馬鈴薯-紅薯收獲機的設計與仿真含proe三維及12張CAD圖,馬鈴薯,土豆,紅薯,收獲,收成,設計,仿真,proe,三維,12,十二,cad 指導記錄 第一次指導記錄： 指導地點 年 月 日 第二次指導記錄： 指導地點 年 月 日 第三次指導記錄： 指導地點 年 月 日 第四次指導記錄： 指導地點 年 月 日 第五次指導記錄： 指導地點 年 月 日 第六次指導記錄： 指導地點 年 月 日 第七次指導記錄： 指導地點 年 月 日 第八次指導記錄： 指導地點 年 月 日 第九次指導記錄： 指導地點 年 月 日 第十次指導記錄： 指導地點 年 月 日 第十一次指導記錄： 指導地點 年 月 日 第十二次指導記錄： 指導地點 年 月 日 第十三次指導記錄： 指導地點 年 月 日 第十四次指導記錄： 指導地點 年 月 日 第十五次指導記錄： 指導地點 年 月 日 XX指導教師評閱表 學院：機電工程學院 專業(yè)：機械設計制造及其自動化 學生：XX 學號：20140601432 題目： ?紅薯收獲機的設計與仿真?? 評價 項目 評價要素 成績評定 優(yōu) 良 中 及格 不及格 工作 態(tài)度 工作態(tài)度認真，按時出勤 能按規(guī)定進度完成設計任務 選題 質量 選題方向和范圍 選題難易度 選題理論意義和實際應用價值 能力 水平 查閱和應用文獻資料能力 綜合運用知識能力 研究方法與手段 實驗技能和實踐能力 創(chuàng)新意識 設計 論文 質量 內容與寫作 結構與水平 規(guī)范化程度 成果與成效 指導 教師 意見 建議成績 是否同意參加答辯 評語： ? ? ? ? ? ? 指導教師簽名： 年 月 日 XX評閱教師評閱表 學院：機電工程學院 專業(yè)：機械設計制造及其自動化 學生：XX 學號：20140601432 題目： ? 紅薯收獲機的設計與仿真?? 評價 項目 評價要素 成績評定 優(yōu) 良 中 及格 不及格 選題 質量 選題方向和范圍 選題難易度 選題理論意義和實際應用價值 能力 水平 查閱和應用文獻資料能力 綜合運用知識能力 研究方法與手段 實驗技能和實踐能力 創(chuàng)新意識 設計 論文 質量 內容與寫作 結構與水平 規(guī)范化程度 成果與成效 評閱 教師 意見 建議成績 是否同意參加答辯 評語： ? ? ? ? ? ? 評閱教師簽名： 年 月 日 XX答辯及綜合成績評定表 學 院 機電工程學院 專 業(yè) 機械設計制造及其自動化 學生姓名 XX 學 號 20140601432 指導教師 XX 設計論文題 目 紅薯收獲機的設計與仿真 答辯時間 年 月 日 時 分至 時 分 答辯地點 敬本樓C502 答辯小組成 員 姓名 陸興華 秦錄芳 楊麗娟 馬西良 黃傳輝 職稱 副教授 副教授 副教授 副教授 教授 答辯 記錄 提問人 提問主要內容 學生回答摘要 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 答辯記錄人簽名： 答辯 小組 意見 答辯評語： ? ? ? 答辯成績： 答辯小組組長簽名： 綜合 成績 評定 指導教師評定成績 評閱教師評定成績 答辯成績 綜合評定成績 答辯委員會主任簽名： ? 年 月 日 ? 畢業(yè)設計（論文） 外文翻譯 學生姓名 XX 班 級 14機械單 學 號 20140601432 學院名稱 機電工程學院 專業(yè)名稱 機械設計制造及其自動化 指導教師 XX 2018年 5月 26日 Characterization of the genetic diversity of Uganda’s sweet potato (Ipomoea batatas) germplasm using microsatellites markers Barbara M. Zawedde ? Marc Ghislain ? Eric Magembe ? Geovani B. Amaro ? Rebecca Grumet ? Jim Hancock Received: 7 April 2014/Accepted: 1 September 2014/Published online: 17September 2014  Springer Science+Business Media Dordrecht 2014 Abstract Knowledge about the genetic diversity and structure of crop cultivars can help make better conservation decisions, and guide crop improvement efforts. Diversity analysis using microsatellite markers was performed to assess the level of genetic diversity in sweet potato in Uganda, and evaluate the genetic relationship between the Uganda’s germplasm and some genotypes obtained from Kenya, Tanzania, Ghana, Brazil and Peru. A total of 260 sweet potato cultivarswerecharacterizedusing93microsatelliteloci. The Ugandan collection showed a large number of Distinct genetic diversitybetweengenotypesobtainedfromthedifferent agro-ecological zones. There was low (6 %) levels of genetic diversity observed between the East African genotypes; however unique alleles were present in collections from the various sources. Pairwise comparisons germplasmwassigni?cantlydifferent(P\0.001)from cultivars from Tanzania, Ghana, Brazil and Peru. The presence of unique alleles in populations from various Uganda’s agro-ecological zones and suggestthateffortsshouldbemadetofurthercollectand characterize the germplasm in more depth. Keywords Characterization  Crop breeding  Ipomoea batatas  Molecular markers  SSR A germplasm collection of crop cultivars with varying environmental adaptive capacity can be both a source of genes for future crop improvement, as well as a critical resource for farmers. The highest levels of genetic diversity for the majority of the important global food crops is in the South, where crop centers of origins are commonly found, and centers of diversity emerged due to prolonged periods of farmer selection (FAO 2008). B. M. Zawedde  R. Grumet  J. Hancock (&) Graduate Program in Plant Breeding, Genetics and Biotechnology, Michigan State University, Plant and Soil Science Building, 1066 Bogue Street, East