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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 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]

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]

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]

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]

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]

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs. [ABSTRACT FROM AUTHOR]

The proliferation of data sharing over the Internet has given rise to pressing concerns surrounding data security. Addressing these concerns, steganography emerges as a viable mechanism to safeguard data during transmission. It involves concealing messages within other media, such as images, exchanged over networks. In this research, we propose an innovative image steganography approach by harnessing the capabilities of bio-inspired algorithms. A central challenge in steganography revolves around the inherent pixel correlations within cover images, which may inadvertently leak sensitive information to potential intruders. To tackle this challenge head-on, we harness the potential of bio-inspired algorithms, which have exhibited promise in efficiently mitigating these vulnerabilities. This paper introduces a steganography strategy rooted in a fusion model that seamlessly integrates diverse bio-inspired algorithms. Our novel embedding approach ensures the production of robust and high-quality cover images and disrupts bit sequences effectively, thereby enhancing resistance against potential attacks. We meticulously evaluate the performance of our method using a comprehensive dataset encompassing grayscale and JPEG color images. Our particular emphasis on color images arises from their superior capacity to conceal a greater volume of information. The results vividly demonstrate our approach's effectiveness in achieving secure and efficient data concealment within images. [ABSTRACT FROM AUTHOR]

The genetic algorithm with aggressive mutations GAAM, is a specialised algorithm for feature selection. This algorithm is dedicated to the selection of a small number of features and allows the user to specify the maximum number of features desired. A major obstacle to the use of this algorithm is its high computational cost, which increases significantly with the number of dimensions to be retained. To solve this problem, we introduce a surrogate model based on machine learning, which reduces the number of evaluations of the fitness function by an average of 48% on the datasets tested, using the standard parameters specified in the original paper. Additionally, we experimentally demonstrate that eliminating the crossover step in the original algorithm does not result in any visible changes in the algorithm's results. We also demonstrate that the original algorithm uses an artificially complex mutation method that could be replaced by a simpler method without loss of efficiency. The sum of the improvements resulted in an average reduction of 53% in the number of evaluations of the fitness functions. Finally, we have shown that these outcomes apply to parameters beyond those utilized in the initial article, while still achieving a comparable decrease in the count of evaluation function calls. Tests were conducted on 9 datasets of varying dimensions, using two different classifiers. [ABSTRACT FROM AUTHOR]

Recently the 1/f signal of human electroencephalography has attracted attention, as it could potentially reveal a quantitative measure of neural excitation and inhibition in the brain, that may be relevant in a clinical setting. The purpose of this short article is to show that the 1/f signal depends on the vigilance state of the brain in both humans and mice. Therefore, proper labelling of the EEG signal is important as improper labelling may obscure disease-related changes in the 1/f signal. We demonstrate this by comparing EEG results from a longitudinal study in a genetic mouse model for synaptic dysfunction in schizophrenia and autism spectrum disorders to results from a large European cohort study with schizophrenia and mild Alzheimer's disease patients. The comparison shows when the 1/f is corrected for vigilance state there is a difference between groups, and this effect disappears when vigilance state is not corrected for. In conclusion, more attention should be paid to the vigilance state during analysis of EEG signals regardless of the species. [ABSTRACT FROM AUTHOR]

The tail wing of box-launched aircraft needs to be folded in the launch box, which can easily cause malfunctions during flight deployment. This article presents a ducted tail wing aircraft that does not require folding of the tail wing. To address the nonlinear problem of lift coefficient in the ducted tail, an aerodynamic optimization method for ducted tails based on the sparrow search algorithm with back-propagation (SSA-BP) neural network approximate model and multi-objective genetic algorithm fusion is proposed, with the goal of improving the lift-to-drag ratio and linearization degree of the lift curve. The linearization degree of the optimized tail lift coefficient curve is significantly improved, and the lift-to-drag ratio is significantly improved under cruising conditions. Based on this optimization result, the shape of the tail wing and fuselage combination was optimized, and the optimal configuration of the ducted tail wing aircraft was selected, providing a reference for the design of ducted tail wing aircraft. [ABSTRACT FROM AUTHOR]

