采用高光谱技术结合化学计量法,对市面上常见的42种不同品牌、型号的黑色染料笔墨迹进行分类鉴别。首先,对42种黑色染料笔的墨迹样品进行450-950 nm区间的高光谱扫描;借助SPSS 26软件和Origin Pro 2023,采用系统聚类分析方法对墨迹样品...采用高光谱技术结合化学计量法,对市面上常见的42种不同品牌、型号的黑色染料笔墨迹进行分类鉴别。首先,对42种黑色染料笔的墨迹样品进行450-950 nm区间的高光谱扫描;借助SPSS 26软件和Origin Pro 2023,采用系统聚类分析方法对墨迹样品进行分类,并用主成分分析来验证分类的结果。对高光谱数据进行系统聚类分析,能将42种墨迹样品分为两大类共7小类,其中油基笔4类,水基笔3类;经主成分分析验证其分类效果合理。结果表明,高光谱技术结合化学计量法能够对黑色染料笔墨迹进行分类和鉴别,为实际案件中黑色染料笔墨迹的检验提供了技术支撑。展开更多
Understanding an underlying structure for phylogenetic trees is very important as it informs on the methods that should be employed during phylogenetic inference. The methods used under a structured population differ ...Understanding an underlying structure for phylogenetic trees is very important as it informs on the methods that should be employed during phylogenetic inference. The methods used under a structured population differ from those needed when a population is not structured. In this paper, we compared two supervised machine learning techniques, that is artificial neural network (ANN) and logistic regression models for prediction of an underlying structure for phylogenetic trees. We carried out parameter tuning for the models to identify optimal models. We then performed 10-fold cross-validation on the optimal models for both logistic regression?and ANN. We also performed a non-supervised technique called clustering to identify the number of clusters that could be identified from simulated phylogenetic trees. The trees were from?both structured?and non-structured populations. Clustering and prediction using classification techniques were?done using tree statistics such as Colless, Sackin and cophenetic indices, among others. Results from 10-fold cross-validation revealed that both logistic regression and ANN models had comparable results, with both models having average accuracy rates of over 0.75. Most of the clustering indices used resulted in 2 or 3 as the optimal number of clusters.展开更多
This study is based on the Tong sheep obtained by the random sampling method of typical colonies in the central area of Baishui County in Shaanxi Province, China. An investigation was undertaken to clarify the gene co...This study is based on the Tong sheep obtained by the random sampling method of typical colonies in the central area of Baishui County in Shaanxi Province, China. An investigation was undertaken to clarify the gene constitution of blood protein and nonprotein types of Tong sheep. Twelve genetic markers were examined by starch-gel electrophoresis and cellulose acetate electrophoresis. Polymorphism in Tong sheep was found at the following 10 loci, transferrin （Tf）, alkaline phosphatase （Alp）, leucine aminopeptidase （Lap）, arylesterase （Ary-Es）, hemoglobin-β （Hb-β）, X-protein （X-p）, carbonic anhydrase （CA）, catalase （Cat）, malate dehydrogenase （MDH）, and lysine （Ly）, whereas, albumin （A1） and postalbumin （Po） loci were monomorphic. Genetic approach degree method and phylogenetic relationship clustering method were used to judge the origin and phylogenetic status of Tong sheep. Results from both methods maintained that Tong sheep belonged to the ＂Mongolia group＂, and Mongolia sheep was the origin of Tong sheep. This was also supported by the history of Tong sheep breeding. Compared to the phylogenetic relationship clustering method, the genetic approach degree method was more reliable for the extraction from East and South of Central Asia, and was more effective in reflecting the breeding course of Tong sheep.展开更多
When microarray gene expression data are used to predict multiple drug resistance(MDR)phenotypes for anticancer drugs,the normalization strategy and the quality of the selected signature genes are usually the main cau...When microarray gene expression data are used to predict multiple drug resistance(MDR)phenotypes for anticancer drugs,the normalization strategy and the quality of the selected signature genes are usually the main causes of inconsistency among different experiments.A stable statistical drug response prediction model is urgently required in oncology.In this study,the microarray gene expression data of multiple cancer cell lines with MDR was analyzed.For each probe-set,the expression value was defined as present/absent(1/0)and was classified into a gene set defined with protein domain organization(PDO).After employing the gene content method of phylogenetic analysis,a phylogenetic model(cell tree)for MDR phenotype prediction was built at the PDO gene set level.The results indicate that classification of cancer cell lines is predominantly affected by both the histopa-thological features and the MDR phenotype(paclitaxel and vinblastine).When applying this model to predict the MDR phenotype of independent samples,the phylogenetic model performs better than signature gene models.Although the utility of our procedure is limited due to sample heterogeneity,it still has potential application in MDR research,especially for hematological tumors or established cell lines.展开更多
