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In the maintenance optimisation framework, grouping maintenance is a promising solution for maintenance planning of multi-component systems, in which maintenance activities are performed together to reduce maintenance costs. One of the most widely identified challenges in real applications of grouping maintenance is that it may disturb the maintenance workload balance (smoothness), causing many difficulties in production and/or labour scheduling and inventory management. In this study, we propose a joint optimisation approach for maintenance grouping and workload balancing to address the above challenge. First, a mathematical model of the joint optimisation problem was derived. A multi-objective grouping optimisation approach based on the Weighted Sum model and Genetic Algorithm was implemented to determine the Pareto-optimal grouping solution. The proposed approach was applied to a real case study of an automotive plant comprising 40 production lines with 1090 components. The results highlighted the advantages, effectiveness, and flexibility of the proposed maintenance approach in real-world applications. [ABSTRACT FROM AUTHOR]

Genetic summary data are broadly accessible and highly useful, including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into summary data, such as allele frequencies, masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted-for substructure limits summary data usability, especially for understudied or admixed populations. There is a need for methods to enable the harmonization of summary data where the underlying substructure is matched between datasets. Here, we present Summix2 , a comprehensive set of methods and software based on a computationally efficient mixture model to enable the harmonization of genetic summary data by estimating and adjusting for substructure. In extensive simulations and application to public data, we show that Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and scans for potential regions of selection due to local substructure deviation. Summix2 increases the robust use of diverse, publicly available summary data, resulting in improved and more equitable research. [Display omitted] We present Summix2 , an open-source software suite of methods to estimate, harmonize, and leverage substructure in genetic summary data, including finer-scale, local, and genetic predisposition to conditions. Summix2 enables more robust use of all genetic summary data, especially for genetically admixed populations, which are typically underrepresented in biomedical research. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases, especially coronary heart disease (CHD), are currently the leading cause of mortality and morbidity. Environmental and genetic factors contribute to the pathophysiology of CHD. In this study, we investigated the associations of miRNA genes MIR222, MIR423, MIR4274, and MIR3117 with the pathogenesis of CHD. Methods: Genetic inheritance models were applied to explore the association between these miRNA genes and CHD. Tetra-Primer Amplification Refractory Mutation System-Polymerase Chain Reaction (T-ARMS-PCR) was used for genotyping, and Sanger sequencing was applied for validation in selected cases. Results: The codominant [χ2 = 8.058, 2; P value = 0.0178], and recessive (CC vs GC + GG) [OR = 0.4535 (0.2439–0.8669); P value = 0.0187] models demonstrated a statistically significant association between MIR222 (rs2858060) and CHD. Similarly, the co-dominant [χ2 = 6.105, 2, 2; P value = 0.0472], and additive [OR = 1.494 (1.024–2.211); P value = 0.0428] models revealed a significant association (P value < 0.05) between MIR423 (rs6505162) and CHD. In MIR4242, a novel triplet insertion (CAC) was identified exclusively in CHD samples, predicated to disrupt the stem-loop structure ofmiR-4274. However, MIR3117 showed no association with CHD, as confirmed by Sanger sequencing of 40 samples. Conclusion: Our findings suggest that functional variants rs2858060, rs6505162, and a novel triplet insertion (CAC) in MIR222, MIR423, and MIR4274 respectively are strongly associated with CHD. These results underline the potential of miRNA gene variants as genetic biomarkers for CHD susceptibility. [ABSTRACT FROM AUTHOR]

We performed the present study to better elucidate the correlation of nitric oxide synthase 3 (NOS3) gene polymorphism with the risk of pulmonary arterial hypertension (PAH). According to the designed search strategy, a systematic literature search was performed through the PubMed, Embase, Web of Science, Cochrane Library, CNKI, VIP and Wan Fang databases to collect published case-control studies on the correlation between NOS3 gene polymorphism and PAH. The search deadline was December 26, 2023. Two reviewers independently screened the literature, extracted data and evaluated the quality according to the inclusion and exclusion criteria. Meta-analysis was performed using RevMan 5.4 software. The odds ratio (OR) and 95 % confidence interval (CI) of the genotype distribution were used as the effect indicators. A total of 11 eligible studies were included, involving three single nucleotide polymorphism (SNP) sites of the NOS3 gene: G894T (rs1799983), 4b/4a (rs61722009), and T-786C (rs2070744). The meta-analysis revealed that for PAH analysis, 4 genetic models of NOS3 G894T polymorphism increased the risk of PAH: the allele model (T vs G, OR = 1.9, 95%CI [1.16, 3.11],P = 0.01), the homozygote model (GG vs TT, OR = 1.91, 95%CI [1.04, 3.51], P = 0.04), the heterozygote model (GG vs GT, OR = 3.19, 95%CI [1.65, 6.19], P = 0.0006) and the dominant model (GT + TT vs GG, OR = 3.06, 95%CI [1.54, 6.09], P = 0.001). In the subgroups analysis, the NOS3 G894T polymorphism was found to be associated with the risk of PAH subgroups, including CHD combined with PAH and COPD combined with PAH, Particularly, there is a highly significant correlation with CHD combined with PAH. 2 genetic models of NOS3 4b/4a polymorphism increased the risk of PAH: the homozygote model (BB vs AA, OR = 2.1, 95%CI [1.02, 4.35], P = 0.04) and the recessive model (BB + BA vs AA, OR = 2.55, 95%CI [1.27, 5.11], P = 0.009). In the subgroups analysis, the NOS3 4b/4a polymorphism was found to be associated with the susceptibility of CHD combined with PAH. The results of the combined analysis of each gene model of NOS3 T-786C gene polymorphism sites were not statistically significant, and their P values were all>0.05. The NOS3 G894T and NOS3 4b/4a gene polymorphism had been found to be associated with the risk of PAH in different regional and racial subgroups. In contrast to the NOS3 G894T gene polymorphism, which increased the risk of PAH development in the yellow race subgroup, the NOS3 4b/4a gene polymorphism reduced the risk of PAH development in the white race subgroup and was a protective factor. The NOS3 G894T (rs1799983) and NOS3 4b/4a (rs61722009) gene polymorphism have a strong correlation with the risk of PAH, with this association varying among different regions and ethnicities. However, it is still necessary to expand the sample size and conduct further studies to confirm whether the NOS3 T-786C (rs2070744) polymorphism tends to increase the incidence of PAH. • The NOS3 G894T (rs1799983) gene polymorphism has a strong correlation with the risk of PAH. • The NOS3 4b/4a (rs61722009) gene polymorphism has a strong correlation with the risk of PAH. • The correlation between the NOS3 T-786C (rs2070744) gene polymorphism and the risk of PAH still requires further investigation. [ABSTRACT FROM AUTHOR]

The sliding mode control (SMC) strategy based on genetic algorithm is proposed for discrete two‐dimensional (2‐D) Markov switching systems with the Roesser model. By means of common 2‐D sliding surface, the 2‐D SMC law is designed for the underlying system. According to Lyapunov stability criteria, sufficient conditions are established for the asymptotic mean‐square stability of the underlying 2‐D Markov switching systems and the reachability of the sliding region is guaranteed. Utilizing an iteration optimizing algorithm, an effective SMC strategy under genetic algorithm is constructed to minimize the sliding region by searching an ideal sliding gain. Finally, the applicability of the proposed 2‐D SMC strategy is verified through an example. [ABSTRACT FROM AUTHOR]

Gaussian process regression (GPR) is a commonly used approach for establishing the nonparametric models of ship maneuvering motion, and its performance depends on the selection of the kernel function. However, no single kernel function can be universally applied to all nonparametric models of ship maneuvering motion, which may compromise the robustness of GPR. To address this issue, an adaptive ensemble of multi-kernel GPRs based on heuristic model screening (AEGPR-HMS) is proposed in this paper. In the proposed method, four kernel functions are involved in constructing the ensemble model. The HMS method is introduced to determine the weights of individual-based GPR models, which can be adaptively assigned according to the baseline GPR model. To determine the hyper-parameters of these kernel functions, the genetic algorithm is also employed to compute the optimal values. The KVLCC2 tanker provided by the SIMMAN 2008 workshop is used to validate the performance of the proposed method. The results demonstrate that the AEGPR-HMS is an efficient and robust method for nonparametric modeling of ship maneuvering motion. [ABSTRACT FROM AUTHOR]

