Model-based Imputation of Below Detection Limit Missing Data and Group Selection in Bayesian Group Index Regression

Investigations into the association between chemical exposure and health outcomes are increasingly focused on the role of chemical mixtures, as opposed to individual chemicals. The analysis of chemical mixture data required the development of novel statistical methods, one of these being Bayesian group index regression. A statistical challenge common to all chemical mixture analyses is the ubiquitous presence of below detection limit (BDL) data. We propose an extension of Bayesian group index regression that treats both regression effects and missing BDL observations as parameters in a model estimated through a Markov Chain Monte Carlo algorithm that we refer to as Pseudo-Gibbs imputation. The Pseudo-Gibbs approach enables the estimated parameters of the health effects model to inform the missing data imputations and vice versa, as well as accounting for the true variance of the BDL imputations. We conduct a simulation study showing greater power to detect chemical indices significantly associated with an outcome and sensitivity for identifying important chemicals within indices at high levels of BDL missing data. We apply our model to a case-control study on the effects of chemical exposure on childhood leukemia. We next address a problem specific to group index models: how to partition a given set of chemicals into groups to form the requisite indices. We first proposed a novel variable clustering algorithm using a variant on the traditional PCA algorithm called Robust PCA. We compared this clustering method with other variable clustering methods from the literature using a simulation study. Finally, we extended the variable clustering method identified previously to incorporate information from an outcome variable. This semi-supervised clustering extension incorporates the ability to constrain clusters based on the direction of association of individual chemicals with the outcome of interest. We apply both unsupervised variable clustering and semi-supervised clustering methods identified to a case-control study on the effects of chemical exposure on non-Hodkin’s lymphoma

The Effect of Single Versus Group μCT on the Detection of Trabecular and Cortical Disease Phenotypes in Mouse Bones

Micro‐computed tomography is a critical assessment tool for bone‐related preclinical research, especially in murine models. To expedite the scanning process, researchers often image multiple bones simultaneously; however, it is unknown if this impacts scan quality and alters the ability to detect differences between experimental groups. The purpose of this study was to assess the effect of multibone scanning on detecting disease‐induced changes in bone microarchitecture and mineral density by group scanning two murine models with known skeletal defects: the Col1a2 G610C/+ model of osteogenesis imperfecta and an adenine‐induced model of chronic kidney disease. Adult male femurs were scanned individually and in random groups of three and eight in a Bruker Skyscan 1172 and 1176, respectively, then assessed for standard trabecular and cortical bone measures. Although scanning methodology altered raw values, with trabecular microarchitecture values more affected than cortical properties, a disease phenotype was still detectable in both group and solo scans. However, tissue mineral density in both trabecular and cortical bone was significantly impacted by group versus solo scanning. Researchers may be able to use small groupings in a single μCT scan to expedite preclinical analyses when the overall bone phenotype is large to decrease costs and increase speed of discoveries; however the details of scanning (single or group) should always be reported

Predictors and temporal trend of flu vaccination in auto-immune rheumatic diseases in the UK: a nationwide prospective cohort study

Objectives: To [1] examine temporal trend in uptake of seasonal influenza vaccine (SIV) in the UK, [2] explore disease and demographic factors associated with vaccination. Methods: 32,751 people with auto-immune rheumatic diseases (AIRDs) prescribed disease modifying anti-rheumatic drugs (DMARDs) between 2006 and 2016 were identified from the Clinical Practice Research Datalink. The proportion vaccinated between 01/September of one year and 31/March of next year was calculated and stratified by age, other indications for vaccination, AIRD type, and number of DMARDs prescribed. Stata and Joinpoint regression programs were used. Results: SIV uptake was high in those aged ≥65 years (82.3% and 80.7% in 2006-07 and 2015-16 respectively). It was significantly lower in other age groups, but improved over time with 51.9% and 61.9% in the 45-64 year age group, and 32.3% and 50.1% in the <45 year age group being vaccinated in 2006-07 and 2015-16 respectively. While 64.9% of the vaccinations in those ≥65 years old occurred by 3rd November, in time to mount a protective immune response before the influenza activity becomes substantial in UK, only 38.9% in the 45-64 year and 26.2% in the <45 year age group without any other reason for vaccination received SIV by this date. Women, those with additional indications for vaccination, on multiple DMARDs and with SLE were more likely to be vaccinated. Conclusion: SIV uptake is low in the under 65s, and the majority of them are not vaccinated in time. Additional effort is required to promote timely uptake of SIV in this population

