Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures

The reliability of critical infrastructures, such as power distribution networks, is of key importance for modern societies. The reliability of such complex systems can, in principle, be assessed by Monte Carlo simulation. However, the size and complexity of these systems, and the rarity of the failure events, can make the calculations quite demanding. Survival signature can help to address this issue, as it allows modelling the structure of the system separately from the probabilistic modelling for the reliability assessment. However, the survival signature calculation of complex, multi-component systems for their reliability assessment suffers from the curse of dimensionality, and both analytical calculation and Monte Carlo Simulation (MCS) are not feasible in practice. Then, in this work, we propose a novel approach to approximate the survival signature of a system, which stands on the use of entropy to drive the sampling by MCS towards non-trivial system structure configurations, so as to save computational cost. The approach is exemplified by calculating the reliability of a generic synthetic multi-component network and the feasibility of its application is shown on a real-world network

Ensemble of Artificial Neural Networks for Approximating the Survival Signature of Critical Infrastructures

International audienceSurvival signature can be useful for the reliability assessment of critical infrastructures. However, analytical calculation and Monte Carlo Simulation (MCS) are not feasible for approximating the survival signature of large infrastructures, because of the complexity and computational demand due to the large number of components. In this case, efficient and accurate approximations are sought. In this paper we formulate the survival signature approximation problem as a missing data problem. An ensemble of artificial neural networks (ANNs) is trained on a set of survival signatures obtained by MCS. The ensemble of trained ANNs is, then, used to retrieve the missing values of the survival signature. A numerical example is worked out and recommendations are given to design the ensemble of ANNs for large-scale, real-world infrastructures. The electricity grid of Great Britain, the New England power grid (IEEE 39-Bus Case), the reduced Berlin metro system and the approximated American Power System (IEEE 118-Bus Case) are, then, eventually, analyzed as particular case studies

New Insights into the Phenotype Switching of Melanoma

Melanoma is considered one of the deadliest skin cancers, partly because of acquired resistance to standard therapies. The most recognized driver of resistance relies on acquired melanoma cell plasticity, or the ability to dynamically switch among differentiation phenotypes. This confers the tumor noticeable advantages. During the last year, two new features have been included in the hallmarks of cancer, namely “Unlocking phenotypic plasticity” and “Non-mutational epigenetic reprogramming”. Such are inextricably intertwined as, most of the time, plasticity is not discernable at the genetic level, as it rather consists of epigenetic reprogramming heavily influenced by external factors. By analyzing current literature, this review provides reasoning about the origin of plasticity and clarifies whether such features already exist among tumors or are acquired by selection. Moreover, markers of plasticity, molecular effectors, and related tumor advantages in melanoma will be explored. Ultimately, as this new branch of tumor biology opened a wide landscape of therapeutic possibilities, in the final paragraph of this review, we will focus on newly characterized drugs targeting melanoma plasticity

<i>In vitro</i> and <i>in planta</i> antagonistic effects of plant growth-promoting rhizobacteria consortium against soilborne plant pathogens of <i>Solanum tuberosum</i> and <i>Solanum lycopersicum</i>

ABSTRACT Potatoes (Solanum tuberosum L.) and tomatoes (Solanum lycopersicum L.), among the main crops belonging to the Solanaceae family, are attacked by several pathogens. Among them Fusarium oxysporum f. sp. radicis-lycopersici and Rhizoctonia solani are very common and cause significant losses. Four plant growth-promoting rhizobacteria, Azospirillum brasilense, Gluconacetobacter diazotrophicus, Herbaspirillum seropedicae and Burkholderia ambifaria were tested against these pathogens. In vitro antagonistic activities of single strains were assessed through dual culture plates. Strains showing antagonistic activity (G. diazotrophicus, H. seropedicae and B. ambifaria) were combined and, after an in vitro confirmation, the consortium was applied on S. lycopersicum and S. tuberosum in a greenhouse pot experiment. The bioprotection was assessed in pre-emergence (infection before germination) and post-emergence (infection after germination). The consortium was able to successfully counteract the infection of both F. oxysporum and R. solani, allowing a regular development of plants. The biocontrol of the fungal pathogens was highlighted both in pre-emergence and post-emergence conditions. This selected consortium could be a valid alternative to agrochemicals and could be exploited as biocontrol agent to counteract losses due to these pathogenic fungi.</jats:p

Comparison of CFD numerical approaches for the simulation of accidental gas release in energy applications

Oil & Gas plants are risk-relevant complex facilities for the presence of toxic, flammable and pressurized fluids. Risk assessment is mandatory to guarantee plant sustainability and compliance with directives. For offshore plants characterized by congested spaces, semi-empirical models for accident consequence simulation often result in risk overestimation. This could be avoided through Computational Fluid Dynamics (CFD), which guarantees more accurate results. Complex phenomena and geometries, however, entail large computational efforts that force limiting the number of simulations to explore the accident scenarios. This calls for new approaches able to model and simulate complex congested geometries in affordable time, while achieving keeping the required accuracy of the results. In this context, a novel CFD model based on ANSYS Fluent, named SBAM (Source Box Accident Model), has been proposed by the research group of the SEADOG lab in Politecnico di Torino with the aim of simulating complex environments with good accuracy and reduced computational cost. In this work, the results provided by the SBAM model on an accidental high pressure flammable gas release in a platform, are compared with those provided by other tools and models available in the market, and widely used in industrial applications, such as FLACS developed by Gexcon US and KFX developed by DNV-GL

