GIS-assisted modelling for debris flow hazard assessment based on the events of May 1998 in the area of Sarno, Southern Italy. II: Velocity and Dynamic Pressure

The velocity and dynamic pressure of debris flows are critical determinants of the impact of these natural phenomena on infrastructure. Therefore, the prediction of these parameters is critical for hazard assessment and vulnerability analysis. We present here an approach to predict the velocity of debris flows on the basis of the energy line concept. First, we obtained empirically and field-based estimates of debris flow peak discharge, mean velocity at peak discharge and velocity, at channel bends and within the fans of ten of the debris flow events that occurred in May 1998 in the area of Sarno, Southern Italy. We used this data to calibrate regression models that enable the prediction of velocity as a function of the vertical distance between the energy line and the surface. Despite the complexity in morphology and behaviour of these flows, the statistical fits were good and the debris flow velocities can be predicted with an associated uncertainty of less than 30% and less than 3 m s-1. We wrote code in Visual Basic for Applications (VBA) that runs within ArcGIS® to implement the results of these calibrations and enable the automatic production of velocity and dynamic pressure maps. The collected data and resulting empirical models constitute a realistic basis for more complex numerical modelling. In addition, the GIS implementation constitutes a useful decision-support tool for real-time hazard mitigation. Copyright © 2008 John Wiley & Sons, Ltd

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COVID-19 infection and rheumatoid arthritis: Faraway, so close!

The outbreak of the new coronavirus infections COVID-19 in December 2019 in China has quickly become a global health emergency. Given the lack of specific anti-viral therapies, the current management of severe acute respiratory syndrome coronaviruses (SARS-CoV-2) is mainly supportive, even though several compounds are now under investigation for the treatment of this life-threatening disease. COVID-19 pandemic is certainly conditioning the treatment strategy of a complex disorder as rheumatoid arthritis (RA), whose infectious risk is increased compared to the general population because of an overall impairment of immune system typical of autoimmune diseases combined with the iatrogenic effect generated by corticosteroids and immunosuppressive drugs. However, the increasing knowledge about the pathophysiology of SARS-CoV-2 infection is leading to consider some anti-rheumatic drugs as potential treatment options for the management of COVID-19. In this review we will critically analyse the evidences on either positive or negative effect of drugs commonly used to treat RA in this particular scenario, in order to optimize the current approach to RA patients

A P2P Platform for real-time multicast video streaming leveraging on scalable multiple descriptions to cope with bandwidth fluctuations

In the immediate future video distribution applications will increase their diffusion thanks tothe ever-increasing user capabilities and improvements in the Internet access speed and performance.The target of this paper is to propose a content delivery system for real-time streaming services based ona peer-to-peer approach that exploits multicast overlay organization of the peers to address thechallenges due to bandwidth heterogeneity. To improve reliability and flexibility, video is coded using ascalable multiple description approach that allows delivery of sub-streams over multiple trees andallows rate adaptation along the trees as the available bandwidth changes. Moreover, we have deployeda new algorithm for tree-based topology management of the overlay network. In fact, tree based overlaynetworks better perform in terms of end-to-end delay and ordered delivery of video flow packets withrespect to mesh based ones. We also show with a case study that the proposed system works better thansimilar systems using only either multicast or multiple trees

GIS-assisted modelling for debris flow hazard assessment based on the events of May 1998 in the area of Sarno, Southern Italy. Part II: Velocity and Dynamic Pressure

The velocity and dynamic pressure of debris flows are critical determinants of the impact of these natural phenomena on infrastructure. Therefore, the prediction of these parameters is critical for haz¬ard assessment and vulnerability analysis. We present here an approach to predict the velocity of de¬bris flows on the basis of the energy line concept. First, we obtained empirically- and field-based esti¬mates of debris flow peak discharge, mean velocity at peak discharge and velocity at channel bends and within the fans of ten of the debris flow events that occurred in May 1998 in the area of Sarno, Southern Italy. We used this data to calibrate regression models that enable the prediction of velocity as a function of the vertical distance between the energy line and the surface. Despite the complexity in morphology and behaviour of these flows, the statistical fits were good and the debris flow veloci¬ties can be predicted with an associated uncertainty of < 30% and < 3 m s-1. We wrote code in Visual Basic for Applications (VBA) that runs within ArcGIS ® to implement the results of these calibrations and enable the automatic production of velocity and dynamic pressure maps. The collected data and resulting empirical models constitute a realistic basis for more complex numerical modelling. In addi¬tion, the GIS-implementation constitutes a useful decision-support tool for real-time hazard mitigatio

First evaluation of neutron induced single event effects on the CMS barrel muon electronics

Neutron irradiation tests of the currently available electronics for the CMS barrel muon detector were performed using Thermal and fast neutrons at E&lt; 11MeV. The Single Event Upset rate on the Static RAM was measured, while upper limits are derived for events having experienced no failure. The results are used to guess the upper limits on the mean time between failures in the whole barrel muon detector

