遗传学重叠效应范文

导语：如何才能写好一篇遗传学重叠效应，这就需要搜集整理更多的资料和文献，欢迎阅读由公务员之家整理的十篇范文，供你借鉴。

篇1

高中生物教材中细胞质遗传是以紫茉莉为例。质基因在正交和反交时出现明显的不同,因为受精时中只带有很少的细胞质,使得受精卵中的细胞质几乎全部来自于卵细胞,这样受细胞质内的遗传物质控制的性状实际上是由卵细胞传给后代的,因此会表现为母系遗传现象。分析花斑紫茉莉遗传的原因:当花斑紫茉莉为母本时,紫茉莉的卵原细胞在减数分裂时,细胞质中的基因并不像核基因那样有规律地分离,而是随机地、不均等地分配到子细胞中去,因此会产生三种卵细胞,从而会产生三种不同的植株。这种随机性和不均等性就导致了细胞质遗传的第二个特点:后代不出现一定的分离比。所以花斑植株可以产生3种子代。而对与动物或人的线粒体DNA的遗传后代性状如何分析?比如一母亲患有某线粒体遗传病,她的后代性状应该是怎样呢?首先,我们知道他的子代不可能出现3种性状,而只有患病或不患病。在我们常见的一些题目里面一般是认为:母亲患病则所有子代都患病,认为这是母系遗传的最好表现。其实不对。我们先来了解下线粒体DNA(mtDNA)的遗传学特征。

与核DNA相比,mtDNA有独特的遗传学特征:

1.mtDNA存在于细胞质中,所以其遗传方式为母系遗传,父系不将其mtDNA传递给子代,因而,发生在生殖细胞中的mtDNA突变能引起母系家族性疾病;而发生在发育过程中或体细胞中的mtDNA突变,则会引起散发性疾病和与年龄有关的氧化磷酸化活性降低。

2.一个细胞中往往有成百上千个线粒体。如果一个细胞内所有mtDNA都一致,称为同质性,当mtDNA发生突变时就会导致一个细胞内同时存在野生型和突变型两种mtDNA,称其为异质性。当异质性细胞分裂时,突变的mtDNA的比例在子代细胞中会发生漂变,分裂旺盛的细胞(如血细胞)往往有排斥突变mtDNA的趋势,朝着具有全部正常型mtDNA的方向发展;也就是说野生型mtDNA对突变型mtDNA有保护和补偿作用,因此,mtDNA突变时并不立即产生严重后果而分裂不旺盛的细胞(如肌细胞、神经细胞),则会逐渐积累突变型mtDNA漂变的结果,使其表型也随之发生变化。

3.mtDNA突变的表型就应主要由某种组织中野生型mtDNA与突变型mtDNA的相对比例及该种组织对线粒体ATP供应的依赖程度决定。中枢神经系统、心脏、骨骼肌、肾脏、内分泌腺和肝脏对能量的需求较高,因而mtDNA的突变表型也往往容易表现出来。同时细胞中突变mtDNA的比例必须达到一定程度才足以引起某器官或组织的功能异常,即具有阈值效应。突变型mtDNA的表达受细胞中线粒体的异质性水平以及组织器官维持正常功能所需的最低能量影响,可产生不同的外显率和表现度。且细胞分裂时,突变型和野生型mtDNA发生分离,随机地分配到子细胞中,使子细胞拥有不同比例的突变型mtDNA分子。

篇2

关键词：人类基因组 基因克隆 基因组学 结构基因组 功能基因组

人类基因组计划（human genome project,HGP）是由美国科学家、诺贝尔奖获得者Renato dulbecco于1986年在杂志《Science》上发表的文章中率先提出的，旨在阐明人类基因组脱氧核糖核酸（DNA）3×109核苷酸的序列，阐明所有人类基因并确定其在染色体的位置，从而破译人类全部遗传信息。美国于1990年正式启动人类基因组计划，估计到2003年完成人类基因组全部序列测定。欧共体、日本、加拿大、巴西、印度、中国也相继提出了各自的基因组研究计划[1]。由于各国政府和科学家的共同努力，HGP目前已在为全球范围的合作项目；随着数理化、信息、材料等学科的渗透和工业化管理模式的引进，HGP已真正成为生命科学领域的科学工程，基因组（genomics）作为一门新兴学科也应运而生。

与此同时，科学界也在思索人类基因组计划完成后的下一步工作，因此就有了“后基因组计划”（post-genome project）的提法。大多数科学家认为原定于2003年所完成的人类基因组计划只是一个以测序为主的结构基因组学（structural genomics）研究，而所谓的“后基因组计划”应该是对基因功能的研究，即所谓的功能基因组学（functional genomics）。此外，一些新的概念如：“蛋白质组（proteome）”、“环境基因组学（environmental genomics）”和“肿瘤基因组解剖学计划（cancer genome anatomy project,CGAP）”等等也在不断向外延伸。

一、结构基因组学

（一）人类基因组作图

人类基因组作图根据使用的标记和手段不同，初期的作图有二种：一是通过计算连锁的遗传标记之间重组频率而确定它们相对距离的遗传连锁图，一般用厘摩（cM）来表示；二是确定各遗传标记之间物理距离的物理图，一般用碱基（bp）或千碱基（kb）或兆碱基（Mb）来表示。1cM的遗传距离大致上相当于1Mb的物理距离。随着研究工作的进展，遗传图和物理图逐渐发生整合，在此基础上大量引入基因标记，从而形成了新一代的转录图[1]。

1.遗传连锁图 遗传连锁图（genetic map）绘制需要遗传标记，早期的遗传标记主要为生化标记，20世纪80年代中期以限制性片段长度多态性（RFLP）、串联重复序列拷贝多态性和小卫星重复顺序等遗传标记为主，这类标记的数量较少，信息也较低；20世纪80年代后期发展的短串联重复序列（short tandem repeat,STR）也称微卫星（microsatellite,MS）标记，主要为二核苷酸重复序列，如：（CA）n，它们在染色体上分布较均匀，信息含量明显高于RFLP，因而成为遗传连锁分析极为有用的标记；近年来，单个碱基的多态性（single nucleotide polymorphism,SNP）标记又被大量使用，其意义已超出了遗传作图的范围，而成为研究基因组多样性和识别、定位疾病相关基因的一种新标记。

2.物理图 物理图（physical map）包含了两层意义，一是获得分布于整个基因组的30000个序列标签位点（sequence tagged site,STS），这可使基因组每隔100kb距离就有一个标记；二是在此基础上构建覆盖每条染色体的大片段DNA克隆，如：酵母人工染色体（yeast ar tificial chromosome,YAC）或细菌人工染色体（bacterial artificial chromosome,BAC）、人工附加染色体（human artificial episomal chromosome,HAEC）和人工噬菌体染色体（P1 bacteriophage artificial chromosome,PAC）等连续克隆。这些图谱的制作进一步定位其它基因座提供了详细的框架[2]。

3.转录图 构建转录图的前提条件是获得大量基因转录本即信使核糖核酸（mRNA）的序列，人类基因组中的基因数目约在10万左右，构建转录图首先需要获得人类基因的表达序列标签（expressed sequence tag,EST），以此建立一张人类的转录图，并与遗传图的交叉参照。

4.DNA序列的生物信息学 HGP一开始就与信息高速公路和数据库技术形成了同步发展。迄今，国际上四个大的生物信息中心即美国的国家生物技术信息中心（NCBI）、基因组序列数据库（GSDB）、欧洲分子生物实验室（EMBL）和日本DNA数据库（DDBJ）已经建立和维持了源自数百种生物的互补DNA（cDNA）和基因组DNA序列的大型数据库。这些中心和全球的基因组研究实验室通过网点、电子邮件或者直接与服务器和数据库联系而获得的搜寻系统，使得研究者可以在多种不同的分析系统中对序列数据库提出质询，这些分析包括基因的发现、蛋白质模体的鉴别、调控元件的分析、重复序列的鉴别、相似性的分析、核苷酸组成的分析以及物种间的比较等。

（二）基因组的基本结构和进化

人类基因组研究的目的，不仅为了单纯地积累数据，而且要提示数据中所蕴藏的内在规律[3]，从而更好地认识生命体。近年来，随着模式生物体测序的相继完成和人类基因组测序速度的加快（到1999年12月已宣布完成人类第22号染色体的完全测序），特别是生物信息所提供的强有力的分析和综合手段，使人人能够逐渐透过浩瀚的基因组序列信息，去探索一些更为本质的问题，如：基因组的复杂度与生物进化、基因组编码序列的结构、基因和蛋白家族、基因家族的大小及其进化。

（三）疾病的基因组学

HGP的直接始动因素是要解决包括肿瘤在内的人类疾病的分子遗传学问题[4]，因此与人类健康密切相关。另一方面，8000多种单基因遗传病和多种大面积危害人群健康的多基因疾病（如：肿瘤、心血管病、代谢性疾病、神经疾病、精神疾病、免疫性疾病）的致病基因和疾病相关基因占人类基因组中相当大的一部分。因此，疾病基因的定位、克隆和鉴定是HGP的核心部分。

