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| 1 | Diversity of microbial plankton across the Three Gorges Dam of the Yangtze River,China显示文摘长江的三个峡水坝(TGD ) ,中国,在世界上是最大的灌溉和水力发电的工程工程之一。效果巨大人造象动物志和 macrophyte 上的 TGD 一样的工程是明显的,主要通过水动力学和流动模式的变化;然而,微生物怎么对如此的变化作出回应,是不太清楚的。这研究被瞄准在不同季节和地点在微生物引起的差异检验差别(在前面并且在 TGD 后面) 。另外,依附粒子、免费生活的社区之间的差别也被检验。在水列在后面并且在 TGD 前面的全部、潜在地活跃的微生物的社区结构与基于 DNA 、基于 RNA 的 16S rRNA 基因被分析在三个不同季节的种系发生的途径。16S rRNA 基因的克隆图书馆从提取 DNA 在扩大以后被准备并且为一些样品,在从提取 rRNA 准备 cDNA 以后。差别在不同季节并且在免费生活、依附粒子的社区之间在地点之间被观察。细菌并且 archaeal 社区比在冬季在夏天是更多样的,由于在夏天的更高滋养的层次和更温暖的温度比在冬季。依附粒子的微生物比免费生活的社区更多样,可能因为在粒子的更高滋养的层次和异构的 geochemical 微型环境。在细菌的社区结构的空间变化被观察,即,在 TGD 后面的水水库(在上游) 比在水坝前面招待了更多样的细菌的人口(下游) ,因为沉积和水从的多样的来源对水库在上游。这些结果在水坝建设影响的河生态系统为我们对环境变化的微生物引起的社区的回答的理解有重要含意。 | Shang Wang Raymond M. Dong Christina Z. Dong Liuqin Huang Hongchen Jiang Yuli Wei Liang Feng Deng Liu Guifang Yang Chuanlun Zhang Hailiang Dong | 2012 | Geoscience Frontiers2012，3，3： | 4 |
| 2 | Problem-Based Cybersecurity Lab with Knowledge Graph as Guidance显示文摘Lecture-based teaching paired with laboratory-based exercises is mostly used in cybersecurity instruction.However,it focuses more on theories and models but fails to provide learners with practical problem-solving skills and opportunities to explore real-world cybersecurity challenges.Problem-based learning(PBL)has been identified as an efficient pedagogy for many disciplines,especially engineering education.It provides learners with real-world complex problem scenarios,which encourages learners to collaborate with classmates,ask questions,and develop a deeper understanding of the concepts while solving realworld cybersecurity problems.This article describes the application of the PBL methodology to enhance professional trainingbased cybersecurity education.The authors developed an online laboratory environment to apply PBL with Knowledge Graph(KG)-based guidance for hands-on labs in cybersecurity training.Learners are provided access to a virtual lab environment with KG guidance to simulated real-life cybersecurity scenarios.Thus,they are forced to think independently and apply their knowledge to create cyber attacks and defend approaches to solve problems provided to them in each lab.Our experimental study shows that learners tend to gain more enhanced learning outcomes by leveraging PBL with KG guidance,become more aware of cybersecurity and relevant concepts,and express interest in keep learning of cybersecurity using our system. | Yuli Deng Zhen Zeng Kritshekhar Jha Dijiang Huang | 2022 | Journal of Artificial Intelligence and Technology2022，2，2： | 1 |
| 3 | Plasmacytoid dendritic cells promote acute kidney injury by producing interferon-α显示文摘Acute kidney injury(AKI)is a common clinical complication associated with high mortality in patients.Immune cells and cytokines have recently been described to play essential roles in AKI pathogenesis.Plasmacytoid dendritic cells(pDCs)are a unique DC subset that specializes in type Ⅰ interferon(IFN)production.Here,we showed that pDCs rapidly infiltrated the kidney in response to AKI and contributed to kidney damage by producing IFN-α.Deletion of pDCs using DTR^(BDCA2) transgenic(Tg)mice suppressed cisplatin-induced AKI,accompanied by marked reductions in proinflammatory cytokine production,immune cell infiltration and apoptosis in the kidney.In contrast,adoptive transfer of pDCs during AKI exacerbated kidney damage.We further identified IFN-α as the key factor that mediated the functions of pDCs during AKI,as IFN-α neutralization significantly attenuated kidney injury.Furthermore,IFN-α produced by pDCs directly induced the apoptosis of renal tubular epithelial cells(TECs)in vitro.In addition,our data demonstrated that apoptotic TECs induced the activation of pDCs,which was inhibited in the presence of an apoptosis inhibitor.Furthermore,similar deleterious effects of pDCs were observed in an ischemia reperfusion(IR)-induced AKI model.Clinically,increased expression of IFN-α in kidney biopsies was observed in kidney transplants with AKI.Taken together,the results of our study reveal that pDCs play a detrimental role in AKI via IFN-α. | Bo Deng Yuli Lin Yusheng Chen Shuai Ma Qian Cai Wenji Wang Bingji Li Tingyan Liu Peihui Zhou Rui He Feng Ding | 2021 | Cellular & Molecular Immunology2021，18，1： | 1 |
| 4 | Reasoning Disaster Chains with Bayesian Network Estimated Under Expert Prior Knowledge显示文摘With the acceleration of global climate change and urbanization,disaster chains are always connected to artificial systems like critical infrastructure.The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers.The Bayesian network(BN)was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies.To capture the interacting relationships among different factors,a scenario representation model of disaster chains was developed,followed by the determination of the BN structure.In deriving the conditional probability tables of the BN model,we found that,due to the lack of data and the significant uncertainty of disaster chains,parameter learning methodologies based on data or expert knowledge alone are insufficient.By integrating both sample data and expert knowledge with the maximum entropy principle,we proposed a parameter estimation algorithm under expert prior knowledge(PEUK).Taking the rainstorm disaster chain as an example,we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior(MAP)algorithm and the direct expert opinion elicitation method.The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions. | Lida Huang Tao Chen Qing Deng Yuli Zhou | 2023 | International Journal of Disaster Risk Science2023，14，6： | 0 |
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