RT education and COVID-19 pneumonia discharge quality.

Background: There is a lack of data assessing the influence of respiratory therapist (RT) education on clinical outcomes. The primary objective of this study was to evaluate the impact of RTs holding advanced degrees or completing adult critical care competencies on discharge outcomes of patients with COVID-19 pneumonia. Study Design and Methods: This retrospective, cross-sectional study included adults with confirmed COVID-19 admitted to the hospital for at least three days between March-May 2020. The academic degree held by each RT was considered advanced (baccalaureate or higher) or associate degree. Discharge outcomes were considered good, compromised, or poor when subjects' hospital discharge was directly to home, long-term care facility/rehabilitation center, or hospice/died, respectively. A time-to-event multi-state regression model was used to determine the impact of RT academic degree and adult critical care competencies on discharge outcomes using α=0.05. Results: A total of 260 subjects (median age 59 y; 166 males) received clinical care from 132 RTs. RT median professional experience was six y (IQR 3-11), 70.8% had an advanced degree, and 70.8% completed adult critical care competencies. The time-to-event multi-state regression model showed that patients with >85% exposure to RTs with advanced degrees transitioned 3.72 times more frequently to good outcomes than RTs without advanced degrees (p=.001). Similarly, patients with >85% exposure to RTs with adult critical care competencies transitioned 5.10 times more frequently to good outcomes than RTs without adult critical care competencies (p<.001). Conclusion: Patients with COVID-19 pneumonia who received greater than 85% of their care by RTs who earned advanced degrees or completed adult critical care competencies had improved discharge outcomes. This preliminary work suggests that advancing education for the respiratory therapist workforce may improve the discharge quality of patients with acute respiratory failure and should be further explored.

Creation of a non-Western humanized gnotobiotic mouse model through the transplantation of rural African fecal microbiota.

IMPORTANCE: There is increasing evidence that microbes residing within the intestines (gut microbiota) play important roles in the well-being of humans. Yet, there are considerable challenges in determining the specific role of gut microbiota in human diseases owing to the complexity of diverse internal and environmental factors that can contribute to diseases. Mice devoid of all microorganisms (germ-free mice) can be colonized with human stool samples to examine the specific contribution of the gut microbiota to a disease. These approaches have been primarily focused on stool samples obtained from individuals in Western countries. Thus, there is limited understanding as to whether the same methods used to colonize germ-free mice with stool from Western individuals would apply to the colonization of germ-free mice with stool from non-Western individuals. Here, we report the results from colonizing germ-free mice with stool samples of Malian children.

COVID-19 infection and mortality among non-pregnant indigenous adults in Mexico 2020-2022: Impact of marginalisation.

Background: Indigenous individuals have higher rates of mortality and poverty in Mexico and more than half are marginalised, and COVID-19 pandemic aggravated the existing burden of health disparities. We aimed to analyse the effects of being indigenous and marginalised on coronavirus (COVID-19) infection fatality in Mexico. Methods: We identified 3 424 690 non-pregnant, COVID-19 positive adults ≥19 years in the Mexico national COVID-19 database with known date of symptom. We used demographic information, indigenous status, marginalisation status, and co-morbidities in binary logistic regression to predict mortality, adjusting for covariates, including hospitalisation, admission to the intensive care unit (ICU), and mechanical ventilation use. We also assessed the interaction between indigenous status and marginalisation. Results: Marginalisation was much higher among indigenous (53.7%) compared to non-indigenous individuals (4.8%). COVID-19 fatalities were approximately 20 years older (64.4 and 63.0 years) than survivors (44.7 and 41.2 years) among indigenous vs non-indigenous individuals, respectively. The unadjusted risk of COVID-19 fatality among indigenous individuals was nearly two-fold (odds ratio (OR) = 1.92)) compared to non-indigenous individuals (OR = 1.05). COVID-19 fatality was higher among highly marginalised individuals (upper quartile) (OR = 1.51; 95% confidence interval (CI) = 1.49-1.54). Marginalised indigenous individuals had a significantly lower likelihood of ICU admission compared to non-indigenous non-marginalised individuals. The likelihood of mechanical ventilation for indigenous individuals was 4% higher compared to non-indigenous individuals. Indigenous marginalised individuals had a significantly lower probability of mechanical ventilation compared to non-indigenous non-marginalised individuals. COVID-19 comorbidity risks of fatality significantly differed between the two groups in the Cox survival analysis. In the fully adjusted model, indigenous individuals were 4% more likely to die from COVID-19 compared to non-indigenous. Conclusions: Indigenous, marginalised individuals with COVID-19 had higher risk of hospitalisation and ICU admission than non-indigenous patients. Marginalised, indigenous individuals were less likely to receive mechanical ventilation compared to non-indigenous, but had a higher risk of COVID-19. Indigenous individuals had a 4% higher COVID-19 mortality risk COVID-19 compared to non-indigenous individuals. Improved community medical care and augmented health services in rural hospitals could mitigate barriers to health care access in indigenous, marginalised populations.

