Menstrual cycle characteristics constitute one of the significant female fertility indicators.Previous epidemiological studies have shown that exposure to environmental chemicals could affect menstrual cycle character...Menstrual cycle characteristics constitute one of the significant female fertility indicators.Previous epidemiological studies have shown that exposure to environmental chemicals could affect menstrual cycle characteristics,but the knowledge remains limited overall.Per-and polyfluoroalkyl substances(PFAS)have been identified as potential reproductive toxicants,while previous studies mainly focused on several legacy PFAS chemicals but generally failed to explore the outcomes from exposure to a complex mixture of both legacy and emerging PFAS.Besides,the modification effect of physical activity is rarely considered.In the present study,we explored the associations of exposure to a suite of legacy and emerging PFAS and menstrual cycle regularity as well as the potential modification by physical activity based on a pre-conception cohort in Shanghai(China)with the participation of 1001 reproductive-aged women.A total of 20 PFAS chemicals with detection frequency>80%,which were derived from the PFAS exposure profile of the same population in our previous study,were included in the confounder-adjusted logistic regression and Bayesian kernel machine regression(BKMR)analysis.In individual PFAS analysis,after adjustment of the covariates,∑2m-PFOS(the sum of all perfluoro-dimethylhexane sulfonates)was significantly associated with menstrual cycle irregularity with an odds ratio(OR)of 1.35(95%confidence interval,CI:1.09,1.67)as well as long cycles(OR=1.37;95%CI:1.08,1.70).In addition,a significant positive association was also found between perfluoro-n-nonanoic acid(PFNA)and long cycles(OR=1.40;95%CI:1.06,1.86).No significant associations were found between the PFAS mixture and the menstrual cycle characteristics as revealed by BKMR analysis,while the significant association between ∑2m-PFOS and menstrual cycle irregularity was also observed in the mixture exposure model.Subgroup analysis stratified by physical activity level showed that the associations between ∑2m-PFOS and menstrual cycle irregularity as well as long cycles were more pronounced in the inactive physical activity subgroup.This study suggested that branched PFOS(i.e.,∑2m-PFOS)might act as the predominant risk factor for menstrual cycle irregularity,and physical activity could influence the risks.展开更多
Let G be a graph of order n with minimum degree δ(G)≥n/2+1. Faudree and Li(2012) conjectured that for any pair of vertices x and y in G and any integer 2≤k≤n/2, there exists a Hamiltonian cycle C such that the dis...Let G be a graph of order n with minimum degree δ(G)≥n/2+1. Faudree and Li(2012) conjectured that for any pair of vertices x and y in G and any integer 2≤k≤n/2, there exists a Hamiltonian cycle C such that the distance between x and y on C is k. In this paper, we prove that this conjecture is true for graphs of sufficiently large order. The main tools of our proof are the regularity lemma of Szemer′edi and the blow-up lemma of Koml′os et al.(1997).展开更多
This paper proposes a novel fault diagnosis method by fusing the information from multi-sensor signals to improve the reliability of the conventional vibration-based wind turbine drivetrain gearbox fault diagnosis met...This paper proposes a novel fault diagnosis method by fusing the information from multi-sensor signals to improve the reliability of the conventional vibration-based wind turbine drivetrain gearbox fault diagnosis methods.The method fully extracts fault features for variable speed,insufficient samples,and strong noise scenarios that may occur in the actual operation of a wind turbine planetary gearbox.First,multiple sensor signals are added to the diagnostic model,and multiple stacked denoising auto-encoders are designed and improved to extract the fault information.Then,a cycle reservoir with regular jumps is introduced to fuse multidimensional fault information and output diagnostic results in response to the insufficient ability to process fused information by the conventional Softmax classifier.In addition,the competitive swarm optimizer algorithm is introduced to address the challenge of obtaining the optimal combination of parameters in the network.Finally,the validation results show that the proposed method can increase fault diagnostic accuracy and improve robustness.展开更多
基金supported by the National Natural Science Foundation of China(No.42277417 and 82130097)the Major Talent Program of Guangdong Provincial(No.2021QN02Y944)+2 种基金The Shanghai Birth Cohort was financially supported by the National Natural Science Foundation of China(Nos.41991314 and 81530086)the Shanghai Municipal Health Commission(Nos.GWIII-26,GWIV-26,and 2020CXJQ01)the Shanghai Jiao Tong University School of Medicine,Xinhua Hospital and the National Human Genetic Resources Sharing Service Platform(No.2005DKA21300).