20世纪90年代之前，绝大多数人类遗传性疾病的原发生化基础尚不清楚，无法用表型-蛋白质-基因的传统途径进行研究。在HGP的遗传和物理作图带动下，出现了最初被称为“反求遗传”、90年代初又改称为“定位克隆法”的全新思路。该思路的关键内容是：应用细胞遗传学定位和家第连锁分析方法，首先将疾病基因定位于染色体的特定位置，然后通过进一步的遗传和物理作图，使相关区域压缩至1Mb之内，此时即可构建YAC、BAC、PAC、HAEC或粘粒（comid）等克隆重叠样，从中分离基因，并在正常人和患者的DNA中进行结构比较，最终识别出疾病基因。包括囊性纤维化、Huntington舞蹈病、遗传性结肠癌、乳腺癌等一大批重要疾病的基因是通过“定位克隆”发现的，从而为这些疾病的基因诊断和未来的基因治疗奠定了基础。随着人类基因图的日臻完善，一旦某个疾病位点被定位，即可从局部的基因图中遴选出结构、功能相关的基因进行分析，将大大提高疾病基因发现的效率。

目前，人类疾病的基因组学研究，已深入到多基因疾病这一难点。多基因疾病难以用一般的家系遗传连锁分析取得突破，需要在人群和遗传标记的选择、数学模型的建立、统计方法的改进等方面进行不断的探索。

二、功能基因组学

HGP当前的整体发展使功能基因组学提到了议事日程[5]，出现了结构和功能基因组学向功能基因组学过渡、转化的过程。一般认为，在功能基因的组研究中可能的核心科学问题有基因组的多样性和进化规律；基因组的 表达及其调控；模式生物体基因组研究等。

（一）基因组多样性

人类是一个具有多样性的群体，不不同群体和个体在生物学性状以及在对疾病的易感性/抗性上的差别，反映了进化过程中基因组与内、外环境相互作用的结果。开展人类基因组多样性的系统研究，无论是对于了解人类的起源和进化，还是对于医学均会产生重大的影响。各种常见多因素疾病（如：高血压、糖尿病和精神分裂症等）相关基因的研究将成为功能基因组时代的研究热点。除了利用多态性遗传标记进行精细定位这一传统途径，也将采用基因组水平再测序的方法直接识别变异序列，即选取一定数量的受累和未受累个体，对所有疾病相关或候选基因的全序列（或其编码区）进行再测序，准确定位其变异相关标记位点。同样，肿瘤研究也需要对肿瘤相关基因进行大规模的再测序。

（二）识别人类基因的共同变异

已知大多数人类基因的等位基因数量是有限的，常仅有2～3种。形成这种遗传多样性局限性的原因，很有可能是因为现代人类来源于一个相当小的群体，这有助于揭开许多疾病敏感性的奥秘。如：载脂蛋白E基因有三种主要变型（E2、E2和E4），可以解释老年痴呆症和心血管疾病的风险性；血管紧张素原转换酶（ACE）与心血管疾病一定相关性；化学趋化因子受体CKR-5在一定程度上影响对人类免疫缺陷病毒（HIV）的敏感性等。非编码区对评价疾病风险也是重要的，精确定位非编码区变异的方法可以是对调控区域变异的系统性筛查，也可利用精密遗传图在人类群体中识别祖先染色体节段。

三、药物基因组学

基因组多样性也在一定程度上决定了人体对药物的反应，通过对影响药物代谢或效应通路有关基因的编码序列的再测序，有可能提示个体对药物反应差异的遗传学基础，这就是“药物基因组学”（pharmacogenomics）的主要内容[6]；以此作为延伸，提示个体对环境反应差异的遗传学基础的环境基因组学也已露端倪。

四、蛋白质组学

蛋白质组学是要从整体上研究蛋白质及其修饰状态。目前正在发展标准化和自动化的二维蛋白质凝胶电泳的工作体系，包括用一个自动系统来提取人类细胞的蛋白质，继而用色谱仪进行部分分离，再用质谱仪检测二维修饰，如：磷酸化和糖基化。此外，也有人在设计和制作各种蛋白质生物芯片；蛋白质的另一个重要工作内容是建立蛋白质相互作用的系统目录。生物大小即蛋白-蛋白和蛋白-核酸之间的互作构成了生命活动的基础，这些互作有可能以通用的或特殊的“陷井”（如：酵母双杂交系统）加以识别[7]。

总之，基因组学正方兴未艾，其现实意义和深远意义已得到全体人类的共识，预期在不远的将来，人类基因组学将对人类的健康、计划生育、优生优育产生重大影响。

参考文献

1 Rowen L. Mahairas G, hood.L.Science,1997;278:605-607

2 Goffeau A,Barrell h,Bussey H et al. Sceince,1996;274:546-567

3 Kleyn PW,Vesell eS.Develop Sci,1998;18:1820

4 Housman D,Ledley fD.Nature Biotech,1998;16:492

5 Hitert P,Boguski m.Science,1997;278:568

篇3

【关键词】 糖尿病；甲状腺疾病；并发症

doi：10.3969/j.issn.1004-7484（s）.2013.08.216 文章编号：1004-7484（2013）-08-4294-01

糖尿病在临床是一种常见的慢性疾病，对患者的危害较大。糖尿病合并甲状腺疾病包括临床甲亢、亚临床甲亢、临床甲减及亚临床甲减，其中前三种各占1-2%左右，临床甲减占10%左右，且随年龄增长，临床甲减患病率升高，女性居多。甲状腺疾患可加速糖尿病加重、恶化，促进某些慢性并发症的发生，尤其当糖尿病患者血糖升高、病情不稳定时，这时甲状腺疾病患者病情有可能加重，应及时采取对症措施，降低患者的病死率，实现对患者病情的良好控制[1].现在就我院2011年10月――2012年10月收治的40例糖尿病合并甲状腺疾病患者，进行回顾分析。

1 资料与方法

1.1 一般资料 我院2011年10月――2012年10月收治的40例2型糖尿病合并甲状腺疾病患者，男11例，女29例，年龄在19-75岁，平均年龄48.25岁。以上均符合1997年美国糖尿病协会（ADA）制定的糖尿病的诊断标准，其中19例符合甲状腺功能减退（甲减）；13例符合亚临床甲减；8例符合甲状腺功能亢进（甲亢）。

1.2 临床表现 19例糖尿病合并甲减患者，12例有口渴多饮，尿频量多，双下肢浮肿、麻凉痛，乏力，畏寒，偶有胸闷气短，便秘，夜寐差，病人无恶心、呕吐、发热等症；7例尿频量多，乏力，双下肢麻凉，双目视物不清，偶有腹胀，大便正常，夜眠尚可。既往无高血压、冠心病等病史。

1.3 实验室检查 将40例患者空腹采血，给予血糖、糖化血红蛋白、血常规、游离T3（FT3）、游离T4（FT4）及TSH检查；口服100克馒头试验：分别于0、30、60、120、180min抽取静脉血进行血糖、C肽水平及胰岛素检测。测得血糖值为8.5-21.7mmol/L，胰岛素释放试验呈高峰延迟出现或低平曲线，葡萄糖耐量试验呈糖尿病型血糖曲线，19例甲减患者FT3、FT4低于正常，TSH升高，13例亚甲减患者FT3、FT4正常，TSH升高，8例糖尿病合并甲亢患者TSH降低，FT3、FT4高于正常。

1.4 治疗及愈后 40例糖尿病患者初步治疗的目的是降低血糖、解除因血糖增高所致症状，强调饮食治疗是一项基础治疗措施，不论病情轻重或有无并发症都应严格执行和长期坚持。总热量和营养成分须适应生理需要，进餐时要定量，力争胰岛功能恢复。糖尿病合并甲亢时，应根据患者病情用药，糖尿病治疗以胰岛素、二甲双胍、格列美脲、瑞格列奈、阿卡波糖等药物为主；对糖尿病酮症酸中毒患者宜给予胰岛素持续静脉滴注或胰岛素泵持续皮下注射，迅速纠正代谢紊乱，争取抢救时机。除上述措施，糖尿病合并甲亢时，常用药物以甲巯咪唑、丙级硫氧嘧啶为主。部分甲减患者根据病情，必要时需口服左甲状腺素治疗；亚临床甲减患者应定期随访，根据病情调整用药，若血脂偏高可给予服药。当甲亢症状控制后，胰岛素应减量10%-30%，口服降糖药也应减少20%-45%。