In-vivo platelet activation and aggregation during and after acute atherothrombotic myocardial infarction in patients with and without Type-2 diabetes mellitus treated with ticagrelor.

INTRODUCTION: Patients with type-2 diabetes are twice as likely to suffer from acute myocardial infarction (AMI) and have a higher incidence of recurrent events than their non-diabetic counterparts. Ticagrelor is a platelet inhibitor known to reduce major adverse cardiovascular events (MACE) in AMI patients. This study measures the level and change in platelet activation and aggregation at the time of and following an AMI in patients with and without diabetes treated with ticagrelor. MATERIALS/METHODS: P2Y12 receptor inhibitor naïve patients presenting with AMI were prospectively enrolled. Blood collection occurred before coronary angiography (baseline: T0), 2, 4, 24, 48 h after baseline, and at a three-month follow-up. Ticagrelor was administered within five minutes of T0. We assessed platelet activation via measurements of surface P-selectin and platelet activated glycoprotein IIb/IIIa-1 (PAC-1) and assessed platelet aggregation via monocyte, lymphocyte, and granulocyte aggregates. We hypothesize that platelet activation and aggregation will be proportionally impacted to the same degree by ticagrelor, regardless of diabetes status. RESULTS: Ninety-seven patients were prospectively enrolled (diabetes, N = 33; no diabetes, N = 64). No difference was observed in the expression of P-selectin and PAC-1 at any given point between diabetes and non-diabetes groups (p > 0.05). No difference was observed in the percentage of platelet bound to leukocytes at any measured timepoint between patients with and without diabetes (p > 0.05). Platelet leukocyte aggregation was suppressed during the acute phase compared to quiescence equally among both groups. DISCUSSION: Ticagrelor demonstrated similar in-vivo effects on platelet activation and aggregation regardless of diabetes status in patients presenting with AMI.

Applications of Bayesian shrinkage prior models in clinical research with categorical responses.

BACKGROUND: Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions such as diabetes, colon cancer, and Alzheimer's disease. Developing accurate prediction and classification methods benefits personalized medicine. Building an excellent predictive model involves selecting the features that are most significantly associated with the outcome. These features can include several biological and demographic characteristics, such as genomic biomarkers and health history. Such variable selection becomes challenging when the number of potential predictors is large. Bayesian shrinkage models have emerged as popular and flexible methods of variable selection in regression settings. This work discusses variable selection with three shrinkage priors and illustrates its application to clinical data such as Pima Indians Diabetes, Colon cancer, ADNI, and OASIS Alzheimer's real-world data. METHODS: A unified Bayesian hierarchical framework that implements and compares shrinkage priors in binary and multinomial logistic regression models is presented. The key feature is the representation of the likelihood by a Polya-Gamma data augmentation, which admits a natural integration with a family of shrinkage priors, specifically focusing on Horseshoe, Dirichlet Laplace, and Double Pareto priors. Extensive simulation studies are conducted to assess the performances under different data dimensions and parameter settings. Measures of accuracy, AUC, brier score, L1 error, cross-entropy, and ROC surface plots are used as evaluation criteria comparing the priors with frequentist methods as Lasso, Elastic-Net, and Ridge regression. RESULTS: All three priors can be used for robust prediction on significant metrics, irrespective of their categorical response model choices. Simulation studies could achieve the mean prediction accuracy of 91.6% (95% CI: 88.5, 94.7) and 76.5% (95% CI: 69.3, 83.8) for logistic regression and multinomial logistic models, respectively. The model can identify significant variables for disease risk prediction and is computationally efficient. CONCLUSIONS: The models are robust enough to conduct both variable selection and prediction because of their high shrinkage properties and applicability to a broad range of classification problems.