Purpose: Meningiomas are central nervous system tumors whose incidence increases with age. Benign meningioma pathogenesis involves germline or somatic mutation of target genes, such as NF2, leading to clonal expansion. We used an established cancer epidemiology model to investigate the number of rate-limiting steps sufficient for benign meningioma development. Methods: Incidence data was obtained from the Surveillance, Epidemiology and End Results Program (SEER) for nonmalignant meningioma from 2004 to 2020. Age-adjusted incidence rates per 100,000 person-years were divided into 5-year bands. This was repeated for vestibular schwannomas as a negative control. The Armitage-Doll methodology was applied. Mathematical solutions correcting for volatile tumor microenvironments were applied to fit higher-order models using polynomial regression when appropriate. A 75:25 training:test split was utilized for validation. Results: 222,509 cases of benign meningiomas were identified. We noted strong linear relationships between log-transformed incidence and age across the cohort and multiple subpopulations: male, white, black, Hispanic, Asian/Pacific Islander, and American Indian subpopulations all demonstrated R2 = 0.99. Slopes were between 3.1 and 3.4, suggesting a four-step process for benign meningioma development. Female patients exhibited nonlinear deviations, but the corrected model demonstrated R2 = 0.99 with a four-hit pathway. This model performed robustly on test data with R2 = 0.99. Vestibular schwannomas demonstrated a slope of 2.1 with R2 = 0.99, suggesting a separate three-step process. Conclusion: Four mutations are uniquely required for the development of benign meningiomas. Correcting for volatile tumor microenvironments reliably accounted for nonlinear deviations in behavior. Further studies are warranted to elucidate genomic findings suggestive of key mutations in this pathway. [ABSTRACT FROM AUTHOR]

Background: We did this study to better clarify the correlations of methylenetetrahydrofolate dehydrogenase 1 (MTHFD1)-G1958A (rs2236225) gene polymorphism with the risk of congenital heart diseases (CHD) and its subgroups. Methods: Relevant articles were searched in PubMed, Web of Science, Cochrane Library, Embase, CNKI, VIP database and Wanfang DATA until October 2023. We will use odds ratios (ORs) and 95% confidence intervals (CIs) to examine the potential associations of MTHFD1- G1958A gene polymorphism with CHD and its subgroups. Results: We included a total of 9 eligible studies, encompassing 1917 children with CHD, 1863 healthy children, 1717 mothers of the children with CHD and 1666 mothers of healthy children. In our study, the meta-analysis of fetal group revealed no significant association between any of the five genetic models for the MTHFD1-G1958A polymorphism and the risk of CHD. Subgroup analysis showed that associations between the MTHFD1-G1958A polymorphism and Tetralogy of Fallot (TOF) risk in the homozygote model (AA vs. GG, OR = 2.82, 95%CI [1.16, 6.86], P = 0.02) and recessive model (AA vs. GG + GA, OR = 3.09, 95%CI [1.36, 7.03], P = 0.007). In addition, the MTHFD1-G1958A polymorphism was associated with the risk of CHD in racial subgroup, increasing the risk of CHD in Caucasians. In maternal analysis, 2 genetic models of MTHFD1-G1958A polymorphism increased the risk of CHD: the heterozygote model (GA vs. GG, OR = 1.22, 95%CI [1.04, 1.42], P = 0.01), and the dominance model (GA + AA vs. GG, OR = 1.17, 95%CI [1.01, 1.34], P = 0.03). Conclusions: The fetal MTHFD1-G1958A (rs2236225) gene polymorphism increase their risk of TOF. The maternal MTHFD1-G1958A polymorphism has a strong correlation with the risk of CHD, and there are racial differences in this correlation. Compared with GG genotype, the GA genotype increases the risk of CHD. [ABSTRACT FROM AUTHOR]

Background: Intermediate phenotypes, such as characteristic neuroimaging patterns, offer unique insights into the genetic and stress-related underpinnings of neuropsychiatric disorders like depression. This study aimed to identify neuroimaging intermediate phenotypes associated with depression, bridging etiological factors to behavioral manifestations and connecting insights from animal models to diverse clinical populations. Methods: We analyzed datasets from both rodents and humans. The rodent studies included a genetic model (P11 knockout) and an environmental stress model (chronic unpredictable mild stress), while the human data comprised 748 participants from three cohorts. Using the amplitude of low-frequency fluctuations, we identified neuroimaging patterns in rodent models. We then applied a machine-learning approach to cluster neuroimaging subtypes of depression. To assess the genetic predispositions and stress-related changes associated with these subtypes, we analyzed genotype and metabolite data. Linear regression was employed to determine which neuroimaging features predicted core depression symptoms across species. Results: The genetic and environmental stress models exhibited distinct neuroimaging patterns in subcortical and sensorimotor regions. Consistent patterns emerged in two neuroimaging subtypes identified across three independent depressed cohorts. The subtype resembling P11 knockout demonstrated higher genetic susceptibility, with enriched expression of risk genes in brain tissues and abnormal metabolites linked to tryptophan metabolism. In contrast, the stress animal-like subtype did not show changes in genetic risk scores but exhibited enriched risk gene expression in somatic and endocrine tissues, along with mitochondrial dysfunction in the antioxidant stress system. Notably, these distinct subcortical-sensorimotor neuroimaging patterns predicted anhedonia, a core symptom of depression, in both rodent models and depressed subtypes. Conclusions: This cross-species validation suggests that these neuroimaging patterns may serve as robust intermediate phenotypes, linking etiology to anhedonia and facilitating the translation of findings from animal models to humans with depression and other psychiatric disorders. [ABSTRACT FROM AUTHOR]

Appraisal models, such as the Scherer's Component Process Model (CPM), represent an elegant framework for the interpretation of emotion processes, advocating for computational models that capture emotion dynamics. Today's emotion recognition research, however, typically classifies discrete qualities or categorised dimensions, neglecting the dynamic nature of emotional processes and thus limiting interpretability based on appraisal theory. In our research, we estimate emotion intensity from multiple physiological features associated to the CPM's neurophysiological component using dynamical models with the aim of bringing insights into the relationship between physiological dynamics and perceived emotion intensity. To this end, we employ nonlinear autoregressive exogeneous (NARX) models, as their parameters can be interpreted within the CPM. In our experiment, emotions of varying intensities are induced for three distinct qualities while physiological signals are measured, and participants assess their subjective feeling in real time. Using data-extracted physiological features, we train intrasubject and intersubject intensity models using a genetic algorithm, which outperform traditional sliding-window linear regression, providing a robust basis for interpretation. The NARX model parameters obtained, interpreted by appraisal theory, indicate consistent heart rate parameters in the intersubject models, suggesting a large temporal contribution that aligns with the CPM-predicted changes. [ABSTRACT FROM AUTHOR]

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by a dismal prognosis. Treatment outcomes exhibit substantial variability across patients, underscoring the urgent need for robust predictive models to effectively estimate survival probabilities and therapeutic responses in PDAC. Methods: Metabolic and immune-related genes exhibiting differential expression were identified using the TCGA-PDAC and GTEx datasets. A genetic prognostic model was developed via univariable Cox regression analysis on a training cohort. Predictive accuracy was assessed using Kaplan-Meier (K-M) curves, calibration plots, and ROC curves. Additional analyses, including GSAE and immune cell infiltration studies, were conducted to explore relevant biological mechanisms and predict therapeutic efficacy. Results: An 8-gene prognostic model (AK2, CXCL11, TYK2, ANGPT4, IL20RA, MET, ENPP6, and CA12) was established. Three genes (AK2, ENPP6, and CA12) were associated with metabolism, while the others were immune-related. Most genes correlated with poor prognosis. Validation in TCGA-PDAC and GSE57495 datasets demonstrated robust performance, with AUC values for 1-, 3-, and 5-year OS exceeding 0.7. The model also effectively predicted responses to adjuvant therapy. Conclusion: This 8-gene signature enhances prognostic accuracy and therapeutic decision-making in PDAC, offering valuable insights for clinical applications and personalized treatment strategies. [ABSTRACT FROM AUTHOR]

