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ZIF-derived holey electrode with enhanced mass transfer and N-rich catalytic sites for high-power and long-life vanadium flow batteries

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Journal of Energy Chemistry 2022年第9期31卷 545-553,I0015页

作者： yongbin liu Lihong Yu Xin liu Le liu Jingyu Xi Institute of Materials Research Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055GuangdongChina School of Materials&Environmental Engineering Shenzhen PolytechnicShenzhen 518055GuangdongChina

Electrode materials with good redox kinetics,excellent mass transfer characteristics and ultra-high stability play a crucial role in reducing the life-cycle cost and prolonging the maintenance-free time of the vanadium flow batteries(VFB).Herein,a nitrogen-doped porous graphite felt electrode(N-PGF)is proposed by growing ZIF-67 nanoparticles on carbon fibers and then calcinating and acid etching.The multi-scale structure of“carbon fiber gap(electrolyte flow),micro/nano pore(active species diffusion)and Nitrogen active center(reaction site)”in N-PGF electrode effectively increases the catalytic sites and promotes mass transfer characteristics.Reasonable electrode design makes the battery show excellent rate performance and ultra-high cycling stability.The peak power density of the battery reaches 1006 mW cm^(-2).During 1000 cycles at 150 mA cm^(-2),the average discharge capacity and average discharge energy of N-PGF increase substantially by 11.6%and 23.4%compared with the benchmark thermal activated graphite felt,respectively.More excitingly,after ultra-long term(5000 cycles)operation at an ultra-high current density(300 mA cm^(-2)),N-PGF exhibits an unprecedented energy efficiency retention(99.79%)and electrochemical performance stability.

关键词： Vanadium flow battery Multi-scale pore electrode Graphite felt Nitrogen-doping Cycling stability

Stabilizing semi-transparent perovskite solar cells with a polymer composite hole transport layer

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Nano Research 2024年第3期17卷 1500-1507页

作者： yongbin Jin Huiping Feng Zheng Fang liu Yang Kaikai liu Bingru Deng Jingfu Chen Xueling Chen Yawen Zhong Jinxin Yang Chengbo Tian Liqiang Xie Zhanhua Wei Xiamen Key Laboratory of Optoelectronic Materials and Advanced Manufacturing Institute of Luminescent Materials and Information DisplaysCollege of Materials Science and EngineeringHuaqiao UniversityXiamen 361021China

Semi-transparent perovskite solar cells(ST-PSCs)have broad applications in building integrated photovoltaics.However,the stability of ST-PSCs needs to be improved,especially in n-i-p ST-PSCs since the doped 2,2',7,7'-tetrakis(N,N-di-p-methoxyphenyl-amine)-9,9'-spirobifluorene(Spiro-OMeTAD)is unstable at elevated temperatures and high humidity.In this work,aπ-conjugated polymer poly[(2,6-(4,8-bis(5-(2-ethylhexyl)thiophene-2-yl)-benzo[1,2-b:4,5-b']dithiophene))-alt-(5,5-(1',3'-di-2-thienyl-5',7'-bis(2-ethylhexyl)benzo[1',2'-c:4',5'-c']dithiophene-4,8-dione)](PBDB-T)is selected to form a polymer composite hole transport layer(HTL)with Spiro-OMeTAD.The sulfur atom of the thiophene unit and the carbonyl group of the polymer interact with the undercoordinated Pb2+at the perovskite surface,which stabilizes the perovskite/HTL interface and passivates the interfacial defects.The incorporation of the polymer also increases the glass transition temperature and the moisture resistance of Spiro-OMeTAD.As a result,we obtain ST-PSCs with a champion efficiency of 13.71%and an average visible light transmittance of 36.04%.Therefore,a high light utilization efficiency of 4.94%can be obtained.Moreover,the encapsulated device can maintain 84%of the initial efficiency after 751 h under continuous one-sun illumination(at 30%relative humidity)at the open circuit and the unencapsulated device can maintain 80%of the initial efficiency after maximum power tracking for more than 1250 h under continuous one-sun illumination.