Prevalence and Treatment Utilization of Patients Diagnosed with Depression and Anxiety Disorders Based on Kentucky Medicaid 2012-2019 Datasets.

Objectives: To examine the prevalence and treatment utilization of patients diagnosed with Depression and Anxiety Disorders (DAD) based on Kentucky Medicaid 2012-2019 datasets. Methods: The study was based on Kentucky Medicaid claims data from 2012 through 2019 for patients 14 years and older. We constructed yearly patient-level databases using ICD_9 CM and ICD_10 CM codes to identify the patients with DAD, using the Current Procedure Terminology (CPT) codes to identify individual psychotherapy and group psychotherapy and using the National drug codes to categorize pharmacotherapy. Based on these data, we constructed summary tables that reflected the trends in prevalence of DAD across eight Kentucky Medicaid regions and for different demographic subgroups. Next, we implemented logistic regression on the constructed yearly patient-level data to formally assess the impact of risk factors and treatments on the prevalence of DAD. The potential risk factors included age, gender, race/ethnicity, geographic characteristics, comorbidities such as alcohol use disorder and tobacco use. Results: The prevalence of DAD increased from 30.84% in 2012 to 36.04% in 2019. The prevalence of DAD was significantly higher in patients with the following characteristics: non-Hispanic white, females, aged between 45 and 54 years old, living in rural areas, having alcohol use disorder, and using tobaccos. Other than 2013, the utilization of pharmacotherapy maintained at about 62%. The utilization of psychotherapy increased over years from 24.4% in 2012 to 36.5% in 2019. Overall, the utilization of any treatment slightly increased from 70.9% in 2012 to 73.3% in 2019 except a drastic decline in 2013 due to the reduction of benzodiazepine prescription. Patients being whites, females, and living in rural areas were more likely to use pharmacotherapy, and patients living in rural areas were less likely to use psychotherapy than those residing in urban areas. Conclusion: The prevalence of DAD has increased over time from 2012 to 2019. The utilization of pharmacotherapy maintained at 62% over eight years except 2013, and the utilization of psychotherapy has steadily increased over time.

Prevalence and Treatment for Alcohol Use Disorders Based on Kentucky Medicaid 2012-2019 Datasets.

Alcohol use is the leading substance use in the United States. Persons with alcohol use disorder (AUD) face enormous health consequences and family problems. Analysis of Medicaid enrollee data is critical to understand different aspects of AUD and the treatment utilization for patients with AUD. Yearly patient-level data were constructed from the Kentucky 2012-2019 Medicaid claims data. ICD-9-CM and ICD-10-CM codes were used to identify patients with AUD and their comorbid conditions, the 11-digit National Drug Codes were used to identify medication treatments, and procedure codes were used to identify psychosocial and behavioral therapies. Logistic regression models were used to examine factors that were associated with AUD prevalence and AUD treatments. The prevalence of AUD trended up over time. Patients living in metro areas, between ages 45-54, having mental disorders, tobacco use, and with a family history of alcoholism had significantly higher rates of AUD. About 60% of patients diagnosed with AUD had major depressive disorder or anxiety. The treatment utilization for AUD also trended up from 2012 to 2019; however, it was still lower than 25% in 2019. Pharmacological treatments were used in only 2.89% of AUD cases in 2012, which increased to 8.13% in 2019. Psychosocial treatments were used in only 1.59% of AUD cases in 2012 that increased to 18.95% in 2019. The prevalence of AUD trended up over years. However, the treatment utilization for AUD was lower than 25%, even as of 2019. There is an urgent need for comprehensive, evidence-based, personalized AUD treatments.

Prevalence of unmasked and improperly masked behavior in indoor public areas during the COVID-19 pandemic: Analysis of a stratified random sample from Louisville, Kentucky.