2 结 果

在患者的长期密切配合及良好的治疗，临床症状缓解，血糖得到了满意的控制，FT3、FT4、TSH检查均恢复正常。

3 讨 论

一般来说，糖尿病合并甲减的发病时间可以以糖尿病症状为先，也可以以甲减症状为先，不过就近年来的临床资料显示，甲减先发生的概率更高。糖尿病是胰岛素分泌减少或/和机体对胰岛素产生抵抗，而导致的一种以慢性高血糖为特征的代谢性疾病。甲状腺功能减退症是由多种原因引起的甲状腺激素合成、分泌或生物效应不足引起的一组内分泌疾病。[1]目前认为，糖尿病与甲状腺疾病有内在联系，其中1型糖尿病与甲状腺患者有共同的遗传学基础和免疫学基础。2型糖尿病由于遗传上的缺陷和易感性，以及免疫平衡的破坏，加上病毒、饮食、环境、情绪等诱发因素，而发生免疫疾病之间的重叠现象[2]研究发现年龄越大患这两种疾病的机率就高，糖尿病合并甲亢的发病率在23%-38%女性高于男性，均有多食、消瘦，虽然两种疾病也各有其相应症状，对于糖尿病患者，病情突然恶化，出现用糖尿病无法解释的惊慌、烦躁、怕热、多汗、心慌、手颤等症状，或三多一少加重，消瘦明显，或者引起酮症酸中毒和心力衰竭，都应怀疑是否合并了甲亢，应及时检查甲状腺功能。[3]糖尿病合并甲减后肝糖原的合成分解，葡萄糖的吸收与利用均发生障碍，一方面由于甲状腺激素缺乏，可使组织代谢所必需的酶产生不足或活性降低，导致机体对碳水化合物的代谢缓慢；另一方面由于甲状腺激素缺乏，机体对降糖药物降解速度减慢，因此糖尿病合并甲减患者更易出现低血糖，应注意监测血糖，避免低血糖的发生。

总之，糖尿病合并甲状腺疾病，一旦明确诊断，治疗时应两者兼顾，需注意胰岛素与甲状腺素之间的互相作用和影响，调节二者的平衡，更好控制血糖改善甲状腺功能。

参考文献

[1] 徐正才，郑晓燕，陈芳建，等.糖尿病患者甲状腺激素的变化与血糖水平的相关性分析[J].放射免疫学杂志，2009，45（6）：87-88.

篇4

（江西省林业科技实验中心，江西 信丰 341600）

【摘要】随着《中国生物多样性保护战略和行动指南（2010-2030）》的贯彻实施，生物多样性监测与评价工作将在全国范围陆续开展。进化生态学作为阐述生物多样性演化规律和机理的基础性学科，其数量研究方法在20世纪70年代后得到了迅速的发展。本文从三个层面系统性总结、筛选了进化生态学在植物生态领域的主流研究方法，其中在生态系统层面，群落演替的主成分分析和聚类分析方法、群落的可恢复性、可持续性、变异性、抗干扰性、边缘效应等主题被筛选为主要分析方法；在种群层面，种间关联指数、相关系数、分离指数、生态位宽度指数、生态位重叠指数等概念可以全面阐释植物种群的演替规律；在遗传层面，哈迪-温伯格平衡度的检测、等位基因频率、多态位点百分数、平均位点的等位基因数、平均位点的预期杂合度、Nei氏遗传分化系数、Nei氏遗传一致度、遗传距离、聚类分析、遗传贡献率等方法在分子进化分析中的应用相对广泛。

关键词 生物多样性评价；生物多样性监测；进化生态学

Review of Evolutionary Ecology Study and Its Application on Biodiversity Monitoring and Assessment

LIU Huan　OUYANG Tianlin　TIAN Cheng-qing

（Jiangxi Provincial Forest science and Technology Experiment Cente， Xinfeng Jiangxi 341600，China）

【Abstract】After Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030) is implemented in China, biodiversity monitoring and assessment projects are increasing steadily in national wide. The statistical methods of evolutionary ecology study have been developed quickly since 1970s, which provides the theory underlying the interpretation of biodiversity evolution in ecosystem. This article summarizes the evolutionary ecology methods which have been relatively broadly applied on botanical species from three layers: for ecosystem diversity, the principle component index (PCI) and cluster analysis for community succession analysis, ecosystem resilience, sustainability, variance, resistance capacity and edge effects are identified as the main analysis methods; for species diversity, the conceptions of inter-specific association, rank correlation coefficient, segregation index, coefficient of niche breadth and coefficient of niche overlap can fully interpret the succession of plant populations in ecosystem; for genetic diversity, the methods including Hardy-Weinberg equilibrium, allele frequency, percentage of polymorphic loci, mean number of alleles per locus, mean expected heterozygosity per locus, Nei’ coefficient of gene differentiation, Nei’ genetic identity, genetic distance and cluster analysis, genetic contribution rate have been identified as main methods for analysis of molecular evolution.

【Key words】Biodiversity assessment；Biodiversity monitoring；Evolutionary ecology

0　Introduction

According to the Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), there are three thorny issues threatening biodiversity conservation in national wide: degradation of ecosystem function in some area; deterioration of endangered species; continuous loss of genetic resources. The methods of evolutionary ecology study from three layers (ecosystem, species, genetics) provides substantial theory explaining these threats so that conservation strategies can be worked out properly.

After Environmental Standard for the Assessment of Regional Biodiversity (HJ623-2011) is implemented in China, multivariate methods of evolutionary ecology study become essential to classify the basic units for biodiversity assessment at both ecosystem layer (classification of communities) and genetic layer (classification of sub-populations).

After Environmental Standard on Classifying the Categories of Genetic Resources (HJ 626-2011) comes into force in China, the methods of evolutionary ecology provide the theoretical basis not only for understanding the evolutionary process of endangered species, but also becomes compulsory for ranking genetic resources (or endangered species) between CategoryⅠand categoryⅡ.

This review article systematically summarizes the main themes of evolutionary ecology study of plant species from three layers, with discussion of selecting suitable methods for biodiversity monitoring and assessment work.

1　Ecosystem Diversity

1.1　Cluster Analysis and Principal Component Analysis (PCA)

According to the Technical Guideline for Ecological Assessment, the significance of dominant plant species is calculated by a combination of density, frequency and dominance, which becomes the basis of cluster analysis or PCA for community classification[1], which becomes the essential units for biodiversity assessment at ecosystem layer. Bu et al.,(2005) adopted both fuzzy cluster analysis and principal component analysis (PCA) methods to classify 13 sampling plots into 5 communities, which included 15 botanical species located in loess hilly region. Both methods led to similar conclusions in terms of community classification. According to the restoration duration required by each community, the temporal succession of 5 plant communities was identified as: Artemisia scoparia community-Leymus scalinus community-Stipa bungeana community-Artemisia gmelinii community-Hippophae rhamnoides community [2].

Anwar et al.,(2009) selected multivariate methods of cluster analysis and principal component analysis to understand corticolous lichen species composition and community structure characteristics in the forest ecosystem of Southern Mounffiins of Urumqi, China. There were thirty nine corticolous lichen species found, which were classified into 5 orders, 13 families and 26 genera. According to the multivariate analysis, three types of communities were classified, including community Lecanora hageni(Ach.)Ach. + Physcia stellaris(L.)Nyl. + L.saligna(Schrad.)Zahlbr; community Physcia aipolia(Humb.)Furm. + Ph.dimidiata(Arn.)Nyl + Cladonia pyxidata(L.)Hoffm; and community Xanthoria fallax (Hepp) Arnold + X.elegans(Link.)Th.Fr, whose structures were significantly influenced by altitude and tree type [3].

The composition and community structure of dominant species were analyzed by Cai et al., (2007) on the basis of multivariate methods of both principal component analysis and cluster analysis with the survey data of phytoplankton in spring, summer, autumn and winter from 1998 to 1999 in the West Guangdong Waters. According to the cluster analysis, phytoplankton species were classified into 2 communities in each season of spring, summer and autumn, with one inshore group and one offshore group, whereas the differentiation of species community was not significant in winter time. The seasonal succession of dominant species was Skeletonema costatum, Navicula subminuscula, Thalassionema nitzschioides, and Thalassiosira subtilis in spring, summer, autumn and winter respectively. However, the freshwater species, Oscillatoria sp. became the dominant species in summer as well [4].

Wang & Peng adopted both species similarity analysis (including coefficient of community, percentage of similarity and coefficient of similarity) and cluster analysis methods to classify plant communities and examine the environmental gradient effects on community succession in Dinghu Mountain, which indicated that Cryptocarya chinensis communities varied with different altitude gradient. Ten plant communities were compared and contrasted, revealing the mutual effects and evolutionary patterns among these communities [5].

1.2　Ecosystem Resilience

Ecological resilience is the capacity of disturbed ecosystem restored into its primitive conditions[6]. Zhang et al., (2013) assessed the ecosystem resilience quantitatively by using social-ecological system (SES) model in Northern Highlands of Yuzhong County, and resulted in the conclusion that the resilience of ecosystem was determined by both drought stress and ecosystem sensitivity to drought condition [7].

