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This study is motivated by the need for a new advanced vibration-based bearing monitoring approach. The ARX-Laguerre model (autoregressive with exogenous) and genetic algorithms (GAs) use collected vibration data to estimate a bearing's remaining useful life (RUL). The concept is based on the actual running conditions of the bearing combined with a new linear ARX-Laguerre representation. The proposed model exploits the vibration and force measurements to reconstruct the Laguerre filter outputs; the dimensionality reduction of the model is subject to an optimal choice of Laguerre poles which is performed using GAs. The paper explains the test rig, data collection, approach, and results. So far and compared to classic methods, the proposed model is effective in tracking the evolution of the bearing's health state and accurately estimates the bearing's RUL. As long as the collected data are relevant to the real health state of the bearing, it is possible to estimate the bearing's lifetime under different operating conditions. [ABSTRACT FROM AUTHOR]

Taking account of their importance in inferring continental geodynamic processes, adakites have been the subject of considerable research since their discovery and identification. With their high Sr and low Y and HREE concentrations and high Sr/Y and La/Yb ratios, adakites were originally considered to be the product of slab melting in subduction zone settings. Adakites were therefore interpreted to yield information pertaining to magmatic processes, the mineralization of economic deposits, and crustal evolution along convergent margins. However, as more 'adakites' are reported, the lack of standardization of the term is becoming more apparent. The uncertainty of the nomenclature mainly evolves from the breadth of lithological definitions and the diversity of proposed adakite genesis. This paper reviews progress on the definition of adakites and related concepts, and formally introduces widely used classifications and genetic models. Using an adakite database compiled from published articles, 979 samples termed as adakites in the database were screened using a number of geochemical definitions, and 356 fully conforming rocks (termed reference adakite samples) were obtained. By comparing and analysing the characteristics of the reference adakite samples, we recognize that: (1) the screened reference adakites have enlightened the origin of anomalous samples; (2) the domain range of the reference adakites in compositional diagrams mainly supports the hypothesis that high-SiO2 adakites are consistent with the original definition of adakites; and (3) numerous genetic models can explain the formation of compositionally heterogeneous adakites, and therefore the use of the term adakite requires caution in their geodynamic interpretation. The reference adakite samples are also used to discuss the applicability of existing geochemical diagrams. There have also been developments in our understanding of the relationship between adakites and porphyry-type Cu–Mo–Au mineralization, which can be quantitatively assessed by constraining the relative oxygen fugacity. [ABSTRACT FROM AUTHOR]

The genetic algorithm (GA) is one of the most common solutions to solve many optimisation problems. Its distributed version, Island Model GA (IMGA), was introduced to overcome more complex and scalable cases. However, there is a recurrent problem in IMGA called premature convergence as a consequence of selection in the migration. This process is a mechanism of migrating individuals from one into another island to keep population diversity. The primary cause is the structural similarity of a migrated individual because of the genetic operator configurations are identical. Localised IMGA (LIMGA) tries to implement different island characteristics to avoid premature convergence. The main motivation of this paper is to investigate the performance of LIMGA capability in maintaining population diversity. In detail, the contributions of this research are (1) to prove LIMGA concept in handling general optimisation problem, (2) to analyse the performance LIMGA in diversity preservation, and (3) compare LIMGA performance with the current solvers. By harmonising three different GA cores, LIMGA could overcome computationally expensive functions with a great result and acceptable execution time. Moreover, because of its success in maintaining the diversity, Localised Island Model Genetic Algorithm (LIMGA) could lead to the among other current solvers for this case. [ABSTRACT FROM AUTHOR]

Study Design.: Meta-analysis. Objective.: To determine the single nucleotide polymorphisms (SNPs) that are related to adult idiopathic scoliosis. Summary and Background Data.: Adolescent idiopathic scoliosis (AIS) is considered one of the most prevalent spinal diseases. Even though the cause of AIS is yet to be determined, family history and sex have shown conclusive associations. Multiple studies have indicated that AIS is more prevalent in families where at least one other first-degree relative is similarly affected, indicating a possible genetic etiology to AIS. Materials and Methods.: Articles were collected from 3 different search engines and then processed in 2 stages for final article selection for quantitative analysis. Five different genetic models were represented to show the association between the different SNPs and AIS. The Hardy-Weinberg equilibrium was examined using Fisher exact test, with significance set at P <0.05. The final analysis paper's quality was evaluated using the Newcastle Ottawa Scale. Kappa interrater agreement was calculated to evaluate the agreement between authors. Results.: The final analysis comprised 43 publications, 19412 cases, 22005 controls, and 25 distinct genes. LBX1 rs11190870 T>C and MATN-1 SNPs were associated with an increased risk of AIS in one or all of the 5 genetic models. IGF-1 , estrogen receptor alfa, and MTNR1B , SNPs were not associated with AIS in all 5 genetic models. Newcastle Ottawa Scale showed good quality for the selected articles. Cohen k = 0.741 and Kappa interrater agreement of 84% showed that the writers were in strong agreement. Conclusions.: There seem to be associations between AIS and genetic SNP. Further larger studies should be conducted to validate the results. [ABSTRACT FROM AUTHOR]

Introduction: Absence epilepsy is a brief non-convulsive seizure associated with sudden abruptness in consciousness. Because of the unpredictable occurrence of absence seizures and the ethical issues of human investigation on the pathogenesis and drug assessment, researchers tend to study animal models. This paper aims to review the advantages and disadvantages of several animal models of nonconvulsive induced seizure. Methods: The articles that were published since 1990 were assessed. The publications that used genetic animals were analyzed, too. Besides, we reviewed possible application methods of each model, clinical types of seizures induced, purposed mechanism of epileptogenesis, their validity, and relevance to the absence epileptic patients. Results: The number of studies that used genetic models of absence epilepsy from years of 2000 was noticeably more than pharmacological models. Genetic animal models have a close correlation of electroencephalogram features and epileptic behaviors to the human condition. Conclusion: The validity of genetic models of absence epilepsy would motivate the researchers to focus on genetic modes in their studies. As there are some differences in the pathophysiology of absence epilepsy between animal models and humans, the development of new animal models is necessary to understand better the epileptogenic process and, or discover novel therapies for this disorder. [ABSTRACT FROM AUTHOR]

