Precise forecasting of solar power is crucial for the development of sustainable energy systems.Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic...Precise forecasting of solar power is crucial for the development of sustainable energy systems.Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic(PV)power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series data.To overcome these challenges,this research presents a cutting-edge,multi-stage forecasting method called D-Informer.This method skillfully merges the differential transformation algorithm with the Informer model,leveraging a detailed array of meteorological variables and historical PV power generation records.The D-Informer model exhibits remarkable superiority over competing models across multiple performance metrics,achieving on average a 67.64%reduction in mean squared error(MSE),a 49.58%decrease in mean absolute error(MAE),and a 43.43%reduction in root mean square error(RMSE).Moreover,it attained an R2 value as high as 0.9917 during the winter season,highlighting its precision and dependability.This significant advancement can be primarily attributed to the incorporation of a multi-head self-attention mechanism,which greatly enhances the model’s ability to identify complex interactions among diverse input variables,and the inclusion of weather variables,enriching the model’s input data and strengthening its predictive accuracy in time series analysis.Additionally,the experimental results confirm the effectiveness of the proposed approach.展开更多
Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from vario...Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.展开更多
Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth,and severe anthropogenic-induced grassland ecosystem function declines have been reported recently.Understanding factors influenci...Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth,and severe anthropogenic-induced grassland ecosystem function declines have been reported recently.Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies.Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate(e.g.,light,water,and wind),and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions.However,the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated.Here,we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China.Aboveground net primary productivity(ANPP)and resilience,two indicators of grassland ecosystem functions,were measured from field data and satellite remote sensing data.Terrestrial laser scanning data were collected to measure canopy structure(represented by mean height and canopy cover).Our results showed that plant species diversity was positively correlated to canopy structural traits,and negatively correlated to human activity intensity.Canopy structure was a significant indicator for ANPP and resilience,but their correlations were inconsistent under different human activity intensity levels.Compared to plant species diversity,canopy structural traits were better indicators for grassland ecosystem functions,especially for ANPP.Through structure equation modeling analyses,we found that plant species diversity did not have a direct influence on ANPP under human disturbances.Instead,it had a strong indirect effect on ANPP by altering canopy structural traits.As to resilience,plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover.This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances,which should be included in future grassland monitoring and management.展开更多
Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale featu...Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical BESSs.Pearson correlation analysis is first employed to identify SOC-related parameters.These parameters are then input into a multi-layer GRU for point-wise feature extraction.Concurrently,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time intervals.Ultimately,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are rendered.Following extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.展开更多
Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance...Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.展开更多
Inhibition of mycobacterial membrane protein large 3(MmpL3)thereby affecting the mycolic acid biosynthetic pathway has been proven to be an effective strategy for developing antitubercular drugs.Based on the X-ray cry...Inhibition of mycobacterial membrane protein large 3(MmpL3)thereby affecting the mycolic acid biosynthetic pathway has been proven to be an effective strategy for developing antitubercular drugs.Based on the X-ray crystal structure of MmpL3 inhibitor complexes,a series of novel 1,2,4-triazole derivatives were designed,synthesized and evaluated antitubercular activity against Mtb strain H37Rv.Comprehensive structure–activity relationship exploration resulted in the identification of compounds 21 and 28,which possess potent antitubercular activity against Mtb strain H37Rv[minimum inhibitory concentration(MIC)=0.03–0.13μg/mL]and the clinical isolates of multidrug resistance(MDR)and extensive drug resistance(XDR)tuberculosis(MIC=0.06–1.0μg/mL).Moreover,compounds 21 and 28 showed neglectable cytotoxicity(IC_(50)≥32μg/mL)to the mammalian Vero cells and favorable physicochemical and pharmacokinetic properties according to the in silico absorption,distribution,metabolism and excretion(ADME)prediction.Finally,the potential target of representative 1,2,4-triazole 28 was identified to be MmpL3 using a microscale thermophoresis(MST)assay.展开更多
