Understanding the nonlinear relationship between hydrological response and key factors and the cause behind this relationship is vital for water resource management and earth system model.In this study,we undertook se...Understanding the nonlinear relationship between hydrological response and key factors and the cause behind this relationship is vital for water resource management and earth system model.In this study,we undertook several steps to explore the relationship.Initially,we partitioned runoff response change(RRC)into multiple components associated with climate and catchment properties,and examined the spatial patterns and smoothness indicated by the Moran's Index of RRC across the contiguous United States(CONUS).Subsequently,we employed a machine learning model to predict RRC using catchment attribute predictors encompassing climate,topography,hydrology,soil,land use/cover,and geology.Additionally,we identified the primary factors influencing RRC and quantified how these key factors control RRC by employing the accumulated local effect,which allows for the representation of not only dominant but also secondary effects.Finally,we explored the relationship between ecoregion patterns,climate gradients,and the distribution of RRC across CONUS.Our findings indicate that:(1)RRC demonstrating significant connections between catchments tends to be well predicted by catchment attributes in space;(2)climate,hydrology,and topography emerge as the top three key attributes nonlinearly influencing the RRC patterns,with their second-order effects determining the heterogeneous patterns of RRC;and(3)local Moran's I signifies a collaborative relationship between the patterns of RRC and their spatial smoothness,climate space,and ecoregions.展开更多
Studies on large-sample gut microbial sequencing data indicate that gut microbiota can be divided into multiple community types;different community types may influence the community function and ecosystem service.Howe...Studies on large-sample gut microbial sequencing data indicate that gut microbiota can be divided into multiple community types;different community types may influence the community function and ecosystem service.However,the knowledge on the classification,diversity,interaction,and assembling of microbial community types in the gut of wild animals is still insufficient.Here,we used pika gut microbiota data as an example to study the microbial community types in large-sample sequencing dataset.Cecal microbial communities from 118 wild plateau pika(Ochotona curzoniae)individuals at 5 elevational regions on the Qinghai–Tibet Plateau were analyzed.Our results show that pika gut microbiota can be separated into 2 community types(Cluster I and Cluster II).Cluster I was mainly distributed on the high-elevation regions with more than 3694 m and was most dominated by Firmicutes.Cluster II was from the low-elevation areas(lower than 3580 m),and was predominated by Bacteroidetes.Cluster I had a higher community alpha-diversity and predicted functional diversity than Cluster II,and the betadiversity and predicted functional profiles of these 2 clusters were significantly different.Network analysis revealed that there were more complex interactions between Cluster I,which had enhanced influence on the co-occurrences of other microbes in the bacterial community when compared to Cluster II.Phylogenetic analysis found that the environmental filtering in the Cluster I was stronger than Cluster II.The assemblages of pika gut bacterial communities were determined mainly by deterministic processes,while the relative importance of deterministic processes accounted for more percentages in the Cluster I than Cluster II.Our results demonstrated that 2 gut microbial community types in pikas had distinct diversity patterns and ecological functions.Current methods are also helpful for identifying gut community types and the related mechanisms behind gut microbiota types in large-sample sequencing data of wild animals.展开更多
Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of the...Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still lacking.This paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost function.By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is derived.Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.展开更多
Epidemiologic studies use outcome-dependent sampling （ODS） schemes where, in addition to a simple random sample, there are also a number of supplement samples that are collected based on outcome variable. ODS scheme...Epidemiologic studies use outcome-dependent sampling （ODS） schemes where, in addition to a simple random sample, there are also a number of supplement samples that are collected based on outcome variable. ODS scheme is a cost-effective way to improve study efficiency. We develop a maximum semiparametric empirical likelihood estimation （MSELE） for data from a two-stage ODS scheme under the assumption that given covariate, the outcome follows a general linear model. The information of both validation samples and nonvalidation samples are used. What is more, we prove the asymptotic properties of the proposed MSELE.展开更多
