ICFRM16-Abstracts-16-002-Farhad Tavassoli
氯化石蜡的环境分析方法研究进展
引用格式：周婷婷, 杨倩玲, 翁冀远, 等. 氯化石蜡的环境分析方法研究进展[J]. 中国测试，2024, 50(4): 1-15. ZHOU Tingting,YANG Qianling, WENG Jiyuan, et al. A review on analysis of chlorinated paraffins in environment[J]. China Measurement & Test,2024, 50(4): 1-15. DOI: 10.11857/j.issn.1674-5124.2022080162氯化石蜡的环境分析方法研究进展周婷婷1,2,3， 杨倩玲1,2,3， 翁冀远1,3， 乔 林1， 高丽荣1,2,3， 郑明辉1,2,3(1. 中国科学院生态环境研究中心，环境化学与生态毒理学国家重点实验室，北京 100085;2. 国科大杭州高等研究院 环境学院，浙江 杭州 310000; 3. 中国科学院大学，北京 100049)摘 要: 短链氯化石蜡（SCCPs ）具有持久性、生物毒性和生物累积性等，是一种持久性有机污染物，被列入《斯德哥尔摩公约》附件A 中，中链氯化石蜡具有SCCPs 相似的性质也备受关注。
氯化石蜡（chlorinated paraffins ，CPs ）拥有成千上万种同系物、异构体、对映体，加之环境基质中存在其他有机卤素化合物，CPs 分离分析困难，目前我国仍没有环境样品中CPs 分析的标准方法。
该文对近年来不同环境基质中的CPs 分析所采用的样品前处理技术和仪器分析方法两个方面进行综述，提供CPs 分析方法最新发展动态，为相关人员对开展此方面的研究提供参考。
关键词: 短链氯化石蜡; 中链氯化石蜡; 分析方法; 前处理方法中图分类号: TB9; X-1文献标志码: A文章编号: 1674–5124(2024)04–0001–15A review on analysis of chlorinated paraffins in environmentZHOU Tingting 1,2,3, YANG Qianling 1,2,3, WENG Jiyuan 1,3, QIAO Lin 1, GAO Lirong 1,2,3, ZHENG Minghui 1,2,3(1. State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; 2. School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310000, China; 3. University of ChineseAcademy of Sciences, Beijing 100049, China)Abstract : Short-chain chlorinated paraffins (SCCPs) are persistent organic pollutants with persistence, toxicity,bioaccumulation, and were listed in Annex A of the Stockholm Convention. Medium-chain chlorinated paraffins (MCCPs) have also gained attention due to its similar properties to SCCPs. Chlorinated paraffins (CPs) have thousands of congeners, isomers and enantiomers and the presence of other organohalogen compounds in the environmental matrices makes the separation and analyze of CPs difficult. At present, there is still no standard analytical method to analysis CPs in the different environmental matrices. This article will review the sample preparation and instrumental analysis of CPs in different environmental matrices in recent years and provide an update on the latest developments in the analytical methods for CPs.Keywords : short-chain chlorinated paraffins; medium-chain chlorinated paraffins; analytical methods;preparation methods收稿日期: 2022-08-27；收到修改稿日期: 2022-10-04基金项目: 国家环境保护环境监测质量控制重点实验室开放基金（KF202203）作者简介: 周婷婷（1997-），女，福建漳州市人，硕士研究生，专业方向为新污染物的分析方法与环境行为的研究。
Analysis of Genetic Diversity and Population Structure
Agricultural Sciences in China2010, 9(9): 1251-1262September 2010Received 30 October, 2009 Accepted 16 April, 2010Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of ChinaLIU Zhi-zhai 1, 2, GUO Rong-hua 2, 3, ZHAO Jiu-ran 4, CAI Yi-lin 1, W ANG Feng-ge 4, CAO Mo-ju 3, W ANG Rong-huan 2, 4, SHI Yun-su 2, SONG Yan-chun 2, WANG Tian-yu 2 and LI Y u 21Maize Research Institute, Southwest University, Chongqing 400716, P.R.China2Institue of Crop Sciences/National Key Facility for Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences,Beijing 100081, P.R.China3Maize Research Institute, Sichuan Agricultural University, Ya’an 625014, P.R.China4Maize Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100089, P.R.ChinaAbstractUnderstanding genetic diversity and population structure of landraces is important in utilization of these germplasm in breeding programs. In the present study, a total of 143 core maize landraces from the South Maize Region (SR) of China,which can represent the general profile of the genetic diversity in the landraces germplasm of SR, were genotyped by 54DNA microsatellite markers. Totally, 517 alleles (ranging from 4 to 22) were detected among these landraces, with an average of 9.57 alleles per locus. The total gene diversity of these core landraces was 0.61, suggesting a rather higher level of genetic diversity. Analysis of population structure based on Bayesian method obtained the samilar result as the phylogeny neighbor-joining (NJ) method. The results indicated that the whole set of 143 core landraces could be clustered into two distinct groups. All landraces from Guangdong, Hainan, and 15 landraces from Jiangxi were clustered into group 1, while those from the other regions of SR formed the group 2. The results from the analysis of genetic diversity showed that both of groups possessed a similar gene diversity, but group 1 possessed relatively lower mean alleles per locus (6.63) and distinct alleles (91) than group 2 (7.94 and 110, respectively). The relatively high richness of total alleles and distinct alleles preserved in the core landraces from SR suggested that all these germplasm could be useful resources in germplasm enhancement and maize breeding in China.Key words :maize, core landraces, genetic diversity, population structureINTRODUCTIONMaize has been grown in China for nearly 500 years since its first introduction into this second biggest pro-duction country in the world. Currently, there are six different maize growing regions throughout the coun-try according to the ecological conditions and farming systems, including three major production regions,i.e., the North Spring Maize Region, the Huang-Huai-Hai Summer Maize Region, and the Southwest MaizeRegion, and three minor regions, i.e., the South Maize Region, the Northwest Maize Region, and the Qingzang Plateau Maize Region. The South Maize Region (SR)is specific because of its importance in origin of Chi-nese maize. It is hypothesized that Chinese maize is introduced mainly from two routes. One is called the land way in which maize was first brought to Tibet from India, then to Sichuan Province in southwestern China. The other way is that maize dispersed via the oceans, first shipped to the coastal areas of southeast China by boats, and then spread all round the country1252LIU Zhi-zhai et al.