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Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

Abstract: Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC–QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients. [Copyright &y& Elsevier]

Background: Snakebite envenoming (SBE) is a potentially life-threatening event that can lead to severe physical, mental, and economic hardships, particularly in under-resourced regions like sub-Saharan Africa. In Rwanda, there have been no epidemiological assessments of SBE to guide the Ministry of Health in its efforts to reduce the burden. This study had two main objectives: first, to estimate the incidence of snakebites across districts, and second, to describe formal versus informal healthcare seeking behaviors among snakebite victims in Eastern Province, Rwanda in 2020., Methodology: This cross-sectional study utilized a cluster sampling approach, involving Community Health Workers (CHWs) who recorded snakebite cases across seven districts. The descriptive analysis considered sampling weights, and healthcare seeking behavior was assessed based on the type of care sought as the first point of treatment., Findings: The study surveyed 390,546 individuals across 763 villages and estimated a provincial annual incidence rate of 4.3 cases per 1,000 individuals. Incidence estimates ranged from 1.1 cases per 1,000 in Nyagatare to 9.1 cases per 1,000 individuals in Bugesera and Ngoma districts. Among the 2,545 cases recorded by CHWs, three resulted in deaths. Regarding healthcare-seeking behavior, 13% of snakebite victims (143 out of 1,098) initially consulted formal care providers (CHWs, health post/center, or hospital), while 87% sought informal care (family/friends, pharmacist, or traditional healer). Approximately half of the victims (583, 53.1%) reported severe symptoms. Unsafe practices included skin cutting/burning, tourniquet application, use of black stones, and venom extraction; only 24 cases (2.2%) received anti-venom., Conclusions: This large-scale community-based assessment highlights variations in snakebite incidence between districts and confirms frequent involvement of traditional healers in management. Improving access to anti-venom and community education on the risks of ineffective practices, along with timely use of formal healthcare, are crucial. Collaboration between healthcare providers, traditional healers, community leaders, and policymakers is essential to implement targeted interventions for enhancing snakebite prevention and management strategies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Hakizimana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The link between cofactor binding and protein activity is well-established. However, how cofactor interactions modulate folding of large proteins remains unknown. We use optical tweezers, clustering and global fitting to dissect the folding mechanism of Drosophila cryptochrome (dCRY), a 542-residue protein that binds FAD, one of the most chemically and structurally complex cofactors in nature. We show that the first dCRY parts to fold are independent of FAD, but later steps are FAD-driven as the remaining polypeptide folds around the cofactor. FAD binds to largely unfolded intermediates, yet with association kinetics above the diffusion-limit. Interestingly, not all FAD moieties are required for folding: whereas the isoalloxazine ring linked to ribitol and one phosphate is sufficient to drive complete folding, the adenosine ring with phosphates only leads to partial folding. Lastly, we propose a dCRY folding model where regions that undergo conformational transitions during signal transduction are the last to fold., (© 2023. The Author(s).)

Recent studies have confirmed the role of miRNA regulation of gene expression in oncogenesis for various cancers. In parallel, prior knowledge about relationships between miRNA and mRNA have been accumulated from biological experiments or statistical analyses. Improved identification of disease-associated miRNA-mRNA pairs may be achieved by incorporating prior knowledge into integrative genomic analyses. In this study we focus on 39 patients with hepatocellular carcinoma (HCC) and 25 patients with liver cirrhosis and use a flexible Bayesian two-step integrative method. We found 66 significant miRNA-mRNA pairs, several of which contain molecules that have previously been identified as potential biomarkers. These results demonstrate the utility of the proposed approach in providing a better understanding of relationships between different biological levels, thereby giving insights into the biological mechanisms underlying the diseases, while providing a better selection of biomarkers that may serve as diagnostic, prognostic, or therapeutic biomarker candidates.

Background: The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error., Methods: We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women's Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women., Results: Simulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women's Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement., Conclusions: Variable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports.

