Soil carbon stock research has gained prominence in environmental studies amidst climate change concerns,especially given that soil is one of the largest terrestrial carbon reserves.Accurate predictions necessitate co...Soil carbon stock research has gained prominence in environmental studies amidst climate change concerns,especially given that soil is one of the largest terrestrial carbon reserves.Accurate predictions necessitate comprehensive soil profile measurements,which are resource-intensive to obtain.To address this,depth functions are employed to derive continuous estimates,aligning with standardized depths.However,global datasets employing depth functions in raster format have not been widely utilized,which could lower financial costs and improve accuracy in data-scarce regions.Furthermore,research into aggregating depth functions for realistic carbon stock estimations remains limited,offering opportunities to streamline cost and time.The aim of this study was to apply equal-area splines to estimate soil carbon stocks,utilizing SoilGrids and iSDAsoil datasets in a 317-km^(2) Quaternary catchment(30°48′E,29°18′S)in KwaZulu-Natal,South Africa.Both datasets were resampled to a 250-m resolution,and the splines were interpolated to a depth of 50 cm per pixel.Various aggregation methods were employed in calculation,including the cumulative sum(definite integral),discrete sum(sum of 1-cm spline predictions),and the mean carbon stock(mean to 50 cm).Quantitative evaluation was performed with 310 external soil samples.SoilGrids showed higher predictions(100–546 kg m^(-2))than iSDAsoil(66.9–225 kg m^(-2))for the cumulative sum.The discrete sum also exhibited higher prediction values for SoilGrids(293–789 kg m^(-2))compared to iSDAsoil(228–557 kg m^(-2)).SoilGrids aggregated with the discrete sum closely matched previous studies,estimating total carbon stock for the catchment at 7126 t,albeit with spatial inconsistencies.However,when evaluating with an external dataset,the results were not satisfactory for any method according to Lin's concordance correlation coefficient(CCC,correlation of a 1:1 line),with all models obtaining a CCC below 0.01.Similarly,all models had a root mean squared error larger than 59 kg m^(-2).It was concluded that SoilGrids and iSDAsoil were spatially inaccurate in the catchment but can still provide information about the total carbon stock.This method could be improved by obtaining more soil samples for the datasets,incorporating local data into the spline,making the method more computationally efficient,and accounting for discrete horizon boundaries.展开更多
The paternally inherited Y chromosome has been widely used in forensics for personal identification, in anthropology and population genetics to understand origin and migration of human populations, and also in medical...The paternally inherited Y chromosome has been widely used in forensics for personal identification, in anthropology and population genetics to understand origin and migration of human populations, and also in medical and clinical studies （Wang and Li, 2013; Wang et al., 2014）. There are two kinds of extremely useful markers in Y chromosome, single nucle- otide polymorphism （SNP） and short tandem repeats （STRs）. With a very low mutation rate on the order of 3.0 x 10-8 mutations/nucleotide/generation （Xue et al., 2009）, SNP markers have been used in constructing a robust phylogeny tree linking all the Y chromosome lineages from world pop- ulations （Karafet et al., 2008）. Those lineages determined by the pattern of SNPs are called haplogroups. That is to say, we have to genotype an appropriate number of SNPs in order to assign a given Y chromosome to a haplogroup. Compared with SNPs, the mutation rates of STR markers are about four to five orders of magnitude higher （Gusmgo et al., 2005; Ballantyne et al., 2010）. Typing STR has advantages of saving time and cost compared with typing SNPs in phylogenetic assignment of a Y chromosome （Wang et al., 2010）. A set of STR values for an individual is called a haplotype. Because of the disparity in mutation rates between SNP and STR, one SNP haplogroup could actually comprise many STR haplotypes （Wang et al., 2010）. It is most interesting that STR variability is clustered more by haplogroups than by populations （Bosch et al., 1999; Behar et al., 2004）, which indicates that STR haplotypes could be used to infer the haplogroup information of a given Y chromosome. There has been increasing interest in this cost- effective strategy for predicting the haplogroup from a given STR haplotype when SNP data are unavailable. For instance, Vadim Urasin＇s YPredictor （http：//predictor.ydna.ru/）, Whit Atheys＇ haplogroup predictor （http：//www.hprg.com/hapest5/） （Athey, 2005, 2006）, and haplogroup classifier of Arizona University （Schlecht et al., 2008） have been widely employed in previous studies for haplogroup prediction （Larmuseau et al., 2010; Bembea et al., 2011; Larmuseau et al., 2012; Tarlykov et al., 2013）.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
