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| 1 | Interannual Climate Variability Change during the Medieval Climate Anomaly and Little Ice Age in PMIP3 Last Millennium Simulations显示文摘In this study, we analyzed numerical experiments undertaken by 10 climate models participating in PMIP3(Paleoclimate Modelling Intercomparison Project Phase 3) to examine the changes in interannual temperature variability and coefficient of variation(CV) of interannual precipitation in the warm period of the Medieval Climate Anomaly(MCA) and the cold period of the Little Ice Age(LIA). With respect to the past millennium period, the MCA temperature variability decreases by 2.0% on average over the globe, and most of the decreases occur in low latitudes. In the LIA, temperature variability increases by a global average of 0.6%, which occurs primarily in the high latitudes of Eurasia and the western Pacific. For the CV of interannual precipitation, regional-scale changes are more significant than changes at the global scale, with a pattern of increased(decreased) CV in the midlatitudes of Eurasia and the northwestern Pacific in the MCA(LIA). The CV change ranges from-7.0% to 4.3%(from -6.3% to 5.4%), with a global average of -0.5%(-0.07%) in the MCA(LIA).Also, the variability changes are considerably larger in December–January–February with respect to both temperature and precipitation. | Kaiqing YANG Dabang JIANG | 2017 | Advances in Atmospheric Sciences2017，34，4： | 4 |
| 2 | Loss of gonadal soma derived factor damaging the pituitary-gonadal axis in medaka显示文摘Medaka sex is determined by dmy/dmrt1b Y,the doublesex and mab-3 domain gene on the Y chromosome[1,2].Most recent studies have revealed a germ cell-intrinsic cue for the sperm-egg fate decision present in medaka,leading to spermatogenesis occurrence in a female gonadal environment without the presence of dmy[3].Targeted disruption of dmy,or sex related genes,such | Kaiqing Sun Lidong Fan Chuanyun Wang Xiaoyu Yang Shuangguang Zhu Xi Zhang Xiaowu Chen Yunhan Hong Guijun Guan | 2017 | Science Bulletin2017，62，3： | 4 |
| 3 | A Versatile Surface Bioengineering Strategy Based on Mussel-Inspired and Bioclickable Peptide Mimic显示文摘In this work,we present a versatile surface engineering strategy by the combination of mussel adhesive peptide mimicking and bioorthogonal click chemistry.The main idea reflected in this work derived from a novel mussel-inspired peptide mimic with a bioclickable azide group(i.e.,DOPA_(4)-azide).Similar to the adhesion mechanism of the mussel foot protein(i.e.,covalent/noncovalent comediated surface adhesion),the bioinspired and bioclickable peptide mimic DOPA_(4)-azide enables stable binding on a broad range of materials,such as metallic,inorganic,and organic polymer substrates.In addition to the material universality,the azide residues of DOPA_(4)-azide are also capable of a specific conjugation of dibenzylcyclooctyne-(DBCO-)modified bioactive ligands through bioorthogonal click reaction in a second step.To demonstrate the applicability of this strategy for diversified biofunctionalization,we bioorthogonally conjugated several typical bioactive molecules with DBCO functionalization on different substrates to fabricate functional surfaces which fulfil essential requirements of biomedically used implants.For instance,antibiofouling,antibacterial,and antithrombogenic properties could be easily applied to the relevant biomaterial surfaces,by grafting antifouling polymer,antibacterial peptide,and NO-generating catalyst,respectively.Overall,the novel surface bioengineering strategy has shown broad applicability for both the types of substrate materials and the expected biofunctionalities.Conceivably,the“clean”molecular modification of bioorthogonal chemistry and the universality of mussel-inspired surface adhesion may synergically provide a versatile surface bioengineering strategy for a wide range of biomedical materials. | Yu Xiao Wenxuan Wang Xiaohua Tian Xing Tan Tong Yang Peng Gao Kaiqing Xiong Qiufen Tu Miao Wang Manfred FMaitz Nan Huang Guoqing Pan Zhilu Yang | 2020 | Research2020，，1： | 3 |
| 4 | Decentralized multi-agent reinforcement learning with networked agents: recent advances显示文摘Multi-agent reinforcement learning(MARL) has long been a significant research topic in both machine learning and control systems. Recent development of(single-agent) deep reinforcement learning has created a resurgence of interest in developing new MARL algorithms, especially those founded on theoretical analysis. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents.In this scenario, multiple agents perform sequential decision-making in a common environment, and without the coordination of any central controller, while being allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. We hope that this review promotes additional research efforts in this exciting yet challenging area. | Kaiqing ZHANG Zhuoran YANG Tamer BAŞAR | 2021 | Frontiers of Information Technology & Electronic Engineering2021，22，6： | 1 |
| 5 | Improvement of surface wetting properties of poly(p-phenylene benzoxazole) by incorporation of ionic groups显示文摘 | Luo Kaiqing Jin Junhong Yang Shenglin | 2006 | Materials Science and Engineering B2006，132，12： | 1 |
| 6 | Corrigendum to “A Versatile Surface Bioengineering StrategyBased on Mussel-Inspired and Bioclickable Peptide Mimic”显示文摘In the article titled,“A Versatile Surface Bioengineering Strategy Based on Mussel-Inspired and Bioclickable Peptide Mimic”[1],there was an error in Figure 2.In panel(e),the cell pictures of PEG after culture for 24 and 72 h were updated.The corrected figure is shown and is listed as Figure 1. | Yu Xiao Wenxuan Wang Xiaohua Tian Xing Tan Tong Yang Peng Gao Kaiqing Xiong Qiufen Tu Miao Wang Manfred F.Maitz Nan Huang Guoqing Pan Zhilu Yang | 2021 | Research2021，，1： | 0 |
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