This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus seve...This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features： three color features （R, a＊, brightness）, width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features,＇ germination percentage increased from 59.3 to 71.8% when a＊〉3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight 〉0.0064 g. The most effective model was based on a multilayer perceptron （MLP） neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.展开更多
目的分析舒巴坦联合胰岛素泵治疗糖尿病并发呼吸道感染的有效性及对患者血糖、TNF-α、IL-6水平的影响。方法以2016年9月至2017年9月我院接收并诊治的74例糖尿病并发呼吸道感染患者为研究对象,采用随机数字法将其分为参照组和试验组,各3...目的分析舒巴坦联合胰岛素泵治疗糖尿病并发呼吸道感染的有效性及对患者血糖、TNF-α、IL-6水平的影响。方法以2016年9月至2017年9月我院接收并诊治的74例糖尿病并发呼吸道感染患者为研究对象,采用随机数字法将其分为参照组和试验组,各37例。参照组在常规治疗的基础上采用舒巴坦联合胰岛素皮下注射进行治疗,试验组在常规治疗的基础上采用舒巴坦联合胰岛素泵进行治疗。比较两组治疗前及治疗3个月后的空腹血糖(FBG)、餐后2 h血糖(2 h PBG)、糖化血红蛋白(HbA1c)、肿瘤坏死因子-α(TNF-α)、白介素(IL-6)水平及治疗3个月后的临床疗效和不良反应发生情况。结果治疗3个月后,两组的FBG、2 h PBG、HbA1c、TNF-α、IL-6水平均降低,且试验组明显低于参照组(P<0.05)。治疗3个月后,试验组的临床治疗总有效率明显高于参照组,不良反应总发生率明显低于参照组(P<0.05)。结论舒巴坦联合胰岛素泵治疗糖尿病并发呼吸道感染的有效性显著,可改善患者的血糖、TNF-α、IL-6水平,降低不良反应发生率,值得临床推广应用。展开更多
基金supported by the Beijing Municipal Science and Technology Project,China (Z151100001015004)
文摘This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features： three color features （R, a＊, brightness）, width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features,＇ germination percentage increased from 59.3 to 71.8% when a＊〉3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight 〉0.0064 g. The most effective model was based on a multilayer perceptron （MLP） neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.
文摘目的分析舒巴坦联合胰岛素泵治疗糖尿病并发呼吸道感染的有效性及对患者血糖、TNF-α、IL-6水平的影响。方法以2016年9月至2017年9月我院接收并诊治的74例糖尿病并发呼吸道感染患者为研究对象,采用随机数字法将其分为参照组和试验组,各37例。参照组在常规治疗的基础上采用舒巴坦联合胰岛素皮下注射进行治疗,试验组在常规治疗的基础上采用舒巴坦联合胰岛素泵进行治疗。比较两组治疗前及治疗3个月后的空腹血糖(FBG)、餐后2 h血糖(2 h PBG)、糖化血红蛋白(HbA1c)、肿瘤坏死因子-α(TNF-α)、白介素(IL-6)水平及治疗3个月后的临床疗效和不良反应发生情况。结果治疗3个月后,两组的FBG、2 h PBG、HbA1c、TNF-α、IL-6水平均降低,且试验组明显低于参照组(P<0.05)。治疗3个月后,试验组的临床治疗总有效率明显高于参照组,不良反应总发生率明显低于参照组(P<0.05)。结论舒巴坦联合胰岛素泵治疗糖尿病并发呼吸道感染的有效性显著,可改善患者的血糖、TNF-α、IL-6水平,降低不良反应发生率,值得临床推广应用。
