A Fast Multifrontal Solver for Non-linear Multi-physics Problems

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Detour-impact index method and traffic gathering algorithm for assessing alternative paths of disrupted roads

Infrastructure plays a key role in society. Recent collapses of bridges have underlined their importance for road functionality, causing disruptions to commuters and emergency vehicles. Major issues arise on rural roads, where the lack of redundancy leads to the isolation of entire communities. Actual approaches to assess the resilience of countryside roads rely on the availability of specific datasets, limiting their practical application; this issue is typically related to traffic data. This research aims to propose innovative algorithms to assess the road network’s vulnerability in rural areas, including a novel traffic data collection process and its calibration. The aggregate metric is called Detour-Impact Index (DII) and compares user costs before and after a disruptive event. The method uses traditional network-impact metrics in combination with a new algorithm that allows us to gather quantitative traffic data starting from qualitative information. User travel time showed good agreement between the proposed procedure and traditional web-based methods. Furthermore, the paper provides user delay costs functions accounting for traffic composition, trip purposes, vehicle operative costs, nonlinear volume–capacity relation, and average daily traffic. A significant aspect is the adaptability of this framework, as it is designed to be coupled with existing approaches. The method is demonstrated on a case study in Tuscany (Italy).The first, third and sixth authors acknowledge that, this work was partly financed by FEDER funds through the Competitivity Factors Operational Programme - COMPETE and by national funds through FCT Foundation for Science and Technology within the scope of the project POCI-01-0145- FEDER-007633. This work was supported by the FCT Foundation for Science and Technology under Grant SFRH/ BD/145478/2019

Risk management for bridges: a case study of unforeseen failure mode

Risk management plays a crucial role in the stakeholders’ decision making because it is directly related to safety, serviceability and economy. There is now a growing concern about how to relocate known risks into an acceptance threshold: this implies the evaluation of several options obtained from hazard scenarios considering the related consequences. In parallel, practitioners usually rely on standard tools for risk assessment, and on structural codes to compute performances. Although this approach is currently widely implemented, this research shows that hazardous situations can arise in properly designed infrastructures, due to errors in management. This paper deals with such issue, also highlighting a gap in current codes that could contribute to losses caused by unforeseen failure modes. In this study, a preliminary FMEA assessment was performed to identify the failure modes that required a deeper quantitative analysis. In a second step, a quantitative analysis was implemented, using a modular methodology that combines reliability theory with a risk-based approach. The results evidenced that a wider analysis focused on the identification of vulnerable areas shall be considered in every stage of the asset management. Furthermore, the dynamic of this process is regulated by the established safety level concerning possible damages to people, production sites and commercial activities.This work was partly financed by FEDER funds through the Competitivity Factors Operational Programme - COMPETE and by national funds through FCT Foundation for Science and Technology within the scope of the project POCI01-0145-FEDER-007633. This work was supported by the FCT Foundation for Science and Technology under Grant SFRH/BD/145478/2019. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769255

Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery

Background Artificial intelligence (AI) has the potential to enhance patient safety in surgery, and all its aspects, including education and training, will derive considerable benefit from AI. In the present study, deep-learning models were used to predict the rates of proficiency acquisition in robot-assisted surgery (RAS), thereby providing surgical programs directors information on the levels of the innate ability of trainees to facilitate the implementation of flexible personalized training. Methods 176 medical students, without prior experience with surgical simulators, were trained to reach proficiency in five tasks on a virtual simulator for RAS. Ensemble deep neural networks (DNN) models were developed and compared with other ensemble AI algorithms, i.e., random forests and gradient boosted regression trees (GBRT). Results DNN models achieved a higher accuracy than random forests and GBRT in predicting time to proficiency, 0.84 vs. 0.70 and 0.77, respectively (Peg board 2), 0.83 vs. 0.79 and 0.78 (Ring walk 2), 0.81 vs 0.81 and 0.80 (Match board 1), 0.79 vs. 0.75 and 0.71 (Ring and rail 2), and 0.87 vs. 0.86 and 0.84 (Thread the rings 2). Ensemble DNN models outperformed random forests and GBRT in predicting number of attempts to proficiency, with an accuracy of 0.87 vs. 0.86 and 0.83, respectively (Peg board 2), 0.89 vs. 0.88 and 0.89 (Ring walk 2), 0.91 vs. 0.89 and 0.89 (Match board 1), 0.89 vs. 0.87 and 0.83 (Ring and rail 2), and 0.96 vs. 0.94 and 0.94 (Thread the rings 2). Conclusions Ensemble DNN models can identify at an early stage the acquisition rates of surgical technical proficiency of trainees and identify those struggling to reach the required expected proficiency level

Environmental stressors and alcoholism development: Focus on molecular targets and their epigenetic regulation

