Hypertension(HT)is a major risk factor for cardiovascular diseases.Krüppel-like factors(KLFs)are important transcription factors in eukaryotes.Studies have reported that KLF4 and KLF5 are correlated with several ...Hypertension(HT)is a major risk factor for cardiovascular diseases.Krüppel-like factors(KLFs)are important transcription factors in eukaryotes.Studies have reported that KLF4 and KLF5 are correlated with several cardiovascular diseases,but population-based studies on associations between HT and KLF4 or KLF5 have rarely been reported.Therefore,the current study investigated the associations of genetic variants and m RNA expression levels of KLF4 and KLF5 with HT,as well as the effects of antihypertensive drugs on the expression levels of these genes.The associations of one single-nucleotide polymorphism(SNP)in KLF4 and three SNPs in KLF5with HT were analyzed using a combination of case-control and cohort studies.The study populations were selected from a community-based cohort in four regions of Jiangsu province.The risks of HT were estimated through logistic and Cox regression analyses.In addition,m RNA expression levels of KLF4 and KLF5 were detected in 246 controls and 385 HT cases selected from the aforementioned cohort.Among the HT cases,263were not taking antihypertensive drugs[AHD(-)]and 122 were taking antihypertensive drugs[AHD(+)].In the case-control study,SNP rs9573096(C>T)in KLF5 was significantly associated with an increased risk of HT in the additive model(adjusted odds ratio[OR],1.106;95%confidence interval[CI],1.009 to 1.212).In the cohort study of the normotensive population,rs9573096 in KLF5 was also significantly associated with an increased risk of HT in the additive model(adjusted hazards ratio[HR],1.199;95%CI,1.070 to 1.344).KLF4 and KLF5m RNA expression levels were significantly higher in the AHD(-)group than in the control group(P<0.05),but lower in the AHD(+)group than in the AHD(-)group(P<0.05).The current study demonstrated the associations of KLF4 and KLF5 genetic variants with hypertension,as well as the association of the indicative variations in m RNA expression levels of KLF4 and KLF5 with the risk of hypertension and antihypertensive treatment.展开更多
Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy....Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy.Although deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale uncertainty.This article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate downscaling.Such a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the upscaling.This dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping process.Experimental findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and quantitatively.Specifically,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation coefficient.In summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.81872686 and 82173611)the National Key Research and Development Program of China(Grant No.2018YFC2000703)the Priority Academic Program for the Development of Jiangsu Higher Education Institutions(Public Health and Preventive Medicine)。
文摘Hypertension(HT)is a major risk factor for cardiovascular diseases.Krüppel-like factors(KLFs)are important transcription factors in eukaryotes.Studies have reported that KLF4 and KLF5 are correlated with several cardiovascular diseases,but population-based studies on associations between HT and KLF4 or KLF5 have rarely been reported.Therefore,the current study investigated the associations of genetic variants and m RNA expression levels of KLF4 and KLF5 with HT,as well as the effects of antihypertensive drugs on the expression levels of these genes.The associations of one single-nucleotide polymorphism(SNP)in KLF4 and three SNPs in KLF5with HT were analyzed using a combination of case-control and cohort studies.The study populations were selected from a community-based cohort in four regions of Jiangsu province.The risks of HT were estimated through logistic and Cox regression analyses.In addition,m RNA expression levels of KLF4 and KLF5 were detected in 246 controls and 385 HT cases selected from the aforementioned cohort.Among the HT cases,263were not taking antihypertensive drugs[AHD(-)]and 122 were taking antihypertensive drugs[AHD(+)].In the case-control study,SNP rs9573096(C>T)in KLF5 was significantly associated with an increased risk of HT in the additive model(adjusted odds ratio[OR],1.106;95%confidence interval[CI],1.009 to 1.212).In the cohort study of the normotensive population,rs9573096 in KLF5 was also significantly associated with an increased risk of HT in the additive model(adjusted hazards ratio[HR],1.199;95%CI,1.070 to 1.344).KLF4 and KLF5m RNA expression levels were significantly higher in the AHD(-)group than in the control group(P<0.05),but lower in the AHD(+)group than in the AHD(-)group(P<0.05).The current study demonstrated the associations of KLF4 and KLF5 genetic variants with hypertension,as well as the association of the indicative variations in m RNA expression levels of KLF4 and KLF5 with the risk of hypertension and antihypertensive treatment.
基金supported by the following funding bodies:the National Key Research and Development Program of China(Grant No.2020YFA0608000)National Science Foundation of China(Grant Nos.42075142,42375148,42125503+2 种基金42130608)FY-APP-2022.0609,Sichuan Province Key Tech nology Research and Development project(Grant Nos.2024ZHCG0168,2024ZHCG0176,2023YFG0305,2023YFG-0124,and 23ZDYF0091)the CUIT Science and Technology Innovation Capacity Enhancement Program project(Grant No.KYQN202305)。
文摘Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy.Although deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale uncertainty.This article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate downscaling.Such a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the upscaling.This dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping process.Experimental findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and quantitatively.Specifically,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation coefficient.In summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.
