Objective: This study aimed to evaluate Chinese tertiary hospital nurses' research output,research ability,and their related training needs regarding scientific research methodology and analyze the relations among...Objective: This study aimed to evaluate Chinese tertiary hospital nurses' research output,research ability,and their related training needs regarding scientific research methodology and analyze the relations among them.Methods: A nationwide survey was conducted in China on a large sample of tertiary hospital nurses (n =27,335) recruited from 22 provinces,autonomous regions,and municipalities.A validated,self-designed questionnaire,consisted of a common questionnaire,the Science Research Skills Self-Rating Questionnaire (SRSQ) and the Scientific Research Training Needs Questionnaire (SRTNQ) were used to assess nurses' research output,self-rated research skills and research-training needs.Results: The nurses' scientific research participation rates (with 4.13%,7.85%,5.35%,and 2.04% in research projects,research attendance,papers published,and patent,respectively) and their self-rated research skills 25.00 (12.50,37.50) were very low.However,the research training needs were relatively high 53.12(37.50,75.00).Significant differences in research participation rates (research projects,research attendance,papers published,and patent),scientific research skills,and research-training needs were determined by age,highest education level,nursing experience,employment,technical title,administrative post,and clinical tutoring experience (P< 0.05).Female and male nurses had different research participation rates (only research projects and studies published) and scientific research skills (P < 0.05).Positive correlations were observed among research output,scientific research skills,and researchtraining needs (P < 0.01).Conclusions: Nurses' scientific research participation and self-rated research ability were below the optimal despite that they had relatively high research-training needs.Nurses should be provided further research training with tailored content to their characteristics and capacity.展开更多
Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This study aimed at developing a method to estimate aboveground...Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This study aimed at developing a method to estimate aboveground biomass(AGB)for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition(VIUPD)from the spectra to estimate AGB.First,we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type.Next,we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables.At last,we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately,all eight vegetation indices provided good estimates of AGB,with the best predictor of AGB varying among different ecosystems.When AGB of all the five ecosystems was estimated together using a simple linear model,VIUPD showed the lowest prediction error among the eight vegetation indices.The regression models containing dummy variables predicted AGB with higher accuracy than the simple models,which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index(VI).These results suggest that VIUPD is the best predictor of AGB among simple regression models.Moreover,both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models.T展开更多
Chang’e-5 explorer successfully acquired lunar regolith core samples from depths of greater than 1 m of lunar surface.This study analyzed the lunar core drilling process based on the telemetry data,image information,...Chang’e-5 explorer successfully acquired lunar regolith core samples from depths of greater than 1 m of lunar surface.This study analyzed the lunar core drilling process based on the telemetry data,image information,and returned samples to optimize the sampling device design and enhance the understanding of the lunar regolith.In particular,a prediction method for the projected drilling path and local terrain fitting of drilling dip angle was proposed based on the flight events recorded during the core drilling process and the image information acquired before,during,and after sampling.The results revealed that the drilling dip angle of Chang’e-5 was approximately2.3.,and the deviation of the drilling length and depth was less than 2 mm.For continuous drilling,a fusion method based on telemetry data and image information was applied to determine the demarcation point of drilling with and without the lunar soil.The position of the demarcation point implied that the drilling point remained at approximately 6 mm loose soil,thereby lagging the action of the force response.Additionally,a characteristic parameter comparison method was proposed for the lunar and ground drilling to analyze the status of the lunar soil.Furthermore,the analysis results revealed that the majority of the Chang’e-5 drilling samples were derived from 0–73.8 cm below the lunar surface and few samples were extracted below 73.8 cm,as the drilling encountered several rocky regions.Moreover,the drilling point exhibited two prominent stratification variations at~28.7 cm and~70 cm below the lunar surface.Ultimately,the preliminary relationship between sample dissected position in soft tube and drilling displacement was analyzed.The segmented estimation results can support research on subsurface lunar soil.展开更多
Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma(HCC).Integrated 15 transcriptomic datasets of HCC clinical samples,the first version of HCC database(HCC...Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma(HCC).Integrated 15 transcriptomic datasets of HCC clinical samples,the first version of HCC database(HCCDB v1.0)was released in 2018.Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets,it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness.With four years having passed,the database now needs integration of recently published datasets.Furthermore,the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture.Here,we present HCCDB v2.0,an updated version that combines bulk,single-cell,and spatial transcriptomic data of HCC clinical samples.It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples,thereby enhancing the reliability of transcriptomic meta-analysis.A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections,respectively.A novel single-cell level and 2-dimension(sc-2D)metric is proposed as well to summarize cell type-specific and dysregulated gene expression patterns.Results are all graphically visualized in our online portal,allowing users to easily retrieve data through a user-friendly interface and navigate between different views.With extensive clinical phenotypes and transcriptomic data in the database,we show two applications for identifying prognosis-associated cells and tumor microenvironment.HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.展开更多
文摘Objective: This study aimed to evaluate Chinese tertiary hospital nurses' research output,research ability,and their related training needs regarding scientific research methodology and analyze the relations among them.Methods: A nationwide survey was conducted in China on a large sample of tertiary hospital nurses (n =27,335) recruited from 22 provinces,autonomous regions,and municipalities.A validated,self-designed questionnaire,consisted of a common questionnaire,the Science Research Skills Self-Rating Questionnaire (SRSQ) and the Scientific Research Training Needs Questionnaire (SRTNQ) were used to assess nurses' research output,self-rated research skills and research-training needs.Results: The nurses' scientific research participation rates (with 4.13%,7.85%,5.35%,and 2.04% in research projects,research attendance,papers published,and patent,respectively) and their self-rated research skills 25.00 (12.50,37.50) were very low.However,the research training needs were relatively high 53.12(37.50,75.00).Significant differences in research participation rates (research projects,research attendance,papers published,and patent),scientific research skills,and research-training needs were determined by age,highest education level,nursing experience,employment,technical title,administrative post,and clinical tutoring experience (P< 0.05).Female and male nurses had different research participation rates (only research projects and studies published) and scientific research skills (P < 0.05).Positive correlations were observed among research output,scientific research skills,and researchtraining needs (P < 0.01).Conclusions: Nurses' scientific research participation and self-rated research ability were below the optimal despite that they had relatively high research-training needs.Nurses should be provided further research training with tailored content to their characteristics and capacity.
