Ariel - Volume 12(13) Number 2

Editor Gary Fishbein Production & Business Manager Rich Davis Layout Editor Lynn Solomon Assistant Layout Editors Bessann Dawson Tonie Kline Becky A. Zuurbier Photography Editor Ben Alma

Optimal Thinning of MCMC Output

The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced. Typically a number of the initial states are attributed to "burn in" and removed, whilst the remainder of the chain is "thinned" if compression is also required. In this paper we consider the problem of retrospectively selecting a subset of states, of fixed cardinality, from the sample path such that the approximation provided by their empirical distribution is close to optimal. A novel method is proposed, based on greedy minimisation of a kernel Stein discrepancy, that is suitable for problems where heavy compression is required. Theoretical results guarantee consistency of the method and its effectiveness is demonstrated in the challenging context of parameter inference for ordinary differential equations. Software is available in the Stein Thinning package in Python, R and MATLAB.Comment: To appear in the Journal of the Royal Statistical Society, Series B, 2021

Change in hematologic indices over time in pediatric inflammatory bowel disease treated with azathioprine

Azathioprine leads to changes in mean corpuscular volume (MCV) and white blood cell (WBC) indices reflecting efficacy or toxicity. Understanding the interactions between bone marrow stem cells and azathioprine could highlight abnormal response patterns as forerunners for hematologic malig-nancies. This study gives a statistical description of factors influencing the relationship between MCV and WBC in children with inflammatory bowel disease treated with azathioprine. We found that leukopenia preceded macro¬cytosis. Macrocytosis is therefore not a good predictor of leukopenia. Further studies will be necessary to determine the subgroup of patients at increased risk of malignancies based on bone marrow response

Altered resting state neuromotor connectivity in men with chronic prostatitis/chronic pelvic pain syndrome: A MAPP: Research Network Neuroimaging Study.

Brain network activity associated with altered motor control in individuals with chronic pain is not well understood. Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) is a debilitating condition in which previous studies have revealed altered resting pelvic floor muscle activity in men with CP/CPPS compared to healthy controls. We hypothesized that the brain networks controlling pelvic floor muscles would also show altered resting state function in men with CP/CPPS. Here we describe the results of the first test of this hypothesis focusing on the motor cortical regions, termed pelvic-motor, that can directly activate pelvic floor muscles. A group of men with CP/CPPS (N = 28), as well as group of age-matched healthy male controls (N = 27), had resting state functional magnetic resonance imaging scans as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. Brain maps of the functional connectivity of pelvic-motor were compared between groups. A significant group difference was observed in the functional connectivity between pelvic-motor and the right posterior insula. The effect size of this group difference was among the largest effect sizes in functional connectivity between all pairs of 165 anatomically-defined subregions of the brain. Interestingly, many of the atlas region pairs with large effect sizes also involved other subregions of the insular cortices. We conclude that functional connectivity between motor cortex and the posterior insula may be among the most important markers of altered brain function in men with CP/CPPS, and may represent changes in the integration of viscerosensory and motor processing

A Double Machine Learning Trend Model for Citizen Science Data

1. Citizen and community-science (CS) datasets have great potential for estimating interannual patterns of population change given the large volumes of data collected globally every year. Yet, the flexible protocols that enable many CS projects to collect large volumes of data typically lack the structure necessary to keep consistent sampling across years. This leads to interannual confounding, as changes to the observation process over time are confounded with changes in species population sizes. 2. Here we describe a novel modeling approach designed to estimate species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double Machine Learning, a statistical framework that uses machine learning methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. Additionally, we develop a simulation method to identify and adjust for residual confounding missed by the propensity scores. Using this new method, we can produce spatially detailed trend estimates from citizen science data. 3. To illustrate the approach, we estimated species trends using data from the CS project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends in the face of real-world confounding. Results showed that the trend estimates distinguished between spatially constant and spatially varying trends at a 27km resolution. There were low error rates on the estimated direction of population change (increasing/decreasing) and high correlations on the estimated magnitude. 4. The ability to estimate spatially explicit trends while accounting for confounding in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species, regions, or seasons without rigorous monitoring data.Comment: 28 pages, 6 figure

High Rate of Microbleed Formation Following Primary Intracerebral Hemorrhage

Background We sought to investigate the frequency of microbleed development following intracerebral hemorrhage in a predominantly African-American population and to identify predictors of new microbleed formation. Aims and/or hypothesis To investigate the frequency and predictors of new microbleeds following intracerebral hemorrhage. Methods The DECIPHER study was a prospective, longitudinal, magnetic resonance-based cohort study designed to evaluate racial/ethnic differences in risk factors for microbleeds and to evaluate the prognostic impact of microbleeds in this intracerebral hemorrhage population. We evaluated new microbleed formation in two time periods: from baseline to 30 days and from 30 days to year 1. Results Of 200 subjects enrolled in DECIPHER, 84 had magnetic resonance imaging at all required time points to meet criteria for this analysis. In the baseline to day 30 analysis, 11 (13·1%) had new microbleeds, compared with 25 (29·8%) in the day 30 to year 1 analysis. Logistic regression analysis demonstrated that baseline number of microbleeds [odds ratio 1·05 (95% confidence interval 1·01, 1·08), P = 0·01] was associated with new microbleed formation at 30 days. A logistic regression model predicting new microbleed at one-year included baseline number of microbleeds [odds ratio 1·05 (1·00, 1·11), P = 0·046], baseline age [odds ratio 1·05 (1·00, 1·10), P = 0·04], and white matter disease score [odds ratio 1·18 (0·96, 1·45). P = 0·115]. Overall, 28 of 84 (33·3%) intracerebral hemorrhage subjects formed new microbleeds at some point in the first year post-intracerebral hemorrhage. Conclusions We found that one-third of intracerebral hemorrhage subjects in this cohort surviving one-year developed new microbleeds, which suggests a dynamic and rapidly progressive vasculopathy. Future studies are needed to examine the impact of new microbleed formation on patient outcomes