Calcium-dependent protein kinases(CDPKs)act as key signal transduction enzymes in plants,especially in response to diverse stresses,including herbivory.In this study,a comprehensive analysis of the CDPK gene family in...Calcium-dependent protein kinases(CDPKs)act as key signal transduction enzymes in plants,especially in response to diverse stresses,including herbivory.In this study,a comprehensive analysis of the CDPK gene family in upland cotton revealed that GhCPKs are widely expressed in multiple cotton tissues and respond positively to various biotic and abiotic stresses.We developed a strategy for screening insect-resistance genes from a CRISPR-Cas9 mutant library of GhCPKs.The library was created using 246 single-guide RNAs targeting the GhCPK gene family to generate 518 independent T0 plants.The average target-gene coverage was 86.18%,the genome editing rate was 89.49%,and the editing heritability was 82%.An insect bioassay in the field led to identification of 14 GhCPK mutants that are resistant or susceptible to insects.The mutant that showed the clearest insect resistance,cpk33/74(in which the homologous genes GhCPK33 and GhCPK74 were knocked out),was selected for further study.Oral secretions from Spodoptera litura induced a rapid influx of Ca2+in cpk33/74 leaves,resulting in a significant increase in jasmonic acid content.S-adenosylmethionine synthase is an important protein involved in plant stress response,and protein interaction experiments provided evidence for interactions of GhCPK33 and GhCPK74 with GhSAMS1 and GhSAM2.In addition,virus-induced gene silencing of GhSAMS1 and GhSAM2 in cotton impaired defense against S.litura.This study demonstrates an effective strategy for constructing a mutant library of a gene family in a polyploid plant species and offers valuable insights into the role of CDPKs in the interaction between plants and herbivorous insects.展开更多
Forest structural complexity can mediate the light and water distribution within forest canopies,and has a direct impact on forest biodiversity and carbon storage capability.It is believed that increases in forest str...Forest structural complexity can mediate the light and water distribution within forest canopies,and has a direct impact on forest biodiversity and carbon storage capability.It is believed that increases in forest structural complexity can enhance tree species diversity and forest productivity,but inconsistent relationships among them have been reported.Here,we quantified forest structural complexity in three aspects(i.e.,horizontal,vertical,and internal structural complexity)from unmanned aerial vehicle light detection and ranging data,and investigated their correlations with tree species diversity and forest productivity by incorporating field measurements in three forest biomes with large latitude gradients in China.Our results show that internal structural complexity had a stronger correlation(correlation coefficient=0.85)with tree species richness than horizontal structural complexity(correlation coefficient=-0.16)and vertical structural complexity(correlation coefficient=0.61),and it was the only forest structural complexity attribute having significant correlations with both tree species richness and tree species evenness.A strong scale effect was observed in the correlations among forest structural complexity,tree species diversity,and forest productivity.Moreover,forest internal structural complexity had a tight positive coordinated contribution with tree species diversity to forest productivity through structure equation model analysis,while horizontal and vertical structural complexity attributes have insignificant or weaker coordinated effects than internal structural complexity,which indicated that the neglect of forest internal structural complexity might partially lead to the current inconsistent observations among forest structural complexity,tree species diversity,and forest productivity.The results of this study can provide a new angle to understand the observed inconsistent correlations among forest structural complexity,tree species diversity,and forest productivity.展开更多
The adenosine 5'-triphosphate(ATP)-binding cassette(ABC)transporter,IrtAB,plays a vital role in the replication and viability of Mycobacterium tuberculosis(Mtb),where its function is to import iron-loaded sideroph...The adenosine 5'-triphosphate(ATP)-binding cassette(ABC)transporter,IrtAB,plays a vital role in the replication and viability of Mycobacterium tuberculosis(Mtb),where its function is to import iron-loaded siderophores.Unusually,it adopts the canonical type IV exporter fold.Herein,we report the structure of unliganded Mtb IrtAB and its structure in complex with ATP,ADP,or ATP analogue(AMP-PNP)at resolutions ranging from 2.8 to 3.5Å.The structure of IrtAB bound ATP-Mg2+shows a“head-to-tail”dimer of nucleotide-binding domains(NBDs),a closed amphipathic cavity within the transmembrane domains(TMDs),and a metal ion liganded to three histidine residues of IrtA in the cavity.Cryo-electron microscopy(Cryo-EM)structures and ATP hydrolysis assays show that the NBD of IrtA has a higher affinity for nucleotides and increased ATPase activity compared with IrtB.Moreover,the metal ion located in the TM region of IrtA is critical for the stabilization of the conformation of IrtAB during the transport cycle.This study provides a structural basis to explain the ATP-driven conformational changes that occur in IrtAB.展开更多
Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological per...Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as living habit.展开更多
