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Effective suppression of surface cation segregations on double perovskite oxides through entropy engineering

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Journal of Rare Earths 2025年第2期43卷 345-353页

作者： Zhe wang Mengke Yuan Juntao Gao Hongru Hao Jingwei Li Lingling Xu Zhe Lv bo Wei School of Physics Harbin Institute of Technology Faculty of Engineering University of Bayreuth State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied ChemistryChinese Academy of Sciences Key Laboratory of Photonic and Electric Band Gap Materials Ministry of EducationSchool of Physics and Electronic EngineeringHarbin Normal University

Cation segregation on cathode surfaces plays a key role in determining the activity and operational stability of solid oxide fuel cells（SOFCs）.The double perovskite oxide PrBa0.8Ca0.2Co2O5+δ（PBCC） has been widely studied as an active cathode but still suffer from serious detrimental *** enhance the cathode stability,a PBCC derived A-site medium-entropy Pr0.6La0.1Nd0.1Sm0.1Gd0.1Ba0.8Ca0.2Co2O5+δ（ME-PBCC） oxide was prepared and its segregation behaviors were investigated under different *** with initial PBCC oxide,the segregations of BaO and Co3O4on the surface of ME-PBCC material are significantly suppressed,especially for Co3O4,which is attributed to its higher configuration *** results also confirm the improved electrochemical performance and structural stability of ME-PBCC material,enabling it as a promising cathode for SOFCs.

关键词： Cathode Cation segregation Configuration entropy Double perovskite Rare earths Solid oxide fuel cells

Machine learning model using immune indicators to predict outcomes in early liver cancer

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World Journal of Gastroenterology 2025年第5期31卷 43-56页

作者： Yi Zhang Ke Shi Ying Feng Xian-bo wang Center of Integrative Medicine Beijing Ditan HospitalCapital Medical UniversityBeijing 100015China

BACKGROUND Patients with early-stage hepatocellular carcinoma(HCC)generally have good survival rates following surgical ***,a subset of these patients experience recurrence within five years *** To develop predictive models utilizing machine learning(ML)methods to detect early-stage patients at a high risk of *** Eight hundred and eight patients with HCC at Beijing Ditan Hospital were randomly allocated to training and validation cohorts in a 2:1 *** models were generated using random survival forests and artificial neural networks(ANNs).These ML models were compared with other classic HCC scoring systems.A decision-tree model was established to validate the contri-bution of immune-inflammatory indicators to the long-term outlook of patients with early-stage *** Immune-inflammatory markers,albumin-bilirubin scores,alpha-fetoprotein,tumor size,and International Normalized Ratio were closely associated with the 5-year survival *** various predictive models,the ANN model gene-rated using these indicators through ML algorithms exhibited superior perfor-mance,with a 5-year area under the curve(AUC)of 0.85(95%CI:0.82-0.88).In the validation cohort,the 5-year AUC was 0.82(95%CI:0.74-0.85).According to the ANN model,patients were classified into high-risk and low-risk groups,with an overall survival hazard ratio of 7.98(95%CI:5.85-10.93,P<0.0001)between the two *** Hepatocellular carcinoma(HCC)is one of the six most prevalent cancers[1]and the third leading cause of cancer-related mortality[2].China has some of the highest incidence and mortality rates for liver cancer,accounting for half of global cases[3,4].The Barcelona Clinic Liver Cancer(BCLC)Staging System is the most widely used framework for diagnosing and treating HCC[5].The optimal candidates for surgical treatment are those with early-stage HCC,classified as BCLC stage 0 or *** with early-stage liver cancer typically hav

关键词： Hepatocellular carcinoma Inflammation Machine learning Prognosis Artificial neural networks Immune biomarkers

Deciphering the role of mtH_(2)O_(2) in hepatic ischemia-reperfusion injury mechanisms with low-background super-resolution fluorogenic probe

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Science China Chemistry 2025年第1期68卷 297-307页

作者： Bin Fang Limin wang Haoqin Li Jiaxin Zhang Yang Ding PanPan Li bo Peng Hua Bai Lin Li Frontiers Science Center for Flexible Electronics Xi’an Institute of Flexible Electronics(IFE)and Xi’an Institute of Biomedical Materials&EngineeringNorthwestern Polytechnical UniversityXi’an 710072China Institute of Flexible Electronics(IFE Future Technologies)Xiamen UniversityXiamen 361005China

Hepatic ischemia-reperfusion injury(HIRI)is characterized by mitochondrial dysfunction and oxidative *** mitochondrial hydrogen peroxide(mtH_(2)O_(2))levels in real-time through super-resolution imaging is crucial for elucidating its distribution in live cells and its mechanism of action during ***,low-background fluorogenic probes have been overlooked in the context of super-resolution *** this study,we developed a low-background fluorogenic probe(MitoWG)with the potential for super-resolution morphology-correlated mitochondrial identification to track the fluctuates of mtH_(2)O_(2)in *** of the desirable fluorescence properties of the probe by mtH_(2)O_(2)was confirmed using structural illumination microscopy(SIM),enabling high-quality mitochondrial imaging with exceptional specificity and *** in mtH_(2)O_(2)levels were successfully observed in both cellular and rat models of ***,we associated the decline in mitochondrial redox homeostasis with accelerated mtH_(2)O_(2)production during HIRI,which triggered mitophagy deficiency and led to cell *** conclusion,Mito-WG possesses excellent photophysical and low-background properties for SIM imaging,making it a promising tool for mtH_(2)O_(2)tracking in HIRI research and clinical diagnosis.