To order to assess community resilience and restoration success, Renaud et al., (2013) developed two indices including Community Structure Integrity Index measuring the proportion of species diversity for the reference community in comparison to the restored or degraded community, as well as the Higher Abundance Index assessing the proportion of the species abundance which was higher than the reference community. Three examples were illustrated for the application of two indices, including fictitious communities; A recently restored (2 years) Mediterranean temporary wetland (Camargue in France) for the assessment of restoration efficiency; and a recently disturbed pseudo-steppe plant community (La Crau area in France) assessing the natural community resilience, which demonstrated that these two indices were not only able to assess the static value of ecosystem function, but also to analyze the temporal and spatial dynamics of ecosystem evolution [8]. Nevertheless, compared with Zhang et al., (2013) model, social disturbance was not integrated into Renaud et al., (2013) model.

Additionally, 5 succession phases of the restoration of degraded ecosystem in Jinyun Mountain were investigated by Li et al.,(2007), including Shrubby grass land, Masson Pine early stage, Masson Pine late stage, Coniferous broad-leaved mixed forest and Evergreen broad --- leaved forest stage. Under the same climate conditions, criteria of species diversity, light absorption, community temperature, cumulate cover of arbor and community pole temperature became the main indicators for the succession of ecosystem restoration. However, among these indicators, both cumulate cover of arbor and community pole temperature were identified to be the best two indicators, and the other indicators were advised as the minor ones for consideration [9].

1.3　Ecosystem Sustainability

Ecosystem sustainability is the potential or manifested ability for ecosystem to perpetually sustain its interior composition, structure and function so that ecosystem is able to develop and evolve healthily [6]. Hu Dan (1997) presented methodology for assessment of ecosystem sustainability on the basis of identifying and evaluating ecosystem components, structure and function, which was consisted of 12 items and more than 30 variables, indicating the dynamics of sustainable ecosystem[10]. However, social factors were not considered in this methodology. In comparison, Yu et al., (2007) developed a quantitative index system for the assessment of eco-tourism sustainability in TianMuShan Natural Reserve, which included 25 criteria selected from three aspects: Environment, Society-Culture and Economics. On the basis of this method, a case study in Tianmushan Nature Reserve was introduced to demonstrate sustainability assessment in ecosystem [11].

1.4　Ecosystem Resistance

Ecosystem resistance is the ability of ecosystem to boycott the external disturbance and sustain its primitive conditions[6]. Hou et al., (2012) pointed out that the criteria of assessing eco-resistance were consisted of decomposition rate of ground combustibles, increase of ground combustibles, spontaneous combustion caused by lightning, indigenous pest, invasive pests and occurrence of pest[12]. However, quantitative method (such as the weight of each criterion) was not presented in this research. In comparison, Guo et al., (2012) presented the criteria for the assessment of eco-resistance which were consisted of the degree of pest invasion (or disease infection) and the fire incidence, with a weight of 0.6891 and 0.3109 respectively [13].

1.5　Ecosystem Variance

Ecosystem variance is divided into spatial heterogeneity and functional heterogeneity, which reflects the complex or variance of species distribution pattern and community structure influenced by available resources and environmental conditions [6]. Liu et al., (2010) adopted β Sorenson index to investigate the variability of plant communities of grass land in Ordos, Inner Mongolia of China, which was restored from grazing land. The relations between restoration duration and variability of plant communities was deduced in this research: compared with stabilized sand (25～30 a), higher variability existed in semi- mobile sand (restoration duration:5～10 a) and semi- stabilized sand (restoration duration:15～20 a). β Sorenson index for plant communities with dominant species Artemisia ordosica or Hedysarum laeve (restoration duration:5～20 a) was approximately 1.2, while the variability index of Artemisia ordosica (restoration duration: 30a) sand was twice than that of Hedysarum laeve (restoration duration: 30a), and faster growth rate was reported in Artemisia ordosica (restoration duration: 30a) sand [14].

Zhang et al.,(1988) analyzed the succession of pioneer meadow communities in abandoned farmland located in the high land of Gansu Province South. Heterogeneity index of H1 was deduced in this study, with value ranging from 0 to 1. Two meadow communities were investigated, with H1 heterogeneity indices of 0.11 and 0.15 respectively, which revealed relatively low heterogeneity between them[15].

1.6　Edge Effect

Edge effect typically exists in the ecotone between different plant communities, which is caused by the mutual interactions between different plant species from various communities, leading to characteristics in terms of species composition, configuration and function differed from the original communities [6]. Wang & Peng (1986) quantified the edge effects of plant communities in DingHuShan Nature Reserve by a model, with discussion of both positive and negative effects of community edges [16].

Eugenie et al., (2001) quantified the edge effects on plant communities caused by 6 recent clearcut edges adjacent to Pinus banksiana and Pinus resinosa plantations in the Great Lakes region. 10 sampling plots were randomly placed at 19 distances along a 240 transect which spanned from clearcut, across the edge, into the forest interior, with an estimation of percentage cover of each understory plant species. Species richness was significantly higher in Pinus banksiana lines than Pinus resinosa lines, with 18 and 2 unique species respectively. Species with clear preference for the clearcut, edge habitats or interior were respectively reflected by depth-of-edge influence, with composition gradient examined by the Detrended correspondence analysis (DCA) of distance sampled on the basis of species richness. Finally a synthesis model was designed to calculate the plant species distributions across forest/clearcut edges [17].

2　Species Diversity

2.1　Inter-specific Association, Rank Correlation Coefficient, Segregation Index

Inter-specific association is the mutual association between different species in terms of spatial distribution patterns in various habitats, which is divided into the competition relationship defined by segregation index (negative correlation), as well as interdependence relation calculated by rank correlation coefficient (positive correlation) [6].

On the basis of 25 sampling plots, 375 quadrats and 150 transect lines, Zhang et al., (2013) adopted eight indices of Diffusion Coefficient (C), Negative Binomial Parameters (K), Average Crowed Degree (m＊), Index of Clumping (I), Index of Patchiness (PI), Green index (GI), Cassie index (CA), Moristia index (Iδ ) and Variance of Percentages (VP) to analyze the spatial distribution patterns and overall correlation between dominant plant species in Gansu Donghuang xihu Desert Wetland ecosystems. The results revealed that significant positive correlation existed between dominant species populations in shrub layer and tree layer, whereas significant negative correlation was reported between dominant species in tree-shrub-grass layer and grass layer. Further more, the 2×2Contigency Table of Chi-square statistics, Association Coefficient (AC), Percentage of Co-occurence (PC) and other methods were conducted additionally to analyze the correlation significance and intensity between dominant species, leading to the results that correlation between dominant species was not significant in most cases and logarithm with significantly negative correlation was more than positive one, which indicated various requirements of habitat and resources for different species [18].

Yan et al., (2009) adopted Contingency Table and Spearman Rank coefficient to analyze the inter-specific association and inter-specific covariance between Artemisia annua and its associated plant species in the natural fostering base from 2006 to 2007. The results showed that flooding disturbance led to insignificant effects on inter-specific association, but significant effects on inter-specific covariance. However, flooding effect on inter-specific covariance varied between different species pairs, indicating that inter-specific covariance of paired species was depended on both environmental conditions and ecological characters, which became more sensitive to environmental disturbance than inter-specific association [19].

Wang et al., (2014) applied statistical methods of 2×2 contingency table V ratio, X2 (Yate’ s correction), Ochiai Index (OI), Dice Index (DI), Point Correlation Coefficient (PCC), Jaccard index (JI), Association Coefficient (AC) and Spearman correlation coefficient to analyze the inter-specific association between epiphytic plant species in ancient cultivated tea plantation. For the 127 tea trees measured at individual scale, significant inter-specific association was reported, whereas insignificant association was found among 31 plots measured at plot scale. Indices of both Association Coefficient (AC) and Spearman correlation coefficient well indicated the inter-specific association between epiphytic species in consistence with X2 test, which revealed positive association between Bulbophyllum sp. and Drynaria propinqua, Davallia cylindrica and Liparis elliptica, Dendrobium capillipes and Lysionotus petelotii，as well as negative association between Bulbophyllum ambrosia and Dendrobium capillipes, Bulbophyllum ambrosia and Lysionotus petelotii, Bulbophyllum nigrescens and Dendrobium chrysanthum, Ascocentrum ampullaceum and Peperomia tetraphylla [20].

2.2　Coefficient of Niche Breadth and Coefficient of Niche Overlap

Niche breadth is the total available resources which can be utilized by a species (or other biological unit), and niche overlap is the competition phenomenon that two or more species with similar niche breadth compete for the limited resources in the common space for survival [6].