Chemoorganotrophic Bacteria From Lake Fryxell, Antarctica, Including Pseudomonas Strain LFY10, a Cold-Adapted, Halotolerant Bacterium Useful in Teaching Labs

Lake Fryxell, situated in the McMurdo Dry Valleys of Antarctica, is an intriguing aquatic ecosystem because of its perennial ice cover, highly stratified water column, and extreme physicochemical conditions, which collectively restrict lake biodiversity to solely microbial forms. To expand our current understanding of the cultivable biodiversity of Lake Fryxell, water samples were collected from depths of 10 and 17 m, and pure cultures of eight diverse strains of aerobic, chemoorganotrophic bacteria were obtained. Despite having high 16S rRNA gene sequence similarity to mesophilic bacteria inhabiting various temperate environments, all Lake Fryxell isolates were psychrotolerant, with growth occurring at 0°C and optimal growth from 18–24°C for all isolates. Phylogenetic analyses showed the isolates to be members of six taxonomic groups, including the genera Brevundimonas, Arthrobacter, Sphingobium, Leifsonia, and Pseudomonas, as well as the family Microbacteriaceae (one strain could not reliably be assigned to a specific genus based on our analysis). Pseudomonas strain LFY10 stood out as a useful tool for teaching laboratory activities because of its substantial cold adaptation (visible growth is evident in 1–2 days at 4°C), beta-hemolytic activity, and halotolerance to 8.5% (w/v) NaCl. These cold-adapted bacteria likely play a role in carbon mineralization and other nutrient cycling in Lake Fryxell, and their characterization broadens our understanding of microbial biodiversity in aquatic polar ecosystems

A Geostratigraphic Map of the Rachmaninoff Basin Area: Integrating Morphostratigraphic and Spectral Units on Mercury

Geological maps of Earth typically incorporate field observations of rock lithology, structure, composition, and more. In contrast, conventional planetary geological maps are often made using primarily qualitative morphostratigraphic remote sensing observations of planetary surfaces. However, it is possible to define independent quantitative spectral units (SUs) of planetary surfaces, which potentially contain information about surface composition, grain size, and space weathering exposure. Here, we demonstrate a generic method to combine independently derived geomorphic and SUs, using the Rachmaninoff basin, Mercury, as an example to create a new geostratigraphic map. From this geostratigraphic map, we can infer some compositional differences within geomorphic units, which clarifies and elaborates on the geological evolution of the region

Measurement of stratospheric HBr using high resolution far infrared spectroscopy

Far infrared spectral features of HBr have been observed in the stratospheric emission spectrum using a balloon borne high resolution Fourier transform spectrometer equipped with a high sensitivity detector specially designed for this purpose. The value of 1.6±0.6 parts per trillion in volume for the HBr mixing ratio has been retrieved, from the global‐fit analysis of 121 spectra, in the 25–36.5 km altitude range. The result is briefly compared with models and previous assessments

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P &gt; 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field

Stratospheric HBr concentration profile obtained from far-infrared emission spectroscopy

Hydrogen bromide (HBr) is the principal bromine sink species for the ozone loss chemistry induced by bromine‐containing gases in the stratosphere. We report a 1994 balloon‐based measurement of the daytime stratospheric HBr profile between 20 and 36.5 km altitude. The average concentration result of 1.31±0.39 parts per trillion in volume (pptv) and an analysis for the concentration versus altitude profile are consistent with previously reported measurements. These results strengthen the evidence for a significantly higher HBr concentration than that predicted by current photochemical models which, on the basis of recent kinetics results, do not include significant HBr production by the reaction branch, BrO + HO2 → HBr + O3