Objective: An interplay of many variant mechanisms is thought to underlie aging or senescence. The Mutation Accumulation Theory proposes the accumulation of mutations in proteins to engender their aging phenotype. Tes...Objective: An interplay of many variant mechanisms is thought to underlie aging or senescence. The Mutation Accumulation Theory proposes the accumulation of mutations in proteins to engender their aging phenotype. Testing whether random mutations lead to the aging phenotype was never done and is deemed infeasible. Bioinformatic algorithms provide an a-priori approach that allows testing;they employ prior knowledge of well-studied proteins to predict the changes brought on by mutations. Here, the Mutation Accumulation Theory of aging is tested using such bioinformatic tools. Methods: This is a simulation study, conducted 2017, September, using algorithms with Web accessibility. Three well-studied proteins implicated in aging were chosen: Collagen, Beta-amyloid Precursor Protein (β-APP) and Low-density-lipoprotein-receptor (LDL-receptor). Random mutations were introduced to their native coding sequences. Then, the mutated sequences were tested using three different prediction algorithms: SPpred for solubility, I-mutant for stability (delta-free energy), SNP and GO for pathogenicity. The new mutated phenotype was then correlated to the aging phenotype of the protein;decrease in solubility for Collagen and β-APP;and accelerated atherosclerosis for LDL-receptor. Results: 15 mutated variants for each protein (45 in total). For collagen and β-APP, the SPpred algorithm did not predict changes in solubility of the naked protein, but the I-mutant and SNP and GO definitely predicted changes that fit the aging phenotype. However, for LDL-receptors, none of the mutated variants when studied could account for the aging phenotype. Conclusion: for Collagen and β-APP, it is shown here that random mutations and their accumulation could explain the aging phenotype of both proteins;backing the Mutation Accumulation Theory for aging.展开更多
传统离线数据分析方法对于处理即时性高和流量大的数据存在缺陷,而在线检测模型可以满足数据流分析的实时性要求。文中提出了一种基于多阈值模板的在线检测方法。该方法结合多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Sm...传统离线数据分析方法对于处理即时性高和流量大的数据存在缺陷,而在线检测模型可以满足数据流分析的实时性要求。文中提出了一种基于多阈值模板的在线检测方法。该方法结合多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法进行在线检测,基于突变点密度更新窗口长度从而提高了突变点检测精度。采用等量分级策略实现对时序数据的自学习、匹配和分类,进而对大规模病变数据进行状态检测和预测。仿真实验和病变数据的实验结果表明,所提方法具有效果高、分类准确等优点,为大规模时序数据进行快速分类研究提供了新方法。展开更多
文摘Objective: An interplay of many variant mechanisms is thought to underlie aging or senescence. The Mutation Accumulation Theory proposes the accumulation of mutations in proteins to engender their aging phenotype. Testing whether random mutations lead to the aging phenotype was never done and is deemed infeasible. Bioinformatic algorithms provide an a-priori approach that allows testing;they employ prior knowledge of well-studied proteins to predict the changes brought on by mutations. Here, the Mutation Accumulation Theory of aging is tested using such bioinformatic tools. Methods: This is a simulation study, conducted 2017, September, using algorithms with Web accessibility. Three well-studied proteins implicated in aging were chosen: Collagen, Beta-amyloid Precursor Protein (β-APP) and Low-density-lipoprotein-receptor (LDL-receptor). Random mutations were introduced to their native coding sequences. Then, the mutated sequences were tested using three different prediction algorithms: SPpred for solubility, I-mutant for stability (delta-free energy), SNP and GO for pathogenicity. The new mutated phenotype was then correlated to the aging phenotype of the protein;decrease in solubility for Collagen and β-APP;and accelerated atherosclerosis for LDL-receptor. Results: 15 mutated variants for each protein (45 in total). For collagen and β-APP, the SPpred algorithm did not predict changes in solubility of the naked protein, but the I-mutant and SNP and GO definitely predicted changes that fit the aging phenotype. However, for LDL-receptors, none of the mutated variants when studied could account for the aging phenotype. Conclusion: for Collagen and β-APP, it is shown here that random mutations and their accumulation could explain the aging phenotype of both proteins;backing the Mutation Accumulation Theory for aging.
文摘传统离线数据分析方法对于处理即时性高和流量大的数据存在缺陷,而在线检测模型可以满足数据流分析的实时性要求。文中提出了一种基于多阈值模板的在线检测方法。该方法结合多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法进行在线检测,基于突变点密度更新窗口长度从而提高了突变点检测精度。采用等量分级策略实现对时序数据的自学习、匹配和分类,进而对大规模病变数据进行状态检测和预测。仿真实验和病变数据的实验结果表明,所提方法具有效果高、分类准确等优点,为大规模时序数据进行快速分类研究提供了新方法。
