PFT ≤1.34 cm, n = 239) and the high PFT group (PFT >1.35 cm, n = 234). A total of 473 healthy participants were included in the control group. Basic clinical characteristics were assessed at the time of renal biopsy, and the relationship between PFT and combined endpoints was analyzed. The renal composite endpoints were defined as a two-fold increase in blood creatinine level, end-stage renal disease (dialysis over 3 months). Kaplan-Meier survival analysis was used to explore the role of PFT in the progression of IgAN. Three clinicopathological models of multivariate Cox regression analysis were established to evaluate the association between PFT and renal prognosis in patients with IgAN. Results: Compared to healthy subjects, patients with IgAN showed significantly higher PFT. After a median follow-up of 50 months, 75 of 473 patients (15.9%) with IgAN reached renal composite endpoints. Among those, 13 of 239 patients (5.4%) were in the low PFT group, and 62 of 234 patients (26.5%) were in the high PFT group (p < 0.001). The results of three Cox regression models (including demographics, pathological and clinical indicators, and PFT) demonstrated that a higher PFT was significantly associated with a higher risk of reaching renal composite endpoints in patients with IgAN. Conclusion: This study indicated a positive relationship between PFT at renal biopsy and renal progression in patients with IgAN, suggesting that perirenal fat might act as a marker of poor prognosis in patients with IgAN

Wheat Rhizosphere Metagenome Reveals Newfound Potential Soil Zn-Mobilizing Bacteria Contributing to Cultivars’ Variation in Grain Zn Concentration

An effective solution to global human zinc (Zn) deficiency is Zn biofortification of staple food crops, which has been hindered by the low available Zn in calcareous soils worldwide. Many culturable soil microbes have been reported to increase Zn availability in the laboratory, while the status of these microbes in fields and whether there are unculturable Zn-mobilizing microbes remain unexplored. Here, we use the culture-independent metagenomic sequencing to investigate the rhizosphere microbiome of three high-Zn (HZn) and three low-Zn (LZn) wheat cultivars in a field experiment with calcareous soils. The average grain Zn concentration of HZn was higher than the Zn biofortification target 40 mg kg–1, while that of LZn was lower than 40 mg kg–1. Metagenomic sequencing and analysis showed large microbiome difference between wheat rhizosphere and bulk soil but small difference between HZn and LZn. Most of the rhizosphere-enriched microbes in HZn and LZn were in common, including many of the previously reported soil Zn-mobilizing microbes. Notably, 30 of the 32 rhizosphere-enriched species exhibiting different abundances between HZn and LZn possess the functional genes involved in soil Zn mobilization, especially the synthesis and exudation of organic acids and siderophores. Most of the abundant potential Zn-mobilizing species were positively correlated with grain Zn concentration and formed a module with strong interspecies relations in the co-occurrence network of abundant rhizosphere-enriched microbes. The potential Zn-mobilizing species, especially Massilia and Pseudomonas, may contribute to the cultivars’ variation in grain Zn concentration, and they deserve further investigation in future studies on Zn biofortification

A Survey of Large Language Models

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.Comment: ongoing work; 51 page

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Case Study Research in Tesla (China) Marketing Strategy Application During Covid-19

Background: In the past two years, the outbreak of the coronavirus has had a major impact on the world economy, and has a considerable negative impact on the performance and sales of the automobile manufacturing industry. Enterprises need to sum up their experience. Tesla's successful case can be used as a reference for analysis. . Purpose: In response to the substantial increase in sales performance of Tesla's Chinese market during the epidemic, relevant market strategy analysis was made, and the researchers tried to summarize relevant experience to provide reference for the automotive industry. Method: The researchers used a relatively flexible and exploratory qualitative approach, conducting semi-structured interviews with seven current Tesla employees and using secondary sources to aid in proving the veracity and viability of the information. Conclusion: The results show that most of the targeted strategies implemented by Tesla during the epidemic are effective, and the application of various strategies is related to changes in sales performance. The researchers collected raw data through interviews, analyzed why Tesla used these strategies, and evaluated the application effects of the main strategies. At the same time, the researchers also put forward our own views and opinions

Case Study Research in Tesla (China) Marketing Strategy Application During Covid-19

Background: In the past two years, the outbreak of the coronavirus has had a major impact on the world economy, and has a considerable negative impact on the performance and sales of the automobile manufacturing industry. Enterprises need to sum up their experience. Tesla's successful case can be used as a reference for analysis. . Purpose: In response to the substantial increase in sales performance of Tesla's Chinese market during the epidemic, relevant market strategy analysis was made, and the researchers tried to summarize relevant experience to provide reference for the automotive industry. Method: The researchers used a relatively flexible and exploratory qualitative approach, conducting semi-structured interviews with seven current Tesla employees and using secondary sources to aid in proving the veracity and viability of the information. Conclusion: The results show that most of the targeted strategies implemented by Tesla during the epidemic are effective, and the application of various strategies is related to changes in sales performance. The researchers collected raw data through interviews, analyzed why Tesla used these strategies, and evaluated the application effects of the main strategies. At the same time, the researchers also put forward our own views and opinions