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The inverse design of nanophotonic devices has been widely concerned by researchers, and on-demand design is the difficulty of inverse design. In inverse design, researchers usually define a target spectrum based on the performance indicators and experiences and then inverse design the structural parameters from the target spectrum. Due to the uncertainty of inverse design and "one-to-many" problem, it is not usually possible to guarantee that the target spectrum is sure to correspond to a real nanostructure. In order to solve these problems, an inverse design method combining generative model and genetic algorithm is proposed in this paper. Before the inverse design, the real spectrum is compressed into a latent space by the generation model, and then, the target spectrum is decoded from the latent space according to the performance index. Finally, the hybrid optimization algorithm combining genetic algorithm and forward prediction network is used to optimize the generated spectrum. The design method follows the process from performance indicators to target spectrum to structural parameter, and we successfully realized the inverse design of multilayer nanofilms on demand by using this method in the experimental part. The inverse design method proposed in this paper provides a possible solution for the inverse design of nanophotonic devices on demand. [ABSTRACT FROM AUTHOR]

The major tree species grown for pulp and paper industry in India are eucalypts, poplars, casuarinas, subabul and acacias. There is a growing demand for pulp and paper products with minimum adverse effect on natural forest and environment. Genetic transformation in these pulp woods are aimed at enhancing growth, wood characteristics and stress tolerance. However, genetic transformation of trees is a time consuming process because of long life cycle, recent domestication status and recalcitrance to in vitro procedures. Though various instances of incorporating desired trait by transformations in trees have been reported, the effect of genetically modified trees on surrounding ecosystems need further studies. Efforts towards making transgenic trees should take in to consideration of alleviation of public concerns on pollen dispersal, contamination of wild germplasm and biosafety. [ABSTRACT FROM AUTHOR]

In online classroom teaching, the function of teaching system can play an important role in the effectiveness of classroom teaching. How to use genetic algorithm to optimize online classroom teaching system has become a research hotspot. Based on genetic algorithm, this paper proposes an adaptive genetic algorithm model based on the traditional algorithm. After setting the appropriate mutation probability, the model can improve the convergence speed. Moreover, based on adaptive genetic algorithm, combined with the direct value method and BT neural network theory, this paper constructs the online classroom teaching quality evaluation model and the teaching system test paper data model, and optimizes adaptive mutation genetic algorithm and BP neural network to evaluate the teaching effectiveness. Simulation experiments are carried out based on the algorithm model, and the visual parameter values are obtained. After experimental comparison, the initial value of the mutation rate is set between 0.002 and 0.004. For the network classroom teaching system, this paper introduces the system demand analysis, function module design, and database design in detail. Finally, through the questionnaire survey, this paper understands the network situation of students in class and the use of online classroom teaching platform in detail, analyzes the problems and influencing factors of online teaching, and finally puts forward the strategies to improve the effectiveness of online classroom teaching. [ABSTRACT FROM AUTHOR]

In order to solve the problem of online inspection of students' theoretical knowledge of Japanese, this paper further optimizes and adjusts the design of the online Japanese examination system and presents an online Japanese examination system based on genetic algorithm. Taking the Japanese test as the research object, on the basis of comprehensively analyzing the problems of slow test paper composition, low success rate, and low quality of traditional online test systems, an intelligent test composition model based on genetic algorithm is proposed, and the implementation process of genetic algorithm and the key steps are described in detail. The results show that the online Japanese examination system based on genetic algorithm can meet the needs of test paper generation in more complex situations. After a long time of operation and continuous improvement, the online Japanese examination system has obtained the adaptability of the best solution. The value of the fitness is 99.666667; when the fitness is at this value, the error of the question type score of the test paper is 0, the average difficulty error on the test paper is 0, and the error of the section test point distribution is 0.666667. This fully illustrates the stability and effectiveness of the Japanese online examination system, which can meet the needs of daily Japanese majors and improve the efficiency of Japanese teaching. [ABSTRACT FROM AUTHOR]

The rapid development of mobile Internet has promoted the rapid rise of cloud computing technology. Mobile terminal devices have greatly expanded the service capacity of mobile terminals by migrating complex computing tasks to run in the cloud. However, in the process of data exchange between mobile terminals and cloud computing centers, on the one hand, it consumes the limited power of mobile terminals, and on the other hand, it results in longer communication time, which negatively affects user QoE. Mobile cloud can effectively improve user QoE by shortening the data transmission distance, reducing the power consumption, and shortening the communication time at the same time. In this paper, we utilize the property that genetic algorithm can perform global search seeking the global optimal solution and construct a dynamic task scheduling model by combining the device-cloud link. The task scheduling model based on genetic algorithm and random scheduling algorithm is compared through comparison experiments, which show that the assignment time of the task scheduling model based on genetic algorithm is shortened by 11.82% to 48.51% and the energy consumption is reduced by 22.28% to 47.52% under different load conditions. [ABSTRACT FROM AUTHOR]

The current commercial intelligent driving systems still take the optimal strategy judged by the machine to be the only goal. Therefore, in order to improve the driving experience of the intelligent driving following scene, based on the assumption that environmental factors remain unchanged for a short time, five important parameters affecting the following scene are selected through correlation analysis, and vehicle-following research is carried out. This paper adopts a driver-following model based on a Genetic Algorithm (GA)-optimized Back Propagation (BP) neural network. Based on the data of next-generation simulation (ngsim), this paper selects vehicle 32 (32 represents the ID of the vehicle in the ngsim project) as the main vehicle in order to study short-term driving habits. A BP neural network is built using MATLAB; 60% of the data of vehicles 32 and 29 is used for the training set, 20% is used for the verification set, and 20% for the test set. Because short-term prediction requires high timeliness, the genetic algorithm is used to optimize the initial weights of the neural network, which not only accelerates the convergence speed but also plays a role in avoiding the local optimal solution. The experimental results show that compared with the traditional stimulus-response vehicle-following model, this model has a following ability that is more in line with the driver's driving habits in terms of ensuring following safety. [ABSTRACT FROM AUTHOR]

