新遺傳變異體 的英文怎麼說

中文拼音 [xīnyízhuànbiànyìtǐ]
新遺傳變異體 英文
new genetic variant
  • 新 : Ⅰ形容詞1 （剛出現的或剛經驗到的） new; fresh; novel; up to date 2 （沒有用過的） unused; new 3 （...
  • 遺 : 遺動詞[書面語] （贈與） offer as a gift; make a present of sth : 遺之千金 present sb with a gener...
  • 傳 : 傳名詞1 （解釋經文的著作） commentaries on classics 2 （傳記） biography 3 （敘述歷史故事的作品）...
  • 異 : 形容詞1 （有分別; 不相同） different 2 （奇異; 特別） strange; unusual; extraordinary 3 （另外的;...
  • 體 : 體構詞成分。
  • 遺傳 : [生物學] heredity; hereditary; inheritance; inherit
  1. This modification includes : ( 1 ) selecting two important molecules as candidates, ( 2 ) choosing a promiscuous t - cell epitope, and two b - cell epitopes or conserved amino acid sequences from the two important molecules, ( 3 ) connecting them adequately through analysis by the molecule designing software. therefore, the synthetic new antigen may interfere with the process of fertilization by multiple ways and its contraceptive effects may be enhancing. based on the molecule designing methods, the b - lymphocyte cell epitope of sperm / testis specific protein sp17 and cyritestin which interfere with fertilization in mouse, as well as the promiscuous th cell epitope of the ribonuclease ( rnase ) in bovine were selected

    本研究以蛋白質分子設計的理論和方法研究避孕疫苗，將sp17和cyritestin關鍵表位和牛核糖核酸酶非選擇性th細胞表位合理組合，獲得新抗原- 35肽序列；並在合成、純化後分別與弗氏佐劑、免疫刺激復合物（ iscoms ）混合后免疫不同遺傳背景的雌性小鼠，觀察血清和生殖道內的特異性抗體滴度的動態變化、生育力的改變以及免疫后小鼠重要臟器的組織病理學改變：以及在ivf下，新抗原的特異性抗血清對精卵相互作用的影響及抗原在精子表面的特異性定位。
  2. So must use information theory method depict and abundant the genetic diversity index system. in addition to, the introduce of molecule biology technology and the research of nucleotide sequence evolutive give a new method for population genetic, so must do deeply research about the analysis method of dna sequence data = the research main about the follows : there are three parts about the information model of population genetic : one about the shannon information entropy property of equilibrium population and the entropy change in the process of establish equilibrium ; another research is about the diversity measure - ment of genetic variation ; lastly, research the shannon information measurement about the disequilibrium gene variation. the result is : 1 to a definite gene distribution, the genotype entropy reach the maximum at the equilibrium population, the process of population from disequilibrium to equilibrium, the entropy get large and large

    此外，分子生物技術的介入及核苷酸序列進化的研究都為群體遺傳學的深入研究提供了新的途徑，但關于dna序列數據的分析方法需要作進一步的研究。本研究主要體現在以下幾個方面： （一）關于群體遺傳學的信息論模型研究，主要分為三部分內容：一是群體平衡的shannon信息熵的性質和群體平衡建立的熵變性質；二是群體遺傳多樣性測度的研究；三是非平衡群體的基因變異測量shannon信息量的方法研究。得到了如下結論： 1 、平衡群體的shannon信息熵最大，群體平衡的過程是熵的增大過程。
  3. Crossover is implemented using arithmetic crossover operator. then unsymmetrical mutation is conducted using the new mutation operator which can expand the scope of chromosome gene value, at the same time, the chromosome with the highest fitness values are retained for each iteration. a lot of experiments are implemented to obtain the optimized initial weighs and bias

    生成了三維矩陣表示的染色體，進行了聯賽選擇，利用算術交叉運算元進行了交叉運算，利用構造的新的變異運算元，進行了非均勻變異，同時保留了每次進化運算后最優的適應值，通過大量實驗，實現了遺傳演算法優化bp網路的初始權值和閾值的目的。
  4. Based on the single genetic algorithms and the features of the distribution network reconfiguration, this dissertation makes a further study on such aspects as selection operator, crossover operator, mutation operator, termination conditions and etc, thus, puts forward improved genetic algorithms. the dissertation makes certain study on the optimization reconfiguration model of distribution network. it puts forward a multi - objective model and according to the theory of variable weight coefficients transforms the multi - objective problem into a single - objective one

    針對目前遺傳演算法在配電網路重構應用中的不足，論文著重從選擇運算元、交叉運算元、變異運算元和收斂準則等方面進行了改進，把最優保存策略和兩兩競爭相結合的方法作為新的選擇運算元，採用隨最優個體相對保留代數自適應變化的交叉和變異運算元，把最優個體最少保留代數作為演算法的終止條件。
  5. The main research work of dissertation can be concluded as follows : 1. two kinds of hybrid strategies of genetic algorithms and tabu search are discussed systematically in this paper. the idea of tabu search is introduced to improve crossover operator and mutation operator of genetic algorithms for the first strategy, while tabu search is executed to a certain proportional selected individuals in the population after genetic evolution of every generation for the second strategy

