Journalistic Media and Fair Trial

The rights of an accused to a fair trial, and freedom of the press, both are fundamental rights guaranteed by the Federal Constitution. Yet these constitutional guarantees, in collision, present one of the most critical current conflicts in the administration of criminal justice. The problem involves what is presently called prejudicial news reporting -news which is prejudicial to the right of the defendant to a fair trial. This paper will try to analyze this conflict, hoping to reach some conclusions which will ultimately aid in the administration of justice without abridging the rights of any parties involved

Optimal control of epidemic spreading in presence of social heterogeneity

The spread of COVID-19 has been thwarted in most countries through non-pharmaceutical interventions. In particular, the most effective measures in this direction have been the stay-at-home and closure strategies of businesses and schools. However, population-wide lockdowns are far from being optimal carrying heavy economic consequences. Therefore, there is nowadays a strong interest in designing more efficient restrictions. In this work, starting from a recent kinetic-type model which takes into account the heterogeneity described by the social contact of individuals, we analyze the effects of introducing an optimal control strategy into the system, to limit selectively the mean number of contacts and reduce consequently the number of infected cases. Thanks to a data-driven approach, we show that this new mathematical model permits to assess the effects of the social limitations. Finally, using the model introduced here and starting from the available data, we show the effectivity of the proposed selective measures to dampen the epidemic trends

Kinetic models for epidemic dynamics with social heterogeneity

We introduce a mathematical description of the impact of sociality in the spread of infectious diseases by integrating an epidemiological dynamics with a kinetic modeling of population-based contacts. The kinetic description leads to study the evolution over time of Boltzmann-type equations describing the number densities of social contacts of susceptible, infected and recovered individuals, whose proportions are driven by a classical SIR-type compartmental model in epidemiology. Explicit calculations show that the spread of the disease is closely related to moments of the contact distribution. Furthermore, the kinetic model allows to clarify how a selective control can be assumed to achieve a minimal lockdown strategy by only reducing individuals undergoing a very large number of daily contacts. We conduct numerical simulations which confirm the ability of the model to describe different phenomena characteristic of the rapid spread of an epidemic. Motivated by the COVID-19 pandemic, a last part is dedicated to fit numerical solutions of the proposed model with infection data coming from different European countries

Asymptotically complexity diminishing schemes (ACDS) for kinetic equations in the diffusive scaling

In this work, we develop a new class of numerical schemes for collisional kinetic equations in the diffusive regime. The first step consists in reformulating the problem by decomposing the solution in the time evolution of an equilibrium state plus a perturbation. Then, the scheme combines a Monte Carlo solver for the perturbation with an Eulerian method for the equilibrium part, and is designed in such a way to be uniformly stable with respect to the diffusive scaling and to be consistent with the asymptotic diffusion equation. Moreover, since particles are only used to describe the perturbation part of the solution, the scheme becomes computationally less expensive – and is thus an asymptotically complexity diminishing scheme (ACDS) – as the solution approaches the equilibrium state due to the fact that the number of particles diminishes accordingly. This contrasts with standard methods for kinetic equations where the computational cost increases (or at least does not decrease) with the number of interactions. At the same time, the statistical error due to the Monte Carlo part of the solution decreases as the system approaches the equilibrium state: the method automatically degenerates to a solution of the macroscopic diffusion equation in the limit of infinite number of interactions. After a detailed description of the method, we perform several numerical tests and compare this new approach with classical numerical methods on various problems up to the full three dimensional case

A convenient approach to characterizing model uncertainty with application to early dark energy solutions of the Hubble tension

Despite increasingly precise observations and sophisticated theoretical models, the discrepancy between measurements of H0 from the cosmic microwave background or from Baryon Acoustic Oscillations combined with Big-Bang Nucleosynthesis versus those from local distance ladder probes -- commonly known as the $H_0$ tension -- continues to perplex the scientific community. To address this tension, Early Dark Energy (EDE) models have been proposed as alternatives to $\Lambda$CDM, as they can change the observed sound horizon and the inferred Hubble constant from measurements based on this. In this paper, we investigate the use of Bayesian Model Averaging (BMA) to evaluate EDE as a solution to the H0 tension. BMA consists of assigning a prior to the model and deriving a posterior as for any other unknown parameter in a Bayesian analysis. BMA can be computationally challenging in that one must approximate the joint posterior of both model and parameters. Here we present a computational strategy for BMA that exploits existing MCMC software and combines model-specific posteriors post-hoc. In application to a comprehensive analysis of cosmological datasets, we quantify the impact of EDE on the H0 discrepancy. We find an EDE model probability of $\sim$90% whenever we include the H0 measurement from Type Ia Supernovae in the analysis, whereas the other data show a strong preference for the standard cosmological model. We finally present constraints on common parameters marginalized over both cosmological models. For reasonable priors on models with and without EDE, the H0 tension is reduced by at least 20%

A Multilevel Monte Carlo Asymptotic-Preserving Particle Method for Kinetic Equations in the Diffusion Limit

We propose a multilevel Monte Carlo method for a particle-based asymptotic-preserving scheme for kinetic equations. Kinetic equations model transport and collision of particles in a position-velocity phase-space. With a diffusive scaling, the kinetic equation converges to an advection-diffusion equation in the limit of zero mean free path. Classical particle-based techniques suffer from a strict time-step restriction to maintain stability in this limit. Asymptotic-preserving schemes provide a solution to this time step restriction, but introduce a first-order error in the time step size. We demonstrate how the multilevel Monte Carlo method can be used as a bias reduction technique to perform accurate simulations in the diffusive regime, while leveraging the reduced simulation cost given by the asymptotic-preserving scheme. We describe how to achieve the necessary correlation between simulation paths at different levels and demonstrate the potential of the approach via numerical experiments.Comment: 20 pages, 7 figures, published in Monte Carlo and Quasi-Monte Carlo Methods 2018, correction of minor typographical error

Are tumor cell lineages solely shaped by mechanical forces?

