Effect of mutations on acetohydroxyacid synthase(AHAS)function in Cyperus difformis L.

Cyperus difformis L.is a troublesome weed in paddy fields and has attracted attention due to its resistance to acetohydroxyacid synthase(AHAS)inhibitors.It was found that the amino acid mutation in AHAS was the primary cause for the resistance of Cyperus difformis.However,the effect of different mutations on AHAS function is not clear in Cyperus difformis.To confirm the effect of mutations on AHAS function,six biotypes were collected,including Pro197Arg,Pro197Ser,Pro197Leu,Asp376Glu,Trp574Leu and wild type,from Hunan,Anhui,Jiangxi and Jiangsu provinces,China and the function of AHAS was characterized.The AHAS in vitro inhibition assay results indicated that the mutations decreased the sensitivity of AHAS to pyrazosulfuron-ethyl,in which the I_(50)(the half maximal inhibitory concentration)of wild type AHAS was 0.04μmol L^(-1)and Asp376Glu,Pro197Leu,Pro197Arg,Pro197Ser and Trp574Leu mutations were 3.98,11.50,40.38,38.19 and 311.43μmol L^(-1),respectively.In the determination of enzyme kinetics parameters,the Km and the maximum reaction velocity(Vmax)of the wild type were 5.18 mmol L^(-1)and 0.12 nmol mg^(-1)min^(-1),respectively,and the Km values of AHAS with Asp376Glu,Trp574Leu,Pro197Leu and Pro197Ser mutations were 0.38-0.93 times of the wild type.The Km value of the Pro197Arg mutation was 1.14times of the wild type,and the Vmax values of the five mutations were 1.17-3.33-fold compared to the wild type.It was found that the mutations increased the affinity of AHAS to the substrate,except for the Pro197Arg mutation.At a concentration of 0.0032-100 mmol L^(-1)branched-chain amino acids(BCAAs),the sensitivity of the other four mutant AHAS biotypes to feedback inhibition decreased,except for the Pro197Arg mutation.This study elucidated the effect of different mutations on AHAS function in Cyperus difformis and provided ideas for further study of resistance development.

THE AVERAGE ABUNDANCE FUNCTION WITH MUTATION OF THE MULTI-PLAYER SNOWDRIFT EVOLUTIONARY GAME MODEL

This article explores the characteristics of the average abundance function with mutation on the basis of the multi-player snowdrift evolutionary game model by analytical analysis and numerical simulation.The specific field of this research concerns the approximate expressions of the average abundance function with mutation on the basis of different levels of selection intensity and an analysis of the results of numerical simulation on the basis of the intuitive expression of the average abundance function.In addition,the biological background of this research lies in research on the effects of mutation,which is regarded as a biological concept and a disturbance to game behavior on the average abundance function.The mutation will make the evolutionary result get closer to the neutral drift state.It can be deduced that this affection is not only related to mutation,but also related to selection intensity and the gap between payoff and aspiration level.The main research findings contain four aspects.First,we have deduced the concrete expression of the expected payoff function.The asymptotic property and change trend of the expected payoff function has been basically obtained.In addition,the intuitive expression of the average abundance function with mutation has been obtained by taking the detailed balance condition as the point of penetration.It can be deduced that the effect of mutation is to make the average abundance function get close to 1/2.In addition,this affection is related to selection intensity and the gap.Secondly,the first-order Taylor expansion of the average abundance function has been deduced for when selection intensity is sufficiently small.The expression of the average abundance function with mutation can be simplified from a composite function to a linear function because of this Taylor expansion.This finding will play a significant role when analyzing the results of the numerical simulation.Thirdly,we have obtained the approximate expressions of the average abundance function corresponding to small and large selection intensity.The significance of the above approximate analysis lies in that we have grasped the basic characteristics of the effect of mutation.The effect is slight and can be neglected when mutation is very small.In addition,the effect begins to increase when mutation rises,and this effect will become more remarkable with the increase of selection intensity.Fourthly,we have explored the influences of parameters on the average abundance function with mutation through numerical simulation.In addition,the corresponding results have been explained on the basis of the expected payoff function.It can be deduced that the influences of parameters on the average abundance function with mutation will be slim when selection intensity is small.Moreover,the corresponding explanation is related to the first-order Taylor expansion.Furthermore,the influences will become notable when selection intensity is large.

η-函数与Mutation

基于文 [2 ]等的工作 ,本文对文 [5]构造的不变量η-函数在 mutation下进行了讨论 .研究表明当mutation仅在链环的一个分支上作和某种情形下在两个分支交叉处作 ,η-函数具有不变性 .此外 ,还得到了链环的第一分支从伴随纽结改变为卫星纽结的 η-函数公式 .