关键词： semi-transparent solar cells π-conjugated polymer composite hole transport layer building integrated photovoltaics

An mRNA vaccine elicits STING-dependent antitumor immune responses

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Acta Pharmaceutica Sinica B 2023年第3期13卷 1274-1286页

作者： Zhe Chen Chaoyang Meng Junhua Mai yongbin liu Hangwen Li Haifa Shen Department of Nanomedicine Houston Methodist Academic InstituteHoustonTX 77030USA Xiangya Hospital of Central South University Changsha 410000China Department of Hepatobiliary and Pancreatic Surgery First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Stemirna Therapeutics Shanghai 201206China ImmunoQ Therapeutics HoustonTX 77401USA

Lipid-formulated RNA vaccines have been widely used for disease prevention and treatment,yet their mechanism of action and individual components contributing to such actions remain to be delineated.Here,we show that a therapeutic cancer vaccine composed of a protamine/mRNA core and a lipid shell is highly potent in promoting cytotoxic CD8+T cell responses and mediating anti-tumor immunity.Mechanistically,both the mRNA core and lipid shell are needed to fully stimulate the expression of type I interferons and inflammatory cytokines in dendritic cells.Stimulation of interferon-βexpression is exclusively dependent on STING,and antitumor activity from the mRNA vaccine is significantly compromised in mice with a defective Sting gene.Thus,the mRNA vaccine elicits STING-dependent antitumor immunity.

关键词： Cancer Immunotherapym RNA Vaccine Dendritic cells EDOPCS TING MAVS

Few-shot Named Entity Recognition with Joint Token and Sentence Awareness

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Data Intelligence 2023年第3期5卷 767-785页

作者： Wen Wen yongbin liu Qiang Lin Chunping Ouyang Computer School University of South ChinaChina Hunan provincial base for scientific and technological innovation cooperation HunanChina

Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks.Recently,few-shot models have been used for Named Entity Recognition(NER).Prototypical network shows high efficiency on few-shot NER.However,existing prototypical methods only consider the similarity of tokens in query sets and support sets and ignore the semantic similarity among the sentences which contain these entities.We present a novel model,Few-shot Named Entity Recognition with Joint Token and Sentence Awareness(JTSA),to address the issue.The sentence awareness is introduced to probe the semantic similarity among the sentences.The Token awareness is used to explore the similarity of the tokens.To further improve the robustness and results of the model,we adopt the joint learning scheme on the few-shot NER.Experimental results demonstrate that our model outperforms state-of-the-art models on two standard Fewshot NER datasets.

关键词： Few-shot Learning Named Entity Recognition Prototypical Network

Relational Topology-based Heterogeneous Network Embedding for Predicting Drug-Target Interactions

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Data Intelligence 2023年第2期5卷 475-493页

作者： Linlin Zhang Chunping Ouyang Fuyu Hu yongbin liu Zheng Gao School of Computer University of South ChinaHengyangHunan421001China Hunan Medical Big Data International Sci.&Tech Innovation Cooperation Base Department of Information and Library Science Indiana University Bloomington Woodlawn AvenueIN 47408BloomingtonAmerica

Predicting interactions between drugs and target proteins has become an essential task in the drug discovery process.Although the method of validation via wet-lab experiments has become available,experimental methods for drug-target interaction(DTI)identification remain either time consuming or heavily dependent on domain expertise.Therefore,various computational models have been proposed to predict possible interactions between drugs and target proteins.However,most prediction methods do not consider the topological structures characteristics of the relationship.In this paper,we propose a relational topologybased heterogeneous network embedding method to predict drug-target interactions,abbreviated as RTHNE_DTI.We first construct a heterogeneous information network based on the interaction between different types of nodes,to enhance the ability of association discovery by fully considering the topology of the network.Then drug and target protein nodes can be represented by the other types of nodes.According to the different topological structure of the relationship between the nodes,we divide the relationship in the heterogeneous network into two categories and model them separately.Extensive experiments on the realworld drug datasets,RTHNE_DTI produces high efficiency and outperforms other state-of-the-art methods.RTHNE_DTI can be further used to predict the interaction between unknown interaction drug-target pairs.