In addition to socioeconomic influences, biological factors are believed to play a role in health disparities. In this paper, we investigate miRNA, mRNA, and DNA methylation patterns that contribute to disparities in hepatocellular carcinoma (HCC). This is accomplished by integration of mRNA-Seq, miRNA-Seq, and DNA methylation data we acquired by analysis of liver tissues from 30 HCC patients consisting of European Americans (EAs), African Americans (AAs), and Asian Americans (Asians). Mixed-ANOVA models are applied to identify miRNAs, mRNAs, and DNA methylation sites that are significantly altered in tumor vs. adjacent normal tissues in a race-specific manner. Through integrated analysis, a refined list of differentially expressed mRNAs is obtained by selecting those that are targets of differentially expressed miRNAs and consist of promoter regions that are differentially methylated.

Background: Adjuvant endocrine therapy reduces risk of recurrence and mortality in women with hormone receptor-positive breast cancer, yet many women never initiate it. We examined the influence of race, sociocultural factors, and process-of-care factors on initiation of adjuvant endocrine therapy in a racially diverse sample., Patients and Methods: Eligible women were originally recruited for the Narrowing the Gaps in Adjuvant Therapy Study (2006-2011). Sociocultural and process-of-care factors were collected via telephone surveys before adjuvant therapy. Clinical factors were abstracted from charts. Penalized LASSO (least absolute shrinkage and selection operator) logistic regression model was used to identify variables associated with initiation., Results: Of the 270 women, 55.6% were black and the rest were white. Most women (74.8%) initiated therapy. A significant interaction (P = .008) was found between race and age. Black women aged ≤ 50 years had the lowest initiation (59.7%) compared to black women > 50 years (87.1%), white women ≤ 50 years (73.7%), or white women > 50 years (72.0%). Multivariate analysis found that younger black women exhibited a marginally higher risk of noninitiation compared to older black women. Additionally, ratings of financial access, presence of comorbidities, and levels of communication were all associated with endocrine therapy initiation., Conclusion: Black women ≤ 50 years of age and women with financial constraints may be important subgroups for interventions. Patient-provider communication appears to be an important leverage point to foster therapy uptake., (Copyright © 2017 Elsevier Inc. All rights reserved.)

We present an ensemble tree-based algorithm for variable selection in high dimensional datasets, in settings where a time-to-event outcome is observed with error. The proposed methods are motivated by self-reported outcomes collected in large-scale epidemiologic studies, such as the Women's Health Initiative. The proposed methods equally apply to imperfect outcomes that arise in other settings such as data extracted from electronic medical records. To evaluate the performance of our proposed algorithm, we present results from simulation studies, considering both continuous and categorical covariates. We illustrate this approach to discover single nucleotide polymorphisms that are associated with incident Type II diabetes in the Women's Health Initiative. A freely available R package icRSF (R Core Team, 2018; Xu et al., 2018) has been developed to implement the proposed methods.

Background: Maternal pre-pregnancy overweight and obese status has been associated with a number of pregnancy complications and adverse offspring outcomes. Mechanisms for observed associations, however, are largely unknown. We investigated associations of pre-pregnancy body mass index with early-mid pregnancy epigenetic biomarkers, circulating microRNAs., Methods: Peripheral blood was collected from participants (16-27 weeks gestation) of two multi-racial pregnancy cohorts, the Omega Study and the Pregnancy Outcomes and Community Health Study. Plasma miRNA expression was characterised using epigenome-wide (319 miRNAs) profiling among 20 pregnant women in each cohort. Cohort-specific linear regression models that included the predictor (pre-pregnancy body mass index), the outcome (microRNA expression), and adjustment factors (maternal age, gestational age at blood collection, and race) were fit., Results: Expression of 27 miRNAs was positively associated with pre-pregnancy body mass index in both cohorts (p-values <0.05). A number of these differentially expressed miRNAs have previously been associated with adipogenesis (e.g. let-7d*, miR-103-2*, -130b, -146b-5-p, -29c, and -26b). Identified miRNAs as well as their experimentally validated targets participate in pathways that involve organismal injury, reproductive system disease, connective tissue disorders, cancer, cellular development, growth and proliferation., Conclusion: Pre-pregnancy body mass index is associated with circulating miRNAs in early-mid pregnancy., (Published by Elsevier Ltd.)