Capability Analysis of Chaotic Mutation and Its Self-Adaption

Through studying several kinds of chaotic mappings' distributions of orbital points, we analyze the capabilityof the chaotic mutations based on these mappings. Nunerical experiments support our conclusions very well. Thecapability analysis also led to a self-adaptive mechanism of chaotic mutation. The introducing of the self-adaptivechaotic mutation can improve the performance of genetic algorithm very prominently.

New era of cystic fibrosis:Full mutational analysis and personalized therapy

Despite its apparently simple genetics,cystic fibrosis(CF) is a rather complex genetic disease.A lot of variability in the steps of the path from the cystic fibrosis transmembrane conductance regulator(CFTR) gene to the clinical manifestations originates an uncertain genotype- phenotype relationship.A major determinant of this uncertainty is the incomplete knowledge of the CFTR mutated genotypes,due to the high number of CFTR mutations and to the higher number of their combinations in trans and in cis.Also the very limited knowledge of functional effects of CFTR mutated alleles severely impairs our diagnostic and prognostic ability.The final phenotypic modulation exerted by CFTR modifier genes and interactome further complicates the framework.The next generation sequencing approach is a rapid,lowcost and high-throughput tool that allows a near complete structural characterization of CFTR mutated genotypes,as well as of genotypes of several other genes cooperating to the final CF clinical manifestations.This powerful method perfectly complements the new personalized therapeutic approach for CF.Drugs active on specific CFTR mutational classes are already available for CF patients or are in phase 3 trials.A complete genetic characterization has been becoming crucial for a correct personalized therapy.However,the need of a functional classification of each CFTR mutation potently arises.Future big efforts towards an ever more detailed knowledge of both structural and functional CFTR defects,coupled to parallel personalized therapeutic interventions decisive for CF cure can be foreseen.

Capability Analysis of Chaotic Mutation and Its Self－Adaption

Through studying several kinds of chaotic mappings' distributions of orbital points, we analyze the capability of the chaotic mutations based on these mappings. Numerical experiments support our conclusions very well. The capability analysis also led to a self-adaptive mechanism of chaotic mutation. The introducing of the self-adaptive chaotic mutation can improve the performance of genetic algorithm very prominently.

Base editor-mediated large-scale screening of functional mutations in bacteria for industrial phenotypes

Base editing,the targeted introduction of point mutations into cellular DNA,holds promise for improving genome-scale functional genome screening to single-nucleotide resolution.Current efforts in prokaryotes,however,remain confined to loss-of-function screens using the premature stop codons-mediated gene inactivation library,which falls far short of fully releasing the potential of base editors.Here,we developed a base editor-mediated functional single nucleotide variant screening pipeline in Escherichia coli.We constructed a library with 31,123 sgRNAs targeting 462 stress response-related genes in E.coli,and screened for adaptive mutations under isobutanol and furfural selective conditions.Guided by the screening results,we successfully identified several known and novel functional mutations.Our pipeline might be expanded to the optimization of other phenotypes or the strain engineering in other microorganisms.

Research on Rosenbrock Function Optimization Problem Based on Improved Differential Evolution Algorithm

The Rosenbrock function optimization belongs to unconstrained optimization problems, and its global minimum value is located at the bottom of a smooth and narrow valley of the parabolic shape. It is very difficult to find the global minimum value of the function because of the little information provided for the optimization algorithm. According to the characteristics of the Rosenbrock function, this paper specifically proposed an improved differential evolution algorithm that adopts the self-adaptive scaling factor F and crossover rate CR with elimination mechanism, which can effectively avoid premature convergence of the algorithm and local optimum. This algorithm can also expand the search range at an early stage to find the global minimum of the Rosenbrock function. Many experimental results show that the algorithm has good performance of function optimization and provides a new idea for optimization problems similar to the Rosenbrock function for some problems of special fields.

Genetic Algorithm for the Thermal Stresses Optimum Design ofFunctionally Gradient Material Plate

Based on the thermal stress distribution for functionally gradient material (FGM) plates, a Genetic Algorithm (GA) method for the thermal stresses optimum design of FGM plate with computer technologies is given. The minimum thermal stresses combination distribution for FGM is obtained.

Adaptive immune-genetic algorithm for global optimization to multivariable function

An adaptive immune-genetic algorithm （AIGA） is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response （secondary response）, adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and decrease locating the local maxima due to the premature convergence. The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly, guarantees the diversity, stability and good searching ability.

