Mutation Probability Research Articles

Computational molecular explanation of Soybean AHAS resistance from P197S mutation

The first enzyme in the pathway involving branched-chain amino is acetohydroxyacid synthase (AHAS, E.C. 2.2.1.6), which is inhibited by five commercial herbicide families. In this work a computational study of a point mutation of Proline-197-Serine of the Soybean AHAS enzyme, which was obtained by mutagenesis, explains the latter's S197 resistance to the commonly used Chlorsulfuron. Using protein-ligand docking and large-scale sampling and distributions from AlphaFold-generated the resistant and susceptible soybean AHAS protein structure. The computational approach here is scaled to screen for mutation probabilities of protein binding sites, similar to screening compounds for potential hits in therapeutic design using the docking software. P197 and S197 AHAS structures were found to be different even if only one amino acid was changed. The non-specific distribution of bindings in the S197 cavity after the P197S change has been rigorously calculated by RMSD analysis that it would require x20 more concentrations to fill the P197 site by the same amount. There is no previously performed detailed chlorsulfuron soybean P197S AHAS binding calculation. In the herbicide site of AHAS, several amino acids interact - a computational study could elucidate the optimal choice of point mutations for herbicidal resistance either individually or collectively by mutations one at a time and analyzing the effects with a set of herbicides individually. With a computational approach, enzymes involved in crop research and development could be analyzed more quickly, enabling faster discovery and development of herbicides.

Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

AbstractFeature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases.

Determinants of rapid adaptation in species with large variance in offspring production.

The speed of population adaptation to changing biotic and abiotic environments is determined by the interaction between genetic drift, positive selection and linkage effects. Many marine species (fish, crustaceans), invertebrates and pathogens of humans and crops, exhibit sweepstakes reproduction characterized by the production of a very large amount of offspring (fecundity phase) from which only a small fraction may survive to the next generation (viability phase). Using stochastic simulations, we investigate whether the occurrence of sweepstakes reproduction affects the efficiency of a positively selected unlinked locus, and thus, the speed of adaptation since fecundity and/or viability have distinguishable consequences on mutation rate, probability and fixation time of advantageous alleles. We observe that the mean number of mutations at the next generation is always the function of the population size, but the variance increases with stronger sweepstakes reproduction when mutations occur in the parents. On the one hand, stronger sweepstakes reproduction magnifies the effect of genetic drift thus increasing the probability of fixation of neutral allele and decreasing that of selected alleles. On the other hand, the time to fixation of advantageous (as well as neutral) alleles is shortened by stronger sweepstakes reproduction. Importantly, fecundity and viability selection exhibit different probabilities and times to fixation of advantageous alleles under intermediate and weak sweepstakes reproduction. Finally, alleles under both strong fecundity and viability selection display a synergistic efficiency of selection. We conclude that measuring and modelling accurately fecundity and/or viability selection are crucial to predict the adaptive potential of species with sweepstakes reproduction.

Vaccine hesitancy promotes emergence of new SARS-CoV-2 variants

The successive emergence of SARS-CoV-2 mutations has led to an unprecedented increase in COVID-19 incidence worldwide. Currently, vaccination is considered to be the best available solution to control the ongoing COVID-19 pandemic. However, public opposition to vaccination persists in many countries, which can lead to increased COVID-19 caseloads and hence greater opportunities for vaccine-evasive mutant strains to arise. To determine the extent that public opinion regarding vaccination can induce or hamper the emergence of new variants, we develop a model that couples a compartmental disease transmission framework featuring two strains of SARS-CoV-2 with game theoretical dynamics on whether or not to vaccinate. We combine semi-stochastic and deterministic simulations to explore the effect of mutation probability, perceived cost of receiving vaccines, and perceived risks of infection on the emergence and spread of mutant SARS-CoV-2 strains. We find that decreasing the perceived costs of being vaccinated and increasing the perceived risks of infection (that is, decreasing vaccine hesitation) will decrease the possibility of vaccine-resistant mutant strains becoming established by about fourfold for intermediate mutation rates. Conversely, we find increasing vaccine hesitation to cause both higher probability of mutant strains emerging and more wild-type cases after the mutant strain has appeared. We also find that once a new variant has emerged, perceived risk of being infected by the original variant plays a much larger role than perceptions of the new variant in determining future outbreak characteristics. Furthermore, we find that rapid vaccination under non-pharmaceutical interventions is a highly effective strategy for preventing new variant emergence, due to interaction effects between non-pharmaceutical interventions and public support for vaccination. Our findings indicate that policies that combine combating vaccine-related misinformation with non-pharmaceutical interventions (such as reducing social contact) will be the most effective for avoiding the establishment of harmful new variants.

