Objective Evidence from prospective studies on the consumption of tea and risk of gout is conflicting and limited.We aimed to investigate the potential causal effects of tea intake on gout using Mendelian randomizatio...Objective Evidence from prospective studies on the consumption of tea and risk of gout is conflicting and limited.We aimed to investigate the potential causal effects of tea intake on gout using Mendelian randomization(MR).Methods Genome-wide association studies in UK Biobank included 349376 individuals and successfully discovered single-nucleotide polymorphisms linked to consumption of one cup of tea per day.Summary statistics from the Chronic Kidney Disease Genetics consortium included 13179 cases and 750634 controls for gout.Two-sample MR analyses were used to evaluate the relationship between tea consumption and gout risk.The inverse-variance weighted(IVW)method was used for primary analysis,and sensitivity analyses were also conducted to validate the potential causal effect.Results In this study,the genetically predicted increase in tea consumption per cup was associated with a lower risk of gout in the IVW method(OR:0.90;95%CI:0.82–0.98).Similar results were found in weighted median methods(OR:0.88;95%CI:0.78–1.00),while no significant associations were found in MR-Egger(OR:0.89;95%CI:0.71–1.11),weighted mode(OR:0.80;95%CI:0.65–0.99),and simple mode(OR:1.01;95%CI:0.75–1.36).In addition,no evidence of pleiotropy was detected by MR-Egger regression(P=0.95)or MR-PRESSO analysis(P=0.07).Conclusion This study provides evidence for the daily consumption of an extra cup of tea to reduce the risk of gout.展开更多
Using deep convolutional neural networks as primary learners and a deep neural network as meta-learner, source ranging is solved as a regression problem with the ensemble learning method. Simulated acoustic data from ...Using deep convolutional neural networks as primary learners and a deep neural network as meta-learner, source ranging is solved as a regression problem with the ensemble learning method. Simulated acoustic data from the acoustic propagation model are used as the training data. Real data from an experiment in the South China Sea are used as the test data to demonstrate the performance. The results indicate that in the direct zone of deep water, signals received by a very deep receiver can be used to estimate the range of underwater sound source.Within 30 km, the mean absolute error of the range predictions is 1.0 km and the mean absolute percentage error is 7.9%.展开更多
基金supported by grants from the Natural Science Foundation of China(No.82102199)the General Program of Shanghai Municipal Commission of Health and Family Planning(No.202040479).
文摘Objective Evidence from prospective studies on the consumption of tea and risk of gout is conflicting and limited.We aimed to investigate the potential causal effects of tea intake on gout using Mendelian randomization(MR).Methods Genome-wide association studies in UK Biobank included 349376 individuals and successfully discovered single-nucleotide polymorphisms linked to consumption of one cup of tea per day.Summary statistics from the Chronic Kidney Disease Genetics consortium included 13179 cases and 750634 controls for gout.Two-sample MR analyses were used to evaluate the relationship between tea consumption and gout risk.The inverse-variance weighted(IVW)method was used for primary analysis,and sensitivity analyses were also conducted to validate the potential causal effect.Results In this study,the genetically predicted increase in tea consumption per cup was associated with a lower risk of gout in the IVW method(OR:0.90;95%CI:0.82–0.98).Similar results were found in weighted median methods(OR:0.88;95%CI:0.78–1.00),while no significant associations were found in MR-Egger(OR:0.89;95%CI:0.71–1.11),weighted mode(OR:0.80;95%CI:0.65–0.99),and simple mode(OR:1.01;95%CI:0.75–1.36).In addition,no evidence of pleiotropy was detected by MR-Egger regression(P=0.95)or MR-PRESSO analysis(P=0.07).Conclusion This study provides evidence for the daily consumption of an extra cup of tea to reduce the risk of gout.
基金the National Natural Science Foundation of China under Grant Nos 11434012 and 11874061
文摘Using deep convolutional neural networks as primary learners and a deep neural network as meta-learner, source ranging is solved as a regression problem with the ensemble learning method. Simulated acoustic data from the acoustic propagation model are used as the training data. Real data from an experiment in the South China Sea are used as the test data to demonstrate the performance. The results indicate that in the direct zone of deep water, signals received by a very deep receiver can be used to estimate the range of underwater sound source.Within 30 km, the mean absolute error of the range predictions is 1.0 km and the mean absolute percentage error is 7.9%.