The analysis of multiple outcomes is becoming increasingly common in modern biomedical studies. It is well-known that joint statistical models for multiple outcomes are more flexible and more powerful than fitting a separate model for each outcome; they yield more powerful tests of exposure or treatment effects by taking into account the dependence among outcomes and pooling evidence across outcomes. It is, however, unlikely that all outcomes are related to the same subset of covariates. Therefore, there is interest in identifying exposures or treatments associated with particular outcomes, which we term outcome-specific variable selection. In this work, we propose a variable selection approach for multivariate normal responses that incorporates not only information on the mean model, but also information on the variance-covariance structure of the outcomes. The approach effectively leverages evidence from all correlated outcomes to estimate the effect of a particular covariate on a given outcome. To implement this strategy, we develop a Bayesian method that builds a multivariate prior for the variable selection indicators based on the variance-covariance of the outcomes. We show via simulation that the proposed variable selection strategy can boost power to detect subtle effects without increasing the probability of false discoveries. We apply the approach to the Normative Aging Study (NAS) epigenetic data and identify a subset of five genes in the asthma pathway for which gene-specific DNA methylations are associated with exposures to either black carbon, a marker of traffic pollution, or sulfate, a marker of particles generated by power plants., (© 2016, The International Biometric Society.)

Multi-omic approaches offer the opportunity to characterize complex diseases such as cancer at various molecular levels. In this paper, we present transcriptomic, proteomic/glycoproteomic, glycomic, and metabolomic (TPGM) data we acquired by analysis of liver tissues from hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. We evaluated changes in the levels of transcripts, proteins, glycans, and metabolites between tumor and cirrhotic tissues by statistical methods. We demonstrated the potential of multi-omic approaches and network analysis to investigate the interactions among these biomolecules in the progression of liver cirrhosis to HCC. Also, we showed the significance of multi-omic approaches to identify pathways altered in HCC.

The genetic architecture of placental abruption (PA) remains poorly understood. We examined variations in SNPs of circadian clock-related genes in placenta with PA risk. We also explored placental and maternal genomic contributions to PA risk. Placental genomic DNA samples were isolated from 280 PA cases and 244 controls. Genotyping was performed using the Illumina Cardio-MetaboChip. We examined 116 SNPs in 13 genes known to moderate circadian rhythms. Logistic regression models were fit to estimate odds ratios (ORs). The combined effect of multiple SNPs on PA risk was estimated using a weighted genetic risk score. We examined independent and joint associations of wGRS derived from placental and maternal genomes with PA. Seven SNPs in five genes (ARNTL2, CRY2, DEC1, PER3 and RORA), in the placental genome, were associated with PA risk. Each copy of the minor allele (G) of a SNP in the RORA gene (rs2899663) was associated with a 30% reduced odds of PA (95% CI 0.52-0.95). The odds of PA increased with increasing placental-wGRS (Ptrend<0.001). The ORs were 1.00, 2.16, 3.24 and 4.48 across quartiles. Associations persisted after the maternal-wGRS was included in the model. There was evidence of an additive contribution of placental and maternal genetic contributions to PA risk. Participants with placental- and maternal-wGRS in the highest quartile, compared with those in the lowest quartile, had a 15.57-fold (95% CI 3.34-72.60) increased odds of PA. Placental variants in circadian clock-related genes are associated with PA risk; and the association persists after control of genetic variants in the maternal genome.

Gas Chromatography coupled with Mass Spectrometry (GC-MS) has been broadly used for the detection of changes in metabolite levels in complex samples. Internal Standards (IS) spiked into a complex background at different concentrations help assess the capability of GC-MS in detecting changes in metabolite levels. This study uses a Latin square design to evaluate the ability of GC-MS in full scan and Single Ion Monitoring (SIM) modes to detect changes among IS spiked into human plasma samples at varying concentrations. Statistical analysis of the data demonstrates the potential of GC-MS to detect true differences over a wide range of concentration levels.