    本文的主要研究成果概括如下: 1 .論文比較系統地探討了遺傳演算法和禁忌搜索演算法的兩種混合策略:第一種是利用禁忌搜索的思想來改進遺傳演算法的交叉運算元和變異運算元;第二種是對每代中交叉變異后得到的新種群中的一定比例的個體進行禁忌搜索處理。
  6. In this thesis, firstly, we give an introduction and analysis to the complexity adaptive system and artificial life which are the mainstream research harvest currently. secondly, we provide a supplement to some theory include stream, diversity and adaptive agent. at last, on the basis of the theories, we complete a validating to the nature selection and heredity variance in computer, a basic conceiving about learning species and a validate model of the theories of the origins of currency and price equilibrium

    本文對這方面最主流的研究成果復雜適應系統及人工生命的研究工作作了分析與介紹，並就其中的流、多樣性、適應性主體等理論作了自己的補充，最後我們結合對這些理論的認識，完成了自然選擇及遺傳變異的初步驗證、學習物種的基本構思、貨幣產生及市場價格均衡理論驗證的模型構建，在後續的工作中，我們除了繼續未完成的工作外，還將添加許多新的理論驗證。
  7. The parameter can be adjusted adaptively by incorporating population entropy into the algorithm to provide a quantitative measure of population diversity of individuals in the population and new individuals can be introduced into the population by a big - mutation operation when the individuals in the population tend to be consistent. the case study shows that the algorithm can alleviate the problem of premature convergence as well as improve the efficiency and scope of searching

    本研究將信息嫡引入遺傳演算法以度量種群的多樣性;當判斷其發生過早收斂時，則通過「大變異」操作來引入新個體，從而有效緩解了成熟前收斂的發生，提高了演算法搜索的范圍與效率。
  8. In the second charper, two reformed metheds has presented, which are competition adaptation - ga based on elitist and dual mutation adaptive - ga which can increase the population diversity and can decrease the dependence of genetic algorithms " result to the control parameter of operators and the status of initial population. to testify their abilities of algorithms, some studies have excuted which included the study to optimize parameter for pid and the study to multivariable intelligent decoupling control for mimo system. the outcomes have showed us that this amendment has a better effective than conventional means and the genetic algorithms which have no change

    第二章中，在已有文獻的基礎上，創新性的提出兩種改進演算法：基於最優保留的聯賽競爭機制遺傳演算法（ competitionadaptation - gabasedonelitist ）和雙變異自適應遺傳演算法（ dualmutationadaptive - ga ） ；通過對演算法中選擇運算元和變異運算元的有效改進，提高了演算法的尋優能力和尋優效率，增加了群體中個體模式的多樣性，對于演算法中存在的欺騙問題、早熟問題以及成熟前收斂問題有明顯的改進作用。
  9. The study of genetic relationship showed that the selected samples and the sequenced mtdna region satisfied the request of the genetic analysis. the novel variable site provided some index to establish a perfect phylogenetic tree

    5個民族之間的遺傳關系分析表明，所選擇的群體樣本及測序區能夠滿足群體遺傳學分析的要求，而且新發現的變異位點可以為建立更完善的系統樹提供指標。
  10. Finally, genetic optimization research is summarized on several typical production scheduling problems. after expounding the general idea of genetic algorithm, the comparative advantages in contrast to the traditional algorithm, the basic characteristics of genetic algorithm and its theoretical base, the paper puts emphasis on the efficiency of genetic algorithm in the scheduling of flow shop, and puts forward an improving genetic algorithm : the ordinal genetic algorithm based on the heuristic rules. the new algorithm introduces into the initial group the solution of heuristic algorithm, and in the group structure adopts a strategy of first ordering according to the priority of the adaptive solution, and then defining a new way of choosing probability by segments, which provides more hybridizing opportunity for optimized individuals, and designs variation - control rule to prevent single population and partial optimal solution

    在論述了遺傳演算法的思想、與傳統搜索演算法的比較優勢、遺傳演算法的基本特徵和遺傳演算法的理論基礎（包括模式定理、隱含并行性、基因塊假設、欺騙問題和收斂性定理）后，重點探討了遺傳演算法在flowshop調度問題中的潛力和有效性；結合啟發式規則，提出了一個改進的遺傳演算法?基於啟發式規則的有序遺傳演算法，新演算法在初始種群中引入了啟發式演算法的解，在種群結構上採用了先按適應值優劣排序再分段確定選擇概率的新策略，使優質個體有更多的雜交機會，在變異中設計了變異控制規則，以防種群單一化，而陷入局部優化解。
  11. When the genetic algorithm is implemented it is usually done in a manner that involves the following cycle : evaluate the fitness of all of the individuals in the population. create a new population by performing operations such as selection, crossover and mutation on the individuals whose fitness has just been measured. discard the old population and iterate using the new population

    遺傳演算法是通過下面簡要步驟的循環最終獲得優化問題的解的：隨機生成一定數目的個體構成初始群體，計算群體中每個個體的適應度，使用遺傳操作即選擇、交叉、變異運算元產生新一代群體然後代替上?代群體并迭代執行。
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