The authors have retracted this article.After publication we found an error in the implementation code that resulted in data leakage in the age-prediction model training process.We have redesigned the prediction model...The authors have retracted this article.After publication we found an error in the implementation code that resulted in data leakage in the age-prediction model training process.We have redesigned the prediction model and tested the mode with an extended dataset(around 2000 subjects,in contrast to the 600 subjects in this article).展开更多
Vegetation maps are important sources of information for biodiversity conservation,ecological studies,vegetation management and restoration,and national strategic decision making.The current Vegetation Map of China(1:...Vegetation maps are important sources of information for biodiversity conservation,ecological studies,vegetation management and restoration,and national strategic decision making.The current Vegetation Map of China(1:1000000)was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s.However,the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades,and it urgently needs to be updated to better represent the distribution of current vegetation types.Here,we describe the process of updating the Vegetation Map of China(1:1000000)generated in the 1980s using a‘‘crowdsourcing-change detection-classification-expert knowledge"vegetation mapping strategy.A total of 203,024 field samples were collected,and 50 taxonomists were involved in the updating process.The resulting updated map has 12 vegetation type groups,55 vegetation types/subtypes,and 866 vegetation formation/sub-formation types.The overall accuracy and kappa coefficient of the updated map are 64.8%and 0.52 at the vegetation type group level,61%and 0.55 at the vegetation type/subtype level and 40%and 0.38 at the vegetation formation/sub-formation level.When compared to the original map,the updated map showed that 3.3 million km^2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change.We expect this updated map to benefit the understanding and management of China’s terrestrial ecosystems.展开更多
In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power dispatch.Conventional univariate wind power forecasting ...In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power dispatch.Conventional univariate wind power forecasting methods at presentonly utilize individual wind farm historical data.However,studies have shown that forecasting accuracy canbe improved by exploring both spatial and temporal correlations among adjacent wind farms.Current research on spatial-temporal wind power forecasting is based on relatively shallow time series models that,to date,have demonstrated unsatisfactory performance.In this paper,a convolution operation is used to capture the spatial and temporal correlations among multiple wind farms.A novel convolution-based spatial-temporal wind power predictor(CSTWPP)is developed.Due to CSTWPP’s high nonlinearity and deep architecture,wind power variation features and regularities included in the historical data can be more effectively extracted.Furthermore,the online training of CSTWPP enables incremental learning,which makes CSTWPP non-stationary and in conformity with real scenarios.Graphics processing units(GPU)is used to speed up the training process,validating the developed CSTWPP for real-time application.Case studies on 28 adjacent wind farms are conducted to show that the proposed model can achieve superior performance on 5-30 minutes ahead wind power forecasts.展开更多
Biostabilization is a cost-effective method for the beneficial utilization of sewage sludge.However,during the operation of sludge biostabilization,some microbial species could be released into the atmospheric environ...Biostabilization is a cost-effective method for the beneficial utilization of sewage sludge.However,during the operation of sludge biostabilization,some microbial species could be released into the atmospheric environment from the solid-phase of sludge easily and present a high risk to human health.This study aimed to evaluate the risk of bioaerosol during sludge biostabilization.We found a total of nine bacterial phyla,one archaeal phylum,and two fungal phyla in the bioaerosol samples.Among them,Proteobacteria,Actinobacteria,Bacteroidetes,and Ascomycota were the dominant phyla.In addition,the bioaerosolization indexes(BI)of prokaryotic phyla and flingal phyla ranged 0-45 and 0-487,respectively.Mass ilia y Pseudarthrobacter,Pseudomonas,Tremellales spp.,and Fusarium were the preferentially aerosolized microbial genera with maximum bioaerosolization indexes of 19962,10360,1802,3055,and 7398.The bioaerosol concentration during the biostabilization ranged from 160 to 1440 cell/m^(3),and we identified species such as Stenotrophomonas rhizophila and Fusarium graminerum with high bioaerosolization indexes that could be threats to human health.Euryachaeota,which belongs to archaeal phyla,had the highest biostabilization index in our study.We also found that Pseudarthrobacter was the easiest to aerosolize during the sludge biostabilization process.展开更多