Background: Globally, common psychiatric disorders such as depression and anxiety are among the leading causes of morbidity and mortality. The 12-item General Health Questionnaire (GHQ-12) is a widely used questionnaire for screening or detecting common psychiatric disorders. The purpose of this study was to examine the reliability, construct validity and factor structure of the GHQ-12 in a large sample of African, Asian and South American young adults., Methods: A cross-sectional study was conducted among 9077 undergraduate students from Chile, Ethiopia, Peru and Thailand. Students aged 18-35 years were invited to complete a self-administered questionnaire that collected information about lifestyle, demographics, and GHQ-12. In each country, the construct validity and factorial structures of the GHQ-12 questionnaire were tested through exploratory and confirmatory factor analyses (EFA and CFA)., Results: Overall the GHQ-12 items showed good internal consistency across all countries as reflected by the Cronbach's alpha: Chile (0.86), Ethiopia (0.83), Peru (0.85), and Thailand (0.82). Results from EFA showed that the GHQ-12 had a two-factor solution in Chile, Ethiopia and Thailand, although a three-factor solution was found in Peru. These findings were corroborated by CFA. Indicators of goodness of fit, comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean squared residual, were all in acceptable ranges across study sites. The CFI values for Chile, Ethiopia, Peru and Thailand were 0.964, 0.951, 0.949, and 0.931, respectively. The corresponding RMSEA values were 0.051, 0.050, 0.059, and 0.059., Conclusion: Overall, we documented cross-cultural comparability of the GHQ-12 for assessing common psychiatric disorders such as symptoms of depressive and anxiety disorders among young adults. Although the GHQ-12 is typically used as single-factor questionnaire, the results of our EFA and CFA revealed the multi- dimensionality of the scale. Future studies are needed to further evaluate the specific cut points for assessing each component within the multiple factors., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: The Berlin and Epworth Sleepiness Scale (ESS) are simple, validated, and widely used questionnaires designed to assess symptoms of obstructive sleep apnea syndrome (OSAS) a common but often unrecognized cause of morbidity and mortality., Methods: A cross-sectional study was conducted among 2,639 college students to examine the extent to which symptoms of OSAS are associated with the odds of common mental disorders (CMDs). The General Health Questionnaire (GHQ-12) was used to evaluate the presence of CMDs while the Berlin and ESS were used to assess high-risk for obstructive sleep apnea (OSA) and excessive daytime sleepiness, respectively. Logistic regression procedures were used to derive odds ratios (OR) and 95% confidence intervals (CI) assessing the independent and joint associations of high-risk for OSA and excessive daytime sleepiness with odds of CMDs., Results: Approximately 19% of students had high-risk for OSA while 26.4% had excessive daytime sleepiness. Compared to students without high-risk for OSA and without excessive daytime sleepiness (referent group), students with excessive daytime sleepiness only (OR=2.01; 95%CI: 1.60-2.52) had increased odds of CMDs. The odds of CMDs for students with high-risk OSA only was 1.26 (OR=1.26; 95%CI 0.94-1.68). Students with both high-risk for OSA and excessive daytime sleepiness, compared to the referent group, had the highest odds of CMDs (OR=2.45; 95%CI: 1.69-3.56)., Conclusion: Our findings indicate that symptoms of OSAS are associated with increased risk of CMDs. These findings emphasize the comorbidity of sleep disorders and CMDs and suggest that there may be benefits to investing in educational programs that extend the knowledge of sleep disorders in young adults.

Purpose: We evaluated the extent to which use of a hypothesized imperfect gold standard, the Composite International Diagnostic Interview (CIDI), biases the estimates of diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9). We also evaluate how statistical correction can be used to address this bias., Methods: The study was conducted among 926 adults where structured interviews were conducted to collect information about participants' current major depressive disorder using PHQ-9 and CIDI instruments. First, we evaluated the relative psychometric properties of PHQ-9 using CIDI as a gold standard. Next, we used a Bayesian latent class model to correct for the bias., Results: In comparison with CIDI, the relative sensitivity and specificity of the PHQ-9 for detecting major depressive disorder at a cut point of 10 or more were 53.1% (95% confidence interval: 45.4%-60.8%) and 77.5% (95% confidence interval, 74.5%-80.5%), respectively. Using a Bayesian latent class model to correct for the bias arising from the use of an imperfect gold standard increased the sensitivity and specificity of PHQ-9 to 79.8% (95% Bayesian credible interval, 64.9%-90.8%) and 79.1% (95% Bayesian credible interval, 74.7%-83.7%), respectively., Conclusions: Our results provided evidence that assessing diagnostic validity of mental health screening instrument, where application of a gold standard might not be available, can be accomplished by using appropriate statistical methods., (Copyright © 2014 Elsevier Inc. All rights reserved.)

