Pairing of segmentation clock genes drives robust pattern formation.

Gene expression is an inherently stochastic process1,2; however, organismal development and homeostasis require cells to coordinate the spatiotemporal expression of large sets of genes. In metazoans, pairs of co-expressed genes often reside in the same chromosomal neighbourhood, with gene pairs representing 10 to 50% of all genes, depending on the species3-6. Because shared upstream regulators can ensure correlated gene expression, the selective advantage of maintaining adjacent gene pairs remains unknown6. Here, using two linked zebrafish segmentation clock genes, her1 and her7, and combining single-cell transcript counting, genetic engineering, real-time imaging and computational modelling, we show that gene pairing boosts correlated transcription and provides phenotypic robustness for the formation of developmental patterns. Our results demonstrate that the prevention of gene pairing disrupts oscillations and segmentation, and the linkage of her1 and her7 is essential for the development of the body axis in zebrafish embryos. We predict that gene pairing may be similarly advantageous in other organisms, and our findings could lead to the engineering of precise synthetic clocks in embryos and organoids.

Prevalence of hazardous alcohol consumption and evaluation of associated factors in university students.

PURPOSE: The aim of the study was to determine the prevalence of hazardous alcohol consumption (HAC) according to gender among university students and associated factors. METHODS: This is a cross-sectional study conducted on undergraduate students. We used a stratified sampling technique to represent 26036 students from all grade levels and 11 faculties, and the survey was administered to 2349 undergraduate students. The prevalence of HAC was determined with the Alcohol Use Disorders Identification Test (AUDIT). HAC was defined as getting 8 points or more from the AUDIT. Multivariate logistic regression analyses were performed to examine HAC related factors in both genders. RESULTS: In this study, 53.2% of the participants were male. The prevalence of HAC in the study group was 13.5% and prevalence of lifetime drinker was 65.3%. In males; those whose fathers [OR = 1.72; 95% CI: (1.17-2.52)], mothers [1.49; (1.02-2.18)], close friends [2.42; (1.28-4.60)] drink alcohol and smoking [3.16; (2.09- 4.77)], use illicit substance [2.35; (1.66-3.34)], have mental health problems [1.65; (1.04-2.62)] were more likely to report HAC. Meanwhile in females, those whose fathers [OR = 1.92; 95%CI: (1.03-3.57)], close friends [5.81; (1.73-19.45)] drink alcohol and smoking [4.33; (2.31-8.15)], use illicit substance [4.34; (2.34-8.06)] have mental health problems [3.01; (1.67-5.43)] were more likely to report HAC. CONCLUSIONS: HAC prevalence is high among university students. The risk of HAC increases with the use of alcohol in family and circle of friends, smoking, illicit substance use and mental health problems. The factors associated with the risk of HAC in both genders are similar.

Helicobacter pylori (H. pylori) risk factor analysis and prevalence prediction: a machine learning-based approach.

BACKGROUND: Although previous epidemiological studies have examined the potential risk factors that increase the likelihood of acquiring Helicobacter pylori infections, most of these analyses have utilized conventional statistical models, including logistic regression, and have not benefited from advanced machine learning techniques. OBJECTIVE: We examined H. pylori infection risk factors among school children using machine learning algorithms to identify important risk factors as well as to determine whether machine learning can be used to predict H. pylori infection status. METHODS: We applied feature selection and classification algorithms to data from a school-based cross-sectional survey in Ethiopia. The data set included 954 school children with 27 sociodemographic and lifestyle variables. We conducted five runs of tenfold cross-validation on the data. We combined the results of these runs for each combination of feature selection (e.g., Information Gain) and classification (e.g., Support Vector Machines) algorithms. RESULTS: The XGBoost classifier had the highest accuracy in predicting H. pylori infection status with an accuracy of 77%-a 13% improvement from the baseline accuracy of guessing the most frequent class (64% of the samples were H. Pylori negative.) K-Nearest Neighbors showed the worst performance across all classifiers. A similar performance was observed using the F1-score and area under the receiver operating curve (AUROC) classifier evaluation metrics. Among all features, place of residence (with urban residence increasing risk) was the most common risk factor for H. pylori infection, regardless of the feature selection method choice. Additionally, our machine learning algorithms identified other important risk factors for H. pylori infection, such as; electricity usage in the home, toilet type, and waste disposal location. Using a 75% cutoff for robustness, machine learning identified five of the eight significant features found by traditional multivariate logistic regression. However, when a lower robustness threshold is used, machine learning approaches identified more H. pylori risk factors than multivariate logistic regression and suggested risk factors not detected by logistic regression. CONCLUSION: This study provides evidence that machine learning approaches are positioned to uncover H. pylori infection risk factors and predict H. pylori infection status. These approaches identify similar risk factors and predict infection with comparable accuracy to logistic regression, thus they could be used as an alternative method.

