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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]

Use of peer instruction and facilitation has surged in undergraduate education at large colleges and universities in recent years. Studies on peer instruction have been directed primarily at student learning gains and affective outcomes among the facilitators. For peer instructors, the relationship between their teaching effectiveness and their foundational content knowledge is assumed but understudied. In an effort to promote instructional coherence (i.e., instructional same-pageness) in the introductory organic chemistry program at the University of Michigan, we observed peer-led study group facilitators' involvement in their study groups (as teachers of groups of 6-12 students) and in a companion course (as learners) designed to reinforce and enhance their content knowledge. Audiovisual recordings of the facilitators in both the companion course and, for ten of them, leading their study groups, were captured over each of the two week periods covering the topics of stereochemistry and also conformational analysis. Recordings were subsequently coded for topic and correctness in presentation of subject matter. Errors made in either study group or the companion course were investigated for error resolution (corrected or uncorrected), source of error, and propagation of corrected errors. Analysis of recordings revealed that facilitators who have their own errors corrected in the companion course, or observe their peers' errors corrected in the companion course, correctly describe these concepts in study groups. On examining errors made by facilitators when they are leading study group sessions, a backwards analysis showed consistently that either the topics had not been addressed in the antecedent companion course, or the facilitator was not actively engaged with the discussion when the topics were being discussed. These findings have implications to inform not only our own implementation of peer-led study groups, but also those interested in designing subject matter companion courses for peer leaders in other instructional settings.

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]

Study assessed the incidence of bullying of students in Years 7, 9, 10, and 11. Significant reductions in victimization were reported only for Year 7 students. A significant increase in support for anti-bullying initiatives was found among students Grade 10 and 11. Anti-bullying activities directed and undertaken by students themselves received the most approval from peers. (Author/JDM)

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]