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Unraveling the complex roles of macrophages in obese adipose tissue:an overview

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Frontiers of Medicine 2024年第2期18卷 205-236页

作者： Chang Peng Jun Chen rui Wu Haowen jiang Jia Li Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou310000China State Key Laboratory of Drug Research Shanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201203China University of Chinese Academy of Sciences Beijing100049China Department of Prosthodontics Shanghai Engineering Research Center of Advanced Dental Technology and MaterialsShanghai Key Laboratory of Stomatology&Shanghai Research Institute of StomatologyNational Clinical Research Center for Oral DiseasesShanghai Ninth People’s HospitalCollege of StomatologyShanghai Jiao Tong University School of MedicineShanghai200011China

Macrophages,a heterogeneous population of innate immune cells,exhibit remarkable plasticity and play pivotal roles in coordinating immune responses and maintaining tissue homeostasis within the context of metabolic diseases.The activation of inflammatory macrophages in obese adipose tissue leads to detrimental effects,inducing insulin resistance through increased inflammation,impaired thermogenesis,and adipose tissue fibrosis.Meanwhile,adipose tissue macrophages also play a beneficial role in maintaining adipose tissue homeostasis by regulating angiogenesis,facilitating the clearance of dead adipocytes,and promoting mitochondrial transfer.Exploring the heterogeneity of macrophages in obese adipose tissue is crucial for unraveling the pathogenesis of obesity and holds significant potential for targeted therapeutic interventions.Recently,the dual effects and some potential regulatory mechanisms of macrophages in adipose tissue have been elucidated using single-cell technology.In this review,we present a comprehensive overview of the intricate activation mechanisms and diverse functions of macrophages in adipose tissue during obesity,as well as explore the potential of drug delivery systems targeting macrophages,aiming to enhance the understanding of current regulatory mechanisms that may be potentially targeted for treating obesity or metabolic diseases.

关键词： obesity inflammation adipose tissue macrophages adipose tissue homeostasis

The Neural Mechanism of Knowledge Assembly in the Human Brain Inspires Artificial Intelligence Algorithm

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Neuroscience Bulletin 2024年第2期40卷 280-282页

作者： Xiang Ji Wentao jiang Xiaoru Zhang Ming song Shan Yu Tianzi jiang University of the Chinese Academy of Sciences Beijing 100049China Brainnetome Center Institute of AutomationThe Chinese Academy of SciencesBeijing 100190China Laboratory of Brain Atlas and Brain-inspired Intelligence Institute of AutomationThe Chinese Academy of SciencesBeijing 100190China CAS Center for Excellence in Brain Science and Intelligence Technology The Chinese Academy of SciencesBeijing 100190China Research Center for Augmented Intelligence Zhejiang LabHangzhou 311100China

When new information enters the brain,a human's prior knowledge of the world can change rapidly through a process referred to as"knowledge assembly".Recently,Nelli et al.investigated the neural correlates of knowledge assembly in the human brain using functional MRI.Further,inspired by the neural mechanism,the authors developed an artificial neural network algorithm to permit rapid knowledge assembly,improving the flexibility of the system[1].Once again,this research demonstrates that studying how the brain works can lead to better computational algorithms.

关键词： neural knowledge artificial

Comparative analysis of DNA-SIP and magnetic-nanoparticle mediated isolation (MMI) on unraveling dimethoate degraders

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Frontiers of Environmental Science & Engineering 2024年第1期18卷 59-69页

作者： Luning Lian Yi Xing Dayi Zhang Longfei jiang Mengke song Bo jiang School of Energy and Environmental Engineering University of Science&Technology BeijingBeijing 100083China Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants University of Science&Technology BeijingBeijing 100083China National Environmental and Energy Science and Technology International Cooperation Base University of Science&Technology BeijingBeijing 100083China College of New Energy and Environment Jilin UniversityChangchun 130021China Guangzhou Institute of Geochemistry Chinese Academy of SciencesGuangzhou 510640China College of Natural Resources and Environment South China Agricultural UniversityGuangzhou 510642China National Engineering Laboratory for Site Remediation Technologies Beijing 100015China