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Intracellular spatial heterogeneity is frequently observed in bacteria, where the chromosome occupies part of the cell's volume and a circuit's DNA often localizes within the cell. How this heterogeneity affects core processes and genetic circuits is still poorly understood. In fact, commonly used ordinary differential equation (ODE) models of genetic circuits assume a well-mixed ensemble of molecules and, as such, do not capture spatial aspects. Reaction-diffusion partial differential equation (PDE) models have been only occasionally used since they are difficult to integrate and do not provide mechanistic understanding of the effects of spatial heterogeneity. In this paper, we derive a reduced ODE model that captures spatial effects, yet has the same dimension as commonly used well-mixed models. In particular, the only difference with respect to a well-mixed ODE model is that the association rate constant of binding reactions is multiplied by a coefficient, which we refer to as the binding correction factor (BCF). The BCF depends on the size of interacting molecules and on their location when fixed in space and it is equal to unity in a well-mixed ODE model. The BCF can be used to investigate how spatial heterogeneity affects the behavior of core processes and genetic circuits. Specifically, our reduced model indicates that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome. The extent of these effects depends on the value of the BCF, which we found to be close to unity. For translation, the value of the BCF is always greater than unity, it increases with mRNA size, and, with biologically relevant parameters, is substantially larger than unity. Our model has broad validity, has the same dimension as a well-mixed model, yet it incorporates spatial heterogeneity. This simple-to-use model can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects. Author summary: A general and simple modeling framework to determine how spatial heterogeneity modulates the dynamics of gene networks is currently lacking. To this end, we provide a simple-to-use ordinary differential equation (ODE) model that can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects. We apply our model to several core biological processes and determine that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome and this difference increases with regulator size. For translation, we predict the effective binding between ribosomes and mRNA is higher than that predicted by a well-mixed model, and it increases with mRNA size. We provide examples where spatial effects are significant and should be considered but also where a traditional well-mixed model suffices despite severe spatial heterogeneity. Finally, we illustrate how the operation of well-known genetic circuits is impacted by spatial effects. [ABSTRACT FROM AUTHOR]

The genetic algorithm is a popular meta-heuristic optimization technique whose performance depends on the quality of the initial population and the crossover operator used to manipulate the individuals to obtain the final optimal solution. It is evident that when similar principle is followed for population seeding and crossover operators, it can enhance the speed of convergence and the quality of final individuals. The recent and popular population seeding technique for combinatorial genetic algorithm is ordered distance vector-based population seeding which works best with respect to convergence rate and diversity. However, the technique could not achieve the zero error rate convergence for the large-sized test instances. Thus, in this paper, an ordered distance vector-based crossover operator is proposed that exclusively exploits the advantages of individuals' generated using the same initialization methods to attain the complete convergence, particularly for most of the large-sized test instances considered. One of the famous combinatorial problems of traveling salesman problem obtained from standard library is chosen as the testbed. From the experimental results, the proposed genetic algorithm model outshines the other existing and popular working genetic algorithm models in the literature. [ABSTRACT FROM AUTHOR]

Key message: Multi-parent populations multi-environment QTL experiments data should be analysed jointly to estimate the QTL effect variation within the population and between environments. Commonly, QTL detection in multi-parent populations (MPPs) data measured in multiple environments (ME) is done by analyzing genotypic values 'averaged' across environments. This method ignores the environment-specific QTL (QTLxE) effects. Running separate single environment analyses is a possibility to measure QTLxE effects, but those analyses do not model the genetic covariance due to the use of the same genotype in different environments. In this paper, we propose methods to analyse MPP-ME QTL experiments using simultaneously the data from several environments and modelling the genotypic covariance. Using data from the EU-NAM Flint population, we show that these methods estimate the QTLxE effects and that they can improve the quality of the QTL detection. Those methods also have a larger inference power. For example, they can be extended to integrate environmental indices like temperature or precipitation to better understand the mechanisms behind the QTLxE effects. Therefore, our methodology allows the exploitation of the full MPP-ME data potential: to estimate QTL effect variation (a) within the MPP between sub-populations due to different genetic backgrounds and (b) between environments. [ABSTRACT FROM AUTHOR]

Improving the prediction accuracy of a complex trait of interest is key to performing genomic selection (GS) for crop breeding. For the complex trait measured in multiple environments, this paper proposes a two-stage method to solve a linear model that jointly models the genetic effects and the genotype × environment interaction (G × E) effects. In the first stage, the least absolute shrinkage and selection operator (LASSO) penalized method was utilized to identify quantitative trait loci (QTL). Then, the ordinary least squares (OLS) approach was used in the second stage to reestimate the QTL effects. As a case study, this approach was used to improve the prediction accuracies of flowering time (FT), oil content (OC), and seed yield per plant (SY) in Brassica napus (B. napus). The results showed that the G × E effects reduced the mean squared error (MSE) significantly. Numerous QTL were environment-specific and presented minor effects. On average, the two-stage method, named OLS post-LASSO, offers the highest prediction accuracies (correlations are 0.8789, 0.9045, and 0.5507 for FT, OC, and SY, respectively). It was followed by the marker × environment interaction (M × E) genomic best linear unbiased prediction (GBLUP) model (correlations are 0.8347, 0.8205, and 0.4005 for FT, OC, and SY, respectively), the LASSO method (correlations are 0.7583, 0.7755, and 0.2718 for FT, OC, and SY, respectively), and the stratified GBLUP model (correlations are 0.6789, 0.6361, and 0.2860 for FT, OC, and SY, respectively). The two-stage method showed an obvious improvement in the prediction accuracy, and this study will provide methods and reference to improve GS of breeding. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to suggest daily bitcoin return model using a genetic algorithm and NARX neural network. We found that the genetic algorithm is effective to decide the architecture of the NARX neural network than information criteria-Akaike information criterion and the Schwarz information criterion using a Monte Carlo simulation and a hypothesis test. Finally, we forecasted daily bitcoin geometric return using this hybrid model of the genetic algorithm and NARX neural network and compare it with a feed-forward neural network forecasting model through a hypothesis test. [ABSTRACT FROM AUTHOR]

