BM-BC: A Bayesian Method of Base Calling for Solexa Sequence Data

Base calling is a critical step in the Solexa next-generation sequencing procedure. It compares the position-specific intensity measurements that reflect the signal strength of four possible bases (A, C, G, T) at each genomic position, and outputs estimates of the true sequences for short reads of DNA or RNA. We present a Bayesian method of base calling, BM-BC, for Solexa-GA sequencing data. The Bayesian method builds on a hierarchical model that accounts for three sources of noise in the data, which are known to affect the accuracy of the base calls: fading, phasing, and cross-talk between channels. We show that the new method improves the precision of base calling compared with currently leading methods. Furthermore, the proposed method provides a probability score that measures the confidence of each base call. This probability score can be used to estimate the false discovery rate of the base calling or to rank the precision of the estimated DNA sequences, which in turn can be useful for downstream analysis such as sequence alignment.NIH/NCI R01 CA132897, K25 CA123344FONDECYT 1100010Institute for Computational Engineering and Sciences (ICES

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

Nonparametric Bayesian Bi-Clustering for Next Generation Sequencing Count Data

Histone modifications (HMs) play important roles in transcription through post-translational modifications. Combinations of HMs, known as chromatin signatures, encode specific messages for gene regulation. We therefore expect that inference on possible clustering of HMs and an annotation of genomic locations on the basis of such clustering can contribute new insights about the functions of regulatory elements and their relationships to combinations of HMs. We propose a nonparametric Bayesian local clustering Poisson model (NoB-LCP) to facilitate posterior inference on two-dimensional clustering of HMs and genomic locations. The NoB-LCP clusters HMs into HM sets and lets each HM set define its own clustering of genomic locations. Furthermore, it probabilistically excludes HMs and genomic locations that are irrelevant to clustering. By doing so, the proposed model effectively identifies important sets of HMs and groups regulatory elements with similar functionality based on HM patterns.NIH R01 CA132897NCI 5 K25 CA123344Mathematic

Facial Mask Use and COVID-19 Protection Measures in Jefferson County, Kentucky: Results from an Observational Survey, November 5−11, 2020

Introduction: The transmission of respiratory infectious diseases such as COVID-19 can significantly decrease by mask-wearing. However, accurate information about the extent and proper use of the facial mask is scarce. This study’s main objective was to observe and analyze mask-wearing behavior and the level of COVID-19 protection measures in indoor public areas (PAs) of Jefferson County, Kentucky. Methods: For conducting the observational survey study, targets were indoor PAs, and zip codes were defined as surveying clusters. The number of selected PAs in each zip code was proportional to the population and the total number of PAs in that zip code. The PA pool in a zip code was divided into four groups, followed by random selection without replacement from each group. Results: A total of 191 PAs were surveyed: 50 of them were grocery stores, 56 were convenience stores or pharmacies, 39 were wine and liquor stores, and 46 were other stores. At least one unmasked and one incorrectly masked staff were observed in 26% and 40% of the sampled PAs, respectively. Also, in 29% and 35% of the PAs, at least one unmasked and one incorrectly masked visitor were observed, respectively. The rates varied by PA size and county district. Eighty percent of unmasked staff and 75% of the unmasked visitors were male. The rate of unmasked males varied from 50% to 100% across districts. About 66% of unmasked staff among all Jefferson County districts were young adults. More than one-fourth of all the PAs provided hand sanitizer for visitors’ use, and only 2% of the PAs provided masks to their visitors. Conclusion: Messaging about mask use and correct usage may need to particularly target the 19-44-year-old male population, as these individuals were the most prevalent among those unmasked and masked incorrectly. Additionally, businesses’ protective measures may depend on their resources to operate in such a manner. Hand sanitizer is easier to offer visitors, while staffing to regularly sanitize carts or funds to provide a sufficient number of wipes, gloves, or masks may present further opportunities for government assistance

Nonparametric Bayesian inference in biostatistics

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve. Riten Mitra is Assistant Professor in the Department of Bioinformatics and Biostatistics at University of Louisville. His research interests include Bayesian graphical models and nonparametric Bayesian methods with a special emphasis on applications in genomics and bioinformatics. Peter Mueller is Professor in the Department of Mathematics and the Department of Statistics & Data Science at the University of Texas at Austin. He has published widely on nonparametric Bayesian statistics, with an emphasis on applications in biostatistics and bioinformatics

Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations

The paper has two major themes. The first part of the paper establishes certain general results for infinite-dimensional optimization problems on Hilbert spaces. These results cover the classical representer theorem and many of its variants as special cases and offer a wider scope of applications. The second part of the paper then develops a systematic approach for learning the drift function of a stochastic differential equation by integrating the results of the first part with Bayesian hierarchical framework. Importantly, our Baysian approach incorporates low-cost sparse learning through proper use of shrinkage priors while allowing proper quantification of uncertainty through posterior distributions. Several examples at the end illustrate the accuracy of our learning scheme.Comment: 32 pages, 4 figure