Aims Boreal forests play an important role in the global carbon cycle.Compared with the boreal forests in North America and Europe,relatively few research studies have been conducted in Siberian boreal forests.Knowled...Aims Boreal forests play an important role in the global carbon cycle.Compared with the boreal forests in North America and Europe,relatively few research studies have been conducted in Siberian boreal forests.Knowledge related to the role of Siberian forests in the global carbon balance is thus essential for a full understanding of global carbon cycle.Methods This study investigated the net ecosystem exchange(NEE)during growing season(May-September)in an eastern Siberian boreal larch forest for a 3-year period in 2004-2006 with contrasting meteorological conditions.Important FindingsThe study found that the forest served as a carbon sink during all of the 3 studied years;in addition,the meteorological conditions essentially influenced the specific annual value of the strength of the carbon sinks in each year.Although 2005 was the warmest year and much wetter than 2004,2005 also featured the greatest amount of ecosystem respiration,which resulted in a minimum value of NEE.The study also found that the phenological changes observed during the three study years had a relatively small effect on annual NEE.Leaf expansion was 26 days earlier in 2005 than in the other 2 years,which resulted in a longer growing season in 2005.However,the NEE in 2005 was counterbalanced by the large rate of ecosystem respiration that was caused by the higher temperatures in the year.This study showed that meteorological variables had larger influences on the interannual variations in NEE for a Siberian boreal larch forest,as compared with phenological changes.The overall results of this study will improve our understanding of the carbon balance of Siberian boreal larch forests and thus can help to forecast the response of these forests to future climate change.展开更多
Canopy structural complexity is a critical emergent forest attribute,and light detection and ranging(lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree level.However,the ...Canopy structural complexity is a critical emergent forest attribute,and light detection and ranging(lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree level.However,the current lidar-based estimation method is highly sensitive to data characteristics,and its scalability from individual trees to forest stands remains unclear.This study proposed an improved method to estimate fractal dimension from lidar data by considering Shannon entropy,and evaluated its scalability from individual trees to forest stands through mathematical derivations.Moreover,a total of 280 forest stand scenes simulated from the terrestrial lidar data of 115 trees spanning large variability in canopy structural complexity were used to evaluate the robustness of the proposed method and the scalability of fractal dimension.The results show that the proposed method can significantly improve the robustness of lidar-derived fractal dimensions.Both mathematical derivations and experimental analyses demonstrate that the fractal dimension of a forest stand is equal to that of the tree with the largest fractal dimension in it,manifesting its nonscalability from individual trees to forest stands.The nonscalability of fractal dimension reveals its limited capability in canopy structural complexity quantification and indicates that the power-law scaling theory of a forest stand underlying fractal geometry is determined by its dominant tree instead of the entire community.Nevertheless,we believe that fractal dimension is still a useful indicator of canopy structural complexity at the individual tree level and might be used along with other stand-level indexes to reflect the“tree-to-stand”correlation of canopy structural complexity.展开更多
Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological per...Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as habits,the family setting,socioeconomic status,and the work environment [1].In contrast to chronological age (CA),brain(or biological) age (BA) is conceptualized as an important index for characterizing the aging process and neuropsychological state,as well as individual cognitiveperformance.Growing evidence indicates that BA can be assessed by neuroimaging techniques,including MRI [2].展开更多
基金supported by the Shenzhen Science and Technology Plan,Sustainable Development Technology Special Project (Dual-Carbon Special Project),Research and Development of Intelligent Virtual Power Plant Technology (KCXST20221021111402006)the Science and Technology project of Tianjin,China (No.22YFYSHZ00330).
文摘Precise forecasting of solar power is crucial for the development of sustainable energy systems.Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic(PV)power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series data.To overcome these challenges,this research presents a cutting-edge,multi-stage forecasting method called D-Informer.This method skillfully merges the differential transformation algorithm with the Informer model,leveraging a detailed array of meteorological variables and historical PV power generation records.The D-Informer model exhibits remarkable superiority over competing models across multiple performance metrics,achieving on average a 67.64%reduction in mean squared error(MSE),a 49.58%decrease in mean absolute error(MAE),and a 43.43%reduction in root mean square error(RMSE).Moreover,it attained an R2 value as high as 0.9917 during the winter season,highlighting its precision and dependability.This significant advancement can be primarily attributed to the incorporation of a multi-head self-attention mechanism,which greatly enhances the model’s ability to identify complex interactions among diverse input variables,and the inclusion of weather variables,enriching the model’s input data and strengthening its predictive accuracy in time series analysis.Additionally,the experimental results confirm the effectiveness of the proposed approach.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA19050401)the National Natural Science Foundation of China(41871332,31971575,41901358).
