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The paper presents an augmented curvilinear virtual element method to determine homogenized in-plane shear material moduli of long-fibre reinforced composites in the framework of asymptotic homogenization method. The new virtual element combine an exact representation of the curvilinear computational geometry for complex fibre cross section shapes through an innovative two-dimensional cubature suite for NURBS-like polygonal domains. A selection of representative numerical tests supports the accuracy and efficiency of the proposed approach for both doubly periodic and random fibre arrangement with matrix domain.

Abstract Chemical reaction networks are powerful tools for modeling cell signaling and its disruptions in diseases like cancer. Realistic chemical reaction networks involve hundreds of proteins and reactions, resulting in a model depending on a consistently large number of kinetic parameters. Since finely calibrating all the parameters would require an unrealistic amount of data, proper sensitivity analysis is required to identify a subset of parameters for which fine tuning is needed and thus provide a fundamental tool for the qualitative analysis of the network. We present a multidisciplinary approach for computing a set of synthetic sensitivity indices. These indices rank the kinetic parameters, based on the impact that errors in their values would have on the protein concentration profile at equilibrium. Our tests on a chemical reaction network devised for colorectal cells demonstrate the effectiveness of the considered sensitivity indices in different scenarios including in-silico drug dosage and novel therapeutic target discovery. The Matlab code for computing the synthetic sensitivity indices and the data concerning the network for colorectal cells are available at https://github.com/theMIDAgroup/CRN_sensitivity.

Abstract The pandemic of coronavirus disease 19 (COVID-19), caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), had severe repercussions for breast cancer patients. Increasing evidence indicates that SARS-CoV-2 infection may directly impact breast cancer biology, but the effects of SARS-CoV-2 on breast tumor cells are still unknown. Here, we analyzed the molecular events occurring in the MCF7, MDA-MB-231 and HCC1937 breast cancer cell lines, representative of the luminal A, basal B/claudin-low and basal A subtypes, respectively, upon SARS-CoV-2 infection. Viral replication was monitored over time, and gene expression profiling was conducted. We found that MCF7 cells were the most permissive to viral replication. Treatment of MCF7 cells with Tamoxifen reduced the SARS-CoV-2 replication rate, suggesting an involvement of the estrogen receptor in sustaining virus replication in malignant cells. Interestingly, a metagene signature based on genes upregulated by SARS-CoV-2 infection in all three cell lines distinguished a subgroup of premenopausal luminal A breast cancer patients with a poor prognosis. As SARS-CoV-2 still spreads among the population, it is essential to understand the impact of SARS-CoV-2 infection on breast cancer, particularly in premenopausal patients diagnosed with the luminal A subtype, and to assess the long-term impact of COVID-19 on breast cancer outcomes.

Over the last few decades, tick-borne encephalitis (TBE) has become a growing public health problem in Europe. The tick-borne encephalitis virus (TBEV) is a zoonotic virus that affects the central nervous system (CNS). TBEV has been detected in 27 European countries, and the rise in TBE cases is mainly due to environmental and ecological factors, and factors that increase the risk of human exposure to infected ticks. The infection via the alimentary route is the second most common means of TBEV transmission to humans. Raw milk from infected goats, sheep, or cows has been identified as a source of human food-borne infections. This study aims to gather new information on the prevalence of tick-borne encephalitis virus (TBEV) in raw goat’s and cow’s milk and related raw products in the Lombard Alps (Italy). This is important due to the close proximity of Lombardy to the Triveneto region, where TBE is endemic, and southern Switzerland, where numerous TBEV-positive mammals have been found. Throughout 2023, a passive monitoring plan was implemented on samples delivered for TBEV analyses from the Alpine pastures. In total, 248 specimens including raw milk, raw milk cheese, and butter were tested. This is the first monitoring of food at risk of TBEV transmission in a non-endemic region with evidence of TBEV circulation. Despite testing a wide range of dairy products, no sample tested positive for RNA-TBEV by real-time RT-PCR. Preliminary results suggest that raw milk and raw dairy products do not pose a significant risk of TBEV transmission to humans in the territory of Lombardy.