This paper investigates cell proliferation dynamics in small tumor cell aggregates using an individual-based model (IBM). The simulation model is designed to study the morphology of the cell population and of the cell lineages as well as the impact of the orientation of the division plane on this morphology. Our IBM model is based on the hypothesis that cells are incompressible objects that grow in size and divide once a threshold size is reached, and that newly born cell adhere to the existing cell cluster. We performed comparisons between the simulation model and experimental data by using several statistical indicators. The results suggest that the emergence of particular morphologies can be explained by simple mechanical interactions

Influence of Menopausal Status on Lipids and Lipoproteins and Fat Mass Distribution: The Pioneer Project

Following menopause, fat redistribution and increased risk for dyslipidemia are common. The influence of menopause; however, on the associations between total and regional fat mass with lipids and lipoproteins remains unclear. PURPOSE: The purpose of this investigation was to determine the influence of menopausal status on associations between total and regional fat mass and lipids and lipoproteins. METHODS: Sedentary, non-smoking women (n=209) were grouped based on current menstrual status: premenopausal (n=143, mean±SD; age=42.7±7.7 yr, BMI=24.5±4.0 kg•m -2, WC=77.4±9.9 cm) or postmenopausal (n=66, mean±SD; age=52.9±5.3 yr, BMI= 24.9±4.2 kg•m -2, WC=78.8±9.9 cm). Fasting (12 hr) serum samples were analyzed for total cholesterol (TC), triglyceride (Tg), LDL-C, HDL-C, HDL2-C, and HDL3-C concentrations. Total (TF), abdominal (AF), hip (HF) and mid-thigh (MTF) fat mass were quantified by DXA. A MANCOVA was used to determine differences between groups for total and regional fat mass and lipids and lipoproteins controlling for HRT status. Stepwise multiple regression analysis was used to determine if menopausal status influenced the association of total and regional fat mass with lipids and lipoproteins. The criterion reference for statistical significance was set at a P \u3c 0.05. RESULTS: Postmenopausal women had significantly greater TC, HDL-C, LDL-C and HDL3-C concentrations than premenopausal women. No significant differences were observed between groups for total and regional fat mass. In premenopausal women, AF predicted TC, but no associations were observed in postmenopausal women. In premenopausal women, AF+HF and AF+TF were significant predictors of Tg and LDL-C, respectively. In contrast, only AF predicted Tg and LDL-C in postmenopausal women. AF+MTF best predicted HDL-C in premenopausal women; however, TF+MTF best predicted HDL-C in postmenopausal women. In premenopausal women, no associations were observed with HDL2-C or HDL3-C. TF and TF+MTF were best predictors of HDL2-C and HDL3-C, respectively in postmenopausal women. CONCLUSION: Menopausal status has an effect on lipid and lipoprotein-cholesterol concentrations, but not on total and regional fat mass. In addition, menopausal status had an influence on the associations of total and regional fat mass with lipids and lipoproteins

Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement

Featured Application The use of highly robust radiomic features is fundamental to reduce intrinsic dependencies and to provide reliable predictive models. This work presents a study on breast tumor DCE-MRI considering the radiomic feature robustness against the quantization settings and segmentation methods. Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, there is a lack of comprehensive guidelines. Therefore, no standardized procedures are available for ROI selection, feature extraction, and processing, with the risk of undermining the effective use of radiomic models in clinical routine. In this study, we aim to assess the impact that the different segmentation methods and the quantization level (defined by means of the number of bins used in the feature-extraction phase) may have on the robustness of the radiomic features. In particular, the robustness of texture features extracted by PyRadiomics, and belonging to five categories-GLCM, GLRLM, GLSZM, GLDM, and NGTDM-was evaluated using the intra-class correlation coefficient (ICC) and mean differences between segmentation raters. In addition to the robustness of each single feature, an overall index for each feature category was quantified. The analysis showed that the level of quantization (i.e., the 'bincount' parameter) plays a key role in defining robust features: in fact, in our study focused on a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset of 111 breast masses, sets with cardinality varying between 34 and 43 robust features were obtained with 'binCount' values equal to 256 and 32, respectively. Moreover, both manual segmentation methods demonstrated good reliability and agreement, while automated segmentation achieved lower ICC values. Considering the dependence on the quantization level, taking into account only the intersection subset among all the values of 'binCount' could be the best selection strategy. Among radiomic feature categories, GLCM, GLRLM, and GLDM showed the best overall robustness with varying segmentation methods