Role of lung ultrasonography in the diagnosis of COVID-19 patients admitted to the emergency department.

Introduction: In this study, the use of lung ultrasonography (LUS) to diagnosis lung findings was evaluated in patients with suspected COVID-19 who were admitted to the emergency department (ED). Methods: This observational clinical study was conducted in the ED of the Ankara City Hospital during the period April 1-30, 2020. Patients who were admitted to the ED were triaged as COVID-19 infected and who agreed to undergo LUS/LCT (lung computed tomography) were included in the study. Results: Included in the study were 40 patients who had been prediagnosed with COVID-19. Pneumonia was detected with LCT in 32 (80%) patients, while the LUS examination identified pneumonia in 23 patients. The most common finding in LCT was ground-glass opacity (n = 29, 90.6%). Of the 23 patients with pneumonia findings in LUS, 15 (65.2%) had direct consolidation. Among the 32 patients who were found to have pneumonia as a result of LCT, 20 (62.5%) had signs of pneumonia on LUS examination, and 12 had no signs of pneumonia. In addition, 3 patients showed no signs of pneumonia with LCT, but they were misdiagnosed with pneumonia by LUS. The sensitivity of LUS in the diagnosis of pneumonia in the COVID-19 patients was 62.5%, while its specificity was 62.5%. In addition, its positive predictive value was 87.0%, and its negative predictive value was 29.4%. Conclusion: LUS may also be used in the diagnosis of pneumonia in COVID-19 patients because it is a valuable and accessible bedside diagnostic tool.

Characterizing building blocks of resource constrained biological networks.

BACKGROUND: Identification of motifs-recurrent and statistically significant patterns-in biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over- or under-estimates), and thus, wrong biological interpretations. RESULTS: In this paper, we develop a novel motif counting algorithm, Partially Overlapping MOtif Counting (POMOC), that considers capacity levels for all interactions in counting motifs. CONCLUSIONS: Our experiments on real and synthetic networks demonstrate that motif count using the POMOC method significantly differs from the existing motif counting approaches, and our method extends to large-scale biological networks in practical time. Our results also show that our method makes it possible to characterize the impact of different stress factors on cell's organization of network. In this regard, analysis of a S. cerevisiae transcriptional regulatory network using our method shows that oxidative stress is more disruptive to organization and abundance of motifs in this network than mutations of individual genes. Our analysis also suggests that by focusing on the edges that lead to variation in motif counts, our method can be used to find important genes, and to reveal subtle topological and functional differences of the biological networks under different cell states.

Shortest path counting in probabilistic biological networks.

BACKGROUND: Biological regulatory networks, representing the interactions between genes and their products, control almost every biological activity in the cell. Shortest path search is critical to apprehend the structure of these networks, and to detect their key components. Counting the number of shortest paths between pairs of genes in biological networks is a polynomial time problem. The fact that biological interactions are uncertain events however drastically complicates the problem, as it makes the topology of a given network uncertain. RESULTS: In this paper, we develop a novel method to count the number of shortest paths between two nodes in probabilistic networks. Unlike earlier approaches, which uses the shortest path counting methods that are specifically designed for deterministic networks, our method builds a new mathematical model to express and compute the number of shortest paths. We prove the correctness of this model. CONCLUSIONS: We compare our novel method to three existing shortest path counting methods on synthetic and real gene regulatory networks. Our experiments demonstrate that our method is scalable, and it outperforms the existing methods in accuracy. Application of our shortest path counting method to detect communities in probabilistic networks shows that our method successfully finds communities in probabilistic networks. Moreover, our experiments on cell cycle pathway among different cancer types exhibit that our method helps in uncovering key functional characteristics of biological networks.

Spatial gradients of protein-level time delays set the pace of the traveling segmentation clock waves.