Microorganisms are crucial in the bioremediation of organophosphorus pesticides. However, most functional microorganisms (> 99%) are yet to be cultivated. This study applied two cultivation-independent approaches, DNA-SIP and magnetic-nanoparticle mediated isolation (MMI), to identify the functional microorganisms in degrading dimethoate in agricultural soils. MMI identified five dimethoate degraders: Pseudomonas, Bacillus, Ramlibacter, Arthrobacter, and Rhodococcus, whereas DNA-SIP identified three dimethoate degraders: Ramlibacter, Arthrobacter, and Rhodococcus. Also, MMI showed higher resolution than DNA-SIP in identifying functional microorganisms. Two organic phosphohydrolase (OPH) genes: ophC2 and ophB, were involved in dimethoate metabolism, as revealed by DNA-SIP and MMI. The degradation products of dimethoate include omethoate, O,O,S-trimethyl thiophosphorothioate, N-methyl-2-sulfanylacetamide, O,O-diethyl S-hydrogen phosphorodithioate, O,O,O-trimethyl thiophosphate, O,O,S-trimethyl thiophosphorodithioate, and O,O,O-trimethyl phosphoric. This study emphasizes the feasibility of using SIP and MMI to explore the functional dimethoate degraders, expanding our knowledge of microbial resources with cultivation-independent approaches.

关键词： Stable isotope probing(SIP) Magnetic-nanoparticle mediated isolation(MMI) Dimethoate Biodegradation Cultivation-independent approach

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

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Advances in Atmospheric Sciences 2024年第7期41卷 1379-1390页

作者： Chentao song jiang ZHU Xichen LI Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

关键词： Arctic sea ice thickness deep learning spatiotemporal sequence prediction transfer learning

Gravitational waves and primordial black hole productions from gluodynamics by holography

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Science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 31-42页

作者： song He Li Li Zhibin Li Shao-jiang Wang Center for Theoretical Physics and College of Physics Jilin UniversityChangchun 130012China Max Planck Institute for Gravitational Physics(Albert Einstein Institute) Golm 14476Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Peng Huanwu Collaborative Center for Research and Education Beihang UniversityBeijing 100191China School of Physics and Microelectronics Zhengzhou UniversityZhengzhou 450001China

Understanding the nature of quantum chromodynamics(QCD)matter is important but challenging due to the presence of nonperturbative dynamics under extreme conditions.We construct a holographic model describing the gluon sector of QCD at finite temperatures in the non-perturbative regime.The equation of state as a function of temperature is in good accordance with the lattice QCD data.Moreover,the Polyakov loop and the gluon condensation,which are proper order parameters to capture the deconfinement phase transition,also agree quantitatively well with the lattice QCD data.We obtain a strong first-order confinement/deconfinement phase transition at Tc=276.5 Me V that is consistent with the lattice QCD prediction.Based on our model for a pure gluon hidden sector,we compute the stochastic gravitational waves and primordial black hole(PBH)productions from this confinement/deconfinement phase transition in the early Universe.The resulting stochastic gravitational-wave backgrounds are found to be within detectability in the International Pulsar Timing Array and Square Kilometre Array in the near future when the associated productions of PBHs saturate the current observational bounds on the PBH abundances from the LIGO-Virgo-Collaboration O3 data.

关键词： AdS/QCD confinement/deconfinement phase transition gravitational wave primordial black hole

Localized CdS homojunctions with optimal ratio of high and low index facets to dynamically boost H_(2)O splitting into H_(2)energy

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Journal of Materials Science & Technology 2024年第4期171卷 94-100页

作者： Xiaojun Wang Yiqi Zhang Shujuan jiang Jiakun Su Shaoqing song School of Materials Science&Chemical Engineering Ningbo UniversityNingbo 330013China School of New Energy Ningbo University of TechnologyNingbo 315336China Tchnology Center China Tobacco Jiangxi Industrial LLCNanchang 330096China

Localized CdS homojunctions with optimal ratio of high and low index facets are constructed to dy-namically boost H_(2)O splitting into H_(2)energy by hydrothermal method in combination with calcination.By density functional theory,hall effect,and in situ diffuse reflectance infrared Fourier transform spec-troscopy,it is revealed that photo-irradiated e^(−)and h^(+)can be spatially separated and directionally trans-ferred to the reductive high-index facet{002}and oxidative low-index facet{110}of localized CdS homo-junction induced by Fermi level difference of both high and low index facets to dehydrogenate ^(∗)-OH and coupled ^(∗)-O intermediates for H_(2)and O_(2)yield,respectively,along with a solar conversion into hydrogen of 1.93%by AM 1.5 G irradiation at 65℃.The study work suggests a scientific perspective on the optimal ratio of high and low index facets to understand photo-generated charge carrier transfer dynamically and their photocatalytic principle for H_(2)O splitting reaction in kinetics.