Increasing Annual Profit of Wind Farm Using Improved Genetic Algorithm

Wind energy, a prominent renewable source of energy, has expanded rapidly in the past few decades. This paper focuses on raising the yearly profit of a possible wind farm in the Kayathar area of India using an enhanced genetic algorithm. Novel dynamic techniques for assigning the probabilities of crossover and mutation operations have been applied for the genetic algorithm-based optimization method along with the conventional static approach. Non-linear functions have been applied for dynamically allocating the crossover and mutation factors for the genetic algorithm-based optimization process. The analysis outcomes of the proposed technique have been compared with the solutions attained by the genetic algorithm with the standard static approach of allocating the crossover and mutation factors. The evaluation outcomes confirm the superiority of the novel non-linearly incrementing methodology over the non-linearly decrementing and static approach of allocating the crossover and mutation probabilities for attaining a more optimal annual profit.

SOLUTION APPROACH TO P-MEDIAN FACILITY LOCATION PROBLEM WITH INTEGRATED PROGRAMMING AND GENETIC ALGORITHM

In the study, the P-median problem that Hakimi brought to science was discussed. With this problem, in short, it is ensured that the locations for the supply of n demand points of p facilities with the lowest cost are determined and the demand points to be served are allocated to these locations. The solution of the p-median problem with the integer programming approach is in the NP (Non-Polynominal) difficult class, and the solution time increases exponentially as the size of the problem increases. For this, the use of heuristic approaches in the P-median problem significantly shortens the solution time of large-sized problems. The data used in the application were taken from the Operation Research-Library site. In practice, the P-median is considered as capacity-constrained and unconstrained. Euclidean distances were calculated in Excel. The solution of mixed integer programming is obtained by using the CPLEX program. In addition to mixed integer programming, genetic algorithm, which is a meta-heuristic method, is applied to the capacity-constrained P-median problem. Different solutions were obtained by changing the population size, maximum number of iterations, crossover and mutation probability values. It has been observed that there is more than one optimum solution in repeated studies. When working with larger data sets, this algorithm will be less likely to find the global optimum. However, when compared to mixed integer programming, the genetic algorithm gave results very close to the optimum result. The use of heuristic algorithms in real-world complex problems will both shorten the solution time considerably compared to the integer programming method and provide results that are very close to the optimum result.

An Approach to Integrated Scheduling of Flexible Job-Shop Considering Conflict-Free Routing Problems.

This study proposes an approach to minimize the maximum makespan of the integrated scheduling problem in flexible job-shop environments, taking into account conflict-free routing problems. A hybrid genetic algorithm is developed for production scheduling, and the optimal ranges of crossover and mutation probabilities are also discussed. The study applies the proposed algorithm to 82 test problems and demonstrates its superior performance over the Sliding Time Window (STW) heuristic proposed by Bilge and the Genetic Algorithm proposed by Ulusoy (UGA). For conflict-free routing problems of Automated Guided Vehicles (AGVs), the genetic algorithm based on AGV coding is used to study the AGV scheduling problem, and specific solutions are proposed to solve different conflicts. In addition, sensors on the AGVs provide real-time data to ensure that the AGVs can navigate through the environment safely and efficiently without causing any conflicts or collisions with other AGVs or objects in the environment. The Dijkstra algorithm based on a time window is used to calculate the shortest paths for all AGVs. Empirical evidence on the feasibility of the proposed approach is presented in a study of a real flexible job-shop. This approach can provide a highly efficient and accurate scheduling method for manufacturing enterprises.

Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method.

Machine learning-based glycolysis-associated molecular classification reveals differences in prognosis, TME, and immunotherapy for colorectal cancer patients.