文摘Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA26010101,XDA23080301).
文摘Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth,and severe anthropogenic-induced grassland ecosystem function declines have been reported recently.Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies.Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate(e.g.,light,water,and wind),and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions.However,the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated.Here,we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China.Aboveground net primary productivity(ANPP)and resilience,two indicators of grassland ecosystem functions,were measured from field data and satellite remote sensing data.Terrestrial laser scanning data were collected to measure canopy structure(represented by mean height and canopy cover).Our results showed that plant species diversity was positively correlated to canopy structural traits,and negatively correlated to human activity intensity.Canopy structure was a significant indicator for ANPP and resilience,but their correlations were inconsistent under different human activity intensity levels.Compared to plant species diversity,canopy structural traits were better indicators for grassland ecosystem functions,especially for ANPP.Through structure equation modeling analyses,we found that plant species diversity did not have a direct influence on ANPP under human disturbances.Instead,it had a strong indirect effect on ANPP by altering canopy structural traits.As to resilience,plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover.This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances,which should be included in future grassland monitoring and management.
基金supported in part by the National Natural Science Foundation of China(No.62172036).
文摘Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical BESSs.Pearson correlation analysis is first employed to identify SOC-related parameters.These parameters are then input into a multi-layer GRU for point-wise feature extraction.Concurrently,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time intervals.Ultimately,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are rendered.Following extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.
基金supported by National Natural Science Foundation of China under grants 52077213 and 62003332Youth Innovation Promotion Association CAS 2021358+1 种基金Shenzhen Science and Technology Research and Development Fund JCYJ20200109114839874NSFC-FDCT under its Joint Scientific Research Project Fund(Grant No.0051/2022/AFJ),China&Macao.
文摘Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.
基金supported by the National Natural Science Foundation of China(Nos.82073706 and 22107031)funded in part with Federal funds from the National Institutes of Health and National Institute of Allergy and Infectious Diseases,Department of Health and Human Services(No.AI155602)。
文摘Inhibition of mycobacterial membrane protein large 3(MmpL3)thereby affecting the mycolic acid biosynthetic pathway has been proven to be an effective strategy for developing antitubercular drugs.Based on the X-ray crystal structure of MmpL3 inhibitor complexes,a series of novel 1,2,4-triazole derivatives were designed,synthesized and evaluated antitubercular activity against Mtb strain H37Rv.Comprehensive structure–activity relationship exploration resulted in the identification of compounds 21 and 28,which possess potent antitubercular activity against Mtb strain H37Rv[minimum inhibitory concentration(MIC)=0.03–0.13μg/mL]and the clinical isolates of multidrug resistance(MDR)and extensive drug resistance(XDR)tuberculosis(MIC=0.06–1.0μg/mL).Moreover,compounds 21 and 28 showed neglectable cytotoxicity(IC_(50)≥32μg/mL)to the mammalian Vero cells and favorable physicochemical and pharmacokinetic properties according to the in silico absorption,distribution,metabolism and excretion(ADME)prediction.Finally,the potential target of representative 1,2,4-triazole 28 was identified to be MmpL3 using a microscale thermophoresis(MST)assay.
基金Biological Breeding of Stress Tolerant and High Yield Cotton Varieties(2023ZD04040)to L.M.National Natural Science Fund of China for Distinguished Young Scholars(32325039)+2 种基金National Natural Science Foundation of China(32272128)to S.J.,the National Natural Science Foundation of China(32401780)Key Scientific and Technological Project of Henan Province(222102110151)to S.L.,Major Science and Technology Project of Xinjiang Uygur Autonomous Region(2023A02003-2)to B.L.