Objectives: This study examined the correlation between circulating receptor activator for nuclear factor-κB ligand (RANKL) levels and rheumatoid arthritis (RA), and investigated the association between polymorphisms in the RANKL gene and susceptibility to RA. Method: We searched the Medline, Embase, and Cochrane databases for relevant publications up to September 2024. A meta-analysis was conducted to assess serum/plasma RANKL levels in patients with RA and controls, and to explore the relationship between RANKL rs9533156 and rs2277438 polymorphisms and RA susceptibility. Results: Ten studies encompassing 1,682 RA patients and 1,288 controls were analyzed. RANKL levels were significantly higher in RA patients compared to controls (SMD = 0.665, 95% CI = 0.290–1.040, P = 0.001). Subgroup analysis affirmed these findings' consistency across different sample sizes and publication years. RANKL levels were positively associated with rheumatoid factor (RF) and Disease Activity Score-28 (DAS28) (RF correlation coefficient = 0.157, 95% CI = 0.028–0.282, P = 0.018; DAS28 correlation coefficient = 0.151, 95% CI = 0.125–0.370, P < 0.001). Additionally, the meta-analysis revealed significant associations between the susceptibility to RA and the RANKL rs9533156 C allele (OR = 0.609, 95% CI = 0.520–0.714, P < 0.010) as well as the rs2277438 G allele (OR = 1.206, 95% CI = 1.003–1.451, P = 0.047). These associations were consistent across homozygote comparisons and different genetic models. Conclusions: This meta-analysis underscores the elevated circulating RANKL levels in RA patients and their significant correlation with RF and DAS28. Additionally, the RANKL rs9533156 and rs2277438 polymorphisms were significantly associated with RA susceptibility. [ABSTRACT FROM AUTHOR]

Anhui Province is rich in geothermal water resources, making the study of its hydrochemical evolution and genetic models essential for scientific development and sustainable utilization. This study combines hydrochemical and hydrogen–oxygen isotopic data from different regions of Anhui Province to analyze the hydrogeochemical evolution characteristics and recharge mechanisms of basin-type and mountainous-type geothermal waters. The results show that basin-type geothermal water is predominantly of the Cl–Na type, with water–rock interactions mainly including halite dissolution, gypsum dissolution, dedolomitization, and silicate hydrolysis. The groundwater system is relatively closed off, with slow flow rates. In contrast, mountainous geothermal water is mainly of the HCO3–Na·Ca, SO4–Na·Ca, and SO4–Na types, with water–rock interactions primarily involving calcite dissolution, dolomite dissolution, and gypsum dissolution. Enhanced precipitation infiltration due to fault structures leads to stronger recharge and an open-system characteristic. The genetic models of the two types of geothermal water reveal the structural and recharge mechanisms of thermal reservoirs under different geological settings, highlighting the significant control of geological background on geothermal water formation. [ABSTRACT FROM AUTHOR]

NFKB1, a core transcription factor critical in various biological process (BP), is increasingly studied for its role in tumors. This research combines literature reviews, meta-analyses, and bioinformatics to systematically explore NFKB1's involvement in tumor initiation and progression. A unique focus is placed on the NFKB1-94 ATTG promoter polymorphism, highlighting its association with cancer risk across diverse genetic models and ethnic groups, alongside comprehensive analysis of pan-cancer expression patterns and drug sensitivity. The study reveals the intricate connections between NFKB1 and tumors, highlighting its significant roles in invasion, metastasis, genomic stability, and metabolic changes. Through meta-analysis, it is evidenced that tumor specimens exhibit increased NFKB1 expression when compared to non-tumor specimens, although its association with cancer incidence requires further investigation. Analysis from the Gene Expression Omnibus (GEO) database suggests that high NFKB1 gene expression may not markedly impact tumor patient prognosis. The noticeable correlation between the NFKB1-94 ATTG promoter polymorphic sequence and elevated cancer susceptibility is highlighted across different genetic models. Furthermore, bioinformatics analysis uncovers NFKB1's association with the sensitivity to various anticancer drugs and its central involvement in crucial BP like the cell cycle, cytoskeleton assembly, and cellular senescence. Overall, NFKB1's expression and polymorphisms are significantly linked to tumor risk, prognosis, and treatment response, highlighting its prospect as a forthcoming aim for cancer treatment. This study offers a robust foundation for further exploration of NFKB1's mechanisms and the development of innovative therapeutic strategies. [ABSTRACT FROM AUTHOR]

The pituitary gland is a key endocrine gland with various physiological functions including metabolism, growth, and reproduction. It comprises several distinct cell populations that release multiple polypeptide hormones. Although the major endocrine cell types are conserved across taxa, the regulatory mechanisms of gene expression and chromatin organization in specific cell types remain poorly understood. Here, we performed simultaneous profiling of the transcriptome and chromatin landscapes in the pituitary cells of the three-spined stickleback (Gasterosteus aculeatus), which represents a good model for investigating the genetic mechanisms underlying adaptive evolution. We obtained pairwise gene expression and chromatin profiles for 5184 cells under short- and long-day conditions. Using three independent clustering analyses, we identified 16 distinct cell clusters and validated their consistency. These results advance our understanding of the regulatory dynamics in the pituitary gland and provide a reference for future research on comparative physiology and evolutionary biology. [ABSTRACT FROM AUTHOR]

Background: The success of selecting high risk or early-stage Alzheimer's disease individuals for the delivery of clinical trials depends on the design and the appropriate recruitment of participants. Polygenic risk scores (PRS) show potential for identifying individuals at risk for Alzheimer's disease (AD). Our study comprehensively examines AD PRS utility using various methods and models. Methods: We compared the PRS prediction accuracy in ADNI (N = 568) and BioFINDER (N = 766) cohorts using five disease risk modelling approaches, three PRS derivation methods, two AD genome-wide association study (GWAS) statistics and two sets of SNPs: the whole genome and microglia-selective regions only. Results: The best prediction accuracy was achieved when modelling genetic risk by using two predictors: APOE and remaining PRS (AUC = 0.72–0.76). Microglial PRS showed comparable accuracy to the whole genome (AUC = 0.71–0.74). The individuals' risk scores differed substantially, with the largest discrepancies (up to 70%) attributable to the GWAS statistics used. Conclusions: Our work benchmarks the best PRS derivation and modelling strategies for AD genetic prediction. [ABSTRACT FROM AUTHOR]

Attention Deficit Hyperactivity Disorder (ADHD) is a common and heritable neurodevelopmental condition that has been the subject of a wealth of genetics research. Because ADHD has an early age of onset, most of this work has focused on children, meaning that less is known about the genetics of ADHD in adults. Additionally, while much research has assessed the heritability of ADHD as a general dimension, less has assessed the heritability of individual subtypes (inattention, hyperactivity) or symptoms of ADHD. It therefore remains unclear whether the genetic factors underlying ADHD symptoms conform to a unidimensional or multidimensional structure. The aim of this study was to assess the genetic and environmental dimensionality of adult ADHD symptoms. We analyzed data from 10,454 twins of the Twins Early Development Study, who provided self-reports of ADHD symptoms using the Conners scale at age 21 years. The data conformed well to a psychometric bifactor model, providing support for a general dimension of ADHD in addition to secondary dimensions for inattention and hyperactivity. However, a bifactor independent pathway twin model provided support for a general dimension only at the level of non-shared environmental effects and not additive genetic or shared environmental effects. This suggests that symptoms of ADHD cluster together under a general dimension of non-shared environmental effects, although the two subtypes of ADHD (inattention and hyperactivity) are meaningfully genetically distinct. We found the overall heritability of ADHD to be 40%, comparable with previous estimates for adult ADHD symptoms. Our results provide useful insights into the genetic and environmental architecture of specific ADHD symptoms. [ABSTRACT FROM AUTHOR]

Corn is a widely grown cereal crop that serves as a model plant for genetic and evolutionary studies. However, the heterosis pattern of sweet corn remains unclear. Here, we analysed the genetic diversity and population structure of 514 sweet corn inbred lines and 181 field corn inbred lines. The population structure study enabled the classification of sweet corn into four groups: temperate sweet corns 1 and 2, tropical sweet corn, and subtropical sweet corn, in addition to the temperate and tropical field corn groups. Temperate sweet corn groups 1 and 2 were merged into the temperate sweet corn cluster in the phylogenetic trees. Principal component analysis divided sweet corn into four groups: temperate groups 1 and 2, tropical, and subtropical. Sweet corn exhibited lower levels of genetic diversity, polymorphism information content, and minor allele frequency than field corn. The average genetic distances and differentiation coefficients between inbreds within each sweet corn group were lower than those within field corn groups, indicating a relatively narrow genetic base in sweet corn. Taken together, the 514 sweet corn inbred lines can be divided into four groups: temperate 1, temperate 2, tropical, and subtropical. The classification of sweet corn groups in this study provides a reference for the breeding of sweet corn. [ABSTRACT FROM AUTHOR]

Plant life is incredibly diverse, with many plant families consisting of thousands or even tens of thousands of species. This extensive diversity must be explained within a creation model of origins. If, as is generally assumed, the taxonomic family is roughly the classification level of the created kinds, then many plant kinds are incredibly diverse. One way to account for this diversity may be created polyploidy. Created polyploidy would allow for an increased number of created alleles, thereby increasing the potential diversity of the original baramins. This increased diversity would enable created polyploids to diversify to the levels we see today within a creationist paradigm. A computer model of the differences in genetic diversity maintained by polyploid and diploid lineages was written in Python, a general-purpose, highlevel, programming language. The model found that polyploids consistently maintained greater diversity than diploids. As such, created polyploidy should be considered as a potential explanation for genetic diversity. This article will propose a unique explanation for the origins of the diversity of many angiosperms as well as some gymnosperms where polyploidy is common. [ABSTRACT FROM AUTHOR]