Field study were conducted by Chen et al.,(2014) to analyze the niche breadth and overlap of 12 plant species on 70 forest plots in Bawangling National Nature Reserve, presenting the descending order of niche breadth for 12 species: Aquilaria sinensis, Nephelium topengii, Camellia sinensis var. assamica, Alseodaphne hainanensis, Keteleeria hainanensis, Podocarpus imbricatus, Firmiana hainanensis, Parakmeria lotungensis, Cephalotaxus mannii, Michelia hedyosperma, Ixonanthes reticulata, Dacrydium pierrei. The results revealed that the niche breadth of a species was determined by its range of spatial distribution; in most cases, higher niche overlap value was usually found between species with broader niche breadth, except Michelia hedyosperma and Firmiana hainanensis species of narrow niche breadth; the low niche breadth of Michelia hedyosperma and Ixonanthes reticulate species partially led to smaller populations, which was advised to give the priority for conservation [21].

Both niche breadth and niche overlap of 10 shrub species and 11 herb species were examined by Gao et al., (2014) under a mixed forest consisted of Picea crassifolia and Betula platyphylla in high hill regions in Datong County, Qinghai Province. The results indicated broader niche breadth for species Potentilla fruticosa and Salix cupularis in shrub layer, as well as species Polygonum viviparum and Fragaria orientalis in herb layer. Higher niche overlap was found usually between populations with broader niche breadth. Nevertheless, some populations with narrow niche breadth also showed high niche overlap. The niche overlap between different species of a genera tended to be smaller, which would be attributed to their evolution and succession [22].

Statistical methods of Variance ratio, χ2-test based on a 2×2 contingency table and the test of association indices (Jaccard, Dice and Ochiai) were selected by Yu et al.,(2012) to examine the inter-specific association of 22 Pyrola decorata communities in Taibai Mountain. Results reported that only 5 paired species showed significant positive association (P<0.05), with 2 paired species showing highly significant positive association (P<0.01), whereas insignificant association was reported between the rest species pairs. For Jaccard index analysis, 84.42% of total species pairs were under 0.25 value of Jaccard index, and 12.31 % of total species pairs ranged from 0.25 to 0.50, while only 3.26% of total species pairs were over 0.50. These results revealed weak inter-specific association between investigated communities which tended to be independent [23].

3　Genetic Diversity

3.1　Hardy-Weinberg Equilibrium

Hardy-Weinberg equilibrium is the principle for the parental generation and their offspring to assess the degree of equilibrium between observed genotypic frequencies and allele frequencies in sexual reproduction process[6]. Both Hardy-Weinberg equilibrium and population structure of 283 Hevea brasiliensis Wickham germplasm were examined and analyzed by Fang et al., (2013), with 25 EST-SSRs loci detected. According to the results, 13 of total 25 EST-SSRs loci deviated Hardy-Weinberg equilibrium. The 283 Hevea brasiliensis Wickham germplasm were divided into 4 groups, and the amount of each group was 155, 110, 61 and 22 respectively. 20 locus combinations (6.67%) were significant linkage disequilibrium (P<0.05), and 5 of them were significant linkage disequilibrium at P<0.01 level [24].

3.2　Genetic Diversity

There are a number of conceptions to quantify genetic diversity, mainly including allele frequency, percentage of polymorphic loci, mean number of alleles per locus, mean expected heterozygosity per locus, Nei’ coefficient of gene differentiation, Nei’ genetic identity.

90 accessions were chosen by Xu et al., (1999) from total 22637 accessions in the National Genebank of soybean species, with selection criteria of nine agronomic traits, including disease resistance to SCN race No.3 and SCN race No.4, rust, SMV, and tolerance to cold, drought, salt, 100 seed weight and protein content. Five maximum and five minimum accessions in the Genebank were selected for comparison for each trait. The genetic diversity of 90 (G. max) soybean and one wild soybean (G. soja) accession were assessed by both agronomic trait analysis and microsatellite DNA or SSR markers. In total twelve pairs of SSR primers were applied and 83 alleles were detected with an average of 6.9 alleles per locus. Simple matching similarity coefficients between each pairs of genotypes were analyzed and clustered by Unweighted Paired Group Method Using Arithmetic Averages (UPGMA), revealing that soybean germplasms could be identified by SSR technique. However, the cluster analysis based on agronomic traits was not identical to SSR markers [25].

The genetic diversity of 38 Paulownia fortunei provenances, with 15 individuals per provenance, was deduced by Li et al., (2011) with technique of inter-simple sequence repeats (ISSR). In total 95 amplified DNA fragments were detected by 9 primers leading to clear and unique polymorphic bands, which were screened from 100 ISSR primers. There were 88 polymorphic loci among 95 amplified DNA fragments, resulting in the percentage of polymorphic loci (PPL) of 92.63%. The PPL at species level ranged from 32.63% (Fuzhou, Jiangxi) to 56.84% (WuZhou Guangxi and Jiu Jiang, Jiangxi) with the mean percentage of 47.16%. The mean values of effective number of alleles (Ne), Nei&acute;s gene diversity index (H) and Shannon&acute;s Information index (I) between different provenances were calculated as 1.3910, 0.2424 and 0.3765 respectively, indicating abundant genetic diversity between them. The Coefficient of Gene Differentiation (GGst) of provenances was 0.3539, and the genetic variation between provenances accounted for 35.39% of total genetic variation, revealing that genetic variation between different individuals of each provenance was higher. Genetic Identity of provenances varied from 0.39 to 0.82, showing the relatively broad genetic basis and abundant genetic variation among provenances. According to Genetic Identity, the provenances of Kaili, Guizhou, and Liuzhou, Guangxi showed closest relationship with Genetic Identity of 0.82, whereas longer genetic distance was reported between Hengyang (Hunan) and Zhuji (Zhejiang) populations, and between Hengyang (Hunan) and Zhenning County (Guizhou) populations, with Genetic Identity of 0.39. In total 38 provenances were classified into 3 groups by UPGMA cluster analysis, with little correlation between genetic distance and geographic distance among those provenances [26].

Genetic diversity of wild soybean population in the region of Beijing China was evaluated by Yan et al., (2008) with 40 primer pairs. In total ten populations were sampled with 28-30 individuals per population. 526 alleles were detected with a mean value of 13.15 per locus. The average value of Expected Heterozygosity per locus (He) and Observed Heterozygosity per locus (Ho) were 0.369 and 1.29% respectively for the wild soybean populations, and the mean Shannon index (I) was 0.658. The mean value of between-population genetic diversity (Hs) and within-population genetic diversity (DST) were 0.446 and 0.362 respectively. The average Coefficient of Gene Differentiation for loci (GGst) between populations was estimated as 0.544. Center-Western ecotype showed more abundant genetic diversity than the Northern and Eastern ecotypes, geographic heterozygosity was found in the genetic divergence patterns of natural populations between the Taihang and the Yanshan mountains. The genetic diversity of drought-tolerant population was poor, indicating the potential value of tolerance gene (s) for breeding [27].

Genetic diversity of totally 13 Cannabis populations from different origins was deduced by Hu et al., (2012) using POPGENE 3.2 Software. AFLP results indicated that the most abundant genetic diversity was found in Yunnan population, with Percentage of Polymorphic Loci (PPL) of 88.82%, Nei&acute;s total genetic diversity (He) of 0.3011, and Shannon Index (I) of 0.4571; and followed by the Heilongjiang population with Percentage of Polymorphic Loci (PPL) of 75.66%, Nei&acute;s total genetic diversity (He) of 0.2572, and Shannon Index (I) of 0.3897. The PPL, Ht and Hs of 13 Cannabis populations was 92.11%, 0.3837 and 0.1640 respectively. Coefficient of genetic differentiation between populations (GGst) was 0.5725, revealing that genetic variation between populations accounted for 57.25% of the total genetic variation, and the other 42.75% of total genetic variation was attributed to the genetic variation between individuals within population. Both genetic distance and genetic identity of Cannabis were calculated on the basis of Nei&acute;s (1978) method, for further analysis of genetic differentiation among populations. Genetic identity among populations ranged from 0.6556 to 0.9258, with the highest value of 0.9258 between Guangxi population and Sichuan population. The genetic identity between Yunnan population and Guizhou population, Yunnan population and Sichuan population were 0.9196 and 0.9173 respectively, while the lowest genetic identity was found between Gansu and Shanxi populations. These findings became the scientific evidence for identification of Cannabis seed and provided the indicators for breeding and evolutionary analysis [28].

The genetic diversity of 120 individuals from six natural populations of Abies chensiensis was analyzed by Li et al., (2012) on the basis of 10 simple sequence repeat markers. The genetic diversity, genetic structure and changes in gene flow between different populations were analyzed, revealing 149 alleles in 10 microsatellite loci with a value of 14.9 as the average number of alleles per locus (A). The effective number of alleles per locus (Ne), the mean expected heterozygosity (He), the mean observed heterozygosities per locus (Ho), the Shannon diversity index (I), the proportion of genetic differentiation among populations (FST), and gene flow between the populations were 7.7, 0.841, 0.243, 2.13, 6.7% and 3.45, respectively. Insignificant correlation was found between genetic distance and geographic distance (r=0.4906, P>0.05). The relatively low genetic diversity was reported in the 6 natural populations of A.chensiensis, and inner-population genetic variation accounted for the majority of total genetic variation [29].

