DEAR EDITOR,The Myanmar or black snub-nosed monkey(Rhinopithecus strykeri) is a recently discovered and critically endangered colobus primate with an unknown gut microbiota. Here, we characterized and compared the gut...DEAR EDITOR,The Myanmar or black snub-nosed monkey(Rhinopithecus strykeri) is a recently discovered and critically endangered colobus primate with an unknown gut microbiota. Here, we characterized and compared the gut microbiota of R. strykeri with those of two closely related snub-nosed monkey species.展开更多
BACKGROUND:Neuroendocrine dysfunction after traumatic brain injury(TBI)has received increased attention due to its impact on the recovery of neural function.The purpose of this study is to investigate the incidence an...BACKGROUND:Neuroendocrine dysfunction after traumatic brain injury(TBI)has received increased attention due to its impact on the recovery of neural function.The purpose of this study is to investigate the incidence and risk factors of adrenocortical insuffi ciency(AI)after TBI to reveal independent predictors and build a prediction model of AI after TBI.METHODS:Enrolled patients were grouped into the AI and non-AI groups.Fourteen preset impact factors were recorded.Patients were regrouped according to each impact factor as a categorical variable.Univariate and multiple logistic regression analyses were performed to screen the related independent risk factors of AI after TBI and develop the predictive model.RESULTS:A total of 108 patients were recruited,of whom 34(31.5%)patients had AI.Nine factors(age,Glasgow Coma Scale[GCS]score on admission,mean arterial pressure[MAP],urinary volume,serum sodium level,cerebral hernia,frontal lobe contusion,diff use axonal injury[DAI],and skull base fracture)were probably related to AI after TBI.Three factors(urinary volume[X4],serum sodium level[X5],and DAI[X8])were independent variables,based on which a prediction model was developed(logit P=-3.552+2.583X4+2.235X5+2.269X8).CONCLUSIONS:The incidence of AI after TBI is high.Factors such as age,GCS score,MAP,urinary volume,serum sodium level,cerebral hernia,frontal lobe contusion,DAI,and skull base fracture are probably related to AI after TBI.Urinary volume,serum sodium level,and DAI are the independent predictors of AI after TBI.展开更多
Viruses can be transmitted from animals to humans(and vice versa)and across animal species.As such,host-virus interactions and transmission have attracted considerable attention.Non-human primates(NHPs),our closest ev...Viruses can be transmitted from animals to humans(and vice versa)and across animal species.As such,host-virus interactions and transmission have attracted considerable attention.Non-human primates(NHPs),our closest evolutionary relatives,are susceptible to human viruses and certain pathogens are known to circulate between humans and NHPs.Here,we generated global statistics on virus infections in NHPs(VI-NHPs)based on a literature search and public data mining.In total,140 NHP species from 12 families are reported to be infected by 186 DNA and RNA virus species,68.8%of which are also found in humans,indicating high potential for crossing species boundaries.展开更多
Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related disasters.The utilisation of multiscale methods is effec...Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related disasters.The utilisation of multiscale methods is effective for a comprehensive evaluation of drought occurrence and progression,given the complex nature of meteorological drought.Nevertheless,the nonlinear spatiotemporal features of meteorological droughts,influenced by various climatological,physical and environmental factors,pose significant challenges to integrated prediction that considers multiple indicators and time scales.To address these constraints,we introduce an innovative deep learning framework based on the shifted window transformer,designed for executing spatiotemporal prediction of meteorological drought across multiple scales.We formulate four prediction indicators using the standardized precipitation index and the standard precipitation evaporation index as core methods for drought definition using the ERA5 reanalysis dataset.These indicators span time scales of approximately 30 d and one season.Short-term indicators capture more anomalous variations,whereas long-term indicators attain comparatively higher accuracy in predicting future trends.We focus on the East Asian region,notable for its diverse climate conditions and intricate terrains,to validate the model's efficacy in addressing the complexities of nonlinear spatiotemporal prediction.The model's performance is evaluated from diverse spatiotemporal viewpoints,and practical application values are analysed by representative drought events.Experimental results substantiate the effectiveness of our proposed model in providing accurate multiscale predictions and capturing the spatiotemporal evolution characteristics of drought.Each of the four drought indicators accurately delineates specific facets of the meteorological drought trend.Moreover,three representative drought events,namely flash drought,sustained drought and severe drought,underscore the significance of selecting appropriate prediction indicators to effectively denote different types of drought events.This study provides methodological and technological support for using a deep learning approach in meteorological drought prediction.Such findings also demonstrate prediction issues related to natural hazards in regions with scarce observational data,complex topography and diverse microclimate systems.展开更多