This work introduces a novel methodology for identifying critical sensor locations and detecting defects in structural components. Initially, a hybrid method is proposed to determine optimal sensor placements by integrating results from both the discrete empirical interpolation method (DEIM) and the random permutation features importance technique (PI). Subsequently, the identified sensors are utilized in a novel defect detection approach, leveraging a semi-intrusive reduced order modeling and genetic search algorithm for fast and reliable defect detection. The proposed algorithm has successfully located defects with low error, especially when using hybrid sensors, which combine the most critical sensors identified through both PI and DEIM. This hybrid method identifies defects with the lowest errors compared to using either the PI or DEIM methods alone. [ABSTRACT FROM AUTHOR]

Circadian rhythms have evolved to synchronize gene expression, physiology, and behavior with time-of-day changes in the external environment. In every mammalian cell exists a core clock mechanism that consists of a transcriptional-translational feedback loop that drives rhythmic gene expression. Circadian disruption, as observed in shift workers and genetic mouse models, contributes to the onset and progression of cardiometabolic disorders. The central clock, located in the hypothalamus, is uniquely sensitive to external light cues, while the peripheral clocks are responsive to non-photic stimuli such as feeding and activity in addition to signals from the central clock. Recent research has illustrated the sensitivity of the skeletal muscle circadian clock to exercise timing, offering a promising avenue for therapeutic intervention in cardiometabolic health. Here we provide an in-depth examination of the molecular mechanisms underlying skeletal muscle clock function and its impact on cardiometabolic pathways, including glucose and lipid metabolism, as well as inflammation. To highlight the role of exercise as a time-cue for the skeletal muscle clock, we discuss evidence of exercise-induced shifts in the skeletal muscle clock and the differential response to exercise performed at different times of the day. Furthermore, we present data in support of time-of-day exercise as a potential therapeutic strategy for mitigating cardiometabolic disease burden. By exploring the relationship between the skeletal muscle clock, exercise timing, and cardiometabolic health, we identify new areas for future research and offer valuable insights into novel therapeutic approaches aimed at improving cardiometabolic disease outcomes. • We provide a review of skeletal muscle circadian clock function and its impact on metabolic and inflammation pathways. • We discuss evidence of time-of-day differential responses to exercse and exercise as a muscle clock time cue. • We present data supporting time-of-day exercise as a potential therapeutic strategy for mitigating cardiometabolic disease burden. • We identify new areas for future research into novel therapeutic approaches aimed at improving cardiometabolic disease outcomes. [ABSTRACT FROM AUTHOR]

Integrating bioinformatics and multiomics approaches is essential for identifying and prioritizing causal genetic variants in complex traits and diseases. The Pla2g4e gene case study in mice illustrates a practical example of using SNPs within regulatory elements to narrow down causative variants, offering a replicable model for similar genetic research. A hierarchical strategy for identifying and ranking SNP-containing regulatory elements is introduced, effectively narrowing down candidates for functional testing. The proposed approach can accelerate research by reducing the time and resources required for future validation of genetic variants for causality. The review introduces a user-friendly bioinformatics pipeline accessible to researchers at all levels without the need for specialized bioinformatics expertise. This review addresses the significant challenge of identifying causal genetic variants within quantitative trait loci (QTLs) for complex traits and diseases. Despite progress in detecting the ever-larger number of such loci, establishing causality remains daunting. We advocate for integrating bioinformatics and multiomics analyses to streamline the prioritization of candidate genes' variants. Our case study on the Pla2g4e gene, identified previously as a positional candidate obesity gene through genetic mapping and expression studies, demonstrates how applying multiomic data filtered through regulatory elements containing SNPs can refine the search for causative variants. This approach can yield results that guide more efficient experimental strategies, accelerating genetic research toward functional validation and therapeutic development. [ABSTRACT FROM AUTHOR]

Objective: To confirm a previously reported association of TRPV1 rs8065080 with the risk of transformation from episodic (EM) to chronic migraine (CM) and to extend knowledge about the role of other TRPV1 single nucleotide polymorphisms (SNPs), we first investigated the impact of three TRPV1 SNPs (rs8065080, rs222747 and rs222749) on the risk of migraine chronification in a case-control study. A systematic review and meta-analysis were then conducted to summarize the accumulated findings. Methods: Genotyping of the selected TRPV1 SNPs was performed using TaqMan real-time PCR in 167 EM and 182 CM participants. Crude and adjusted odds ratios with associated 95% confidence intervals were calculated in the log-additive, dominant, and recessive genetic models. A comprehensive literature search was performed in PubMed, Web of Knowledge, Cochrane Library, and OpenGrey until February 2024. Results: In our case-control study, no association was found between TRPV1 SNPs and the risk of migraine chronification, both in the unadjusted logistic regression models and after adjustment for confounding clinical variables. The results of the meta-analysis with a total of 241 participants with EM and 223 with CM confirmed no association between TRPV1 SNPs and the risk of migraine chronification in any of the genetic models tested. Conclusion: The results of the present case-control study and meta-analysis exclude a major role of TRPV1 rs8065080, rs222747, and rs222749 as risk factors for migraine chronification. However, further research is needed to investigate the gene-gene and gene-environment interactions of TRPV1 SNPs on the risk of transformation from episodic to chronic migraine. [ABSTRACT FROM AUTHOR]

Rail freight planning problems pose specific challenges that have attracted the attention of academics and industry professionals for many decades. They involve multiple types of assets (trains, stations, terminals, etc.) and are subjected to structural, operational and safety constraints. Even though various approaches have been proposed, few can address the complexity and size of real‐world scenarios, and decentralized techniques, like multi‐agent systems (MAS), have become more prevalent. The current state of the art in disciplines such as agent technology, reinforcement learning and discrete‐event simulation allows the implementation of complex architectures, with multiple actors interacting and learning simultaneously. Therefore, this study takes advantage of these current advances and proposes an innovative approach to real‐time traffic management problems in freight railway networks through multi‐agent deep reinforcement learning (MADRL). This study was motivated by the decision‐making scheduling problems arising in the Hunter Valley Coal Chain (HVCC), located in New South Wales, Australia. The MADRL algorithm uses as the training environment the simulation model currently utilized for capacity planning of the HVCC, allowing experiments with actual data. Thus, we enhanced the simulation model to accommodate a MAS with intelligent agents representing system elements, such as trains, dump stations, and load points. Furthermore, these agents act in a decentralized fashion based on local observations, constituting a partially‐observed Markov decision process (dec‐POMDP). Three variations of the MADRL approach are presented: a baseline model, an extended model, and one that directly addresses deadlocks. Finally, we present a transfer learning method that improves deadlock resolution and leverages performance. In the experiments, we explore specific, complex scenarios arising in the HVCC, where trains frequently face deadlock conditions. The baseline model outperforms a first‐come‐first‐serve (FCFS) based heuristic used by HVCC's simulation model and a genetic algorithm in instances with up to 60 trains – but fails in more complex scenarios. On the other hand, the most advanced model, which addresses deadlocks via transfer learning, always finds feasible solutions and produces policies that outperform the FCFS‐based heuristic in 94% of the instances. [ABSTRACT FROM AUTHOR]

Many influential mathematical models of sexual selection have stressed that mating preferences evolve due to correlations that build between mating preferences and preferred display traits—that is, through indirect selection. Nevertheless, there is a perception that indirect selection should generally be overwhelmed by direct selection, for example, in the form of search costs. Recent work by Fry has used quantitative genetic models to argue that in many cases, including when there are direct benefits (a fecundity advantage to mating with the preferred male), direct and indirect selection may be of similar magnitude. Here, I use population genetic models, in which the strength of the genetic correlation is an emergent property of evolution at mating preference and display trait loci, to assess the relative contributions of direct and indirect selection to the evolution of mating preferences. For the cases of direct benefits and of indirect benefits with fixed and frequency-dependent search costs, I outline parameter values of fecundity benefits, preference strengths, and search costs for which indirect selection on female preferences can potentially predominate. I also analyze male mate choice under polygyny, showing that direct selection will always outweigh indirect selection except when there are direct benefits. [ABSTRACT FROM AUTHOR]