However, it is worthwhile mentioning that the analysis of genetic diversity is significantly influenced by sampling size. For example, the genetic integrity of Sorghum bicolor L. Moench. was studied by Xu et al.,(2012) adopting SSRs technique, as one of the most commonly used markers for the assessment of genetic diversity, population structure studies and marker-assisted selection. In total ten groups of sorghum with different sample sizes (including 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 individuals per group) were selected randomly and 25 polymorphic microsatellite primers were conducted for the assessment of genetic diversity indices (the average number of alleles, effective number of alleles, Shannon Index, Observed Heterozygosity, Expected Heterozygosity, Percentage of Polymorphic loci and the Frequency of Rare Alleles). According to the correlation between genetic diversity indices and sample sizes, the number of alleles, effective number of alleles, Shannon index increased correspondingly to the increase of sample sizes, with the peak increase rate at a sample size of 40 individuals. Consequently, the sample size of 40 individuals accounted for 98.5% of total numbers of alleles, 99.1 % of total effective numbers of alleles and 98.5% of total Shannon indexes among 100 individuals, indicating the 40 individuals as optimal sample size for the SSRs technique in gorghum integrity assessment [30].

3.3　Genetic Variation

Genetic variation assessment mainly adopts the conception of genetic distance and evolutionarily significant unit (ESU), usually deduced by cluster analysis, PCA, or evolutionary tree analysis. However, both cytological and DNA molecular markers are able to achieve this.

The karyotype of characteristics and evolutionary relationships among the traditional Chinese medicine Sophora flavescens from four different origins was investigated by Duan et al., (2014). The karyotypes and chromosome numbers of Sophora flavescens were calculated by using root-tip squashing method and clustered by the karyotype resemblance-near coefficient, which linked all the genetic materials.

The chromosome numbers of Sophora flavescens from Chifeng Inner Mongolia， Changzhi Shaanxi, Meixian Shaanxi and Chengdu Sichuan all were 18 and belonged to 1 A type, with karyotype formulas of 2n = 2x = 18 = 18m(2SAT), 2n = 2x = 18 = 14m(1SAT) + 4sm(1SAT), 2n = 2x = 18 = 16m(2SAT) + 2sm and 2n = 2x = 18 = 18m(2SAT) respectively. The karyotype asymmetry index of Sophora flavescens from Chifeng Inner Mongolia, Changzhi Shaanxi, Meixian Shaanxi and Chengdu Sichuan were 56.32%, 57.88%, 59.41 % and 54. 32%, respectively. According to Karyotype clustering analysis, the closest genetic relationship was reported between S. flavescens from Chengdu and Chifeng, with the highest karyotype resemblance-near coefficient of 0.9929, and their evolution distance was 0.0072. In comparison, the farthest genetic relationship was found between S. flavescens from Chengdu and Meixian, with the lowest karyotype resemblance-near coefficient of 0.9533, and their evolution distance was 0.0478. Karyotype of Sophora flavescents from Chengdu was the most primitive among them, followed by those from Chifeng, Changzhi and Meixian. The conclusion of this study provided cytological information for germplasms identification, and became the basis of genetic variation and genetic relationship analysis of Sophora flavescens[31].

To explore the genetic distance in evolutionary process among 6 Bupleurum medical plants, including B.longeradiatum, B.smityii, B. longicaule var. amplexicaule, B. scorzonerifolium, B. chinense, B. falcatum, karyotype parameters identification was adopted by Song et al.,(2012), which used the cluster analysis of karyotype resemblance-near coefficient and evolutionary distance, based on the calculation of the relative length, arm ratio, centromere index. The highest karyotype resemblance-near coefficient (0.9920) and smallest evolutionary distance (De = 0.0080) existed between B. scorzonerifolium and B. chinense, revealing the closest relationship between them. In comparison, the minimum karyotype resemblance-near coefficient (0.4794) and the maximum evolutionary distance (De = 0.7352) was reported between B. smityii and B. falcatum [32].

In Luo et al., (2006) study, 200 two-line combinations were matched by mating 5 photo/ thermal-sensitive genic male-sterile lines and 40 varieties. The genetic distance (GD) between 5 sterile lines and 40 varieties was examined by SSR markers, with the discussion between genetic distance and heterosis. The correlation of genetic distance varied with yield per F1 plant, heterobeltiosis of F1 yield, effective panicles, panicle length spikelets per panicle, density of spikelet setting, seed setting rate, and 1000 grain weight, due to various gene materials or different range of genetic distance. When the genetic distance between Tianfeng S and its paternal varieties ranged from 0.6286 to 2.5257, the correlation of genetic distance with yield per F1 plant or its heterobeltiosis appeared to be significant at P<0.05 level; As the genetic distance between Peiai 64S and paternal varieties ranged from 0.8247 to 1.5315, their correlation between genetic distance and yield per F1 plant was significant at P<0.05 level; furthermore, for all parents of two-line combinations with genetic distance ranged from 0. 5333 to 1.5, the correlation between heterobeltiosis of yield per F1 plant and genetic distance appeared to be significant at P < 0. 05 level; the correlation of yield per F1 plant with genetic distance was significant at P < 0. 05 level, as the genetic distance ranged from 0.5333 to 1.0; the significance of correlation between yield per F1 plant and genetic distance was at P < 0. 01 level, when genetic distance ranged at three layers: between 1.0 and 1.5; 0.5333 and 1.5; 0.5333 and 2.5257. This genetic distance analysis indicated the appropriate range for mating combinations of hybrid rice [33].

An endemic species of Sinomanglietia glauca, which is unique in Yichun in Jiangxi Province and Yongshun of Hunan Province in Central China, has been listed in Category I of the National Key Protected Wild Plants in 1999 (as asynonym of Manglietia decidua). Xiong et al.,(2014) study covered all of four populations of S. glauca, which had been identified so far, and the genetic diversity and genetic variation was investigated by nuclear microsatellite markers. According to the results, S. glauca showed relatively low genetic diversity with the average number of alleles (A) of 2.604 and the mean expected heterozygosity (HE) of 0.423, but presented significant genetic variation with high genetic differentiation FST of 0.425. Cluster analysis by STRUCTURE and Principal Coordinated Analysis indicated that Jiangxi and Hunan populations were classified into two independent groups. Only one natural breeding population was identified in Jiangxi, while two were found in Hunan, with significant genetic variation. The heterozygosity was found to be excessive significantly, which might be caused by allelic frequencies differed between male and female parents occasionally in a small population. The results indicated that S. glauca would experience bottleneck(s) in recent evolution history, which led to reduction of population size, loss of genetic diversity and strong population differentiation. The genetic diversity study resulted in the advices that S.glauca should be classified as three conservation units according to their evolutionary units: Jiangxi unit and Hunan unit, and the Hunan populations could be further divided into two sub-management units (YPC and LJC) [34].

3.4　Genetic Contribution Rate

Genetic contribution rate was firstly proposed by Petit et al., (1998). For the standardization of the allelic richness results across populations, the technique of rarefaction is established to facilitate assessment of the expected number of different alleles among equal-sized samples derived from different populations, which is divided into two components: the first is relevant to the degree of population diversity and the second is related to its divergence from the other populations [35].

4　Conclusion

As discussed above, the multivariate methods of evolutionary ecology study become essential to classify the communities and sub-populations at ecosystem layer and at genetic layer respectively for biodiversity assessment work. Due to three thorny issues threatening biodiversity defined by Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), biodiversity monitoring projects need to be implemented at three layers, and application of 3S technology on biodiversity monitoring with high-resolution remote sensing imagines is advised by Liu et al., (2014) [36], e.g. investigation of the distribution change of dominant plant species over ten years in a national park by using object-oriented classification of Quickbird remote sensing imagines, and then the temporal and spatial dynamics of biodiversity evolution at both ecosystem layers and species layers should be discussed on the basis of evolutionary ecology study. Additionally, biodiversity monitoring projects should be conducted according to the Technical Guidelines for Biodiversity Monitoring --- Terrestrial Vascular Plant (HJ 710.1-2014).

For genetic layer, a combination of cytological markers and DNA molecular markers is advised by Liu et al., (2014) for classification of sub-populations [37], mainly due to the consideration of saving the cost and reliability of differentiation methods. Nevertheless, it is worthwhile mentioning that the conclusion drawn by multivariate cluster analysis between cytological markers and DNA molecular markers would not be consistent, possibly due to gene recombination and gene mutation. Consequently, the multivariate cluster analysis for sub-population classification would be more reliable on the basis of DNA molecular markers. The software of computing both polygenetic (gene by gene analysis) and phylogenomic (the whole genome comparison) methods is suggested by Ahmed (2009) [38].

