Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature ...Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential data.To address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric recognition.The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency domains.Meanwhile,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information.In addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics.Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.展开更多
Background:Microbiome-gut-brain axis may be involved in the progression of age-related cognitive impairment and relevant brain structure changes,but evidence from large human cohorts is lacking.This study was aimed to...Background:Microbiome-gut-brain axis may be involved in the progression of age-related cognitive impairment and relevant brain structure changes,but evidence from large human cohorts is lacking.This study was aimed to investigate the associations of gut microbiome with cognitive impairment and brain structure based on multi-omics from three independent populations.Methods:We included 1430 participants from the Guangzhou Nutrition and Health Study(GNHS)with both gut microbiome and cognitive assessment data available as a discovery cohort,of whom 272 individuals provided fecal samples twice before cognitive assessment.We selected 208 individuals with baseline microbiome data for brain magnetic resonance imaging during the follow-up visit.Fecal 16S rRNA and shotgun metagenomic sequencing,tar-geted serum metabolomics,and cytokine measurements were performed in the GNHS.The validation analyses were conducted in an Alzheimer’s disease case-control study(replication study 1,n=90)and another community-based cohort(replication study 2,n=1300)with cross-sectional dataset.Results:We found protective associations of specific gut microbial genera(Odoribacter,Butyricimonas,and Bac-teroides)with cognitive impairment in both the discovery cohort and the replication study 1.Result of Bacteroides was further validated in the replication study 2.Odoribacter was positively associated with hippocampal volume(β,0.16;95%CI 0.06-0.26,P=0.002),which might be mediated by acetic acids.Increased intra-individual alterations in gut microbial composition were found in participants with cognitive impairment.We also identified several serum metabolites and inflammation-associated metagenomic species and pathways linked to impaired cognition.Conclusions:Our findings reveal that specific gut microbial features are closely associated with cognitive impair-ment and decreased hippocampal volume,which may play an important role in dementia development.展开更多
基金supported by National Nature Science Foundation of China(No.62276093)in part by Natural Science Foundation of Shandong Province,China(No.2022MF86).
文摘Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential data.To address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric recognition.The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency domains.Meanwhile,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information.In addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics.Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.
基金the National Natural Science Foundation of China(82073529,81903316,81773416,and 82103826)Zhejiang Ten-thousand Talents Program(2019R52039)+3 种基金Zhejiang Provincial Natural Science Foundation of China(LQ21H260002)CHNS received funding from the National Institutes of Health(NIH)(R01HD30880,R01AG065357,P30DK056350,and R01HD38700)from 1989 to 2019was supported by the National Institutes of Health and National Institute of Diabetes and Digestive and Kidney Diseases(R01DK104371)the Carolina Population Center P2CHD050924,P30AG066615.The funders had no role in collecting data,study design,interpretation of data or the decision to submit the manuscript for publication.
文摘Background:Microbiome-gut-brain axis may be involved in the progression of age-related cognitive impairment and relevant brain structure changes,but evidence from large human cohorts is lacking.This study was aimed to investigate the associations of gut microbiome with cognitive impairment and brain structure based on multi-omics from three independent populations.Methods:We included 1430 participants from the Guangzhou Nutrition and Health Study(GNHS)with both gut microbiome and cognitive assessment data available as a discovery cohort,of whom 272 individuals provided fecal samples twice before cognitive assessment.We selected 208 individuals with baseline microbiome data for brain magnetic resonance imaging during the follow-up visit.Fecal 16S rRNA and shotgun metagenomic sequencing,tar-geted serum metabolomics,and cytokine measurements were performed in the GNHS.The validation analyses were conducted in an Alzheimer’s disease case-control study(replication study 1,n=90)and another community-based cohort(replication study 2,n=1300)with cross-sectional dataset.Results:We found protective associations of specific gut microbial genera(Odoribacter,Butyricimonas,and Bac-teroides)with cognitive impairment in both the discovery cohort and the replication study 1.Result of Bacteroides was further validated in the replication study 2.Odoribacter was positively associated with hippocampal volume(β,0.16;95%CI 0.06-0.26,P=0.002),which might be mediated by acetic acids.Increased intra-individual alterations in gut microbial composition were found in participants with cognitive impairment.We also identified several serum metabolites and inflammation-associated metagenomic species and pathways linked to impaired cognition.Conclusions:Our findings reveal that specific gut microbial features are closely associated with cognitive impair-ment and decreased hippocampal volume,which may play an important role in dementia development.