This paper adopts the multi-criterion decision-making model of fuzzy-TODIM and genetic algorithm (GA) for optimal portfolio allocation. We applied Markowitz's portfolio parameters as inputs for the fuzzy TODIM model to rank stocks that are constituents of each index from three different markets. Portfolios are then generated dynamically using three weighting techniques and subject to multi-objective criteria and additional constraints. The results indicate a significant variation in performance metrics between the model-generated portfolios and the market indices. Replication of the procedure produces a similar outcome. Moreover, the out-of-sample tests conducted over 3 years validate the results' robustness, indicating that fuzzy TODIM, combined with GA, can achieve superior performance in dynamic portfolio allocation. [ABSTRACT FROM AUTHOR]

Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a softwaredefined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%-15.61%, 11.2%-26.4%, 8.3%-17.6% and 7.8%-14.4% than that of the compared algorithm, respectively. [ABSTRACT FROM AUTHOR]

Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters. [ABSTRACT FROM AUTHOR]

The bandgap properties of elastic metamaterials can be efficiently utilized to tailor the propagation characteristics of elastic and acoustic waves, which have promising applications in noise and vibration reduction and isolation. In this paper, an elastic metamaterial with a multilayered honeycomb structure (EMHS) is proposed to enlarge the bandgaps in the low-frequency range and its bandgap properties are analyzed. To meet the requirement of the lightweight design, an optimization model for maximizing the total relative bandgap width with a mass constraint is established. A novel optimization approach combining the Kriging surrogate model with the genetic algorithm (GA) is proposed to reduce the huge computational cost of the corresponding optimization problem. In the Kriging-GA approach, a high-precision Kriging-based surrogate model with addition of supplementary points is constructed to predict the bandgap objective function value, and the GA is employed to search for the optimal parameters. The performance of the proposed Kriging-GA approach is investigated by numerical examples, and the results are compared with those obtained by the commonly used FEM-GA method. The results show that the proposed Kriging-GA approach is highly efficient for the design optimization of the EMHS and can remarkably reduce the computational cost of the considered optimization problem, which has promising prospects in a wide range of engineering applications. [ABSTRACT FROM AUTHOR]

Summary: Exploring the associations between microRNAs (miRNAs) and diseases can identify potential disease features. Prediction of miRNA‐skin cancer associations has become an effective method for detecting skin cancer, as direct detection of skin cancer is challenging. However, traditional binary associations prediction methods overlook high‐order feature differences of miRNAs in different types of skin cancer. Although current tensor decomposition methods have addressed this, they assume a consistent composition standard of global tensor elements, ignoring differences in the composition standards of local sub‐tensors. This leads to poor prediction accuracy and comprehensiveness and fails to consider the credibility and diversity requirements of patients and doctors in practical applications. In this paper, we represent miRNA‐skin cancer associations as a tensor and employ tensor decomposition for tensor completion to achieve prediction purposes. First, we propose a credibility evaluation indicator and introduce four objective functions: accuracy, comprehensiveness, credibility, and diversity, to construct a many‐objective local tensor factorization model (MOLTF). Then, to avoid wrong individuals when solving this model with genetic algorithms, we propose a corrected single‐point crossover operator and a corrected multi‐point mutation operator. On the miRNA‐disease dataset HMDD v3.2, our algorithm improves accuracy by 14.5% compared to the recent baseline, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we consider a genetic evolution model associated to a given Feynman–Kac flow (called also the simple genetic algorithm). We first obtain an estimate of the contraction coefficient of this interacting particle system in some suitable metric, independent of the number of particles in the system. Second, by transport-entropy inequality technique, we obtain some concentration inequalities for the particle system, uniform in time and in the number of particles. [ABSTRACT FROM AUTHOR]

The article highlights the contributions of women in evolutionary developmental biology and celebrates their significant achievements in the field. Topics discussed include the impact of female researchers on the study of Drosophila development, advances in understanding sea urchin embryology and gene regulatory networks, and the genetic basis of phenotypic variation in mammals, including insights into human evolution.