Wearing a facial mask can limit COVID-19 transmission. Measurements of communities' mask use behavior have mostly relied on self-report. This study's objective was to devise a method to measure the prevalence of improper mask use and no mask use in indoor public areas without relying on self-report. A stratified random sample of retail trade stores (public areas) in Louisville, Kentucky, USA, was selected and targeted for observation by trained surveyors during December 14-20, 2020. The stratification allowed for investigating mask use behavior by city district, retail trade group, and public area size. The total number of visited public areas was 382 where mask use behavior of 2,080 visitors and 1,510 staff were observed. The average prevalence of mask use among observed visitors was 96%, while the average prevalence of proper use was 86%. In 48% of the public areas, at least one improperly masked visitor was observed and in 17% at least one unmasked visitor was observed. The average prevalence of proper mask use among staff was 87%, similar to the average among visitors. However, the percentage of public areas where at least one improperly masked staff was observed was 33. Significant disparities in mask use and its proper use were observed among both visitors and staff by public area size, retail trade type, and geographical area. Observing unmasked and improperly masked visitors was more common in small (less than 1500 square feet) public areas than larger ones, specifically in food and grocery stores as compared to other retail stores. Also, the majority of the observed unmasked persons were male and middle-aged.

County-level demographic, social, economic, and lifestyle correlates of COVID-19 infection and death trajectories during the first wave of the pandemic in the United States.

BACKGROUND: The US COVID-19 epidemic impacted counties differently across space and time, though large-scale transmission dynamics are unclear. The study's objective was to group counties with similar trajectories of COVID-19 cases and deaths and identify county-level correlates of the distinct trajectory groups. METHODS: Daily COVID-19 cases and deaths were obtained from 3141 US counties from January through June 2020. Clusters of epidemic curve trajectories of COVID-19 cases and deaths per 100,000 people were identified with Proc Traj. We utilized polytomous logistic regression to estimate Odds Ratios for trajectory group membership in relation to county-level demographics, socioeconomic factors, school enrollment, employment and lifestyle data. RESULTS: Six COVID-19 case trajectory groups and five death trajectory groups were identified. Younger counties, counties with a greater proportion of females, Black and Hispanic populations, and greater employment in private sectors had higher odds of being in worse case and death trajectories. Percentage of counties enrolled in grades 1-8 was associated with earlier-start case trajectories. Counties with more educated adult populations had lower odds of being in worse case trajectories but were generally not associated with worse death trajectories. Counties with higher poverty rates, higher uninsured, and more living in non-family households had lower odds of being in worse case and death trajectories. Counties with higher smoking rates had higher odds of being in worse death trajectory counties. DISCUSSION: In the absence of clear guidelines and personal protection, smoking, racial and ethnic groups, younger populations, social, and economic factors were correlated with worse COVID-19 epidemics that may reflect population transmission dynamics during January-June 2020. After vaccination of high-risk individuals, communities with higher proportions of youth, communities of color, smokers, and workers in healthcare, service and goods industries can reduce viral spread by targeting vaccination programs to these populations and increasing access and education on non-pharmaceutical interventions.

Development and Validation of Biosafety Climate Scale for Biological and Biomedical Science Laboratories in the United States.

Introduction: Industry-specific safety climate scales that measure safety status have been published, however, nothing specific to biological laboratories has ever been established. Objective: This study aimed to develop and validate a biosafety climate (BSCL) scale unique for research professionals (RPs) and biosafety professionals (BPs) at teaching and research biological laboratories affiliated to public universities in the United States. Methods: BSCL scale was developed from literature review. In study 1, 15-item biosafety climate (BSCL-15) scale with 15 items and 5 factors was pretested with n = 9 RPs and n = 7 BPs to perform reliability, content, and face validity analyses. In study 2, revised 17-item biosafety climate (BSCL-17) scale with 17 items and 5 factors was pilot tested with n = 91 RPs and n = 88 BPs. Correlation tests, Kaiser-Mayer-Olkin, Bartlett's test of sphericity, Cronbach's alpha, and exploratory factor analysis (EFA) were conducted to validate the BSCL-17 scale. Results: EFA resulted in a 3-factor 17-item BSCL scale for both RPs and BPs. Internal consistency of the scale was > 0.8 for the BSCL scale and the underlying three factors, indicating high reliability. The factors identified for RPs are 1) management priority, communication and participation, 2) group norms, and 3) supervisor commitment. The factors identified for BPs are 1) management priority and communication, 2) group norms and participation, and 3) supervisor commitment. Discussion: A valid and reliable BSCL scale to measure safety climate and quantify safety culture in biological laboratories has been presented. It can be used as a key performance indicator and aid in targeted interventions as part of process improvement of biological safety programs.