Internet of things(IoT)has become more popular due to the development and potential of smart technology aspects.Security concerns against IoT infrastructure,applications,and devices have grown along with the need for ...Internet of things(IoT)has become more popular due to the development and potential of smart technology aspects.Security concerns against IoT infrastructure,applications,and devices have grown along with the need for IoT technologies.Enhanced system security protocols are difficult due to the diverse capabilities of IoT devices and the dynamic,ever-changing environment,and simply applying basic security requirements is dangerous.Therefore,this proposed work designs a malware detection and prevention approach for secure data transmission among IoT gadgets.The malware detection approach is designed with the aid of a deep learning approach.The initial process is identifying attack nodes from normal nodes through a trust value using contextual features.After discovering attack nodes,these are considered for predicting different kinds of attacks present in the network,while some preprocessing and feature extraction strategies are applied for effective classification.The Deep LSTM classifier is applied for this malware detection approach.Once completed malware detection,prevention is performed with the help of the Improved Elliptic Curve Cryptography(IECC)algorithm.A hybrid MA-BW optimization is adopted for selecting the optimal key during transmission.Python 3.8 software is used to test the performance of the proposed approach,and several existing techniques are considered to evaluate its performance.The proposed approach obtained 95%of accuracy,5%of error value and 92%of precision.In addition,the improved ECC algorithm is also compared with some existing algorithm which takes 6.02 s of execution time.Compared to the other methods,the proposed approach provides better security to IoT gadgets during data transmission.展开更多
文摘Soil carbon stock research has gained prominence in environmental studies amidst climate change concerns,especially given that soil is one of the largest terrestrial carbon reserves.Accurate predictions necessitate comprehensive soil profile measurements,which are resource-intensive to obtain.To address this,depth functions are employed to derive continuous estimates,aligning with standardized depths.However,global datasets employing depth functions in raster format have not been widely utilized,which could lower financial costs and improve accuracy in data-scarce regions.Furthermore,research into aggregating depth functions for realistic carbon stock estimations remains limited,offering opportunities to streamline cost and time.The aim of this study was to apply equal-area splines to estimate soil carbon stocks,utilizing SoilGrids and iSDAsoil datasets in a 317-km^(2) Quaternary catchment(30°48′E,29°18′S)in KwaZulu-Natal,South Africa.Both datasets were resampled to a 250-m resolution,and the splines were interpolated to a depth of 50 cm per pixel.Various aggregation methods were employed in calculation,including the cumulative sum(definite integral),discrete sum(sum of 1-cm spline predictions),and the mean carbon stock(mean to 50 cm).Quantitative evaluation was performed with 310 external soil samples.SoilGrids showed higher predictions(100–546 kg m^(-2))than iSDAsoil(66.9–225 kg m^(-2))for the cumulative sum.The discrete sum also exhibited higher prediction values for SoilGrids(293–789 kg m^(-2))compared to iSDAsoil(228–557 kg m^(-2)).SoilGrids aggregated with the discrete sum closely matched previous studies,estimating total carbon stock for the catchment at 7126 t,albeit with spatial inconsistencies.However,when evaluating with an external dataset,the results were not satisfactory for any method according to Lin's concordance correlation coefficient(CCC,correlation of a 1:1 line),with all models obtaining a CCC below 0.01.Similarly,all models had a root mean squared error larger than 59 kg m^(-2).It was concluded that SoilGrids and iSDAsoil were spatially inaccurate in the catchment but can still provide information about the total carbon stock.This method could be improved by obtaining more soil samples for the datasets,incorporating local data into the spline,making the method more computationally efficient,and accounting for discrete horizon boundaries.