The aim was to estimate the relative contribution of imprinting effects from both paternal and maternal sides to phenotypic variation in milk production traits including 305 days milk yield (MY), average daily milk production (ADM), fat percentage (F%), protein percentage (P%), 305 days fat yield (FY), 305 days protein yield (PY), ratio of fat percentage to protein percentage (F:P) and somatic cell score (SCS) in Iranian Holstein cows. To do this, each trait was analysed with a series of four animal models, which were identical for fixed and additive genetic effects but differed for combinations of paternal and maternal imprinting effects. The log‐likelihood ratio test (LRT) and Akaike's information criteria (AIC) were used to select the best model for each trait. Correlations between traits due to additive and imprinting effects were estimated by bivariate analyses. For all traits studied, fitting the imprinting effect led to a better data fit. Also, it resulted in a noticeable decrease in additive genetic variance from 8% (SCS) to 28% (F:P). A significant maternal imprinting effect was detected on all traits studied. Estimates of maternal imprinting heritability (hmi2) were 0.07 ± 0.02, 0.04 ± 0.01, 0.06 ± 0.01, 0.05 ± 0.01, 0.5 ± 0.01, 0.09 ± 0.02, 0.07 ± 0.02 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. For F:P, in addition to the maternal imprinting effect, a significant paternal imprinting component was also detected with a 7% contribution to phenotypic variance of F:P. Estimates of direct heritability (ha2) were 0.29 ± 0.02, 0.17 ± 0.01, 0.22 ± 0.02, 0.11 ± 0.01, 0.18 ± 0.02, 0.22 ± 0.02, 0.15 ± 0.04 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. Maternal imprinting correlations (rmi) were in a wide range between −0.75 ± 0.15 (P%‐SCS) and 0.95 ± 0.11 (MY‐ADM). Additive genetic correlations (ra) ranged between −0.54 ± 0.05 (MY‐P%) and 0.99 ± 0.01 (MY‐ADM) and phenotypic correlations (rp) ranged from −0.30 ± 0.01 (MY‐F%) to 0.93 ± 0.01 (MY‐ADM). The Spearman's correlation between additive breeding values including and excluding imprinting effects deviated from unity especially for top‐ranked animals implying re‐ranking of top animals following the inclusion of imprinting effects in the model. Since including imprinting effects in the model resulted in better data fit and re‐ranking of top animals, including these effects in the genetic evaluation models for milk production traits was recommended. [ABSTRACT FROM AUTHOR]

Anthropogenically fragmented populations may have reduced fitness due to loss of genetic diversity and inbreeding. The extent of such fitness losses due to fragmentation and potential gains from conservation actions are infrequently assessed together empirically. Controlled crosses within and among populations can identify whether populations are at risk of inbreeding depression and whether inter-population crossing alleviates fitness loss. Because fitness depends on the environment and life stage, studies quantifying cumulative fitness over a large portion of the lifecycle in conditions that mimic natural environments are most informative. To assess the fitness consequences of habitat fragmentation, we leveraged controlled within-family, within-population, and between-population crosses to quantify inbreeding depression and heterosis in seven populations of Echinacea angustifolia within a 6,400-ha area. We then assessed cumulative offspring fitness after 14 yr of growth in a natural experimental plot (N  = 1,136). The mean fitness of progeny from within-population crosses varied considerably, indicating genetic differentiation among source populations, even though these sites are all less than 9 km apart. The fitness consequences of within-family and between-population crosses varied in magnitude and direction. Only one of the seven populations showed inbreeding depression of high effect, while four populations showed substantial heterosis. Outbreeding depression was rare and slight. Our findings indicate that local crossings between isolated populations yield unpredictable fitness consequences ranging from slight decreases to substantial increases. Interestingly, inbreeding depression and heterosis did not relate closely to population size, suggesting that all fragmented populations could contribute to conservation goals as either pollen recipients or donors. [ABSTRACT FROM AUTHOR]

In this article, a fuzzy controller mathematical model synthesising method that uses cognitive computing and a genetic algorithm for automated tuning and adaptation to changing environmental conditions has been developed. The technique consists of 12 stages, including creating the control objects' mathematical model and tuning the controller coefficients using classical methods. The research pays special attention to the error parameters and their derivative fuzzification, which simplifies the development of logical rules and helps increase the stability of the systems. The fuzzy controller parameters were tuned using a genetic algorithm in a computational experiment based on helicopter flight data. The results show an increase in the integral quality criterion from 85.36 to 98.19%, which confirms an increase in control efficiency by 12.83%. The fuzzy controller use made it possible to significantly improve the helicopter turboshaft engines' gas-generator rotor speed control performance, reducing the first and second types of errors by 2.06...12.58 times compared to traditional methods. [ABSTRACT FROM AUTHOR]

To establish a finite element model that accurately represents the dynamic characteristics of actual super high-rise building and improve the accuracy of the finite element simulation results, a finite element model updating method for super high-rise building is proposed based on the response surface method (RSM). Taking a 120 m super high-rise building as the research object, a refined initial finite element model is firstly established, and the elastic modulus and density of the main concrete and steel components in the model are set as the parameters to be updated. A significance analysis was conducted on 16 parameters to be updated including E1–E8, D1–D8, and the first 10 natural frequencies of the structure, and 6 updating parameters are ultimately selected. A sample set of updating parameters was generated using central composite design (CCD) and then applied to the finite element model for calculation. The response surface equations for the first ten natural frequencies were obtained through quadratic polynomial fitting, and the optimal solution of the objective function was determined using a genetic algorithm. The results of the engineering case study indicate that the errors in the first ten natural frequencies of the updated finite element model are all within 5%. The updated model accurately reflects the current situation of the super high-rise building and provides a basis for super high-rise building health monitoring, damage detection, and reliability assessment. [ABSTRACT FROM AUTHOR]

The flexible rotor, as a crucial component of the traveling wave rotary ultrasonic motor, effectively reduces radial friction. However, issues such as uneven contact between the stator and rotor, as well as rotor-deformation-induced stress, still persist. This paper presents an optimization method that combines the Kriging response surface model with a multi-objective genetic algorithm (MOGA). Drawing on the existing rotor structure, a novel rotor design is proposed to match the improved TRUM60 stator. During the optimization process, the contact surface between the stator and rotor is taken as the optimization target, and an objective function is established. The Kriging response surface model is constructed using Latin hypercube sampling, and an MOGA is employed to optimize this model, allowing the selection of the optimal balanced solution from multiple candidate designs. Following stator optimization, the objective function value decreased from 0.631 to 0.036, and the maximum contact stress on the rotor inner ring was reduced from 32.77 MPa to 9.96 MPa. Experimental validation confirmed the reliability of this design, significantly improving the overall performance and durability of the motor. [ABSTRACT FROM AUTHOR]

Osteoclasts are large multinucleated cells that degrade bone mineral and extracellular matrix. Investigating the epigenetic mechanisms orchestrating osteoclast differentiation is key to our understanding of the pathogenesis of skeletal related diseases such as periodontitis and osteoporosis. Lysine specific demethylase 1 (LSD1/KDM1A) is a member of the histone demethylase family that mediates the removal of mono- and dimethyl groups from H3K4 and H3K9 to elicit dichotomous effects on gene expression. Prior to our study, little was known about the contributions of LSD1 to skeletal development and osteoclast differentiation. Here we show that conditional deletion of Lsd1 within the myeloid lineage or macrophage/osteoclast precursors results in enhanced bone mass of male and female mice accompanied by diminished osteoclast size in vivo. Furthermore, Lsd1 deletion decreased osteoclast differentiation and activity within in vitro assays. Our bulk RNA-SEQ data suggest Lsd1 ablation in male and female mice inhibits osteoclast differentiation due to enhanced expression of interferon-β target genes. Lastly, we demonstrate that LSD1 forms an immune complex with HDAC1 and HDAC2. These data suggest that the combination of methylation and acetylation of histone residues, facilitated by LSD1, mechanistically promotes osteoclast gene expression. [ABSTRACT FROM AUTHOR]