TSH RECEPTOR GENETIC ALTERATIONS IN THE AUTONOMOUSLY FUNCTIONING THYROID ADENOMAS

Objective To determine the relationship between TSH receptor gene mutations and autonomously functioning thyroid adenomas (AFTAs). Methods The thyroid samples from 14 cases of diagnosed AFTAs were analyzed, with normal thyroid specimens adjacent to the tumors as controls. The 155 base pairs DNA fragments which encompassed the third cytoplasmic loop and the sixth transmembrane segments in the TSH receptor gene exon 10 were amplified by Polymerase chain reaction (PCR) and analyzed by the single-strand conformation polymorphism (SSCP). Direct sequencing of the PCR products was performed with Prism Dye Terminator Cycle Sequencing Core Kit. Results 6 of 14 AFTA specimens displayed abnormal migration in SSCP analysis. In sequence analysis of 3 abnormally migrated samples, one base substitution at nucleotide 1957 (A to C) and two same insertion mutations of one adenosine nucleotide between nucleotide 1972 and 1973 were identified. No mutations were found in controls. Conclusion This study confirmed the presence of TSH receptor gene mutations in AFTAs; both one-point substitution mutation and one-base insertion mutation were found to be responsible for the pathogenesis of AFTAs.

基于改进蜘蛛猴算法的永磁同步电机多参数辨识

为解决一类智能算法对永磁同步电机(PMSM)参数辨识存在不稳定和精度不高的问题,现提出一种基于改进Tent混沌映射的反向学习蜘蛛猴优化(CSMO)算法。首先,采用改进的Tent混沌映射产生多样性强的初始种群,保证搜索空间的遍历性;其次,在局部领导阶段使用自适应步长取代随机步长,使其适应不同阶段的搜索,有效平衡了全局探索能力与局部开发能力;然后,在局部领导者决策阶段采用柯西变异策略使个体跳出局部最优,从而进一步加强了群体搜索能力,有助于在当前解的局部区域中发现较优解,使得算法的探索速度和寻优精度得到平衡;最后,使用标准测试函数来检测CSMO算法的性能,建立用于PMSM参数辨识的数学模型,再将该模型应用在改进的算法中来辨识电机参数。仿真与实验分析表明,CSMO算法对永磁同步电机参数的辨识有着较好的精度和收敛速度。

PD-1抑制剂联合一线化疗方案对晚期驱动基因阴性肺腺癌的效果及安全性分析

目的探讨程序性细胞死亡蛋白-1(PD-1)抑制剂联合一线化疗方案治疗晚期驱动基因阴性肺腺癌的效果及安全性。方法回顾性收集2019年1月至2021年12月收治的60例晚期驱动基因阴性的肺腺癌患者病历资料,依据治疗方式不同分组,将30例接受培美曲塞、顺铂联合PD-1抑制剂(信迪利单抗)治疗的病历资料纳入观察组另30例接受培美曲塞联合顺铂治疗的病历资料纳入对照组。对比2组患者治疗前、治疗30 d时2组临床疗效[客观缓解率(ORR)、疾病控制率(DCR)]、免疫功能[CD_(4)^(+)、CD_(8)^(+)、CD_(4)^(+)/CD_(8)^(+)]、肿瘤标志物[癌胚抗原(CEA)、细胞角蛋白19片段(CYFRA21-1)]、不良反应[恶心、粒细胞减少、肝功能异常、甲状腺功能异常、肺炎、皮疹、血小板降低]。结果观察组ORR显著高于对照组,差异有统计学意义(P<0.05);2组DCR比较差异无统计学意义(P>0.05);治疗后,观察组CD_(4)^(+)、CD_(4)^(+)/CD_(8)^(+)水平高于治疗前,且高于对照组(P<0.05);治疗后,2组CYFRA21-1、CEA水平均下降,且观察组低于对照组(P<0.05)。观察组甲状腺功能异常、肺炎、皮疹等发生例数显著多于对照组,差异有统计学意义(P<0.05)。结论PD-1抑制剂联合化疗一线治疗可有效改善晚期驱动基因阴性肺腺癌患者免疫功能,并降低肿瘤标志物水平,疗效确切。

Adaptive mutation sparrow search algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground.The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration.In AM-SSA,firstly,the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow,enhancing the global search ability.Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered,expanding the local search ability.Finally,the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal.In addition,it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA.To demonstrate the effectiveness and reliability of AM-SSA,23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions(CEC2005),are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms.The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces.By utilizing the AdaBoost algorithm,multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output.Additionally,the on-site monitoring data acquired from a deep excavation project in Ningbo,China,were selected as the training and testing sample.Meanwhile,the predictive outcomes are compared with those of other different optimization and machine learning techniques.In the end,the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model,illustrating its power and superiority in terms of computational efficiency,accuracy,stability,and robustness.More critically,by observing data in real time on daily basis,the structural safety associated with metro tunnels could be supervised,which enables decision-makers to take concrete control and protection measures.