(Xu 2001; Zhou 2000). SR contains all of the coastal provinces and regions lie in southeastern China.In the long-term cultivation history of maize in south-ern China, numerous landraces have been formed, in which a great amount of genetic variation was observed (Li 1998). Similar to the hybrid swapping in Europe (Reif et al. 2005a), the maize landraces have been al-most replaced by hybrids since the 1950s in China (Li 1998). However, some landraces with good adapta-tions and yield performances are still grown in a few mountainous areas of this region (Liu et al.1999). Through a great effort of collection since the 1950s, 13521 accessions of maize landraces have been cur-rently preserved in China National Genebank (CNG), and a core collection of these landraces was established (Li et al. 2004). In this core collection, a total of 143 maize landrace accessions were collected from the South Maize Region (SR) (Table 1).Since simple sequence repeat ( SSR ) markers were firstly used in human genetics (Litt and Luty 1989), it now has become one of the most widely used markers in the related researches in crops (Melchinger et al. 1998; Enoki et al. 2005), especially in the molecular characterization of genetic resources, e.g., soybean [Glycine max (L.) Merr] (Xie et al. 2005), rice (Orya sativa L.) (Garris et al. 2005), and wheat (Triticum aestivum) (Chao et al. 2007). In maize (Zea mays L.), numerous studies focusing on the genetic diversity and population structure of landraces and inbred lines in many countries and regions worldwide have been pub-lished (Liu et al. 2003; Vegouroux et al. 2005; Reif et al. 2006; Wang et al. 2008). These activities of documenting genetic diversity and population structure of maize genetic resources have facilitated the under-standing of genetic bases of maize landraces, the utili-zation of these resources, and the mining of favorable alleles from landraces. Although some studies on ge-netic diversity of Chinese maize inbred lines were con-ducted (Yu et al. 2007; Wang et al. 2008), the general profile of genetic diversity in Chinese maize landraces is scarce. Especially, there are not any reports on ge-netic diversity of the maize landraces collected from SR, a possibly earliest maize growing area in China. In this paper, a total of 143 landraces from SR listed in the core collection of CNG were genotyped by using SSR markers, with the aim of revealing genetic diver-sity of the landraces from SR (Table 2) of China and examining genetic relationships and population struc-ture of these landraces.MATERIALS AND METHODSPlant materials and DNA extractionTotally, 143 landraces from SR which are listed in the core collection of CNG established by sequential strati-fication method (Liu et al. 2004) were used in the present study. Detailed information of all these landrace accessions is listed in Table 1. For each landrace, DNA sample was extracted by a CTAB method (Saghi-Maroof et al. 1984) from a bulk pool constructed by an equal-amount of leaves materials sampled from 15 random-chosen plants of each landrace according to the proce-dure of Reif et al. (2005b).SSR genotypingA total of 54 simple sequence repeat (SSR) markers covering the entire maize genome were screened to fin-gerprint all of the 143 core landrace accessions (Table 3). 5´ end of the left primer of each locus was tailed by an M13 sequence of 5´-CACGACGTTGTAAAACGAC-3´. PCR amplification was performed in a 15 L reac-tion containing 80 ng of template DNA, 7.5 mmol L-1 of each of the four dNTPs, 1×Taq polymerase buffer, 1.5 mmol L-1 MgCl2, 1 U Taq polymerase (Tiangen Biotech Co. Ltd., Beijing, China), 1.2 mol L-1 of forward primer and universal fluorescent labeled M13 primer, and 0.3 mol L-1 of M13 sequence tailed reverse primer (Schuelke 2000). The amplification was carried out in a 96-well DNA thermal cycler (GeneAmp PCR System 9700, Applied Biosystem, USA). PCR products were size-separated on an ABI Prism 3730XL DNA sequencer (HitachiHigh-Technologies Corporation, Tokyo, Japan) via the software packages of GENEMAPPER and GeneMarker ver. 6 (SoftGenetics, USA).Data analysesAverage number of alleles per locus and average num-ber of group-specific alleles per locus were identifiedAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1253Table 1 The detailed information about the landraces used in the present studyPGS revealed by Structure1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysis140-150tian 00120005AnH-06Jingde Anhui 0.0060.994Group 2170tian00120006AnH-07Jingde Anhui 0.0050.995Group 2Zixihuangyumi00120007AnH-08Zixi Anhui 0.0020.998Group 2Zixibaihuangzayumi 00120008AnH-09Zixi Anhui 0.0030.997Group 2Baiyulu 00120020AnH-10Yuexi Anhui 0.0060.994Group 2Wuhuazi 00120021AnH-11Yuexi Anhui 0.0030.997Group 2Tongbai 00120035AnH-12Tongling Anhui 0.0060.994Group 2Yangyulu 00120036AnH-13Yuexi Anhui 0.0040.996Group 2Huangli 00120037AnH-14Tunxi Anhui 0.0410.959Group 2Baiyumi 00120038AnH-15Tunxi Anhui 0.0030.997Group 2Dapigu00120039AnH-16Tunxi Anhui 0.0350.965Group 2150tianbaiyumi 00120040AnH-17Xiuning Anhui 0.0020.998Group 2Xiuning60tian 00120042AnH-18Xiuning Anhui 0.0040.996Group 2Wubaogu 00120044AnH-19ShitaiAnhui 0.0020.998Group 2Kuyumi00130001FuJ-01Shanghang Fujian 0.0050.995Group 2Zhongdouyumi 00130003FuJ-02Shanghang Fujian 0.0380.962Group 2Baixinyumi 