Crowdtesting has emerged as an attractive and economical testing paradigm that features testers from different countries,with various backgrounds and working conditions.Recent developments in crowdsourcing testing sug...Crowdtesting has emerged as an attractive and economical testing paradigm that features testers from different countries,with various backgrounds and working conditions.Recent developments in crowdsourcing testing suggest that it is feasible to manage test populations and processes,but they are often outside the scope of standard testing theory.This paper explores how to allocate service-testing tasks to proper testers in an ever-changing crowdsourcing environment.We formalize it as an optimization problem with the objective to ensure the testing quality of the crowds,while considering influencing factors such as knowledge capability,the rewards,the network connections,and the geography and the skills required.To solve the proposed problem,we design a task assignment algorithm based on the Differential Evolution(DE)algorithm.Extensive experiments are conducted to evaluate the efficiency and effectiveness of the proposed algorithm in real and synthetic data,and the results show better performance compared with other heuristic-based algorithms.展开更多
In this paper,we propose a fast second-order approximation to the variable-order(VO)Caputo fractional derivative,which is developed based on L2-1σformula and the exponential-sum-approximation technique.The fast evalu...In this paper,we propose a fast second-order approximation to the variable-order(VO)Caputo fractional derivative,which is developed based on L2-1σformula and the exponential-sum-approximation technique.The fast evaluation method can achieve the second-order accuracy and further reduce the computational cost and the acting memory for the VO Caputo fractional derivative.This fast algorithm is applied to construct a relevant fast temporal second-order and spatial fourth-order scheme(F L2-1σscheme)for the multi-dimensional VO time-fractional sub-diffusion equations.Theoretically,F L2-1σscheme is proved to fulfill the similar properties of the coefficients as those of the well-studied L2-1σscheme.Therefore,F L2-1σscheme is strictly proved to be unconditionally stable and convergent.A sharp decrease in the computational cost and the acting memory is shown in the numerical examples to demonstrate the efficiency of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China (31821001, 32070404)Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31000000,XDA23080000)State Forestry Administration of China。
文摘DEAR EDITOR,The Myanmar or black snub-nosed monkey(Rhinopithecus strykeri) is a recently discovered and critically endangered colobus primate with an unknown gut microbiota. Here, we characterized and compared the gut microbiota of R. strykeri with those of two closely related snub-nosed monkey species.
基金a grant from the National Clinical Specialty Construction Project of China(2013-544).
文摘BACKGROUND:Neuroendocrine dysfunction after traumatic brain injury(TBI)has received increased attention due to its impact on the recovery of neural function.The purpose of this study is to investigate the incidence and risk factors of adrenocortical insuffi ciency(AI)after TBI to reveal independent predictors and build a prediction model of AI after TBI.METHODS:Enrolled patients were grouped into the AI and non-AI groups.Fourteen preset impact factors were recorded.Patients were regrouped according to each impact factor as a categorical variable.Univariate and multiple logistic regression analyses were performed to screen the related independent risk factors of AI after TBI and develop the predictive model.RESULTS:A total of 108 patients were recruited,of whom 34(31.5%)patients had AI.Nine factors(age,Glasgow Coma Scale[GCS]score on admission,mean arterial pressure[MAP],urinary volume,serum sodium level,cerebral hernia,frontal lobe contusion,diff use axonal injury[DAI],and skull base fracture)were probably related to AI after TBI.Three factors(urinary volume[X4],serum sodium level[X5],and DAI[X8])were independent variables,based on which a prediction model was developed(logit P=-3.552+2.583X4+2.235X5+2.269X8).CONCLUSIONS:The incidence of AI after TBI is high.Factors such as age,GCS score,MAP,urinary volume,serum sodium level,cerebral hernia,frontal lobe contusion,DAI,and skull base fracture are probably related to AI after TBI.Urinary volume,serum sodium level,and DAI are the independent predictors of AI after TBI.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23080201,XDA19050202)National Natural Science Foundation of China(31821001)+1 种基金National Key R&D Program of China(2016YFC0503200)。
文摘Viruses can be transmitted from animals to humans(and vice versa)and across animal species.As such,host-virus interactions and transmission have attracted considerable attention.Non-human primates(NHPs),our closest evolutionary relatives,are susceptible to human viruses and certain pathogens are known to circulate between humans and NHPs.Here,we generated global statistics on virus infections in NHPs(VI-NHPs)based on a literature search and public data mining.In total,140 NHP species from 12 families are reported to be infected by 186 DNA and RNA virus species,68.8%of which are also found in humans,indicating high potential for crossing species boundaries.