We conducted this study to evaluate the prevalence of daytime sleepiness and evening chronotype, and to assess the extent to which both are associated with the use of caffeinated stimulants among 3,000 Thai college students. Demographic and behavioral characteristics were collected using a self-administered questionnaire. The Epworth Sleepiness Scale and the Horne and Ostberg Morningness-Eveningness Questionnaire were used to evaluate prevalence of daytime sleepiness and circadian preference. Multivariable logistic regression models were used to evaluate the association between sleep disorders and consumption of caffeinated beverages. Overall, the prevalence of daytime sleepiness was 27.9 % (95% CI: 26.2-29.5%) while the prevalence of evening chronotype was 13% (95% CI: 11.8-14.2%). Students who use energy drinks were more likely to be evening types. For instance, the use of M100/M150 energy drinks was associated with a more than 3-fold increased odds of evening chronotype (OR 3.50; 95% CI 1.90-6.44), while Red Bull users were more than twice as likely to have evening chronotype (OR 2.39; 95% CI 1.02-5.58). Additionally, those who consumed any energy drinks were more likely to be daytime sleepers. For example, Red Bull (OR 1.72; 95% CI 1.08-2.75) or M100/M150 (OR 1.52; 95% CI 1.10-2.11) consumption was associated with increased odds of daytime sleepiness. Our findings emphasize the importance of implementing educational and prevention programs targeted toward improving sleep hygiene and reducing the consumption of energy drinks among young adults.

Background: The effects of hepatocellular carcinoma on liver metabolism and circulating metabolites have been subjected to continuing investigation. This study compares the levels of selected metabolites in sera of hepatocellular carcinoma cases versus patients with liver cirrhosis and evaluates the influence of gender, race, and alcoholic cirrhosis on the performance of the metabolites as candidate biomarkers for hepatocellular carcinoma., Methods: Targeted quantitation of 15 metabolites is performed by selected research monitoring in sera from 89 Egyptian subjects (40 hepatocellular carcinoma cases and 49 cirrhotic controls) and 110 U.S. subjects (56 hepatocellular carcinoma cases and 54 cirrhotic controls). Logistic regression models are used to evaluate the ability of these metabolites in distinguishing hepatocellular carcinoma cases from cirrhotic controls. The influences of gender, race, and alcoholic cirrhosis on the performance of the metabolites are analyzed by stratified logistic regression., Results: Two metabolites are selected on the basis of their significance to both cohorts. Although both metabolites discriminate hepatocellular carcinoma cases from cirrhotic controls in males and Caucasians, they are insignificant in females and African Americans. One metabolite is significant in patients with alcoholic cirrhosis and the other in nonalcoholic cirrhosis., Conclusions: The study demonstrates the potential of two metabolites as candidate biomarkers for hepatocellular carcinoma by combining them with α-fetoprotein (AFP) and gender. Stratified statistical analyses reveal that gender, race, and alcoholic cirrhosis affect the relative levels of small molecules in serum., Impact: The findings of this study contribute to a better understanding of the influence of gender, race, and alcoholic cirrhosis in investigating small molecules as biomarkers for hepatocellular carcinoma.

Objectives: To estimate the prevalence of daytime sleepiness and circadian preferences, and to examine the extent to which caffeine consumption and Khat (a herbal stimulant) use are associated with daytime sleepiness and evening chronotype among Ethiopian college students., Methods: A cross-sectional study was conducted among 2,410 college students. A self-administered questionnaire was used to collect information about sleep, behavioral risk factors such as caffeinated beverages, tobacco, alcohol, and Khat consumption. Daytime sleepiness and chronotype were assessed using the Epworth Sleepiness Scale (ESS) and the Horne & Ostberg Morningness /Eveningness Questionnaire (MEQ), respectively. Linear and logistic regression models were used to evaluate associations., Results: Daytime sleepiness (ESS≥10) was present in 26% of the students (95% CI: 24.4-27.8%) with 25.9% in males and 25.5% in females. A total of 30 (0.8%) students were classified as evening chronotypes (0.7% in females and 0.9% in males). Overall, Overall, Khat consumption, excessive alcohol use and cigarette smoking status were associated with evening chronotype. Use of any caffeinated beverages (OR=2.18; 95%CI: 0.82-5.77) and Khat consumption (OR=7.43; 95%CI: 3.28-16.98) increased the odds of evening chronotype., Conclusion: The prevalence of daytime sleepiness among our study population was high while few were classified as evening chronotypes. We also found increased odds of evening chronotype with caffeine consumption and Khat use amongst Ethiopian college students. Prospective cohort studies that examine the effects of caffeinated beverages and Khat use on sleep disorders among young adults are needed.