00130004FuJ-03Liancheng Fujian 0.0040.996Group 2Hongxinyumi 00130005FuJ-04Liancheng Fujian 0.0340.966Group 2Baibaogu 00130008FuJ-05Changding Fujian 0.0030.997Group 2Huangyumi 00130011FuJ-06Jiangyang Fujian 0.0020.998Group 2Huabaomi 00130013FuJ-07Shaowu Fujian 0.0020.998Group 2Huangbaomi 00130014FuJ-08Songxi Fujian 0.0020.998Group 2Huangyumi 00130016FuJ-09Wuyishan Fujian 0.0460.954Group 2Huabaogu 00130019FuJ-10Jian’ou Fujian 0.0060.994Group 2Huangyumi 00130024FuJ-11Guangze Fujian 0.0010.999Group 2Huayumi 00130025FuJ-12Nanping Fujian 0.0040.996Group 2Huangyumi 00130026FuJ-13Nanping Fujian 0.0110.989Group 2Hongbaosu 00130027FuJ-14Longyan Fujian 0.0160.984Group 2Huangfansu 00130029FuJ-15Loangyan Fujian 0.0020.998Group 2Huangbaosu 00130031FuJ-16Zhangping Fujian 0.0060.994Group 2Huangfansu 00130033FuJ-17Zhangping Fujian0.0040.996Group 2Baolieyumi 00190001GuangD-01Guangzhou Guangdong 0.9890.011Group 1Nuomibao (I)00190005GuangD-02Shixing Guangdong 0.9740.026Group 1Nuomibao (II)00190006GuangD-03Shixing Guangdong 0.9790.021Group 1Zasehuabao 00190010GuangD-04Lechang Guangdong 0.9970.003Group 1Zihongmi 00190013GuangD-05Lechang Guangdong 0.9880.012Group 1Jiufengyumi 00190015GuangD-06Lechang Guangdong 0.9950.005Group 1Huangbaosu 00190029GuangD-07MeiGuangdong 0.9970.003Group 1Bailibao 00190032GuangD-08Xingning Guangdong 0.9980.002Group 1Nuobao00190038GuangD-09Xingning Guangdong 0.9980.002Group 1Jinlanghuang 00190048GuangD-10Jiangcheng Guangdong 0.9960.004Group 1Baimizhenzhusu 00190050GuangD-11Yangdong Guangdong 0.9940.006Group 1Huangmizhenzhusu 00190052GuangD-12Yangdong Guangdong 0.9930.007Group 1Baizhenzhu 00190061GuangD-13Yangdong Guangdong 0.9970.003Group 1Baiyumi 00190066GuangD-14Wuchuan Guangdong 0.9880.012Group 1Bendibai 00190067GuangD-15Suixi Guangdong 0.9980.002Group 1Shigubaisu 00190068GuangD-16Gaozhou Guangdong 0.9960.004Group 1Zhenzhusu 00190069GuangD-17Xinyi Guangdong 0.9960.004Group 1Nianyaxixinbai 00190070GuangD-18Huazhou Guangdong 0.9960.004Group 1Huangbaosu 00190074GuangD-19Xinxing Guangdong 0.9950.005Group 1Huangmisu 00190076GuangD-20Luoding Guangdong 0.940.060Group 1Huangmi’ai 00190078GuangD-21Luoding Guangdong 0.9980.002Group 1Bayuemai 00190084GuangD-22Liannan Guangdong 0.9910.009Group 1Baiyumi 00300001HaiN-01Haikou Hainan 0.9960.004Group 1Baiyumi 00300003HaiN-02Sanya Hainan 0.9970.003Group 1Hongyumi 00300004HaiN-03Sanya Hainan 0.9980.002Group 1Baiyumi00300011HaiN-04Tongshi Hainan 0.9990.001Group 1Zhenzhuyumi 00300013HaiN-05Tongshi Hainan 0.9980.002Group 1Zhenzhuyumi 00300015HaiN-06Qiongshan Hainan 0.9960.004Group 1Aiyumi 00300016HaiN-07Qiongshan Hainan 0.9960.004Group 1Huangyumi 00300021HaiN-08Qionghai Hainan 0.9970.003Group 1Y umi 00300025HaiN-09Qionghai Hainan 0.9870.013Group 1Accession name Entry code Analyzing code Origin (county/city)Province/Region1254LIU Zhi-zhai et al .Baiyumi00300032HaiN-10Tunchang Hainan 0.9960.004Group 1Huangyumi 00300051HaiN-11Baisha Hainan 0.9980.002Group 1Baihuangyumi 00300055HaiN-12BaishaHainan 0.9970.003Group 1Machihuangyumi 00300069HaiN-13Changjiang Hainan 0.9900.010Group 1Hongyumi00300073HaiN-14Dongfang Hainan 0.9980.002Group 1Xiaohonghuayumi 00300087HaiN-15Lingshui Hainan 0.9980.002Group 1Baiyumi00300095HaiN-16Qiongzhong Hainan 0.9950.005Group 1Y umi (Baimai)00300101HaiN-17Qiongzhong Hainan 0.9980.002Group 1Y umi (Xuemai)00300103HaiN-18Qiongzhong Hainan 0.9990.001Group 1Huangmaya 00100008JiangS-10Rugao Jiangsu 0.0040.996Group 2Bainian00100012JiangS-11Rugao Jiangsu 0.0080.992Group 2Bayebaiyumi 00100016JiangS-12Rudong Jiangsu 0.0040.996Group 2Chengtuohuang 00100021JiangS-13Qidong Jiangsu 0.0050.995Group 2Xuehuanuo 00100024JiangS-14Qidong Jiangsu 0.0020.998Group 2Laobaiyumi 00100032JiangS-15Qidong Jiangsu 0.0050.995Group 2Laobaiyumi 00100033JiangS-16Qidong Jiangsu 0.0010.999Group 2Huangwuye’er 00100035JiangS-17Hai’an Jiangsu 0.0030.997Group 2Xiangchuanhuang 00100047JiangS-18Nantong Jiangsu 0.0060.994Group 2Huangyingzi 00100094JiangS-19Xinghua Jiangsu 0.0040.996Group 2Xiaojinhuang 00100096JiangS-20Yangzhou Jiangsu 0.0010.999Group 2Liushizi00100106JiangS-21Dongtai Jiangsu 0.0030.997Group 2Kangnandabaizi 00100108JiangS-22Dongtai Jiangsu 0.0020.998Group 2Shanyumi 00140020JiangX-01Dexing Jiangxi 0.9970.003Group 1Y umi00140024JiangX-02Dexing Jiangxi 0.9970.003Group 1Tianhongyumi 00140027JiangX-03Yushan Jiangxi 0.9910.009Group 1Hongganshanyumi 00140028JiangX-04Yushan Jiangxi 0.9980.002Group 1Zaoshuyumi 00140032JiangX-05Qianshan Jiangxi 0.9970.003Group 1Y umi 00140034JiangX-06Wannian Jiangxi 0.9970.003Group 1Y umi 00140038JiangX-07De’an Jiangxi 0.9940.006Group 1Y umi00140045JiangX-08Wuning Jiangxi 0.9740.026Group 1Chihongyumi 00140049JiangX-09Wanzai Jiangxi 0.9920.008Group 1Y umi 00140052JiangX-10Wanzai Jiangxi 0.9930.007Group 1Huayumi 00140060JiangX-11Jing’an Jiangxi 0.9970.003Group 1Baiyumi 00140065JiangX-12Pingxiang Jiangxi 0.9940.006Group 1Huangyumi00140066JiangX-13Pingxiang Jiangxi 0.9680.032Group 1Nuobaosuhuang 00140068JiangX-14Ruijin Jiangxi 0.9950.005Group 1Huangyumi 00140072JiangX-15Xinfeng Jiangxi 0.9960.004Group 1Wuningyumi 00140002JiangX-16Jiujiang Jiangxi 0.0590.941Group 2Tianyumi 00140005JiangX-17Shangrao Jiangxi 0.0020.998Group 2Y umi 00140006JiangX-18Shangrao Jiangxi 0.0310.969Group 2Baiyiumi 00140012JiangX-19Maoyuan Jiangxi 0.0060.994Group 260riyumi 00140016JiangX-20Maoyuan Jiangxi 0.0020.998Group 2Shanyumi 00140019JiangX-21Dexing Jiangxi 0.0050.995Group 2Laorenya 00090002ShangH-01Chongming Shanghai 0.0050.995Group 2Jinmeihuang 00090004ShangH-02Chongming Shanghai 0.0020.998Group 2Zaobaiyumi 