Bayesian Decision-Theoretic Methods for Survival Data using Stochastic Optimization.

We introduce a principled method for Bayesian subgroup analysis. The approach is based on casting subgroup analysis as a Bayesian decision problem. The two main innovations are: (1) the explicit consideration of a "subgroup report," comprising multiple subpopulations; and (2) adapting an inhomogeneous Markov chain Monte Carlo simulation scheme to implement stochastic optimization. The latter makes the search for "subgroup reports" practically feasible.

Change in matrix metalloproteinase 2, 3, and 9 levels at the time of and after acute atherothrombotic myocardial infarction.

Elevated measures of matrix metalloproteinases (MMPs) are associated with acute myocardial infarction (MI), but it is not known how long these changes persist post-MI or if these measures differ between atherothrombotic versus non-atherothrombotic MI. MMPs-2, 3, and 9 were measured in 80 subjects with acute MI (atherothrombotic and non-atherothrombotic MI) or stable coronary artery disease (CAD). Measurements were made at, the time of acute MI, and > 3-month following acute MI (quiescent phase). Outcome measures were compared between groups and between time of acute MI and quiescent post-MI follow-up using Wilcoxon's and repeated measures analysis of variance. Forty-nine subjects met the criteria for acute MI with clearly defined atherothrombotic (n = 22) and non-atherothrombotic (n = 12) subsets. Fifteen subjects met criteria for stable CAD. MMP-3 was higher in acute MI versus stable CAD subjects at the time of acute MI: (453 vs. 217 pg/mL, p = 0.010) but not at quiescent phase follow-up (p > 0.05). MMP-9 was higher in acute MI versus stable CAD subjects at the time of acute MI: (412 vs. 168 pg/mL, p = 0.002) but not at the quiescent phase follow-up (p > 0.05). MMP-9 was higher at the time of acute MI versus quiescent phase follow-up in acute MI (412 vs. 213 pg/mL, p = 0.001) and atherothrombotic MI specifically (458 vs. 212 pg/mL, p = 0.001). No difference in MMP-2, 3, or 9 was observed between atherothrombotic versus non-atherothrombotic MI subgroups. MMPs-3 and 9 are significantly elevated in acute MI verses stable CAD subjects at time of acute MI but not different at quiescent phase follow-up. MMP-9 is elevated at the time of acute MI and specifically in acute atherothrombotic MI at time of MI versus quiescent phase follow-up.

Bayesian Hierarchical Model for Protein Identifications.

In proteomics, identification of proteins from complex mixtures of proteins extracted from biological samples is an important problem. Among the experimental technologies, Mass-Spectrometry (MS) is the most popular one. Protein identification from MS data typically relies on a "two-step" procedure of identifying the peptide first followed by the separate protein identification procedure next. In this setup, the interdependence of peptides and proteins are neglected resulting in relatively inaccurate protein identification. In this article, we propose a Markov chain Monte Carlo (MCMC) based Bayesian hierarchical model, a first of its kind in protein identification, which integrates the two steps and performs joint analysis of proteins and peptides using posterior probabilities. We remove the assumption of independence of proteins by using clustering group priors to the proteins based on the assumption that proteins sharing the same biological pathway are likely to be present or absent together and are correlated. The complete conditionals of the proposed joint model being tractable, we propose and implement a Gibbs sampling scheme for full posterior inference that provides the estimation and statistical uncertainties of all relevant parameters. The model has better operational characteristics compared to two existing "one-step" procedures on a range of simulation settings as well as on two well-studied datasets.

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data.