The value of source data verification in a cancer clinical trial

Background Source data verification (SDV) is a resource intensive method of quality assurance frequently used in clinical trials. There is no empirical evidence to suggest that SDV would impact on comparative treatment effect results from a clinical trial. Methods Data discrepancies and comparative treatment effects obtained following 100% SDV were compared to those based on data without SDV. Overall survival (OS) and Progression-free survival (PFS) were compared using Kaplan-Meier curves, log-rank tests and Cox models. Tumour response classifications and comparative treatment Odds Ratios (ORs) for the outcome objective response rate, and number of Serious Adverse Events (SAEs) were compared. OS estimates based on SDV data were compared against estimates obtained from centrally monitored data. Findings Data discrepancies were identified between different monitoring procedures for the majority of variables examined, with some variation in discrepancy rates. There were no systematic patterns to discrepancies and their impact was negligible on OS, the primary outcome of the trial (HR (95% CI): 1.18(0.99 to 1.41), p = 0.064 with 100% SDV; 1.18(0.99 to 1.42), p = 0.068 without SDV; 1.18(0.99 to 1.40), p = 0.073 with central monitoring). Results were similar for PFS. More extreme discrepancies were found for the subjective outcome overall objective response (OR (95% CI): 1.67(1.04 to 2.68), p = 0.03 with 100% SDV; 2.45(1.49 to 4.04), p = 0.0003 without any SDV) which was mostly due to differing CT scans. Interpretation Quality assurance methods used in clinical trials should be informed by empirical evidence. In this empirical comparison, SDV was expensive and identified random errors that made little impact on results and clinical conclusions of the trial. Central monitoring using an external data source was a more efficient approach for the primary outcome of OS. For the subjective outcome objective response, an independent blinded review committee and tracking system to monitor missing scan data could be more efficient than SDV

The combined use of VIGl@ct (R) (bioMerieux) and fluorescent amplified length fragment polymorphisms in the investigation of potential outbreaks

Even with good surveillance programmes, hospital-acquired infections (HAls) are not always recognized and this may lead to an outbreak. In order to reduce this risk, we propose a model for prompt detection of HAls, based on the use of a real-time epidemiological information system called VIGI@ct (R) (bioMerieux, Las Balmas, France) and on the rapid confirmation or exclusion of the genetic relationship among pathogens using fluorescent amplified length fragment polymorphism (f-AFLP) microbial fingerprinting. We present the results of one year's experience with the system, which identified a total, of 306 suspicious HAls. Of these, 281 (92%) were 'confirmed' by clinical evidence, 16 (5%) were considered to be simple colonization and the tatter nine (3%) were archived as 'not answered' because of the absence of the physician's cooperation. There were seven suspected outbreaks; of these, f-AFLP analysis confirmed the clonal relationship among the isolates in four cases: outbreak 1 (four isolates of Pseudomonas aeruginosa), outbreak 2 (three Escherichia coli isolates), outbreak 6 (two Candida parapsilosis isolates) and outbreak 7 (30 ESPL-producing Klebsiella pneumoniae subsp. pneumoniae). Based on our results, we conclude that the combination of VIGI@ct (R) and f-AFLP is useful in the rapid assessment of an outbreak due to Gram-positive or Gramnegative bacteria and yeasts. (C) 2007 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved

Factors Predicting Early Failure of Etanercept in Rheumatoid Arthritis: An Analysis From the Gruppo Italiano di Studio sulla Early Arthritis (Italian Group for the Study of Early Arthritis) Registry

Objectives: This study aims to investigate the factors associated with early discontinuation (within one year) of etanercept (ETA) in rheumatoid arthritis (RA) patients who began ETA as first biologic disease-modifying antirheumatic drug (bDMARD) and who were entered into the Gruppo Italiano di Studio sulla Early Arthritis (Italian Group for the Study of Early Arthritis; GISEA) registry.Patients and methods: This registry-based cohort study included 477 RA patients (95 males, 382 females; median age 53 years; range 18 to 83 years) who began ETA as first bDMARD. Patient demographics, disease features and drugs were re-evaluated after 12 months. Baseline predictors of ETA discontinuation were estimated by univariate and multivariate analyses using Cox regression model.Results: Seventy patients (14.7%) discontinued ETA during the first year (for inefficacy in 55.8%, adverse events in 28.6%, and other reasons in 6.5%). Concurrent conventional synthetic DMARDs (csDMARDs) were reported in 54.3% of patients, mainly methotrexate (MTX), while 52.4% of subjects took low doses of glucocorticoids. Patients stopping ETA more frequently showed one or more comorbidities, mainly cardiovascular diseases (28.6% vs. 15.7% in patients stopping and continuing ETA, respectively, p=0.009). The presence of comorbidities and a combination therapy with csDMARDs other than MTX were independent factors associated with early discontinuation of ETA at multivariate Cox analysis.Conclusion: Although ETA demonstrated a high persistence in biologic-naive RA patients, about 15% of patients discontinued the treatment within 12 months. The presence of comorbidities and a combination therapy with csDMARDs other than MTX were the main factors for an early withdrawal of the drug