In this paper, a new bi-tubular corrugated composite tube, consisting of inner and outer cylindrical and conical tubes is proposed. Different models with various geometrical parameters including the radius of curvatures and their numbers are considered and studied numerically in axial and oblique crushing in order to achieve favorable crashworthiness parameters. Moreover, quasi-static compression tests have been conducted to obtain results in order to validate the finite element model. There has been a sensible agreement between the numerical results and experimental data. Finite element models are also validated using the analytical solutions for both straight and corrugated composite tubes. Regardless of the number and radius of curvatures, as the crashworthiness of bi-tubular corrugated structures both in axial and oblique crushing is investigated and compared with their single-wall and bi-tubular straight peers, a considerable improvement is achieved in all crashworthiness parameters, including desirable increase in specific energy absorption, favorable reduction in peak force, and consequently a beneficial rise in crushing force efficiency. In addition, an optimization study using a suitable multi-objective function is done to choose the best model among the existing models, in addition to finding an optimum model via genetic algorithm. In the next step, a parametric study is conducted on the best model to inspect how well it undergoes oblique crushing at different angles. Finally, this best model and two other candidates have been chosen to investigate the effect of using foams and then the energy absorption capability of the empty and foam-filled tubes has been compared. [ABSTRACT FROM AUTHOR]

This paper presents a case study of the optimal ALINEA ramp metering system model of a corridor of the metro Atlanta freeway. Based on real-world traffic data, this study estimates the origin-destination matrix for the corridor. Using a stochastic simulation-based optimization framework that combines a micro-simulation model and a genetic algorithm-based optimization module, we determine the optimal parameter values of a combined ALINEA ramp metering system with a queue flush system that minimizes total vehicle travel time. We found that the performance of ramp metering with optimized parameters, which is very sensitive possibly because bottlenecks are correlated, outperforms the no control model with its optimized parameters in terms of reducing total travel time. [ABSTRACT FROM AUTHOR]

The experiments reported in this research paper were aimed at assessing the genetic responses of a Holstein cow population, as a response to the variations in environmental temperature, through the analysis of the effects resulting from the genotype by environment interaction (GEI), based on reaction norms. Therefore, milk production data was collected from the database of the Paraná Holstein Breeders Association in Brazil for 67,360 primiparous cows born between 1990 and 2015, with the purpose of evaluating the temperature effect, considered as an environmental variable, distinguished under six gradients (17 ºC to 19.5 ºC) over the region. A random regression model was adopted, utilizing the fourth order under the Legendre polynomials, applying the mixed models of analysis by the REML method, and using the WOMBAT software. Additionally, the breeding value of the 15 most representative bulls was assessed, in response to the changes in the temperature gradient. The total milk production on average was estimated at 8,412.83 ± 2,012.08 kg. The heritabilities estimates were found in the low to moderate range, from 0.18 to 0.23, displaying a decline with a rise in the temperature, highlighting the influence it exerted on the heritabilities. Variations in the genetic expression of some bulls were noted to show differences of up to 289 kg of milk in response to the increase in the temperature from 17 ºC to 19.5 ºC. However, all the genetic correlations between the gradients for milk yield were above 0.80, in the range of 0.873 to 0.998, revealing no remarkable interaction between the genotype and environment. This result indicates that the application of the temperature variable in the models of genetic analysis in southern Brazil is not required. [ABSTRACT FROM AUTHOR]

Background: To evaluate the association between apolipoprotein B gene polymorphism and coronary heart disease in some populations at home and abroad by means of meta-analysis.Methods: Using the strict exclusion criteria for primary screening of the literature and applying the Hardy-Weinberg equilibrium to test the genetic balance of the selected literature. The corresponding models were selected according to the results of the heterogeneity test. The Begg's test and Egger's test were used to evaluate publication bias, and meta-analysis was performed using Stata 12.0.Results: The study included twelve articles. In the literature, a total of 1596 patients with coronary heart disease and 1431 controls.Meta-analysis results showed no statistical value in the following three genetic models: allelic comparison (a vs A,P = 0.811,OR = 0.95, 95%CI = 0.62-1.46), recessive genetic models (aa vs Aa/AA, P = 0.86,OR = 0.94, 95%CI = 0.45-1.96), or dominant genetic models (aa/Aa vs AA, P = 0.73,OR = 0.92, 95%CI = 0.58-1.47). Subgroup analysis based on ethnicity showed allelic comparison (a vs A,P = 0.464,OR = 1.32, 95%CI = 0.63-2.78), recessive genetic models (aa vs Aa/AA, P = 0.422,OR = 1.52, 95%CI = 0.55-4.21), and dominant genetic models (aa/Aa vs AA, P = 0.551,OR = 1.26, 95%CI = 0.58-2.73) in Asians, allelic comparison (a vs A,P = 0.410,OR = 0.79, 95%CI = 0.45-1.39), recessive genetic models (aa vs Aa/AA, P = 0.041,OR = 0.75,95%CI = 0.57-0.99),dominant genetic models (aa/Aa vs AA, P = 0.385,OR = 0.75, 95%CI = 0.40-1.43) in Caucasian; CONCLUSION: The ApoB(apolipoprotein B) XbaI locus is not a risk factor when it comes to the development of coronary heart disease in the domestic and international populations included in this paper. In Caucasians, people carrying the aa genotype may be less susceptible to CHD (coronary heart disease). The results of recessive genetic models have to take the effect of heterogeneity and sample sizes into account. Further research may require a larger and more rigorous research design. [ABSTRACT FROM AUTHOR]

Air pollution is one of the major environmental concerns in recent time. The majority of the population in the developed world live in urban area, hence air pollution concern is even more in cities. The worst gaseous pollutants are Caron Monoxide (CO), Nitrogen dioxide (NO2) and OZONE (O3). In this paper, we propose two predictive models for estimation of concentration of gases in the air, namely Carbon Monoxide (CO), Nitrogen dioxide (NO2) and OZONE (O3). The first proposed model is a combination of linear regression and Genetic Algorithm (GA). The second proposed model estimates concentration of gasses using Multivariable Polynomial Regression. First model uses a linear regression for prediction of concentration of gases, whereby errors like MAPE, R2 obtained by linear regression are optimized using a genetic algorithm (GA). Multivariable Polynomial Regression is adopted as a second proposed method for the prediction of concentration of same gases. A detailed comparative study has been carried out on the performances of GA and Multivariable Polynomial Regression. In addition, predictive equations are formed for CO, O3 and NO2 based on temperature, relative humidity, benzene and Nox (oxides of nitrogen). [ABSTRACT FROM AUTHOR]

