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The Traf2 and NcK interacting kinase inhibitor NCB-0846 suppresses seizure activity involving the decrease of GRIA1

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Genes & Diseases 2024年第3期11卷 452-467页

作者： Min Wang Yixue Gu Qiubo Li Bangzhe Feng Xinke Lv Hao Zhang Qingxia Kong Zhifang Dong Xin Tian Yanke Zhang Department of Neurology Affiliated Hospital of Jining Medical UniversityJiningShandong 272000China Department of Neurology The First Affiliated Hospital of Chongqing Medical UniversityChongqing Key Laboratory of NeurologyChongqing 400016China Department of Pediatrics Affiliated Hospital of Jining Medical UniversityJiningShandong 272000China Department of Neurosurgery Affliated Hospital of Jining Medical UniversityJiningShandong 272000China Pediatric Research Institute Ministry of Education Key Laboratory of Child Development and DisordersNational Clinical Research Center for Child Health and DisordersChongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory DisordersChildren's Hospital of Chongqing Medical UniversityChongqing 400014China center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Epilepsy,one of the most common neurological disorders,is characterized by spon-taneous recurrent *** lobe epilepsy(TLE)is one of the most common medically intractable seizure ***2-and NcK-interacting kinase(TNIK)has recently attracted attention as a critical modulation target of many neurological and psychiatric disorders,but its role in epilepsy remains *** this study,we hypothesized the involvement of TNIK in epilepsy and investigated TNIK expression in patients with intractable TLE and in a pilocarpineinduced rat model of epilepsy by western blotting,immunofluorescence,and immunohistochemistry.A pentylenetetrazole(PTZ)-induced epilepsy rat model was used to determine the effect of the TNIK inhibitor NCB-0846 on behavioral manifestations of ***(Co-IP)/mass spectrometry(MS)was used to identify the potential *** Co-IP,we detected and confirmed the main potential TNIK *** fractionation was used to establish the effect of NCB-0846 on the expression of the main interactors in postsynaptic density(PSD)*** found that TNIK was primarily located in neurons and decreased significantly in epilepsy model rats and TLE patients compared with ***-0846 delayed kindling progression and decreased seizure ***-IP/MS identified 63 candidate TNIK interactors in rat hippocampi,notably ***-IP showed that TNIK might correlate with endogenous GRIA1,SYN2,PSD-95,CaMKIV,GABRG1,and *** addition,the significant decrease in GRIA1 in hippocampal total lysate and PSDs after NCB-0846 treatment might help modify the progression of PTZ *** results suggest that TNIK contributes to epileptic pathology and is a potential antiepileptic drug target.

关键词： Epilepsy NCB-0846 Rat TLE TNIK Traf2-and NcK-interacting kinase

Power of SAR Imagery and Machine learning in Monitoring Ulva prolifera: A Case Study of Sentinel-1 and Random Forest

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Chinese Geographical Science 2024年

作者： ZHENG Longxiao WU Mengquan XUE Mingyue WU Hao LIANG Feng LI Xiangpeng HOU Shimin LIU Jiayan College of Resources and Environmental Engineering Ludong University North China Sea Marine Forecasting and Hazard Mitigation center Ministry of Natural Resources School of Life Sciences Lanzhou University Marine development and Fisheries Bureau of Yantai Modern Marine Industry development Promotion center of Yantai

Automatically detecting Ulva prolifera(U. prolifera) in rainy and cloudy weather using remote sensing imagery has been a long-standing problem. Here, we address this challenge by combining high-resolution Synthetic Aperture Radar(SAR) imagery with the machine learning, and detect the U. prolifera of the South Yellow Sea of China(SYS) in 2021. The findings indicate that the Random Forest model can accurately and robustly detect U. prolifera, even in the presence of complex ocean backgrounds and speckle *** inspection confirmed that the method successfully identified the majority of pixels containing U. prolifera without misidentifying noise pixels or seawater pixels as U. prolifera. Additionally, the method demonstrated consistent performance across different images, with an average Area Under Curve(AUC) of 0.930(±0.028). The analysis yielded an overall accuracy of over 96%, with an average Kappa coefficient of 0.941(±0.038). Compared to the traditional thresholding method, Random Forest model has a lower estimation error of 14.81%. Practical application indicates that this method can be used in the detection of unprecedented U. prolifera in 2021to derive continuous spatiotemporal changes. This study provides a potential new method to detect U. prolifera and enhances our understanding of macroalgal outbreaks in the marine environment.