Introduction: This article provides an overview of several experimental models, including in vivo, genetics, chemical, knock-in, knock-out, electrical, in vitro, and optogenetics models, that have been employed to investigate epileptogenesis. The present review introduces a novel categorization of these models, taking into account the fact that the most recent classification that gained widespread acceptance was established by Fisher in 1989. A significant number of such models have become virtually outdated. Objective: This paper specifically examines the models that have contributed to the investigation of partial seizures, generalized seizures, and status epilepticus. Discussion: A description is provided of the primary features associated with the processes that produce and regulate the symptoms of various epileptogenesis models. Numerous experimental epilepsy models in animals have made substantial contributions to the investigation of particular brain regions that are capable of inducing seizures. Experimental models of epilepsy have also enabled the investigation of the therapeutic mechanisms of anti-epileptic medications. Typically, animals are selected for the development and study of experimental animal models of epilepsy based on the specific form of epilepsy being investigated. Conclusions: Currently, it is established that specific animal species can undergo epileptic seizures that resemble those described in humans. Nevertheless, it is crucial to acknowledge that a comprehensive assessment of all forms of human epilepsy has not been feasible. However, these experimental models, both those derived from channelopathies and others, have provided a limited comprehension of the fundamental mechanisms of this disease. [ABSTRACT FROM AUTHOR]

In this paper, we explore the regulation of one-way station-based vehicle-sharing system (OSVS) through dynamic-hybrid parking reservation policies. We first propose a dynamic-hybrid parking reservation policy. This policy only requires trips with expected travel distances shorter than a specific threshold to make a parking reservation. The distance threshold varies with time. We develop a discrete event simulation model based on the O2DES (object-oriented discrete event simulation) framework to compare the dynamic-hybrid parking reservation (DHPR) strategy with the no-reservation (NR), static-hybrid parking reservation (SHPR) and complete parking reservation (CPR) strategies. Furthermore, we propose a simulation-optimization model and an Elitism-based Genetic algorithm with the optimal computation budget allocation to determine the fleet size, station capacity, and dynamic reservation distance threshold. The analysis of case studies of a real-world system indicates that DHPR is always superior to NR, SHPR and CPR. [ABSTRACT FROM AUTHOR]

In this paper, genetic algorithms are applied to fine-tune the parameters of a system model characterized by unknown transfer functions utilizing the Strejc method. In this method, the high-order plant dynamic is approximated by the reduced-order multiple inertial transfer function. The primary objective of this research is to optimize the parameter values of the Strejc model using genetic algorithms to obtain the optimal value of the integral quality indicator for the model and step responses which fit the plant response. In the analysis, various structures of transfer functions will be considered. For fifth-order plants, different structures of a transfer function will be employed: second-order inertia and multiple-inertial models of different orders. The genotype structure is composed in such a way as to ensure the convergence of the method. A numerical example demonstrating the utility of the method of high-order plants is presented. [ABSTRACT FROM AUTHOR]

The study presented in this paper evaluated gene expression profiles from The Cancer Genome Atlas (TCGA). To reduce complexity, we focused on genes in the cGAS–STING pathway, crucial for cytosolic DNA detection and immune response. The study analyzes three clinical variables: disease-specific survival (DSS), overall survival (OS), and tumor stage. To effectively utilize the high-dimensional gene expression data, we needed to find a way to project these data meaningfully. Since gene pathways can be represented as graphs, a novel method of presenting genomics data using graph data structure was employed, rather than the conventional tabular format. To leverage the gene expression data represented as graphs, we utilized a graph convolutional network (GCN) machine learning model in conjunction with the genetic algorithm optimization technique. This allowed for obtaining an optimal graph representation topology and capturing important activations within the pathway for each use case, enabling a more insightful analysis of the cGAS–STING pathway and its activations across different cancer types and clinical variables. To tackle the problem of unexplainable AI, graph visualization alongside the integrated gradients method was employed to explain the GCN model's decision-making process, identifying key nodes (genes) in the cGAS–STING pathway. This approach revealed distinct molecular mechanisms, enhancing interpretability. This study demonstrates the potential of GCNs combined with explainable AI to analyze gene expression, providing insights into cancer progression. Further research with more data is needed to validate these findings. [ABSTRACT FROM AUTHOR]

Genetic oscillators have been widely used in modeling key processes of biological systems, especially cell cycles and circadian rhythms. In particular, repressilatory genetic oscillators have been employed in modeling the dynamics of mRNA and protein interactions with transcriptional and translational feedback loops at the molecular level. In addition, synchronization of these oscillators is crucial for understanding the underlying mechanisms of the associated biological processes. In this paper, models of fractional-order genetic oscillators and their coupling are established, where the aspects of time delay, coupling strength, noise, and stability are all taken into consideration. Communication in the proposed coupling model is based on quorum sensing. The synchronization of the fractional-order repressilator model has been examined through simulations which show three main findings. Firstly, the synchronization of the fractional-order repressilator model can be optimized through coupling weight selection. Secondly, the synchronization can be enhanced by increasing the fractional order and decreasing the time delay and the noise intensity. Finally, transitions between the states of the fractional-order repressilatory oscillator can be achieved through varying the fractional order. The simulation results verify the biological relevance of the genetic oscillator models, and their potential for explaining the underlying mechanisms of the associated biological processes. [ABSTRACT FROM AUTHOR]

Context: Perennial crop variety selection trials are often conducted over several seasons or years. These field trials often exhibit spatial correlation between plots. When data from multiple assessment times are analysed, it is necessary to account for both spatial and temporal correlation. A current approach is to use linear mixed models with separable spatial and temporal residual covariance structures. A limitation of these separable models is that they assume the same spatial correlation structure for each assessment time, which may not hold in practice. Aims: This study aims to provide more flexible methods for modelling the spatio-temporal correlation in multi-assessment perennial crop data, allowing for differing spatial parameters for each time, together with modelling genetic effects over time. Methods: The paper investigates the suitability of two-directional invariant multivariate autoregressive (2DIMVAR1) models for analysis of multi-assessment perennial crop data. The analysis method is applied to persistence data from a pasture breeding trial. Key results: The multivariate autoregressive spatio-temporal residual models are a significant improvement on separable residual models under different genetic models. The paper demonstrates how to fit the models in practice using the software ASReml-R. Conclusions: A flexible modelling approach for multi-assessment perennial crop data is presented, allowing differing spatial correlation parameters for each time. The models allow investigation into genotype × time interactions, while optimally accounting for spatial and temporal correlation. Implications: The models provide improvements on current approaches and hence will result in more accurate genetic predictions in multi-assessment perennial crop variety selection trials. Selecting the best perennial crop varieties requires accurate genetic prediction, from multiple assessments on variety selection trials, in which spatial and temporal trends need to be modelled. Current analysis approaches involve rigid models that assume the same spatial correlation parameters at each time. We present more flexible models allowing for differing spatial parameters over time that are a significant improvement on current methods, thereby improving accuracy of genetic prediction. [ABSTRACT FROM AUTHOR]