According to the Environmental Standard on Classifying the Categories of Genetic Resources (HJ 626-2011) in China, there are three kinds of DNA molecular methods pointed out for ranking genetic resources (or endangered species) between categoryⅠand categoryⅡ, including assessment of genetic diversity, evolutionarily significant unit (ESU), or genetic contribution rate, which have been substantially discussed above. However, it is worthwhile noting that any one of these three methods is acceptable for environmental engineers to conduct this environmental standard, although there is debate between these methods in terms of selection priority, such as Chen et al., (2002) [39].

According to the Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), prediction of climate change effects on biodiversity conservation is significant, and the application of CTMs model on prediction of climate change effects on biodiversity is advised by Liu Huan (2014) [40]. However, the knowledge of evolutionary ecology study derived from the biodiversity monitoring projects in the past may be required for this prediction work.

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篇5

作者单位：330006 南昌大学研究生院医学部,江西赣州市立医院神经内科（陈锦琼）；南昌大学附属赣州医院神经内科（李广生）

缺血性脑卒中是全世界最主要的致死和致残性疾病，主要病理基础是颅内外动脉粥样硬化斑块形成和动脉狭窄，越来越多的证据表明颈动脉斑块破裂和继发的血栓形成是比动脉狭窄更重要的卒中危险因素，颈动脉斑块的研究也越来越受到重视，本文就近年来颈动脉斑块的相关研究进行综述报告。

1 颈动脉斑块的形成和分类

颈动脉斑块的形成是外界环境因素和内在多基因调控异常共同作用的结果，颈动脉斑块的发展是一个动态平衡过程，即平滑肌细胞产生的胶原纤维组成斑块帽与通过金属蛋白酶等介导的基质降解之间的平衡，打破了平衡，斑块的稳定性下降，则将成为不稳定性斑块或易损性斑块［1］。在临床实践中，一般笼统的将动脉粥样硬化斑块划分为稳定斑块（硬斑块）和易损斑块（软斑块/不稳定斑块）两类，易损斑块是临床干预的对象。

所谓易损斑块，是指易于形成血栓或可能迅速进展为罪犯病变的斑块［2］。按照2003年Naghavi M等以尸检研究资料为依据提出的诊断标准，易损斑块包括五个主要特征及五个次要特征［3-4］：五个主要特征包括：①斑块内活动性炎症――斑块内单核细胞、巨噬细胞浸润，有时会有T淋巴细胞浸润。②薄纤维帽及大脂质核心：一般认为纤维帽厚度小于100 μm、脂核占斑块体积40%以上时，粥样斑块易于发生破裂。③血管内皮侵蚀伴有表面血小板凝集。④裂隙样斑块。⑤管腔狭窄大于90%。而五个次要特征包括：①斑块表面结节样钙化。②仅在血管内镜下可见的黄亮斑块。③斑块内出血。④血管内皮功能障碍。⑤血管重塑形。

但是既往研究对易损斑块的定义是针对冠状动脉而言，对于大血管的颈动脉显然不合适。最近Mauriello A等［5］通过组织病理学研究，将颈动脉易损斑块定义为纤维帽厚度＜165 μm并且巨噬细胞浸润＞25个/高倍视野，这是否合适需要后续的研究进行证实。

2 颈动脉斑块的影像学

目前在体评估颈动脉斑块性质的方法主要包括无创性（如超声、CT和MRI）和有创性（如DSA、血管内超声、血管内MRI）检查，每种检查方法各有优缺点。

2.1 超声 在各种无创检查中，血管超声是最早,也是应用最广泛的检查手段之一；超声检查可以观察血管管壁及管腔的形态, 测量血管的内径、外径、截面积、管壁厚度，根据血管壁回声强弱分析血管内膜有无斑块形成，并可测量斑块大小、长度；一般低回声和等回声斑块内多含有富脂成分、坏死物质和出血，常与易损斑块有关，而高回声斑块多富含纤维和钙化，提示稳定斑块，斑块表面不规则提示溃疡形成；其不足之处在于受操作者技术熟练程度、图像的空间分辨率和组织对比分辨率的限制，对斑块内部的组织学特性评价有一定局限性。

三维超声能够重建血管的三维图像，显示血管在空间上的变化，有助于更好地区分斑块表面和血管壁的解剖结构。Heliopoulos J等［7］证实三维超声可以显著提高颈动脉溃疡斑块的检出率。

血管内超声虚拟组织成像利用不同组织不同频率信号回声强度，连同采集血管内超声成像资料的振幅，可以将不同组织成分呈现不同颜色区分纤维斑块、混合斑块、钙化斑块和坏死核心。Diethrich EB等［8］经组织病理学对照，发现血管内超声虚拟组织成像对薄帽纤维粥样斑块诊断的准确性为99.4%，钙化薄帽纤维粥样斑块为96.1%，纤维粥样斑块为85.9%，纤维钙化斑块为85.5%，病理性内膜增厚为83.4%，认为血管内超声虚拟组织成像对斑块的鉴别与组织病理学的结果有很强的一致性。Tamakawa N等［9］的研究也证实血管内超声虚拟组织成像能有效客观评价颈动脉斑块组成，而且重复性好。但是目前临床应用的血管内超声的组织分辨率为100～150 μm，对于厚度小于100 μm的纤维帽尚无法准确识别。

声辐射力脉冲成像（acoustic radiation force impulse,ARFI）是一种新的超声成像方法，成像时先确定需要进行弹性检测的感兴趣区, 探头发射推力脉冲, 组织受力后产生纵向压缩和横向振动, 收集这些细微变化并演算出横向剪切波速度值, 间接反映该区域组织的弹性程度。由于血管壁、软组织、斑块、钙化的弹性度的差异，ARFI能够很好的加于区分。在Allen JD等［10］的研究认为ARFI能够识别软斑块和硬斑块，而且能够鉴别易损或稳定斑块，这给颈动脉斑块的检测提供了新方法。

2.2 多层螺旋CT 多层螺旋CT血管成像 （multi-section spial CT angiography，MSCTA）空间分辨率高，对颈动脉斑块的成分、形态、管腔的狭窄程度、斑块位置以及斑块周围组织的评价均很有价值，尤其对斑块的脂核和钙化显示较好，并且具有安全、方便、快速等特点。MSCTA容积重建（volume rendering technique，VRT）及最大密度投影（maximum intensity project，MIP）可从各个不同角度显示观察，VRT技术可对血管成像的透明度进行调节，因而可以把管壁和斑块与管腔分离观察，有利于表面规则和不规则的斑块发现。MSCTA通过对斑块密度的CT值测量可以把斑块进行分类［11］，血栓CT值约20 HU，密度均匀，位于管腔内侧面；脂质斑块CT值40～50 HU，纤维斑块50～120 HU，钙化斑块CT值>120 HU。由于富含脂质的坏死核心、结缔组织、出血的密度有明显重叠，钙化所致部分容积效应也影响密度的测量，导致在评价斑块表面形态和组织成分处于弱势，而且放射线剂量和碘剂也限制了CTA的应用。

然而，近年来随着多层CT血管成像技术的进步，它对颈动脉斑块的成分、形态、管腔的狭窄程度、斑块位置以及斑块周围组织的评价的特异性和敏感性均有很大提高。在识别钙化斑块方面，多层CT的敏感性和特异性均达100%［12］；而在识别斑块表面的溃疡的敏感性、特异性、阳性预测值、阴性预测值分别可达93.75%、98.59%、96.74%、97.2%［13］；在斑块成分识别上，MSCTA利用全自动分析软件可以明确标记富脂的坏死核心、钙化、出血产物以及剩余的结缔组织［14］；在识别易损斑块和稳定斑块方面也有一定的可行性，比如Haraguchi K等的研究发现易损颈动脉斑块其平均CT值是（27.7±7.5） HU，而稳定颈动脉斑块其CT值为（60.4±20.8 HU）［15］。

2.3 核磁共振 MRI有较高的软组织密度和空间分辨力，可以直接观察血管管壁情况，对斑块的大小、体积及斑块组成提供较为准确的信息，不但可以较准确地显示病变区域的整体解剖形态，而且可以根据斑块的信号变化判断其不同的结构成分等，有利于斑块易损性的评价。近几年随着MRI新序列的开展，对于斑块检查所采用的序列除了传统的T1WI，T2WI，还包括了黑血技术和亮血技术。黑血技术的优势在于显示斑块的形态及其组成成分，如脂质、出血及纤维组织，不足之处是采集时间相对较长。亮血技术即时间飞越成像（Time Of Flight，TOF）, 采集时间短，在显示斑块表面的纤维帽等低信号成分和鉴别斑块内出血方面出血等方面有优势。两种技术相配合提高了对颈动脉斑块检查的精确性。此外，各种靶向标记的增强MRI技术可以更精确地帮助分析斑块成分，甚至精确到细胞学水平。常用的钆造影剂和新型的超微顺磁铁氧化物（Ultrasmall Superparamagnetic Particles of Iron Oxide，USPIOs）均已经用于粥样斑块成分的显示。但MRI成像时间较长，呼吸运动、血管搏动、吞咽及不自主运动均可引起运动伪影，是目前难以克服的缺点。