基金supported by the National Excellent Youth Science Foundation of China(No.31222030)the National Natural Science Foundation of China(No.91131002)+3 种基金the Shanghai Rising-Star Program(No.12QA1400300)the China Ministry of Education Scientific Research Major Project(Nos. 311016 and 113022A)the MOE University Doctoral Research Supervisor's Funds(No.20120071110021)the Shanghai Professional Development Funding(No.2010001)
文摘The paternally inherited Y chromosome has been widely used in forensics for personal identification, in anthropology and population genetics to understand origin and migration of human populations, and also in medical and clinical studies （Wang and Li, 2013; Wang et al., 2014）. There are two kinds of extremely useful markers in Y chromosome, single nucle- otide polymorphism （SNP） and short tandem repeats （STRs）. With a very low mutation rate on the order of 3.0 x 10-8 mutations/nucleotide/generation （Xue et al., 2009）, SNP markers have been used in constructing a robust phylogeny tree linking all the Y chromosome lineages from world pop- ulations （Karafet et al., 2008）. Those lineages determined by the pattern of SNPs are called haplogroups. That is to say, we have to genotype an appropriate number of SNPs in order to assign a given Y chromosome to a haplogroup. Compared with SNPs, the mutation rates of STR markers are about four to five orders of magnitude higher （Gusmgo et al., 2005; Ballantyne et al., 2010）. Typing STR has advantages of saving time and cost compared with typing SNPs in phylogenetic assignment of a Y chromosome （Wang et al., 2010）. A set of STR values for an individual is called a haplotype. Because of the disparity in mutation rates between SNP and STR, one SNP haplogroup could actually comprise many STR haplotypes （Wang et al., 2010）. It is most interesting that STR variability is clustered more by haplogroups than by populations （Bosch et al., 1999; Behar et al., 2004）, which indicates that STR haplotypes could be used to infer the haplogroup information of a given Y chromosome. There has been increasing interest in this cost- effective strategy for predicting the haplogroup from a given STR haplotype when SNP data are unavailable. For instance, Vadim Urasin＇s YPredictor （http：//predictor.ydna.ru/）, Whit Atheys＇ haplogroup predictor （http：//www.hprg.com/hapest5/） （Athey, 2005, 2006）, and haplogroup classifier of Arizona University （Schlecht et al., 2008） have been widely employed in previous studies for haplogroup prediction （Larmuseau et al., 2010; Bembea et al., 2011; Larmuseau et al., 2012; Tarlykov et al., 2013）.
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].
文摘Internet of things(IoT)has become more popular due to the development and potential of smart technology aspects.Security concerns against IoT infrastructure,applications,and devices have grown along with the need for IoT technologies.Enhanced system security protocols are difficult due to the diverse capabilities of IoT devices and the dynamic,ever-changing environment,and simply applying basic security requirements is dangerous.Therefore,this proposed work designs a malware detection and prevention approach for secure data transmission among IoT gadgets.The malware detection approach is designed with the aid of a deep learning approach.The initial process is identifying attack nodes from normal nodes through a trust value using contextual features.After discovering attack nodes,these are considered for predicting different kinds of attacks present in the network,while some preprocessing and feature extraction strategies are applied for effective classification.The Deep LSTM classifier is applied for this malware detection approach.Once completed malware detection,prevention is performed with the help of the Improved Elliptic Curve Cryptography(IECC)algorithm.A hybrid MA-BW optimization is adopted for selecting the optimal key during transmission.Python 3.8 software is used to test the performance of the proposed approach,and several existing techniques are considered to evaluate its performance.The proposed approach obtained 95%of accuracy,5%of error value and 92%of precision.In addition,the improved ECC algorithm is also compared with some existing algorithm which takes 6.02 s of execution time.Compared to the other methods,the proposed approach provides better security to IoT gadgets during data transmission.