Improving the quality of tennis teaching and learning is an important part of the modernization of education in China. The limitations of traditional tennis teaching quality evaluation methods have made them controversial, and it is crucial to improve the scientific, rational, and timely evaluation of tenn is teaching quality. Therefore, it is necessary to establish a scientific and rational quality evaluation model for tennis education to evaluate the quality of tennis education. Based on the principle of constructing a perfect tennis teaching quality evaluation system, this paper analyzes the advantages and disadvantages of the previous tennis teaching quality evaluation methods and summarizes the problems existing in the current tennis teaching quality evaluation system in a university. On this basis, to break the limitations of the existing tennis teaching quality evaluation system in a university, a tennis teaching quality evaluation model based on genetic algorithm (GA) and back-propagation (BP) neural network is proposed and a more scientific reasonable tennis teaching quality evaluation system. The evaluation results of BP neural network optimization method based on adaptive mutation genetic algorithm are very satisfactory. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to use a derivative of genetic programming to predict the bond strength of glass fiber-reinforced polymer (GFRP) bars in concrete under the effects of design guidelines. In developing bond strength prediction models, this paper prioritized simplicity and meaningfulness over extreme accuracy. Design/methodology/approach: Assessing the bond strength of GFRP bars in concrete is a critical issue in designing and building reinforced concrete structures. Findings: Ultimately, the equation of a linear form of a particular design guideline was suggested as the optimal prediction model. Improvements to the current design guidelines suggested by this model include setting a 1.31 magnification and considering the effects of the three significant parameters of bar diameter (db), minimum cover-to-bar diameter (C/db) and development length to bar diameter (l/db) under an acceptable root mean square error accuracy of around 2 MPa. Furthermore, the model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations. Originality/value: The model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations. [ABSTRACT FROM AUTHOR]

Induction heating is a clear, cheap, and highly effective technology used for many industrial and commercial applications. Generally, a time-varying magnetic field produces the required heat in the workpiece with a specially designed coil. The efficiency of the heating process depends highly on the coil design and the geometrical arrangement. A detailed and accurate finite element analysis of the induction heating process usually needs to resolve a coupled thermoelastic–magnetic problem, whose parameters values depend on the solution of another field. The paper deals with a shrink-fitting process design problem: a gear should be assembled with an axe. The interesting part of this case study is given the prescribed low limits for critical stress, the temperature of the gear material, and the heat-treated wearing surfaces. A coupled finite-element-based model and a genetic algorithm-based parameter determination methodology were presented. A thermal imaging-based measurement validated the presented numerical model and parameter determination task. The results show that the proposed methodology can be used to calibrate and validate the numerical model and optimize an induction heating process. [ABSTRACT FROM AUTHOR]

Verification, validation, and uncertainty quantification is generally recognized as a standard for assessing the credibility of mechanical models. This is especially evident in biomechanics, with intricate models, such as knee joint models, and highly subjective acquisition of parameters. Propagation of uncertainty is numerically expensive but required to evaluate the model reliability. An alternative to this is to analyze the worst-case models obtained within the specific bounds set on the parameters. The main idea of the paper is to search for two models with the greatest different response in terms of displacement-load curve. Real-Coded Genetic Algorithm is employed to effectively explore the high-dimensional space of uncertain parameters of a 2D dynamic knee model, while Radial Basis Function surrogates reduce the computation by orders of magnitude to near real-time, with negligible impact on the quality. It is expected that the studied knee joint model is very sensitive to uncertainty in the geometrical parameters. The obtained worst-case knee models showcase unrealistic behavior with one of them unable to fully extend, and the other largely overextending. Their relative difference in extension is up to 35% under ±1 mm bound set on the geometry. This unrealistic behavior of knee joint model is confirmed by the large standard deviation obtained from a classical sampling-based sensitivity analysis. The results confirm the viability of the method in assessing the reliability of biomechanical models. The proposed approach is general and could be applied to other mechanical systems as well. [ABSTRACT FROM AUTHOR]

Purpose: Despite the enormous need to succeed in the urban model, scientists and policymakers should work consistently to create blueprints to regulate urbanization. The absence of coordination between the crucial requirements and the regional strategies of the local authorities leads to a lack of conformance in urban development. The purpose of this paper is to address this issue. Design/methodology/approach: This study intends to manage future urban growth patterns using integrated methods and then employ the results in the genetic algorithm (GA) model to considerably improve growth behavior. Multi-temporal land-use datasets have been derived from remotely sensed images for the years 1990, 2000, 2010 and 2020. Urban growth patterns and processes were then analyzed with land-use-and-land-cover dynamics. Results were examined for simulation and utilization of the GA. Findings: Model parameters were derived and evaluated, and a preliminary assessment of the effective coefficient in the formation of urbanization is analyzed, showing the city's urbanization pattern has followed along with the transportation infrastructure and outward growth, and the scattering rates are high, with an increase of 5.64% in building area associated with a decrease in agricultural lands and rangelands. Originality/value: The research achieved a considerable improvement over the growth behavior. The conducted research design was the first of its type in that field to be executed to any specific growth pattern parameters in terms of regulating and policymaking. The method has integrated various artificial intelligence models to monitor, measure and optimize the projected growth by applying this design. Other research on the area was limited to projecting the future of Amman as it is an urbanized distressed city. [ABSTRACT FROM AUTHOR]

