Using genetic tools to inform conservation of fragmented populations of Asian elephants (Elephas maximus) across their range in China     

Ying CHEN  Yakuan SUN  Mei HUA  Kun SHI  David DUDGEON 《Integrative zoology》2023,18(3):453-468

A herd of 15 Chinese elephants attracted international attention during their 2021 northward trek, motivating the government to propose establishment of an Asian elephant national park. However, planning is hampered by a lack of genetic information on the remaining populations in China. We collected DNA from 497 dung samples from all 5 populations encompassing the entire range of elephants in China and used mitochondrial and microsatellite markers to investigate their genetic and demographic structure. We identified 237 unique genotypes (153 females, 84 males), representing 81% of the known population. However, the effective population size was small (28, range 25–32). Historic demographic contraction appeared to account for low haplotype diversity (H_d = 0.235), but moderate nucleotide and nuclear diversity (π = 0.6%, H_e = 0.55) was attributable to post-bottleneck recovery involving recent population expansion plus historical gene exchange with elephants in Myanmar, Lao PDR, and Vietnam. The 5 populations fell into 3 clusters, with Nangunhe elephants differing consistently from the other 4 populations (F_ST = 0.23); elephants from Mengyang, Simao, and Jiangcheng belonged to a single population (henceforth, MSJ), and differed from the Shangyong population (F_ST = 0.11). Interpopulation genetic variation reflected isolation by distance and female-biased dispersal. Chinese elephants should be managed as 2 distinct units: Nangunhe and another combining Shangyong and MSJ; their long-term viability will require restoring gene flow between Shangyong and MSJ, and between elephants in China and neighboring countries. Our results have the potential to inform conservation planning for an iconic megafaunal species.  相似文献   

Genetic and non-genetic factors influencing KLH binding natural antibodies and specific antibody response to Newcastle disease in Kenyan chicken populations     

Sophie Miyumo  Chrilukovian B. Wasike  Evans D. Ilatsia  Jörn Bennewitz  Mizeck G. Chagunda 《Zeitschrift für Tierzüchtung und Züchtungsbiologie》2023,140(1):106-120

This study aimed at investigating the influence of genetic and non-genetic factors on immune traits to inform on possibilities of genetic improvement of disease resistance traits in local chicken of Kenya. Immune traits such as natural and specific antibodies are considered suitable indicators of an individual's health status and consequently, used as indicator traits of disease resistance. In this study, natural antibodies binding to Keyhole Limpet Hemocyanin (KLH-NAbs) was used to measure general disease resistance. Specific antibodies binding to Newcastle disease virus (NDV-IgG) post vaccination was used to measure specific disease resistance. Titers of KLH-NAbs isotypes (KLH-IgM, KLH-IgG and KLH-IgA) and NDV-IgG were measured in 1,540 chickens of different ages ranging from 12 to 56 weeks. A general linear model was fitted to determine the effect of sex, generation, population type, phylogenetic cluster, line, genotype and age on the antibody traits. A multivariate animal mixed model was fitted to estimate heritability and genetic correlations among the antibody traits. The model constituted of non-genetic factors found to have a significant influence on the antibody traits as fixed effects, and animal and residual effects as random variables. Overall mean (±SE) concentration levels for KLH-IgM, KLH-IgG, KLH-IgA and NDV-IgG were 10.33 ± 0.04, 9.08 ± 0.02, 6.00 ± 0.02 and 10.12 ± 0.03, respectively. Sex, generation and age (linear covariate) significantly (p < 0.05) influenced variation across all the antibody traits. Genotype effects (p < 0.05) were present in all antibody traits, apart from KLH-IgA. Interaction between generation and line was significant (p < 0.05) in KLH-IgM and NDV-IgG while nesting phylogenetic cluster within population significantly (p < 0.05) influenced all antibody traits, apart from KLH-IgA. Heritability estimates for KLH-IgM, KLH-IgG, KLH-IgA and NDV-IgG were 0.28 ± 0.08, 0.14 ± 0.06, 0.07 ± 0.04 and 0.31 ± 0.06, respectively. There were positive genetic correlations (0.40–0.61) among the KLH-NAbs while negative genetic correlations (−0.26 to −0.98) were observed between the KLH-NAbs and NDV-IgG. Results from this study indicate that non-genetic effects due to biological and environmental factors influence natural and specific antibodies and should be accounted for to reduce bias and improve accuracy when evaluating the traits. Subsequently, the moderate heritability estimates in KLH-IgM and NDV-IgG suggest selection possibilities for genetic improvement of general and specific immunity, respectively, and consequently disease resistance. However, the negative correlations between KLH-NAbs and NDV-IgG indicate the need to consider a suitable approach that can optimally combine both traits in a multiple trait selection strategies.  相似文献   

