The COVID-19 pandemic triggered a global research effort to define and assess timely and effective containment policies. Understanding the role that specific venues play in the dynamics of epidemic spread is critical to guide the implementation of fine-grained non-pharmaceutical interventions (NPIs). In this paper, we present a new model of context-dependent interactions that integrates information about the surrounding territory and the social fabric.

Motivation Inflammation is part of the complex function that addresses harmful stimuli, and the first phase of wound healing (WH), which guarantees living systems' homeostasis. Deviances from physiology make inflammation turn acute (sepsis, 11M death/y) or chronic (non-communicable diseases, 41M death/y). Therefore, tackling inflammation is a key priority.

We propose a numerical method for approximating integro-differential equations arising in age-of-infection epidemic models. The method is based on a non-standard finite differences approximation of the integral term appearing in the equation. The study of convergence properties and the analysis of the qualitative behavior of the numerical solution show that it preserves all the basic properties of the continuous model with no restrictive conditions on the step-length h of integration and that it recovers the continuous dynamic as h tends to zero.

Chandipura vesiculovirus (CHPV) is a rapidly emerging pathogen responsible for causing acute encephalitis. Due to its widespread occurrence in Asian and African countries, this has become a global threat, and there is an urgent need to design an effective and nonallergenic vaccine against this pathogen. The present study aimed to develop a multi-epitope vaccine using an immunoinformatics approach. The conventional method of vaccine design involves large proteins or whole organism which leads to unnecessary antigenic load with increased chances of allergenic reactions.

The sparse multivariate method of simulated quantiles (S-MMSQ) is applied to solve a portfolio optimization problem under value-at-risk constraints where the joint returns follow a multivariate skew-elliptical stable distribution. The S-MMSQ is a simulation-based method that is particularly useful for making parametric inference in some pathological situations where the maximum likelihood estimator is difficult to compute.

A recently introduced approach to the classical gravitational dynamics of binary systems involves intricate integrals (linked to a combination of nonlocal-in-time interactions with iterated 1r-potential scattering) which have so far resisted attempts at their analytical evaluation.

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses.

In this article, we deal with the model selection problem for estimating a Gaussian Graphical Model (GGM) by regression based techniques. In fact, although regression based techniques are well understood and have good theoretical properties, it is still not clear which criterion is more appropriate for model selection. In this work we do a comparative study between CV and BIC, obtaining important conclusions that can be of practical interest in different contexts of data analysis.

One of the most distinctive hallmarks of many-body systems far from equilibrium is the spontaneous emergence of order under conditions where disorder would be plausibly expected. Here, we report on the self-transition between ordered and disordered emulsions in divergent microfluidic channels, i.e., from monodisperse assemblies to heterogeneous polydisperse foamlike structures, and back again to ordered ones.

Network embedding, also known as network representation learning, aims at defining low-dimensional, continuous vector representation of nodes to maximally preserve the network structure. Recent efforts attempt to extend network embedding to attributed networks where nodes are enriched with descriptors, to enhance interpretability. However, most of these efforts seldom consider the additional knowledge relevant to the aim of the downstream network analysis, i.e. task-related information. When they do, they are analysis-specific and thus lack adaptability to alternative tasks.