The vertebrate segmentation clock is a gene expression oscillator controlling rhythmic segmentation of the vertebral column during embryonic development. The period of oscillations becomes longer as cells are displaced along the posterior to anterior axis, which results in traveling waves of clock gene expression sweeping in the unsegmented tissue. Although various hypotheses necessitating the inclusion of additional regulatory genes into the core clock network at different spatial locations have been proposed, the mechanism underlying traveling waves has remained elusive. Here, we combined molecular-level computational modeling and quantitative experimentation to solve this puzzle. Our model predicts the existence of an increasing gradient of gene expression time delays along the posterior to anterior direction to recapitulate spatiotemporal profiles of the traveling segmentation clock waves in different genetic backgrounds in zebrafish. We validated this prediction by measuring an increased time delay of oscillatory Her1 protein production along the unsegmented tissue. Our results refuted the need for spatial expansion of the core feedback loop to explain the occurrence of traveling waves. Spatial regulation of gene expression time delays is a novel way of creating dynamic patterns; this is the first report demonstrating such a control mechanism in any tissue and future investigations will explore the presence of analogous examples in other biological systems.

Differential transcriptional regulation by alternatively designed mechanisms: A mathematical modeling approach.

Cells maintain cellular homeostasis employing different regulatory mechanisms to respond external stimuli. We study two groups of signal-dependent transcriptional regulatory mechanisms. In the first group, we assume that repressor and activator proteins compete for binding to the same regulatory site on DNA (competitive mechanisms). In the second group, they can bind to different regulatory regions in a noncompetitive fashion (noncompetitive mechanisms). For both competitive and noncompetitive mechanisms, we studied the gene expression dynamics by increasing the repressor or decreasing the activator abundance (inhibition mechanisms), or by decreasing the repressor or increasing the activator abundance (activation mechanisms). We employed delay differential equation models. Our simulation results show that the competitive and noncompetitive inhibition mechanisms exhibit comparable repression effectiveness. However, response time is fastest in the noncompetitive inhibition mechanism due to increased repressor abundance, and slowest in the competitive inhibition mechanism by increased repressor level. The competitive and noncompetitive inhibition mechanisms through decreased activator abundance show comparable and moderate response times, while the competitive and noncompetitive activation mechanisms by increased activator protein level display more effective and faster response. Our study exemplifies the importance of mathematical modeling and computer simulation in the analysis of gene expression dynamics.

Short-lived Her proteins drive robust synchronized oscillations in the zebrafish segmentation clock.

Oscillations are prevalent in natural systems. A gene expression oscillator, called the segmentation clock, controls segmentation of precursors of the vertebral column. Genes belonging to the Hes/her family encode the only conserved oscillating genes in all analyzed vertebrate species. Hes/Her proteins form dimers and negatively autoregulate their own transcription. Here, we developed a stochastic two-dimensional multicellular computational model to elucidate how the dynamics, i.e. period, amplitude and synchronization, of the segmentation clock are regulated. We performed parameter searches to demonstrate that autoregulatory negative-feedback loops of the redundant repressor Her dimers can generate synchronized gene expression oscillations in wild-type embryos and reproduce the dynamics of the segmentation oscillator in different mutant conditions. Our model also predicts that synchronized oscillations can be robustly generated as long as the half-lives of the repressor dimers are shorter than 6 minutes. We validated this prediction by measuring, for the first time, the half-life of Her7 protein as 3.5 minutes. These results demonstrate the importance of building biologically realistic stochastic models to test biological models more stringently and make predictions for future experimental studies.

Hierarchical decomposition of dynamically evolving regulatory networks.

BACKGROUND: Gene regulatory networks describe the interplay between genes and their products. These networks control almost every biological activity in the cell through interactions. The hierarchy of genes in these networks as defined by their interactions gives important insights into how these functions are governed. Accurately determining the hierarchy of genes is however a computationally difficult problem. This problem is further complicated by the fact that an intrinsic characteristic of regulatory networks is that the wiring of interactions can change over time. Determining how the hierarchy in the gene regulatory networks changes with dynamically evolving network topology remains to be an unsolved challenge. RESULTS: In this study, we develop a new method, named D-HIDEN (Dynamic-HIerarchical DEcomposition of Networks) to find the hierarchy of the genes in dynamically evolving gene regulatory network topologies. Unlike earlier methods, which recompute the hierarchy from scratch when the network topology changes, our method adapts the hierarchy based on the wiring of the interactions only for the nodes which have the potential to move in the hierarchy. CONCLUSIONS: We compare D-HIDEN to five currently available hierarchical decomposition methods on synthetic and real gene regulatory networks. Our experiments demonstrate that D-HIDEN significantly outperforms existing methods in running time, accuracy, or both. Furthermore, our method is robust against dynamic changes in hierarchy. Our experiments on human gene regulatory networks suggest that our method may be used to reconstruct hierarchy in gene regulatory networks.