Cricket is a popular and increasingly wealthy bat-and-ball sport. Cricket bats made from willow are the focus of increasing scientific research and innovation, and in this paper, an innovative approach to improving the performance of cricket bats is described. Parametric modeling and genetic algorithms were used to converge the location of two points on a bat that are associated with increased velocity of a ball rebounding off bats: vibrational nodal points and center of percussion (COP). This modeling was able to reduce the distance between nodal points and COP from 174.5 to 98.1 mm. This change occurred as a result of modifications to the geometry of the bat notably shifting its mass toward its end, and keel-shaped sculpting of the back of the bat. These features are seen in some modern high-performance bats. However, the keel-shaped sculpting of the new bat was more sharply incurvate at the center and ends of the bat. The authors conclude that the combination of parametric modeling and optimization using genetic algorithms is a powerful tool for exploring virtual designs for cricket bats that are based on performance criteria, and suggest that additional novel bat designs could arise through multi-objective modeling and optimization using genetic algorithms. [ABSTRACT FROM AUTHOR]

Some certain genetic polymorphisms have been considered to implicate in the pathogenesis and progression of autoimmune diseases and may predispose to an early stage of general autoimmune susceptibility. Recent studies have been conducted to investigate the association between macrophage migration inhibitory factor- (MIF-) 173G/C gene polymorphism and autoimmune diseases; however, the results were not exactly identical. In the present study, a systematic review and meta-analysis of case-control studies was performed to estimate the relationship. A comprehensive search of PubMed, Ebsco, EMbase, WanFang databases and CNKI was done. Odds ratio (ORs) and corresponding 95% confidence intervals (CIs) were combined to pool the effect size. The publication bias was examined by Begg's funnel plots and Egger's test. RevMan 5.3 and STATA 12.0 software were used for statistical processing. 23 papers were included, and the results revealed that MIF-173G/C was significantly associated with an increased risk of autoimmune diseases in five genetic models (recessive genetic model: OR = 1.95 , 95% CI: 1.52-2.50; dominant genetic model: OR = 1.35 , 95% CI: 1.24-1.46; allele model: OR = 1.32 , 95% CI: 1.23-1.41; homozygote model: OR = 1.92 , 95% CI: 1.57-2.35; heterozygote model: OR = 4.92 , 95% CI: 4.03-6.02), whether in Asia, Europe, or North America. Furthermore, subgroup analysis showed an increasing risk in rheumatoid arthritis (RA), ulcerative colitis (UC), Crohn's disease (CD), atopic dermatitis (AD), Henoch-Schonlein purpura (HSP), and Henoch-Schonlein purpura nephritis (HSPN), but it was not related to the susceptibility of autoimmune hepatitis (AIH). Therefore, it could be considered that MIF-173G/C polymorphism could increase the susceptibility of autoimmune diseases, while there may be the discrepancy of disease entity. [ABSTRACT FROM AUTHOR]

Price is an important guideline for measuring the changes in the oil market. Therefore, the forecasting of oil prices has become an important issue in oil market research. One of the problems, however, is that oil price is a non-linear or chaotic time-series, leading to difficulties in such research. In the forecasting methods commonly used, pattern matching model is a good method because of its simplicity, non-linearity, and accuracy, but when calculating its important input parameters, pattern matching model encounters certain problems in terms of accuracy and stability. In this case, the accuracy of the model prediction results will be affected. In this paper, the loss function is used to detect the source of the complexity of oil price forecast. On the basis of generalised pattern matching model based on genetic algorithm (GPGA), we introduce empirical distribution into genetic algorithm, which can dynamically compare the fitness among populations and tracks changes in individual evolutionary fitness to improve multiple modules. By using these information, directional evolution and full search elements are ensured. Finally, a generalised pattern matching model based on empirical genetic algorithm (GPEGA) is proposed. Empirical studies show that the accuracy and stability of GPEGA are 59.0% and 0.8% higher than that of GPGA. Moreover, the performance is 71.2% and 72.2% better than that of BPNN and ARIMA on mean square error. This study can help decision makers quickly and accurately grasp market information and provide support and reference for decision making on stabilizing economic markets and people's lives. [ABSTRACT FROM AUTHOR]

The paper presents an advanced parametric method of blade stacking lines in terms of sweep and lean based on controlled curvature. To the knowledge of the authors, there is no related approach reported in open literature that uses Bezier spline as the radial curvature distribution to improve the smoothness of the blade surface; most previous studies ignored the discontinuous slopes of curvature of the parametric curves. The parametric method called curvature-controlled stacking-line method (CCSLM) is performed by changing the magnitude of the sweep or lean. A fourth Bezier spline is adopted to define the curvature of spanwise stacking line directly ensuring surface smoothness. Then, the redesign cascades are created by sectional profiles stacked along the radial stacking lines which are obtained by twice integrating the Bezier spline. Then, the advanced method is conducted to optimize a high-subsonic controlled diffusion airfoil at design point, where the blade shape is generated in terms of lean. A single-objective optimization is performed using Kriging model and genetic algorithm to optimize total pressure loss, and the optimized geometry is obtained. The optimization results show that the blade design CCSLM has significant effects on the endwall flow vortex as well as radial loading distribution. The reduction of total pressure loss and secondary flow is also observed, and the aerodynamic performance is well improved compared with the original cascade. [ABSTRACT FROM AUTHOR]

Maximum k Satisfiability logical rule (MAX-kSAT) is a language that bridges real life application to neural network optimization. MAX-kSAT is an interesting paradigm because the outcome of this logical rule is always negative/false. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when the logical rule is embedded in HNN. Increasing the storage capacity during the learning phase of HNN has been a challenging problem for most neural network researchers. Development of Metaheuristics algorithms has been crucial in optimizing the learning phase of Neural Network. The most celebrated metaheuristics model is Genetic Algorithm (GA). GA consists of several important operators that emphasize on solution improvement. Although GA has been reported to optimize logic programming in HNN, the learning complexity increases as the number of clauses increases. GA is more likely to be trapped in suboptimal fitness as the number of clauses increases. In this paper, metaheuristic algorithm namely Artificial Bee Colony (ABC) were proposed in learning MAX-kSAT programming. ABC is swarm-based metaheuristics that capitalized the capability of Employed Bee, Onlooker Bee, and Scout Bee. To this end, all the learning models were tested in a new restricted learning environment. Experimental results obtained from the computer simulation demonstrate the effectiveness of ABC in modelling MAX-kSAT. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters‐based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52%, whereas the average predicted accuracy of model (with stress effect) is 99.42%. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies. [ABSTRACT FROM AUTHOR]