文摘Calcium-dependent protein kinases(CDPKs)act as key signal transduction enzymes in plants,especially in response to diverse stresses,including herbivory.In this study,a comprehensive analysis of the CDPK gene family in upland cotton revealed that GhCPKs are widely expressed in multiple cotton tissues and respond positively to various biotic and abiotic stresses.We developed a strategy for screening insect-resistance genes from a CRISPR-Cas9 mutant library of GhCPKs.The library was created using 246 single-guide RNAs targeting the GhCPK gene family to generate 518 independent T0 plants.The average target-gene coverage was 86.18%,the genome editing rate was 89.49%,and the editing heritability was 82%.An insect bioassay in the field led to identification of 14 GhCPK mutants that are resistant or susceptible to insects.The mutant that showed the clearest insect resistance,cpk33/74(in which the homologous genes GhCPK33 and GhCPK74 were knocked out),was selected for further study.Oral secretions from Spodoptera litura induced a rapid influx of Ca2+in cpk33/74 leaves,resulting in a significant increase in jasmonic acid content.S-adenosylmethionine synthase is an important protein involved in plant stress response,and protein interaction experiments provided evidence for interactions of GhCPK33 and GhCPK74 with GhSAMS1 and GhSAM2.In addition,virus-induced gene silencing of GhSAMS1 and GhSAM2 in cotton impaired defense against S.litura.This study demonstrates an effective strategy for constructing a mutant library of a gene family in a polyploid plant species and offers valuable insights into the role of CDPKs in the interaction between plants and herbivorous insects.
基金supported by the Frontier Science Key Programs of the Chinese Academy of Sciences(QYZDY-SSW-SMC011)the National Natural Science Foundation of China(41871332,31971575,41901358).
文摘Forest structural complexity can mediate the light and water distribution within forest canopies,and has a direct impact on forest biodiversity and carbon storage capability.It is believed that increases in forest structural complexity can enhance tree species diversity and forest productivity,but inconsistent relationships among them have been reported.Here,we quantified forest structural complexity in three aspects(i.e.,horizontal,vertical,and internal structural complexity)from unmanned aerial vehicle light detection and ranging data,and investigated their correlations with tree species diversity and forest productivity by incorporating field measurements in three forest biomes with large latitude gradients in China.Our results show that internal structural complexity had a stronger correlation(correlation coefficient=0.85)with tree species richness than horizontal structural complexity(correlation coefficient=-0.16)and vertical structural complexity(correlation coefficient=0.61),and it was the only forest structural complexity attribute having significant correlations with both tree species richness and tree species evenness.A strong scale effect was observed in the correlations among forest structural complexity,tree species diversity,and forest productivity.Moreover,forest internal structural complexity had a tight positive coordinated contribution with tree species diversity to forest productivity through structure equation model analysis,while horizontal and vertical structural complexity attributes have insignificant or weaker coordinated effects than internal structural complexity,which indicated that the neglect of forest internal structural complexity might partially lead to the current inconsistent observations among forest structural complexity,tree species diversity,and forest productivity.The results of this study can provide a new angle to understand the observed inconsistent correlations among forest structural complexity,tree species diversity,and forest productivity.
基金supported by grants from the National Key Research and Development Program of China(Grant No.2022YFC2302900)the National Natural Science Foundation of China(Grant No.32171217 to B.Z.)+5 种基金Shanghai Sailing Program(Grant No.21YF1429700 to B.Z.)Young Elite Scientists Sponsorship Program by CAST(Grant No.2021QNRC001)the Lingang Laboratory(Grant No.LG202101-01-08)Shanghai Municipal Science and Technology Major Project(Grant No.ZD2021CY001)Science and Technology Commission of Shanghai Municipality(Grant No.20XD1422900 to H.Y.)the Shanghai Frontiers Science Center for Biomacromolecules and Precision Medicine,Shanghaitech University.