Diurnal Preference Predicts Phase Differences in Expression of Human Peripheral Circadian Clock Genes.

BACKGROUND: Circadian rhythms play an integral role in human behavior, physiology and health. Individual differences in daily rhythms (chronotypes) can affect individual sleep-wake cycles, activity patterns and behavioral choices. Diurnal preference, the tendency towards morningness or eveningness among individuals, has been associated with interpersonal variation in circadian clock-related output measures, including body temperature, melatonin levels and clock gene mRNA in blood, oral mucosa, and dermal fibroblast cell cultures. METHODS: Here we report gene expression data from two principal clock genes sampled from hair follicle cells, a peripheral circadian clock. Hair follicle cells from fourteen individuals of extreme morning or evening chronotype were sampled at three time points. RNA was extracted and quantitative PCR assays were used to measure mRNA expression patterns of two clock genes, Per3 and Nr1d2. RESULTS: We found significant differences in clock gene expression over time between chronotype groups, independent of gender or age of participants. Extreme evening chronotypes have a delay in phase of circadian clock gene oscillation relative to extreme morning types. Variation in the molecular clockwork of chronotype groups represents nearly three-hour phase differences (Per3: 2.61 hours; Nr1d2: 3.08 hours, both: 2.86) in circadian oscillations of these clock genes. CONCLUSIONS: The measurement of gene expression from hair follicles at three time points allows for a direct, efficient method of estimating phase shifts of a peripheral circadian clock in real-life conditions. The robust phase differences in temporal expression of clock genes associated with diurnal preferences provide the framework for further studies of the molecular mechanisms and gene-by-environment interactions underlying chronotype-specific behavioral phenomena, including social jetlag.

A primer on thermodynamic-based models for deciphering transcriptional regulatory logic.

A rigorous analysis of transcriptional regulation at the DNA level is crucial to the understanding of many biological systems. Mathematical modeling has offered researchers a new approach to understanding this central process. In particular, thermodynamic-based modeling represents the most biophysically informed approach aimed at connecting DNA level regulatory sequences to the expression of specific genes. The goal of this review is to give biologists a thorough description of the steps involved in building, analyzing, and implementing a thermodynamic-based model of transcriptional regulation. The data requirements for this modeling approach are described, the derivation for a specific regulatory region is shown, and the challenges and future directions for the quantitative modeling of gene regulation are discussed.

Global parameter estimation for thermodynamic models of transcriptional regulation.

Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort.

Mathematical modeling of gene expression: a guide for the perplexed biologist.

The detailed analysis of transcriptional networks holds a key for understanding central biological processes, and interest in this field has exploded due to new large-scale data acquisition techniques. Mathematical modeling can provide essential insights, but the diversity of modeling approaches can be a daunting prospect to investigators new to this area. For those interested in beginning a transcriptional mathematical modeling project, we provide here an overview of major types of models and their applications to transcriptional networks. In this discussion of recent literature on thermodynamic, Boolean, and differential equation models, we focus on considerations critical for choosing and validating a modeling approach that will be useful for quantitative understanding of biological systems.

Analysis of the gut microbiome in sled dogs reveals glucosamine- and activity-related effects on gut microbial composition.

The composition of the microbiome influences many aspects of physiology and health, and can be altered by environmental factors, including diet and activity. Glucosamine is a dietary supplement often administered to address arthritic symptoms in humans, dogs, and other mammals. To investigate how gut microbial composition varies with glucosamine supplementation, we performed 16S rRNA sequence analysis of fecal samples from 24 Alaskan and Inuit huskies and used mixed effects models to investigate associations with activity, age, and additional factors. Glucosamine ingestion, age, activity, sex, and diet were correlated with differences in alpha-diversity, with diversity decreasing in dogs consuming glucosamine. Beta-diversity analysis revealed clustering of dogs based on glucosamine supplementation status. Glucosamine supplementation and exercise-related activity were associated with greater inter-individual pairwise distances. At the family level, Lactobacillaceae and Anaerovoracaceae relative abundances were lower in supplemented dogs when activity was accounted for. At the genus level, Eubacterium [brachy], Sellimonus, Parvibacter, and an unclassified genus belonging to the same family as Parvibacter (Eggerthellaceae) all were lower in supplemented dogs, but only significantly so post-activity. Our findings suggest that glucosamine supplementation alters microbiome composition in sled dogs, particularly in the context of exercise-related activity.