一种改进的自适应遗传算法

针对现有遗传算法在求解多参数问题时出现收敛精度低、收敛速度慢、易陷入局部最优等问题,提出一种改进的自适应遗传算法。该算法引入复制算子、种群密集度函数和精英选择策略,提出根据种群迭代次数和个体适应度的自适应策略调节交叉概率和变异概率,很好地平衡了遗传算法的全局搜索能力和局部寻优能力。总结出具有代表意义的测试函数,通过求解测试函数和旅行商问题,证明改进的自适应遗传算法的收敛精度、收敛速度等均有明显的提高。

Functional analysis of low-density lipoprotein receptor in homozygous familial hypercholesterolemia patients with novel 1439 C →T mutation of low-density lipoprotein receptor gene

Background Familial hypercholesterolemia （FH）, caused by low density lipoprotein （LDL） receptor （LDL-R） gene mutations, is associated with increased risk of premature coronary heart disease. Until now, limited molecular data concerning FH are available in China. The present study described the clinical profiles and cell biological defects of a Chinese FH kindred with novel LDL-R gene mutation. Methods The patient＇s LDL-R gene coding region was sequenced. The patient＇s lymphocytes were isolated and the LDL-R expression, binding and up-take functions were observed by immunohistochemistry staining and flow cytometry detection. The patient＇s heart and the major large vessels were detected by vessel ultrasound examination and myocardial perfusion imaging （MPI）. Results The patient＇s LDL-R expression, LDL binding and up-take functions were significantly lower than normal control （39%, 63% and 76% respectively）. A novel homozygous 1439 C→T mutation of the LDL-R gene was detected in the patient and his family. ECG showed atypical angina pectoris. Echocardiogram showed stenosis of the coronary artery and calcification of the aortic valve and its root. Blood vessel ultrasound examination showed the thickness of large vessel intima, and the vessel lumen was narrowed by 71%. MPI showed ischemic changes. Conclusions The LDL-R synthesis dysfunction of FH patients leads to arterial stenosis and calcification, which are the major phenotype of the clinical disorder. The mutation of the LDL-R gene is determined. These data increase the mutational spectrum of FH in China.

Genetic Algorithm Based on New Evaluation Function and Mutation Model for Training of BPNN

A local minimum is frequently encountered in the training of back propagation neural networks (BPNN), which sharply slows the training process. In this paper, an analysis of the formation of local minima is presented, and an improved genetic algorithm (GA) is introduced to overcome local minima. The Sigmoid function is generally used as the activation function of BPNN nodes. It is the flat characteristic of the Sigmoid function that results in the formation of local minima. In the improved GA, pertinent modifications are made to the evaluation function and the mutation model. The evaluation of the solution is associated with both the training error and gradient. The sensitivity of the error function to network parameters is used to form a self adapting mutation model. An example of industrial application shows the advantage of the improved GA to overcome local minima.

客户等级划分视阈下的车辆路径遗传算法研究

针对当前车辆路径规划算法存在的车辆满载率低、车辆路径求解时间长、车辆配送成本高的问题,文中设计了考虑客户等级划分的车辆路径遗传算法求解过程。在描述车辆路径相关问题和函数的基础上,给出相关假设和约束条件,确定目标函数并考虑客户等级划分,然后构建时间窗车辆路径模型。采用遗传算法,通过染色体编码生成初始种群,再通过选择、交叉以及变异输出最优解,从而求解时间窗车辆路径。实验结果表明:该方法能够有效提升车辆满载率,并缩短求解时间、降低配送成本。

改进萤火虫算法的武器目标分配问题研究

武器目标分配(WTA)是指挥控制和任务规划领域的关键问题之一,萤火虫算法求解武器目标分配问题具有参数设置简单、执行效率高等优点。针对传统萤火虫算法易陷入局部极值,收敛速度和精度不高的弊端,从以下三个方面进行改进:初始化萤火虫序列编码时融入PWLCM混沌优化以提高全局搜索能力;设置受迭代次数控制的服从半高斯分布的非均匀步长因子以兼顾算法的搜索能力和收敛性;设计基于排序的萤火虫更新策略同时融入交叉变异操作以提高算法的效率和准确性。为验证改进算法的先进性,先利用其求解4种典型测试函数的最优解,再求解WTA问题,并与传统萤火虫算法进行对比,仿真实验结果表明,改进后算法的评价函数值提高了2.1%,迭代次数减少了52.9%,改善了传统算法易陷入局部极值的缺点,提高了收敛速度和精度,提升了武器目标分配的效果。