Source localization from M/EEG data is a fundamental step in many analysis pipelines, including those aiming at clinical applications such as the pre-surgical evaluation in epilepsy. Among the many available source localization algorithms, SESAME (SEquential SemiAnalytic Montecarlo Estimator) is a Bayesian method that distinguishes itself for several good reasons: it is highly accurate in localizing focal sources with comparably little sensitivity to input parameters; it allows the quantification of the uncertainty of the reconstructed source(s); it accepts user-defined a priori high- and low-probability search regions in input; it can localize the generators of neural oscillations in the frequency domain. Both a Python and a MATLAB implementation of SESAME are available as open-source packages under the name of SESAMEEG and are well integrated with the main software packages used by the M/EEG community; moreover, the algorithm is part of the commercial software BESA Research (from version 7.0 onwards). While SESAMEEG is arguably simpler to use than other source modeling methods, it has a much richer output that deserves to be described thoroughly. In this article, after a gentle mathematical introduction to the algorithm, we provide a complete description of the available output and show several use cases on experimental M/EEG data.

OH, HO2, total and partially speciated RO2, and OH reactivity (kOH′) were measured during the July 2015 ICOZA (Integrated Chemistry of OZone in the Atmosphere) project that took place at a coastal site in north Norfolk, UK. Maximum measured daily OH, HO2 and total RO2 radical concentrations were in the range 2.6–17 × 106, 0.75–4.2 × 108 and 2.3–8.0 × 108 molec. cm−3, respectively. kOH′ ranged from 1.7 to 17.6 s−1, with a median value of 4.7 s−1. ICOZA data were split by wind direction to assess differences in the radical chemistry between air that had passed over the North Sea (NW–SE sectors) and that over major urban conurbations such as London (SW sector). A box model using the Master Chemical Mechanism (MCMv3.3.1) was in reasonable agreement with the OH measurements, but it overpredicted HO2 observations in NW–SE air in the afternoon by a factor of ∼ 2–3, although slightly better agreement was found for HO2 in SW air (factor of ∼ 1.4–2.0 underprediction). The box model severely underpredicted total RO2 observations in both NW–SE and SW air by factors of ∼ 8–9 on average. Measured radical and kOH′ levels and measurement–model ratios displayed strong dependences on NO mixing ratios, with the results suggesting that peroxy radical chemistry is not well understood under high-NOx conditions. The simultaneous measurement of OH, HO2, total RO2 and kOH′ was used to derive experimental (i.e. observationally determined) budgets for all radical species as well as total ROx (i.e. OH + HO2 + RO2). In NW–SE air, the ROx budget could be closed during the daytime within experimental uncertainty, but the rate of OH destruction exceeded the rate of OH production, and the rate of HO2 production greatly exceeded the rate of HO2 destruction, while the opposite was true for RO2. In SW air, the ROx budget analysis indicated missing daytime ROx sources, but the OH budget was balanced, and the same imbalances were found with the HO2 and RO2 budgets as in NW–SE air. For HO2 and RO2, the budget imbalances were most severe at high-NO mixing ratios, and the best agreement between HO2 and RO2 rates of production and destruction rates was found when the RO2 + NO rate coefficient was reduced by a factor of 5. A photostationary-steady-state (PSS) calculation underpredicted daytime OH in NW–SE air by ∼ 35 %, whereas agreement (∼ 15 %) was found within instrumental uncertainty (∼ 26 % at 2σ) in SW air. The rate of in situ ozone production (P(Ox)) was calculated from observations of ROx, NO and NO2 and compared to that calculated from MCM-modelled radical concentrations. The MCM-calculated P(Ox) significantly underpredicted the measurement-calculated P(Ox) in the morning, and the degree of underprediction was found to scale with NO.