The quality of a product obtained by hydroforming process is influenced by the geometrical, material and process parameters. In this paper, to predict an acceptable T-shaped tube with minimum wall thickness variations, and accomplishes the industrial requirements, a methodology based on the coupling of three-dimensional finite element incremental simulation based on Explicit Dynamic approach and an automatic surrogate model are proposed. The surrogate model is based on an adaptive moving target zone, and both Moving Least Square (MLS) and the Kriging technique. The optimization results are presented and compared in term of efficiency. Five quality criteria are used, an objective function defining the thickness variation with four nonlinear constraints functions, to reduce the risk of necking and to fulfil the industrial requirements. The proposed approach will provide a numerical estimation of the "best" tool dimensions and ideal punch stroke in order to obtain a final feasible workpiece. [ABSTRACT FROM AUTHOR]

This paper presents a semi-analytical model that facilitates the optimum design of small-scale hydropower systems, so that maximum possible energy can be harvested under low head and streamflow conditions in run-of-the-stream settings. The hydropower harvesting model developed and tested at the University of Connecticut (Malla et al., Renewable Energy 36(5):1568–1577, 2011) comprises a rotating cylinder attached to a piston producing reciprocating motion when placed in moving water. Taking this model as the base, the semi-analytical model employs genetic algorithm–based optimization and develops optimal dimensions for the system, with the objective to minimize the time period while at the same time maximizing the stroke of the piston. The semi-analytical model is tested in steps: first with single-objective optimization for the time period and the stroke of the piston separately and then using multi-objective optimization to produce a set of Pareto optimal solutions. As many energy harvesting approaches are based on the reciprocating motion of the mechanical/structural system, which is greatly affected by the geometric dimensions of the system, optimization of the system geometry becomes crucial for energy harvesting. Compared to the hydropower harvesting model tested before (Malla et al., Renewable Energy 36(5):1568–1577, 2011), the semi-analytical model is able to reduce the arms' dimensions while obtaining a much higher stroke of piston for much lower time period. The model has some limitations but is able to produce optimization results comparable to laboratory experimental data and applicable to actual flow data from Shetucket River in Willimantic, CT, USA. [ABSTRACT FROM AUTHOR]

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning a teaching event in a certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. This problem becomes complicated for universities with a large number of students and lecturers. Moreover, several universities are implementing student sectioning, which is a problem of assigning students to classes of a subject while respecting individual student requests, along with additional constraints. Such implementation also implies the complexity of constraints, which is larger accordingly. However, current and generic solvers have failed to meet the scalability and reliability requirements for student sectioning UCTP. In this paper, we introduce the localized island model genetic algorithm with dual dynamic migration policy (DM‐LIMGA) to solve student sectioning UCTP. Our research shows that DM‐LIMGA can produce a feasible timetable for the student sectioning problem and get better results than previous works and the current UCTP solver. Our proposed solution also consistently yield lower violation number than other algorithms, as evidenced by UCTP benchmark experiment results. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

The objective of the present paper is to model the genetic influence on prostate cancer with multivariate adaptive regression splines (MARS) and artificial neural networks (ANNs) techniques for classification. These models will be able to classify subjects that have cancer according to the values of the selected proteins from the genes selected with the models as most relevant. Subjects are selected as cases and controls from the MCC-Spain database and represent a heterogeneous group. Multivariate adaptive regression splines models allow to select a set of the most relevant proteins from the database. These models were trained in nine different degrees and chosen regarding its performance and complexity. Artificial neural networks models were trained on with data restricted to the most significant variables. The performance of both types of models was analyzed in terms of the area under the curve of the receiver operating characteristics curve. The ANN technique resulted in a model with AUC of 0.62006, while for MARS technique, the value was 0.569312 in the best situation. Then, the artificial neural network model obtained can determine whether a patient suffers prostate cancer significantly better than MARS models and with high rate of success. The best model presented was based on support vector machines, reaching values of AUC of 0.65212. [ABSTRACT FROM AUTHOR]

Tip geometry modification is frequently used to suppress the tip leakage flow in the turbine cascade however a universally beneficial tip geometry modification design has not been fully discovered. In this paper, the two-surface coupling arbitrary blade tip design method in three-dimensional physical space which satisfies the simple trigonometric function law is proposed and the mathematical parametric description is presented. The effects of different arbitrary blade tips on tip leakage flow have been studied numerically in a highly loaded axial turbine cascade. The aerodynamic performance of different tips is assessed by the tip leakage mass flow rate and the total pressure loss coefficient at the exit section. The Kriging model and genetic optimization algorithm are used to optimize the arbitrary blade tips to obtain the optimal arbitrary blade tip. Compared with the flat tip, the tip leakage mass flow rate is decreased by 10.57% and the area-average total pressure loss coefficient at the exit section is reduced by 8.91% in the optimal arbitrary blade tip. [ABSTRACT FROM AUTHOR]

Background: Gene regulatory networks (GRNs) can be inferred from both gene expression data and genetic perturbations. Under different conditions, the gene data of the same gene set may be different from each other, which results in different GRNs. Detecting structural difference between GRNs under different conditions is of great significance for understanding gene functions and biological mechanisms. Results: In this paper, we propose a Bayesian Fused algorithm to jointly infer differential structures of GRNs under two different conditions. The algorithm is developed for GRNs modeled with structural equation models (SEMs), which makes it possible to incorporate genetic perturbations into models to improve the inference accuracy, so we name it BFDSEM. Different from the naive approaches that separately infer pair-wise GRNs and identify the difference from the inferred GRNs, we first re-parameterize the two SEMs to form an integrated model that takes full advantage of the two groups of gene data, and then solve the re-parameterized model by developing a novel Bayesian fused prior following the criterion that separate GRNs and differential GRN are both sparse. Conclusions: Computer simulations are run on synthetic data to compare BFDSEM to two state-of-the-art joint inference algorithms: FSSEM and ReDNet. The results demonstrate that the performance of BFDSEM is comparable to FSSEM, and is generally better than ReDNet. The BFDSEM algorithm is also applied to a real data set of lung cancer and adjacent normal tissues, the yielded normal GRN and differential GRN are consistent with the reported results in previous literatures. An open-source program implementing BFDSEM is freely available in Additional file 1. [ABSTRACT FROM AUTHOR]

