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| 1 | White Matter Abnormalities in Major Depression Biotypes Identified by Diffusion Tensor Imaging显示文摘Identifying data-driven biotypes of major depressive disorder(MDD) has promise for the clarification of diagnostic heterogeneity. However, few studies have focused on white-matter abnormalities for MDD subtyping. This study included 116 patients with MDD and118 demographically-matched healthy controls assessed by diffusion tensor imaging and neurocognitive evaluation.Hierarchical clustering was applied to the major fiber tracts, in conjunction with tract-based spatial statistics, to reveal white-matter alterations associated with MDD.Clinical and neurocognitive differences were compared between identified subgroups and healthy controls. With fractional anisotropy extracted from 20 fiber tracts, cluster analysis revealed 3 subgroups based on the patterns of abnormalities. Patients in each subgroup versus healthy controls showed a stepwise pattern of white-matter alterations as follows: subgroup 1(25.9% of patient sample),widespread white-matter disruption;subgroup 2(43.1% of patient sample), intermediate and more localized abnormalities in aspects of the corpus callosum and left cingulate;and subgroup 3(31.0% of patient sample),possible mild alterations, but no statistically significant tract disruption after controlling for family-wise error. The neurocognitive impairment in each subgroup accompanied the white-matter alterations: subgroup 1, deficits in sustained attention and delayed memory;subgroup 2, dysfunction in delayed memory;and subgroup 3, no significant deficits. Three subtypes of white-matter abnormality exist in individuals with major depression, those having widespread abnormalities suffering more neurocognitive impairments, which may provide evidence for parsing the heterogeneity of the disorder and help optimize typespecific treatment approaches. | Sugai Liang Qiang Wang Xiangzhen Kong Wei Deng Xiao Yang Xiaojing Li Zhong Zhang Jian Zhang Chengcheng Zhang Xin-min Li Xiaohong Ma Junming Shao Andrew J. Greenshaw Tao Li | 2019 | Neuroscience Bulletin2019，35，5： | 9 |
| 2 | Arm Voltage Balancing Control of Modular Multilevel Resonant Converter显示文摘Modular multilevel resonant converter is an promising candidate for high voltage applications since it has advantageous features,such as high efficiency,high voltage capability and easy fault-tolerant operation.However,the inequality of arm inductance in practice will lead to imbalance between the upper and lower arm voltages,which will induce large ripples in the circulating current and a dc bias on the voltage generated by modular circuits.To compensate for the voltage imbalance,effects of arm duty cycle changes on arm voltages are discussed.An arm voltage balancing control method is proposed:adjust arm duty cycle according to arm voltage deviation in every switching cycle.Simulation and experimental results are presented to validate the theoretical analysis and the proposed control method. | Jianjia Zhang Shuai Shao Yucen Li Junming Zhang Kuang Sheng | 2020 | CES Transactions on Electrical Machines and Systems2020，4，4： | 7 |
| 3 | Location Prediction on Trajectory Data: A Review显示文摘Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few. This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications. First, we introduce the types of trajectory data and related basic concepts. Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction. We also discuss and analyze the advantages and disadvantages of these algorithms and briefly summarize current applications of location prediction in diverse fields. Finally, we identify the potential challenges and future research directions in location prediction. | Ruizhi Wu Guangchun Luo Junming Shao Ling Tian Chengzong Peng | 2018 | Big Data Mining and Analytics2018，1，2： | 5 |
| 4 | Integration of data-intensive,machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions显示文摘Technological advancements in recent decades have greatly transformed the field of material chemistry.Juxtaposing the accentuating energy demand with the pollution associated,urgent measures are required to ensure energy maximization,while reducing the extended experimental time cycle involved in energy production.In lieu of this,the prominence of catalysts in chemical reactions,particularly energy related reactions cannot be undermined,and thus it is critical to discover and design catalyst,towards the optimization of chemical processes and generation of sustainable energy.Most recently,artificial intelligence(AI)has been incorporated into several fields,particularly in advancing catalytic processes.The integration of intensive data set,machine learning models and robotics,provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques.The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst,with extreme accuracy and swiftness comparable to a number of human researchers.Although,the utilization of robots in catalyst discovery is still in its infancy,in this review we summarize current sway of artificial intelligence in catalyst discovery,briefly describe the application of databases,machine learning models and robots in this field,with emphasis on the consolidation of these monomeric units into a tripartite flow process.We point out current trends of machine learning and hybrid models of first principle calculations(DFT)for generating dataset,which is integrable into autonomous flow process of catalyst discovery.Also,we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors. | Oyawale Adetunji Moses Wei Chen Mukhtar Lawan Adam Zhuo Wang Kaili Liu Junming Shao Zhengsheng Li Wentao Li Chensu Wang Haitao Zhao Cheng Heng Pang Zongyou Yin Xuefeng Yu | 2021 | Materials Reports(Energy)2021，1，3： | 3 |