关键词： Link prediction Heterogeneous information network Drug-target interaction Network embedding Feature representation

Multi-head attention graph convolutional network model:End-to-end entity and relation joint extraction based on multi-head attention graph convolutional network

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 468-477页

作者： Zhihua Tao Chunping Ouyang yongbin liu Tonglee Chung Yixin Cao School of Computer Science University of South ChinaHengyangChina Department of Computer Science and technology Tsinghua UniversityBeijingChina School of Computer and Information Systems Singapore Management UniversitySingaporeSingapore

At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure information.The adjacency matrix constructed by the dependency tree can convey syntactic information.Dependency trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic description.At the same time,a large amount of irrelevant information will cause redundancy.This paper presents a novel end-to-end entity and relation joint extraction based on the multihead attention graph convolutional network model(MAGCN),which does not rely on external *** generates an adjacency matrix through a multi-head attention mechanism to form an attention graph convolutional network model,uses head selection to identify multiple relations,and effectively improve the prediction result of overlapping relations.The authors extensively experiment and prove the method's effectiveness on three public datasets:NYT,WebNLG,and CoNLL04.The results show that the authors’method outperforms the state-of-the-art research results for the task of entities and relation extraction.

关键词： information retrieval natural language processing

Ensemble Making Few-Shot Learning Stronger

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Data Intelligence 2022年第3期4卷 529-551页

作者： Qiang Lin yongbin liu Wen Wen Zhihua Tao Chunping Ouyang Yaping Wan Computer School University of South China 421001China Hunan provincial base for scientific and technological innovation cooperation HunanChina

Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks.Many few-shot models have been widely used for relation learning tasks.However,each of these models has a shortage of capturing a certain aspect of semantic features,for example,CNN on long-range dependencies part,Transformer on local features.It is difficult for a single model to adapt to various relation learning,which results in a high variance problem.Ensemble strategy could be competitive in improving the accuracy of few-shot relation extraction and mitigating high variance risks.This paper explores an ensemble approach to reduce the variance and introduces fine-tuning and feature attention strategies to calibrate relation-level features.Results on several few-shot relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.

关键词： Few-shot learning Relation extraction Ensemble learning Attention Mechanism Fine-tuning

First-line camrelizumab(a PD-1 inhibitor)plus apatinib(an VEGFR-2 inhibitor)and chemotherapy for advanced gastric cancer(SPACE):a phase 1 study

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Signal Transduction and Targeted Therapy 2024年第4期9卷 1760-1767页