This paper explores the application of complex network models and genetic algorithms in epidemiological modeling. By considering the small-world and Barabási–Albert network models, we aim to replicate the dynamics of disease spread in urban environments. This study emphasizes the importance of accurately mapping individual contacts and social networks to forecast disease progression. Using a genetic algorithm, we estimate the input parameters for network construction, thereby simulating disease transmission within these networks. Our results demonstrate the networks' resemblance to real social interactions, highlighting their potential in predicting disease spread. This study underscores the significance of complex network models and genetic algorithms in understanding and managing public health crises. [ABSTRACT FROM AUTHOR]

Settlement prediction based on monitoring data holds significant importance for engineering maintenance of seawalls. In practical engineering, the volume of the collected monitoring data is often limited due to the restrictions of devices and engineering budgets. Previous studies have applied the fractional-order grey model to time series prediction under the situation of limited data volume. However, the performance of the fractional-order grey model is easily affected by the inappropriate settings of fractional order. Also, the model cannot make dynamic predictions due to the characteristic of fixed step size. To solve the above problems, in this paper, the genetic algorithm with enhanced search capabilities was employed to solve the premature convergence problem. Additionally, to solve the problem of the fractional-order grey model associated with fixed step size, the real-time tracing algorithm was introduced to conduct equal-dimensionally recursive calculation. The proposed model was validated using monitoring data of four monitoring points at Haiyan seawall in Zhejiang province, China. The prediction performance of the proposed model was then compared with those of the fractional-order GM(1,1), integer-order GM(1,1), and fractal theory model. Results indicate that the proposed model significantly improves the prediction performance compared to other models. [ABSTRACT FROM AUTHOR]

The implementation of a joint preventive maintenance (JPM) strategy for complex product systems can significantly enhance maintenance effectiveness during the warranty period, alleviate the manufacturer's warranty burden, and empower users with greater autonomy in maintenance activities. However, inadequate task allocation may lead to reduced product availability and increased financial burden on the manufacturer. This study proposes a JPM strategy for two-dimensional warranted complex product systems, with preventive maintenance being regular imperfect maintenance. Furthermore, the research examines the dynamics of the two-dimensional failure rate function under JPM strategies, leading to the development of nuanced warranty cost and availability models, and further develops a cost-effectiveness model. In pursuit of optimizing cost-effectiveness, a decision optimization model is formulated. This model encompasses decision variables such as the two-dimensional preventive maintenance intervals along with the timing and frequency of manufacturer intervention in preventive maintenance. The approach employed combines pattern search algorithms and genetic algorithms for model solutions. Finally, a JPM strategy for large engineering vehicles was developed. Comparing the JPM strategy given in this paper with several other strategies, the results show that the proposed strategy can effectively reduce warranty costs, improve availability, and ultimately improve cost-effectiveness. Through sensitivity analysis, the research provides managerial recommendations to guide the implementation of the proposed JPM strategies. [ABSTRACT FROM AUTHOR]

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple Genetic Algorithm and Parallel Genetic Algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward. [ABSTRACT FROM AUTHOR]

One of the major areas of machine learning application is in medical diagnosis. Machine learning algorithms can detect patterns in patients' data and generates a diagnosis based on those patterns. There are several machine learning classification algorithms each having different strengths and weaknesses, and this makes it difficult to determine the best one for classification problems. In this paper, machine learning techniques used to classify the clinical depression dataset are Fuzzy Logic, Neural Network, Neuro-Fuzzy System, and Genetic Neuro-Fuzzy System. A total of 134 clinical diagnosis first report depression datasets were used in arriving at prediction. The outcome of the experiment showed that the Genetic Neuro-Fuzzy model generated the best result with a prediction accuracy of 95 %, and cross-validation of 83.2 %. This shows that the model is robust and can make accurate prediction on new, unseen data. This research work will guide future researchers and practitioners to identify new directions for advanced development opportunities in using machine learning in depression diagnosis. It will help policymakers in the area of depression to make informed decisions, especially in the area of best machine learning technique for classification problem related to depression diagnosis. The research is limited to clinical depression diagnosis; future work could be expanded to compute the severity ranks of other depression-connected dysfunctions similar to diabetes, lungs, and cancer diseases. [ABSTRACT FROM AUTHOR]

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The optimization of drilling parameters is crucial for resolving the drilling problems in low-pressure and leaky formations using the annulus aerated dual gradient drilling technology. However, the previous studies have mostly focused on engineering applications and wellbore fluid flow models, with less emphasis on parameter optimization. This paper combines the wellbore multiphase flow model with genetic algorithms for the first time, proposing a key parameter optimization method for annulus aerated dual gradient drilling based on genetic algorithms. The study investigates the impact of selection operators on the performance of genetic algorithms and compares genetic algorithms with PSO algorithm and SAA. The results indicate that the convergence and stability of genetic algorithms can be improved by enhancing the selection operators. Compared to the gas–liquid ratio parameter optimization method, the IRSGA optimization method reduces the cost coefficient by 36.46%. Through comparative analysis of different optimization methods, the IRSGA demonstrates over 95% accuracy in large-scale computations. The research findings contribute to the optimization of parameters design under low-cost conditions and are of significant importance for promoting the use of this technology to address the serious issue of lost circulation in drilling technology. [ABSTRACT FROM AUTHOR]

Construction projects may be subjected to various risks which must be identified, evaluated, and a suitable response to each risk must be determined. The risk response stage is a crucial and significant phase in risk management that requires particular attention. This paper proposes an effective mathematical model for determining the most suitable strategy and action in dealing with both primary and secondary risk events in different risk categories that may arise in a construction project. It also provides a method for estimating or forecasting the anticipated budget for a risk response plan. Another contribution of this study is the development of an innovative approach that combines binary programming with the genetic algorithm. The efficacy of the proposed methodology was examined by its implementation in a real geothermal project. The results demonstrated that the proposed framework serves as a useful tool to tackle the challenges related to the selection and optimization of risk response strategies, as well as setting an appropriate budget for the risk response plan. The suggested model can help decision-makers to assess the variety of viable risk response actions and strategies and arrive at a more well-informed decision. [ABSTRACT FROM AUTHOR]

