Chromosome segregation in mitosis is orchestrated by the interaction of the kinetochore with spindle microtubules. Our recent study shows that NEK2A interacts with MAD 1 at the kinetochore and possibly functions as a ...Chromosome segregation in mitosis is orchestrated by the interaction of the kinetochore with spindle microtubules. Our recent study shows that NEK2A interacts with MAD 1 at the kinetochore and possibly functions as a novel integrator of spindle checkpoint signaling. However, it is unclear how NEK2A regulates kinetochore-microtubule attachment in mitosis. Here we show that NEK2A phosphorylates human Sgo 1 and such phosphorylation is essential for faithful chromosome congression in mitosis. NEK2A binds directly to HsSgol in vitro and co-distributes with HsSgol to the kinetochore of mitotic cells. Our in vitro phosphorylation experiment demonstrated that HsSgo 1 is a substrate of NEK2A and the phosphorylation sites were mapped to Ser^14 and Ser^507 as judged by the incorporation of 32^P. Although such phosphorylation is not required for assembly of HsSgo 1 to the kinetochore, expression of non-phosphorylatable mutant HsSgo 1 perturbed chromosome congression and resulted in a dramatic increase in microtubule attachment errors, including syntelic and monotelic attachments. These findings reveal a key role for the NEK2A-mediated phosphorylation ofHsSgo 1 in orchestrating dynamic kinetochore-microtubule interaction. We propose that NEK2A-mediated phosphorylation of human Sgo 1 provides a link between centromeric cohesion and spindle microtubule attachment at the kinetochores.展开更多
Shugoshin-1(Sgo1)is necessary for maintaining sister centromere cohesion and ensuring accurate chromosome segregation during mitosis.It has been reported that the localization of Sgo1 at the centromere is dependent on...Shugoshin-1(Sgo1)is necessary for maintaining sister centromere cohesion and ensuring accurate chromosome segregation during mitosis.It has been reported that the localization of Sgo1 at the centromere is dependent on Bub1-mediated phosphorylation of histone H2A at T120.However,it remains uncertain whether other centromeric proteins play a role in regulating the localization and function of Sgo1 during mitosis.Here,we show that CENP-A interacts with Sgo1 and determines the localization of Sgo1 to the centromere during mitosis.Further biochemical characterization revealed that lysine and arginine residues in the C-terminal domain of Sgo1 are critical for binding CENP-A.Interestingly,the replacement of these basic amino acids with acidic amino acids perturbed the localization of Sgo1 and Aurora B to the centromere,resulting in aberrant chromosome segregation and premature chromatid separation.Taken together,these findings reveal a previously unrecognized but direct link between Sgo1 and CENP-A in centromere plasticity control and illustrate how the Sgo1–CENP-A interaction guides accurate cell division.展开更多
地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算...地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算法(Gradient-Based Optimizer,GBO),引入地震反演.相比于差分进化等其他全局优化算法,GBO算法通过梯度随机搜索机制和局部逃逸算子进行全局搜索,能有效降低地震反演的多解性.但是,GBO算法收敛速度慢和局部随机性强,难以满足大批量的地震反演计算需求.因此,本文在GBO算法迭代过程中引入Wolfe线性局部搜索机制,提出基于Wolfe搜索的随机梯度优化算法(Stochastic—Gradient Optimization Based on Wolfe's Search,SGO-WS).在全局搜索过程中,通过线性搜索算子,充分挖掘当前迭代解周围的局部最优,既保证了反演解精度,又大幅提高了原GBO算法的计算效率,同时还有效降低了反演解的局部随机性.Marmousi-2模型测试验证了SGO-WS算法的可行性和准确性,厄瓜多尔Tapir油田地震资料也验证了SGO-WS算法的实用性.展开更多
This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often lim...This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often limit its adaptability,as they don’t account for variations across organizations.By fine-tuning these parameters with SGO,we aim to improve estimation accuracy.We train and validate our SGO-enhanced model using historical project data,evaluating its performance with metrics like the mean magnitude of relative error(MMRE)and Manhattan distance(MD).Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost models,offering valuable insights for project managers and practitioners in the field.However,the approach’s effectiveness may vary depending on the quality and quantity of available historical data,and its scalability across diverse project types and sizes remains a key consideration for future research.展开更多
Till now,several novel metaheuristic algorithms are proposed for global search.But only specific algorithms have become popular or attracted researchers,who are efficient in solving global optimization problems as wel...Till now,several novel metaheuristic algorithms are proposed for global search.But only specific algorithms have become popular or attracted researchers,who are efficient in solving global optimization problems as well as real-world application problems.The Social Group Optimization(SGO)algorithm is a new metaheuristic bioinspired algorithm inspired by human social behavior that attracted researchers due to its simplicity and problem-solving capability.In this study,to deal with the problems of low accuracy and local convergence in SGO,the chaos theory is introduced into the evolutionary process of SGO.Since chaotic mapping has certainty,ergodicity,and stochastic property,by replacing the constant value of the self-introspection parameter with chaotic maps,the proposed chaotic social group optimization algorithm increases its convergence rate and resulting precision.The proposal chaotic SGO is validated through 13 benchmark functions and after that 9 structural engineering design problems have been solved.The simulated results have been noticed as competent with that of state-of-art algorithms regarding convergence quality and accuracy,which certifies that improved SGO with chaos is valid and feasible.展开更多
基金We thank members of our group for insightful discussion during the course of this study.This work was supported by grants from Chinese Academy of Science(KSCX1-YW-R65,KSCX2-YW-H10)National Basic Research Program of China(2002CB713700)+4 种基金Hi-Tech Research and Development Program of China(2001AA215331)Chinese Minister of Education(20020358051 to XY,PCSIRT0413 to XD)National Natural Science Foundation of China(39925018,30270293 to XY,30500183 to XD,30600222 to JY)National Institutes of Health(USA)(DK56292,CA92080)to XY(a Georgia Cancer Coalition Eminent Scholar)JY was supported by China Postdoctor(2005037560).
