Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-...Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.展开更多
We start with a description of the statistical inferential framework and the duality between observed data and the true state of nature that underlies it. We demonstrate here that the usual testing of dueling hypothes...We start with a description of the statistical inferential framework and the duality between observed data and the true state of nature that underlies it. We demonstrate here that the usual testing of dueling hypotheses and the acceptance of one and the rejection of the other is a framework which can often be faulty when such inferences are applied to individual subjects. This follows from noting that the statistical inferential framework is predominantly based on conclusions drawn for aggregates and noting that what is true in the aggregate frequently does not hold for individuals, an ecological fallacy. Such a fallacy is usually seen as problematic when each data record represents aggregate statistics for counties or districts and not data for individuals. Here we demonstrate strong ecological fallacies even when using subject data. Inverted simulations, of trials rightly sized to detect meaningful differences, yielding a statistically significant p-value of 0.000001 （1 in a million） and associated with clinically meaningful differences between a hypothetical new therapy and a standard therapy, had a proportion of instances of subjects with standard therapy effect better than new therapy effects close to 30%. A ―winner take all‖ choice between two hypotheses may not be supported by statistically significant differences based on stochastic data. We also argue the incorrectness across many individuals of other summaries such as correlations, density estimates, standard deviations and predictions based on machine learning models. Despite artifacts we support the use of prospective clinical trials and careful unbiased model building as necessary first steps. In health care, high touch personalized care based on patient level data will remain relevant even as we adopt more high tech data-intensive personalized therapeutic strategies based on aggregates.展开更多
Objective: This study aimed to investigate the association between single-nucleotide polymorphisms (SNPs) ofPCSK1 (proprotein convertase subtilisin/kexin type 1) related to obesity and nonalcoholic fatty liver disease...Objective: This study aimed to investigate the association between single-nucleotide polymorphisms (SNPs) ofPCSK1 (proprotein convertase subtilisin/kexin type 1) related to obesity and nonalcoholic fatty liver disease (NAFLD).Methods: In this case-control observational study, four candidate SNPs (rs6234, rs155971, rs6232, rs3811951) ofPCSK1 were genotyped in 732 NAFLD patients and 823 healthy control participants, all of whom were of ethnic Han Chinese descent. All participants came from Shanghai, China, and joined our study during 2015 to 2016. The frequencies of each allele and genotype, paired linkage disequilibrium, and haplotype were calculated on the SHEsis platform. In addition to SHEsis, five different genetic models (codominant, dominant, recessive, overdominant, and log-additive) were employed to identify the correlation between genotype frequency and NAFLD. This study was approved by the Medical Ethics Committee of Shanghai University of Traditional Chinese Medicine (approved No. 2017LCSY069).Results: In a comparison of NAFLD patients and healthy participants, none of the fourPCSK1 SNPs were significantly correlated with the occurrence of NAFLD (P>0.05), in either genotypic or allelic distribution. The recessive model of rs3811951 appeared to show a correlation (odds ratio=1.077;95% confidence interval=0.924-1.256;P=0.04), but there was no statistical significance after Bonferroni correction (Pcorr>0.0125).Conclusions: Four obesity-relatedPCSK1 SNPs (rs6234, rs155971, rs6232, rs3811951) showed no significant correlation with the development of NAFLD in a Han Chinese population.展开更多
基金National Key Research and Development Program of China,No.2016YFB0502300。
文摘Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.
文摘We start with a description of the statistical inferential framework and the duality between observed data and the true state of nature that underlies it. We demonstrate here that the usual testing of dueling hypotheses and the acceptance of one and the rejection of the other is a framework which can often be faulty when such inferences are applied to individual subjects. This follows from noting that the statistical inferential framework is predominantly based on conclusions drawn for aggregates and noting that what is true in the aggregate frequently does not hold for individuals, an ecological fallacy. Such a fallacy is usually seen as problematic when each data record represents aggregate statistics for counties or districts and not data for individuals. Here we demonstrate strong ecological fallacies even when using subject data. Inverted simulations, of trials rightly sized to detect meaningful differences, yielding a statistically significant p-value of 0.000001 （1 in a million） and associated with clinically meaningful differences between a hypothetical new therapy and a standard therapy, had a proportion of instances of subjects with standard therapy effect better than new therapy effects close to 30%. A ―winner take all‖ choice between two hypotheses may not be supported by statistically significant differences based on stochastic data. We also argue the incorrectness across many individuals of other summaries such as correlations, density estimates, standard deviations and predictions based on machine learning models. Despite artifacts we support the use of prospective clinical trials and careful unbiased model building as necessary first steps. In health care, high touch personalized care based on patient level data will remain relevant even as we adopt more high tech data-intensive personalized therapeutic strategies based on aggregates.
基金Innovation Funding in Shanghai(Nos.20JC1418600 and 18JC1413100)National Nature Science Foundation of China(Nos.82071262 and 81671326)+2 种基金Natural Science Foundation of Shanghai(Nos.20ZR1427200 and 20511101900)Shanghai Municipal Science and Technology Major Project(No.2017SHZDZX01)Shanghai Leading Academic Discipline Project(No.B205).
文摘Objective: This study aimed to investigate the association between single-nucleotide polymorphisms (SNPs) ofPCSK1 (proprotein convertase subtilisin/kexin type 1) related to obesity and nonalcoholic fatty liver disease (NAFLD).Methods: In this case-control observational study, four candidate SNPs (rs6234, rs155971, rs6232, rs3811951) ofPCSK1 were genotyped in 732 NAFLD patients and 823 healthy control participants, all of whom were of ethnic Han Chinese descent. All participants came from Shanghai, China, and joined our study during 2015 to 2016. The frequencies of each allele and genotype, paired linkage disequilibrium, and haplotype were calculated on the SHEsis platform. In addition to SHEsis, five different genetic models (codominant, dominant, recessive, overdominant, and log-additive) were employed to identify the correlation between genotype frequency and NAFLD. This study was approved by the Medical Ethics Committee of Shanghai University of Traditional Chinese Medicine (approved No. 2017LCSY069).Results: In a comparison of NAFLD patients and healthy participants, none of the fourPCSK1 SNPs were significantly correlated with the occurrence of NAFLD (P>0.05), in either genotypic or allelic distribution. The recessive model of rs3811951 appeared to show a correlation (odds ratio=1.077;95% confidence interval=0.924-1.256;P=0.04), but there was no statistical significance after Bonferroni correction (Pcorr>0.0125).Conclusions: Four obesity-relatedPCSK1 SNPs (rs6234, rs155971, rs6232, rs3811951) showed no significant correlation with the development of NAFLD in a Han Chinese population.
