Understanding gene expression variations between species is pivotal for deciphering the evolutionary diversity in phenotypes. Rhesus macaques(Macaca mulatta, MMU)and crab-eating macaques(M. fascicularis, MFA) serve as...Understanding gene expression variations between species is pivotal for deciphering the evolutionary diversity in phenotypes. Rhesus macaques(Macaca mulatta, MMU)and crab-eating macaques(M. fascicularis, MFA) serve as crucial nonhuman primate biomedical models with different phenotypes. To date, however, large-scale comparative transcriptome research between these two species has not yet been fully explored. Here, we conducted systematic comparisons utilizing newly sequenced RNA-seq data from84 samples(41 MFA samples and 43 MMU samples)encompassing 14 common tissues. Our findings revealed a small fraction of genes(3.7%) with differential expression between the two species, as well as 36.5% of genes with tissue-specific expression in both macaques. Comparison of gene expression between macaques and humans indicated that 22.6% of orthologous genes displayed differential expression in at least two tissues. Moreover,19.41% of genes that overlapped with macaque-specific structural variants showed differential expression between humans and macaques. Of these, the FAM220A gene exhibited elevated expression in humans compared to macaques due to lineage-specific duplication. In summary,this study presents a large-scale transcriptomic comparison between MMU and MFA and between macaques and humans. The discovery of gene expression variations not only enhances the biomedical utility of macaque models but also contributes to the wider field of primate genomics.展开更多
Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a prom...Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.展开更多
针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并...针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。展开更多
Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanc...Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating formula.The proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction tasks.iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets.It ranked first in 11 out of 17 datasets in terms of average classification accuracy.Moreover,iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets.The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.展开更多
In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLS...In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.展开更多
为探明同域分布的鼬獾(Melogale moschata)和食蟹獴(Herpestes urva)的日活动节律、时间生态位及其两者共存机制,于2016年12月至2017年12月,在广西弄岗国家级自然保护区开展红外相机监测获取数据,后采用核密度估计(Kernel density estim...为探明同域分布的鼬獾(Melogale moschata)和食蟹獴(Herpestes urva)的日活动节律、时间生态位及其两者共存机制,于2016年12月至2017年12月,在广西弄岗国家级自然保护区开展红外相机监测获取数据,后采用核密度估计(Kernel density estimation)和重叠指数(Coefficient of overlap)对该地区的鼬獾和食蟹獴进行了研究。结果表明:鼬獾属于典型的夜行性动物,其日活动模式为双峰型,活动峰值出现在01:00—05:00和21:00—24:00;与雨季相比,旱季的活动峰值均提前1 h,活动高峰期延长3 h。食蟹獴为典型昼行性动物,其日活动模式为单峰型,活动峰值出现在11:00—18:00,与雨季相比,旱季活动高峰期提前并延长1 h。两者间的日活动模式重叠指数较低(Δ=0.17),但旱季(Δ=0.22)高于雨季(Δ=0.12)。两者主要通过改变时间生态位来避免对食物资源和领域的竞争,从而实现共存。展开更多
This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but...This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.展开更多
The Egyptia n mon goose (Herpestes ichneumon Linn aeus, 1758) is a medium-sized car nivore that experienced remarkable geographic expansion over the last 3 decades in the Iberian Peninsula. In this study, we investiga...The Egyptia n mon goose (Herpestes ichneumon Linn aeus, 1758) is a medium-sized car nivore that experienced remarkable geographic expansion over the last 3 decades in the Iberian Peninsula. In this study, we investigated the association of species-related and abiotic factors with spleen weight (as a proxy for immunocompete nee) in the species. We assessed the relationship of body con dition, sex, age, seas on, and envir onmental conditi ons with splee n weight established for 508 hunted specimens. Our results indicate that the effects of sex and season outweigh those of all other variables, including body condition. Spleen weight is higher in males than in females, and heavier spleens are more likely to be found in spring, coinciding with the highest period of investment in reproduction due to mating, gestation, birth, and lactation. Coupled with the absence of an effect of body condition, our findi ngs suggest that splee n weight variation in this species is mostly influe need by lifehistory traits linked to reproduction, rather than overall energy availability, winter immunoenhancement, or energy partitioning effects, and prompt further research focusing on this topic.展开更多