文摘Chromosome segregation in mitosis is orchestrated by the interaction of the kinetochore with spindle microtubules. Our recent study shows that NEK2A interacts with MAD 1 at the kinetochore and possibly functions as a novel integrator of spindle checkpoint signaling. However, it is unclear how NEK2A regulates kinetochore-microtubule attachment in mitosis. Here we show that NEK2A phosphorylates human Sgo 1 and such phosphorylation is essential for faithful chromosome congression in mitosis. NEK2A binds directly to HsSgol in vitro and co-distributes with HsSgol to the kinetochore of mitotic cells. Our in vitro phosphorylation experiment demonstrated that HsSgo 1 is a substrate of NEK2A and the phosphorylation sites were mapped to Ser^14 and Ser^507 as judged by the incorporation of 32^P. Although such phosphorylation is not required for assembly of HsSgo 1 to the kinetochore, expression of non-phosphorylatable mutant HsSgo 1 perturbed chromosome congression and resulted in a dramatic increase in microtubule attachment errors, including syntelic and monotelic attachments. These findings reveal a key role for the NEK2A-mediated phosphorylation ofHsSgo 1 in orchestrating dynamic kinetochore-microtubule interaction. We propose that NEK2A-mediated phosphorylation of human Sgo 1 provides a link between centromeric cohesion and spindle microtubule attachment at the kinetochores.
基金supported by grants from the Ministry of Science and Technology of China(2022YFA1303100,2022YFA0806800,2022YFA1302700,and 2017YFA0503600)the National Natural Science Foundation of China(32090040,92254302,92153302,32170733,31621002,and 22177106)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB19040000 and XDB37010105)the Ministry of Education(IRT_17R102,20113402130010,and YD2070006001).
文摘Shugoshin-1(Sgo1)is necessary for maintaining sister centromere cohesion and ensuring accurate chromosome segregation during mitosis.It has been reported that the localization of Sgo1 at the centromere is dependent on Bub1-mediated phosphorylation of histone H2A at T120.However,it remains uncertain whether other centromeric proteins play a role in regulating the localization and function of Sgo1 during mitosis.Here,we show that CENP-A interacts with Sgo1 and determines the localization of Sgo1 to the centromere during mitosis.Further biochemical characterization revealed that lysine and arginine residues in the C-terminal domain of Sgo1 are critical for binding CENP-A.Interestingly,the replacement of these basic amino acids with acidic amino acids perturbed the localization of Sgo1 and Aurora B to the centromere,resulting in aberrant chromosome segregation and premature chromatid separation.Taken together,these findings reveal a previously unrecognized but direct link between Sgo1 and CENP-A in centromere plasticity control and illustrate how the Sgo1–CENP-A interaction guides accurate cell division.
文摘地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算法(Gradient-Based Optimizer,GBO),引入地震反演.相比于差分进化等其他全局优化算法,GBO算法通过梯度随机搜索机制和局部逃逸算子进行全局搜索,能有效降低地震反演的多解性.但是,GBO算法收敛速度慢和局部随机性强,难以满足大批量的地震反演计算需求.因此,本文在GBO算法迭代过程中引入Wolfe线性局部搜索机制,提出基于Wolfe搜索的随机梯度优化算法(Stochastic—Gradient Optimization Based on Wolfe's Search,SGO-WS).在全局搜索过程中,通过线性搜索算子,充分挖掘当前迭代解周围的局部最优,既保证了反演解精度,又大幅提高了原GBO算法的计算效率,同时还有效降低了反演解的局部随机性.Marmousi-2模型测试验证了SGO-WS算法的可行性和准确性,厄瓜多尔Tapir油田地震资料也验证了SGO-WS算法的实用性.
文摘This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often limit its adaptability,as they don’t account for variations across organizations.By fine-tuning these parameters with SGO,we aim to improve estimation accuracy.We train and validate our SGO-enhanced model using historical project data,evaluating its performance with metrics like the mean magnitude of relative error(MMRE)and Manhattan distance(MD).Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost models,offering valuable insights for project managers and practitioners in the field.However,the approach’s effectiveness may vary depending on the quality and quantity of available historical data,and its scalability across diverse project types and sizes remains a key consideration for future research.
文摘Till now,several novel metaheuristic algorithms are proposed for global search.But only specific algorithms have become popular or attracted researchers,who are efficient in solving global optimization problems as well as real-world application problems.The Social Group Optimization(SGO)algorithm is a new metaheuristic bioinspired algorithm inspired by human social behavior that attracted researchers due to its simplicity and problem-solving capability.In this study,to deal with the problems of low accuracy and local convergence in SGO,the chaos theory is introduced into the evolutionary process of SGO.Since chaotic mapping has certainty,ergodicity,and stochastic property,by replacing the constant value of the self-introspection parameter with chaotic maps,the proposed chaotic social group optimization algorithm increases its convergence rate and resulting precision.The proposal chaotic SGO is validated through 13 benchmark functions and after that 9 structural engineering design problems have been solved.The simulated results have been noticed as competent with that of state-of-art algorithms regarding convergence quality and accuracy,which certifies that improved SGO with chaos is valid and feasible.