Sex-specific associations between circadian-related genes and depression in UK Biobank participants highlight links to glucose metabolism, inflammation and neuroplasticity pathways.

Depressive disorders have increased in global prevalence, making improved management of these disorders a public health priority. Prior research has linked circadian clock genes to depression, either through direct interactions with mood-related pathways in the brain or by modulating the phase of circadian rhythms. Using machine learning and statistical techniques, we explored associations between 157,347 SNP variants from 51 circadian-related genes and depression scores from the patient health questionnaire 9 (PHQ-9) in 99,939 UK Biobank participants. Our results highlight multiple pathways linking the circadian system to mood, including metabolic, monoamine, immune, and stress-related pathways. Notably, genes regulating glucose metabolism and inflammation (GSK3B, LEP, RORA, and NOCT) were prominent factors in females, in addition to DELEC1 and USP46, two genes of unknown function. In contrast, FBXL3 and DRD4 emerged as significant risk factors for male depression. We also found epistatic interactions involving RORA, NFIL3, and ZBTB20 as either risk or protective factors for depression, underscoring the importance of transcription factors (ZBTB20, NFIL3) and hormone receptors (RORA) in depression etiology. Understanding the complex, sex-specific links between circadian genes and mood disorders will facilitate the development of therapeutic interventions and enhance the efficacy of multi-target treatments for depression.

Effect of different masses, ages, and coats on the thermoregulation of dogs before and after exercise across different seasons.

Domestic dogs demonstrate a positive correlation between body mass and whole-animal metabolism but a negative relationship between body mass and lifespan in dogs. Additionally, essential physiological mechanisms in domestic dogs, such as the relationships between thermal relations and body size and age, remain poorly understood. In this study, we looked at thermoregulation in dogs of different sizes, ages, coat types, and head morphologies across three different seasons. We used tympanic membrane temperatures (Tear) and infrared thermography to observe temperature regulation in pet dogs before and after a 45-min moderate walking exercise trial. We hypothesized that Tear and heat dissipation is positively correlated with body mass. Using network analysis, we found that body mass was among the most central features for spring and summer trials, but not for the winter trials. Similarly, leg length, snout length, and paw width were the central predictors in two of the three seasons. Mediation analysis demonstrated that nose and snout length act as significant mediators of the effects of body mass on mouth temperatures in the spring. For the summer trials, nose length and paw width significantly mediated the effect of body mass on mouth temperatures. Age, however, does not seem to be a major determinant of thermoregulation in dogs according to best subset models. A cross-seasonal examination of repeated measurements showed that mouth temperature heat dissipation rates decreased with increasing temperature and humidity. Overall, our findings support our hypothesis that Tear and heat dissipation rates are positively correlated with body mass in dogs, thus, negatively correlated with mass-specific metabolism. This finding suggests that small dogs allocate a bigger proportion of their metabolism to "inefficiencies" of heat production to offset greater heat loss.

Machine learning and network analysis of the gut microbiome from patients with schizophrenia and non-psychiatric subject controls reveal behavioral risk factors and bacterial interactions.

Recent findings have supported an association between deviations in gut microbiome composition and schizophrenia. However, the extent to which the gut microbiota contributes to schizophrenia remains unclear. Moreover, studies have yet to explore variations in ecological associations among bacterial types in subjects with schizophrenia, which can reveal differences in community interactions and gut stability. We examined the dataset collected by Nguyen et al. (2021) to investigate the similarities and differences in gut microbial constituents between 48 subjects with schizophrenia and 48 matched non-psychiatric comparison cases. We re-analyzed alpha- and beta-diversity differences and completed modified differential abundance analyses and confirmed the findings of Nguyen et al. (2021) that there was little variation in alpha-diversity but significant differences in beta-diversity between individuals with schizophrenia and non-psychiatric subjects. We also conducted mediation analysis, developed a machine learning (ML) model to predict schizophrenia, and completed network analysis to examine community-level interactions among bacterial taxa. Our study offers new insights, suggesting that the gut microbiome mediates the effects between schizophrenia and smoking status, BMI, anxiety score, and depression score. Our differential abundance and network analysis findings suggest that the differential abundance of Lachnospiraceae and Ruminococcaceae taxa fosters a decrease in stabilizing competitive interactions in the gut microbiome of subjects with schizophrenia. Loss of this competition may promote ecological instability and dysbiosis, altering gut-brain axis interactions in these subjects.

