Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especially...Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especiallyimportant issue that requires proper evaluation.This paper introduces a spatiotemporal deep learning model thatincorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts.Thegiven method is a combination of the Spatial-Temporal-Assisted Deep Belief Network(StaDBN)and a hybrid WhaleOptimization Algorithm and Tiki-Taka Algorithms(WOA-TTA)that would model intricate patterns of contamination.Historical ground water data sets with the hydrochemical data and time are preprocessed and pertinent and nonredundant features are determined with the Addax Optimization Algorithm(AOA).Spatial and temporal dependenciesare explicitly integrated in StaDBN architecture to facilitate representation learning,and network hyperparametersare optimized by the WOA-TTA module to increase the training efficiency and predictive performance.The modelwas coded in Python and tested based on common statistical measures,such as root mean square error(RMSE),Nash Sutcliffe efficiency(NSE),mean absolute error(MAE),and the correlation coefficient(R).The proposedGWQP-StaDBN-WOA-TTA framework demonstrates superior predictive performance and interpretability comparedto conventional machine learning and deep learning models,achieving higher correlation(R=0.963),improvedNash-Sutcliffe efficiency(NSE=0.84),and substantially lower prediction errors(MAE=0.29,RMSE=0.48),therebyvalidating its effectiveness for groundwater quality assessment under industrial and atmospheric pollution scenarios.展开更多
Extreme heat and chronic water scarcity present formidable challenges to large desert-dwelling mammals.In addition to camels,antelopes within the Hippotraginae and Alcelaphinae subfamilies also exhibit remarkable phys...Extreme heat and chronic water scarcity present formidable challenges to large desert-dwelling mammals.In addition to camels,antelopes within the Hippotraginae and Alcelaphinae subfamilies also exhibit remarkable physiological and genetic specializations for desert survival.Among them,the critically endangered addax(Addax nasomaculatus)represents the most desert-adapted antelope species.However,the evolutionary and molecular mechanisms underlying desert adaptations remain largely unexplored.Herein,a high-quality genome assembly of the addax was generated to investigate the molecular evolution of desert adaptation in camels and desert antelopes.Comparative genomic analyses identified 136 genes harboring convergent amino acid substitutions implicated in crucial biological processes,including water reabsorption,fat metabolism,and stress response.Notably,a convergent R146S amino acid mutation in the prostaglandin EP2 receptor gene PTGER2 significantly reduced receptor activity,potentially facilitating large-mammal adaptation to arid environments.Lineage-specific innovations were also identified in desert antelopes,including previously uncharacterized conserved non-coding elements.Functional assays revealed that several of these elements exerted significant regulatory effects in vitro,suggesting potential roles in adaptive gene expression.Additionally,signals of introgression and variation in genetic load were observed,indicating their possible influence on desert adaptation.These findings provide insights into the sequential evolutionary processes that drive physiological resilience in arid environments and highlight the importance of convergent evolution in shaping adaptive traits in large terrestrial mammals.展开更多
以茂名野生动物园斑鼻羚体内分离出的毛首线虫为研究对象,用保守引物PCR扩增其核糖体DNA（rDNA）的内转录间隔区（ITS）和5.8 S序列,并进行克隆、转化、测序和序列分析,对样品进行分子鉴定。结果获得2个毛首线虫样品的ITS及5.8 S rDNA序...以茂名野生动物园斑鼻羚体内分离出的毛首线虫为研究对象,用保守引物PCR扩增其核糖体DNA（rDNA）的内转录间隔区（ITS）和5.8 S序列,并进行克隆、转化、测序和序列分析,对样品进行分子鉴定。结果获得2个毛首线虫样品的ITS及5.8 S rDNA序列,总长为1 316 bp,样品间序列相似性为99.2%。将序列与GenBankTM公布的相关序列进行比较分析,结果显示与羊毛首线虫的ITS1、5.8 S和ITS2序列相似性高,分别为97.3%-97.6%、100%和97.8%-98.0%,表明斑鼻羚体内分离的毛首线虫属于羊毛首线虫。展开更多
基金Fund for funding this research work under Research Support Program for Central labs at King Khalid University through the project number CL/CO/B/6.
文摘Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especiallyimportant issue that requires proper evaluation.This paper introduces a spatiotemporal deep learning model thatincorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts.Thegiven method is a combination of the Spatial-Temporal-Assisted Deep Belief Network(StaDBN)and a hybrid WhaleOptimization Algorithm and Tiki-Taka Algorithms(WOA-TTA)that would model intricate patterns of contamination.Historical ground water data sets with the hydrochemical data and time are preprocessed and pertinent and nonredundant features are determined with the Addax Optimization Algorithm(AOA).Spatial and temporal dependenciesare explicitly integrated in StaDBN architecture to facilitate representation learning,and network hyperparametersare optimized by the WOA-TTA module to increase the training efficiency and predictive performance.The modelwas coded in Python and tested based on common statistical measures,such as root mean square error(RMSE),Nash Sutcliffe efficiency(NSE),mean absolute error(MAE),and the correlation coefficient(R).The proposedGWQP-StaDBN-WOA-TTA framework demonstrates superior predictive performance and interpretability comparedto conventional machine learning and deep learning models,achieving higher correlation(R=0.963),improvedNash-Sutcliffe efficiency(NSE=0.84),and substantially lower prediction errors(MAE=0.29,RMSE=0.48),therebyvalidating its effectiveness for groundwater quality assessment under industrial and atmospheric pollution scenarios.
基金supported by the National Key R&D Program of China(2022YFF1000100)Shaanxi Program for Support of Top-notch Young ProfessionalsFundamental Research Funds for the Central Universities。
文摘Extreme heat and chronic water scarcity present formidable challenges to large desert-dwelling mammals.In addition to camels,antelopes within the Hippotraginae and Alcelaphinae subfamilies also exhibit remarkable physiological and genetic specializations for desert survival.Among them,the critically endangered addax(Addax nasomaculatus)represents the most desert-adapted antelope species.However,the evolutionary and molecular mechanisms underlying desert adaptations remain largely unexplored.Herein,a high-quality genome assembly of the addax was generated to investigate the molecular evolution of desert adaptation in camels and desert antelopes.Comparative genomic analyses identified 136 genes harboring convergent amino acid substitutions implicated in crucial biological processes,including water reabsorption,fat metabolism,and stress response.Notably,a convergent R146S amino acid mutation in the prostaglandin EP2 receptor gene PTGER2 significantly reduced receptor activity,potentially facilitating large-mammal adaptation to arid environments.Lineage-specific innovations were also identified in desert antelopes,including previously uncharacterized conserved non-coding elements.Functional assays revealed that several of these elements exerted significant regulatory effects in vitro,suggesting potential roles in adaptive gene expression.Additionally,signals of introgression and variation in genetic load were observed,indicating their possible influence on desert adaptation.These findings provide insights into the sequential evolutionary processes that drive physiological resilience in arid environments and highlight the importance of convergent evolution in shaping adaptive traits in large terrestrial mammals.