00090006ShangH-03Chongming Shanghai 0.0020.998Group 2Chengtuohuang 00090007ShangH-04Chongming Shanghai 0.0780.922Group 2Benyumi (Huang)00090008ShangH-05Shangshi Shanghai 0.0020.998Group 2Bendiyumi 00090010ShangH-06Shangshi Shanghai 0.0040.996Group 2Baigengyumi 00090011ShangH-07Jiading Shanghai 0.0020.998Group 2Huangnuoyumi 00090012ShangH-08Jiading Shanghai 0.0040.996Group 2Huangdubaiyumi 00090013ShangH-09Jiading Shanghai 0.0440.956Group 2Bainuoyumi 00090014ShangH-10Chuansha Shanghai 0.0010.999Group 2Laorenya 00090015ShangH-11Shangshi Shanghai 0.0100.990Group 2Xiaojinhuang 00090016ShangH-12Shangshi Shanghai 0.0050.995Group 2Gengbaidayumi 00090017ShangH-13Shangshi Shanghai 0.0020.998Group 2Nongmeiyihao 00090018ShangH-14Shangshi Shanghai 0.0540.946Group 2Chuanshazinuo 00090020ShangH-15Chuansha Shanghai 0.0550.945Group 2Baoanshanyumi 00110004ZheJ-01Jiangshan Zhejiang 0.0130.987Group 2Changtaixizi 00110005ZheJ-02Jiangshan Zhejiang 0.0020.998Group 2Shanyumibaizi 00110007ZheJ-03Jiangshan Zhejiang 0.0020.998Group 2Kaihuajinyinbao 00110017ZheJ-04Kaihua Zhejiang 0.0100.990Group 2Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group1 Group2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/RegoinAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1255Liputianzi00110038ZheJ-05Jinhua Zhejiang 0.0020.998Group 2Jinhuaqiuyumi 00110040ZheJ-06Jinhua Zhejiang 0.0050.995Group 2Pujiang80ri 00110069ZheJ-07Pujiang Zhejiang 0.0210.979Group 2Dalihuang 00110076ZheJ-08Yongkang Zhejiang 0.0140.986Group 2Ziyumi00110077ZheJ-09Yongkang Zhejiang 0.0020.998Group 2Baiyanhandipinzhong 00110078ZheJ-10Yongkang Zhejiang 0.0030.997Group 2Duosuiyumi00110081ZheJ-11Wuyi Zhejiang 0.0020.998Group 2Chun’an80huang 00110084ZheJ-12Chun’an Zhejiang 0.0020.998Group 2120ribaiyumi 00110090ZheJ-13Chun’an Zhejiang 0.0020.998Group 2Lin’anliugu 00110111ZheJ-14Lin’an Zhejiang 0.0030.997Group 2Qianhuangyumi00110114ZheJ-15Lin’an Zhejiang 0.0030.997Group 2Fenshuishuitianyumi 00110118ZheJ-16Tonglu Zhejiang 0.0410.959Group 2Kuihualiugu 00110119ZheJ-17Tonglu Zhejiang 0.0030.997Group 2Danbaihuang 00110122ZheJ-18Tonglu Zhejiang 0.0020.998Group 2Hongxinma 00110124ZheJ-19Jiande Zhejiang 0.0030.997Group 2Shanyumi 00110136ZheJ-20Suichang Zhejiang 0.0030.997Group 2Bai60ri 00110143ZheJ-21Lishui Zhejiang 0.0050.995Group 2Zeibutou 00110195ZheJ-22Xianju Zhejiang 0.0020.998Group 2Kelilao00110197ZheJ-23Pan’an Zhejiang 0.0600.940Group 21)The figures refered to the proportion of membership that each landrace possessed.Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/Regoin Table 2 Construction of two phylogenetic groups (SSR-clustered groups) and their correlation with geographical locationsGeographical location SSR-clustered groupChi-square testGroup 1Group 2Total Guangdong 2222 χ2 = 124.89Hainan 1818P < 0.0001Jiangxi 15621Anhui 1414Fujian 1717Jiangsu 1313Shanghai 1515Zhejiang 2323Total5588143by the software of Excel MicroSatellite toolkit (Park 2001). Average number of alleles per locus was calcu-lated by the formula rAA rj j¦1, with the standarddeviation of1)()(12¦ r A AA rj jV , where A j was thenumber of distinct alleles at locus j , and r was the num-ber of loci (Park 2001).Unbiased gene diversity also known as expected heterozygosity, observed heterozygosity for each lo-cus and average gene diversity across the 54 SSR loci,as well as model-based groupings inferred by Struc-ture ver. 2.2, were calculated by the softwarePowerMarker ver.3.25 (Liu et al . 2005). Unbiased gene diversity for each locus was calculated by˅˄¦ 2ˆ1122ˆi x n n h , where 2ˆˆ2ˆ2¦¦z ji ijij i X X x ,and ij X ˆwas the frequency of genotype A i A jin the sample, and n was the number of individuals sampled.The average gene diversity across 54 loci was cal-culated as described by Nei (1987) as follows:rh H rj j ¦1ˆ, with the variance ,whereThe average observed heterozygosity across the en-tire loci was calculated as described by (Hedrick 1983)as follows: r jrj obsobs n h h ¦1, with the standard deviationrn h obs obsobs 1V1256LIU Zhi-zhai et al.Phylogenetic analysis and population genetic structureRelationships among all of the 143 accessions collected from SR were evaluated by using the unweighted pair group method with neighbor-joining (NJ) based on the log transformation of the proportion of shared alleles distance (InSPAD) via PowerMarker ver. 3.25 (FukunagaTable 3 The PIC of each locus and the number of alleles detected by 54 SSRsLocus Bin Repeat motif PIC No. of alleles Description 2)bnlg1007y51) 1.02AG0.7815Probe siteumc1122 1.06GGT0.639Probe siteumc1147y41) 1.07CA0.2615Probe sitephi961001) 2.00ACCT0.298Probe siteumc1185 2.03GC0.7215ole1 (oleosin 1)phi127 2.08AGAC0.577Probe siteumc1736y21) 2.09GCA T0.677Probe sitephi453121 3.01ACC0.7111Probe sitephi374118 3.03ACC0.477Probe sitephi053k21) 3.05A TAC0.7910Probe sitenc004 4.03AG0.4812adh2 (alcohol dehydrogenase 2)bnlg490y41) 4.04T A0.5217Probe sitephi079 4.05AGATG0.495gpc1(glyceraldehyde-3-phosphate dehydrogenase 1) bnlg1784 4.07AG0.6210Probe siteumc1574 4.09GCC0.719sbp2 (SBP-domain protein 2)umc1940y51) 4.09GCA0.4713Probe siteumc1050 4.11AA T0.7810cat3 (catalase 3)nc130 5.00AGC0.5610Probe siteumc2112y31) 5.02GA0.7014Probe sitephi109188 5.03AAAG0.719Probe siteumc1860 5.04A T0.325Probe sitephi085 5.07AACGC0.537gln4 (glutamine synthetase 4)phi331888 5.07AAG0.5811Probe siteumc1153 5.09TCA0.7310Probe sitephi075 6.00CT0.758fdx1 (ferredoxin 1)bnlg249k21) 6.01AG0.7314Probe sitephi389203 6.03AGC0.416Probe sitephi299852y21) 6.07AGC0.7112Probe siteumc1545y21)7.00AAGA0.7610hsp3(heat