The article discusses a comment made by Bourke et al. regarding the method described in Chen et al.'s paper on mapping quantitative trait loci (QTL) in autotetraploids. Chen et al. argue that their comment is correct based on facts and statistical principles. They explain that the method used in Bourke et al.'s paper lacks an essential component, which is genetic linkage analysis between QTLs and surrounding markers for QTL mapping in an outbred autotetraploid segregating population. Chen et al. also address the terminology and methodology used by Bourke et al., pointing out inconsistencies and inaccuracies. They conclude that their own method enables QTL mapping in autotetraploid species under a novel tetrasomic inheritance model and is based on rigorous scientific and statistical principles. [Extracted from the article]

In recent years, the petroleum industry has devoted considerable attention to studying fluid flow inside fracture channels due to the discovery of naturally fractured reservoirs. The behavior prediction of these reservoirs is a well‐known challenging task, in which the initial stage consists of identifying reservoir hydromechanical parameters. This work proposes an artificial intelligence‐based approach to identify hydromechanical parameters from borehole injection pressure curves acquired through minifrac tests. This approach combines proxy modeling with a stochastic optimization algorithm to match observed and predicted borehole pressure curves. Therefore, a gradient boosting‐based proxy model is built to predict borehole pressure curves, considering a proper strategy to develop time series modeling. Moreover, a Bayesian optimization algorithm is applied to compute the gradient boosting hyperparameters. In this optimization scenario, this paper proposes an appropriate objective function established from the assumed time series prediction strategy and the k‐fold cross‐validation. Finally, a genetic algorithm is adopted to identify unknown hydromechanical parameters, solving an inverse problem. Based on the proposed workflow, a study of the importance of the hydromechanical parameters is developed. To assess the methodology applicability, the approach is employed to identify parameters in synthetic and field minifrac tests. The results present how this approach can adequately identify hydromechanical parameters of hydraulic fracturing problems. [ABSTRACT FROM AUTHOR]

The safety and stability of metal hydride reactor (MHR) structure are significantly important for normal operations of hydrogen isotope storage and supply system. Moreover, considering that the experiments of studying the fatigue and creep properties of MHR structure are always time, money, difficulty and energy consuming. To further improve the service life of MHR as well as lower the failure risk, this paper proposes a system integration method based on ASME code validation, which combines genetic algorithm (GA) and back propagation neural network (BPNN) for rapid assessment of creep-fatigue life (CFL) and optimal design of structural parameters. First, numerical simulation is applied to perform thermal-mechanical coupled finite element analysis and verified by ASME code. Then, parametric sensitivity analysis and experimental design of parameter-based training set based on Taguchi method were performed, and response values are obtained from physical models combined with numerical simulations. Finally, a parametric optimization procedure combining BPNN and GA is developed based on the training data. In the workflow, parametric data flow is passed through the experimental design, numerical simulation, prediction and optimization processes. The results show that the proposed BPNN proxy model based on ASME code validation has good performance in predicting the life of MHR. Based on this, the life of the MHR is improved by 8% compared to the reference sample. • The correlation between creep-fatigue life (CFL) of metal hydride reactor (MHR) and design parameters is established. • Numerical models combining BPNN with ASME Code are available for assessment of CFL of MHR. • The sensitivity analysis is applied to measure the effect of design parameters on CFL of MHR. • Optimal design scheme of MHR is performed through an integrated design optimization method. [ABSTRACT FROM AUTHOR]