Introduction: Glutathione S-transferase (GST) has the ability to detoxify the cellular environment of xenobiotic compounds and by-products of oxidative stress. The expression levels of GST genes and their polymorphisms are associated with various human diseases. Methamphetamine and opiate addiction also account for a significant proportion of SUDs in Iran. Considering the oxidative stress induced by morphine and methamphetamine and the potential of GST as a therapeutic option for SUD, we aimed to investigate the association of common genetic variations of two genes from GST family, GSTT1 and GSTM1, with addiction to morphine and METH in Iranian population. Material and methods: A total of 160 blood and urine samples were randomly collected from 50 opiums and 30 methamphetamine users and 80 healthy controls. All samples were processed by thin layer chromatography (TLC), high performance liquid chromatography, and Gas Chromatography-Mass Spectrometry (GC-MS) techniques to detect opium alkaloids (morphine, codeine, papaverine, noscapine, etc.), methamphetamine stimulants, and other legal and illegal drugs. The genotypes of GSTM1 and GSTT1 polymorphisms were determined by PCR. Statistical analysis was performed using the SPSS. This project was approved by the Research Ethics Committee of Legal Medicine Organization, Tehran, Iran. Results: A statistically significant association was observed between the GSTM1 polymorphisms and morphine addiction under a recessive genetic model. The reference group consisted of pooled n/p and p/p genotypes, with an odds ratio (OR) of 2.15, a 95% confidence interval (CI) of 1.05 to 4.39, and a P-value of 0.03. In contrast, there was no statistically significant association between genetic variations in the GSTT1 gene and morphine or methamphetamine addiction. The results revealed no significant association between GSTT1 and GSTM1 allele frequencies and morphine and methamphetamine addiction when divided into risk allele carriers and noncarriers. Conclusion: These findings suggest that the GSTM1 gene may be involved in the development of morphine addiction. However, further studies with larger sample sizes are required to verify these results and investigate the underlying molecular mechanisms. [ABSTRACT FROM AUTHOR]

Steel–concrete hybrid towers (SCHTs) have been adopted as a popular supporting structure for wind turbines with the increasing hub height and rotor diameter. The excessive transverse vibration of the supporting tower adversely compromises the turbine’s operation and leads to structural deteriorations. The tuned mass damper (TMD) has been validated as an efficient device integrated with high-rise structures in vibration suppressions. This study aims to provide a reliable method for determining the key parameters of the TMD to obtain the optimal performance in vibration suppressions. This study begins with a rigorous theoretical analysis to investigate the dynamic responses of the SCHT. The dynamic analysis serves as a basis for developing a dynamics model of the SCHT integrated with a TMD, which provides a programmable method to analyze the effect of the TMD on the dynamic responses of the SCHT. Then, an optimization model based on the genetic algorithm (GA) is established to obtain the optimal parameters of the TMD. Finally, a numerical test comprising 12 loading conditions is conducted to analyze the vibration mitigation effect of the TMD. The validation process also provides discussions about the influence of the wind turbine operation states and the fitness function adopted in the GA on the efficiency of the TMD. Those findings demonstrate the effectiveness and limitations of the TMD for mitigating vibrations of the SCHT. It thereby contributes to the understanding and improvement of vibration control strategies for SCHT structures and can guide future design and optimization efforts. [ABSTRACT FROM AUTHOR]

ABSTRACT We estimated the desired genetic gains for important traits in Beluga sturgeon (Huso huso) using an analytic hierarchy process (AHP) and weighted goal programming (WGP). Two questionnaires were distributed to 122 private Beluga sturgeon farmers in Iran in two stages. The initial stage involved the first questionnaire, which aimed to gather information on the environmental and management aspects of the farms. Additionally, farmers were requested to rate 4 important traits from a list of 12 traits for genetic improvement in the Beluga breeding program. Based on the results, four traits, that is, ovarian fat lobe weight (OFW), total caviar weight (CW), body weight of broodstock (BW) and larval body area at hatching (LBA) were rated highest. In the second stage, the second questionnaire asked farmers to make six pairwise comparisons between the above traits to determine preferences of these traits. The analytical hierarchy process was used to estimate individual preference (Ind‐P) values using Super Decisions software. The medians of the Ind‐P values were OFW and CW (0.30), BW (0.27) and LBA (0.10). Social group preference (Soc‐P) values were estimated for five categories, with 13 social groups using the WGP model in LINGO software. Most disagreements in Soc‐P values were found between the commercial products and water temperature categories. Consensus preference values (Con‐P) for the above two categories were obtained by the extended WGP model with the range of λ between 0 and 1 in LINGO software. The average best Con‐P values were OFW = BW (0.29), LBA (0.21) and CW (0.19). Desired genetic gains (%) were calculated using the percentage genetic improvement (G%) multiplied by the mean of Con‐P, yielding values of 2.09, 1.88, 1.44 and 0.62 for BW, CW, LBA and OFW, respectively. The most important achievements of this research are the determination of the economic traits importance and multi‐traits selection in the Beluga breeding program. [ABSTRACT FROM AUTHOR]

The association between Vitamin D Receptor (VDR) gene polymorphisms and essential hypertension (EH) remains controversial. We searched databases (Cochrane Library, EBSCO, EMBASE, LILACS, ProQuest, PubMed, Science Direct, Springer) for studies on VDR gene polymorphisms and EH until May 30, 2024, following PRISMA guidelines. RevMan 5.4.1 provided pooled odds ratio (OR) under Hardy-Weinberg Equilibrium based on allele, additive, dominant, and recessive genetic models. Meta-regression was performed using Comprehensive Meta Analysis V3. Twenty-two studies from thirteen countries were analyzed. The recessive model suggested lower EH risk in individuals with the recessive allele (bb) of BsmI (OR: 0.81; 95%CI, 0.69 to 0.94, p = 0.007; I2 = 35%, p = 0.13). No significant associations were found for FokI, ApaI, and TaqI polymorphisms. Methodological quality significantly influenced EH risk associated with the FokI polymorphism across allele, additive, and dominant models (All p<0.0005). Male proportion influenced EH risk in the additive model for the FokI polymorphism (p = 0.0235), while age impacted risk in the recessive model (p = 0.0327). FokI polymorphism's influence on EH risk varies by sex, age, and study quality. BsmI polymorphism is independently associated with lower EH risk in recessive homozygotes, with no significant associations found for ApaI and TaqI polymorphisms. [ABSTRACT FROM AUTHOR]

Summary Mate limitation in small populations can reduce reproductive fitness, hinder population growth, and increase extinction risk. Mate limitation is exacerbated in self‐incompatible (SI) taxa, where shared S‐alleles further restrict mating. Theory suggests genetic drift as a predictor of mate limitation and the breakdown of SI systems. We tested this prediction by evaluating mate availability and S‐allele number in populations of a tetraploid herb with gametophytic SI (GSI) spanning a range of effective population sizes. We performed controlled crosses in 13 populations of Argentina anserina to quantify mate availability and S‐allele diversity, which were compared with simulations of tetraploid populations with GSI. We further evaluated mechanisms at the pollen–pistil interface contributing to outcross failure and leakiness in self‐recognition. Mate availability declined in small populations, and closely fit tetraploid GSI population genetic models where maternal plants receive pollen with diverse S‐alleles generated through tetrasomic inheritance. The failure to arrest self‐pollen in the style was common in some populations. Specifically, leaky SI was more common in small populations with low mate availability, where it explained higher seed production in natural populations. The restriction of leaky self‐recognition to the smallest populations is consistent with mate limitation as a pressure driving the breakdown of self‐incompatibility. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is a leading cause of cancer-related mortality worldwide, with hereditary forms accounting for approximately 10% of cases. In Chile, significant gaps exist in genetic counseling and testing, particularly within the public health system. This study presents the implementation and outcomes of the first regional hereditary cancer program in the Maule region of Chile, aimed at improving detection and management of hereditary breast cancer. Methods: A cohort of 48 high-risk breast cancer patients from the Hospital Regional de Talca received genetic counseling and underwent Next-Generation Sequencing multigene panel testing. The program was established through collaboration between multiple institutions, leveraging telemedicine and outsourcing sequencing analysis to address regional gaps. Results: Pathogenic or likely pathogenic variants were identified in 12% of patients, including in BRCA1, BRCA2, TP53, and PALB2. Notably, novel pathogenic variants in BRCA1 (rs80357505) and TP53 (rs1131691022) were discovered, highlighting the unique genetic landscape of the Chilean population. Additionally, 70 variants of uncertain significance were found across 42 genes, particularly in FAN1, MSH6, and FANCI, underscoring the need for further research. The program's collaborative approach effectively bridged critical gaps in genetic services, providing high-quality care within the public health system despite limited resources. Conclusions: The Regional Hereditary Cancer Program addresses significant gaps in genetic counseling and testing in Chile's public health system. This scalable model enhances early detection and personalized treatment for hereditary cancer patients and could be adapted to other regions across Latin America. [ABSTRACT FROM AUTHOR]