shock protein 3)phi1127.01AG0.5310o2 (opaque endosperm 2)phi4207018.00CCG0.469Probe siteumc13598.00TC0.7814Probe siteumc11398.01GAC0.479Probe siteumc13048.02TCGA0.335Probe sitephi1158.03A TAC0.465act1(actin1)umc22128.05ACG0.455Probe siteumc11218.05AGAT0.484Probe sitephi0808.08AGGAG0.646gst1 (glutathione-S-transferase 1)phi233376y11)8.09CCG0.598Probe sitebnlg12729.00AG0.8922Probe siteumc20849.01CTAG0.498Probe sitebnlg1520k11)9.01AG0.5913Probe sitephi0659.03CACCT0.519pep1(phosphoenolpyruvate carboxylase 1)umc1492y131)9.04GCT0.2514Probe siteumc1231k41)9.05GA0.2210Probe sitephi1084119.06AGCT0.495Probe sitephi4488809.06AAG0.7610Probe siteumc16759.07CGCC0.677Probe sitephi041y61)10.00AGCC0.417Probe siteumc1432y61)10.02AG0.7512Probe siteumc136710.03CGA0.6410Probe siteumc201610.03ACAT0.517pao1 (polyamine oxidase 1)phi06210.04ACG0.337mgs1 (male-gametophyte specific 1)phi07110.04GGA0.515hsp90 (heat shock protein, 90 kDa)1) These primers were provided by Beijing Academy of Agricultural and Forestry Sciences (Beijing, China).2) Searched from Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China1257et al. 2005). The unrooted phylogenetic tree was finally schematized with the software MEGA (molecular evolu-tionary genetics analysis) ver. 3.1 (Kumar et al. 2004). Additionally, a chi-square test was used to reveal the correlation between the geographical origins and SSR-clustered groups through FREQ procedure implemented in SAS ver. 9.0 (2002, SAS Institute, Inc.).In order to reveal the population genetic structure (PGS) of 143 landrace accessions, a Bayesian approach was firstly applied to determine the number of groups (K) that these materials should be assigned by the soft-ware BAPS (Bayesian Analysis of Population Structure) ver.5.1. By using BAPS, a fixed-K clustering proce-dure was applied, and with each separate K, the num-ber of runs was set to 100, and the value of log (mL) was averaged to determine the appropriate K value (Corander et al. 2003; Corander and Tang 2007). Since the number of groups were determined, a model-based clustering analysis was used to assign all of the acces-sions into the corresponding groups by an admixture model and a correlated allele frequency via software Structure ver.2.2 (Pritchard et al. 2000; Falush et al. 2007), and for the given K value determined by BAPS, three independent runs were carried out by setting both the burn-in period and replication number 100000. The threshold probability assigned individuals into groupswas set by 0.8 (Liu et al. 2003). The PGS result carried out by Structure was visualized via Distruct program ver. 1.1 (Rosenberg 2004).RESULTSGenetic diversityA total of 517 alleles were detected by the whole set of54 SSRs covering the entire maize genome through all of the 143 maize landraces, with an average of 9.57 alleles per locus and ranged from 4 (umc1121) to 22 (bnlg1272) (Table 3). Among all the alleles detected, the number of distinct alleles accounted for 132 (25.53%), with an av-erage of 2.44 alleles per locus. The distinct alleles dif-fered significantly among the landraces from different provinces/regions, and the landraces from Guangdong, Fujian, Zhejiang, and Shanghai possessed more distinct alleles than those from the other provinces/regions, while those from southern Anhui possessed the lowest distinct alleles, only counting for 3.28% of the total (Table 4).Table 4 The genetic diversity within eight provinces/regions and groups revealed by 54 SSRsProvince/Region Sample size Allele no.1)Distinct allele no.Gene diversity (expected heterozygosity)Observed heterozygosity Anhui14 4.28 (4.19) 69 (72.4)0.51 (0.54)0.58 (0.58)Fujian17 4.93 (4.58 80 (79.3)0.56 (0.60)0.63 (0.62)Guangdong22 5.48 (4.67) 88 (80.4)0.57 (0.59)0.59 (0.58)Hainan18 4.65 (4.26) 79 (75.9)0.53 (0.57)0.55 (0.59)Jiangsu13 4.24 700.500.55Jiangxi21 4.96 (4.35) 72 (68.7)0.56 (0.60)0.68 (0.68)Shanghai15 5.07 (4.89) 90 (91.4)0.55 (0.60)0.55 (0.55)Zhejiang23 5.04 (4.24) 85 (74)0.53 (0.550.60 (0.61)Total/average1439.571320.610.60GroupGroup 155 6.63 (6.40) 91 (89.5)0.57 (0.58)0.62 (0.62)Group 2887.94 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Total/Average1439.571320.610.60Provinces/Regions within a groupGroup 1Total55 6.69 (6.40) 910.57 (0.58)0.62 (0.62)Guangdong22 5.48 (4.99) 86 (90.1)0.57 (0.60)0.59 (0.58)Hainan18 4.65 (4.38) 79 (73.9)0.53 (0.56)0.55 (0.59)Jiangxi15 4.30 680.540.69Group 2Total887.97 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Anhui14 4.28 (3.22) 69 (63.2)0.51 (0.54)0.58 (0.57)Fujian17 4.93 (3.58) 78 (76.6)0.56 (0.60)0.63 (0.61)Jiangsu13 4.24 (3.22) 71 (64.3)0.50 (0.54)0.55 (0.54)Jiangxi6 3.07 520.460.65Shanghai15 5.07 (3.20) 91 (84.1)0.55 (0.60)0.55 (0.54)Zhejiang23 5.04 (3.20) 83 (61.7)0.53 (0.54)0.60 (0.58)1258LIU Zhi-zhai et al.Among the 54 loci used in the study, 16 (or 29.63%) were dinucleotide repeat SSRs, which were defined as type class I-I, the other 38 loci were SSRs with a longer repeat motifs, and two with unknown repeat motifs, all these 38 loci were defined as the class of I-II. In addition, 15 were located within certain functional genes (defined as class II-I) and the rest were defined as class II-II. The results of comparison indicated that the av-erage number of alleles per locus captured by class I-I and II-II were 12.88 and 10.05, respectively, which were significantly higher than that by type I-II and II-I (8.18 and 8.38, respectively). The gene diversity re-vealed by class I-I (0.63) and II-I (0.63) were some-what higher than by class I-II (0.60) and II-II (0.60) (Table 5).Genetic relationships of the core landraces Overall, 143 landraces were clustered into two groups by using neighbor-joining (NJ) method based on InSPAD. All the landraces from provinces of Guangdong and Hainan and 15 of 21 from Jiangxi were clustered together to form group 1, and the other 88 landraces from the other provinces/regions formed group 2 (Fig.