Hearts of Dystonia musculorum Mice Display Normal Morphological and Histological Features but Show Signs of Cardiac Stress

Dystonin is a giant cytoskeletal protein belonging to the plakin protein family and is believed to crosslink the major filament systems in contractile cells. Previous work has demonstrated skeletal muscle defects in dystonin-deficient dystonia musculorum (dt) mice. In this study, we show that the dystonin muscle isoform is localized at the Z-disc, the H zone, the sarcolemma and intercalated discs in cardiac tissue. Based on this localization pattern, we tested whether dystonin-deficiency leads to structural defects in cardiac muscle. Desmin intermediate filament, microfilament, and microtubule subcellular organization appeared normal in dt hearts. Nevertheless, increased transcript levels of atrial natriuretic factor (ANF, 66%) β-myosin heavy chain (beta-MHC, 95%) and decreased levels of sarcoplasmic reticulum calcium pump isoform 2A (SERCA2a, 26%), all signs of cardiac muscle stress, were noted in dt hearts. Hearts from two-week old dt mice were assessed for the presence of morphological and histological alterations. Heart to body weight ratios as well as left ventricular wall thickness and left chamber volume measurements were similar between dt and wild-type control mice. Hearts from dt mice also displayed no signs of fibrosis or calcification. Taken together, our data provide new insights into the intricate structure of the sarcomere by situating dystonin in cardiac muscle fibers and suggest that dystonin does not significantly influence the structural organization of cardiac muscle fibers during early postnatal development

Simpson's aggregation paradox in nonparametric statistical analysis: Theory, computation, and susceptibility in public health data

This study establishes sufficient conditions for observing instances of Simpson's (data aggregation) Paradox under rank sum scoring (RSS), as used, e.g., in the Wilcoxon-Mann-Whitney (WMW) rank sum test. The WMW test is a primary nonparametric statistical test in FDA drug product evaluation and other prominent medical settings. Using computational nonparametric statistical methods, we also establish the relative frequency with which paradox-generating Simpson Reversals occur under RSS when an initial data sequence is pooled with its ordinal replicate. For each 2-sample, n-element per sample or 2 x n case of RSS considered, strict Reversals occurred for between 0% and 1.74% of data poolings across the whole sample space, roughly similar to that observed for 2 x 2 x 2 contingency tables and considerably less than that observed for path models. The Reversal rate conditional on observed initial sequence is highly variable. Despite a mode at 0%, this rate exceeds 20% for some initial sequences. Our empirical application identifies clusters of Simpson Reversal susceptibility for publicly-released mobile phone radiofrequency exposure data. Simpson Reversals under RSS are not simply a theoretical concern but can reverse nonparametric or parametric biostatistical results even in vitally important public health settings. Conceptually, Paradox incidence can be viewed as a robustness check on a given WMW statistical test result. When an instance of Paradox occurs, results constituting this instance are found to be data-scale dependent. Given that the rate of Reversal can vary substantially by initial sequence, the practice of calculating this rate conditional on observed initial sequence represents a potentially important robustness check upon a result

An Open Source Simulation Model for Soil and Sediment Bioturbation

Bioturbation is one of the most widespread forms of ecological engineering and has significant implications for the structure and functioning of ecosystems, yet our understanding of the processes involved in biotic mixing remains incomplete. One reason is that, despite their value and utility, most mathematical models currently applied to bioturbation data tend to neglect aspects of the natural complexity of bioturbation in favour of mathematical simplicity. At the same time, the abstract nature of these approaches limits the application of such models to a limited range of users. Here, we contend that a movement towards process-based modelling can improve both the representation of the mechanistic basis of bioturbation and the intuitiveness of modelling approaches. In support of this initiative, we present an open source modelling framework that explicitly simulates particle displacement and a worked example to facilitate application and further development. The framework combines the advantages of rule-based lattice models with the application of parameterisable probability density functions to generate mixing on the lattice. Model parameters can be fitted by experimental data and describe particle displacement at the spatial and temporal scales at which bioturbation data is routinely collected. By using the same model structure across species, but generating species-specific parameters, a generic understanding of species-specific bioturbation behaviour can be achieved. An application to a case study and comparison with a commonly used model attest the predictive power of the approach

Characterizing Long COVID: Deep Phenotype of a Complex Condition.

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or long COVID ), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FINDINGS: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements