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.

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.

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.

Risk for Seasonal Affective Disorder (SAD) Linked to Circadian Clock Gene Variants.

Molecular pathways affecting mood are associated with circadian clock gene variants and are influenced, in part, by the circadian clock, but the molecular mechanisms underlying this link are poorly understood. We use machine learning and statistical analyses to determine the circadian gene variants and clinical features most highly associated with symptoms of seasonality and seasonal affective disorder (SAD) in a deeply phenotyped population sample. We report sex-specific clock gene effects on seasonality and SAD symptoms; genotypic combinations of CLOCK3111/ZBTB20 and PER2/PER3B were significant genetic risk factors for males, and CRY2/PER3C and CRY2/PER3-VNTR were significant risk factors for females. Anxiety, eveningness, and increasing age were significant clinical risk factors for seasonality and SAD for females. Protective factors for SAD symptoms (in females only) included single gene variants: CRY1-GG and PER3-VNTR-4,5. Clock gene effects were partially or fully mediated by diurnal preference or chronotype, suggesting multiple indirect effects of clock genes on seasonality symptoms. Interestingly, protective effects of CRY1-GG, PER3-VNTR-4,5, and ZBTB20 genotypes on seasonality and depression were not mediated by chronotype, suggesting some clock variants have direct effects on depressive symptoms related to SAD. Our results support previous links between CRY2, PER2, and ZBTB20 genes and identify novel links for CLOCK and PER3 with symptoms of seasonality and SAD. Our findings reinforce the sex-specific nature of circadian clock influences on seasonality and SAD and underscore the multiple pathways by which clock variants affect downstream mood pathways via direct and indirect mechanisms.

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.

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.

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.

Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep Disturbance.

Introduction: Sleep disturbances often co-occur with mood disorders, with poor sleep quality affecting over a quarter of the global population. Recent advances in sleep and circadian biology suggest poor sleep quality is linked to disruptions in circadian rhythms, including significant associations between sleep features and circadian clock gene variants. Methods: Here, we employ machine learning techniques, combined with statistical approaches, in a deeply phenotyped population to explore associations between clock genotypes, circadian phenotypes (diurnal preference and circadian phase), and risk for sleep disturbance symptoms. Results: As found in previous studies, evening chronotypes report high levels of sleep disturbance symptoms. Using molecular chronotyping by measuring circadian phase, we extend these findings and show that individuals with a mismatch between circadian phase and diurnal preference report higher levels of sleep disturbance. We also report novel synergistic interactions in genotype combinations of Period 3, Clock and Cryptochrome variants (PER3B (rs17031614)/ CRY1 (rs228716) and CLOCK3111 (rs1801260)/ CRY2 (rs10838524)) that yield strong associations with sleep disturbance, particularly in males. Conclusion: Our results indicate that both direct and indirect mechanisms may impact sleep quality; sex-specific clock genotype combinations predictive of sleep disturbance may represent direct effects of clock gene function on downstream pathways involved in sleep physiology. In addition, the mediation of clock gene effects on sleep disturbance indicates circadian influences on the quality of sleep. Unraveling the complex molecular mechanisms at the intersection of circadian and sleep physiology is vital for understanding how genetic and behavioral factors influencing circadian phenotypes impact sleep quality. Such studies provide potential targets for further study and inform efforts to improve non-invasive therapeutics for sleep disorders.

Gene copy number and negative feedback differentially regulate transcriptional variability of segmentation clock genes.

Timely progression of a genetic program is critical for embryonic development. However, gene expression involves inevitable fluctuations in biochemical reactions leading to substantial cell-to-cell variability (gene expression noise). One of the important questions in developmental biology is how pattern formation is reproducibly executed despite these unavoidable fluctuations in gene expression. Here, we studied the transcriptional variability of two paired zebrafish segmentation clock genes (her1 and her7) in multiple genetic backgrounds. Segmentation clock genes establish an oscillating self-regulatory system, presenting a challenging yet beautiful system in studying control of transcription variability. In this study, we found that a negative feedback loop established by the Her1 and Her7 proteins minimizes uncorrelated variability whereas gene copy number affects variability of both RNAs in a similar manner (correlated variability). We anticipate that these findings will help analyze the precision of other natural clocks and inspire the ideas for engineering precise synthetic clocks in tissue engineering.

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.

Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data.

