Feasibility and user-experience of a virtual environment for social connection and education after stroke: A pilot study.

OBJECTIVES: To evaluate the feasibility and usability of stroke survivor participation in an 8-week virtual environment intervention that provides opportunities for social support exchanges, social network interactions, and recovery education. MATERIALS AND METHODS: A single-group, pre- and post-test measure design was used. Descriptive statistics were used to examine enrollment and retention rates, proportion of questionnaires completed, and virtual environment process data (e.g., number of log-ins) and usability scores. Changes in pre- and post-intervention questionnaire (e.g., usability, social support, depression, anxiety, loneliness, and self-efficacy) scores were explored using Wilcoxon signed-rank tests and paired t-test. RESULTS: Fifteen (65 %) of the eligible stroke survivors enrolled (60 % white, 27 % black), 12 (80 %) had an ischemic stroke, ages ranged from 33 to 74 years (mean 44 years), and mean months since stroke was 33 ± 23. Retention and questionnaire completion rates were both 93 % (n = 14). Survivors logged into the virtual environment a total of 122 times, logged an average of 49 min/log-in, and 12 (80 %) attended support groups and social activities. Median usability score indicated lower than average usability. Improvement trends in social support, loneliness, and depressive symptoms were found, but significant changes in mean questionnaire scores were not found. CONCLUSIONS: Overall, the results suggest that using a virtual environment to foster social support exchanges, social network interactions, and recovery education after stroke is feasible. Similar to other chronic disease populations, stroke survivor adoption of a virtual environment likely requires ongoing technical assistance, repetition of instructions, and opportunities for practice to reinforce engagement. TRIAL REGISTRATION: NCT05487144.

Antibody Duration After Infection From SARS-CoV-2 in the Texas Coronavirus Antibody Response Survey.

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not).

Methodology to estimate natural- and vaccine-induced antibodies to SARS-CoV-2 in a large geographic region.

Accurate estimates of natural and/or vaccine-induced antibodies to SARS-CoV-2 are difficult to obtain. Although model-based estimates of seroprevalence have been proposed, they require inputting unknown parameters including viral reproduction number, longevity of immune response, and other dynamic factors. In contrast to a model-based approach, the current study presents a data-driven detailed statistical procedure for estimating total seroprevalence (defined as antibodies from natural infection or from full vaccination) in a region using prospectively collected serological data and state-level vaccination data. Specifically, we conducted a longitudinal statewide serological survey with 88,605 participants 5 years or older with 3 prospective blood draws beginning September 30, 2020. Along with state vaccination data, as of October 31, 2021, the estimated percentage of those 5 years or older with naturally occurring antibodies to SARS-CoV-2 in Texas is 35.0% (95% CI = (33.1%, 36.9%)). This is 3× higher than, state-confirmed COVID-19 cases (11.83%) for all ages. The percentage with naturally occurring or vaccine-induced antibodies (total seroprevalence) is 77.42%. This methodology is integral to pandemic preparedness as accurate estimates of seroprevalence can inform policy-making decisions relevant to SARS-CoV-2.

Power calculator for detecting allelic imbalance using hierarchical Bayesian model.

OBJECTIVE: Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI exist, but methods are needed to estimate type I error and power for detecting AI and difference of AI between conditions. As the costs of the technology plummet, what is more important: reads or replicates? RESULTS: We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10, 20, and 30%, respectively, deviation from allelic balance in a condition with power > 80%. A minimum of 960 and 240 allele specific reads divided equally among 8 replicates is needed to detect a 20 or 30% difference in AI between conditions with comparable power. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions.

Testcrosses are an efficient strategy for identifying cis-regulatory variation: Bayesian analysis of allele-specific expression (BayesASE).

Allelic imbalance (AI) occurs when alleles in a diploid individual are differentially expressed and indicates cis acting regulatory variation. What is the distribution of allelic effects in a natural population? Are all alleles the same? Are all alleles distinct? The approach described applies to any technology generating allele-specific sequence counts, for example for chromatin accessibility and can be applied generally including to comparisons between tissues or environments for the same genotype. Tests of allelic effect are generally performed by crossing individuals and comparing expression between alleles directly in the F1. However, a crossing scheme that compares alleles pairwise is a prohibitive cost for more than a handful of alleles as the number of crosses is at least (n2-n)/2 where n is the number of alleles. We show here that a testcross design followed by a hypothesis test of AI between testcrosses can be used to infer differences between nontester alleles, allowing n alleles to be compared with n crosses. Using a mouse data set where both testcrosses and direct comparisons have been performed, we show that the predicted differences between nontester alleles are validated at levels of over 90% when a parent-of-origin effect is present and of 60%-80% overall. Power considerations for a testcross, are similar to those in a reciprocal cross. In all applications, the testing for AI involves several complex bioinformatics steps. BayesASE is a complete bioinformatics pipeline that incorporates state-of-the-art error reduction techniques and a flexible Bayesian approach to estimating AI and formally comparing levels of AI between conditions. The modular structure of BayesASE has been packaged in Galaxy, made available in Nextflow and as a collection of scripts for the SLURM workload manager on github (https://github.com/McIntyre-Lab/BayesASE).