基金The field investigation was partly supported by a program on long-term monitoring of alpine ecosystems on the Tibetan Plateau from the Ministry of Environment,Japan to T.Y.Program for New Century Excellent Talents in University to C.J.Director-encouragement fund from National Institute for Environmental Studies to S.A.
文摘Aims There are numerous grassland ecosystem types on the Tibetan Plateau.These include the alpine meadow and steppe and degraded alpine meadow and steppe.This study aimed at developing a method to estimate aboveground biomass(AGB)for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition(VIUPD)from the spectra to estimate AGB.First,we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type.Next,we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables.At last,we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately,all eight vegetation indices provided good estimates of AGB,with the best predictor of AGB varying among different ecosystems.When AGB of all the five ecosystems was estimated together using a simple linear model,VIUPD showed the lowest prediction error among the eight vegetation indices.The regression models containing dummy variables predicted AGB with higher accuracy than the simple models,which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index(VI).These results suggest that VIUPD is the best predictor of AGB among simple regression models.Moreover,both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models.T
基金supported by the National Medium and Longterm Science and Technology Major Special Project of ChinaYoung Top Talents Foundation of China Aerospace Science and Technology Corporation+1 种基金Pre-research project on Civil Aerospace Technologies by CNSA under Grant D020201the National Natural Science Foundation of China(Research on Supporting Management Strategy of Scientific Research Activities in Lunar Exploration under Grant 42142033)。
文摘Chang’e-5 explorer successfully acquired lunar regolith core samples from depths of greater than 1 m of lunar surface.This study analyzed the lunar core drilling process based on the telemetry data,image information,and returned samples to optimize the sampling device design and enhance the understanding of the lunar regolith.In particular,a prediction method for the projected drilling path and local terrain fitting of drilling dip angle was proposed based on the flight events recorded during the core drilling process and the image information acquired before,during,and after sampling.The results revealed that the drilling dip angle of Chang’e-5 was approximately2.3.,and the deviation of the drilling length and depth was less than 2 mm.For continuous drilling,a fusion method based on telemetry data and image information was applied to determine the demarcation point of drilling with and without the lunar soil.The position of the demarcation point implied that the drilling point remained at approximately 6 mm loose soil,thereby lagging the action of the force response.Additionally,a characteristic parameter comparison method was proposed for the lunar and ground drilling to analyze the status of the lunar soil.Furthermore,the analysis results revealed that the majority of the Chang’e-5 drilling samples were derived from 0–73.8 cm below the lunar surface and few samples were extracted below 73.8 cm,as the drilling encountered several rocky regions.Moreover,the drilling point exhibited two prominent stratification variations at~28.7 cm and~70 cm below the lunar surface.Ultimately,the preliminary relationship between sample dissected position in soft tube and drilling displacement was analyzed.The segmented estimation results can support research on subsurface lunar soil.
基金funded by the National Key R&D Program of China(Grant No.2021YFF1200901 awarded to Jin Gu)the National Natural Science Foundation of China(Grant Nos.62133006,61721003,and 62103273 awarded to Jin Gu)+3 种基金the Tsinghua University Initiative Scientific Research Program(Grant No.20221080076 awarded to Jin Gu)the Beijing Municipal Natural Science Foundation(Grant No.7222130 awarded to Yongchang Zheng)the Special Clinical Research Project of Peking Union Medical College Hospital(Grant No.2022-PUMCH-A-236 awarded to Yongchang Zheng)the CHEN XIAO-PING Foundation for the Development of Science and Technology of Hubei Province(Grant No.CXPJJH1200008-10 awarded to Yongchang Zheng),China.
文摘Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma(HCC).Integrated 15 transcriptomic datasets of HCC clinical samples,the first version of HCC database(HCCDB v1.0)was released in 2018.Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets,it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness.With four years having passed,the database now needs integration of recently published datasets.Furthermore,the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture.Here,we present HCCDB v2.0,an updated version that combines bulk,single-cell,and spatial transcriptomic data of HCC clinical samples.It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples,thereby enhancing the reliability of transcriptomic meta-analysis.A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections,respectively.A novel single-cell level and 2-dimension(sc-2D)metric is proposed as well to summarize cell type-specific and dysregulated gene expression patterns.Results are all graphically visualized in our online portal,allowing users to easily retrieve data through a user-friendly interface and navigate between different views.With extensive clinical phenotypes and transcriptomic data in the database,we show two applications for identifying prognosis-associated cells and tumor microenvironment.HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.