文摘The adenosine 5'-triphosphate(ATP)-binding cassette(ABC)transporter,IrtAB,plays a vital role in the replication and viability of Mycobacterium tuberculosis(Mtb),where its function is to import iron-loaded siderophores.Unusually,it adopts the canonical type IV exporter fold.Herein,we report the structure of unliganded Mtb IrtAB and its structure in complex with ATP,ADP,or ATP analogue(AMP-PNP)at resolutions ranging from 2.8 to 3.5Å.The structure of IrtAB bound ATP-Mg2+shows a“head-to-tail”dimer of nucleotide-binding domains(NBDs),a closed amphipathic cavity within the transmembrane domains(TMDs),and a metal ion liganded to three histidine residues of IrtA in the cavity.Cryo-electron microscopy(Cryo-EM)structures and ATP hydrolysis assays show that the NBD of IrtA has a higher affinity for nucleotides and increased ATPase activity compared with IrtB.Moreover,the metal ion located in the TM region of IrtA is critical for the stabilization of the conformation of IrtAB during the transport cycle.This study provides a structural basis to explain the ATP-driven conformational changes that occur in IrtAB.
基金supported by the National Natural Science Foundation of China(61971420)Beijing Brain Initiative of the Beijing Municipal Science and Technology Commission(Z181100001518003)+1 种基金Special Projects of Brain Science of the Beijing Municipal Science and Technology Commission(Z161100000216139)International Cooperation and Exchange of the National Natural Science Foundation of China(31620103905).
文摘Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as living habit.
文摘The authors have retracted this article.After publication we found an error in the implementation code that resulted in data leakage in the age-prediction model training process.We have redesigned the prediction model and tested the mode with an extended dataset(around 2000 subjects,in contrast to the 600 subjects in this article).
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(XDA19050401)Maps in this article were reviewed by Ministry of Natural Resources of the People’s Republic of China(GS(2020)1044)。
文摘Vegetation maps are important sources of information for biodiversity conservation,ecological studies,vegetation management and restoration,and national strategic decision making.The current Vegetation Map of China(1:1000000)was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s.However,the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades,and it urgently needs to be updated to better represent the distribution of current vegetation types.Here,we describe the process of updating the Vegetation Map of China(1:1000000)generated in the 1980s using a‘‘crowdsourcing-change detection-classification-expert knowledge"vegetation mapping strategy.A total of 203,024 field samples were collected,and 50 taxonomists were involved in the updating process.The resulting updated map has 12 vegetation type groups,55 vegetation types/subtypes,and 866 vegetation formation/sub-formation types.The overall accuracy and kappa coefficient of the updated map are 64.8%and 0.52 at the vegetation type group level,61%and 0.55 at the vegetation type/subtype level and 40%and 0.38 at the vegetation formation/sub-formation level.When compared to the original map,the updated map showed that 3.3 million km^2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change.We expect this updated map to benefit the understanding and management of China’s terrestrial ecosystems.
文摘In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power dispatch.Conventional univariate wind power forecasting methods at presentonly utilize individual wind farm historical data.However,studies have shown that forecasting accuracy canbe improved by exploring both spatial and temporal correlations among adjacent wind farms.Current research on spatial-temporal wind power forecasting is based on relatively shallow time series models that,to date,have demonstrated unsatisfactory performance.In this paper,a convolution operation is used to capture the spatial and temporal correlations among multiple wind farms.A novel convolution-based spatial-temporal wind power predictor(CSTWPP)is developed.Due to CSTWPP’s high nonlinearity and deep architecture,wind power variation features and regularities included in the historical data can be more effectively extracted.Furthermore,the online training of CSTWPP enables incremental learning,which makes CSTWPP non-stationary and in conformity with real scenarios.Graphics processing units(GPU)is used to speed up the training process,validating the developed CSTWPP for real-time application.Case studies on 28 adjacent wind farms are conducted to show that the proposed model can achieve superior performance on 5-30 minutes ahead wind power forecasts.
基金the National Key R&D Program of China(No.2018YFD-1100600).
文摘Biostabilization is a cost-effective method for the beneficial utilization of sewage sludge.However,during the operation of sludge biostabilization,some microbial species could be released into the atmospheric environment from the solid-phase of sludge easily and present a high risk to human health.This study aimed to evaluate the risk of bioaerosol during sludge biostabilization.We found a total of nine bacterial phyla,one archaeal phylum,and two fungal phyla in the bioaerosol samples.Among them,Proteobacteria,Actinobacteria,Bacteroidetes,and Ascomycota were the dominant phyla.In addition,the bioaerosolization indexes(BI)of prokaryotic phyla and flingal phyla ranged 0-45 and 0-487,respectively.Mass ilia y Pseudarthrobacter,Pseudomonas,Tremellales spp.,and Fusarium were the preferentially aerosolized microbial genera with maximum bioaerosolization indexes of 19962,10360,1802,3055,and 7398.The bioaerosol concentration during the biostabilization ranged from 160 to 1440 cell/m^(3),and we identified species such as Stenotrophomonas rhizophila and Fusarium graminerum with high bioaerosolization indexes that could be threats to human health.Euryachaeota,which belongs to archaeal phyla,had the highest biostabilization index in our study.We also found that Pseudarthrobacter was the easiest to aerosolize during the sludge biostabilization process.