Time trends and predictors of survival in surgically resected early-stage non-small cell lung cancer patients.

BACKGROUND: The improvement in the management of lung cancer have the potential to improve survival in patients undergoing resection for early-stage (stage I and II) non-small cell lung cancer (NSCLC), but few studies have evaluated time trends and identified predictors of overall survival (OS). METHODS: We identified surgically resected early-stage NSCLC between 1998 and 2016. The 3-year OS (1998-2014) and 5-year OS (1998-2012) rates were calculated for each year. Joinpoint regression was used to calculate annual percentage changes (APC) and to test time trends in OS. Multivariable Cox regression was used to identify predictors of OS. RESULTS: There was a significant upward trend in the 3-year (1998, 56%; 2014, 83%; APC = 1.8) and 5-year (1998, 47%; 2012, 76%; APC = 3.1) OS. Older age; male sex; history of diabetes, coronary artery disease, and chronic obstructive pulmonary disease; high ASA score; smoking pack-years; high-grade tumor; pneumonectomy; thoracotomy; neoadjuvant therapy; nodal disease; and positive tumor margin were predictors of poor OS. CONCLUSION: The upward time trend in OS suggests that improved staging, patient selection, and management have conferred a survival benefit in early-stage NSCLC patients. The prediction model of OS could be used to refine selection criteria for resection and improve survival outcomes.

Time Trends of Perioperative Outcomes in Early Stage Non-Small Cell Lung Cancer Resection Patients.

BACKGROUND: Advances in perioperative and operative management hold great promise for improving perioperative outcomes in patients undergoing resection for early stage non-small cell lung cancer (NSCLC). The objective of this study was to evaluate time trends in the incidence of perioperative outcomes and to identify predictors of pulmonary complication in early stage NSCLC resection patients. METHODS: An institutional database was reviewed to identify patients with primary, clinical stage I and II NSCLC who underwent resection from 1998 to 2016. Rates of perioperative pulmonary complication, pneumonia, and cardiovascular complication; and 30-day and 90-day mortality were calculated for each year. Joinpoint regression was used to calculate annual percentage change (APC) and to evaluate time trends in rates of these outcomes. Multivariable logistic regression was conducted to identify predictors of pulmonary complication. RESULTS: Of the 3045 patients identified, 80% had stage I and 20% had stage II NSCLC. From 1998 to 2016, there was no trend in the rate of pulmonary complication, but there was a significant downward trend in the rates of pneumonia (APC -3.7), cardiovascular complication (APC -3.5), 30-day mortality (APC -9.8), and 90-mortality (APC -7.4). Older age, male sex, smoking status, percentage of predicted forced expiratory volume in 1 second and percentage of diffusion capacity of lung for carbon monoxide, and intraoperative blood transfusion were identified as predictors of pulmonary complication. CONCLUSIONS: Decrease in the rates of perioperative outcomes parallels improvements in patient selection and perioperative management of early stage NSCLC resection patients. Predictors of pulmonary complication could be used to improve selection criteria for surgery and to reduce the incidence of pulmonary complication in these patients.

ADAPTIVE-WEIGHT BURDEN TEST FOR ASSOCIATIONS BETWEEN QUANTITATIVE TRAITS AND GENOTYPE DATA WITH COMPLEX CORRELATIONS.

High-throughput sequencing has often been used to screen samples from pedigrees or with population structure, producing genotype data with complex correlations rendered from both familial relation and linkage disequilibrium. With such data, it is critical to account for these genotypic correlations when assessing the contribution of variants by gene or pathway. Recognizing the limitations of existing association testing methods, we propose Adaptive-weight Burden Test (ABT), a retrospective, mixed-model test for genetic association of quantitative traits on genotype data with complex correlations. This method makes full use of genotypic correlations across both samples and variants, and adopts "data-driven" weights to improve power. We derive the ABT statistic and its explicit distribution under the null hypothesis, and demonstrate through simulation studies that it is generally more powerful than the fixed-weight burden test and family-based SKAT in various scenarios, controlling for the type I error rate. Further investigation reveals the connection of ABT with kernel tests, as well as the adaptability of its weights to the direction of genetic effects. The application of ABT is illustrated by a whole genome analysis of genes with common and rare variants associated with fasting glucose from the NHLBI "Grand Opportunity" Exome Sequencing Project.

A Bayesian analysis of quantal bioassay experiments incorporating historical controls via Bayes factors.