Lansing, MI 48824, USA e-mail: hancock@msu.edu B. M. Zawedde e-mail: zawedde@msu.edu Present Address: B. M. Zawedde Uganda Biosciences Information Center (UBIC), National Crop Resources Research Institute, 27 km Kampala Zirobwe Road, Namulonge, Kampala, Uganda M. Ghislain  E. Magembe CIP Sub-Saharan Africa, International Potato Center, P.O. Box 25171, Nairobi, Kenya G. B. Amaro Embrapa Vegetable Crops, P.O. Box 218, Bras? ′lia, DF CEP 70359-970, Brazil Sweet potato, Ipomoea batatas (L.) Lam., is the ?fth most important food crop in terms of weight harvested in Eastern Africa (FAO 2012). Sweet potato was introduced to the East African borders from South America by Portuguese explorers during the 16th century (Zhang et al. 2004). The oldest remains of sweet potato have been found in the caves of the Chilca Canyon in Peru and dated as 8,000 years old (Lebot 2010). However, based on morphological relationships among related species, the center of origin appears to be between the Yucatan Peninsula in Mexico and the Orinoco River in Venezuela (Austin 1977). It is also in that region that the wild species of the section Batatas, considered as putative ancestors and wild relatives of the cultivated sweet potato, are found (Andersson and de Vicente 2010). Evaluations of genetic diversity patterns among germplasm from different parts of the world have resulted in the suggestion that China, Southeast Asia, New Guinea and East Africa are secondary centers of diversity (Yen 1982; Austin 1983). Uganda has the highest production per capita in Sub-Saharan Africa and numerous, diverse sweet potato landraces are grown. In 2005, the national sweet potato program collected over 1,300 landraces, which were characterized using morphological methodologies to determine the level of genetic diversity and 946 of these were found to be morphologically distinct genotypes (Yada et al. 2010a). This high level of diversity can be attributed primarily to the allogamous and hexaploidy nature of sweet potato (Lebot 2010), as well as variations in farmers’ preferences (Veasey et al. 2008). The method of propagation by vine cuttings contributes also indirectly by maintaining cultivar diversity. Knowledge about the genetic diversity and structure of existing crop cultivars can aid in making better conservation decisions, and help direct breeding programs. Characterization of crop diversity can be achieved through morphological and molecular tools. Morphological characterization is an important ?rst step in assessment of diversity; however there are major limitations in relying only on morphological characterization including low levels of polymorphism, low repeatability, late expression for certain traits; phenotypic plasticity and parallel evolution (Karuri et al. 2010; Yada et al. 2010b). A number of molecular markers including random ampli?ed polymorphic DNAs (RAPDs), restriction fragment length genetic diversity for each population included number of polymorphic loci (P), percentage of polymorphic loci (%P) and Nei’s (1973) gene diversity (D), estimated from binary data using GenAlex 6.4 (Peakall and Smouse 2006). Analysisofmolecularvariance(AMOVA)was also performed using GenAlEx version 6.4 to estimate the total variance and distribution of diversity within and between populations. Wright’s F-Statistic (FST, ?xation index) was also computed, using GenAlEx software, to estimate the amount of genetic variance that can be explained by population structure (Holsinger and Bruce 2009). Fixation index; FST ? HT HI HT where HI is the mean observed heterozygosity per individual within subpopulations and HT is the expected heterozygosity in a