基金The National Science Foundation of China(41301020)National Key Basic Research Program of China(2013CB956604)Core Research for Evolutional Science and Technology of the Japan Science and Technology.
文摘Aims Boreal forests play an important role in the global carbon cycle.Compared with the boreal forests in North America and Europe,relatively few research studies have been conducted in Siberian boreal forests.Knowledge related to the role of Siberian forests in the global carbon balance is thus essential for a full understanding of global carbon cycle.Methods This study investigated the net ecosystem exchange(NEE)during growing season(May-September)in an eastern Siberian boreal larch forest for a 3-year period in 2004-2006 with contrasting meteorological conditions.Important FindingsThe study found that the forest served as a carbon sink during all of the 3 studied years;in addition,the meteorological conditions essentially influenced the specific annual value of the strength of the carbon sinks in each year.Although 2005 was the warmest year and much wetter than 2004,2005 also featured the greatest amount of ecosystem respiration,which resulted in a minimum value of NEE.The study also found that the phenological changes observed during the three study years had a relatively small effect on annual NEE.Leaf expansion was 26 days earlier in 2005 than in the other 2 years,which resulted in a longer growing season in 2005.However,the NEE in 2005 was counterbalanced by the large rate of ecosystem respiration that was caused by the higher temperatures in the year.This study showed that meteorological variables had larger influences on the interannual variations in NEE for a Siberian boreal larch forest,as compared with phenological changes.The overall results of this study will improve our understanding of the carbon balance of Siberian boreal larch forests and thus can help to forecast the response of these forests to future climate change.
基金This study is supported by the Frontier Science Key Programs of the Chinese Academy of Sciences(QYZDY-SSW-SMC011)the National Natural Science Foundation of China(41871332,31971575,and 41901358)。
文摘Canopy structural complexity is a critical emergent forest attribute,and light detection and ranging(lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree level.However,the current lidar-based estimation method is highly sensitive to data characteristics,and its scalability from individual trees to forest stands remains unclear.This study proposed an improved method to estimate fractal dimension from lidar data by considering Shannon entropy,and evaluated its scalability from individual trees to forest stands through mathematical derivations.Moreover,a total of 280 forest stand scenes simulated from the terrestrial lidar data of 115 trees spanning large variability in canopy structural complexity were used to evaluate the robustness of the proposed method and the scalability of fractal dimension.The results show that the proposed method can significantly improve the robustness of lidar-derived fractal dimensions.Both mathematical derivations and experimental analyses demonstrate that the fractal dimension of a forest stand is equal to that of the tree with the largest fractal dimension in it,manifesting its nonscalability from individual trees to forest stands.The nonscalability of fractal dimension reveals its limited capability in canopy structural complexity quantification and indicates that the power-law scaling theory of a forest stand underlying fractal geometry is determined by its dominant tree instead of the entire community.Nevertheless,we believe that fractal dimension is still a useful indicator of canopy structural complexity at the individual tree level and might be used along with other stand-level indexes to reflect the“tree-to-stand”correlation of canopy structural complexity.
基金supported by the National Natural Science Foundation of China(61971420)the Beijing Brain Initiative of the Beijing Municipal Science and Technology Commission(Z181100001518003)+1 种基金Special Projects of Brain Science of the Beijing Municipal Science and Technology Commission(Z161100000216139 and Z171100000117002)the International Cooperation and Exchange of the National Natural Science Foundation of China(31620103905)。
文摘Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as habits,the family setting,socioeconomic status,and the work environment [1].In contrast to chronological age (CA),brain(or biological) age (BA) is conceptualized as an important index for characterizing the aging process and neuropsychological state,as well as individual cognitiveperformance.Growing evidence indicates that BA can be assessed by neuroimaging techniques,including MRI [2].