Aerobic glycolysis is a process that metabolizes glucose under aerobic conditions, finally producing pyruvate, lactic acid, and ATP for tumor cells. Nevertheless, the overall significance of glycolysis-related genes in colorectal cancer and how they affect the immune microenvironment have not been investigated. By combining the transcriptome and single-cell analysis, we summarize the various expression patterns of glycolysis-related genes in colorectal cancer. Three glycolysis-associated clusters (GAC) were identified with distinct clinical, genomic, and tumor microenvironment (TME). By mapping GAC to single-cell RNA sequencing analysis (scRNA-seq), we next discovered that the immune infiltration profile of GACs was similar to that of bulk RNA sequencing analysis (bulk RNA-seq). In order to determine the kind of GAC for each sample, we developed the GAC predictor using markers of single cells and GACs that were most pertinent to clinical prognostic indications. Additionally, potential drugs for each GAC were discovered using different algorithms. GAC1 was comparable to the immune-desert type, with a low mutation probability and a relatively general prognosis; GAC2 was more likely to be immune-inflamed/excluded, with more immunosuppressive cells and stromal components, which also carried the risk of the poorest prognosis; Similar to the immune-activated type, GAC3 had a high mutation rate, more active immune cells, and excellent therapeutic potential. In conclusion, we combined transcriptome and single-cell data to identify new molecular subtypes using glycolysis-related genes in colorectal cancer based on machine-learning methods, which provided therapeutic direction for colorectal patients.

Mutation of amino acids in SARS-CoV-2 may be responsible for CoV-2 vaccine long term inefficiency

Abstract Objectives SARS-CoV-2 vaccines confer protection for ~2 months, hence the need for a booster dose. The inefficiency of the vaccines may be attributed to mutated amino acids leading to changes in the structure and function of immunogenetic viral particles. Therefore, literature search was carried out with a view to identifying problems of CoV-2 vaccine long term inefficiency, using missensed amino acids of the immunogens. Methods Narrative review of six different COVID-19 vaccines administered at different centres to a total population of 98 979 individuals aged ≥18–95 years was adopted. The number of individuals that came down with infection postvaccination, vaccine dose administered, recorded mortality, postvaccinated infection-free individuals, immunogenicity status, missense mutation, incidence, probability and quality of mutation among amino acids sequences of the vaccinated viral particles were determined. Key findings Findings have shown that some live-attenuated vaccines such as BBIBP-CorV, WBIP, ChAdOxnCoV and Ad26.CoV2.5 are efficacious but could induce mortal infection and mutation of amino acids such as aspartic acid, glycine, cysteine, aspartate, tyrosine, phenylalanine, threonine, serine, alanine, methionine, leucine and lysine. Conclusion Mutation of some specific amino acids could be responsible for the severe pathogenicity of SARS-CoV-2 and vaccine failure. Modalities that regulate the synthesis of nucleobases and amino acids could be used to avert vaccine failure and improves the immunogenicity of the vaccines.

An Improved Genetic-XGBoost Classifier for Customer Consumption Behavior Prediction

AbstractIn an increasingly competitive market, predicting the customer’s consumption behavior has a vital role in customer relationship management. In this study, a new classifier for customer consumption behavior prediction is proposed. The proposed methods are as follows: (i) A feature selection method based on least absolute shrinkage and selection operator (Lasso) and Principal Component Analysis (PCA), to achieve efficient feature selection and eliminate correlations between variables. (ii) An improved genetic-eXtreme Gradient Boosting (XGBoost) for customer consumption behavior prediction, to improve the accuracy of prediction. Furthermore, the global search ability and flexibility of the genetic mechanism are used to optimize the XGBoost parameters, which avoids inaccurate parameter settings by manual experience. The adaptive crossover and mutation probabilities are designed to prevent the population from falling into the local extremum. Moreover, the grape-customer consumption behavior dataset is employed to compare the six Lasso-based models from the original, normalized and standardized data sources with the Isometric Mapping, Locally Linear Embedding, Multidimensional Scaling, PCA and Kernel Principal Component Analysis methods. The improved genetic-XGBoost is compared with several well-known parameter optimization algorithms and state-of-the-art classification approaches. Furthermore, experiments are conducted on the University of California Irvine datasets to verify the improved genetic-XGBoost algorithm. All results show that the proposed methods outperform the existing ones. The prediction results provide the decision-making basis for enterprises to formulate better marketing strategies.

Three-Dimensional Path Planning of UAV Based on Improved Particle Swarm Optimization

The traditional particle swarm optimization algorithm is fast and efficient, but it is easy to fall into a local optimum. An improved PSO algorithm is proposed and applied in 3D path planning of UAV to solve the problem. Improvement methods are described as follows: combining PSO algorithm with genetic algorithm (GA), setting dynamic inertia weight, adding sigmoid function to improve the crossover and mutation probability of genetic algorithm, and changing the selection method. The simulation results show that the improved PSO algorithm solves better route results and is faster and more stable.