Today, investigating gait recognition as a biometric technology has become necessary, especially after the COVID-19 pandemic broke out in the world. This paper proposes a deep structure procedure for precise human identification from the walking manner quickly, as each human has a unique way of walking. The proposed generative adversarial network (GAN) generates gait images from pedestrians of the CASIA and OU-ISIR gait datasets for normalizing the number of frames in each class in both datasets. Then, we extract the gait features from the normalized datasets by the pretrained convolutional neural network (CNN) models; AlexNet, Inception, VGG16, VGG19, ResNet, and Xception, and balance them using the synthetic minority oversampling technique (SMOTE) to enhance the recognition results. Several feature selectors are selecting the best features, including particle swarm optimization (PSO), grey wolf optimization (GWO), Chi-square, and genetic models. Finally, the proposed deep neural network recognizes the gait images precisely. Several performance assessment measures were generated to assess the model's quality, including accuracy, precision, sensitivity, specificity, false negative rate (FNR), intersection of union (IoU), and time. Experimental results showed that the inception model with genetic feature selector performed better in all used datasets than the current studies and was more robust against any change, achieving 99.3%, 99.1%, and 99.09% accuracy in identifying CASIA-B, CASIA-A, and OU-ISIR datasets, respectively in low time. [ABSTRACT FROM AUTHOR]

The objective of this study was to estimate the breeding value and heritability for calving score using an animal model for population from Aberdeen Angus breed. Data consisted of records of 1297 calves of Aberdeen Angus breed from Aberdeen Angus Association Romania. The mean for calving score was 1.007±0.002. The breeding values for calving score for calves were between -0.013 and 0.075. The heritability for calving score was low 0.08 in Aberdeen Angus breed. The animal model was adequate due it is simpler than threshold model for calculate the genetic parameters for calving score. [ABSTRACT FROM AUTHOR]

The stochastic kinetics of biochemical reaction networks is described by a chemical master equation (CME) and the underlying laws of mass action. Assuming network-free simulations of the rule-based models of biochemical reaction networks (BRNs), this paper departs from the usual analysis of network dynamics as the time-dependent distributions of chemical species counts, and instead considers statistically evaluating the sequences of reaction events generated from the stochastic simulations. The reaction event-time series can be used for reaction clustering, identifying rare events, and recognizing the periods of increased or steady-state activity. However, the main aim of this paper is to device an effective method for identifying causally and anti-causally related sub-sequences of reaction events using their empirical probabilities. This allows discovering some of the causal dynamics of BRNs as well as uncovering their short-term deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related. Moreover, since the time-ordering of reaction events is locally irrelevant, the reaction sub-sequences can be transformed into the reaction sets or multi-sets. The distance metrics can be then used to define the equivalences among the reaction events. The proposed method for identifying the causally related reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The method was evaluated for five models of genetic networks in seven defined numerical experiments. The models were simulated in BioNetGen using the open-source network-free simulator NFsim. This simulator had to be modified first to allow recording the traces of reaction events, and it is available in the Github repository, ploskot/nfsim_1.20. The generated event time-series were analyzed with Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts. This demonstrates the opportunities for substantially increasing the research productivity by creating automated data generation and processing pipelines. [Display omitted] • Biochemical reaction networks can be studied as traces of reaction events. • Causally related sub-sequences of reaction events can be identified. • Reaction sub-sequences can be represented as a query and the corresponding response patterns. • NFsim software was modified to allow recording the reaction traces. • The simulations and evaluations of biochemical reaction networks were nearly fully automated. [ABSTRACT FROM AUTHOR]

Chronic demyelination and oligodendrocyte loss deprive neurons of crucial support. It is the degeneration of neurons and their connections that drives progressive disability in demyelinating disease. However, whether chronic demyelination triggers neurodegeneration and how it may do so remain unclear. We characterize two genetic mouse models of inducible demyelination, one distinguished by effective remyelination and the other by remyelination failure and chronic demyelination. While both demyelinating lines feature axonal damage, mice with blocked remyelination have elevated neuronal apoptosis and altered microglial inflammation, whereas mice with efficient remyelination do not feature neuronal apoptosis and have improved functional recovery. Remyelination incapable mice show increased activation of kinases downstream of dual leucine zipper kinase (DLK) and phosphorylation of c-Jun in neuronal nuclei. Pharmacological inhibition or genetic disruption of DLK block c-Jun phosphorylation and the apoptosis of demyelinated neurons. Together, we demonstrate that remyelination is associated with neuroprotection and identify DLK inhibition as protective strategy for chronically demyelinated neurons. The mechanisms that trigger neurodegeneration in demyelinating disease are unclear. Here, the authors find that impaired remyelination induces a DLK-mediated loss of retinal ganglion cells (RGCs), and that efficient remyelination or DLK inhibition block RGC death. [ABSTRACT FROM AUTHOR]

