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Type A uncertainty evaluation can significantly benefit from incorporating prior knowledge about the precision of an employed measurement device, which allows for reliable uncertainty assessments with limited observations. The Bayesian framework, employing Bayes theorem and Markov Chain Monte Carlo (MCMC), is recommended to incorporate such prior knowledge in a statistically rigorous way. While MCMC is recommended, metrologists are usually well-familiar with plain Monte Carlo sampling and previous work demonstrated the integration of similar prior knowledge into an uncertainty evaluation framework following the plain Monte Carlo sampling of JCGM 101–the Supplement 1 to the GUM. In this work, we explore the potential and limitations of such an approach, presenting classes of data distributions for informative Type A uncertainty evaluations. Our work justifies an informative extension of the JCGM 101 Type A uncertainty evaluation from a statistical perspective, providing theoretical insight and practical guidance. Explicit distributions are proposed for input quantities in Type A scenarios, aligning with Bayesian uncertainty evaluations. In addition, inherent limitations of the JCGM 101 Monte Carlo approach are discussed concerning general Bayesian inference. Metrological examples support the theoretical findings, significantly expanding the applicability of the JCGM 101 Monte Carlo technique from a Bayesian perspective.

Determination of infarct and scar size following myocardial infarction (MI) is commonly used to evaluate the efficacy of potential cardioprotective treatments in animal models. However, histological methods to determine morphological features in the infarcted heart have barely improved since implementation while still consuming large parts of the tissue and offering little options for parallel analyses. We aim to develop a new fluorescence technology for determining infarct area and area at risk that is comparable to 2,3,5-triphenyltetrazolium chloride (TTC) staining but allows for multiple analyses on the same heart tissue. For early and late time points following MI, we compared classical histochemical approaches with fluorescence staining methods. Reperfused MI was induced in male mice, the hearts were extracted 24 h, 7-, 21-, or 28-days later and fluorescently stained by combining Hoechst and phalloidin. This approach allowed for clear visualization of the infarct area, the area at ischemic risk and the remote area not affected by MI. The combined fluorescence staining correlated with the classic TTC/Evans Blue staining 24 h after MI (r = 0.8334). In later phases (>7 d) post-MI, wheat germ agglutinin (WGA) is equally accurate as classical Sirius Red (r = 0.9752), Masson's (r = 0.9920) and Gomori's Trichrome (r = 0.8082) staining for determination of scar size. Additionally, feasibility to co-localize fluorescence-stained immune cells in specific regions of the infarcted myocardium was demonstrated with this protocol. In conclusion, this new procedure for determination of post-MI infarct size is not inferior to classical TTC staining, yet provides substantial benefits, including the option for unbiased software-assisted analysis while sparing ample residual tissue for additional analyses. Overall, this enhances the data quality and reduces the required animal numbers consistent with the 3R concept of animal experimentation.

The tilted-wave interferometer is a promising technique for the development of a reference measurement system for the highly accurate form measurement of aspheres and freeform surfaces. The technique combines interferometric measurements, acquired with a special setup, and sophisticated mathematical evaluation procedures. To determine the form of the surface under test, a computational model is required that closely mimics the measurement process of the physical measurement instruments. The parameters of the computational model, comprising the surface under test sought, are then tuned by solving an inverse problem. Due to this embedded structure of the real experiment and computational model and the overall complexity, a thorough uncertainty evaluation is challenging. In this work, a Bayesian approach is proposed to tackle the inverse problem, based on a statistical model derived from the computational model of the tilted-wave interferometer. Such a procedure naturally allows for uncertainty quantification to be made. We present an approximate inference scheme to efficiently sample quantities of the posterior using Monte Carlo sampling involving the statistical model. In particular, the methodology derived is applied to the tilted-wave interferometer to obtain an estimate and corresponding uncertainty of the pixel-by-pixel form of the surface under test for two typical surfaces taking into account a number of key influencing factors. A statistical analysis using experimental design is employed to identify main influencing factors and a subsequent analysis confirms the efficacy of the method derived.

Background: An abdominal aortic aneurysm (AAA) is a life-threatening cardiovascular disease. Although its pathogenesis is still poorly understood, recent evidence suggests that AAA displays autoimmune disease characteristics. Particularly, T cells responding to AAA-related antigens in the aortic wall may contribute to an initial immune response. Single-cell RNA (scRNA) T cell receptor (TCR) and B cell receptor (BCR) sequencing is a powerful tool for investigating clonality. However, difficulties such as limited numbers of isolated cells must be considered during implementation and data analysis, making biological interpretation challenging. Here, we perform a representative single-cell immune repertoire analysis in experimental murine AAA and show a reliable bioinformatic processing pipeline highlighting opportunities and limitations of this approach., Methods: We performed scRNA TCR and BCR sequencing of isolated lymphocytes from the infrarenal aorta of male C57BL/6J mice 3, 7, 14, and 28 days after AAA induction via elastase perfusion of the aorta. Sham-operated mice at days 3 and 28 and non-operated mice served as controls., Results: Comparison of complementarity-determining region (CDR3) length distribution of 179 B cells and 796 T cells revealed neither differences between AAA and control nor between the disease stages. We found no clonal expansion of B cells in AAA. For T cells, we identified several clones in 11 of 16 AAA samples and one of eight control samples. Immune receptor repertoire comparison indicated that only a few clones were shared between the individual AAA samples. The most frequently used V-genes in the TCR beta chain in AAA were TRBV3, TRBV19, and the splicing variant TRBV12-2 + TRBV13-2., Conclusion: We found no clonal expansion of B cells but evidence for clonal expansion of T cells in elastase-induced AAA in mice. Our findings imply that a more precise characterization of TCR and BCR distribution requires a more extensive number of lymphocytes to prevent undersampling and potentially detect rare clones. Thus, further experiments are necessary to confirm our findings. In summary, this paper examines TCR and BCR sequencing results, identifies limitations and pitfalls, and offers guidance for future studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Elster, Ommer-Bläsius, Lang, Vajen, Pfeiler, Feige, Yau Pang, Böttenberg, Verheyen, Lê Quý, Chernigovskaya, Kelm, Winkels, Schmidt, Greiff and Gerdes.)