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Accelerating the practical application of MOFs for hydrogen storage——from performance-driven to application-oriented

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Green Energy & Environment 2024年第8期9卷 1193-1198页

作者： Yifan Wang Jinghui Wu Yidi Gao Keqing Li Chi Wang Xiaochun Cui Mingxin Huo Xianze Wang Science and Technology Innovation center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Engineering Research center of Low-Carbon Treatment and Green development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University

Metal-organic frameworks(MOFs) are highly promising porous materials known for their exceptional porosity, extensive surface area, and customizable pore structures, making them an ideal solution for hydrogen storage. However, most MOFs research remains confined to the laboratory, lacking practical applications. To address this, the author proposes a shift towards practical applications, the creation of a comprehensive MOFs database, alignment of synthesis with practical considerations, and diversification of MOFs applications. These steps are crucial for harnessing the full potential of MOFs in real-world energy challenges.

关键词： Metal–organic frameworks Hydrogen gas storage Molecular simulation Computational chemistry Machine learning

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Improving link prediction models through a performance enhancement scheme:a study on semi-supervised learning and model soup

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The Journal of China Universities of Posts and Telecommunications 2024年第4期31卷 43-53页

作者： Qi Donglin Chen Shudong Du Rong Yu Yong Tong Da Intelligent Manufacturing Electronics Research and development center Institute of Microelectronics of the Chinese Academy of SciencesBeijing 100029China School of Integrated Circuits University of Chinese Academy of SciencesBeijing 100049China

As an important method for knowledge graph(KG)complementation,link prediction has become a hot research topic in recent *** this paper,a performance enhancement scheme for link prediction models based on the idea of semi-supervised learning and model soup is proposed,which effectively improves the model performance on several mainstream link prediction models with small changes to their *** novel scheme consists of two main parts,one is predicting potential fact triples in the graph with semi-supervised learning strategies,the other is creatively combining semi-supervised learning and model soup to further improve the final model performance without adding significant computational *** validate the effectiveness of the scheme for a variety of link prediction models,especially on the dataset with dense *** terms of CompGCN,the model with the best overall performance among the tested models improves its Hits@1 metric by 14.7%on the FB15K-237 dataset and 7.8%on the WN18RR dataset after using the enhancement ***,it is observed that the semi-supervised learning strategy in the augmentation scheme has a significant improvement for multi-class link prediction models,and the performance improvement brought by the introduction of the model soup is related to the specific tested models,as the performances of some models are improved while others remain largely unaffected.

关键词： natural language processing knowledge graph(KG) link prediction model soup semi-supervised learning

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miR-429-3p mediates memory decline by targeting MKP-1 to reduce surface GluA1- containing AMPA receptors in a mouse model of Alzheimer’s disease

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Acta Pharmaceutica Sinica B 2024年第2期14卷 635-652页

作者： Man Luo Yayan Pang Junjie Li Lilin Yi Bin Wu Qiuyun Tian Yan He Maoju Wang Lei Xia Guiqiong He Weihong Song Yehong Du Zhifang Dong Pediatric Research Institute Ministry of Education Key Laboratory of Child Development and DisordersNational Clinical Research Center for Child Health and DisordersChina International Science and Technology Cooperation Base of Child Development and Critical DisordersChongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory DisordersChildren’s Hospital of Chongqing Medical UniversityChongqing 400014China Department of Anatomy Basic Medical CollegeChongqing Medical UniversityChongqing 400016China Townsend Family Laboratories Department of PsychiatryThe University of British ColumbiaVancouver BC V6T 1Z3Canada Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Institute of AgingKey Laboratory of Alzheimer’s Disease of Zhejiang ProvinceZhejiang Clinical Research Center for Mental DisordersSchool of Mental Health and the Affiliated Kangning HospitalWenzhou Medical UniversityWenzhou 325000China Institute for Brain Science and Disease of Chongqing Medical University Chongqing 400016China