关键词： hepatic ischemia-reperfusion injury low-background mtH_(2)O_(2)-activatable super-resolution imaging fluorogenic probe

Building Li-S batteries with enhanced temperature adaptability via a redox-active COF-based barrier-trapping electrocatalyst

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Journal of Energy Chemistry 2025年第2期101卷 702-712页

作者： Jie Xu Acheng Zhu Zhangyu Zheng Yiming Qi Yuwen Cheng Yongjie Cao bo Peng Lianbo Ma Yonggang wang School of Materials Science and Engineering Key Laboratory of Efficient Conversion and Solid-state Storage of Hydrogen & Electricity of Anhui Province Key Laboratory of Green Fabrication and Surface Technology of Advanced Metal Materials of Ministry of Education Anhui University of Technology Department of Chemistry and Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Institute of New Energy iChEM (Collaborative Innovation Center of Chemistry for Energy Materials) Fudan University

Covalent organic frameworks（COFs） are promising materials for mitigating polysulfide shuttling in lithium-sulfur（Li-S） batteries,but enhancing their ability to convert polysulfides across a wide temperature range remains a challenge,Herein,we introduce a redox-active COF（RaCOF） that functions as both a physical barrier and a kinetic enhancer to improve the temperature adaptability of Li-S batteries,The RaCOF constructed from redox-active anthraquinone units accelerates polysulfide conversion kinetics through reversible C=O/C-OLi transformations within a voltage range of 1,7 to 2.8 V（***+/Li）,optimizing sulfur redox reactions in ether-based *** conventional COFs,RaCOF provides bidentate trapping of polysulfides,increasing binding energy and facilitating more effective polysulfide ***-situ XRD and ToF-SIMS analyses confirm that RaCOF enhances polysulfide adsorption and promotes the transformation of lithium sulfide（Li2S）,leading to better sulfur cathode ***,RaCOF-modified Li-S batteries demonstrate low self-discharge（4.0% decay over a 7-day rest）,excellent wide-temperature performance（stable from-10 to+60℃）,and high-rate cycling stability（94% capacity retention over 500 cycles at 5.0 C）.This work offers valuable insights for designing COF structures aimed at achieving temperature-adaptive performance in rechargeable batteries.

关键词： Covalent organic framework Lithium-sulfur batteries Redox-active property Wide-temperature adaptability

Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI bo wang Chong wang Jiayi wang Tiantian wang Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

Graph based recurrent network for context specific synthetic lethality prediction

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Science China(Life Sciences) 2025年第2期68卷 527-540页

作者： Yuyang Jiang Jing wang Yixin Zhang ZhiWei Cao Qinglong Zhang Jinsong Su Song He Xiaochen bo Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjin 300072China Department of Bioinformatics Institute of Health Service and Transfusion MedicineBeijing 100850China School of Medicine Tsinghua UniversityBeijing 100084China School of Informatics Xiamen UniversityXiamen 361005China

The concept of synthetic lethality(SL)has been successfully used for targeted *** further explore SL for cancer therapy,identifying more SL interactions with therapeutic potential are ***,graph neural network-based deep learning methods have been proposed for SL prediction,which reduce the SL search space of wet-lab based ***,these methods ignore that most SL interactions depend strongly on genetic context,which limits the application of the predicted *** this study,we proposed a graph recurrent network-based model for specific context-dependent SL prediction(SLGRN).In particular,we introduced a Graph Recurrent Network-based encoder to acquire a context-specific,low-dimensional feature representation for each node,facilitating the prediction of novel *** leveraged gate recurrent unit(GRU)and it incorporated a context-dependent-level state to effectively integrate information from all *** a result,SLGRN outperforms the state-of-the-arts models for SL *** subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival *** in vitro experiments and retrospective clinical analysis,we emphasize the potential clinical significance of this context-specific SL prediction model.

