维生素E(Ve)是大豆油中一种天然抗氧化剂,是评价大豆油营养价值的重要指标。本研究利用含有264份的大豆自然群体在2021年和2022年测定了籽粒中α-、γ-和δ-生育酚含量,并进行全基因组关联分析(Genome-wide association study,GWAS)。...维生素E(Ve)是大豆油中一种天然抗氧化剂,是评价大豆油营养价值的重要指标。本研究利用含有264份的大豆自然群体在2021年和2022年测定了籽粒中α-、γ-和δ-生育酚含量,并进行全基因组关联分析(Genome-wide association study,GWAS)。本研究共检测到199个与大豆Ve含量显著关联的SNP位点,其中9个可在2个环境或者2个性状被重复检测到,分别位于3号、7号、11号、12号、13号、15号、17号和18号染色体上。其中位于7号染色体上的显著关联信号是控制α-生育酚含量的主效位点,可在2年环境中被检测到,表型变异解释率为9.83%。对该位点候选基因进行筛选,获得一个编码myb转录因子的基因Glyma.07G054000,可能是这个位点的效应基因。另外,在12号染色体上得到2个编码γ-生育酚甲基转移酶的基因Glyma.12G014200和Glyma.12G014300,有可能是影响Ve含量的重要基因。本研究结果有助于解析大豆籽粒Ve含量的遗传基础及其调控机制,为大豆品质遗传改良奠定了基础。展开更多
An innovative inversion code, named “Curupira v1.0”, has been developed using Matlab to determine the vertical distribution of resistivity beneath the subsoil. The program integrates Vertical Electrical Sounding (VE...An innovative inversion code, named “Curupira v1.0”, has been developed using Matlab to determine the vertical distribution of resistivity beneath the subsoil. The program integrates Vertical Electrical Sounding (VES), successful in shallow subsurface exploration and Time Domain Electromagnetic (TEM) techniques, better suited for deeper exploration, both of which are widely employed in geophysical exploration. These methodologies involve calculating subsurface resistivity through appropriate inversion processes. To address the ill-posed nature of inverse problems in geophysics, a joint inversion scheme combining VES and TEM data has been incorporated into Curupira v1.0. The software has been tested on both synthetic and real-world data, the latter of which was acquired from the Parana sedimentary basin which we summarise here. The results indicate that the joint inversion of VES and TEM techniques offers improved recovery of simulated models and demonstrates significant potential for hydrogeological studies.展开更多
The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of t...The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm tofind the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Thefindings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,specificity,and a decrease in misclassification as 9.3%,6.4%,12.5%,5.2%and 81%.展开更多
As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medi...As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques,we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine(SVM),Naïve Bayes,K-Nearest Neighbor(KNN),and Decision Tree)in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique,which is a supervised machine learning method,in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods,the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.展开更多
Background: Adequate selection of a prospective whole blood donor protects his health and safety of the recipient. Objectives: The main objective of this study was to determine the haematology parameters of apparently...Background: Adequate selection of a prospective whole blood donor protects his health and safety of the recipient. Objectives: The main objective of this study was to determine the haematology parameters of apparently healthy prospective whole blood donors. Participants and Methods: This was a hospital based prospective study carried out from August to October 2020 at the blood transfusion unit of the Lagos State University Teaching Hospital (LASUTH), Ikeja, Nigeria. A structured pretested questionnaire was used for data collection. The socio demographic status and the haematology parameters of apparently healthy prospective whole blood donors who tested negative for HIV, hepatitis B and C markers were captured. Obtained data were analysed with the statistical package for the social scientist software version 20. Results: One hundred male (97.1%) and three female (2.9%) apparently healthy prospective whole blood donors were studied. The median age of study subjects was 30 years. Obtained median haematology parameter values were 13 g/dl, 40%, 4.9/nl and 203.9/nl for haemoglobin concentration, haematocrit, total white cell and platelet counts respectively. The median values for the mean corpuscular haemoglobin concentration (MCHC), mean corpuscular haemoglobin (MCH) and mean corpuscular volume (MCV) of participants were 32.6 g/dl, 27.7 pg and 85.7 fl respectively. Observed prevalence of subnormal haematology parameters for haemoglobin concentration, total white cells, platelets were 12.6%, 25.2%, and 13.6% respectively. Also subnormal values for MCHC, MCH, MCV were 11.7%, 26.2%, and 16.5% respectively among prospective whole blood donors in this study. No higher than normal haematology parameter values were observed. Median values for erythrocyte sedimentation rate was 8.4 mm/hr. Conclusion: A significant percentage of apparently healthy prospective whole blood donors had subnormal haematology parameters values. Obtained normal values in our study are comparable with local reference range reports from previous studies in Nigeria and other parts of Africa. 124947 .展开更多