Alzheimer ’s disease(AD) is a leading cause of dementia in the ***-activated protein kinase phosphatase 1(MKP-1) plays a neuroprotective role in ***,the molecular mechanisms underlying the effects of MKP-1 on AD have not been extensively ***(miRNAs) regulate gene expression at the post-transcriptional level,thereby repressing mRNA ***,we reported that the microRNA-429-3p(miR-429-3p) was significantly increased in the brain of APP23/PS45 AD model mice and N2AAPPAD model *** further found that miR-429-3p could downregulate MKP-1 expression by directly binding to its 3’-untranslated region(3’ UTR).Inhibition of miR-429-3p by its antagomir(A-miR-429) restored the expression of MKP-1 to a control level and consequently reduced the amyloidogenic processing of APP and Aβ *** importantly,intranasal administration of A-miR-429 successfully ameliorated the deficits of hippocampal CA1 long-term potentiation and spatial learning and memory in AD model mice by suppressing extracellular signal-regulated kinase(ERK1/2)-mediated GluAl hyperphosphorylation at Ser831 site,thereby increasing the surface expression of GluAl-containing α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors(AMPARs).Together,these results demonstrate that inhibiting miR-429-3p to upregulate MKP-1 effectively improves cognitive and synaptic functions in AD model mice,suggesting that miR-429/MKP-1 pathway may be a novel therapeutic target for AD treatment.

关键词： Alzheimer's disease MKP-1 miR-429-3p AMPAreceptor learning and memory Long-term potentiation

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Machine learning model based on non-convex penalized huberized-SVM

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Journal of Electronic Science and Technology 2024年第1期22卷 81-94页

作者： Peng Wang Ji Guo Lin-Feng Li School of Finance and Economics Xizang Minzu UniversityXianyang712082China Research center for Quality development of Xizang Special Industries Xianyang712082China School of Computer Science and Technology Xinjiang UniversityUrumqi830017China

The support vector machine(SVM)is a classical machine learning *** the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional ***,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle *** this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional *** is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier *** are also superior in terms of variable selection,especially when there is a high linear correlation between the *** they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered *** all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest *** companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.

关键词： Huberized loss Machine learning Non-convex penalties Support vector machine(SVM)

A genomic association study revealing subphenotypes of childhood steroid-sensitive nephrotic syndrome in a larger genomic sequencing cohort

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Genes & Diseases 2024年第4期11卷 400-410页

作者： Han Chan Fenfen Ni Bo Zhao Huimin Jiang Juanjuan Ding Li Wang Xiaowen Wang Jingjing Cui Shipin Feng Xiaojie Gao Xueying Yang Huan Chi Hao Lee Xuelan Chen Xiaoqin Li Jia Jiao Daoqi Wu Gaofu Zhang Mo Wang Yupeng Cun Xiongzhong Ruan Haiping Yang Qiu Li Department of Nephrology Children's Hospital of Chongqing Medical UniversityNational Clinical Research Center for Child Health and DisordersMinistry of Education Key Laboratory of Child Development and DisordersChongqing Key Laboratory of PediatricsChongqing 400014China Department of Nephrology Shenzhen Children's HospitalShenzhenGuangdong 518034China Department of Nephrology Kunming Children's HospitalKunming Medical UniversityKunmingYunnan 650228China Department of Nephrology Wuhan Children's HospitalTongji Medical College of Huazhong University of Science and TechnologyWuhanHubei 430015China Department of Nephrology Chengdu Women and Children Central HospitalChengduSichuan 610073China Pediatric Research Institute Ministry of Education Key Laboratory of Child Development and DisordersNational Clinical Research Center for Child Health and DisordersChina International Science and Technology Cooperation Base of Child Development and Critical DisordersChongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory DisordersChildren's Hospital of Chongqing Medical UniversityChongqing 400014China Department of Nephrology John Moorhead Research LaboratoryUniversity College London Medical SchoolRoyal Free CampusUniversity College LondonLondon NW32PFUnited Kingdom