| 5 | Biological roles and potential clinical values of circular RNAs in gastrointestinal malignancies显示文摘Circular RNAs(circ RNAs),a class of endogenous RNA molecules,are produced by alternative splicing of precursor RNA and are covalently linked at the 5′and 3′ends.Recent studies have revealed that dysregulated circ RNAs are closely related to the occurrence and progression of gastrointestinal malignancies.Accumulating evidence indicates that circ RNAs,including circ PVT1,circ LARP4,circ-SFMBT2,cir-ITCH,circ RNA_100782,circ_100395,circ-DONSON,hsa_circ_0001368,circ NRIP1,circ FAT1(e2),circ CCDC66,circ SMARCA5,circ-ZNF652,and circ_0030235 play important roles in the proliferation,differentiation,invasion,and metastasis of cancer cells through a variety of mechanisms,such as acting as micro RNA sponges,interacting with RNA-binding proteins,regulating gene transcription and alternative splicing,and being translated into proteins.With the characteristics of high abundance,high stability,extensive functions,and certain tissue-,time-and diseasespecific expressions,circ RNAs are expected to provide novel perspectives for the diagnoses and treatments of gastrointestinal malignancies. | Xueping Tao Yongfu Shao Jianing Yan Liyang Yang Qihua Ye Qingling Wang Rongdan Lu Junming Guo | 2021 | Cancer Biology & Medicine2021，18，2： | 2 |
| 6 | lncRNA-AC130710 targeting by miR-129-5p is upregulated in gastric cancer and associates with poor prognosis显示文摘 | Chunjing Xu Yongfu Shao Tian Xia Yunben Yang Jiawei Dai Lin Luo Xinjun Zhang Weiliang Sun Haojun Song Bingxiu Xiao Junming Guo | 2014 | Tumor Biology2014，，10： | 1 |
| 7 | Using circular RNA as a novel type of biomarker in the screening of gastric cancer显示文摘 | Peifei Li Shengcan Chen Huilin Chen Xiaoyan Mo Tianwen Li Yongfu Shao Bingxiu Xiao Junming Guo | 2015 | Clinica Chimica Acta2015，，： | 1 |
| 8 | Electrochemical performances of NiO/Ni2N nanocomposite thin film as anode material for lithium ion batteries显示文摘Despite the high specific capacities,the practical application of transition metal oxides as the lithium ion battery(LIB)anode is hindered by their low cycling stability,severe polarization,low initial coulombic efficiency,etc.Here,we report the synthesis of the NiO/Ni2N nanocomposite thin film by reactive magnetron sputtering with a Ni metal target in an atmosphere of 1 vol.% O2 and 99 vol.%N2.The existence of homogeneously dispersed nano Ni2N phase not only improves charge transfer kinetics,but also contributes to the one-off formation of a stable solid electrolyte interphase(SEI).In comparison with the NiO electrode,the NiO/Ni2N electrode exhibits significantly enhanced cycling stability with retention rate of 98.8%(85.6%for the NiO electrode)after 50 cycles,initial coulombic efficiency of 76.6%(65.0%for the NiO electrode)and rate capability with 515.3 mA·h·g^−1(340.1 mA·h·g^−1 for the NiO electrode)at 1.6 A·g^−1. | Yanlin JIA Zhiyuan MA Zhicheng LI Zhenli HE Junming SHAO Hong ZHANG | 2019 | Frontiers of Materials Science2019，13，4： | 1 |
| 9 | 一种用于淮河上游日径流预测的增强型LSTM模型显示文摘径流预报对防洪具有重要意义。然而,由于径流过程的复杂性和随机性,对日径流量进行准确预测是困难的,尤其是对峰值径流量的预测。为了解决这一问题,本研究提出了一种用于径流预测的增强型长短期记忆(LSTM)模型,其中引入了新的损失函数并集成了特征提取器。设计了峰值误差tanh(peak error tanh,PET)和峰值误差swish(peak error swish,PES)两个损失函数,增强了峰值径流预测的重要性,弱化了正常径流预测的权重。为每个气象站建立由3个LSTM网络组成的特征提取器,目的是提取每个气象站输入数据的时间特征。以中国淮河上游为例,利用增强型LSTM模型对1960—2016年的日径流量进行了预测。结果表明,改进后的LSTM模型表现良好,在验证期内(2005年11月至2016年12月),Nash-Sutcliffe效率(NSE)系数在0.917-0.924之间,优于广泛使用的集总水文模型(Australian Water Balance model(AWBM)、Sacramento、Sim Hyd和Tank模型)和数据驱动模型(人工神经网络(ANN)、支持向量回归(SVR)和门控循环单元(GRU))。以PES为损失函数的增强型LSTM对洪水极端径流的预测效果最好,平均NSE为0.873。此外,海拔较高的气象站降水对径流预测的贡献比最近的气象站更大。该研究为流域日径流预测提供了有效工具,有利于流域防洪和水安全管理。 | Yuanyuan Man Qinli Yang Junming Shao Guoqing Wang Linlong Bai Yunhong Xue | 2023 | Engineering2023，，5： | 0 |
| 10 | Wide-range color-tunable afterglow emission by the modulation of triplet exciton transition processes based on buckybowl structure显示文摘Buckybowl structures as non-uniform electrostatic potential distributions of poly-cyclic aromatic materials show a unique photoelectric performance.In this work,OTC was utilized for dynamic modulation of triplet exciton transition processes.Five host molecules with different functional units were selected,thus providing dif-ferent intermolecular interactions in the host/guest systems.Therefore,the delayed emissions were regulated from 536 to 624 nm via the tuning of the triplet exciton transition processes of OTC in different hosts.Experimental data and theoretical calculations revealed that the varied triplet transition behaviors resulted from the competition between the intersystem crossing(ISC)process of OTC-monomer and the reverse intersystem crossing(RISC)process of OTC-aggregates.This work proves the superior structure of buckybowl-based luminophore for controlling triplet exciton transition processes and supplies a new perspective for persistent afterglow luminophore design. | Yongfeng Zhang Chenchen Xiong Wenbo Wang Wenbo Dai Yue Ren Junming Xia Gengchen Li Jianbing Shi Bin Tong Xiaoyan Zheng Xiangfeng Shao Zhengxu Cai Yuping Dong | 2023 | Aggregate2023，4，4： | 0 |
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