关键词： Photocatalytic H_(2)evolution Homojunction Localized field Ratio of high low index facets In-situ DRIFTS

Surface morphologies of Mg-Gd alloy particle during its reactions with O_(2) and Teflon

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Defence Technology（防务技术） 2024年第3期33卷 209-221页

作者： Yifan Li Hongtao Yang Aifeng jiang Dongming song Yanchun Li Department of Chemical Engineering Nanjing University of Science and TechnologyNanjing 210097China Xi'an Modern Chemistry Research Institute Xi'an 710065China Southwest Technology and Engineering Research Institute Chongqing 400039China

Mg-Gd alloy particle has exhibited its unique combustion properties as the fuel of Mg/Teflon/Viton(MTV).Mg-Gd alloy/Teflon/Viton(MGTV)could burn at lower ambient pressure than MTV.To further investigate the reaction of MGTV in air,it was investigated via thermo gravity-differential scanning calorimetry(TG-DSC).Meanwhile,the morphologies and element distributions on the alloy surface during the reaction of MGTV in air were investigated via scanning electronic microscope-mapping-electronic differential spectrometer.Meanwhile,a similar experimental protocol on the Mg-Gd alloy particle during oxidation was also applied.The results showed that owning to a protective oxide shell,the onset oxidation temperature of Mg-Gd alloy is higher than Mg.However,the onset oxidation temperature of the exceeded Mg-Gd alloy in MGTV is significantly lower than that of the exceeded Mg in MTV.It was due to the existence of GdOF,which could significantly lower the oxidation temperature of the exceeded fuel.Furthermore,a possible reaction mechanism was proposed.The fascinating oxidation properties of Mg-Gd alloy suggested its promising applications in energetic materials.

关键词： Mg-Gd alloy Energetic materials Morphology EDS GdOF

Jellyfish bio-inspired Fe@CNT@CuNC derived from ZIF-8 for cathodic oxygen reduction

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Nano Research 2024年第4期17卷 2352-2359页

作者： Kai Cheng Zequan Liu Demin jiang Min song Yuqiao Wang Research Center for Nano Photoelectrochemistry and Devices School of Chemistry and Chemical EngineeringSoutheast UniversityNanjing 211189China Yangtze River Delta Carbon Neutrality Strategy Development Institute Southeast UniversityNanjing 210096China School of Energy and Environment Southeast UniversityNanjing 210096China

Biomimetics provides guidance to design and synthesize advanced catalysts for oxygen reduction reaction in microbial fuel cells(MFCs).Herein,jellyfish-inspired Fe clusters on carbon nanotubes connected with CuNC(Fe@CNT@CuNC)were designed and prepared by using zeolitic imidazolate framework(ZIF)-8 precursors to imitate the organic texture and function of jellyfish.The antibacterial effect of Cu^(+)ions depressed the growth of cathode biofilm to ensure rapid mass transport.Fe clusters and CuNC connected by CNTs accelerated the electron transfer from Fe to CuNC.The optimization of oxygen adsorption was caused by electron redistribution between sites of Fe and Cu.Jellyfish-like catalysts achieved a half-wave potential of 0.86 V and onset potential of 0.95 V vs.reversible hydrogen electrode(RHE).MFCs gained the maximum power density of 1600 mW·m^(-2) after 500 h measurement.This work provides insights into the special design of advanced catalysts based on bio-inspiration and biomimetics.

关键词： bio-inspiration oxygen reduction reaction antibacterial ability microbial fuel cells density functional theory(DFT)rational design

Multimodal Fusion of Brain Imaging Data: Methods and Applications

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Machine Intelligence Research 2024年第1期21卷 136-152页

作者： Na Luo Weiyang Shi Zhengyi Yang Ming song Tianzi jiang Brainnetome Center and National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Research Center for Augmented Intelligence Zhejiang LaboratoryHangzhou 311100China

Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the brain.To lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality information.With the exploited rich information,it is becoming popular to combine multiple modality data to ex-plore the structural and functional characteristics of the brain in both health and disease status.In this paper,we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data,broadly categorized into unsupervised and supervised learning strategies.Followed by this,some representative applications are discussed,including how they help to under-stand the brain arealization,how they improve the prediction of behavioral phenotypes and brain aging,and how they accelerate the biomarker exploration of brain diseases.Finally,we discuss some exciting emerging trends and important future directions.Collectively,we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications,along with the chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.

关键词： Multimodal fusion supervised learning unsupervised learning brain atlas cognition brain disorders