random mating total population. FST can range from 0.0 (no differentiation) to 1.0 (complete differentiation, that is, subpopulations ?xed for different alleles). The phylogenetic relationship among populations was assessed using DARwin version 5 (Perrier and Jacquemoud-Collet 2006). Similarity matrices were constructed from the binary data with Jaccard’s coef?cients (Jaccard 1908). Jaccard’s coef?cient = Nab/(Na ? Nb), where Nab is the number of alleles shared by two individuals a and b, Na is total number of alleles in sample a, and Nb is total number of alleles in sample b. Genetic distances between populations were obtained by computing the usual Euclidian distance matrix based on haplotype frequencies. From this matrix, a dendrogram was constructed using the neighbor joining method (NJ) from Saitou and Nei (1987). The signi?cance of each node was evaluated by bootstrappingdataovera locusfor 5,000 replications of the original matrix. We examined hierarchical genetic variation between individuals using the un-weighted pair group method analysis (UPGMA), as suggested by Sneath and Sokal (1973). Clustering patterns of individuals and populations were examined using STRUCTURE version 2.3.3 (Pritchard et al. 2000), which is reported to have the capability to generate population structuring (Pritchard et al. 2009). Using the allele dosage (MAC-PR) data for each individual, individuals were assigned probabilistically to genetic clusters (K). The STRUCTURE program was run using no prior assumptions of population structure withan admixture ancestrymodel and the recommended methods for recessive alleles, and allele frequencies correlated. The analysis was used to determine whether biologically relevant clusters could be determined among the plants sampled, and establish the proportion of an individual’s genome (Q) that originated from each cluster. For all analyses, the Markov chain Monte Carlo (MCMC) parameters were set to a burn-in period of 50,000 with 50,000 iterations. The optimum K, indicating the number of true clusters in the data, was determined from 20 replicate runs for each value of K (K set to 10) using the method described by Evanno et al. (2005) and the adhocQuantityDeltaK,based ontherateofchangein the log probability of the data between successive K values. Parameters of the method of Evanno et al. (2005) were calculated using the program Structure Harvester version 0.6.92 (Earl and vonHoldt 2012). Similarity among different runs was calculated by the method of Jakobsson and Rosenberg (2007) as used in their computer program CLUMPP 1.1.2. This method calculates a similarity coef?cient h0, which allows the assessment of the similarity of individual runs of the program STRUCTURE. The optimal alignment of 20 replicates of K values was determined using the computer program CLUMPP 1.1.2 (Jakobsson and Rosenberg 2007) and clusters were visualized using the program DISTRUCT 1.1 (Rosenberg 2004). Results SSR markers ampli?cation A total of 107 alleles were scored for the 19 SSR markers (Table 2). The number of alleles per locus ranged from 3 to 9. Three markers had very low PIC; IB-S07(0.22),JB1809(0.19)andIBSSR09(0.31) thus were excluded from further analyses. Determining relatedness between cultivars in Uganda A total of 10 newly improved cultivars released by the national program were compared with 158 Ugandan landraces. The unweighted neighbor joining (NJ) algorithm cluster analysis generated numerous clusters (Fig. 1). Improved cultivars were scattered into many of these clusters together with landraces. Noteworthy (6/10) improved cultivars were grouped together with a Kenyan cultivar Kakamega, which was