利用RAPD标记对12种白粉菌种间的亲缘关系进行了研究。结果表明:(1)用5个扩增结果比较明显的随机引物扩增得到的45个RAPD位点中,有38个具多态性,多态性位点频率(单卫星方法统计)为84.4%。(2)根据PopG ene 32软件计算的Shannon多样性指数...利用RAPD标记对12种白粉菌种间的亲缘关系进行了研究。结果表明:(1)用5个扩增结果比较明显的随机引物扩增得到的45个RAPD位点中,有38个具多态性,多态性位点频率(单卫星方法统计)为84.4%。(2)根据PopG ene 32软件计算的Shannon多样性指数与N e i基因多样度指数分别为0.511 4和0.339 5,多态性频率为97.78%,说明白粉菌种间具有丰富的遗传多样性。聚类分析结果得到的亲缘关系与形态学分类结果并不十分相符,说明分子技术鉴定和形态学鉴定之间还有一定差异。展开更多
文摘采用高光谱技术结合化学计量法,对市面上常见的42种不同品牌、型号的黑色染料笔墨迹进行分类鉴别。首先,对42种黑色染料笔的墨迹样品进行450-950 nm区间的高光谱扫描;借助SPSS 26软件和Origin Pro 2023,采用系统聚类分析方法对墨迹样品进行分类,并用主成分分析来验证分类的结果。对高光谱数据进行系统聚类分析,能将42种墨迹样品分为两大类共7小类,其中油基笔4类,水基笔3类;经主成分分析验证其分类效果合理。结果表明,高光谱技术结合化学计量法能够对黑色染料笔墨迹进行分类和鉴别,为实际案件中黑色染料笔墨迹的检验提供了技术支撑。
文摘Understanding an underlying structure for phylogenetic trees is very important as it informs on the methods that should be employed during phylogenetic inference. The methods used under a structured population differ from those needed when a population is not structured. In this paper, we compared two supervised machine learning techniques, that is artificial neural network (ANN) and logistic regression models for prediction of an underlying structure for phylogenetic trees. We carried out parameter tuning for the models to identify optimal models. We then performed 10-fold cross-validation on the optimal models for both logistic regression?and ANN. We also performed a non-supervised technique called clustering to identify the number of clusters that could be identified from simulated phylogenetic trees. The trees were from?both structured?and non-structured populations. Clustering and prediction using classification techniques were?done using tree statistics such as Colless, Sackin and cophenetic indices, among others. Results from 10-fold cross-validation revealed that both logistic regression and ANN models had comparable results, with both models having average accuracy rates of over 0.75. Most of the clustering indices used resulted in 2 or 3 as the optimal number of clusters.
基金the International Cooperation Item of the National Natural Science Foundation of China (No. 30213009, 30310103007, 30410103150)Natural Science Foundation of Jiangsu Province of China (No. BK2007556)+1 种基金Basic Natura Science Foundation for Colleges and Universities Jiangsu Province (No. NK051039) the New Century Talent Project of Yangzhou University in China.
文摘This study is based on the Tong sheep obtained by the random sampling method of typical colonies in the central area of Baishui County in Shaanxi Province, China. An investigation was undertaken to clarify the gene constitution of blood protein and nonprotein types of Tong sheep. Twelve genetic markers were examined by starch-gel electrophoresis and cellulose acetate electrophoresis. Polymorphism in Tong sheep was found at the following 10 loci, transferrin （Tf）, alkaline phosphatase （Alp）, leucine aminopeptidase （Lap）, arylesterase （Ary-Es）, hemoglobin-β （Hb-β）, X-protein （X-p）, carbonic anhydrase （CA）, catalase （Cat）, malate dehydrogenase （MDH）, and lysine （Ly）, whereas, albumin （A1） and postalbumin （Po） loci were monomorphic. Genetic approach degree method and phylogenetic relationship clustering method were used to judge the origin and phylogenetic status of Tong sheep. Results from both methods maintained that Tong sheep belonged to the ＂Mongolia group＂, and Mongolia sheep was the origin of Tong sheep. This was also supported by the history of Tong sheep breeding. Compared to the phylogenetic relationship clustering method, the genetic approach degree method was more reliable for the extraction from East and South of Central Asia, and was more effective in reflecting the breeding course of Tong sheep.
基金supported by the National High Technology Research and Development Program of China(2007AA02Z332,2008AA02Z126 and 2009AA02Z308)Shanghai Great Project Program Foundation(07DZ19505)
文摘When microarray gene expression data are used to predict multiple drug resistance(MDR)phenotypes for anticancer drugs,the normalization strategy and the quality of the selected signature genes are usually the main causes of inconsistency among different experiments.A stable statistical drug response prediction model is urgently required in oncology.In this study,the microarray gene expression data of multiple cancer cell lines with MDR was analyzed.For each probe-set,the expression value was defined as present/absent(1/0)and was classified into a gene set defined with protein domain organization(PDO).After employing the gene content method of phylogenetic analysis,a phylogenetic model(cell tree)for MDR phenotype prediction was built at the PDO gene set level.The results indicate that classification of cancer cell lines is predominantly affected by both the histopa-thological features and the MDR phenotype(paclitaxel and vinblastine).When applying this model to predict the MDR phenotype of independent samples,the phylogenetic model performs better than signature gene models.Although the utility of our procedure is limited due to sample heterogeneity,it still has potential application in MDR research,especially for hematological tumors or established cell lines.
文摘利用RAPD标记对12种白粉菌种间的亲缘关系进行了研究。结果表明:(1)用5个扩增结果比较明显的随机引物扩增得到的45个RAPD位点中,有38个具多态性,多态性位点频率(单卫星方法统计)为84.4%。(2)根据PopG ene 32软件计算的Shannon多样性指数与N e i基因多样度指数分别为0.511 4和0.339 5,多态性频率为97.78%,说明白粉菌种间具有丰富的遗传多样性。聚类分析结果得到的亲缘关系与形态学分类结果并不十分相符,说明分子技术鉴定和形态学鉴定之间还有一定差异。