BACKGROUND: Previous epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors. METHODS: In this study, we used data from a survey of 54 risk factors for intestinal parasitosis in 954 Ethiopian school children. We investigated whether machine learning approaches can supplement traditional logistic regression in identifying intestinal parasite infection risk factors. We used feature selection methods such as InfoGain (IG), ReliefF (ReF), Joint Mutual Information (JMI), and Minimum Redundancy Maximum Relevance (MRMR). Additionally, we predicted children's parasitic infection status using classifiers such as Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF) and XGBoost (XGB), and compared their accuracy and area under the receiver operating characteristic curve (AUROC) scores. For optimal model training, we performed tenfold cross-validation and tuned the classifier hyperparameters. We balanced our dataset using the Synthetic Minority Oversampling (SMOTE) method. Additionally, we used association rule learning to establish a link between risk factors and parasitic infections. KEY FINDINGS: Our study demonstrated that machine learning could be used in conjunction with logistic regression. Using machine learning, we developed models that accurately predicted four parasitic infections: any parasitic infection at 79.9% accuracy, helminth infection at 84.9%, any STH infection at 95.9%, and protozoan infection at 94.2%. The Random Forests (RF) and Support Vector Machines (SVM) classifiers achieved the highest accuracy when top 20 risk factors were considered using Joint Mutual Information (JMI) or all features were used. The best predictors of infection were socioeconomic, demographic, and hematological characteristics. CONCLUSIONS: We demonstrated that feature selection and association rule learning are useful strategies for detecting risk factors for parasite infection. Additionally, we showed that advanced classifiers might be utilized to predict children's parasitic infection status. When combined with standard logistic regression models, machine learning techniques can identify novel risk factors and predict infection risk.

SEALNET: Facial recognition software for ecological studies of harbor seals.

Methods for long-term monitoring of coastal species such as harbor seals (Phoca vitulina) are often costly, time-consuming, and highly invasive, underscoring the need for improved techniques for data collection and analysis. Here, we propose the use of automated facial recognition technology for identification of individual seals and demonstrate its utility in ecological and population studies. We created a software package, SealNet, that automates photo identification of seals, using a graphical user interface (GUI) software to detect, align, and chip seal faces from photographs and a deep convolutional neural network (CNN) suitable for small datasets (e.g., 100 seals with five photos per seal) to classify individual seals. We piloted the SealNet technology with a population of harbor seals located within Casco Bay on the coast of Maine, USA. Across two years of sampling, 2019 and 2020, at seven haul-out sites in Middle Bay, we obtained a dataset optimized for the development and testing of SealNet. We processed 1752 images representing 408 individual seals and achieved 88% Rank-1 and 96% Rank-5 accuracy in closed set seal identification. In identifying individual seals, SealNet software outperformed a similar face recognition method, PrimNet, developed for primates but retrained on seals. The ease and wealth of image data that can be processed using SealNet software contributes a vital tool for ecological and behavioral studies of marine mammals in the developing field of conservation technology.

Machine learning and expression analyses reveal circadian clock features predictive of anxiety.

Mood disorders, including generalized anxiety disorder, are associated with disruptions in circadian rhythms and are linked to polymorphisms in circadian clock genes. Molecular mechanisms underlying these connections may be direct-via transcriptional activity of clock genes on downstream mood pathways in the brain, or indirect-via clock gene influences on the phase and amplitude of circadian rhythms which, in turn, modulate physiological processes influencing mood. Employing machine learning combined with statistical approaches, we explored clock genotype combinations that predict risk for anxiety symptoms in a deeply phenotyped population. We identified multiple novel circadian genotypes predictive of anxiety, with the PER3(rs17031614)-AG/CRY1(rs2287161)-CG genotype being the strongest predictor of anxiety risk, particularly in males. Molecular chronotyping, using clock gene expression oscillations, revealed that advanced circadian phase and robust circadian amplitudes are associated with high levels of anxiety symptoms. Further analyses revealed that individuals with advanced phases and pronounced circadian misalignment were at higher risk for severe anxiety symptoms. Our results support both direct and indirect influences of clock gene variants on mood: while sex-specific clock genotype combinations predictive of anxiety symptoms suggest direct effects on mood pathways, the mediation of PER3 effects on anxiety via diurnal preference measures and the association of circadian phase with anxiety symptoms provide evidence for indirect effects of the molecular clockwork on mood. Unraveling the complex molecular mechanisms underlying the links between circadian physiology and mood is essential to identifying the core clock genes to target in future functional studies, thereby advancing the development of non-invasive treatments for anxiety-related disorders.