-B). The geographical origins of all these 143 landraces with the clustering results were schematized in Fig.-D. Revealed by the chi-square test, the phylogenetic results (SSR-clustered groups) of all the 143 landraces from provinces/regions showed a significant correlation with their geographical origin (χ2=124.89, P<0.0001, Table 2).Revealed by the phylogenetic analysis based on the InSPAD, the minimum distance was observed as 0.1671 between two landraces, i.e., Tianhongyumi (JiangX-03) and Hongganshanyumi (JiangX-04) collected from Jiangxi Province, and the maximum was between two landraces of Huangbaosu (FuJ-16) and Hongyumi (HaiN-14) collected from provinces of Fujian and Hainan, respectively, with the distance of 1.3863 (data not shown). Two landraces (JiangX-01 and JiangX-21) collected from the same location of Dexing County (Table 1) possessing the same names as Shanyumi were separated to different groups, i.e., JiangX-01 to group1, while JiangX-21 to group 2 (Table 1). Besides, JiangX-01 and JiangX-21 showed a rather distant distance of 0.9808 (data not shown). These results indicated that JiangX-01 and JiangX-21 possibly had different ances-tral origins.Population structureA Bayesian method was used to detect the number of groups (K value) of the whole set of landraces from SR with a fixed-K clustering procedure implemented in BAPS software ver. 5.1. The result showed that all of the 143 landraces could also be assigned into two groups (Fig.-A). Then, a model-based clustering method was applied to carry out the PGS of all the landraces via Structure ver. 2.2 by setting K=2. This method as-signed individuals to groups based on the membership probability, thus the threshold probability 0.80 was set for the individuals’ assignment (Liu et al. 2003). Accordingly, all of the 143 landraces were divided into two distinct model-based groups (Fig.-C). The landraces from Guangdong, Hainan, and 15 landraces from Jiangxi formed one group, while the rest 6 landraces from the marginal countries of northern Jiangxi and those from the other provinces formed an-other group (Table 1, Fig.-D). The PGS revealed by the model-based approach via Structure was perfectly consistent with the relationships resulted from the phy-logenetic analysis via PowerMarker (Table 1).DISCUSSIONThe SR includes eight provinces, i.e., southern Jiangsu and Anhui, Shanghai, Zhejiang, Fujian, Jiangxi, Guangdong, and Hainan (Fig.-C), with the annual maize growing area of about 1 million ha (less than 5% of theTable 5 The genetic diversity detected with different types of SSR markersType of locus No. of alleles Gene diversity Expected heterozygosity PIC Class I-I12.880.630.650.60 Class I-II8.180.600.580.55 Class II-I8.330.630.630.58。
SCI收录的《中国神经再生研究(英文版)》(NRR)杂志
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染色质重塑复合体ATP酶CHR16在拟南芥响应干旱胁迫中的作用
染色质重塑复合体ATP酶CHR16在拟南芥响应干旱胁迫中的作用中文摘要染色质重塑通过形成一系列重塑复合体对染色质修饰进行精确调控，影响各类转录调节因子与染色质结合的难易程度，进而使细胞感受各种信号和环境剌激，并在调控基因时空特异性表达和沉默方面发挥重要作用，形成了一系列分子开关。
SNF2类染色质重塑复合体A TP酶在调控植物生长发育和对逆境的响应中扮演极其重要的角色。
CHR16是SNF2家族中的成员，目前关于CHR16的生物学功能鲜有报道。
本课题组在前期工作中发现拟南芥CHR16基因通过调控CLV3，WUS，STM，PLT2，SCR，CYCB，QC46和QC184等基因的表达，在SAM 和RAM的形成及干细胞身份维持方面发挥重要的作用。
为了探究CHR16的其它生物学功能，我们以拟南芥野生型Col-0、CHR16 T-DNA插入突变体chr16-1、chr16-2、chr16-3和chr16-4突变体为实验材料，通过表型分析、组织化学染色、酵母双杂交、qPCR、生理以及转录组等方法，对CHR16的生物学功能做了进一步研究，主要研究结果如下：1、通过表型分析、三引物法纯杂合鉴定以及RT-PCR,我们发现chr16-1、chr16-2、chr16-3和chr16-4突变体皆为单基因隐形突变，纯合突变体为完全缺失突变；与野生型相比，纯合突变体出现短根、子叶较小的表型，能完成生活史，但抽薹时间较晚，可以开花但是不育；2、通过构建pCHR16-GUS转基因株系，我们对CHR16在拟南芥中的组织定位进行了研究。
GUS染色结果表明，在拟南芥的种皮、子叶、真叶、下胚轴、长角果、莲座叶、花器官以及花药中都有很强的GUS信号，这说明CHR16在植物中的表达相当广谱；3、通过酵母双杂交方法筛选了拟南芥酵母文库，初步得到了14个与CHR16N可能有相互作用的候选底物，通过比对发现RD2、DI19和PP2C62都参与干旱胁迫响应。
Chapter 16-Enterobacteriaceae
Escherichia coli (cont’d)
Infections
Wide range including meningitis, gastrointestinal, urinary tract, wound, and bacteremia Gastrointestinal Infections
Clinical Significance of Enterics
Enterics are ubiquitous in nature Except for few, most are present in the intestinal tract of animals and humans as commensal flora; therefore, they are sometimes call “fecal coliforms” Some live in water, soil and sewage
All ferment glucose (dextrose) All reduce nitrates to nitrites All are oxidase negative All except Klebsiella, Shigella and Yersinia are motile
Microscopic and Colony Morphology
Usually found in intestinal tract Wide variety of infections, primarily pneumonia, wound, and UTI General characteristics:
Some species are non-motile Simmons citrate positive H2S negative Phenylalanine deaminase negative Some weakly urease positive MR negative; VP positive
国际畜牧、动物医学、水产类核心期刊中英文对照表
693C0052
英国
22
The Journal of veterinary medical science
兽医学杂志
693D0001
日本
23
The Journal of the American Animal Hospital Association
美国动物医院协会志
693B0103
美国
加拿大兽医杂志
693NA052
加拿大
27
Medical and veterinary entomology
医用与兽医昆虫学
631C0086
英国
28
Journal of veterinary pharmacology and herapeutics
兽医药理学与治疗杂志
693C0060
英国
29
The Indian veterinary Journal
兽医学文献
智利
42
Turk veterinerlik ve hayvancilik dergisi
土耳其兽医与动物科学杂志
693IC001
土耳其
43
Meat science
肉类科学
834C0059
英国
79.2
序号
刊名
中文译名
中图刊号
岀版国
1
The Veterinary record
兽医记录
693C0006
国际畜牧、动物医学、水产类核心期刊中英文对 照表