作者： Xiaofeng Chen Hao Xu Xiaobing Chen Tongpeng Xu Yitong Tian Deqiang Wang Fen Guo Kangxin Wang Guangfu Jin Xiao Li Rong Wang Fengyuan Li yongbin Ding Jie Tang Yueyu Fang Jing Zhao Liang liu Ling Ma Lijuan Meng Zhiguo Hou Rongrong Zheng Yang liu Ni Guan Bei Zhang Shuang Tong Shiqing Chen Xing Li Yongqian Shu Department of Oncology The First Affiliated Hospital with Nanjing Medical UniversityNanjingChina Gusu School Nanjing medical UniversitySuzhouChina Jiangsu Key Lab of Cancer Biomarkers Prevention and TreatmentCollaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjingChina Department of General surgery The First Affiliated Hospital with Nanjing Medical UniversityNanjingChina Department of Oncology Henan Cancer Hospital Affiliated Cancer Hospital of Zhengzhou UniversityZhengzhouChina Henan Engineering Research Center of Precision Therapy of Gastrointestinal Cancer&Zhengzhou Key Laboratory for Precision Therapy of Gastrointestinal Cancer ZhengzhouChina Department of Oncology Digestive Disease Institute&Cancer Institute of Jiangsu UniversityAffiliated Hospital of Jiangsu UniversityZhenjiangChina Department of Oncology Suzhou Hospital of Nanjing Medical UniversitySuzhouChina Department of Oncology Nanjing PuKou People’s HospitalNanjingChina Department of Epidemiology School of Public HealthNanjing Medical UniversityNanjingChina Department of Pathology The First Affiliated Hospital with Nanjing Medical UniversityNanjingChina Department of general surgery Jurong Branch hospital of Jiangsu Province People HospitalJurongChina Department of Medical Oncology Liyang People’s HospitalLiyangChina Department of radiology Nanjing PuKou People’s HospitalNanjingChina Jiangsu Hengrui Pharmaceuticals ShanghaiChina Department of Medical Affairs 3D Medicines Inc.ShanghaiChina Shanghai OrigiMed Co. Ltd.ShanghaiChina

Patients with advanced gastric cancer typically face a grim prognosis.This phase 1a(dose escalation)and phase 1b(dose expansion)study investigated safety and efficacy of first-line camrelizumab plus apatinib and chemotherapyfor advanced gastric or gastroesophageal junction adenocarcinoma.The primary endpoints included maximum tolerated dose(MTD)in phase 1a and objective response rate(ORR)across phase 1a and 1b.Phase 1a tested three dose regimens of camrelizumab,apatinib,oxaliplatin,and S-1.Dose regimen 1:camrelizumab 200 mg on day 1,apatinib 250 mg every other day,oxaliplatin 100 mg/m^(2) on day 1,and S-140 mg twice a day on days 1-14.Dose regimen 2:same as dose regimen 1,but oxaliplatin 130mg/m.Dose regimen3:same as dose regimen 2,but apatinib 250 mg daily.Thirty-four patients were included(9 in phase 1a,25 in phase 1b).No dose-limiting toxicities occurred so no MTD was identified.Dose 3 was set for the recommended phase 2 doses and administered in phase 1b.The confrmed ORR was 76.5%(95%CI 58.8-89.3).The median progression-free survival was 8.4 months(95%CI 5.9-not evaluable[NE]),and the median overall survival(OS)was not mature(11.6-NE).Ten patients underwent surgery after treatment and the multidisciplinary team evaluation.Among 24 patients without surgery,the median OS was 19.6 months(7.8-NE).Eighteen patients(52.9%)developed grade≥3 treatment-emergent adverse events.Camrelizumab plus apatinib and chemotherapy showed favorable clinical outcomes and manageable safety for untreated advanced gastric cancer(ChiCTR2000034109).

关键词： regimen cancer doses

How Do Pronouns Affect Word Embedding

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Tsinghua Science and Technology 2017年第6期22卷 586-594页

作者： Tonglee Chung Bin Xu yongbin liu Juanzi Li Chunping Ouyang the Department of Computer Science and Technology Tsinghua University Beijing 100084 China the School of Computer Science and Technology University of South China Hengyang 421001 China.

Word embedding has drawn a lot of attention due to its usefulness in many NLP tasks. So far a handful of neural-network based word embedding algorithms have been proposed without considering the effects of pronouns in the training corpus. In this paper, we propose using co-reference resolution to improve the word embedding by extracting better context. We evaluate four word embeddings with considerations of co-reference resolution and compare the quality of word embedding on the task of word analogy and word similarity on multiple data sets.Experiments show that by using co-reference resolution, the word embedding performance in the word analogy task can be improved by around 1.88%. We find that the words that are names of countries are affected the most,which is as expected.

关键词： word embedding co-reference resolution representation learning