PA-X基因的长度变化对H3N2犬流感病毒复制能力和致病性的影响     

李舜  李桂珍  原耀贤  李康健  马春全  李守军  黄良宗  马骏 《畜牧兽医学报》2023,54(1):272-280

H3N2犬流感病毒(canine influenza virus, CIV)已在中国多地的犬群中流行,是禽流感跨宿主感染并形成新分支的近期案例。研究表明,PA-X基因与甲型流感病毒适应新宿主的能力相关,且其长度能够影响甲型流感病毒的复制及致病能力。为了解PA-X基因的长度变化对H3N2 CIV复制能力及致病力的影响,本研究利用H3N2 CIV的8质粒操作系统,拯救了三株重组H3N2 CIV毒株：PA-X基因表达大小为232个氨基酸多肽的亲本病毒CIV＿PA-X＿232;对PA编码区第191、192位氨基酸的密码子进行改造,PA-X基因不表达蛋白的重组病毒CIV＿PA-X＿Knock;对PA+1编码区第232位氨基酸进行突变,PA-X基因表达大小为252个氨基酸多肽的重组病毒CIV＿PA-X＿252。通过比较3株重组病毒的聚合酶活性,在MDCK细胞中的复制效率及对小鼠致病性的差异,来评价表达不同长度的PA-X基因对H3N2 CIV的影响。结果显示,CIV＿PA-X＿252和CIV＿PA-X＿Knock的聚合酶活性显著(P<0.05)高于CIV＿PA-X＿232,且CIV＿PA-X＿...  相似文献   

Structure-activity Relationship of Flavonoids Antioxidants     

LIU Jie  WANG Bo-chu  PENG Liang  ZHANG Guang-qiunistry of Education  College of Bioengineering  Chongqing University  Chongqing  Chin 《保鲜与加工》2004,(2):120-124

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Distribution Network Structure Planning Based on Improved Immune Genetic Algorithm     

YAN Wei  WANG Li-na 《保鲜与加工》2007,(1):28-31

Distribution Network Structure planning is a complex combinatorial optimization problem,which is difficult to solve properly by using traditional optimization methods.In order to solve this problem,Improved Immune Genetic Algorithm is introduced to the distribution network optimal planning. Improved Immune Genetic Algorithm draws into the immune diversity and antibody's density mechanism to maintain the individual's diversity and remains evolution algorithm's global stochastic searching ability,so it can promote diversity and the whole optimal-searching ability of genetic algorithm.The optimal module takes the minimum annual cost as its object,and the capacity and voltage drop of feeder and the radiation of distribution network as its restrictions.According to the require of radiation of distribution network,the spanning tree of the alternative network is taken as the initial solution to speed up the calculation.And the branch-exchange method is used in designing crossover operator and mutation operator to avoid the radiation checking and enhance the optimizing ability.This algorithm has been illustrated effectively by examples,at the same time,the calculation example demonstrates that,the algorithm has higher calculation speed than the traditional immune genetic algorithm.  相似文献   

Improved Genetic Algorithm and Its Application in TSP     

ZHANG Chun-tao~ 《保鲜与加工》2004,(7):78-81

Crossover operator and mutation operator are basic operators of genetic algorithm , which play important roles in improving the quality of the solution populations. We propose the improved crossover operator , mutation operator and rotation operator on the basis of good point set with the traveling salesman problem , the experimental result shows these operators are very efficient.  相似文献   

New Algorithms for Data Discretization Based on Rough Set Theory     

ZHAO Jun  WANG Guo_yin  WU Zhong_fu  LI Hua 《保鲜与加工》2002,(3):18-21

The discretization of real values is always one of the key problems to be solved in the domain of machine learning for its great contribution to speeding up the followed learning algorithms, cutting down the real demand of algorithms on running space and time, and improving the clustering capability of the ultimate learning results. The basic characteristics and framework of discretization approaches based on rough set model are analyzed at first, then the different measurements of the importance of candidate cuts are discussed and researched. Two new heuristic algorithms are put forward to finally select the useful cuts from a candidate set. The selected cuts of the two algorithms will adequately maintain the discernible relation of information systems for their full considering the specialty of rough set, which perfectly embodies the advantages of this theory. Moreover, excellent discretization results may be expected through these heuristic algorithms.  相似文献   

Exploration of Gas Chromatographic Retention Indices (RIGC) of Phenols by Molecular Electronegativity-Distance Vector (MEDV)     

HU Li-mei 《保鲜与加工》2004,(4):67-71

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Automatic Fingerprint Classification by GA-Based Neural Network     

HUANG Xi yue  MA Xiao xiao  SHEN Zhi xi  WANG Peng  ZHOU Xin 《保鲜与加工》2001,(1):74-77

Fingerprint classification can provide an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for large database. In the paper, by combining genetic algorithm and neural network is presented a fingerprint classification algorithm which is able to achieve an accurate classification. By inputting the global feature represented by directional image to three layer neural network trained by genetic algorithm, the fingerprints were classified into six categories: whorl, right loop, left loop, arch, double loop and undiscerning type successfully.  相似文献   

Avoiding Obstacle Planning for Robots on Molecular Optimization Algorithm     

YANG Da-di  ZHANG Lei 《保鲜与加工》2007,(1):102-105

Interest in DNA computing has increased overwhelmingly since Adleman successfully demonstrated its capability to solve Hamiltonian Path Problem. This article introduces the improving method in virtue of the biological thery of DNA technology, a new molecular algorithm is advanced. After a numerical simulation, the result shows that it avoids the prematurely and lower convergent speed of the classic genetic algorithm, and inherits global search capability, the validity and the speed of the genetic algorithm have been increased. The best result can be obtained in few iterative times. It is fit for solving path planning problem.  相似文献