A Bayesian multilevel functional mixed-effects model with group specific random-effects is presented for analysis of liquid chromatography-mass spectrometry (LC-MS) data. The proposed framework allows alignment of LC-MS spectra with respect to both retention time (RT) and mass-to-charge ratio (m/z). Affine transformations are incorporated within the model to account for any variability along the RT and m/z dimensions. Simultaneous posterior inference of all unknown parameters is accomplished via Markov chain Monte Carlo method using the Gibbs sampling algorithm. The proposed approach is computationally tractable and allows incorporating prior knowledge in the inference process. We demonstrate the applicability of our approach for alignment of LC-MS spectra based on total ion count profiles derived from two LC-MS datasets.

In this paper, a framework of probabilistic-based mixture regression models (PMRM) is presented for multi-class alignment of liquid chromatography-mass spectrometry (LC-MS) data. The proposed framework performs the alignment in both time and measurement spaces of the LC-MS spectra. The expectation maximization (EM) algorithm is used to estimate the joint parameters of spline-based mixture regression models and prior transformation densities. The latter are incorporated to account for variability in time and measurement spaces of the data. As a proof of concept, the proposed method is applied to align a single-class replicate LC-MS spectra generated from proteins of lysed E.coli cells. Its performance is compared with the dynamic time warping (DTW) and continuous profile model (CPM) approaches.

DNA microarrays in conjunction with statistical models may help gain a deeper understanding of the molecular basis for specific diseases. An intense area of research is concerned with the identification of genes related to particular phenotypes. The technology, however, is subject to various sources of error that may lead to expression readings that are substantially different from the true transcript levels. Few methods for microarray data analysis have accounted for measurement error in a substantial way and that is the purpose of this investigation. We describe a Bayesian error-in-variable model for the analysis of microarray data from a clinical study of patients with acute lymphoblastic leukemia. We focus in particular on the problem of identifying genes whose expression patterns are associated with duration of remission. This is a question of great practical interest since relapse is a major concern in the treatment of this disease. We explore the effects of ignoring the uncertainty in the expression estimates on the selection and ranking of genes.

The false discovery rate (FDR) procedure has become a popular method for handling multiplicity in high-dimensional data. The definition of FDR has a natural Bayesian interpretation; it is the expected proportion of null hypotheses mistakenly rejected given a measure of evidence for their truth. In this article, we propose controlling the positive FDR using a Bayesian approach where the rejection rule is based on the posterior probabilities of the null hypotheses. Correspondence between Bayesian and frequentist measures of evidence in hypothesis testing has been studied in several contexts. Here we extend the comparison to multiple testing with control of the FDR and illustrate the procedure with an application to wavelet thresholding. The problem consists of recovering signal from noisy measurements. This involves extracting wavelet coefficients that result from true signal and can be formulated as a multiple hypotheses-testing problem. We use simulated examples to compare the performance of our approach to the Benjamini and Hochberg (1995, Journal of the Royal Statistical Society, Series B57, 289-300) procedure. We also illustrate the method with nuclear magnetic resonance spectral data from human brain.

In DNA microarray analysis, there is often interest in isolating a few genes that best discriminate between tissue types. This is especially important in cancer, where different clinicopathologic groups are known to vary in their outcomes and response to therapy. The identification of a small subset of gene expression patterns distinctive for tumor subtypes can help design treatment strategies and improve diagnosis. Toward this goal, we propose a methodology for the analysis of high-density oligonucleotide arrays. The gene expression measures are modeled as censored data to account for the quantification limits of the technology, and two gene selection criteria based on contrasts from an analysis of covariance (ANCOVA) model are presented. The model is formulated in a hierarchical Bayesian framework, which in addition to making the fit of the model straightforward and computationally efficient, allows us to borrow strength across genes. The elicitation of hierarchical priors, as well as issues related to parameter identifiability and posterior propriety, are discussed in detail. We examine the performance of our proposed method on simulated data, then present a detailed case study of an endometrial cancer dataset.