Simple Summary: Hair whorls in horses are a hereditary trait that may be associated with various factors, including the temperament or the coat color of the animals. Hair whorls are described as changes in the hair pattern and may take various forms: circular whorls (spirals of hair with a round epicenter) and linear whorls (a line where the hairs span out on both sides from the center, producing an oval shape similar to a feather). The aim of this study is to estimate the frequency and genetic parameters of the number and position of circular and linear hair whorls (on head, body-neck and limbs) of the Pura Raza Española horse according to different factors such as gender, level of inbreeding, birth period and coat color. In this breed, circular whorls are more prevalent than linear whorls, with both showing a relevant symmetry. The laterality of hair whorls has been also evidenced and are most concentrated on the left-hand side. Most horses, particularly gray ones, showed circular hair whorls below the central line of the eyes; in a previous paper, this was associated with a calmer and more docile temperament. Hair whorls have medium-high heritability and can be included in a breeding program due to their relationship with behavior. Hair whorls are a hereditary feature in horses that may be associated with temperament and coat color. Hair whorls are described as changes in the hair pattern and may take various forms, such as circular and linear whorls. We first carried out a frequency analysis of hair whorls (circular and linear). Next, a Generalized Non-Linear Model was computed to assess the significance of some potential influencing factors, and a genetic parameter estimation was performed. ENDOG software v4.8 was used to estimate the inbreeding coefficient of all the animals analyzed. It was more common to find horses with circular hair whorls than with linear whorls. The heritability ranges obtained were, in general, medium-high for both circular whorls (0.20 to 0.90) and linear whorls (0.44 to 0.84). High positive correlations were found on the between left and right positions, indicating a tendency to symmetry in certain locations. The laterality of hair whorls was also evidenced, with the biggest concentration on the left-hand side, particularly in gray horses, showing circular whorls below the central line of eyes, which has been associated in a previous paper with a calmer and more docile temperament. [ABSTRACT FROM AUTHOR]

Neural tube defects (NTDs) are congenital abnormalities in the central nervous system. The exact etiology of NTDs is still not determined, but several genetic and epigenetic factors have been studied. Folate supplementation during gestation is recommended to reduce the risk of NTDs. In this review we examine single nucleotide polymorphisms (SNPs) of the genes in the folate pathway associated with NTD. We reviewed the literature for all papers discussing both NTDs and SNPs in the folate pathway. Data were represented through five different genetic models. Quality assessment was performed using the Newcastle–Ottawa Scale (NOS) and Cohen's Kappa inter-rater coefficient assessed author agreement. Fifty-nine papers were included. SNPs in MTHFR, MTRR, RFC genes were found to be highly associated with NTD risk. NOS showed that high quality papers were selected, and Kappa Q-test was 0.86. Our combined results support the notion that SNPs significantly influence NTDs across the population, particularly in Asian ethnicity. Additional high-quality research from diverse ethnicities is needed and meta-regression analysis based on a range of criteria may provide a more complete understanding of the role of folate metabolism in NTDs. [ABSTRACT FROM AUTHOR]

Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions. Author summary: An important question in genetics is how the effects of mutations combine to alter phenotypes. Genetic interactions (epistasis) describe non-additive effects of pairs of mutations, but can also involve higher-order (three- and four-way etc.) combinations. Quantifying higher-order interactions is experimentally very challenging requiring a large number of measurements. Techniques based on deep mutational scanning (DMS) represent valuable sources of data to study epistasis. However, the best way to extract the relevant pairwise and higher-order epistatic coefficients (genetic interaction terms) from this data for the task of phenotypic prediction remains an unresolved problem. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values. Critically, this formalism currently only allows for two alleles (amino acid or nucleotide states) per sequence position, hampering applications in more biologically realistic scenarios. Here we present an extension of the Walsh-Hadamard transform that overcomes this limitation and demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic genetic landscapes. [ABSTRACT FROM AUTHOR]

Clustering ensemble can be regarded as a mathematical optimization problem, and the genetic algorithm has been widely used as a powerful tool for solving such optimization problems. However, the existing research on clustering ensemble based on the genetic algorithm model has mainly focused on unsupervised approaches and has been limited by parameters like crossover probability and mutation probability. This paper presents a semi-supervised clustering ensemble based on the genetic algorithm model. This approach utilizes pairwise constraint information to strengthen the crossover process and mutation process, resulting in enhanced overall algorithm performance. To validate the effectiveness of the proposed approach, extensive comparative experiments were conducted on 9 diverse datasets. The results of the experiments demonstrate the superiority of the proposed algorithm in terms of clustering accuracy and robustness. In summary, this paper introduces a novel semi-supervised approach based on the genetic algorithm model. The utilization of pair-wise constraint information enhances the algorithm's performance, making it a promising solution for real-world clustering problems. [ABSTRACT FROM AUTHOR]

Phylogenetic Trees are critical in human genome research for investigating human evolution and identifying disease-associated genetic markers. New high-throughput genome sequencing technologies raise an urgent need to develop statistical methods that can construct phylogenetic trees from long genome sequences with quick computation speeds, while considering various biological complexities. Though an ancestral mixture model has been proposed [Chen SC, Lindsay BG. Building mixture trees from binary sequence data. Biometrika. 2006;93(4):843–860. doi: 10.1093/biomet/93.4.843] to this end by allowing genetic mutations over generations, another essential evolution factor, genetic recombination, is missed. Therefore, in this paper, we develop a novel genetic recombination model for tree construction and propose to use Markov chain composite likelihood (MCCL) to make model estimation computationally feasible. To further reduce computation complexity, a hierarchical estimator is constructed to estimate unknown ancestral distributions through MCCL. Simulation studies and real data example show that our proposed methods perform well and fast, so have the potential for implementation in long sequence genome data. [ABSTRACT FROM AUTHOR]

Calcareous interbeds control the migration of oil and water in marine strata. However, in China, the origins of such calcareous interbeds have not been investigated in detail. In this paper, we present a study of calcareous interbeds in marine strata of the Zhujiang Formation in the Wenchang Oil Field, which is located in the southeast of Hainan Province, China. The lithological characteristics, types and features of diagenesis, and formation of the calcareous interbeds were investigated using core, thin-section, scanning electron microscopy, and cathodoluminescence observations, and stable carbon and oxygen isotope data. The calcareous interbeds consist of mixed sediments, which are dominated by bioclastic limestones containing terrigenous clasts, along with subordinate calcareous sandstone. The interbeds are densely cemented. The bioclasts are dominantly brachiopods, pleopods, and foraminifera, with minor amounts of echinodermata, bivalves, red algae, ostracods, and bryozoa. Diagenesis involved calcitic cementation, associated with relatively weak compaction. Carbon and oxygen isotopic data indicate the pore water that formed the carbonate cement was mostly sourced from seawater and minor amounts of meteoric water. The degree of carbonate cementation was significantly related to the bioclast content. On the basis of our study, a genetic model for the macroscopic and microscopic formation of calcareous interbeds is proposed. [ABSTRACT FROM AUTHOR]