Eco-friendly raft-pile foundation (ERP) system is one of the most recent developed types of pile foundations that the original materials can be provided from local Bakau. A precise prediction of its behaviour is of interest for many engineers. This paper presents three intelligent systems, namely, adaptive neuro-fuzzy inference system (ANFIS), conventional artificial neural network (ANN), and optimized ANN model with genetic algorithm (GA) for prediction of vertical settlement in ERP system. In this regard, a database compiled from 43 load-settlement results obtained from full-scale maintained load test (PLT). Note that, these floating raft-pile system piles were subjected to vertical axial loading. The ERP system was installed at the marine soft clay soil site at Rantau Panjang Kapar, Selangor, Malaysia. The values of subgrade modulus (Ks), Young's modulus (Es), soil properties beneath the footing, and applied load were set as model input to predict vertical settlement (s). To evaluate the reliability of the network output, several well-known statistical indexes were used. The results show that the new proposed GA-ANN model could provide a better performance in estimating the maximum settlement of ERP system. In terms of statistical indexes (R2, and RMSE), the values of (0.998, 0.0259, and 99.99) and (0.997, 0.0324, and 99.998) were obtained for both data sets of training and testing, respectively. Besides, comparing the training and testing data sets, R2 values of (0.994, 0.9884, 0.995, and 0.9984) and (0.996, 0.985, 0.994, and 0.9973) were found for ANN–LMBP, ANFIS, GA, and GA-ANN models, respectively, which proves the superiority of the proposed GA-ANN model comparing to other methods. [ABSTRACT FROM AUTHOR]

In some researches of vehicle system dynamics, it's necessary to optimise various of vehicle parameters to meet the requirements of different objectives. In this paper, an articulated monorail dynamic model together with a genetic algorithm is developed to optimise the curving dynamics of the articulated monorail vehicle. The articulated monorail is modelled as a rigid multi-body dynamic model consisting of six car bodies and seven straddle-type bogies, in which each straddle-type bogie is comprised of two driving wheels, four guide wheels and two stable wheels. The Magic tire model is used to model the nonlinearities in the driving wheels, guide wheels and stable wheels. The validity of the proposed monorail vehicle model is demonstrated through comparing the car body acceleration responses with those obtained in a field test. The genetic algorithm considering 50 individuals and the mutation probability of 0.01 is developed and further combined with the monorail vehicle dynamic model to serve as a multi-parameters and objective optimisation platform. This optimisation platform is employed to optimise the suspension parameters in the monorail vehicle to improve the ride comfort of the vehicle and minimise the wheel unload, overturning coefficients and wear generation in the tire. [ABSTRACT FROM AUTHOR]

Background: Non-small cell lung cancer (NSCLC) accounts for approximately 80% of diagnosed lung cancer patients. RAD52 has been reported to be associated with the development of squamous cell lung carcinoma. In this study, we assessed the relationships of RAD52 genetic polymorphisms and NSCLC risk among the Chinese population at high altitude. Methods: Eight single nucleotide polymorphisms (SNPs) of RAD52 were genotyped in the Agena MassARRAY platform among 506 NSCLC patients and 510 healthy controls. We examined the association of RAD52 polymorphisms with NSCLC risk using odds ratios (ORs) and 95% confidence intervals (CIs) via multiple genetic models. Results: The rs10774474 A allele was related to a decreased risk of NSCLC in a high altitude population of China (OR = 0.82, 95% CI = 0.69–0.98, p = 0.032), whereas mutant alleles of rs1051672, rs7310449, rs1051669, rs6413436, rs4766377 and rs10849605 significantly increased NSCLC risk. Haplotype analysis showed that four haplotypes of RAD52 polymorphisms conferred an enhanced susceptibility to NSCLC (Ars1051672Grs7310449Trs1051669Ars6413436: OR = 1.29, p = 0.021; Grs1051672Ars7310449Crs1051669Grs6413436: OR = 1.21, p = 0.027; Grs4766377Crs12822733Trs10774474Crs10849605: OR = 1.26, p = 0.032; Ars4766377Crs12822733Ars10774474Trs10849605: OR = 1.21, p = 0.032). Conclusions: Our findings suggested the remarkable association of RAD52 polymorphisms with NSCLC risk among the Chinese population in a high altitude area. The reviews of this paper are available via the supplemental material section. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a technique which helps to find the most optimal feature subset to develop an efficient pattern recognition model under consideration. The use of genetic algorithm (GA) and particle swarm optimization (PSO) in the field of FS is profound. In this paper, we propose an insightful way to perform FS by amassing information from the candidate solutions produced by GA and PSO. Our aim is to combine the exploitation ability of GA with the exploration capacity of PSO. We name this new model as binary genetic swarm optimization (BGSO). The proposed method initially lets GA and PSO to run independently. To extract sufficient information from the feature subsets obtained by those, BGSO combines their results by an algorithm called average weighted combination method to produce an intermediate solution. Thereafter, a local search called sequential one-point flipping is applied to refine the intermediate solution further in order to generate the final solution. BGSO is applied on 20 popular UCI datasets. The results were obtained by two classifiers, namely, k nearest neighbors (KNN) and multi-layer perceptron (MLP). The overall results and comparisons show that the proposed method outperforms the constituent algorithms in 16 and 14 datasets using KNN and MLP, respectively, whereas among the constituent algorithms, GA is able to achieve the best classification accuracy for 2 and 7 datasets and PSO achieves best accuracy for 2 and 4 datasets, respectively, for the same set of classifiers. This proves the applicability and usefulness of the method in the domain of FS. [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]

The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, a genetic algorithm, which consists of a boundary search heuristic (BSH), a linear program, and a dual simplex method, is developed for an UA-FLP. To test the performance of the proposed technique, several test problems taken from the literature are used in the analysis. The results show that the proposed heuristic performs well with respect to solution quality and computational time. [ABSTRACT FROM AUTHOR]

