芝麻是八大类食物过敏原之一,快速准确识别芝麻过敏原对预防其过敏有重要意义。核酸适配体可以高效识别靶标过敏原,在过敏原检测中有良好的应用前景。为了获得芝麻主要过敏原Ses i 2的特异性核酸适体,本研究以Ses i 2为靶标,通过磁珠筛...芝麻是八大类食物过敏原之一,快速准确识别芝麻过敏原对预防其过敏有重要意义。核酸适配体可以高效识别靶标过敏原,在过敏原检测中有良好的应用前景。为了获得芝麻主要过敏原Ses i 2的特异性核酸适体,本研究以Ses i 2为靶标,通过磁珠筛选法(磁珠-SELEX)开展10轮筛选,经由高通量测序获得6条候补序列(S1~S6),并进行家族性、同源性分析及二级结构预测。结果表明,6条候选核酸适体的重复率可达46.38%,其自由能在-9.02到-2.47 kcal·moL^(-1)之间,根据自由能能量稳定原则,S1和S5吉布斯自由能最低最稳定,分别为-6.70和-9.02 kcal·moL^(-1)。利用ELISA试验进行亲和力测试,结果表明核酸适体S1和S2的亲和能力较强,S1:KD=67.02 nmol·L^(-1),R2=0.925 8,S2:KD=97.65 nmol·L^(-1),R2=0.795 1。核酸适体S1与过敏原Ses i 2的结合力和其他过敏原蛋白相比有显著差异,可视为具有特异性。本研究最终获得一条兼具良好亲和力和特异性的核酸适体S1,为芝麻过敏原快速检测提供了技术支撑。展开更多
This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for ar...This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.展开更多
文摘芝麻是八大类食物过敏原之一,快速准确识别芝麻过敏原对预防其过敏有重要意义。核酸适配体可以高效识别靶标过敏原,在过敏原检测中有良好的应用前景。为了获得芝麻主要过敏原Ses i 2的特异性核酸适体,本研究以Ses i 2为靶标,通过磁珠筛选法(磁珠-SELEX)开展10轮筛选,经由高通量测序获得6条候补序列(S1~S6),并进行家族性、同源性分析及二级结构预测。结果表明,6条候选核酸适体的重复率可达46.38%,其自由能在-9.02到-2.47 kcal·moL^(-1)之间,根据自由能能量稳定原则,S1和S5吉布斯自由能最低最稳定,分别为-6.70和-9.02 kcal·moL^(-1)。利用ELISA试验进行亲和力测试,结果表明核酸适体S1和S2的亲和能力较强,S1:KD=67.02 nmol·L^(-1),R2=0.925 8,S2:KD=97.65 nmol·L^(-1),R2=0.795 1。核酸适体S1与过敏原Ses i 2的结合力和其他过敏原蛋白相比有显著差异,可视为具有特异性。本研究最终获得一条兼具良好亲和力和特异性的核酸适体S1,为芝麻过敏原快速检测提供了技术支撑。
文摘This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.