Model-based testing provides an effective means for ensuring the quality of Android apps. Nevertheless, existing models that focus on event sequences and abstract them into Finite State Machines (FSMs) may lack precision and determinism because of the different data values of events that can result in various states of Android applications. To address this issue, a novel model based on Extended Finite State Machines (EFSMs) for Android apps is proposed in this paper. The approach leverages machine learning to infer data constraints on events and annotates them on state transitions, leading to a more precise and deterministic model. Additionally, a state abstraction strategy is presented to further refine the model. Besides, test diversity plays a vital role in enhancing test suite effectiveness. To achieve high coverage and fault detection, test cases are generated from the EFSM model with the help of a Genetic Algorithm (GA), guided by test diversity. To evaluate the effectiveness of our approach, this paper carries out experiments on 93 open-source apps. The results show that our approach performs better in code coverage and crash detection than the existing open-source model-based testing tools. Particularly, the 19 unique crashes that involve complex data constraints are detected by our approach. [ABSTRACT FROM AUTHOR]

The proliferation of misinformation, as insidious and pervasive as water, presents an unprecedented challenge to public discourse and comprehension. Often propagated to further specific ideologies or political objectives, misinformation not only misleads the populace but also fuels online advertising revenue generation. As such, the urgent need to pinpoint and eliminate misinformation from digital platforms has never been more critical. In response to this dilemma, this paper proposes a solution built on the backbone of massive data generation in today's digital landscape. By leveraging advanced technologies, such as AI-driven systems with deep learning models and natural language processing capabilities, we can monitor and analyze an extensive scope of social media data. This, in turn, facilitates the identification of misinformation across multiple platforms and alerts users to potential propaganda. Central to our study is the development of misinformation classifiers based on a deep bi-directional long short-term memory (Bi-LSTM) model. This model is further enhanced by employing a genetic algorithm (GA), which automates the search for an optimal neural architecture, thereby significantly impacting the training behavior of the deep learning algorithm and the performance of the model being trained. To validate our approach, we compared the efficacy of our proposed model with nine traditional machine learning algorithms and a deep learning model rooted in long short-term memory (LSTM). The results affirmed the superiority of our GA-tuned Bi-LSTM model, which outperformed all other models in detecting misinformation with remarkable accuracy. Our intention with this paper is not to present our model as a comprehensive solution to misinformation but rather as a technological tool that can aid in the process, supplementing and bolstering the existing methodologies in the field of misinformation detection. [ABSTRACT FROM AUTHOR]

Multiobjective flexible workshop scheduling is an important subject to improve resource utilization and production efficiency and enhance the competitiveness of enterprises. As the situation of resource constraints becomes more and more severe, the problem of companies rationally allocating limited resources in production is becoming more and more serious. Today, the manufacturing industry widely adopts advanced manufacturing modes such as computer-integrated manufacturing and intelligent manufacturing, but in these semi-intelligent manufacturing modes with a high degree of uncertainty and a high degree of personnel dependence, it is difficult to adapt to the work of large-scale production. Therefore, suitable clustering algorithms are urgently needed to help solve these problems, and this paper selects a clustering algorithm based on the genetic simulation annealing algorithm. This article is aimed at studying the problem of efficiency improvement in the production process of large-scale manufacturing and at finding a stronger and more effective production mode for the manufacturing industry. Firstly, this paper introduces the basic principles of simulated annealing genetic algorithm and regularized clustering algorithm. These algorithms have excellent performance in searching for global optimal solutions. They can be constantly tested and computed to keep the calculation results close to the global optimal solution. In this paper, the K -means clustering algorithm is used to select the shortest completion time to represent the clustering target. According to the minimum distance principle, the machine, workpiece, and other objects are input into the clustering of the algorithm, and the K -means algorithm will send out the sorting plan. Therefore, a multiobjective flexible job shop scheduling model based on genetic simulated annealing algorithm and clustering algorithm is established. Then, by using hypothetical production data to simulate the operation of the workshop, the scheduling model was applied to conduct a deduction and empirical comparative study. The experimental results showed that the model shortened the completion time of the workpiece by 4.4% and increased the average load rate of the machine by 10%. [ABSTRACT FROM AUTHOR]

In order to explore a quantitative and multiobjective optimization method of land use spatial allocation, this paper proposes a multiobjective algorithm for urban land spatial layout optimization. In this paper, the optimal multiobjective particle swarm optimization (MSO) algorithm is used to construct the optimal land use allocation model, and the variation characteristics of the optimized land use allocation scheme in quantity structure and spatial layout are analyzed. The results show that the total running time of the MSO model and the ordinary genetic algorithm spatial optimal allocation model is 8.57 h and 3.31 h, respectively, and the running efficiency of the mosolua model is 61.38% higher than that of the ordinary genetic algorithm spatial optimal allocation model. The configuration was optimized by using the model of land use spatial pattern from the plaque compactness, adjacency, aggregation degree, environmental compatibility, and the overall degree of resource-saving and environmental friendliness than the ordinary genetic algorithm model of optimal configuration results, and the model of overall fitness model compared with the ordinary genetic algorithm improved by 12.57%. [ABSTRACT FROM AUTHOR]