基金supported by the National Natural Science Foundation of China (82021001 and 31825018 to Q.S., 32370658 to Y.M.,82001372 to X.Y.)National Key Research and Development Program of China (2022YFF0710901)+2 种基金National Science and Technology Innovation2030 Major Program (2021ZD0200900) to Q.S.Shanghai Pujiang Program (22PJ1407300)Shanghai Jiao Tong University 2030 Initiative (WH510363001-7) to Y.M。
文摘Understanding gene expression variations between species is pivotal for deciphering the evolutionary diversity in phenotypes. Rhesus macaques(Macaca mulatta, MMU)and crab-eating macaques(M. fascicularis, MFA) serve as crucial nonhuman primate biomedical models with different phenotypes. To date, however, large-scale comparative transcriptome research between these two species has not yet been fully explored. Here, we conducted systematic comparisons utilizing newly sequenced RNA-seq data from84 samples(41 MFA samples and 43 MMU samples)encompassing 14 common tissues. Our findings revealed a small fraction of genes(3.7%) with differential expression between the two species, as well as 36.5% of genes with tissue-specific expression in both macaques. Comparison of gene expression between macaques and humans indicated that 22.6% of orthologous genes displayed differential expression in at least two tissues. Moreover,19.41% of genes that overlapped with macaque-specific structural variants showed differential expression between humans and macaques. Of these, the FAM220A gene exhibited elevated expression in humans compared to macaques due to lineage-specific duplication. In summary,this study presents a large-scale transcriptomic comparison between MMU and MFA and between macaques and humans. The discovery of gene expression variations not only enhances the biomedical utility of macaque models but also contributes to the wider field of primate genomics.
基金This research work was funded by Institutional Fund Projects under grant no.(IFPIP:14-611-1443)Therefore,the authors gratefully acknowledge technical and financial support provided by the Ministry of Education and Deanship of Scientific Research(DSR),King Abdulaziz University(KAU),Jeddah,Saudi Arabia.
文摘Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.
文摘针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。
基金supported by the Deanship of Scientific Research and Innovation at Al-Balqa Applied University in Jordan.
文摘Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating formula.The proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction tasks.iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets.It ranked first in 11 out of 17 datasets in terms of average classification accuracy.Moreover,iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets.The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.
基金National Natural Science Foundation of China,Grant No.52375264.
文摘In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.
文摘为探明同域分布的鼬獾(Melogale moschata)和食蟹獴(Herpestes urva)的日活动节律、时间生态位及其两者共存机制,于2016年12月至2017年12月,在广西弄岗国家级自然保护区开展红外相机监测获取数据,后采用核密度估计(Kernel density estimation)和重叠指数(Coefficient of overlap)对该地区的鼬獾和食蟹獴进行了研究。结果表明:鼬獾属于典型的夜行性动物,其日活动模式为双峰型,活动峰值出现在01:00—05:00和21:00—24:00;与雨季相比,旱季的活动峰值均提前1 h,活动高峰期延长3 h。食蟹獴为典型昼行性动物,其日活动模式为单峰型,活动峰值出现在11:00—18:00,与雨季相比,旱季活动高峰期提前并延长1 h。两者间的日活动模式重叠指数较低(Δ=0.17),但旱季(Δ=0.22)高于雨季(Δ=0.12)。两者主要通过改变时间生态位来避免对食物资源和领域的竞争,从而实现共存。
文摘This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
文摘The Egyptia n mon goose (Herpestes ichneumon Linn aeus, 1758) is a medium-sized car nivore that experienced remarkable geographic expansion over the last 3 decades in the Iberian Peninsula. In this study, we investigated the association of species-related and abiotic factors with spleen weight (as a proxy for immunocompete nee) in the species. We assessed the relationship of body con dition, sex, age, seas on, and envir onmental conditi ons with splee n weight established for 508 hunted specimens. Our results indicate that the effects of sex and season outweigh those of all other variables, including body condition. Spleen weight is higher in males than in females, and heavier spleens are more likely to be found in spring, coinciding with the highest period of investment in reproduction due to mating, gestation, birth, and lactation. Coupled with the absence of an effect of body condition, our findi ngs suggest that splee n weight variation in this species is mostly influe need by lifehistory traits linked to reproduction, rather than overall energy availability, winter immunoenhancement, or energy partitioning effects, and prompt further research focusing on this topic.