序号
刊名
中文译名
中图刊号
岀版国
1
Journal of animal science
畜牧学杂志
211132683_东南亚十二节段RNA病毒荧光定量qRT-PCR和常规RT-PCR检测方法的建立
·研究论文·Chinese Journal of Animal Infectious Diseases中国动物传染病学报摘 要：东南亚十二节段RNA 病毒属为呼肠孤病毒科中的一个新属，该属包括版纳病毒（BAV ）、芒市病毒（MSV ）、卡皮罗病毒（KDV ）和辽宁病毒（LNV ）四种病毒。
为建立上述四种病毒的群特异性核酸检测方法，本研究根据BAV 、MSV 、KDV 和LNV 毒株VP12基因序列的保守区，设计特异性引物和TaqMan 探针，建立了荧光定量qRT-PCR 和常规RT-PCR 检测方法，并分别进行了特异性、灵敏性和重复性的检测。
实验结果表明，两种检测方法分别对四种病毒均有特异性的扩增和荧光信号检出，而对流行性出血病病毒（EHDV ）、蓝舌病病毒（BTV ）、中山病病毒（CHUV ）、广西环状病毒（GXOV ）、阿卡斑病毒（AKAV ）、云南环状病毒（YUOV ）等病毒无扩增和无有效荧光信号检出，具有较好的特异性。
灵敏性实验结果显示，荧光定量qRT-PCR 检测四种病毒核酸的下限可达10 copies/μL ，常规RT-PCR 的检测下限为102 copies/μL ，荧光定量方法灵敏度是常规方法的10倍。
四种病毒荧光定量qRT-PCR 方法的组内和组间重复试验标准差均小于0.8，变异系数均小于3%，表明该方法均具有良好的稳定性。
以上结果表明，本研究建立的BAV 、MSV 、KDV 和LNV 四种病毒的荧光定量qRT-PCR 和常规RT-PCR 方法具有快速、准确、稳定等优点，可为四种病毒的早期诊断、快速检测、流行病学调查和实验研究等提供有效的技术方法。
关键词：版纳病毒；芒市病毒；卡地皮诺病毒；辽宁病毒；RT-PCR ；病毒检测中图分类号：S852.65文献标志码：A文章编号：1674-6422（2023）01-0098-08Development of Real-time Fluorescent Quantitative RT-PCR and ConventionalRT-PCR for Detection of Southeastern Asian Dodeca RNA Viruses收稿日期：2020-09-28基金项目：国家重点研发计划（2017YFC1200505）；国家公益性行业（农业）科研专项（201303035）；云南省中青年学术和技术带头人后备人才培养项目（2017HB055）作者简介：杨振兴，男，硕士，副研究员，主要从事牛羊虫媒病毒研究；李占鸿，男，硕士，助理研究员，主要从事牛羊虫媒病毒研究通信作者：廖德芳，E-mail：*******************；杨恒，E-mail：*************************东南亚十二节段RNA 病毒荧光定量qRT-PCR 和常规RT-PCR 检测方法的建立杨振兴，李占鸿，李卓然，李华春，廖德芳，杨 恒（云南省畜牧兽医科学院 热带亚热带动物病毒病重点实验室，昆明650224）2023,31(1):98-105Abstract: Southeastern Asian dodeca RNA viruses (Seadornavirus ) belong to a new genus of the Reoviridae family, which includes Banna virus (BA V), Mangshi virus (MSV), Kadipiro Virus (KDV) and Liaoning virus (LNV). In order to develop a group-specifi c nucleic acid detection method for BA V , MSV , KDV and LNV , specifi c primers and TaqMan probes were designed based on the conserved regions of the VP12 gene sequences of the 4 viruses for development of a real-time quantitative RT-PCR (qRT-PCR) and conventional RT-PCR. The results showed that both assays specifi cally amplifi ed the 4 viruses b u t not for epizootic hemorrhagic disease virus (EHDV), bluetongue Virus (BTV), Chuzan virus (CHUV), Guangxi Orbivirus (GXOV), Akabane virus (AKA V) and Yunnan Orbivirus (YUOV). The detectionYANG Zhenxing, LI Zhanhong, LI Zhuoran, LI Huachun, LIAO Defang, YANG Heng(Yunnan Tropical and Subtropical Animal Virus Disease Laboratory, Yunnan V eterinary and Animal Science Institute, Kunming 650224, China)· 99 ·杨振兴等：东南亚十二节段RNA 病毒荧光定量qRT-PCR 和常规RT-PCR 检测方法的建立第31卷第1期东南亚十二节段RNA病毒属（Seadornavirus ）隶属呼肠孤病毒科（Reoviridae），为2005年国际病毒分类委员会（International Committee for the Taxonomy of Viruses, ICTV）第八次会议新确认的一个病毒属[1]。
First-principles study of the structural, vibrational, phonon and thermodynamic
1. Introduction Ultra-high temperature ceramics (UHTCs) with melting temperatures in excess of 3000 K are usually composed by the refractory borides, carbides and nitrides of early transition metals [1–7]. Among the UHTCs, transition metal carbides (TMC) such as TiC, ZrC and HfC are metallic compounds with unique physical and chemical properties including an extremely high melting point and hardness, chemical stability, corrosion resistance combined with metallic electrical and thermal conductivities [5–10]. These features give transition metal carbides the capability to withstand high temperatures in oxidizing environments, making them candidates for applications in the atmosphere of extreme thermal and chemical environments [6,7]. The structural, vibrational, phonon and thermodynamic properties of IVb group transition metal carbides have been investigated experimentally [10–17] and theoretically [13,18–28] in the earlier reports. In the 1970s, the phonon dispersion relations of TiC, ZrC and HfC were measured using inelastic neutron scattering by Pintschovius et al. [10] and Smith et al. [15–17]. Lattice dynamics calculation and the phonon dispersion relations of transition metal carbides such as ZrC and HfC were reported using a phenomenological ‘‘double-shell’’ model theory [18] where long-range interatomic interactions were taken into account in order to get a
基于MIMIC-IV数据库中发生血流感染的危重疾病患者相关数据构建革兰阴性菌血流感染风险预测模型
基于MIMIC -IV 数据库中发生血流感染的危重疾病患者相关数据构建革兰阴性菌血流感染风险预测模型陈秋宇1，秦泽辉2，刘享田3，叶莉萍3，田行瀚31 滨州医学院第二临床医学院，山东烟台 264000；2 潍坊医学院附属医院重症医学科；3 烟台毓璜顶医院重症医学科摘要：目的 基于重症医学信息数据库（MIMIC -IV ）中发生血流感染的危重疾病患者相关数据构建预测危重疾病患者革兰阴性菌血流感染发生风险预测模型，以期为预测危重疾病患者发生革兰阴性菌血流感染风险提供新的方法。
方法 收集MIMIC -IV 中2 503例发生血流感染的危重疾病患者的临床资料及实验室检查指标［血常规（红细胞、白细胞、血小板等）、血生化（钾离子、钙离子、氯离子、碳酸氢根、阴离子间隙和尿素氮等）、凝血功能指标（INR 、PT 、PTT ）］数据。
将所有危重疾病患者以7∶3的比例分为训练集（1 752例）和验证集（751例）。
在训练集中使用LASSO 回归初步筛选出危重疾病患者发生革兰阴性菌血流感染的影响因素，并将筛选出来的影响因素行多因素Logistic 回归分析，建立危重疾病患者革兰阴性菌血流感染风险预测模型（列线图模型）。
在训练集和验证集中通过受试者工作特征曲线（ROC ）、校准曲线和决策曲线（DCA ）分别对列线图模型的区分度、一致性、临床适用性进行评价。
结果 年龄、患有肿瘤、肝胆系统疾病、嗜酒史、钾离子、钙离子、碳酸氢根、阴离子间隙和尿素氮为危重疾病患者发生革兰阴性菌血流感染风险的影响因素，基于以上影响因素采用Logistic 回归分析，构建列线图模型。
训练集和验证集中，列线图模型预测危重疾病患者发生革兰阴性菌血流感染的ROC 下面积分别为0.711（95% CI 0.667~0.756）、0.705（95% CI 0.678~0.733）；校准曲线表明列线图模型预测革兰阴性菌血流感染发生的结果与实际结果之间具有良好的一致性（P = 0.764）；DCA 显示列线图模型具有良好的临床适用性。
纤维素键合手性固定相拆分6种萘满酮衍生物对映体