Alcohol exposure and stressful events in life can induce long-lasting changes in physiology, behavior and gene expression patterns, eventually facilitating the development of psychiatric diseases like alcohol use disorders (AUD). Epigenetic mechanisms have been recently proposed to play a role in the cellular actions of alcohol via chromatin remodeling. Here we discuss interactions between stress and the pharmacological effects of alcohol, including the possibility that early exposure to, or withdrawal of, alcohol might induce stressful effects of their own. A specific aim is to describe novel molecular mechanisms by which stress, alcohol or their combined presentation impact on the epigenome. A key question is why only a fraction of the population progresses from regular, non-problematic, alcohol use to AUD, despite suffering from similar alcohol exposure. It is important to analyze how environmental factors, most notably stress, interact with the epigenetic machinery to increase vulnerability for AUD. The knowledge derived from this endeavor will be critical for the development of preventive strategies and new, drug- or gene-based, therapies.Fil: Pucci, Mariangela. University of Teramo; ItaliaFil: Micioni Di Bonaventura, Maria Vittoria. Universita Degli Di Camerino; ItaliaFil: Wille-Bille, Aranza. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Fernandez, Macarena Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Maccarrone, Mauro. Università di Roma; ItaliaFil: Pautassi, Ricardo Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; ArgentinaFil: Cifani, Carlo. Universita Degli Di Camerino; ItaliaFil: D'Addario, Claudio. University of Teramo; Italia. Karolinska Huddinge Hospital. Karolinska Institutet; Sueci

Sustainable safety evaluation of roads network in case of extreme weather events

Recent failures in road networks highlight their vulnerability towards natural hazards, particularly to extreme weather events. This paper proposes a method to evaluate the safety of road networks in case of collapse of one or more bridges. In addition, relevant consequences in terms of safety of human life, direct and indirect cost are crucial aspects to consider. The framework described here is based on the knowledge of road and river network, of the individual bridges and of the traffic data. However, this approach can be generalized in case of interruption of road network due to other causes. An algorithm has been developed to extract traffic data from Google and elaborate it throughout a procedure based on the application of the USA Highway Capacity Manual. This consents to have a quantitative definition of the road traffic directly from the users and to get updated traffic data. The maps are processed throughout a GIS software and, thanks to the application of a routing algorithm and proper constraints, it is possible to evaluate the effects of the interruption of one or more bridges. The consequences are evaluated in terms of drivers’ delay and time cost. This provides useful information about priority of intervention with the aim of proposing to stakeholders a suitable instrument for disaster prevention and management

Protopine/Gemcitabine Combination Induces Cytotoxic or Cytoprotective Effects in Cell Type-Specific and Dose-Dependent Manner on Human Cancer and Normal Cells

The natural alkaloid protopine (PRO) exhibits pharmacological properties including anticancer activity. We investigated the effects of PRO, alone and in combination with the chemotherapeutic gemcitabine (GEM), on human tumor cell lines and non-tumor human dermal fibroblasts (HDFs). We found that treatments with different PRO/GEM combinations were cytotoxic or cytoprotective, depending on concentration and cell type. PRO/GEM decreased viability in pancreatic cancer MIA PaCa-2 and PANC-1 cells, while it rescued the GEM-induced viability decline in HDFs and in tumor MCF-7 cells. Moreover, PRO/GEM decreased G1, S and G2/M phases, concomitantly with an increase of subG1 phase in MIA PaCa-2 and PANC-1 cells. Differently, PRO/GEM restored the normal progression of the cell cycle, altered by GEM, and decreased cell death in HDFs. PRO alone increased mitochondrial reactive oxygen species (ROS) in MIA PaCa-2, PANC-1 cells and HDFs, while PRO/GEM increased both intracellular and mitochondrial ROS in the three cell lines. These results indicate that specific combinations of PRO/GEM may be used to induce cytotoxic effects in pancreatic tumor MIA PaCa-2 and PANC-1 cells, but have cytoprotective or no effects in HDFs

REGIONAL MAPPING OF MYOCARDIAL HIBERNATION PHENOTYPE IN IDIOPATHIC END-STAGE DILATED CARDIOMYOPATHY

Myocardial hibernation (MH) is a well-known feature of human ischaemic cardiomyopathy (ICM), whereas its presence in human idiopathic dilated cardiomyopathy (DCM) is still controversial. We investigated the histological and molecular features of MH in left ventricle (LV) regions of failing DCM or ICM hearts. We examined failing hearts from DCM (n = 11; 41.9 ± 5.45 years; left ventricle-ejection fraction (LV-EF), 18 ± 3.16%) and ICM patients (n = 12; 58.08 ± 1.7 years; LVEF, 21.5 ± 6.08%) undergoing cardiac transplantation, and normal donor hearts (N, n = 8). LV inter-ventricular septum (IVS) and antero-lateral free wall (FW) were transmurally (i.e. sub-epicardial, mesocardial and sub-endocardial layers) analysed. LV glycogen content was shown to be increased in both DCM and ICM as compared with N hearts (P < 0.001), with a U-shaped transmural distribution (lower values in mesocardium). Capillary density was homogenously reduced in both DCM and ICM as compared with N (P < 0.05 versus N), with a lower decrease independent of the extent of fibrosis in sub-endocardial and sub-epicardial layers of DCM as compared with ICM. HIF1-α and nestin, recognized ischaemic molecular hallmarks, were similarly expressed in DCM-LV and ICM-LV myocardium. The proteomic profile was overlapping by ~50% in DCM and ICM groups. Morphological and molecular features of MH were detected in end-stage ICM as well as in end-stage DCM LV, despite epicardial coronary artery patency and lower fibrosis in DCM hearts. Unravelling the presence of MH in the absence of coronary stenosis may be helpful to design a novel approach in the clinical management of DCM