各种斑块内成分在高分辨MRI中的特点表现如下:①脂质主要成分为胆固醇及胆固醇酯，T1WI和PDW为高信号，TOF像为等信号，T2WI可以显示为低、等信号。②纤维组织主要为细胞外基质，粥样斑块的纤维帽是由富含胶原的基质和平滑肌细胞组成的。TOF像为接近高信号，T1WI为等信号，PDW高信号，T2WI信号变化较大。稳定的纤维帽相对较厚而完整，而不规则、不连续的信号带与组织病理上发现的破裂、薄弱及溃疡的纤维帽一致。③钙化在各序列上均呈现低信号，但是斑块表面钙化和伸展至管腔的钙化结节由于易为黑血序列掩盖而无法判别，而亮血序列易于检测，另外较小的钙化因与脂质、坏死并存而出现混杂信号，单纯MRI影像不易判断。④出血随时间变化信号改变较大。近期出血TOF像为高信号，T1WI为等信号，PDW和T2WI可有不同变化。⑤新生血管：多采用MRI增强检查，斑块内新生血管表现为明显强化区域。⑥血栓由于形成时间不同，信号变化不定。

T1加权三维磁化强度预备梯度回波序列（T1WI-3 d-MP RAGE）属于快速容积扫描技术,具有较高的空间分辨率和时间分辨率,对脑内结构（如白质、灰质和脑脊液）的对比度良好,能三维显示人脑内部精细解剖结构,有利于显示小病灶及其细节。Hishikawa T等［16］运用T1WI-3 d-MP RAGE与组织病理比较，对35个颈动脉内膜剥脱术的患者进行研究，在T1WI-3 d-MP RAGE序列上呈高信号的颈动脉斑块与没有高信号的颈动脉斑块比较，脂质坏死核心区显著更大，中位数分别为51.2%和49.0% （P 0.029），呈高信号的颈动脉斑块与低信号的斑块比较有更严重的斑块内出血（P< 0.0001），而且斑块内出血的严重程度与脂质坏死核心的大小显著相关（P< 0.01）。

动态对比度增强（dynamic contrast-enhanced, DCE）MRI是一种对斑块进行定量评估的MRI对比增强技术，可量化斑块的新血管生成以及与之密切相关的斑块炎症。Kerwin WS等［17］利用DCE-MRI技术对动脉粥样硬化斑块进行定量分析，研究证实分数血浆容量 （fractional plasma volume，Vp）与微血管的面积相关，而对比剂的转移常数（transfer constant， Ktrans）与微血管的通透性相关，指明Ktrans值作为斑块炎症的定量非入侵标记，可以区分不同的斑块成分，并能对斑块进行可靠的分期，以预测斑块的进展。

3 颈动脉斑块与临床

3.1 颈动脉斑块与卒中 颈动脉斑块引起缺血性卒中的原因包括： ①斑块不稳定破裂，破裂的斑块栓塞远端的血管。②斑块不断增大，直接阻塞血管。③狭窄的颈动脉使远端的灌注压下降，导致分水岭区供血不足，形成低灌注性梗死。④破裂或未破裂的斑块表面粗糙，血小板和凝血因子被激活，形成血栓。

一般认为颈动脉斑块如果具有如下特征则为易损斑块：溃疡、破损纤维帽、薄纤维帽、大的脂质核心、斑块内大量新生血管形成等，而易损斑块是脑卒中的危险因素。Saba L等［18］研究证实了破损纤维帽和同侧症状存在相关性，提示破损纤维帽可以作为潜在脑血管病的预测因子。而Takaya等［19］的研究进一步指出，对于颈动脉50-70%狭窄的患者，有薄或破损的纤维帽、斑块内出血、坏死脂质核心的比例和血管壁的厚度与后续脑血管事件的发生密切相关。Homburg PJ等［20］ 发现颈动脉溃疡斑块与斑块体积、狭窄程度和富于脂质的坏死核心成分显著相关，即使在轻度狭窄的患者，相反钙化与溃疡斑块没有相关性。

但是Nandalur KR等［21］的研究则提示钙化率可以作为预测卒中发生危险的指标，总斑块体积、非钙化斑块体积或钙化斑块体积与症状之间均无显著相关性，颈动脉斑块的钙化率而不是颈动脉斑块的体积与狭窄患者的病情稳定相关，特别是钙化率>45%可能是无症状的一个界限。当然，各个试验间的结论还有不一致性，比如Saam T等［22］发现无症状性斑块和症状性斑块在富含脂质坏死核心区大小、钙化、斑块内出血发生率方面，两者间无显著性差异；与无症状性斑块比较，症状性斑块的纤维帽破裂发生率更高 （P0.007），近管腔的出血或血栓发生率也更高（P0.039）；而且有更大的斑块出血区（P0.003）和松散基质区（P 0.014），以及更小的管腔面积（P0.008）。但是在富含脂质坏死核心区、钙化、斑块内出血发生率方面则两者间无显著性差异。

3.2 颈动脉斑块与冠心病 颈动脉粥样硬化与冠状动脉粥样硬化具有共同的发病机制，颈动脉斑块的易损性可预示冠状动脉斑块的易损性。

Morito N等［23］的研究提示高斑块积分、低HDL-C、高空腹血糖依次是预测冠状动脉狭窄和/或狭窄严重程度的前3大因素；根据ROC曲线，颈动脉斑块积分预测冠状动脉狭窄的临界值为1.9。Sugioka K等［24］的研究同样证实颈动脉斑块积分与冠心病的病变程度相关，并发现斑块积分（P0.001）和面积狭窄率（P 0.004）与冠心病多支病变有独立的相关性。Kwon TG等［25］报道在韩国冠状动脉粥样硬化患者中颈动脉斑块发生率为30.3% （516/1705），多因素分析提示老年（≥65岁）、高血压、颈动脉内中膜增厚（≥1.0 mm）是颈动脉斑块发生的的独立预测因素，同样发现颈动脉斑块是冠状动脉多支病变的独立预测因素。但是Takase B等［26］的研究则认为颈动脉斑块负荷不能有效预测冠脉事件，而肱动脉血流介导的内皮依赖血管舒张功能和运动负荷试验则是冠脉事件的强烈预测因子。

在颈动脉粥样斑块的成分与心血管事件相关性的一项前瞻性研究中［27］，发现局部斑块组成是未来心血管事件发生的独立预测因子。若斑块有出血或显著斑块内血管形成更易发生心血管事件；而巨噬细胞浸润、巨大脂质核心、钙化、胶原以及平滑肌细胞浸润与临件没有相关性。提示局部斑块出血及斑块内大量血管形成与临件独立相关，且可作为临床风险及药物治疗的独立因子。

3.3 其他 在对一些特殊人群的研究中，证实颈动脉斑块的发生与多因素相关。

Roepke SK等［28］的研究提示阿尔茨海默病照看者比非照看者有更高的颈动脉斑块发生率，认为在长期压力下的阿尔茨海默病照看者对急性应急的持续交感反应可能促增加了动脉粥样硬化的发生。另外的一项研究发现TSH下降增加了颈动脉斑块的发生，同时也增加了卒中的发生率，但是TSH正常与颈动脉斑块或者卒中没有相关性［29］。Kim JY等［30］在40例男性垂体功能减退症和对照组比较研究中，发现颈动脉斑块发生率显著升高（59.5% ：2.5%,P

个体基因型差异与不稳定斑块的发生也有相关性，在一项早发型冠心病的非裔家族后代的遗传学研究中［36］，发现MCP-1 rs2857656 CC基因型与颈动脉粥样斑块的发生存在独立相关性；同时携带MCP-1 CC纯合基因型和CCR2 V64I杂合或纯合基因型的个体，与颈动脉硬化斑块的高发生率存在密切关系。另外的研究提示携带血小板膜糖蛋白Ⅲa基因Leu33Pro多态性的个体，可能发生动脉粥样硬化斑块破裂的风险增加［37］；而国内报告ALOX5AP基因SG13S114 A/T多态性可能与动脉粥样斑块的稳定性有关［38］。

4 小结

颈动脉作为动脉粥样硬化的好发部位，位置表浅、容易检测，同时可与颈动脉内膜剥脱术中获取的斑块标本进行对比研究，目前已经成为研究动脉硬化斑块的首选部位。如何准确的判断颈动脉斑块的稳定性，理解斑块的发生发展过程，以及干预斑块的形成，需要更多的研究来进一步解答。

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