Perivascular mural cells including vascular smooth cells (VSMCs) and pericytes are integral components of the vascular system. In the central nervous system (CNS), pericytes are also indispensable for the blood-brain barrier (BBB), blood-spinal cord barrier, and blood-retinal barrier and play key roles in maintaining cerebrovascular and neuronal functions. However, the functional specifications of pericytes between CNS and peripheral organs have not been resolved at the genetic and molecular levels. Hence, the generation of reliable CNS pericyte-specific models and genetic tools remains very challenging. Here, we report a new CNS pericyte marker in mice. This putative cation-transporting ATPase 13A5 (Atp13a5)marker was identified through single-cell transcriptomics, based on its specificity to brain pericytes. We further generated a knock-inmodel with both tdTomato reporter and Cre recombinase. Using this model to trace the distribution of Atp13a5-positive pericytes in mice, we found that the tdTomato reporter reliably labels the CNS pericytes, including the ones in spinal cord and retina but not peripheral organs. Interestingly, brain pericytes are likely shaped by the developing neural environment, as Atp13a5-positive pericytes start to appear around murine embryonic day 15 (E15) and expand along the cerebrovasculature. Thus, Atp13a5 is a specific marker of CNS pericyte lineage, and this Atp13a5-based model is a reliable tool to explore the heterogeneity of pericytes and BBB functions in health and diseases. [ABSTRACT FROM AUTHOR]

Background: To investigate the association of vitamin D receptor (VDR) gene polymorphism with caries risk in children(< 18 years). Methods: The electronic databases PubMed, Cochrane, EMBASE, Web of Science, CNKI, Cqvip, and Wanfang were searched for observational studies on the relationship between VDR single nucleotide polymorphism(SNP) and caries, including cohort, case–control, and cross-sectional studies. Quality assessment of selected studies was conducted using the Newcastle Ottawa scale. Odds ratios (OR) with 95% confidence intervals (CI) values for associations of individual VDR SNP with dental caries were calculated based on four genetic models: allelic, recessive, dominant, and over-dominant. Results: Of 79 studies considered, 10 (nine case–control and one cross-sectional) were selected for analysis; the studies involved seven VDR SNPs: ApaI(rs7975232),BsmI(rs1544410),FokI(rs2228570),TaqI(rs731236), TaqI/BglI(rs739837), FokI(rs10735810) and Cdx-2(rs11568820). Alleles C and T of FokI(rs10735810) were significantly differently distributed in the caries and caries-free groups (OR = 1.33, 95% CI: 1.30–2.30, P = 0.03), with CC + CT genotypes at this locus associated with greater risk of developing caries than the TT genotype (OR = 1.87, 95%CI: 1.15–3.04, P = 0.01). Further, TT + CC genotype at TaqI(rs731236) was associated with a 1.33-fold higher risk of caries development than the TC genotype (OR = 1.33, 95%CI: 1.06–1.67, P = 0.02). On subgroup analysis, the association between TaqI(rs731236) and caries risk was affected by dentition type, and ethnicity (permanent dentition: OR = 1.48, 95%CI: 1.07–2.03, P = 0.02; Asian: OR = 1.38, 95%CI: 1.02–1.87, P = 0.03;). Genotype distributions at BsmI(rs1544410), TaqI/BglI(rs739837), FokI(rs2228570), and ApaI(rs7975232) did not differ significantly between the caries and caries-free groups. Conclusions: Caries risk could be associated with TaqI(rs731236) and FokI(rs10735810) genotypes, and TaqI(rs731236) may be a risk factor for permanent teeth caries among Asian people. [ABSTRACT FROM AUTHOR]