Objective: To investigate the association of specific genetic polymorphisms (rs2371597 in STON2 , rs11720822 in PDIA5 , rs387907358 in WNT1 , and rs77542162 in ABCA6) in a Saudi cohort of keratoconus (KC) patients compared to controls. Methods: A retrospective case-control genetic association study was conducted. The study included 99 KC patients and 193 healthy controls. Genotyping was performed using real-time PCR with TaqMan assays. Associations between genetic polymorphisms and KC were assessed using various genetic models and binary logistic regression analysis. Results: None of the tested polymorphisms showed an overall association with KC risk. Specifically, the rs2371597 polymorphism in STON2 did not demonstrate a significant association with KC risk across different genetic models. However, a gender-specific effect of rs2371597 was noted: in men, the C/G genotype was associated with a higher risk of KC, particularly in the dominant model, while no significant association was observed in women. Age and sex were identified as significant predictors of KC risk, but rs2371597 did not significantly affect KC risk in regression analysis. Conclusion: Preliminary evidence suggests a gender-specific effect of the rs2371597 polymorphism in STON2 , with an increased KC risk associated with C/G-C/C genotypes in men which was age-dependent. This result highlights the importance of considering population-specific genetic factors and the potential gender-specific effects on KC susceptibility. However, these findings need further validation with larger age- and sex-matched samples of diverse populations. [ABSTRACT FROM AUTHOR]

The main material used in the construction of roads is asphalt. Therefore, the recognition of asphalt's mechanical aspects is very important. One of the important features of asphalt is its shear strength, which should be measured accurately. However, the methods that have been presented to measure this important factor of asphalt always encounter weaknesses. So, it is necessary to find a suitable method to determine the shear strength of asphalt with more accurate results and high compatibility with reality. In this regard, the purpose of the present research was to design jaws in order to measure the shear strength in the direction and opposite direction of the traffic path and provide a model to predict shear strength using Marshall stability resulting from invented jaws. In order to examine the accuracy of the designed jaw in this study, two different types of asphalt, Binder 0–25 and Topeka 0–19 grading, were used. For this purpose, Marshall stability and shear strength tests in the direction and opposite direction of the Marshall were conducted with 12 repetitions on these samples. Also, the genetic programming (GP) evolutionary algorithm was applied in this study to provide a prediction model of shear strength. The results of this study indicated that there was a significant relationship between the Marshall stability and the shear strength in the direction and opposite direction of the Marshall applying the invented jaws in both asphalt types, and the coefficient of determination (R2) for the Binder and Topeka were 0.93 and 0.97 in the Marshall's direction and 0.96 and 0.95 for the Marshall's opposite direction, respectively. Also, the results of the GP method indicated that the relationships between predicted and actual values of shear strength for Binder and Topeka asphalt types were appropriately described by R2 of 99.47% and 99.21% with RMSE of 8.0177 and 5.0143 in the traffic direction, and R2 of 97.45% and 98.08% with RMSE of 1.2684 and 0.7035 in the traffic opposite direction, respectively. Therefore, GP provided a more suitable fit of all experimental data for both Binder and Topeka asphalts, and it can be said that with the help of new designed jaws, the shear strength in the direction and opposite direction of the Marshall can be estimated with high accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hyperactivation of mTOR results in increased dendritic spine density. • Rapamycin and analogues show potential in reversing some ASD-related behaviors. • Environmental models support the role of mTOR in mediating environmental risk factors. • Behavioral deficits are observed in genetic and ambiental models of ASD. Autism spectrum disorder (ASD) is a highly prevalent multifactorial disorder characterized by social deficits and stereotypies. Despite extensive research efforts, the etiology of ASD remains poorly understood. However, studies using preclinical models have identified the mechanistic target of rapamycin kinase (mTOR) signaling pathway as a key player in ASD-related features. This review examines genetic and environmental models of ASD, focusing on their association with the mTOR pathway. We organize findings on alterations within this pathway, providing insights about the potential mechanisms involved in the onset and maintenance of ASD symptoms. Our analysis highlights the central role of mTOR hyperactivation in disrupting autophagic processes, neural organization, and neurotransmitter pathways, which collectively contribute to ASD phenotypes. The review also discusses the therapeutic potential of mTOR pathway inhibitors, such as rapamycin, in mitigating ASD characteristics. These insights underscore the importance of the mTOR pathway as a target for future research and therapeutic intervention in ASD. This review innovates by bringing the convergence of disrupted mTOR signaling in preclinical models and clinical data associated with ASD. [ABSTRACT FROM AUTHOR]

The primary cilium, a non‐motile organelle present in most human cells, plays a crucial role in detecting microenvironmental changes and regulating intracellular signaling. Its dysfunction is linked to various diseases, including cancer. We explored the role of ciliated cells in prostate cancer by using Gefitinib and Jasplakinolide compounds to induce ciliated cells in both normal and tumor‐like prostate cell lines. We assessed GLI1 and IFT20 expression and investigated YAP1 protein's role, which is implicated in primary cilium regulation. Finally, we examined these compounds in 3D cell models, aiming to simulate in vivo conditions. Our study highlights YAP1 as a potential target for novel genetic models to understand the primary cilium's role in mediating resistance to anticancer treatments. [ABSTRACT FROM AUTHOR]

Supergenes are genetic architectures resulting in the segregation of alternative combinations of alleles underlying complex phenotypes. The co‐segregation of alleles at linked loci is often facilitated by polymorphic chromosomal rearrangements suppressing recombination locally. Supergenes are involved in many complex polymorphisms, including sexual, colour or behavioural polymorphisms in numerous plants, fungi, mammals, fish, and insects. Despite a long history of empirical and theoretical research, the formation of supergenes remains poorly understood. Here, using a two‐island population genetic model, we explore how gene flow and the evolution of overdominant chromosomal inversions may jointly lead to the formation of supergenes. We show that the evolution of inversions in differentiated populations, both under disruptive selection, leads to an increase in frequency of poorly adapted, immigrant haplotypes. Indeed, rare allelic combinations, such as immigrant haplotypes, are more frequently reshuffled by recombination than common allelic combinations, and therefore benefit from the recombination suppression generated by inversions. When an inversion capturing a locally adapted haplotype spreads but is associated with a fitness cost hampering its fixation (e.g. a recessive mutation load), the maintenance of a non‐inverted haplotype in the population is enhanced; under certain conditions, the immigrant haplotype persists alongside the inverted local haplotype, while the standard local haplotype disappears. This establishes a stable, local polymorphism with two non‐recombining haplotypes encoding alternative adaptive strategies, that is, a supergene. These results bring new light to the importance of local adaptation, overdominance, and gene flow in the formation of supergenes and inversion polymorphisms in general. [ABSTRACT FROM AUTHOR]

[Display omitted] Metabolic dysfunction-associated steatotic liver disease (MASLD) remains a rapidly growing global health burden. Here, we report that the nonessential amino acid (NEAA) transporter SLC7A11 plays a key role in MASLD. In patients with MASLD, we found high expression levels of SLC7A11 that were correlated directly with clinical grade. Using both loss-of-function and gain-of-function genetic models, we found that Slc7a11 deficiency accelerated MASLD progression via classic cystine/cysteine deficiency-induced ferroptosis, while serine deficiency and a resulting impairment in de novo cysteine production were attributed to ferroptosis-induced MASLD progression in mice overexpressing hepatic Slc7a11. Consistent with these findings, we found that both serine supplementation and blocking ferroptosis significantly alleviated MASLD, and the serum serine/glutamate ratio was significantly lower in these preclinical disease models, suggesting that it might serve as a prognostic biomarker for MASLD in patients. These findings indicate that defects in NEAA metabolism are involved in the progression of MASLD and that serine deficiency-triggered ferroptosis may provide a therapeutic target for its treatment. [ABSTRACT FROM AUTHOR]

This study investigates platelet‐related subtypes in non‐small cell lung cancer (NSCLC) and seeks to identify genes associated with prognosis, focusing on the clinical significance of the chloride ion channel gene BEST3. We utilised sequencing and clinical data from GEO, TCGA and the Xena platform, building a risk model based on genetic features. TCGA and GSE37745 served as training cohorts, while GSE50081, GSE13213, GSE30129 and GSE42127 were validation cohorts. Immunotherapy datasets (GSE135222, TCGA‐SKCM) were also analysed. Differentially expressed genes (DEGs) were identified using Limma, subtypes through ConsensusClusterPlus and key prognostic genes using COX regression, Random Forest and LASSO‐COX. BEST3 expression was validated by flow cytometry (FCM) and functional assays in A549 cells with lentiviral overexpression evaluated its impact on apoptosis, proliferation and migration. Three platelet‐related subtypes were identified, with ten key prognostic genes (including BEST3). Gene Ontology (GO) analysis showed six genes involved in platelet pathways. BEST3 was highly expressed in the platelet subtype 1. Flow cytometry confirmed elevated BEST3 levels in NSCLC (35.9% vs. 27.3% in healthy individuals). Overexpression of BEST3 in NSCLC cells suppressed apoptosis and promoted proliferation and migration. The discovery of three platelet subtypes and the role of BEST3 in promoting tumour growth and migration highlights its potential as a therapeutic target and prognostic marker in NSCLC. [ABSTRACT FROM AUTHOR]