purposely included in this analysis because it is a known maternal parent for many of these improved cultivars. Genetic relationship between genotypes from Uganda’s agro-ecological zones and cultivars collected from other East African countries AnalysisofMolecularVariance(AMOVA)indicatedthatonly6 %ofthegeneticvariationwasexplainedby differences among the sources (Table 3). Analysis of the sixteen microsatellites yielded a total of 93 presumptive loci in the 228-sweet potato genotypes from the eight prede?ned populations (Table 4). An average of 70 polymorphic loci was observed in each population. The level of genetic diversity varied among the different populations. Most regions in Uganda had populations with few unique alleles (1–4), except the south-western region, which had none. Tanzanian cultivars also had few unique alleles (4), but had the highest level of heterozygosity (D). Overall the level of heterozygosity (D) for the collected samples was low. The signi?cant difference between the cultivars from Tanzania and the populations from Uganda and Kenya is clearly shown in the genetic distance matrix (Fig. 2). Genetic relationship between Ugandan genotypes and cultivars collected from elsewhere Analysis of Molecular Variance (AMOVA) indicated that only 24 % of the genetic variation was explained by differences among the countries (Table 5). Pairwise comparisons of genetic differentiation among countries indicated that Uganda’s germplasm was signi?cantly different (P\0.001) from genotypes from Brazil, Peru and Ghana (Table 6). The Jaccard’s similarity coef?cients ranged from 0.0 to 0.95 with a mean of 0.56. More than 70 % of the pair-wise similarity coef?cients were between 0.50 and 0.63. The dendrogram generated by DARwin software revealed three clusters (Fig. 2). To ef?ciently visualize the results, the dendrogram was pruned from the complete tree to show clustering only between genotypes that had bootstrap values greater than 60. This pruned tree showed similar broad clustering patterns as the complete tree (data not shown). East African germplasm and cultivars from USA were found in Cluster A, the majority of cultivars from Brazil and Peru were in cluster B while most Ghanaian cultivars were found in Cluster C. The Bayesian model of STRUCTURE (Pritchard et al. 2000) assigned the individuals to two major genetic clusters, as the highest Delta K was observed at K = 2. All individuals appeared to have a component of both clusters in their genome; however, the Ugandan and Kenyan cultivars had a very high proportion of their genome originating from cluster K1, Brazil, Ghana and Peru had a very high proportion of their genome originating from cluster K2, while Tanzaniancultivarswerecomposedofamixtureofthe two clusters (Fig. 3). Discussion The number of alleles per primer pair observed in this work is close to that obtained by Yada et al. (2010b) using the same SSR markers. However, our number of alleles varies somewhat from those reported by Tumwegamireetal.(2011)onsimilarsweetpotatogermplasm using the same markers. Higher number of alleles was observedforsomemarkersinthisworkandthisislikely due to a larger genotype sample size and a higher resolution of DNA fragment. Yada et al. (2010b) assessed 192 samples using the ABI system similar to what was used in this work, while Tumwegamire et al. (2011) screened 75 samples with the LiCOR system. A total of 92 out of 106 markers were highly polymorphic, which con?rms the high discriminating power of the SSR markers (Hwang et al. 2002; Gichuru et al. 2006, Veasey et al. 2008, Yada et al. 2010a, b; Tumwegamire et al. 2011). Hwang et al. Conclusions Overall, the sweet