Interbull's multiple across-country evaluaftion provides national breeding organizations with breeding values for internationally used bulls, which integrate performance data obtained in different breeding populations, environments, and production systems. However, breeding value-based selection decisions on domestic individuals born to foreign sires can only benefit from Interbull breeding values if they are integrated such that their information can contribute to the breeding values of all related domestic animals. For that purpose, several methods have been proposed which either model Interbull breeding values as prior information in a Bayesian approach, as additional pseudo data points, or as correlated traits, where these methods also differ in their software and parameterization requirements. Further, the complexity of integration also depends on the traits and genetic evaluation models. Especially random regression models require attention because of the dimensionality discrepancy between the number of Interbull breeding values and the number of modeled genetic effects. This paper presents the results from integrating 16,063 Interbull breeding values into the domestic single-step random regression test-day model for milk, fat, and protein yield for Australian Red dairy cattle breeds. Interbull breeding values were modeled as pseudo data points with data point-specific residual variances derived within animal across traits, ignoring relationships between integrated animals. Results suggest that the integration was successful with regard to alignment of Interbull breeding values with their domestic equivalent as well as with regard to the individual and population-wide increase in reliabilities. Depending on the relationship structure between integration candidates, further work is required to account for those relationships in a computationally feasible manner. Other traits with separate parity effects nationally could use a similar approach, even if not modeled with a test-day model. [ABSTRACT FROM AUTHOR]

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• A two-stage adaptive surrogate model improves the surrogate model accuracy. • Trust region GA improves the stability of the contamination source inversion results. • TSASM-TRGA framework is a robust tool to improve the accuracy and reliability of GCSI. The inversion of contamination source characteristics is of great importance to groundwater remediation and risk management. The surrogate model-based simulation-optimization method is the most commonly used groundwater contamination source inversion (GCSI) approach. However, the static surrogate model and the genetic algorithm (GA) suffer from inaccuracy and instability problems when the nonlinearity of the numerical model or the dimension of the variables to be identified increase. In this paper, a two-stage adaptive surrogate model-assisted trust region GA (TSASM-TRGA) framework was developed to improve the accuracy and stability of the complex high dimensional nonlinear GCSI. In this framework, the two-stage adaptive surrogate model was applied to balance the global and local accuracy of the surrogate model, and the trust region GA was adopted to accelerate the convergence and improve the stability of optimal solutions. By comparing the accuracy of the TSASM-TRGA, two-stage adaptive surrogate model-assisted GA (TSASM-GA), one-stage adaptive surrogate model-assisted GA (OSASM-GA), and static surrogate model-assisted GA (SSM-GA) frameworks, the results showed that the identified contamination source locations with TSASM-TRGA framework are more consistent with the actual locations, and the identified contamination source fluxes are closer to the actual ones. The mean relative error (MRE) between TSASM-TRGA framework and the actual values is 0.31%, which is much smaller than that for the other three frameworks. The inversion results with the TSASM-TRGA framework show a higher accuracy than those with the simulation-statistical method. The present study suggests that the proposed TSASM-TRGA framework provides a more effective approach to improve the accuracy and stability of GCSI. [ABSTRACT FROM AUTHOR]

Thermal barrier coatings (TBCs) are widely used on turbine guide vanes (TGVs) in aero engines. The construction of a reasonable TBC fatigue life prediction model is of great significance to the development of aero engines. A 2D axisymmetric finite element model (FEM) is established, based on experimental data from a tube with a TBC. Then, a reasonable TBC life fatigue prediction model is established by combining the Manson–Coffin equation, linear cumulative damage theory, and growth characteristics of the oxide layer. The fitting problem is transformed into an optimization problem in the process of establishing the TBC fatigue life prediction model, and the coefficients of the model are solved by a genetic algorithm (GA). Finally, a strain analysis FEM for TGVs with a TBC is established, based on the master–slave model method, and TGVs coating fatigue life is predicted by a fatigue life prediction model. The results show that the maximum fatigue life prediction error for tubes with a TBC is 104.7%, which is 114.4% lower than that obtained in previous studies, and most of the coating fatigue life prediction values are distributed within 50% confidence bounds. The coating fatigue life of the TGV on the trailing edge is 1948 cycles, which is a reasonable result. The efforts of this study provide a framework to predict the coating fatigue life of aero engine hot components. [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]

Limb reduction has occurred multiple times in tetrapod history. Among ratites, wing reductions range from mild vestigialization to complete loss, with emus (Dromaius novaehollandiae) serving as a model for studying the genetic mechanisms behind limb reduction. Here, we explore the developmental mechanisms underlying wing reduction in emu. Our analyses reveal that immobilization resulting from the absence of distal muscles contributes to skeletal shortening, fusion and left-right intraindividual variation. Expression analysis and single cell-RNA sequencing identify muscle progenitors displaying a dual lateral plate mesodermal and myogenic signature. These cells aggregate at the proximal region of wing buds and undergo cell death. We propose that this cell death, linked to the lack of distal muscle masses, underlines the morphological features and variability in skeletal elements due to reduced mechanical loading. Our results demonstrate that differential mobility during embryonic development may drive morphological diversification in vestigial structures. The flightless emu wings have a striking resemblance to immobilization phenotypes. Here, authors report that cell death of dual-identity muscle progenitors during embryonic development contributes to distal muscle absence, linked to skeletal shortening and asymmetry in this vestigial structure. [ABSTRACT FROM AUTHOR]

Mouse (Mus musculus) models have been heavily utilized in developmental biology research to understand mammalian embryonic development, as mice share many genetic, physiological, and developmental characteristics with humans. New explorations into the integration of temporal (stage-specific) and transcriptional (tissue-specific) data have expanded our knowledge of mouse embryo tissue-specific gene functions. To better understand the substantial impact of synonymous mutational variations in the cell-state-specific transcriptome on a tissue's codon and codon pair usage landscape, we have established a novel resource—Mouse Embryo Codon and Codon Pair Usage Tables (Mouse Embryo CoCoPUTs). This webpage not only offers codon and codon pair usage, but also GC, dinucleotide, and junction dinucleotide usage, encompassing four strains, 15 murine embryonic tissue groups, 18 Theiler stages, and 26 embryonic days. Here, we leverage Mouse Embryo CoCoPUTs and employ the use of heatmaps to depict usage changes over time and a comparison to human usage for each strain and embryonic time point, highlighting unique differences and similarities. The usage similarities found between mouse and human central nervous system data highlight the translation for projects leveraging mouse models. Data for this analysis can be directly retrieved from Mouse Embryo CoCoPUTs. This cutting-edge resource plays a crucial role in deciphering the complex interplay between usage patterns and embryonic development, offering valuable insights into variation across diverse tissues, strains, and stages. Its applications extend across multiple domains, with notable advantages for biotherapeutic development, where optimizing codon usage can enhance protein expression; one can compare strains, tissues, and mouse embryonic stages in one query. Additionally, Mouse Embryo CoCoPUTs holds great potential in the field of tissue-specific genetic engineering, providing insights for tailoring gene expression to specific tissues for targeted interventions. Furthermore, this resource may enhance our understanding of the nuanced connections between usage biases and tissue-specific gene function, contributing to the development of more accurate predictive models for genetic disorders. [ABSTRACT FROM AUTHOR]