Glycerol kinase (GK) participates in triglyceride (TG) synthesis by catalyzing glycerol metabolism. Whether GK contributes to nonalcoholic fatty liver (NAFL) is unclear. The expression of hepatic Gk is found to be increased in diet‐induced and genetic mouse models of NAFL and is positively associated with hepatic SREBP‐1c expression and TG levels. Cholesterol and fatty acids stimulate GK expression in hepatocytes. In HFD‐induced NAFL mice, knockdown of hepatic Gk decreases expression of SREBP‐1c and its target lipogenic genes as well as DGAT1/2, increases serum glycerol levels, decreases serum TG levels, and attenuates hepatic TG accumulation. Overexpression of GK in hepatocytes in mice or in culture produces opposite results. Mechanistic studies reveal that GK stimulates SREBP‐1c transcription directly by binding to its gene promoter and indirectly by binding to SREBP‐1c protein, thereby increasing lipogenic gene expression and de novo lipogenesis. Studies with truncated GK and mutant GKs indicate that GK induces SREBP‐1c transcription independently of its enzyme activity. GK contributes to lipid homeostasis under physiological conditions by catalyzing glycerol metabolism rather than by regulating SREBP‐1c transcription. Collectively, these results demonstrate that increased hepatic GK promotes de novo lipogenesis and TG synthesis in NAFL by stimulating SREBP‐1c transcription and DGAT1/2 expression and catalyzing glycerol metabolism. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRS) aim to predict a trait from genetic information, relying on common genetic variants with low to medium effect sizes. As genotype data are high‐dimensional in nature, it is crucial to develop methods that can be applied to large‐scale data (large n$$ n $$ and large p$$ p $$). Many PRS tools aggregate univariate summary statistics from genome‐wide association studies into a single score. Recent advancements allow simultaneous modeling of variant effects from individual‐level genotype data. In this context, we introduced snpboost, an algorithm that applies statistical boosting on individual‐level genotype data to estimate PRS via multivariable regression models. By processing variants iteratively in batches, snpboost can deal with large‐scale cohort data. Having solved the technical obstacles due to data dimensionality, the methodological scope can now be broadened—focusing on key objectives for the clinical application of PRS. Similar to most methods in this context, snpboost has, so far, been restricted to quantitative and binary traits. Now, we incorporate more advanced alternatives—targeted to the particular aim and outcome. Adapting the loss function extends the snpboost framework to further data situations such as time‐to‐event and count data. Furthermore, alternative loss functions for continuous outcomes allow us to focus not only on the mean of the conditional distribution but also on other aspects that may be more helpful in the risk stratification of individual patients and can quantify prediction uncertainty, for example, median or quantile regression. This work enhances PRS fitting across multiple model classes previously unfeasible for this data type. [ABSTRACT FROM AUTHOR]

Coelimycin (CPK) producer Streptomyces coelicolor A3(2) is a well-established model for the genetic studies of bacteria from the genus Streptomyces, renowned for their ability to produce a plethora of antibiotics and other secondary metabolites. Expression regulation of natural product biosynthetic gene clusters (BGCs) is highly complex, involving not only regulatory proteins, like transcription factors, but also the products of the biosynthetic pathway that may act as ligands for some regulators and modulate their activity. Here, we present the evidence that intracellular CPK precursor(s) (preCPK) is involved in a negative feedback loop repressing the CPK BGC. Moreover, we provide a characterization of the cluster-encoded efflux pump CpkF. We show that CpkF is essential for the extracellular CPK production. In order to track down which CPK compounds — intra- or extracellular — are the ones responsible for the feedback signal, a luciferase-based reporter system was applied to compare the activity of 13 CPK gene promoters in the wild-type (WT) and two mutated strains. The first strain, lacking the CPK-specific exporter CpkF (ΔcpkF), was unable to produce the extracellular CPK. The second one did not produce any CPK at all, due to the disruption of the CpkC polyketide synthase subunit (ΔcpkC). All tested promoters were strongly upregulated in ΔcpkC strain, while in the ΔcpkF strain, promoter activity resembled the one of WT. These results lead to the conclusion that the CPK polyketide acts as a silencer of its own production. Supposedly this function is exerted via binding of the preCPK by an unidentified regulatory protein. Key points: •Intracellular coelimycin precursor takes part in a negative cpk cluster regulation •CpkF exporter is essential for the extracellular coelimycin production •Simple method for the analysis of coelimycin P2 production in agar medium [ABSTRACT FROM AUTHOR]

With the development of the economy, the demand for 3D model animation in fields such as games, film and television, and education is constantly increasing. At present, many 3D model animations are built based on genetic algorithm. However, due to the slow convergence speed and poor local search ability of genetic algorithm, this model still has many shortcomings. Therefore, a cellular automaton is used to optimize the genetic algorithm, and a cellular genetic algorithm is proposed. It is integrated with WebAR technology, which has the characteristics of lightweight and low cost, aiming to improve the separation and dynamic binding mechanism of 3D model animation. Experimental analysis showed that when the number of chromosome genes was 90, the vertex gene value of the research algorithm was the highest, ranging from 281 to 501, and the convergence was the highest. The file size of the model dataset based on the research method was reduced by 50% compared with before. The FPDT based on the animation separation and dynamic binding mechanism was decreased by 15.2 s on average. To sum up, it can be seen that the designed 3D model animation is more detailed and the animation dynamic is also the best. This research method can significantly improve the separation and dynamic binding mechanism of 3D model animation. This research will improve the integration of genetic algorithm and WebAR technology, which can not only effectively deal with complex 3D models and animations, but also has broad application prospects. [ABSTRACT FROM AUTHOR]

Since males and females have different number of X chromosome, different mechanisms have evolved to equalize dosage of gene products from the X chromosome between XX females and XY males. The aim of this study was to study X chromosome dosage compensation for growth rate (GR), Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR) in Zandi sheep. A two steps procedure was adopted to analysis data. In the first step, each trait was analyzed with a series of 6 animal models including different combinations of direct and maternal effects. Using Akanke's Information Criterion (AIC) the best model (Model I) was selected for each trait. In the second step, five additional models were fitted by adding X chromosome effects to the Model I, considering 5 strategies for modeling X chromosome dosage compensation: (1) no global dosage compensation (ngdc), (2) random inactivation in the homogametic sex (hori), (3) doubling of the single shared sex chromosome in the heterogametic sex (hedo), (4) halving expression of both sex chromosomes in the homogametic sex (hoha) or (5) inactivation of the paternal sex chromosome in the homogametic sex (hopi). Predictive ability of models was measured using the mean squared error of prediction (MSE) and Pearson's correlation coefficient between the real and predicted values of records Correlations between traits due to autosomal- and X-linked genetic effects were estimated by bi-variate analyses. For GR and KR, models including X-linked effects lead to a much better fit of data, expressed by the strong decrease in the AIC criterion. Models including X-linked effects had also better predictive ability as they provided smaller MSE and higher For GR and KR, although all strategies for modeling X chromosome dosage compensation improved general properties of the model, the model "ngdc" fitted the data significantly better than other models. Including X-linked genetic effects in the model led to 10% (GR, KR) decrease in the autosomal additive variance, and 7% (KR) to 19% (GR) decrease in the residual variance. Estimates of autosomal heritability (), were 0.15 ± 0.03, 0.13 ± 0.03, 0.9 ± 0.03 and 0.13 ± 0.03 for GR, KR, EF and RGR, respectively. X-linked heritability () was 0.08 ± 0.03 for GR and 0.04 ± 0.03 for KR, respectively. Maternal heritability () were 0.02 ± 0.01, 0.01 ± 0.01, 0.03 ± 0.02 and 0.03 ± 0.02 for GR, KR, EF and RGR, respectively. For GR and KR, the Spearman's correlation between breeding values obtained from the best model and model I deviated from unity, indicating re-ranking of top animals across models. The X-linked additive genetic correlation and autosomal additive genetic correlation were similar in terms of sign and magnitude in a way that they were all positive and high. As considering X-linked genetic effects resulted to an improvement in the general properties of the model and possibility of re-ranking of top animals, including these effects in the model, considering dosage compensation on the X chromosome was recommended. [ABSTRACT FROM AUTHOR]