文摘维生素E(Ve)是大豆油中一种天然抗氧化剂,是评价大豆油营养价值的重要指标。本研究利用含有264份的大豆自然群体在2021年和2022年测定了籽粒中α-、γ-和δ-生育酚含量,并进行全基因组关联分析(Genome-wide association study,GWAS)。本研究共检测到199个与大豆Ve含量显著关联的SNP位点,其中9个可在2个环境或者2个性状被重复检测到,分别位于3号、7号、11号、12号、13号、15号、17号和18号染色体上。其中位于7号染色体上的显著关联信号是控制α-生育酚含量的主效位点,可在2年环境中被检测到,表型变异解释率为9.83%。对该位点候选基因进行筛选,获得一个编码myb转录因子的基因Glyma.07G054000,可能是这个位点的效应基因。另外,在12号染色体上得到2个编码γ-生育酚甲基转移酶的基因Glyma.12G014200和Glyma.12G014300,有可能是影响Ve含量的重要基因。本研究结果有助于解析大豆籽粒Ve含量的遗传基础及其调控机制,为大豆品质遗传改良奠定了基础。
文摘An innovative inversion code, named “Curupira v1.0”, has been developed using Matlab to determine the vertical distribution of resistivity beneath the subsoil. The program integrates Vertical Electrical Sounding (VES), successful in shallow subsurface exploration and Time Domain Electromagnetic (TEM) techniques, better suited for deeper exploration, both of which are widely employed in geophysical exploration. These methodologies involve calculating subsurface resistivity through appropriate inversion processes. To address the ill-posed nature of inverse problems in geophysics, a joint inversion scheme combining VES and TEM data has been incorporated into Curupira v1.0. The software has been tested on both synthetic and real-world data, the latter of which was acquired from the Parana sedimentary basin which we summarise here. The results indicate that the joint inversion of VES and TEM techniques offers improved recovery of simulated models and demonstrates significant potential for hydrogeological studies.
文摘The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm tofind the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Thefindings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,specificity,and a decrease in misclassification as 9.3%,6.4%,12.5%,5.2%and 81%.
文摘As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques,we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine(SVM),Naïve Bayes,K-Nearest Neighbor(KNN),and Decision Tree)in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique,which is a supervised machine learning method,in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods,the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.
文摘Background: Adequate selection of a prospective whole blood donor protects his health and safety of the recipient. Objectives: The main objective of this study was to determine the haematology parameters of apparently healthy prospective whole blood donors. Participants and Methods: This was a hospital based prospective study carried out from August to October 2020 at the blood transfusion unit of the Lagos State University Teaching Hospital (LASUTH), Ikeja, Nigeria. A structured pretested questionnaire was used for data collection. The socio demographic status and the haematology parameters of apparently healthy prospective whole blood donors who tested negative for HIV, hepatitis B and C markers were captured. Obtained data were analysed with the statistical package for the social scientist software version 20. Results: One hundred male (97.1%) and three female (2.9%) apparently healthy prospective whole blood donors were studied. The median age of study subjects was 30 years. Obtained median haematology parameter values were 13 g/dl, 40%, 4.9/nl and 203.9/nl for haemoglobin concentration, haematocrit, total white cell and platelet counts respectively. The median values for the mean corpuscular haemoglobin concentration (MCHC), mean corpuscular haemoglobin (MCH) and mean corpuscular volume (MCV) of participants were 32.6 g/dl, 27.7 pg and 85.7 fl respectively. Observed prevalence of subnormal haematology parameters for haemoglobin concentration, total white cells, platelets were 12.6%, 25.2%, and 13.6% respectively. Also subnormal values for MCHC, MCH, MCV were 11.7%, 26.2%, and 16.5% respectively among prospective whole blood donors in this study. No higher than normal haematology parameter values were observed. Median values for erythrocyte sedimentation rate was 8.4 mm/hr. Conclusion: A significant percentage of apparently healthy prospective whole blood donors had subnormal haematology parameters values. Obtained normal values in our study are comparable with local reference range reports from previous studies in Nigeria and other parts of Africa. 124947 .