纤维素键合手性固定相拆分6种萘满酮衍生物对映体赵允凤;宋佳新;孙嘉仪;袁晓薇;郭兴杰【摘要】A high performance liquid chromatographic method( HPLC)was developed for the chiral separation of six α-aryl tetralone derivatives using a Chiralpak IC column. The factors influencing the chiral separation including the type and concentration of organic modifier,col-umn temperature and flow rate were investigated. The results showed that high enantiomeric separation can be obtained with isopropanol as modifier for the six compounds. The thermody-namic study indicated that the enantioseparation was enthalpically driven and showed that low column temperature was beneficial to separation. Complete separation of compound Ⅰ was achieved with a binary solvent mixture of n-hexane-isopropanol(90 : 10,v / v)as the recom-mended mobile phase. The compounds Ⅱ,Ⅲ and Ⅳ were separated with the mobile phase of the mixture of n-hexane-isopropanol(99 :1,v / v)and the compound Ⅴ was separated with the mixture of n-hexane-isopropanol(85 :15,v / v). The compound Ⅵ was separated wi th the mix-ture of n-hexane-isopropanol(80 :20,v / v). The column temperature was 25 ℃ ,and the flow rate was 1. 0 mL / min. The six tetralone derivative enantiomers were separated on a chiral sta-tionary phase of Chiralpak IC by HPLC. The column has high enantiomeric selectivity to the six tetralone derivative enantiomers.%使用 Chiralpak IC（纤维素-三（3，5-二氯苯基氨基甲酸酯）共价键合硅胶）手性柱，建立了采用手性固定相高效液相色谱拆分6种α-芳基萘满酮类衍生物对映体的方法。
负载HPV16E6基因树突状细胞疫苗的构建
负载HPV16E6基因树突状细胞疫苗的构建陈东晓;朱远丰【期刊名称】《临床和实验医学杂志》【年(卷),期】2011(010)015【摘要】Objective HPV16E6 gene was transfered into dendritic cells ( DCs ) for constructing dendritic cell tumor - vaccine with the carrier of cationic lipid.Then its efficiency and safety were studied.Methods The plasmid of pcDNA 3.1 - HPV16E6 gene was mixed with cationic lipid to form complex, then it was transfect to DCs.The morphology of cells was observed by inverted contrast microscopy.Its efficiency was detected by flowcytometry and immuocytochemistry ( ICC ).Results The transfected DCs developed more particles and dendrite ecphyma, and the growth of cells was well and the efficiency ratio reached 49.8 ％ ,which had been confirmed by ICC and flowcytometry.Conclusion By using cationic lipid for constructing DCs vaccine, it is high efficient and safe.%目的以阳离子脂质体为载体,构建载入人乳头瘤病毒16型E6蛋白(HPV16E6)基因树突状细胞(DC)疫苗,研究其安全性及转染效率.方法将pcDNA3.1-HPV16E6质粒与阳离子脂质体混合形成复合物后转染树突状细胞,倒置相差显微镜观察细胞形态,流式细胞仪及免疫细胞化学检测基因表达效率.结果转染后的DC可见胞浆内颗粒增多,胞质突起增加,细胞生长良好.免疫细胞化学、流式细胞仪结果DC转染率49.8%.结论阳离子脂质体可用于DC疫苗构建,具有高效性及安全性.【总页数】2页(P1185-1186)【作者】陈东晓;朱远丰【作者单位】汕头大学医学院第二附属医院内分泌科,广东,汕头,515041;汕头大学医学院第二附属医院内分泌科,广东,汕头,515041【正文语种】中文【相关文献】1.转染HPV16E6基因人树突状细胞疫苗的制备及生物学特性 [J], 任会均;张锦堃;陈东晓;李军;魏锡云2.负载EB病毒潜伏膜蛋白基因的树突状细胞疫苗的体内抗肿瘤免疫作用 [J], 盘鹰;王锦芝;张鲁勉;何小英;郑锦鸿3.HPV16E6/E7融合基因真核表达载体的构建 [J], 张秦忠;陈艳;李卉;姜孝芳;李艳;李惠武4.重叠PCR合成HPV16E6、E7基因并构建植物表达双元载体 [J], 芦春斌;高忱5.HPV16E6基因树突状细胞疫苗的体内抗宫颈癌研究 [J], 郑燕君;卓静;吴林;陈慎仁因版权原因，仅展示原文概要，查看原文内容请购买。
自主神经系统与肝纤维化关系的研究进展
自主神经系统与肝纤维化关系的研究进展万亿【摘要】肝纤维化( Heptic fibrosis,HF)是各种致病原因引起肝脏受到慢性损伤时,细胞外基质(cxtra～cllular matrix,ECM)在肝内可逆性沉积的创伤愈合过程.包括胶原在内的各种基质蛋白合成与分泌增多,而降解相对不足,沉积在肝脏中引起纤维化,如果得不到有效控制则可引起肝实质细胞坏死、肝脏组织重构、血流动力学改变、再生结节和假小叶形成,最终会发展为肝硬化甚至肝癌,以及出现其他各种严重的并发症,影响患者生活质量甚至威胁生命.近年来,随着对肝纤维化发病机制的广泛深入研究,学者们在大量动物和临床试验的数据基础上,明确提出肝纤维化是可逆的病理过程[1].所以积极探索肝纤维化发病机制,寻求有效治疗措施对降低肝硬化发病率和死亡率具有重要意义.【期刊名称】《贵州医药》【年(卷),期】2011(035)012【总页数】6页(P1133-1138)【作者】万亿【作者单位】中国药科大学药理教研室,南京210009【正文语种】中文【中图分类】R575.5肝纤维化（Heptic fibrosis，HF）是各种致病原因引起肝脏受到慢性损伤时，细胞外基质（cxtra～cllular matrix，ECM）在肝内可逆性沉积的创伤愈合过程。
包括胶原在内的各种基质蛋白合成与分泌增多，而降解相对不足，沉积在肝脏中引起纤维化，如果得不到有效控制则可引起肝实质细胞坏死、肝脏组织重构、血流动力学改变、再生结节和假小叶形成，最终会发展为肝硬化甚至肝癌，以及出现其他各种严重的并发症，影响患者生活质量甚至威胁生命。
近年来，随着对肝纤维化发病机制的广泛深入研究，学者们在大量动物和临床试验的数据基础上，明确提出肝纤维化是可逆的病理过程［1］。
所以积极探索肝纤维化发病机制，寻求有效治疗措施对降低肝硬化发病率和死亡率具有重要意义。
肝纤维化的发病机制非常复杂，涉及到肝内外多种细胞和细胞因子。
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EFFECT OF HELIUM ON STAINLESS STEEL MECHANICAL AND
MICROSTRUCTURAL PROPERTIES USING TRITIUM DECAY
Farhad Tavassoli
Commissariat à l'Energie Atomique, France
More than three decades ago, CEA used tritium decay to generate large amounts of helium, uniformly distributed in thick section austenitic stainless steels 304L and 316. This little known work can make a major contribution to the understanding of helium effects in fusion environment particularly in view of the advanced ITER options considered for an early EU DEMO.
The method used consists of diffusing tritium through specimens at high temperature (up to 450°C) and at high pressure (10 bars). Once the desired quantity of tritium is reached, the specimens are rapidly quenched to cryogenic temperatures. Tritium is thus trapped inside the specimens and kept there for long periods, months to several years. 3He is formed from the decay of tritium. Post heat treatments of specimens at higher temperatures (up to 750°C) allow removal of remaining tritium and eventually formation of helium bubbles. These specimens can be handled without special precautions after surface cleaning. However, they cannot be used for neutron irradiation due to thermal neutron interaction with 3He, but can be used for ion irradiation.
Microstructural examinations of specimens gone through the above tritium exposure show fine and homogenous distributions of helium inside the matrix, at grain boundaries and on dislocation loops. Further heat treatment of specimens at 800 °C for 100 h results in growth of helium bubbles, particularly at the grain boundaries.
Tension tests performed on specimens with different amounts of helium show little degradation of properties. At the highest He concentration investigated (280 appm) a slight reduction in tensile elongation is observed. Only when tests are carried out at 800°C a more pronounced reduction in ductility is observed.
These results are used to support recommendations made in recent European assessment report on potential of 316L(N)-IG for application in the 2 early EU DEMO models derived from ITER technology, using extended pulses at low or high temperatures and target doses of 10-20 dpa and 100-200 appm He.
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*Farhad Tavassoli。