关键词： synthetic lethality graph recurrent network context-specific graph combination therapy

Latitudinal patterns of tree β-diversity and relevant ecological processes vary across spatial extents in forests of southeastern China

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Plant Diversity 2025年第1期47卷 89-97页

作者： Maochou Liu Wenxiang Wu Ke wang Xinshuai Ren Xueqin Zhang Lei wang Jing Geng bo Yang Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Department of Environment and Resources University of Chinese Academy of SciencesBeijing 100049China Zhai Mingguo Academician Work Station Sanya UniversitySanya 572022China

Latitudinal patterns of treeβ-diversity reveal important insights into the biogeographical processes that influence forest *** previous studies have extensively documentedβ-diversity within relatively small spatial extents,the potential drivers ofβ-diversity along latitudinal gradients are still not well understood at larger spatial *** this study,we determined whether treeβ-diversity is correlated with latitude in forests of southeastern China,and if so,what ecological processes contribute to these patterns of treeβ-*** specifically aimed to disentangle the relative contributions from interspecific aggregation and environmental filtering across various spatial *** delineated regional communities comprising multiple nearby national forest inventory(NFI)plots around random focal *** number of NFI plots in a regional community served as a surrogate for spatial *** also used a null model to simulate randomly assembled communities and quantify the deviation(β-deviation)between observed and expectedβ-*** found thatβ-diversity decreased along a latitudinal gradient and that this pattern was clearer at larger spatial *** addition,latitudinal patterns ofβ-deviation were explained by the degree of species spatial *** also identified environmental factors that driveβ-deviation in these forests,including precipitation,seasonality,and temperature *** larger spatial extents,these environmental variables explained up to 84%of theβ-*** results reinforce that ecological processes are scale-dependent and collectively contribute to theβ-gradient in subtropical *** recommend that conservation efforts maintain diverse forests and heterogeneous environments at multiple spatial extents to mitigate the adverse effects of climate change.

关键词： Environmental filtering Latitudinal pattern Forest beta diversity Spatial extent Species spatial aggregation Regional community

Dispatchable Capability of Aggregated Electric Vehicle Charging in Distribution Systems

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Energy Engineering 2025年第1期122卷 129-152页

作者： Shiqian wang bo Liu Yuanpeng Hua Qiuyan Li Binhua Tang Jianshu Zhou Yue Xiang Economic and Technology Research Institute State Grid Henan Electric Power CompanyZhengzhou450052China College of Electrical Engineering Sichuan UniversityChengdu610065China

This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable *** methodology involves developing a model for aggregated EV charging at the charging station level,estimating its physical dispatchable capability,determining its economic dispatchable capability under economic incentives,modeling its participation in the grid,and investigating the effects of different scenarios and EV penetration on the aggregated load dispatch and dispatchable *** results indicate that using economic dispatchable capability reduces charging prices by 9.7%compared to physical dispatchable capability and 9.3%compared to disorderly ***,the peak-to-valley difference is reduced by 64.6%when applying economic dispatchable capability with 20%EV penetration and residential base load,compared to disorderly charging.

关键词： Aggregated charging dispatchable capability peak shaving and valley filling the economics of charging demand response

Deep learning identification of novel autophagic protein-protein interactions and experimental validation of Beclin 2-Ubiquilin 1 axis in triple-negative breast cancer

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Oncology Research 2025年第1期33卷 67-81页

作者： XIANG LI WENKE JIN LIFENG WU HUAN wang XIN XIE WEI HUANG bo LIU State Key Laboratory of Southwestern Chinese Medicine Resources School of Pharmacy and College of Medical TechnologyChengdu University of Traditional Chinese MedicineChengdu611137China Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041China

Background:Triple-negative breast cancer(TNBC),characterized by its lack of traditional hormone receptors and HER2,presents a significant challenge in oncology due to its poor response to conventional *** is an important process for maintaining cellular homeostasis,and there are currently autophagy biomarkers that play an effective role in the clinical treatment of *** contrast to targeting protein activity,intervention with proteinprotein interaction(PPI)can avoid unrelated crosstalk and regulate the autophagy process with minimal interference ***:Here,we employed Naive Bayes,Decision Tree,and k-Nearest Neighbors to elucidate the complex PPI network associated with autophagy in TNBC,aiming to uncover novel therapeutic ***,the candidate proteins interacting with Beclin 2 were initially screened in MDA-MB-231 cells using Beclin 2 as bait protein by immunoprecipitation-mass spectrometry assay,and the interaction relationship was verified by molecular docking and CO-IP experiments after *** formation,cellular immunofluorescence,cell scratch and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT)tests were used to predict the clinical therapeutic effects of manipulating candidate ***:By developing three PPI classification models and analyzing over 13,000 datasets,we identified 3733 previously unknown autophagy-related *** network analysis revealed the central role of Beclin 2 in autophagy regulation,uncovering its interactions with 39 newly identified ***,the CO-IP studies identified the substantial interaction between Beclin 2 and Ubiquilin 1,which was anticipated by our model and discovered in immunoprecipitation-mass spectrometry assay ***,in vitro investigations showed that overexpressing Beclin 2 increased Ubiquilin 1,promoted autophagy-dependent cell death,and inhibited proliferation and metastasis in MDA-MB-231 ***:This study not

关键词： Triple-negative breast cancer(TNBC) Autophagy Protein-protein interactions(PPI) Artificial intelligence(AI) Beclin 2 Ubiquilin 1