文摘Menstrual cycle characteristics constitute one of the significant female fertility indicators.Previous epidemiological studies have shown that exposure to environmental chemicals could affect menstrual cycle characteristics,but the knowledge remains limited overall.Per-and polyfluoroalkyl substances(PFAS)have been identified as potential reproductive toxicants,while previous studies mainly focused on several legacy PFAS chemicals but generally failed to explore the outcomes from exposure to a complex mixture of both legacy and emerging PFAS.Besides,the modification effect of physical activity is rarely considered.In the present study,we explored the associations of exposure to a suite of legacy and emerging PFAS and menstrual cycle regularity as well as the potential modification by physical activity based on a pre-conception cohort in Shanghai(China)with the participation of 1001 reproductive-aged women.A total of 20 PFAS chemicals with detection frequency>80%,which were derived from the PFAS exposure profile of the same population in our previous study,were included in the confounder-adjusted logistic regression and Bayesian kernel machine regression(BKMR)analysis.In individual PFAS analysis,after adjustment of the covariates,∑2m-PFOS(the sum of all perfluoro-dimethylhexane sulfonates)was significantly associated with menstrual cycle irregularity with an odds ratio(OR)of 1.35(95%confidence interval,CI:1.09,1.67)as well as long cycles(OR=1.37;95%CI:1.08,1.70).In addition,a significant positive association was also found between perfluoro-n-nonanoic acid(PFNA)and long cycles(OR=1.40;95%CI:1.06,1.86).No significant associations were found between the PFAS mixture and the menstrual cycle characteristics as revealed by BKMR analysis,while the significant association between ∑2m-PFOS and menstrual cycle irregularity was also observed in the mixture exposure model.Subgroup analysis stratified by physical activity level showed that the associations between ∑2m-PFOS and menstrual cycle irregularity as well as long cycles were more pronounced in the inactive physical activity subgroup.This study suggested that branched PFOS(i.e.,∑2m-PFOS)might act as the predominant risk factor for menstrual cycle irregularity,and physical activity could influence the risks.
基金supported by National Natural Science Foundation of China (Grant Nos. 11601093 and 11671296)
文摘Let G be a graph of order n with minimum degree δ(G)≥n/2+1. Faudree and Li(2012) conjectured that for any pair of vertices x and y in G and any integer 2≤k≤n/2, there exists a Hamiltonian cycle C such that the distance between x and y on C is k. In this paper, we prove that this conjecture is true for graphs of sufficiently large order. The main tools of our proof are the regularity lemma of Szemer′edi and the blow-up lemma of Koml′os et al.(1997).
基金supported by the Shanghai Rising-Star Program(No.21QC1400200)the Natural Science Foundation of Shanghai(No.21ZR1425400)the National Natural Science Foundation of China(No.52377111).
文摘This paper proposes a novel fault diagnosis method by fusing the information from multi-sensor signals to improve the reliability of the conventional vibration-based wind turbine drivetrain gearbox fault diagnosis methods.The method fully extracts fault features for variable speed,insufficient samples,and strong noise scenarios that may occur in the actual operation of a wind turbine planetary gearbox.First,multiple sensor signals are added to the diagnostic model,and multiple stacked denoising auto-encoders are designed and improved to extract the fault information.Then,a cycle reservoir with regular jumps is introduced to fuse multidimensional fault information and output diagnostic results in response to the insufficient ability to process fused information by the conventional Softmax classifier.In addition,the competitive swarm optimizer algorithm is introduced to address the challenge of obtaining the optimal combination of parameters in the network.Finally,the validation results show that the proposed method can increase fault diagnostic accuracy and improve robustness.
