5-HT和DA对虾蟹打斗行为影响的研究进展

5-羟色胺(5-HT)和多巴胺(DA)是两种神经递质,可与众多不同类型的受体结合发挥多种重要的生理功能.现已证明其广泛分布于多种动物的不同组织中,在动物的打斗行为活动中起着重要的调节作用.目前,在虾蟹中已被报道的5-HT受体主要有5种,分别是5-HT1A、5-HT1B、5HT2A、5HT2B和5-HT7;DA受体主要为DA1A、DA1B、DA2和DA4.5-HT和DA及其受体分布存在明显的种属和组织特异性.5-HT和DA参于了虾蟹打斗行为的调节过程并有不同的调节机理.5-HT可以调节环磷酸腺苷(cAMP)或高血糖激素(CHH)的释放,促进或抑制虾蟹打斗行为;而DA同样能够通过调节cAMP及COMT等物质的释放来调节虾蟹打斗行为.     

秦岭南坡陕西洋县辖区哺乳动物物种多样性的空间分布格局

物种多样性的空间分布格局一直是生态学和生物地理学研究的一个热点问题。山地生态系统的生境异质性和物种多样性高, 适合研究物种多样性空间分布格局及其相关机制。2016年11月至2017年11月, 本研究选取秦岭南坡陕西洋县辖区作为研究区域, 采用样线法、红外相机法和笼捕/夹捕法, 系统分析了8目21科48种哺乳动物物种多样性的空间分布格局。研究结果发现秦岭南坡洋县辖区哺乳动物物种丰富度的空间分布格局大致是中南部低, 北部和东部高; 物种多样性指数大致是中南部和北部低, 东部高。啮齿类动物和非啮齿类动物的空间分布格局存在差异。哺乳动物物种丰富度和多样性指数的垂直分布格局都符合中峰模式, 但啮齿类动物和非啮齿类动物间存在差异。最优线性模型结果表明, 研究地区哺乳动物物种多样性的空间分布格局受到多种环境因素的共同影响。其中, 年均温与物种多样性的相关性最强, 在6个最优线性模型中贡献都是最大。综上, 秦岭南坡洋县辖区中高海拔区域的物种多样性较高, 应加强对中高海拔地区的保护, 以维系该区域较高的生物多样性。     

植物光信号途径重要新调控因子TZP的研究进展

TZP (TANDEM ZINC-FINGER/PLUS3)是近年来鉴定到的一个光信号转导途径新组分, 在光介导的植物生长发育过程中发挥重要调控作用。TZP不仅负调控蓝光信号途径, 参与光敏色素B (phyB)介导的开花调控过程, 还参与调控phyA在体内的蛋白质磷酸化。对TZP生化活性和作用机制的深入研究, 不仅有助于进一步完善光信号调控网络, 也可为设计和培育具有耐密理想株型及高光效作物新品种提供理论依据。该文系统总结了TZP在植物光信号途径中发挥的重要调控作用, 并提出未来TZP功能研究的重要问题。     

Cryo-EM structures reveal the dynamic transformation of human alpha-2-macroglobulin working as a protease inhibitor

Science China Life Sciences - Human alpha-2-macroglobulin is a well-known inhibitor of a broad spectrum of proteases and plays important roles in immunity, inflammation, and infections. Here, we...     

Engineering Saccharomyces cerevisiae-based biosensors for copper detection

Heavy metals, that is Cu(II), are harmful to the environment. There is an increasing demand to develop inexpensive detection methods for heavy metals. Here, we developed a yeast biosensor with reduced-noise and improved signal output for potential on-site copper ion detection. The copper-sensing circuit was achieved by employing a secondary genetic layer to control the galactose-inducible (GAL) system in Saccharomyces cerevisiae. The reciprocal control of the Gal4 activator and Gal80 repressor under copper-responsive promoters resulted in a low-noise and sensitive yeast biosensor for copper ion detection. Furthermore, we developed a betaxanthin-based colorimetric assay, as well as 2-phenylethanol and styrene-based olfactory outputs for the copper ion detection. Notably, our engineered yeast sensor confers a narrow range switch-like behaviour, which can give a ‘yes/no’ response when coupled with a betaxanthin-based visual phenotype. Taken together, we envision that the design principle established here might be applicable to develop other sensing systems for various chemical detections.     