BACKGROUND: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. METHODS: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes "Prior interaction map + TCGA data → Posterior interaction map." RESULTS: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. CONCLUSIONS: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.

A Bayesian hierarchical model for inference across related reverse phase protein arrays experiments.

We consider inference for functional proteomics experiments that record protein activation over time following perturbation under different dose levels of several drugs. The main inference goal is the dependence structure of the selected proteins. A critical challenge is the lack of sufficient data under any one drug and dose level to allow meaningful inference on dependence structure. We propose a hierarchical model to implement the desired inference. The key element of the model is a shared dependence structure on (latent) binary indicators of protein activation.

Characterizing Cancer-Specific Networks by Integrating TCGA Data.

The Cancer Genome Atlas (TCGA) generates comprehensive genomic data for thousands of patients over more than 20 cancer types. TCGA data are typically whole-genome measurements of multiple genomic features, such as DNA copy numbers, DNA methylation, and gene expression, providing unique opportunities for investigating cancer mechanism from multiple molecular and regulatory layers. We propose a Bayesian graphical model to systemically integrate multi-platform TCGA data for inference of the interactions between different genomic features either within a gene or between multiple genes. The presence or absence of edges in the graph indicates the presence or absence of conditional dependence between genomic features. The inference is restricted to genes within a known biological network, but can be extended to any sets of genes. Applying the model to the same genes using patient samples in two different cancer types, we identify network components that are common as well as different between cancer types. The examples and codes are available at https://www.ma.utexas.edu/users/yxu/software.html.

Bayesian hierarchical models for protein networks in single-cell mass cytometry.

We propose a class of hierarchical models to investigate the protein functional network of cellular markers. We consider a novel data set from single-cell proteomics. The data are generated from single-cell mass cytometry experiments, in which protein expression is measured within an individual cell for multiple markers. Tens of thousands of cells are measured serving as biological replicates. Applying the Bayesian models, we report protein functional networks under different experimental conditions and the differences between the networks, ie, differential networks. We also present the differential network in a novel fashion that allows direct observation of the links between the experimental agent and its putative targeted proteins based on posterior inference. Our method serves as a powerful tool for studying molecular interactions at cellular level.

Bayesian Nonparametric Inference - Why and How.

We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. We discuss inference for density estimation, clustering, regression and for mixed effects models with random effects distributions. While we focus on arguing for the need for the flexibility of BNP models, we also review some of the more commonly used BNP models, thus hopefully answering a bit of both questions, why and how to use BNP.

Toward breaking the histone code: bayesian graphical models for histone modifications.

BACKGROUND: Histones are proteins that wrap DNA around in small spherical structures called nucleosomes. Histone modifications (HMs) refer to the post-translational modifications to the histone tails. At a particular genomic locus, each of these HMs can either be present or absent, and the combinatory patterns of the presence or absence of multiple HMs, or the histone codes, are believed to coregulate important biological processes. We aim to use raw data on HM markers at different genomic loci to (1) decode the complex biological network of HMs in a single region, and (2) demonstrate how the HM networks differ in different regulatory regions. We suggest that these differences in network attributes form a significant link between histones and genomic functions. METHODS AND RESULTS: We develop a powerful graphical model under the Bayesian paradigm. Posterior inference is fully probabilistic, allowing us to compute the probabilities of distinct dependence patterns of the HMs using graphs. Furthermore, our model-based framework allows for easy but important extensions for inference on differential networks under various conditions, such as the different annotations of the genomic locations (eg, promoters versus insulators). We applied these models to ChIP-Seq data based on CD4+ T lymphocytes. The results confirmed many existing findings and provided a unified tool to generate various promising hypotheses. Differential network analyses revealed new insights on coregulation of HMs of transcriptional activities in different genomic regions. CONCLUSIONS: The use of Bayesian graphical models and borrowing strength across different conditions provide high power to infer histone networks and their differences.

Propriety conditions for the Bayesian autologistic model - Inference for histone modifications.

Motivated by inference for a set of histone modifications we consider an improper prior for an autologistic model. We state sufficient conditions for posterior propriety under a constant prior on the coefficients of an autologistic model. We use known results for a multinomial logistic regression to prove posterior propriety under the autologistic model. The conditions are easily verified.