This paper addresses model-based Bayesian inference in the analysis of data arising from bioassay experiments. In such experiments, increasing doses of a chemical substance are given to treatment groups (usually rats or mice) for a fixed period of time (usually 2 years). The goal of such an experiment is to determine whether an increased dosage of the chemical is associated with increased probability of an adverse effect (usually presence of adenoma or carcinoma). The data consists of dosage, survival time, and the occurrence of the adverse event for each unit in the study. To determine whether such relationship exists, this paper proposes using Bayes factors to compare two probit models, the model that assumes increasing dose effects and the model that assumes no dose effect. These models account for the survival time of each unit through a Poly-k type correction. In order to increase statistical power, the proposed approach allows the incorporation of information from control groups from previous studies. The proposed method is able to handle data with very few occurrences of the adverse event. The proposed method is compared with a variation of the Peddada test via simulation and is shown to have higher power. We demonstrate the method by applying it to the two bioassay experiment datasets previously analyzed by other authors. Copyright © 2017 John Wiley & Sons, Ltd.

Buffering of Genetic Regulatory Networks in Drosophila melanogaster.

Regulatory variation in gene expression can be described by cis- and trans-genetic components. Here we used RNA-seq data from a population panel of Drosophila melanogaster test crosses to compare allelic imbalance (AI) in female head tissue between mated and virgin flies, an environmental change known to affect transcription. Indeed, 3048 exons (1610 genes) are differentially expressed in this study. A Bayesian model for AI, with an intersection test, controls type I error. There are ∼200 genes with AI exclusively in mated or virgin flies, indicating an environmental component of expression regulation. On average 34% of genes within a cross and 54% of all genes show evidence for genetic regulation of transcription. Nearly all differentially regulated genes are affected in cis, with an average of 63% of expression variation explained by the cis-effects. Trans-effects explain 8% of the variance in AI on average and the interaction between cis and trans explains an average of 11% of the total variance in AI. In both environments cis- and trans-effects are compensatory in their overall effect, with a negative association between cis- and trans-effects in 85% of the exons examined. We hypothesize that the gene expression level perturbed by cis-regulatory mutations is compensated through trans-regulatory mechanisms, e.g., trans and cis by trans-factors buffering cis-mutations. In addition, when AI is detected in both environments, cis-mated, cis-virgin, and trans-mated-trans-virgin estimates are highly concordant with 99% of all exons positively correlated with a median correlation of 0.83 for cis and 0.95 for trans We conclude that the gene regulatory networks (GRNs) are robust and that trans-buffering explains robustness.

Early childhood gut microbiomes show strong geographic differences among subjects at high risk for type 1 diabetes.

OBJECTIVE: Gut microbiome dysbiosis is associated with numerous diseases, including type 1 diabetes. This pilot study determines how geographical location affects the microbiome of infants at high risk for type 1 diabetes in a population of homogenous HLA class II genotypes. RESEARCH DESIGN AND METHODS: High-throughput 16S rRNA sequencing was performed on stool samples collected from 90 high-risk, nonautoimmune infants participating in The Environmental Determinants of Diabetes in the Young (TEDDY) study in the U.S., Germany, Sweden, and Finland. RESULTS: Study site-specific patterns of gut colonization share characteristics across continents. Finland and Colorado have a significantly lower bacterial diversity, while Sweden and Washington state are dominated by Bifidobacterium in early life. Bacterial community diversity over time is significantly different by geographical location. CONCLUSIONS: The microbiome of high-risk infants is associated with geographical location. Future studies aiming to identify the microbiome disease phenotype need to carefully consider the geographical origin of subjects.

A flexible Bayesian method for detecting allelic imbalance in RNA-seq data.

BACKGROUND: One method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data. RESULTS: Here we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test. CONCLUSIONS: To improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation.

Bayesian decision theoretic multiple comparison procedures: an application to phage display data.

We discuss a case study that highlights the features and limitations of a principled Bayesian decision theoretic approach to massive multiple comparisons. We consider inference for a mouse phage display experiment with three stages. The data are tripeptide counts by tissue and stage. The primary aim of the experiment is to identify ligands that bind with high affinity to a given tissue. The inference goal is to select from a large list of peptide and tissue pairs those with significant increase over stages. The desired inference summary involves a massive multiplicity problem. We consider two alternative approaches to address this multiplicity issue. First we propose an approach based on the control of the posterior expected false discovery rate. We notice that the implied solution ignores the relative size of the increase. This motivates a second approach based on a utility function that includes explicit weights for the size of the increase.

Semiparametric Bayesian inference for phage display data.

We discuss inference for a human phage display experiment with three stages. The data are tripeptide counts by tissue and stage. The primary aim of the experiment is to identify ligands that bind with high affinity to a given tissue. We formalize the research question as inference about the monotonicity of mean counts over stages. The inference goal is then to identify a list of peptide-tissue pairs with significant increase over stages. We use a semiparametric Dirichlet process mixture of Poisson model. The posterior distribution under this model allows the desired inference about the monotonicity of mean counts. However, the desired inference summary as a list of peptide-tissue pairs with significant increase involves a massive multiplicity problem. We consider two alternative approaches to address this multiplicity issue. First we propose an approach based on the control of the posterior expected false discovery rate. We notice that the implied solution ignores the relative size of the increase. This motivates a second approach based on a utility function that includes explicit weights for the size of the increase.