potato has high levels of genetic diversity. However, the presence of unique alleles in populations from various Uganda’s agro-ecological zones and other global regions, as well as the regional diversity patterns, indicates the value of collecting and characterizing the germplasm in more depth. The use of microsatellite marker data can be particularlyuseful tomakebetter choicesofwhatneeds tobe preservedin order to increase genetic diversity and representation of landraces across Africa. These genotypes need to be incorporated in the collections at the national gene bank and managed to ensure their long-term conservation. Finally, the origin of sweet potato germplasm in East Africa doesn’t appear to be strictly of a single Brazilian origin but rather successive introduction from several sources. Acknowledgments We are very grateful to the Norman E. Borlaug Leadership Enhancement in Agriculture Program (LEAP) for funding this research. Our sincere gratitude goes to Dr. Joseph Nduguru and Luambano Nessie at Mikocheni Agricultural Research Institute, Tanzania for providing us with the samples from Tanzania. We are also grateful to Francis Osingada and Jimmy Akono at the Biosciences Facility of the National Crop Resources Research Institute, Uganda, Bramwel Wanjala of Biosciences eastern and central Africa (BecA) Hub and Maggie Mwathi of CIP-Of?ce at the International Livestock Research InstituteinNairobi,Kenya, forthetechnical assistance provided to conduct the research. 外文下載地址：https://wenku.baidu.com/view/92938a48767f5acfa1c7cd89.html 利用微衛(wèi)星標記對烏干達紅薯(番薯)的遺傳多樣性進行了表征 芭芭拉·m·扎維德·馬克·吉斯林2014年4月7日/ 2014年9月1日在網上發(fā)布:2014年9月17日施普林格科學+商業(yè)媒體。 對作物品種的遺傳多樣性和結構的認識可以幫助做出更好的保護決策，并指導作物改良工作。利用微衛(wèi)星標記進行多樣性分析，以評估烏干達甘薯的遺傳多樣性水平，并評估烏干達的種質與從肯尼亞、坦桑尼亞、加納、巴西和秘魯獲得的一些基因型之間的遺傳關系?？偣灿?60顆甘薯品種。烏干達的收藏品顯示了大量的。 不同的農業(yè)生態(tài)帶中，不同的土壤類型和不同的遺傳多樣性水平非常低(3%)。東非基因型的遺傳多樣性水平低(6%);然而，各種來源的收藏品中有獨特的等位基因。對遺傳分化的配對比較表明，烏干達的種質與坦桑尼亞、加納、巴西和秘魯?shù)钠贩N不同(P\0.001)。在不同的烏干達農業(yè)生態(tài)區(qū)和其他全球區(qū)域，以及區(qū)域多樣性模式的種群中，存在著獨特的等位基因，這表明，需要更深入地研究和描述種質。 關鍵詞表征作物育種;巴氏分子標記物不同環(huán)境適應能力的作物品種的種質資源可能既是未來作物改良的基因來源，也是農民的重要資源。大多數(shù)重要的全球糧食作物的遺傳多樣性最高的地方是南方，那里的作物中心通常都有，而且由于長期的農民選擇(糧農組織2008年)，多樣性的中心出現(xiàn)了。 劉明輝，《植物育種、遺傳與生物技術研究》，北京大學出版社，2001年，第4期。 B. M. Zawedde電郵:zawedde@msu.edu。 目前的地址:烏干達國家作物資源研究所，烏干達生物科學信息中心(UBIC)， 27公里坎帕拉齊魯瓦路，Namulonge，坎帕拉，烏干達。 M. Ghislain E. Magembe CIP撒哈拉以南非洲，國際馬鈴薯中心，P.O. Box 25171，內羅畢，肯尼亞。 巴西紅薯、巴西番薯、巴西番薯、巴西番薯、巴西番薯、巴西番薯、巴西番薯、巴西番薯等。林。這是非洲東部收獲的第五種最重要的糧食作物(糧農組織2012)。16世紀時，葡萄牙探險家將甘薯從南美洲引進到東非邊界(Zhang et al. 2004)。在秘魯Chilca峽谷的洞穴中發(fā)現(xiàn)了最古老的甘薯遺骨，距今已有8000年的歷史(Lebot 2010)。然而，基于相關物種之間的形態(tài)學關系，起源中心似乎位于墨西哥的尤卡坦半島和委內瑞拉的奧里諾科河之間(奧斯汀1977年)。在這一地區(qū)，被認為是公認的祖先和栽培甘薯的野生親戚的巴塔塔的野生物種被發(fā)現(xiàn)(Andersson和de Vicente 2010)。對世界各地種質遺傳多樣性模式的評價結果表明，中國、東南亞、新幾內亞和東非是多樣性的二級中心(日元1982;奧斯汀1983)。烏干達是撒哈拉以南非洲地區(qū)人均產量最高的地區(qū)，種植了多種多樣的甘薯。在2005年，國家甘薯項目收集了1300多個landraces，使用形態(tài)學方法來確定遺傳多樣性的水平，其中946個被發(fā)現(xiàn)是形態(tài)上不同的基因型(Yada et al. 2010a)。這種高度的多樣性主要歸功于甘薯的特性，以及農民偏好的變化。葡萄扦插繁殖的方法也間接地通過保持品種的多樣性。對現(xiàn)有作物品種的遺傳多樣性和結構的了解有助于更好地進行保護決策，并幫助直接育種項目?？梢酝ㄟ^形態(tài)學和分子工具來表征作物的多樣性。形態(tài)特征是評價多樣性的重要第一步;然而，僅依賴于形態(tài)學特征，包括低水平的多態(tài)性、低重復性、晚期表達的某些性狀，有很大的局限性;表型可塑性和平行進化。許多分子標記，包括隨機擴增的多態(tài)，限制片段長度。 多態(tài)性(RFLPs)，擴增片段長度多態(tài)性(AFLP)，微衛(wèi)星或簡單序列重復(SSRs)，單核苷酸多態(tài)性(SNPs)已被開發(fā)，用于補充形態(tài)特征。在作物中選擇任何特定的DNA標記在很大程度上取決于研究的目標、可利用的資源和技術技能(Otoo等人，2009)。在甘薯研究中，一些分子標記物被用于研究作物的遺傳多樣性，被認為起源于幾種野生物種之間的自然雜交。 每個種群的遺傳多樣性包括多態(tài)位點(P)、多態(tài)位點的百分比(%P)和Nei(1973)基因多樣性(D)，用GenAlex 6.4 (Peakall和Smouse 2006)的二進制數(shù)據(jù)估計。分析分子方差(AMOVA)也使用GenAlEx version 6.4來估計種群內部和種群之間的多樣性的總方差和分布。Wright的f統(tǒng)計(FST，固定指數(shù))也被計算，使用GenAlEx軟件，來估計遺傳變異的數(shù)量，可以解釋人口結構(Holsinger和Bruce 2009)。固定指數(shù);置?HT嗨HT 其中HI是次