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Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. We present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest. [ABSTRACT FROM AUTHOR]

We present a macroscale constitutive model that couples magnetism with thermal, elastic, plastic, and damage effects in an Internal State Variable (ISV) theory. Previous constitutive models did not include an interdependence between the internal magnetic (magnetostriction and magnetic flux) and mechanical fields. Although constitutive models explaining the mechanisms behind mechanical deformations caused by magnetization changes have been presented in the literature, they mainly focus on nanoscale structure–property relations. A fully coupled multiphysics macroscale ISV model presented herein admits lower length scale information from the nanoscale and microscale descriptions of the multiphysics behavior, thus capturing the effects of magnetic field forces with isotropic and anisotropic magnetization terms and moments under thermomechanical deformations. For the first time, this ISV modeling framework internally coheres to the kinematic, thermodynamic, and kinetic relationships of deformation using the evolving ISV histories. For the kinematics, a multiplicative decomposition of deformation gradient is employed including a magnetization term; hence, the Jacobian represents the conservation of mass and conservation of momentum including magnetism. The first and second laws of thermodynamics are used to constrain the appropriate constitutive relations through the Clausius–Duhem inequality. The kinetic framework employs a stress–strain relationship with a flow rule that couples the thermal, mechanical, and magnetic terms. Experimental data from the literature for three different materials (iron, nickel, and cobalt) are used to compare with the model's results showing good correlations. [ABSTRACT FROM AUTHOR]

This article discusses the advancements in molecular genetic testing for Tuberous Sclerosis Complex (TSC). In the past, sequencing and deletion/duplication analysis of specific coding regions were used for testing. However, recent developments in next-generation sequencing (NGS) and bioinformatic dosage analysis have improved variant detection. The study found that deep NGS of the entire genomic TSC gene regions led to the identification of challenging variants in patients with no mutation identified (NMI) cases. This approach has proven to be more effective in detecting TSC variants compared to analyzing only the coding regions. [Extracted from the article]

Objectives: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) reduce heart failure (HF) in at-risk patients and may possess antitumour effects. We examined the effect of SGLT2i on HF and mortality among patients with cancer and diabetes.Methods: This was a retrospective propensity score-matched cohort study involving adult patients with type 2 diabetes mellitus diagnosed with cancer between January 2010 and December 2021. The primary outcomes were hospitalisation for incident HF and all-cause mortality. The secondary outcomes were serious adverse events associated with SGLT2i.Results: From a total of 8640 patients, 878 SGLT2i recipients were matched to non-recipients. During a median follow-up of 18.8 months, SGLT2i recipients had a threefold lower rate of hospitalisation for incident HF compared with non-SGLT2i recipients (2.92 vs 8.95 per 1000 patient-years, p=0.018). In Cox regression and competing regression models, SGLT2i were associated with a 72% reduction in the risk of hospitalisation for HF (HR 0.28 (95% CI: 0.11 to 0.77), p=0.013; subdistribution HR 0.32 (95% CI: 0.12 to 0.84), p=0.021). The use of SGLT2i was also associated with a higher overall survival (85.3% vs 63.0% at 2 years, p<0.001). The risk of serious adverse events such as hypoglycaemia and sepsis was similar between the two groups.Conclusions: The use of SGLT2i was associated with a lower rate of incident HF and prolonged overall survival in patients with cancer with diabetes mellitus. [ABSTRACT FROM AUTHOR]