In this paper, according to the requirements of clothing sales forecast, the forecast model of clothing sales is constructed. Through the design of cyclic structure, the adverse effects of uncertainty, hysteresis, and time-varying factors of the predicted object are overcome, and the prediction procedure is theoretically standardized. Aiming at the shortcomings of traditional NN sales forecasting algorithm, such as low learning efficiency, slow convergence speed, and easy to fall into local minimum, this paper puts forward some improvement measures. Adaptive learning efficiency is used to improve the effectiveness and convergence of the algorithm, additional momentum method is used to improve the adaptability of the algorithm, and improved GA is used to optimize the weights of NN. Improve the global optimization characteristics of GA to achieve the purpose of fast optimization and accurate prediction. Finally, an example is used to verify the algorithm. On this basis, the correlation adaptability and prediction accuracy of clothing prediction methods are compared and analyzed, combined with the theoretical analysis of various methods, to explore the practical applicability of various methods under different prediction conditions. It provides an important basis for the decision-making of garment enterprises. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease is a neurodegenerative disorder of the central nervous system that is one of the mental disorders that cause tremors, rigidity, and bradykinesia. Many factors determine the development of disease. A comprehensive physical examination and medical history of the patient should be part of the differential diagnosis for Parkinson's disease (PD). According to epidemiology, Parkinson's disease majorly affects elderly persons and frequency of affecting men is more as compared to women where the worldwide burden of Parkinson's disease (PD) increased more than twice in the past 20 years. In this review paper, we discussed screening models, recent clinical trials, cellular and molecular pathways, and genetic variants (mutations) responsible for induction of Parkinson's disease. The paper also aims to study the pathophysiology, epidemiology, general mechanism of action, risk factors, neurotoxin models, cellular and molecular pathway, clinical trials genetic variants of Parkinson's disease. These models correspond to our research into the pathogenesis of Parkinson's disease. The collected data for the review have been obtained by studying the combination of research and review papers from different databases such as PubMed, Elsevier, Web of Science, Medline, Science Direct, Medica Database, Elton B. Stephens Company (EBSCO), and Google open-access publications from the years 2017–2023, using search keywords such as "Cellular and molecular pathways, Clinical trials, Genetic mutation, Genetic models, Neurotoxin, Parkinson's disease, Pathophysiology." Short Conclusion: Microglia and astrocytes can cause neuroinflammation, which can speed the course of pathogenic damage to substantia nigra (SN). The mechanism of Parkinson's disease (PD) that causes tremors, rigidity, and bradykinesia is a decrease in striatal dopamine. Genes prominently CYP1A2 (Cytochrome P450 A2), GRIN2A, and SNCA are Parkinson's disease (PD) hazard factor modifiers. The most well-known neurotoxin is 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which destroys dopaminergic neurons, resulting in the development of Parkinson's disease (PD). Dopamine auto-oxidation in dopaminergic (DA) neurons is a significant source of reactive oxygen species (ROS) that causes neuronal oxidative stress. Most common genes which when affected by mutation lead to development and progression of Parkinson's disease (PD) are LRRK2, SNCA (alpha-synuclein protein), DJ-1, PRKN (Parkin protein), PINK1, GBA1, and VPS35. The commonly used neurotoxin models for inducing Parkinson's disease are 6-hydroxydopamine (6-OHDA), rotenone, paraquat, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), and genetic models. Anti-apoptic drugs, gene mutation therapy, cell-based therapy, and plasma therapy were all discontinued due to insufficient efficacy. Because it is unclear how aging affects these molecular pathways and cellular functions, future research into these pathways and their interactions with one another in healthy and diseased states is essential to creating disease-specific therapeutics. [ABSTRACT FROM AUTHOR]

To boost the operational performance of a non-aqueous DES electrolyte-based vanadium-iron redox flow battery (RFB), our previous work proposed a double-layer porous electrode spliced by carbon paper and graphite felt. However, this electrode's architecture still needs to be further optimized under different operational conditions. Hence, this paper proposes a multi-layer artificial neural network (ANN) model to predict the relationship between vanadium-iron RFB's performance and double-layer electrode structural characteristics. A training dataset of ANN is generated by three-dimensional finite-element numerical simulations of the galvanostatic discharging process. In addition, a genetic algorithm (GA) is coupled to an ANN regression training process for optimizing the model parameters to elevate the accuracy of ANN prediction. The novelty of this work lies in this modified optimal method of a double-layer electrode for non-aqueous RFB driven by a machine learning (ML) model coupled with GA. The comparative result shows that the ML model reaches a satisfactory predictive accuracy, and the mean square error of this model is lower than other popular ML regression models. Based on the known region of operating conditions, the obtained results prove that this well-trained ML algorithm can be used to estimate whether a double-layer electrode should be applied to a non-aqueous vanadium-iron RFB and determine an appropriate thickness ratio for this double-layer electrode. [ABSTRACT FROM AUTHOR]

Through-wall imaging is capable of detecting various living and non-living things behind the wall. The characteristics of the wall under the investigation, amount of clutter and noise govern the quality and reliability of the image as well as the detection ability of the targets using through the wall imaging system. The characteristics of the wall are not known prior, in the literature only the intensity profile is investigated for the unknown wall characteristics using a single dielectric target and the effect of the wall characteristics on the contrast imaging and impact on time or frequency domain features are not investigated. The target with less dielectric is having less reflectivity; hence its detection in the presence of a high reflective target and a noisy environment becomes difficult. In this paper, to enhance the detection ability of the imaging system attenuation constant (α) of the wall is estimated with the proposed wall parameter estimation methods and used as a normalizing factor. To achieve effective beamforming different weighting strategies are developed and the obtained images are compared with the traditional beamforming. Furthermore, a novel approach to finding the effective rank in the low-rank estimation using a statistical model and multi-objective genetic algorithm is proposed for de-noising. [ABSTRACT FROM AUTHOR]

With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm. [ABSTRACT FROM AUTHOR]

Building green energy-saving is an important area of current architectural application research. Artificial intelligence algorithm can effectively improve the design of building energy-saving digital, intelligent level. Therefore, this paper proposes the use of BIM model based on genetic algorithms to optimize the application of building energy conservation research. In this paper, the basic structure of genetic algorithm and advantages and disadvantages of the algorithm are described. According to the need of optimal design of building energy-saving integration, multiobjective optimization function is introduced and a multiobjective optimization genetic algorithm is established to improve the prediction effect of building energy efficiency model. Finally, results of analysis of some of the influencing factors and the simulation test of building energy-saving integrated optimization algorithm in small high-rise office building show that the improved genetic algorithm can effectively improve the effect of energy-saving integrated optimization and has good application prospects. [ABSTRACT FROM AUTHOR]