基金This work is supported by the National Key Research and Development Program of China(2022YFE0195900,2021YFC3101600,2020YFA0607900,and 2020YFA0608000)the National Natural Science Foundation of China(42125503 and 42075137).
文摘Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related disasters.The utilisation of multiscale methods is effective for a comprehensive evaluation of drought occurrence and progression,given the complex nature of meteorological drought.Nevertheless,the nonlinear spatiotemporal features of meteorological droughts,influenced by various climatological,physical and environmental factors,pose significant challenges to integrated prediction that considers multiple indicators and time scales.To address these constraints,we introduce an innovative deep learning framework based on the shifted window transformer,designed for executing spatiotemporal prediction of meteorological drought across multiple scales.We formulate four prediction indicators using the standardized precipitation index and the standard precipitation evaporation index as core methods for drought definition using the ERA5 reanalysis dataset.These indicators span time scales of approximately 30 d and one season.Short-term indicators capture more anomalous variations,whereas long-term indicators attain comparatively higher accuracy in predicting future trends.We focus on the East Asian region,notable for its diverse climate conditions and intricate terrains,to validate the model's efficacy in addressing the complexities of nonlinear spatiotemporal prediction.The model's performance is evaluated from diverse spatiotemporal viewpoints,and practical application values are analysed by representative drought events.Experimental results substantiate the effectiveness of our proposed model in providing accurate multiscale predictions and capturing the spatiotemporal evolution characteristics of drought.Each of the four drought indicators accurately delineates specific facets of the meteorological drought trend.Moreover,three representative drought events,namely flash drought,sustained drought and severe drought,underscore the significance of selecting appropriate prediction indicators to effectively denote different types of drought events.This study provides methodological and technological support for using a deep learning approach in meteorological drought prediction.Such findings also demonstrate prediction issues related to natural hazards in regions with scarce observational data,complex topography and diverse microclimate systems.
基金supported by the National Natural Science Foundation of China under Grant Nos.61672122,61902050,61602077the Fundamental Research Funds for the Central Universities of China under Grant No.3132019355the CERNET Innovation Project under Grant No.NGII20190627.
文摘Crowdtesting has emerged as an attractive and economical testing paradigm that features testers from different countries,with various backgrounds and working conditions.Recent developments in crowdsourcing testing suggest that it is feasible to manage test populations and processes,but they are often outside the scope of standard testing theory.This paper explores how to allocate service-testing tasks to proper testers in an ever-changing crowdsourcing environment.We formalize it as an optimization problem with the objective to ensure the testing quality of the crowds,while considering influencing factors such as knowledge capability,the rewards,the network connections,and the geography and the skills required.To solve the proposed problem,we design a task assignment algorithm based on the Differential Evolution(DE)algorithm.Extensive experiments are conducted to evaluate the efficiency and effectiveness of the proposed algorithm in real and synthetic data,and the results show better performance compared with other heuristic-based algorithms.
基金supported in part by research grants of the Science and Technology De-velopment Fund,Macao SAR(0122/2020/A3)University of Macao(MYRG2020-00224-FST).
文摘In this paper,we propose a fast second-order approximation to the variable-order(VO)Caputo fractional derivative,which is developed based on L2-1σformula and the exponential-sum-approximation technique.The fast evaluation method can achieve the second-order accuracy and further reduce the computational cost and the acting memory for the VO Caputo fractional derivative.This fast algorithm is applied to construct a relevant fast temporal second-order and spatial fourth-order scheme(F L2-1σscheme)for the multi-dimensional VO time-fractional sub-diffusion equations.Theoretically,F L2-1σscheme is proved to fulfill the similar properties of the coefficients as those of the well-studied L2-1σscheme.Therefore,F L2-1σscheme is strictly proved to be unconditionally stable and convergent.A sharp decrease in the computational cost and the acting memory is shown in the numerical examples to demonstrate the efficiency of the proposed method.