Routing Optimization of Regional Flexible Transit Under the Mixed Demand Mode

To meet trip demands and avoid transit capacity waste or shortages, this study investigates the routing optimization of flexible transit with time windows. We introduce the time penalty costs to accommodate the impacts of early and late vehicle arrivals on passengers’ satisfaction. A routing optimization model is developed to minimize the system operation costs and the costs incurred by passengers' time penalties. The problem is solved by a designed adaptive genetic algorithm that adopts an adaptive mutation strategy to dynamically adjust the mutation probability and mutation operator. The numerical experiments compare the results of the mixed demand model, in which vehicles can pick up and drop off passengers simultaneously, to those of the separate pick-up and delivery modes. Finally, a sensitive analysis is conducted to explore the impact of operational factors (vehicle speed, maximum one-way travel time, and weighting ratios between operating and penalty costs) on the system's performance (total costs, per capita mileage, and average seat occupancy rate). Our results confirm the advantages of the developed adaptive genetic algorithm over traditional ones with respect to the convergence speed and optimality gap. Moreover, the numerical results indicate that the mixed demand operation mode of transit reduces total costs by an average of 2.35% compared to separate pick-up and delivery modes. The results also reveal that an increase in the weights of the operating cost can reduce the total cost. The findings of this work can provide guidance to the operation of regional flexible transit.

Identification of FDA-approved drugs with triple targeting mode of action for the treatment of monkeypox: a high throughput virtual screening study.

According to the Center for Disease Control and Prevention, as of August 23, 94 countries had confirmed 42,954 Monkeypox Virus cases. As specific monkeypox drugs are not yet developed, the treatment depends on repurposed FDA-approved drugs. According to a recent study, the Monkeypox outbreak is caused by a strain with a unique mutation, raising the likelihood that the virus will develop resistance to current drugs by acquiring mutations in the targets of currently used drugs. The probability of multiple mutations in two or more drug targets at a time is always low than mutation in a single drug target. Therefore, we identified 15 triple-targeting FDA-approved drugs that can inhibit three viral targets, including topoisomerase1, p37, and thymidylate kinase, using high throughput virtual screening approach. Further, the molecular dynamics simulation analysis of the top hits such as Naldemedine and Saquinavir with their respective targets reveals the formation of stable conformational changes of the ligand-protein complexes inside the dynamic biological environment. We suggest further research on these triple-targeting molecules to develop an effective therapy for the currently spreading Monkeypox.

Bi-objective bus scheduling optimization with passenger perception in mind

With the development of big traffic data, bus schedules should be changed from the traditional "empirical" rough scheduling to "responsive" accurate scheduling to meet the travel needs of passengers. Based on passenger flow distribution, considering passengers' feelings of congestion and waiting time at the station, we establish a Dual-Cost Bus Scheduling Optimization Model (Dual-CBSOM) with the optimization objectives of minimizing bus operation and passenger travel costs. Improving the classical Genetic Algorithm (GA) by adaptively determining the crossover probability and mutation probability of the algorithm. We use an Adaptive Double Probability Genetic Algorithm (A_DPGA) to solve the Dual-CBSOM. Taking Qingdao city as an example for optimization, the constructed A_DPGA is compared with the classical GA and Adaptive Genetic Algorithm (AGA). By solving the arithmetic example, we get the optimal solution that can reduce the overall objective function value by 2.3%, improve the bus operation cost by 4.0%, and reduce the passenger travel cost by 6.3%. The conclusions show that the Dual_CBSOM built can better meet the passenger travel demand, improve passenger travel satisfaction, and reduce the passenger travel cost and waiting for cost. It is demonstrated that the A_DPGA built in this research has faster convergence and better optimization results.

Multi-Objective Path Optimization of Highway-Railway Multimodal Transport Considering Carbon Emissions

With the increase in carbon emissions from railway transit, green transportation has attracted worldwide attention due to its low pollution and low consumption. In order to improve the transportation efficiency of multimodal transport and reduce carbon emissions, this paper makes a systematic study on the comprehensive optimization model and method of multiple transport tasks and transport modes considering carbon emissions. Firstly, an optimization model is established with transportation distance, transportation time, and carbon emission as transportation objectives. Secondly, an improved fuzzy adaptive genetic algorithm is designed to adaptively select crossover and mutation probabilities to optimize the path and transportation mode by using population variance. Finally, an example is designed, and the method proposed in this paper is compared with the ordinary genetic algorithm and adaptive genetic algorithm, which proves the proposed model and algorithm are effective. In conclusion, it is found that the present multi-objective optimization model based on the improved genetic algorithm can adjust multimodal transport plans and reduce carbon dioxide emissions, which provides a reference basis for logistics enterprises to carry out multimodal transport.