Background: The association between MTHFR gene polymorphisms (C677T and A1298C) and prostate cancer risk remains controversial. Methods: Two independent researchers searched the PubMed, Embase, Cochrane and Web of Science databases for all papers published up to 12/19/2023 and used various genetic models to evaluate the relationship between MTHFR polymorphisms and prostate cancer risk. Results: The meta-analysis included 26 case-control studies with a total of 12,455 cases and 13,900 controls with the C677T polymorphism and 6,396 cases and 8,913 controls with the A1298C polymorphism. Overall, no significant association was found between the MTHFR gene polymorphisms and prostate cancer risk. However, the C677T polymorphism was associated with reduced prostate cancer risk in the Asian population (T allele vs. C allele: OR = 0.759, 95% CI 0.669-0.861, p < 0.001; TT + CT vs. CC: OR = 0.720, 95% CI 0.638-0.812, p < 0.001; TT vs. CC + CT: OR = 0.719, 95% CI 0.617-0.838, p < 0.001; TT vs. CC: OR = 0.620, 95% CI 0.522-0.737, p < 0.001); however, the A1298C polymorphism was associated with an increased risk in the mixed race group from the United States (CC + AC vs. AA: OR = 1.464, 95% CI 1.052-2.037, p = 0.024; AC vs. AA: OR = 1.615, 95% CI 1.037-2.514, p = 0.034). Conclusion: The meta-analysis suggested that MTHFR gene polymorphisms (C677T and A1298C) may have different effects on prostate cancer risk in specific populations. [ABSTRACT FROM AUTHOR]

The rapid advancement of genetic technologies has made it possible to modify various plants through both genetic transformation and gene editing techniques. Poplar, with its rapid in vitro growth and regeneration enabling high rates of micropropagation, has emerged as a model system for the genetic transformation of woody plants. In this study, Populus × berolinensis K. Koch. (Berlin poplar) was chosen as the model organism due to its narrow leaves and spindle-shaped crown, which make it highly suitable for in vitro manipulations. Various protocols for the Agrobacterium-mediated transformation of poplar species have been developed to date. However, the genetic transformation procedures are often constrained by the complexity of the nutrient media used for plant regeneration and growth, which could potentially be simplified. Our study presents a cheaper, simplified, and relatively fast protocol for the Agrobacterium-mediated transformation of Berlin poplar. The protocol involved using internode sections without axillary buds as explants, which were co-cultivated in 10 µL droplets of bacterial suspension directly on the surface of a solid agar-based medium without rinsing and sterile paper drying after inoculation. We used only one regeneration Murashige and Skoogbased medium supplemented with BA (0.2 mg·L−1), TDZ (0.02 mg·L−1), and NAA (0.01 mg·L−1). Acetosyringone was not used as an induction agent for vir genes during the genetic transformation. Applying our protocol and using the binary plasmid pBI121 carrying the nptII selective and uidA reporter genes, we obtained the six transgenic lines of poplar. Transgenesis was confirmed through a PCR-based screening of kanamycin-selected regenerants for the presence of both mentioned genes, Sanger sequencing, and tests for detecting the maintained activity of both genes. The transformation efficiency, considering the 100 explants taken originally, was 6%. [ABSTRACT FROM AUTHOR]

Diabetes is a chronic disease affecting millions of people worldwide. Several studies have been carried out to control the diabetes problem, involving both linear and non-linear models. However, the complexity of linear models makes it impossible to describe the diabetic population dynamic in depth. To capture more detail about this dynamic, non-linear terms were introduced into the mathematical models, resulting in more complicated models strongly consistent with reality (capable of re-producing observable data). The most commonly used methods for control estimation are Pantryagain’s maximum principle and Gumel’s numerical method. However, these methods lead to a costly strategy regarding material and human resources; in addition, diabetologists cannot use the formulas implemented by the proposed controls. In this paper, the authors propose a straightforward and well-performing strategy based on non-linear models and genetic algorithms (GA) that consists of three steps: 1) discretization of the considered non-linear model using classical numerical methods (trapezoidal rule and Euler–Cauchy algorithm); 2) estimation of the optimal control, in several points, based on GA with appropriate fitness function and suitable genetic operators (mutation, crossover, and selection); 3) construction of the optimal control using an interpolation model (splines). The results show that the use of the GA for non-linear models was successfully solved, resulting in a control approach that shows a significant decrease in the number of diabetes cases and diabetics with complications. Remarkably, this result is achieved using less than 70% of available resources. [ABSTRACT FROM AUTHOR]

Huntington's disease (HD) is an autosomal-dominant inherited neurological disorder caused by a genetic mutation in the IT15 gene. This neurodegenerative disorder is caused by a polyglutamine repeat expansion mutation in the widely expressed huntingtin (HTT) protein. HD is characterized by the degeneration of basal ganglia neurons and progressive cell death in intrinsic neurons of the striatum, accompanied by dementia and involuntary abnormal choreiform movements. Animal models have been extensively studied and have proven to be extremely valuable for therapeutic target evaluations. They reveal the hallmark of the age-dependent formation of aggregates or inclusions consisting of misfolded proteins. Animal models of HD have provided a therapeutic strategy to treat HD by suppressing mutant HTT (mHTT). Transgenic animal models have significantly increased our understanding of the molecular processes and pathophysiological mechanisms underlying the HD behavioral phenotype. Since effective therapies to cure or interrupt the course of the disease are not yet available, clinical research will have to make use of reliable animal models. This paper reviews the main studies of rodents as HD animal models, highlighting the neurological and behavioral differences between them. The choice of an animal model depends on the specific aspect of the disease to be investigated. Toxin-based models can still be useful, but most experimental hypotheses depend on success in a genetic model, whose choice is determined by the experimental question. There are many animal models showing similar HD symptoms or pathologies. They include chemical-induced HDs and genetic HDs, where cell-free and cell culture, lower organisms (such as yeast, Drosophila, C. elegans, zebrafish), rodents (mice, rats), and non-human primates are involved. These models provide accessible systems to study molecular pathogenesis and test potential treatments. For developing more effective pharmacological treatments, better animal models must be available and used to evaluate the efficacy of drugs. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including genetic groups in quantitative genetic animal models and how genetic mixture of multiple source populations can start spread of biological invasions.

Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9–10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background. [ABSTRACT FROM AUTHOR]