德州汉族人群37个Y⁃STR基因座遗传多态性分析

调查德州汉族人群598名男性无关个体37个Y-STR基因座的遗传多态性,分析其在法医学和群体遗传学方面的应用价值,用AGCU Y37荧光检测试剂盒对德州汉族群体的37个Y-STR基因座进行扩增,用3500xL基因分析仪对其进行检测。用MEGA 7.0软件,通过邻接法（neighbor-joining, NJ）构建德州汉族群体和其他15个参考群体的系统发生树,探索群体间的遗传关系。结果共检出593种单倍型,基因多样性（genetic diversity, GD）值为0.113 9（DYS645）～0.971 4（DYS385a/b）,单倍型多样性（haplotype diversity, HD）和识别能力（discriminative capacity, DC）分别为0.999 971 989和0.991 6。结果表明,这37个Y-STR基因座在德州汉族人群中有较高的多态性分布。群体遗传分析中,遗传距离、多维尺度分析( multi-dimensional scaling,MDS) 和系统发生树分析结果表明,德州汉族与其他地区的汉族群体遗传距离更近。不同群体的遗传特征与语系划分、历史形成、地理分布等方面具有一致性。研究结果可为德州汉族人群的法庭科学和群体遗传学研究提供基础数据支持。     

An unconventionally secreted effector from the root knot nematode Meloidogyne incognita,Mi-ISC-1, promotes parasitism by disrupting salicylic acid biosynthesis in host plants

Plant-parasitic nematodes need to deliver effectors that suppress host immunity for successful parasitism. We have characterized a novel isochorismatase effector from the root-knot nematode Meloidogyne incognita, named Mi-ISC-1. The Mi-isc-1 gene is expressed in the subventral oesophageal glands and is up-regulated in parasitic-stage juveniles. Tobacco rattle virus-induced gene silencing targeting Mi-isc-1 attenuated M. incognita parasitism. Enzyme activity assays confirmed that Mi-ISC-1 can catalyse hydrolysis of isochorismate into 2,3-dihydro-2,3-dihydroxybenzoate in vitro. Although Mi-ISC-1 lacks a classical signal peptide for secretion at its N-terminus, a yeast invertase secretion assay showed that this protein can be secreted from eukaryotic cells. However, the subcellular localization and plasmolysis assay revealed that the unconventional secretory signal present on the Mi-ISC-1 is not recognized by the plant secretory pathway and that the effector was localized within the cytoplasm of plant cells, but not apoplast, when transiently expressed in Nicotiana benthamiana leaves by agroinfiltration. Ectopic expression of Mi-ISC-1 in N. benthamiana reduced expression of the PR1 gene and levels of salicylic acid (SA), and promoted infection by Phytophthora capsici. The cytoplasmic localization of Mi-ISC-1 is required for its function. Moreover, Mi-ISC-1 suppresses the production of SA following the reconstitution of the de novo SA biosynthesis via the isochorismate pathway in the cytoplasm of N. benthamiana leaves. These results demonstrate that M. incognita deploys a functional isochorismatase that suppresses SA-mediated plant defences by disrupting the isochorismate synthase pathway for SA biosynthesis to promote parasitism.     

Cost-aware job scheduling for cloud instances using deep reinforcement learning

As the services provided by cloud vendors are providing better performance, achieving auto-scaling, load-balancing, and optimized performance along with low infrastructure maintenance, more and more companies migrate their services to the cloud. Since the cloud workload is dynamic and complex, scheduling the jobs submitted by users in an effective way is proving to be a challenging task. Although a lot of advanced job scheduling approaches have been proposed in the past years, almost all of them are designed to handle batch jobs rather than real-time workloads, such as that user requests are submitted at any time with any amount of numbers. In this work, we have proposed a Deep Reinforcement Learning (DRL) based job scheduler that dispatches the jobs in real time to tackle this problem. Specifically, we focus on scheduling user requests in such a way as to provide the quality of service (QoS) to the end-user along with a significant reduction of the cost spent on the execution of jobs on the virtual instances. We have implemented our method by Deep Q-learning Network (DQN) model, and our experimental results demonstrate that our approach can significantly outperform the commonly used real-time scheduling algorithms.