Investigation of Multiple Susceptibility Loci for Inflammatory Bowel Disease in an Italian Cohort of Patients

BACKGROUND: Recent GWAs and meta-analyses have outlined about 100 susceptibility genes/loci for inflammatory bowel diseases (IBD). In this study we aimed to investigate the influence of SNPs tagging the genes/loci PTGER4, TNFSF15, NKX2-3, ZNF365, IFNG, PTPN2, PSMG1, and HLA in a large pediatric- and adult-onset IBD Italian cohort. METHODS: Eight SNPs were assessed in 1,070 Crohn's disease (CD), 1,213 ulcerative colitis (UC), 557 of whom being diagnosed at the age of ≤16 years, and 789 healthy controls. Correlations with sub-phenotypes and major variants of NOD2 gene were investigated. RESULTS: The SNPs tagging the TNFSF15, NKX2-3, ZNF365, and PTPN2 genes were associated with CD (P values ranging from 0.037 to 7×10(-6)). The SNPs tagging the PTGER4, NKX2-3, ZNF365, IFNG, PSMG1, and HLA area were associated with UC (P values 0.047 to 4×10(-5)). In the pediatric cohort the associations of TNFSF15, NKX2-3 with CD, and PTGER4, NKX2-3, ZNF365, IFNG, PSMG1 with UC, were confirmed. Association with TNFSF15 and pediatric UC was also reported. A correlation with NKX2-3 and need for surgery (P  =  0.038), and with HLA and steroid-responsiveness (P  =  0.024) in UC patients was observed. Moreover, significant association in our CD cohort with TNFSF15 SNP and colonic involvement (P  =  0.021), and with ZNF365 and ileal location (P  =  0.024) was demonstrated. CONCLUSIONS: We confirmed in a large Italian cohort the associations with CD and UC of newly identified genes, both in adult and pediatric cohort of patients, with some influence on sub-phenotypes

Computing resilience of process plants under Na-Tech events: Methodology and application to sesmic loading scenarios

Resilience is a performance measure representing both the capability of a system to survive a disruptive event and the ability of rapidly restoring the operational status recovering the initial capacity. However, literature is lacking about methods allowing to compute resilience of process plants from a technical point of view. In fact, in the process industry the scarce literature about resilience mainly focused on organizational issues. In order to contribute to fill this gap a methodology has been developed to estimate resilience in case of Na-Tech events for process plants. The methodology provides a direct estimation of capacity loss after the disruptive event, and the time trend of recovery as well as the related economic loss. The model can be applied both in deterministic and probabilistic manner and is generalizable to any kind of Na-Tech hazard. However, in this paper specific reference is made to seismic hazard. In order to show the capabilities of the methodology a case study is also described referring to a Nitric Acid plant. Results show the predictive capabilities of this approach and the usefulness as a decision making tool for facility planners and emergency managers in the process industry

A probabilistic framework for the estimation of resilience of process plants under Na-Tech seismic events

Industrial plants are complex structures, highly vulnerable with respect to seismic loading. Past seismic events have demonstrated the devastating impact and huge economic losses that an industrial plant can experience not only due to physical damage of equipment, but also due to interruption of the production processes. In order to quantify these economic losses, plant seismic resilience evaluation is required. The current paper presents a probabilistic process flow-based framework for assessment of industrial plant resilience and economic losses in case of seismic events. Uncertainties are considered in the ability of plant equipment to withstand the perturbation, and also in the recovery process including equipment recovery durations and recovery costs. Monte Carlo Simulation is used to account for the uncertainties of the model. A black carbon plant is used as a case study to show the applicability of the model. Results and capability of the proposed model shows that it can be a useful tool for decision makers, plant owners, insurance companies, emergency managers and plant designers in their decision making process

Probabilistic risk analysis of process plants under seismic loading based on Monte Carlo simulations

The vulnerability of process plants to natural hazards has been demonstrated in the last decades by a number of catastrophic events. Unfortunately, despite the continuous evolution of the knowledge on this matter, there is a lack of widely accepted and standardized procedures to perform a risk assessment of process plants subjected to Na-Tech hazards. In this paper, a new tool for the probabilistic seismic risk assessment of process plants is thus proposed, based on Monte Carlo simulations. Starting from the seismic hazard curve of the site in which the plant is placed, a multi-level approach is proposed. In this approach, the first level is represented by the components seismically damaged, whereas the following levels are treated through a classical consequence analysis, including propagation of multiple simultaneous and interacting chains of accidents. This latter is applied through the definition, for all relevant equipment, of proper correspondences between structural damage (i.e., damage states) and loss of containment events. The procedure has been implemented in the software “PRIAMUS” (Probabilistic RIsk Assessment with Monte Carlo simulations of Process Plants Under Seismic Loading). By automatically generating samples of damage propagation chains, the risk of the plant can be easily quantified in terms of economic losses, content losses, damage propagations or final damage scenarios. The application to a petrochemical plant shows the potentiality of the method and envisages possible further evolutions

On the use of proper fragility models for Quantitative Seismic Risk Assessment of process plants in seismic prone areas

Quantitative Risk Assessment (QRA) is a classical method for the calculation of risk in process plants, which is based on the logic of the consequence analysis. This intrinsically probabilistic method has been thought for classical accident conditions, where the damage events and the relevant consequences start from a preselected component and a standard loss of containment (LOC) and follow all possible scenarios for the calculation of individual and societal risk. This final risk metric is usually expressed in terms of probability of fatality in a specific location of the surrounding area or a certain number of fatalities in the area surrounding the accident. In presence of Na-Tech events, like earthquakes, a multi-source condition can be caused by multi-damage conditions simultaneously involving more than one equipment, which in turn can generate a multiple-chain of events and consequences. In literature, several attempts of modifying the classic QRA approach to account for this important aspect have been formalized without converging toward a unified approach. In this paper, a fragility-based method for Quantitative Seismic Risk Analysis (QSRA) of a process plant is investigated. This method takes into account all possible damage/losses of containment conditions in the most critical equipment, e.g., storage tanks. Fragility curves, which are analytically evaluated for each unit with respect to its seismic damage conditions, are utilized inside the procedure. The Monte Carlo Simulation (MCS) method is then used with the aim to follow all steps of QSRA. In particular, starting from the seismic hazard curve of the site where the plant is placed, a multi-level approach is proposed. In this approach, the first level is represented by the components seismically damaged, whereas the following levels are treated through a classical consequence analysis, including the propagation of multiple simultaneous and interacting chains of accidents. These latter are applied by defining proper correspondences for all relevant equipment between structural damage (i.e., limit states) and LOC events. The application of the method to an actual process plant permits to investigate its high potentiality and the dependency of the risk assessment from the proper fragility models