Machine learning analyses reveal circadian clock features predictive of anxiety among UK biobank participants.

Mood disorders, including depression and anxiety, affect almost one-fifth of the world's adult population and are becoming increasingly prevalent. Mutations in circadian clock genes have previously been associated with mood disorders both directly and indirectly through alterations in circadian phase, suggesting that the circadian clock influences multiple molecular pathways involved in mood. By targeting previously identified single nucleotide polymorphisms (SNPs) that have been implicated in anxiety and depressive disorders, we use a combination of statistical and machine learning techniques to investigate associations with the generalized anxiety disorder assessment (GAD-7) scores in a UK Biobank sample of 90,882 individuals. As in previous studies, we observed that females exhibited higher GAD-7 scores than males regardless of genotype. Interestingly, we found no significant effects on anxiety from individual circadian gene variants; only circadian genotypes with multiple SNP variants showed significant associations with anxiety. For both sexes, severe anxiety is associated with a 120-fold increase in odds for individuals with CRY2_AG(rs1083852)/ZBTB20_TT(rs1394593) genotypes and is associated with a near 40-fold reduction in odds for individuals with PER3-A_CG(rs228697)/ZBTB20_TT(rs1394593) genotypes. We also report several sex-specific associations with anxiety. In females, the CRY2/ZBTB20 genotype combination showed a > 200-fold increase in odds of anxiety and PER3/ZBTB20 and CRY1 /PER3-A genotype combinations also appeared as female risk factors. In males, CRY1/PER3-A and PER3-B/ZBTB20 genotype combinations were associated with anxiety risk. Mediation analysis revealed direct associations of CRY2/ZBTB20 variant genotypes with moderate anxiety in females and CRY1/PER3-A variant genotypes with severe anxiety in males. The association of CRY1/PER3-A variant genotypes with severe anxiety in females was partially mediated by extreme evening chronotype. Our results reinforce existing findings that females exhibit stronger anxiety outcomes than males, and provide evidence for circadian gene associations with anxiety, particularly in females. Our analyses only identified significant associations using two-gene combinations, underscoring the importance of combined gene effects on anxiety risk. We describe novel, robust associations between gene combinations involving the ZBTB20 SNP (rs1394593) and risk of anxiety symptoms in a large population sample. Our findings also support previous findings that the ZBTB20 SNP is an important factor in mood disorders, including seasonal affective disorder. Our results suggest that reduced expression of this gene significantly modulates the risk of anxiety symptoms through direct influences on mood-related pathways. Together, these observations provide novel links between the circadian clockwork and anxiety symptoms and identify potential molecular pathways through which clock genes may influence anxiety risk.

Feature selection and association rule learning identify risk factors of malnutrition among Ethiopian schoolchildren.

Introduction: Previous studies have sought to identify risk factors for malnutrition in populations of schoolchildren, depending on traditional logistic regression methods. However, holistic machine learning (ML) approaches are emerging that may provide a more comprehensive analysis of risk factors. Methods: This study employed feature selection and association rule learning ML methods in conjunction with logistic regression on epidemiological survey data from 1,036 Ethiopian school children. Our first analysis used the entire dataset and then we reran this analysis on age, residence, and sex population subsets. Results: Both logistic regression and ML methods identified older childhood age as a significant risk factor, while females and vaccinated individuals showed reduced odds of stunting. Our machine learning analyses provided additional insights into the data, as feature selection identified that age, school latrine cleanliness, large family size, and nail trimming habits were significant risk factors for stunting, underweight, and thinness. Association rule learning revealed an association between co-occurring hygiene and socio-economical variables with malnutrition that was otherwise missed using traditional statistical methods. Discussion: Our analysis supports the benefit of integrating feature selection methods, association rules learning techniques, and logistic regression to identify comprehensive risk factors associated with malnutrition in young children.