为了建立一种可以快捷、灵敏、安全地检测D型流感病毒(Influenza D virus,IDV)的实时荧光定量PCR(quantitative real time PCR,RT-qPCR)方法,试验首先将IDV NP基因作为检测目标,根据其序列的保守区设计了一对RT-qPCR引物,然后采用单一...为了建立一种可以快捷、灵敏、安全地检测D型流感病毒(Influenza D virus,IDV)的实时荧光定量PCR(quantitative real time PCR,RT-qPCR)方法,试验首先将IDV NP基因作为检测目标,根据其序列的保守区设计了一对RT-qPCR引物,然后采用单一变量法对RT-qPCR反应条件中的引物浓度和退火温度进行优化,建立了一种可检测IDV的RT-qPCR方法,并对该方法的灵敏度、重复性和特异性进行检测,最后应用该方法进行临床样本检测。结果表明:所建立的RT-qPCR方法的最佳引物浓度和退火温度分别为400 nmol/L和61℃,标准曲线为y=-3.572x+42.02(R^(2)=0.9993);该方法能检测到的最低拷贝数为8.30×10^(1)copies,灵敏度是常规PCR的100倍左右;组内重复和组间重复变异系数(CV)值均小于3%;同时检测牛呼吸道合胞体病毒(BRSV)、牛传染性鼻气管炎病毒(IBRV)、牛病毒性腹泻病毒(BVDV)、牛冠状病毒(BCoV)、牛副流感病毒3型(BPIV-3)、牛疱疹病毒4型(BHV-4)和牛流行热病毒(BEFV)的结果均为阴性;应用该方法对收集到的244份有呼吸道症状的牛鼻拭子样品进行检测,阳性率为1.23%。说明成功建立了IDV RT-qPCR检测方法,该方法具有灵敏度高、重复性和特异性好的特点,为IDV的防控提供了可靠的技术支撑。展开更多
Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of soil.Unfortunately,EET suffers from a lack of predictive models.Herein,an intricately crafted machine learning model has bee...Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of soil.Unfortunately,EET suffers from a lack of predictive models.Herein,an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density(j_(max))and Coulombic charge(C_(out))as dependent output variables.An autoencoder ensemble stacking(AES)model was developed to address the aforementioned issue by integrating support vector machine,multilayer perceptron,extreme gradient boosting,and light gradient boosting machine algorithms as the stacking algorithms.With 10-fold crossvalidation,the AES model exhibited notable improvements in predicting j_(max)and C_(out),with average test R^(2)values of 0.83 and 0.84,respectively,surpassing those of single machine learning(ML)models and the basic ensemble model.By utilizing partial correlation plots(PDPs),Shapley Additive explanations(SHAP)values,and SHAP decision plots,we quantitatively explained the impact and contribution of the input molecules on the AES model’s predictions of j_(max)and C_(out).In the context of the SHAP method for the AES model,total carbon(TC)was identified as the most correlated descriptor for j_(max),while total organic carbon(TOC)stood out as the most relevant descriptor for C_(out).In the prediction tasks of j_(max)and C_(out)within the AES model,employing a multitask ML approach allowed the model to benefit from the shared information of input variables,thereby enhancing its overall generalizability.This study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability.展开更多
文摘为了建立一种可以快捷、灵敏、安全地检测D型流感病毒(Influenza D virus,IDV)的实时荧光定量PCR(quantitative real time PCR,RT-qPCR)方法,试验首先将IDV NP基因作为检测目标,根据其序列的保守区设计了一对RT-qPCR引物,然后采用单一变量法对RT-qPCR反应条件中的引物浓度和退火温度进行优化,建立了一种可检测IDV的RT-qPCR方法,并对该方法的灵敏度、重复性和特异性进行检测,最后应用该方法进行临床样本检测。结果表明:所建立的RT-qPCR方法的最佳引物浓度和退火温度分别为400 nmol/L和61℃,标准曲线为y=-3.572x+42.02(R^(2)=0.9993);该方法能检测到的最低拷贝数为8.30×10^(1)copies,灵敏度是常规PCR的100倍左右;组内重复和组间重复变异系数(CV)值均小于3%;同时检测牛呼吸道合胞体病毒(BRSV)、牛传染性鼻气管炎病毒(IBRV)、牛病毒性腹泻病毒(BVDV)、牛冠状病毒(BCoV)、牛副流感病毒3型(BPIV-3)、牛疱疹病毒4型(BHV-4)和牛流行热病毒(BEFV)的结果均为阴性;应用该方法对收集到的244份有呼吸道症状的牛鼻拭子样品进行检测,阳性率为1.23%。说明成功建立了IDV RT-qPCR检测方法,该方法具有灵敏度高、重复性和特异性好的特点,为IDV的防控提供了可靠的技术支撑。
基金supported by Guangdong Basic and Applied Basic Research Foundation(Grant No.2023B1515040022)the National Natural Science Foundation of China(Grant Nos.42177270 and 42207340).
文摘Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of soil.Unfortunately,EET suffers from a lack of predictive models.Herein,an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density(j_(max))and Coulombic charge(C_(out))as dependent output variables.An autoencoder ensemble stacking(AES)model was developed to address the aforementioned issue by integrating support vector machine,multilayer perceptron,extreme gradient boosting,and light gradient boosting machine algorithms as the stacking algorithms.With 10-fold crossvalidation,the AES model exhibited notable improvements in predicting j_(max)and C_(out),with average test R^(2)values of 0.83 and 0.84,respectively,surpassing those of single machine learning(ML)models and the basic ensemble model.By utilizing partial correlation plots(PDPs),Shapley Additive explanations(SHAP)values,and SHAP decision plots,we quantitatively explained the impact and contribution of the input molecules on the AES model’s predictions of j_(max)and C_(out).In the context of the SHAP method for the AES model,total carbon(TC)was identified as the most correlated descriptor for j_(max),while total organic carbon(TOC)stood out as the most relevant descriptor for C_(out).In the prediction tasks of j_(max)and C_(out)within the AES model,employing a multitask ML approach allowed the model to benefit from the shared information of input variables,thereby enhancing its overall generalizability.This study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability.