Dissecting the genetic components that contribute to the two main subphenotypes of steroid-sensitive nephrotic syndrome(SSNS)using genome-wide association studies(GWAS)strategy is important for understanding the *** conducted a multicenter cohort study(360 patients and 1835 controls)combined with a GWAS strategy to identify susceptibility var-iants associated with the following two subphenotypes of ssNS:steroid-sensitive nephrotic syn-drome without relapse(SSNswR,181 patients)and steroid-dependent/frequent relapse nephrotic syndrome(SDNS/FRNS,179 patients).The distribution of two single-nucleotide poly-morphisms(SNPs)in ANKRD36 and ALPG was significant between SSNSWR and healthy controls,and that of two SNPs in GAD1 and HLA-DQA1 was significant between SDNS/FRNS and healthy ***,rs1047989 in HLA-DQA1 was a candidate locus for SDNS/FRNS but not for *** significant SNPs were observed between SSNSWR and SDNS/***,chromosome 2:171713702 in GAD1 was associated with a greater steroid dose(>0.75 mg/kg/d)upon relapse to first remission in patients with SDNS/FRNS(odds ratio=3.14;95%confidence interval,0.97-9.87;P=0.034).rs117014418 in APOL4 was significantly associated with a decrease in eGFR of greater than 20%compared with the baseline in SDNS/FRNS patients(P=0.0001).Protein-protein intersection network construction suggested that HLA-DQA1 and HLA-DQB1 function together through ***,SSNSWR belongs to non-HLA region-dependent nephropathy,and the HLA-DQA/DQB region is likely strongly associated with dis-ease relapse,especially in SDNS/*** study provides a novel approach for the GWAS strategy of SsNS and contributes to our understanding of the pathological mechanisms of SSNSWRandSDNS/FRNS.

关键词： Frequentrelapse Genome-wide association study Human leukocyte antigen region Steroid-sensitive nephrotic syndrome

Climate bonds toward achieving net zero emissions and carbon neutrality:Evidence from machine learning technique

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Journal of Management Science and Engineering 2024年第1期9卷 1-15页

作者： Hermas Abudu Presley K.Wesseh Jr. Boqiang Lin College of Overseas Education Chengdu UniversityChengdu610106SichuanChina School of Management China Institute for Studies in Energy PolicyCollaborative Innovation Center for Energy Economics and Energy PolicyXiamen UniversityFujian361005China center for African development Strategy(CFADS) MonroviaLiberia

The Conference of the Parties(COP26 and 27)placed significant emphasis on climate financing policies with the objective of achieving net zero emissions and carbon ***,studies on the implementation of this policy proposition are *** address this gap in the literature,this study employs machine learning techniques,specifically natural language processing(NLP),to examine 77 climate bond(CB)policies from 32 countries within the context of climate *** findings indicate that“sustainability”and“carbon emissions control”are the most outlined policy objectives in these CB ***,the study highlights that most CB funds are invested toward energy projects(i.e.,renewable,clean,and efficient initiatives).However,there has been a notable shift in the allocation of CB funds from climate-friendly energy projects to the construction sector between 2015 and *** shift raises concerns about the potential redirection of funds from climate-focused investments to the real estate industry,potentially leading to the greenwashing of climate ***,policy sentiment analysis revealed that a minority of policies hold skeptical views on climate change,which may negatively influence climate ***,the findings highlight that the effective implementation of CB policies depends on policy goals,objectives,and ***,this study contributes to the literature by employing NLP techniques to understand policy sentiments in climate financing.

关键词： Climate bonds funds utilization Climate bonds policy text mining Machine learning technique Net zero emissions Policy sentiment analysis

An explainable AI framework for robust and transparent data-driven wind turbine power curve models

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Energy and AI 2024年第1期15卷 312-327页

作者： Simon Letzgus Klaus-Robert Müller Machine learning Group Technische Universitβe BerlinStraβe des 17.Juni 135Berlin10623Germany Berlin Institute for the Foundations of learning and Data Berlin10587Germany Department of Artificial Intelligence Korea UniversitySeoul136-713South Korea Max Planck Institute for Informatics Saarbrücken66123Germany

In recent years,increasingly complex machine learning methods have become state-of-the-art in modelling wind turbine power curves based on operational *** these methods often exhibit superior performance on test sets,they face criticism due to a perceived lack of transparency and concerns about their robustness in dynamic,non-stationary environments encountered by wind *** this work,we address these issues and present a framework that leverages explainable artificial intelligence methods to gain systematic insights into data-driven power curve *** its core,we propose a metric to quantify how well a learned model strategy aligns with the underlying physical principles of the *** novel tool enables model validation beyond the conventional error metrics in an automated *** demonstrate,for instance,its capacity as an indicator for model generalization even when limited data is ***,it facilitates understanding how decisions made during the machine learning development process,such as data selection,pre-processing,or training parameters,affect learned *** a result,we obtain physically more reasonable models,a prerequisite not only for robustness but also for meaningful insights into turbine operation by domain *** latter,we illustrate in the context of wind turbine performance *** summary,the framework aims to guide researchers and practitioners alike toward a more informed selection and utilization of data-driven wind turbine power curve models.

关键词： Explainable AI(XAI) Machine learning Wind energy Wind turbine power curve SCADA Condition monitoring