Pattern Discovery in Multilayer Networks.

MOTIVATION: In bioinformatics, complex cellular modeling and behavior simulation to identify significant molecular interactions is considered a relevant problem. Traditional methods model such complex systems using single and binary network. However, this model is inadequate to represent biological networks as different sets of interactions can simultaneously take place for different interaction constraints (such as transcription regulation and protein interaction). Furthermore, biological systems may exhibit varying interaction topologies even for the same interaction type under different developmental stages or stress conditions. Therefore, models which consider biological systems as solitary interactions are inaccurate as they fail to capture the complex behavior of cellular interactions within organisms. Identification and counting of recurrent motifs within a network is one of the fundamental problems in biological network analysis. Existing methods for motif counting on single network topologies are inadequate to capture patterns of molecular interactions that have significant changes in biological expression when identified across different organisms that are similar, or even time-varying networks within the same organism. That is, they fail to identify recurrent interactions as they consider a single snapshot of a network among a set of multiple networks. Therefore, we need methods geared towards studying multiple network topologies and the pattern conservation among them. Contributions: In this paper, we consider the problem of counting the number of instances of a user supplied motif topology in a given multilayer network. We model interactions among a set of entities (e.g., genes)describing various conditions or temporal variation as multilayer networks. Thus a separate network as each layer shows the connectivity of the nodes under a unique network state. Existing motif counting and identification methods are limited to single network topologies, and thus cannot be directly applied on multilayer networks. We apply our model and algorithm to study frequent patterns in cellular networks that are common in varying cellular states under different stress conditions, where the cellular network topology under each stress condition describes a unique network layer. RESULTS: We develop a methodology and corresponding algorithm based on the proposed model for motif counting in multilayer networks. We performed experiments on both real and synthetic datasets. We modeled the synthetic datasets under a wide spectrum of parameters, such as network size, density, motif frequency. Results on synthetic datasets demonstrate that our algorithm finds motif embeddings with very high accuracy compared to existing state-of-the-art methods such as G-tries, ESU (FANMODE)and mfinder. Furthermore, we observe that our method runs from several times to several orders of magnitude faster than existing methods. For experiments on real dataset, we consider Escherichia coli (E. coli)transcription regulatory network under different experimental conditions. We observe that the genes selected by our method conserves functional characteristics under various stress conditions with very low false discovery rates. Moreover, the method is scalable to real networks in terms of both network size and number of layers.

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.

ANCA: Alignment-Based Network Construction Algorithm.

Dynamic biological networks model changes in the network topology over time. However, often the topologies of these networks are not available at specific time points. Existing algorithms for studying dynamic networks often ignore this problem and focus only on the time points at which experimental data is available. In this paper, we develop a novel alignment based network construction algorithm, ANCA, that constructs the dynamic networks at the missing time points by exploiting the information from a reference dynamic network. Our experiments on synthetic and real networks demonstrate that ANCA predicts the missing target networks accurately, and scales to large-scale biological networks in practical time. Our analysis of an E. coli protein-protein interaction network shows that ANCA successfully identifies key temporal changes in the biological networks. Our analysis also suggests that by focusing on the topological differences in the network, our method can be used to find important genes and temporal functional changes in the biological networks.

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.

The relationship between climate change and political instability: the case of MENA countries (1985:01-2016:12).

The aim of this study is to examine the relationship between climate change and political instability in the MENA region. To this extent, 18 Middle East and North African (MENA) countries are analyzed covering the period 1985:01-2016:12 with monthly data. In econometric analysis, at first cross-sectional dependency analysis is applied, and existence of cross-sectional dependency among countries is found. Therefore, CADF-second generation panel unit root test applied, and finally, Dumitrescu and Hurlin (2012) panel causality test that consider the cross-sectional dependency are utilized. For empirical analysis, temperature and precipitation data representing climate change, political instability, and conflict data are employed. According to the findings, there is a causal relationship from climate change to political instability in 16 countries and to conflict in 15 countries. In addition to this, at least one causal relationship is determined from climate change to political instability or conflict in all MENA countries. Therefore, empirical results support the assumption that climate change acts as a threat multiplier in MENA countries since it triggers, accelerates, and deepens the current instabilities.

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.