Quantification of Uncertainty in CFD Simulation of Accidental Gas Release for O & G Quantitative Risk Assessment

International audienceQuantitative Risk Assessment (QRA) of Oil & Gas installations implies modeling accidents’ evolution. Computational Fluid Dynamics (CFD) is one way to do this, and off-the-shelf tools are available, such as FLACS developed by Gexcon US and KFX developed by DNV-GL. A recent model based on ANSYS Fluent, named SBAM (Source Box Accident Model) was proposed by the SEADOG lab at Politecnico di Torino. In this work, we address one major concern related to the use of CFD tools for accident simulation, which is the relevant computational demand that limits the number of simulations that can be performed. This brings with it the challenge of quantifying the uncertainty of the results obtained, which requires performing a large number of simulations. Here we propose a procedure for the Uncertainty Quantification (UQ) of FLACX, KFX and SBAM, and show its performance considering an accidental high-pressure methane release scenario in a realistic offshore Oil & Gas (O & G) platform deck. The novelty of the work is that the UQ of the CFD models, which is performed relying on well-consolidated approaches such as the Grid Convergence Index (GCI) method and a generalization of Richardson’s extrapolation, is originally propagated to a set of risk measures that can be used to support the decision-making process to prevent/mitigate accidental scenarios

Quantification of Uncertainty in CFD Simulation of Accidental Gas Release for O &amp; G Quantitative Risk Assessment

Quantitative Risk Assessment (QRA) of Oil &amp; Gas installations implies modeling accidents’ evolution. Computational Fluid Dynamics (CFD) is one way to do this, and off-the-shelf tools are available, such as FLACS developed by Gexcon US and KFX developed by DNV-GL. A recent model based on ANSYS Fluent, named SBAM (Source Box Accident Model) was proposed by the SEADOG lab at Politecnico di Torino. In this work, we address one major concern related to the use of CFD tools for accident simulation, which is the relevant computational demand that limits the number of simulations that can be performed. This brings with it the challenge of quantifying the uncertainty of the results obtained, which requires performing a large number of simulations. Here we propose a procedure for the Uncertainty Quantification (UQ) of FLACX, KFX and SBAM, and show its performance considering an accidental high-pressure methane release scenario in a realistic offshore Oil &amp; Gas (O &amp; G) platform deck. The novelty of the work is that the UQ of the CFD models, which is performed relying on well-consolidated approaches such as the Grid Convergence Index (GCI) method and a generalization of Richardson’s extrapolation, is originally propagated to a set of risk measures that can be used to support the decision-making process to prevent/mitigate accidental scenarios.</jats:p

AMBRA1 levels predict resistance to MAPK inhibitors in melanoma.

peer reviewedIntrinsic and acquired resistance to mitogen-activated protein kinase inhibitors (MAPKi) in melanoma remains a major therapeutic challenge. Here, we show that the clinical development of resistance to MAPKi is associated with reduced tumor expression of the melanoma suppressor Autophagy and Beclin 1 Regulator 1 (AMBRA1) and that lower expression levels of AMBRA1 predict a poor response to MAPKi treatment. Functional analyses show that loss of AMBRA1 induces phenotype switching and orchestrates an extracellular signal-regulated kinase (ERK)-independent resistance mechanism by activating focal adhesion kinase 1 (FAK1). In both in vitro and in vivo settings, melanomas with low AMBRA1 expression exhibit intrinsic resistance to MAPKi therapy but higher sensitivity to FAK1 inhibition. Finally, we show that the rapid development of resistance in initially MAPKi-sensitive melanomas can be attributed to preexisting subclones characterized by low AMBRA1 expression and that cotreatment with MAPKi and FAK1 inhibitors (FAKi) effectively prevents the development of resistance in these tumors. In summary, our findings underscore the value of AMBRA1 expression for predicting melanoma response to MAPKi and supporting the therapeutic efficacy of FAKi to overcome MAPKi-induced resistance

GSNOR deficiency promotes tumor growth via FAK1 S-nitrosylation

Summary: Nitric oxide (NO) production in the tumor microenvironment is a common element in cancer. S-nitrosylation, the post-translational modification of cysteines by NO, is emerging as a key transduction mechanism sustaining tumorigenesis. However, most oncoproteins that are regulated by S-nitrosylation are still unknown. Here we show that S-nitrosoglutathione reductase (GSNOR), the enzyme that deactivates S-nitrosylation, is hypo-expressed in several human malignancies. Using multiple tumor models, we demonstrate that GSNOR deficiency induces S-nitrosylation of focal adhesion kinase 1 (FAK1) at C658. This event enhances FAK1 autophosphorylation and sustains tumorigenicity by providing cancer cells with the ability to survive in suspension (evade anoikis). In line with these results, GSNOR-deficient tumor models are highly susceptible to treatment with FAK1 inhibitors. Altogether, our findings advance our understanding of the oncogenic role of S-nitrosylation, define GSNOR as a tumor suppressor, and point to GSNOR hypo-expression as a therapeutically exploitable vulnerability in cancer