Hyperemia in response to neural activity is essential for brain health. A hyperemic response delivers O2 and nutrients, clears metabolic waste, and concomitantly exposes cerebrovascular endothelial cells to hemodynamic forces. While neurovascular research has primarily centered on the front end of hyperemia—neuronal activity-to-vascular response—the mechanical consequences of hyperemia have gone largely unexplored. Piezo1 is an endothelial mechanosensor that senses hyperemia-associated forces. Using genetic mouse models and pharmacologic approaches to manipulate endothelial Piezo1 function, we evaluated its role in blood flow control and whether it impacts cognition. We provide evidence of a built-in brake system that sculpts hyperemia, and specifically show that Piezo1 activation triggers a mechano-feedback system that promotes blood flow recovery to baseline. Further, genetic Piezo1 modification led to deficits in complementary memory tasks. Collectively, our findings establish a role for endothelial Piezo1 in cerebral blood flow regulation and a role in its behavioral sequelae. On-demand blood flow increases are essential for brain health, but how flow recovers is unclear. Here, the authors show that brain perfusion triggers vascular Piezo1-mediated mechano-feedback that promotes blood flow recovery to baseline levels. [ABSTRACT FROM AUTHOR]

Aim: Marginal tree populations, either those located at the edges of the species' range or in suboptimal environments, are often a valuable genetic resource for biological conservation. However, there is a lack of knowledge about the genetic consequences of population marginality, estimated across entire species' ranges. Our study addresses this gap by providing information about several genetic indicators and their variability in marginal and core populations identified using quantitative marginality indices. Location: Southwestern Europe and North Africa. Methods: Using 10,185 SNPs across 82 populations of maritime pine (Pinus pinaster Ait.), a widespread conifer characterised by a fragmented range, we modelled the relationship of seven genetic indicators potentially related to population evolutionary resilience, namely genetic diversity (based on both all SNPs and outlier SNPs), inbreeding, genetic differentiation, recessive genetic load and genomic offset, with population geographical, demo‐historical and ecological marginality (as estimated by nine quantitative indices). Models were constructed for both regional (introducing gene pool as a random factor) and range‐wide spatial scales. Results: We showed a trend towards decreasing overall genetic diversity and increasing differentiation with geographic marginality, supporting the centre‐periphery hypothesis (CPH). However, we found no correlation between population inbreeding and marginality, while geographically marginal populations had a lower recessive genetic load (only models without the gene pool effect). Ecologically marginal populations had a higher genomic offset, suggesting higher maladaptation to future climate, albeit some of these populations also had high genetic diversity for climate outliers. Main Conclusions: Overall genetic diversity (but not outlier‐based estimates) and differentiation patterns support the CPH. Ecologically marginal populations and those at the southern edge could be more vulnerable to climate change due to higher climate maladaptation, as predicted by genomic offsets, and/or lower potentially adaptive genetic diversity. This risk is exacerbated by typically small effective population sizes and increasing human impact in marginal populations. [ABSTRACT FROM AUTHOR]

Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly crucial, as it can not only significantly improve the prognosis of patients but also provide valuable reference information for clinical treatment. This study selected the BRFSS (Behavioral Risk Factor Surveillance System) dataset, which is publicly available on the Kaggle platform, as the research object, aiming to provide a scientific basis for the early diagnosis and treatment of diabetes through advanced machine learning techniques. Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. The results show: (1) Random oversampling, ADASYN, SMOTE, and SMOTEENN were used for data balance processing, among which SMOTEENN showed better efficiency and effect in dealing with data imbalance. (2) The GA-XGBoost model optimized the hyperparameters of the XGBoost model through a genetic algorithm to improve the model's predictive accuracy. Combined with the better-performing LightGBM model and random forest model, a two-layer Stacking model was constructed. This model not only outperforms single machine learning models in predictive effect but also provides a new idea and method in the field of model integration. (3) Shapley value analysis identified features that have a significant impact on the prediction of diabetes, such as age and body mass index. This analysis not only enhances the transparency of the model but also provides more precise treatment decision support for doctors and patients. In summary, this study has not only improved the accuracy of predicting the risk of diabetes by adopting advanced machine learning techniques and model integration strategies but also provided a powerful tool for the early diagnosis and personalized treatment of diabetes. [ABSTRACT FROM AUTHOR]