基金National Natural Science Foundation of China,No.U2243203,No.51979069Natural Science Foundation of Jiangsu Province,China,No.BK20211202Research Council of Norway,No.FRINATEK Project 274310。
文摘Understanding the nonlinear relationship between hydrological response and key factors and the cause behind this relationship is vital for water resource management and earth system model.In this study,we undertook several steps to explore the relationship.Initially,we partitioned runoff response change(RRC)into multiple components associated with climate and catchment properties,and examined the spatial patterns and smoothness indicated by the Moran's Index of RRC across the contiguous United States(CONUS).Subsequently,we employed a machine learning model to predict RRC using catchment attribute predictors encompassing climate,topography,hydrology,soil,land use/cover,and geology.Additionally,we identified the primary factors influencing RRC and quantified how these key factors control RRC by employing the accumulated local effect,which allows for the representation of not only dominant but also secondary effects.Finally,we explored the relationship between ecoregion patterns,climate gradients,and the distribution of RRC across CONUS.Our findings indicate that:(1)RRC demonstrating significant connections between catchments tends to be well predicted by catchment attributes in space;(2)climate,hydrology,and topography emerge as the top three key attributes nonlinearly influencing the RRC patterns,with their second-order effects determining the heterogeneous patterns of RRC;and(3)local Moran's I signifies a collaborative relationship between the patterns of RRC and their spatial smoothness,climate space,and ecoregions.
基金The funding of this work was supported by the National Natural Science Foundation of China(42007026 and 32070460).
文摘Studies on large-sample gut microbial sequencing data indicate that gut microbiota can be divided into multiple community types;different community types may influence the community function and ecosystem service.However,the knowledge on the classification,diversity,interaction,and assembling of microbial community types in the gut of wild animals is still insufficient.Here,we used pika gut microbiota data as an example to study the microbial community types in large-sample sequencing dataset.Cecal microbial communities from 118 wild plateau pika(Ochotona curzoniae)individuals at 5 elevational regions on the Qinghai–Tibet Plateau were analyzed.Our results show that pika gut microbiota can be separated into 2 community types(Cluster I and Cluster II).Cluster I was mainly distributed on the high-elevation regions with more than 3694 m and was most dominated by Firmicutes.Cluster II was from the low-elevation areas(lower than 3580 m),and was predominated by Bacteroidetes.Cluster I had a higher community alpha-diversity and predicted functional diversity than Cluster II,and the betadiversity and predicted functional profiles of these 2 clusters were significantly different.Network analysis revealed that there were more complex interactions between Cluster I,which had enhanced influence on the co-occurrences of other microbes in the bacterial community when compared to Cluster II.Phylogenetic analysis found that the environmental filtering in the Cluster I was stronger than Cluster II.The assemblages of pika gut bacterial communities were determined mainly by deterministic processes,while the relative importance of deterministic processes accounted for more percentages in the Cluster I than Cluster II.Our results demonstrated that 2 gut microbial community types in pikas had distinct diversity patterns and ecological functions.Current methods are also helpful for identifying gut community types and the related mechanisms behind gut microbiota types in large-sample sequencing data of wild animals.
基金supported in part by the National Natural Science Foundation of China(62201447)the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China(2022JQ-640)。
文摘Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still lacking.This paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost function.By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is derived.Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.
基金Jie-li DING is supported by the National Natural Science Foundation of China(No.11101314),Yan-yan LIU s supported by the National Natural Science Foundation of China(No.11171263,No.11371299)
文摘Epidemiologic studies use outcome-dependent sampling （ODS） schemes where, in addition to a simple random sample, there are also a number of supplement samples that are collected based on outcome variable. ODS scheme is a cost-effective way to improve study efficiency. We develop a maximum semiparametric empirical likelihood estimation （MSELE） for data from a two-stage ODS scheme under the assumption that given covariate, the outcome follows a general linear model. The information of both validation samples and nonvalidation samples are used. What is more, we prove the asymptotic properties of the proposed MSELE.