Mutagenesis is an important tool in crop improvement and free of the regulatory restrictions imposed on genetically modified organisms. Barley (Hordeum vulgare L.) is a diploid species with a genome smaller than those of other members of the Triticeae crops, making it an attractive model for genetic studies in Triticeae crops. In this study, we report an ethyl methane sulfonate (EMS)-mutagenized population in the Chinese barley landrace TX9425, which is tolerant to both abiotic and biotic stress. A TILLING (Targeting Induced Locus Lesion in Genomes) population consisting of 2000 M2 lines was also constructed based on the CEL I enzyme with subsequent polyacrylamide electrophoresis, which decreased the cost and labor investment. The mutant phenotypes of the M2 and M3 generations were scored and revealed the presence of a wide spectrum of morphological diversity. The population was evaluated by screening for induced mutations in five genes of interest. A detailed analysis was performed for the HvGLR3.5 gene and three mutations were identified by screening in 2000 M2 lines. Two of three mutations displayed tuft and yellow striped leaves compared to the wild type. Altogether, our study shows the efficiency of screening and the great potential of the new TILLING population for genetic studies in the barley crop model system. [ABSTRACT FROM AUTHOR]

Age-related macular degeneration (AMD) is a major global health problem as it is the leading cause of irreversible loss of central vision in the aging population. Anti-vascular endothelial growth factor (anti-VEGF) therapies are effective but do not respond optimally in all patients. This study investigates the genetic factors associated with susceptibility to AMD and response to treatment, focusing on key polymorphisms in the ARMS2 (rs10490924), IL1B1 (rs1143623), TNFRSF1B (rs1061622), TNFRSF1A (rs4149576), VEGFA (rs3024997), ARMS2, IL1B1, TNFRSF1B, TNFRSF1A, and VEGFA serum levels in AMD development and treatment efficacy. This study examined the associations of specific genetic polymorphisms and serum protein levels with exudative and early AMD and the response to anti-VEGF treatment. The AA genotype of VEGFA (rs3024997) was significantly associated with a 20-fold reduction in the odds of exudative AMD compared to the GG + GA genotypes. Conversely, the TT genotype of ARMS2 (rs10490924) was linked to a 4.2-fold increase in the odds of exudative AMD compared to GG + GT genotypes. In females, each T allele of ARMS2 increased the odds by 2.3-fold, while in males, the TT genotype was associated with a 5-fold increase. Lower serum IL1B levels were observed in the exudative AMD group compared to the controls. Early AMD patients had higher serum TNFRSF1B levels than controls, particularly those with the GG genotype of TNFRSF1B rs1061622. Exudative AMD patients with the CC genotype of TNFRSF1A rs4149576 had lower serum TNFRSF1A levels compared to the controls. Visual acuity (VA) analysis showed that non-responders had better baseline VA than responders but experienced decreased VA after treatment, whereas responders showed improvement. Central retinal thickness (CRT) reduced significantly in responders after treatment and was lower in responders compared to non-responders after treatment. The T allele of TNFRSF1B rs1061622 was associated with a better response to anti-VEGF treatment under both dominant and additive genetic models. These findings highlight significant genetic and biochemical markers associated with AMD and treatment response. This study found that the VEGFA rs3024997 AA genotype reduces the odds of exudative AMD, while the ARMS2 rs10490924 TT genotype increases it. Lower serum IL1B levels and variations in TNFRSF1B and TNFRSF1A levels were linked to AMD. The TNFRSF1B rs1061622 T allele was associated with better anti-VEGF treatment response. These markers could potentially guide risk assessment and personalized treatment for AMD. [ABSTRACT FROM AUTHOR]

Climate change is altering the geographical distribution and abundance of species. Abundant genetic variation generally indicates a stronger adaptability and evolutionary potentiality, especially in case of sharply changing climates or environments. With the past global climate fluctuations, especially the climate oscillation since the Quaternary, the global temperature changes related to glaciation, many relict plant species have formed possible refugia in humid subtropical/warm temperate forests, thus retaining a high level of genetic diversity patterns. Based on the contraction and expansion of the geographical distribution of Taxus wallichiana Zucc. in the past driven by climate change, combined with the contemporary genetic diversity modeling, the distribution performance of Taxus wallichiana Zucc. in future climate change was predicted. The areas of highly suitable habitat will increase with climate change in the future. There were continuous and stable high suitable areas of T. wallichiana in the southeastern Tibet and northwestern Yunnan as long-term stable climate refugia. We made the genetic landscape surface of T. wallichiana complex and discovered geographical barriers against gene flow. Genetic barriers spatially isolated the center of genetic diversity into three regions: west (east Himalaya), middle (Yunnan plateau, Sichuan basin, Shennongjia, and the junction of Guizhou and Guangxi provinces), and east (Mt. Huangshan and Fujian). Southern Tibet was isolated from other populations. The central and western Yunnan, the Sichuan basin, and surrounding mountains were isolated from the southern China populations. We found that the positive correlationships between the present species genetic diversity and suitability index during LGM, MH, and 2070. This infers that T. wallichiana has provisioned certain genetic diversity and has strong evolutionary potential under conditions of climate change. [ABSTRACT FROM AUTHOR]

A number of powerful demographic inference methods have been developed in recent years, with the goal of fitting rich evolutionary models to genetic data obtained from many populations. In this paper we investigate the statistical performance of these methods in the specific case where there is continuous migration between populations. Compared with earlier work, migration significantly complicates the theoretical analysis and requires new techniques. We employ the theories of phase-type distributions and concentration of measure in order to study the two-island and isolation-with-migration models, resulting in both upper and lower bounds on rates of convergence for parametric estimators in migration models. For the upper bounds, we consider inferring rates of coalescent and migration on the basis of directly observing pairwise coalescent times, and, more realistically, when (conditionally) Poisson-distributed mutations dropped on latent trees are observed. We complement these upper bounds with information-theoretic lower bounds which establish a limit, in terms of sample size, below which inference is effectively impossible. [ABSTRACT FROM AUTHOR]