Neutral Models of De Novo Gene Emergence Suggest that Gene Evolution has a Preferred Trajectory.

New protein coding genes can emerge from genomic regions that previously did not contain any genes, via a process called de novo gene emergence. To synthesize a protein, DNA must be transcribed as well as translated. Both processes need certain DNA sequence features. Stable transcription requires promoters and a polyadenylation signal, while translation requires at least an open reading frame. We develop mathematical models based on mutation probabilities, and the assumption of neutral evolution, to find out how quickly genes emerge and are lost. We also investigate the effect of the order by which DNA features evolve, and if sequence composition is biased by mutation rate. We rationalize how genes are lost much more rapidly than they emerge, and how they preferentially arise in regions that are already transcribed. Our study not only answers some fundamental questions on the topic of de novo emergence but also provides a modeling framework for future studies.

Research on Relay Network Method of Aerial Platform

As a regional wireless communication means, a wireless broadband communication system mainly provides communication services for mountainous areas with imperfect infrastructure construction. When the system provides communication services in mountainous areas, the vehicle-mounted base stations encounter obstacles to block communication, which causes the problem of non-cascade communication. In this paper, an improved genetic algorithm based on the adaptive change of crossover and mutation probability is proposed to formulate the air platform relay network planning scheme. The simulation results show that the crossover and mutation probabilities of the adaptive improved genetic algorithm change with the iteration of the algorithm, which improves the search ability of the algorithm and prevents it from falling into the local optimal solution.

Ribonucleic acid genome mutations induced by the Casimir effect

In this paper, we investigate the Casimir effect within a virus RNA, particularizing the study to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Then, we discuss the possibility of occurring damage or mutation in its genome due to the presence of quantum vacuum fluctuations inside and around the RNA ribbon. For this, we consider the geometry and the nontrivial topology of the viral RNA as having a simple helical structure. We initially compute the non-thermal Casimir energy associated to that geometry, considering boundary conditions that constrain the zero point oscillations of a massless scalar field to the cylindrical cavity containing a helix pitch of RNA ribbon. Then we extend the obtained result to the electromagnetic field and, following, we calculate the probability of occurring damage or mutation in RNA by using the normalized inverse exponential distribution, which suppresses very low energies, and consider cutoff (threshold) energies corresponding to UV-A and UV-C rays, surely responsible by mutations. Then, by taking into account UV-A, we arrive at a mutation rate per base per infection cycle, which in the case of the SARS-CoV-2 is non-negligible. We find a maximum value of this mutation rate for an RNA ribbon radius, applying it for SARS-CoV-2, in particular. We also calculate a characteristic longitudinal oscillation frequency for the helix pitch value corresponding to the local minimum of the Casimir energy. Finally, we consider thermal fluctuations of classical and quantum nature and show that the corresponding probability of mutation is completely negligible for that virus. Therefore, we conclude that only the nontrivial topology and the geometric attributes of the RNA molecule contribute to the possible mutations caused by quantum vacuum fluctuations in the viral genome.

An adaptive fuzzing method based on transformer and protocol similarity mutation

Industrial control protocols have a large number of vulnerabilities due to lacking authentication and misuse of function codes, which seriously threaten the production safety. Fuzzing, as a common method for vulnerability mining, has the disadvantages of low reception rate of generated test cases and blind mutation, which leads to poor vulnerability mining. To address these issues, we propose an adaptive fuzzing method based on Transformer and protocol similarity mutation. Firstly, the Transformer network is trained to learn the semantics information of the commonly used industrial control protocol Modbus TCP, which can generate test cases with a high reception rate in a short time. Secondly, during the test case generation stage, compare the semantic similarity and the size of random values between the newly generated bytes and the model input fields to determine whether to perform bit-flip mutation for the newly generated bytes, so as to reduce the overall similarity of the test cases and improve the test system abnormal rate. Finally, the byte importance self-adaptive algorithm is used to improve the mutation probability of bytes that are prone to trigger vulnerabilities. Experimental results indicate that compared with the traditional method, our method not only effectively improves the testing efficiency, but also increases the test system’s abnormal rate. In addition, the ability of vulnerability mining capability has been effectively improved.