SARS-CoV-2 Breakthrough Infections: Incidence and Risk Factors in a Large European Multicentric Cohort of Health Workers

The research aimed to investigate the incidence of SARS-CoV-2 breakthrough infections and their determinants in a large European cohort of more than 60,000 health workers

A hierarchical architecture for on-line control of private cloud-based systems

Several enterprise data centers are adopting the private cloud computing paradigm as a scalable, cost-effective, robust way to provide services to their end users. The management and control of the underlying hw/sw infrastructure pose several interesting problems. In this paper we are interested to evidence that the monitoring process needs to scale to thousands of heterogeneous resources at different levels (system, network, storage, application) and at different time scales; it has to cope with missing data and detect anomalies in the performance samples; it has to transform all data into meaningful information and pass it to the decision process (possibly through different, ad-hoc algorithms for different resources). In most cases of interest for this paper, the control management system must operate under real-time constraints. We propose a hierarchical architecture that is able to support the efficient orchestration of an on-line management mechanism for a private cloud-based infrastructure. This architecture integrates a framework that collects samples from monitors, validates and aggregates them. We motivate the choice of a hierarchical scheme and show some data manipulation, orchestration and control strategies at different time scales. We then focus on a specific context referring to mid-term management objectives.We have applied the proposed hierarchical architecture successfully to data centers made of a large number of nodes that require short to mid-term control and in our experience we can conclude that it is a viable approach for the control of private cloud-based systems

Selective resource characterization for evaluation of system dynamics

Management decisions to achieve peak performance operations, scalability and availability in distributed systems require a continuous statistical characterization of data setscoming from server and network monitors. Due to the increasing sizes of data centers and their continuous dynamicchanges, the traditional approaches that work on all datasets in a centralized way are impractical. We propose astrategy for data processing that is able to limit the analysis of the large sets of collected measures to a smaller subsetof significant information for a twofold purpose: to classifythe collected data sets in few classes characterized by similarstatistical behaviors, to evaluate the dynamics of the overallsystem and its most relevant changes. The proposed strategy works at the level of server resources and of significantaggregation of servers of the overall distributed system. Several experimental results demonstrate the feasibility of theproposed strategy that is validated in real contexts

Self-adaptive techniques for the load trend evaluation of internal system resources

Modern distributed systems that have to avoid performance degradation and system overload require several runtime management decisions for load balancing and load sharing, overload and admission control,job dispatching and request redirection. As the external workload and the internal resource behavior of themodern system is highly complex and variable, selfadaptive techniques require a stable vision of the system behavior. In this paper we propose a trend modelthat guarantees a robust interpretation for load-awaredecision algorithms. Various experimental results in aWeb cluster demonstrate that the proposed models andalgorithms guarantee better stability of the load and areduction of the response time experienced by the users

Supporting data center management through clustering of system data streams

Aggregating large data sets related to hardware and software resources into clusters is at the basis of several operations and strategies for management and control. High variability and noise characterizing data collected from system resources monitoring prevent the application of existing solutions that are affected by low accuracy and scarce robustness. We present a new algorithm which extends the clustering method to data center management because it is able to find groups of related objects even when correlation is hidden by high variability. Our experimental evaluation performed on both synthetic and real data shows the accuracy and robustness of the proposed solution, and its ability in clustering servers with correlated functionalit

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load

Antibacterial Agent Discovery Using Thymidylate Synthase Biolibrary Screening

see attachedJRC.I.5-Physical and chemical exposure

Inappropriatezza prescrittiva dell'ossigenoterapia domiciliare a lungo termine

Long-term oxygen therapy (LTOT) at home is an established treatment, which is potentially subjected to inappropriate prescription. This retrospective study aims to identify the determinants of inappropriate prescription through the analysis of existing prescribing information for LTOT and routes of supply within the Northern area of the Emilia-Romagna Region. We selected all first time prescriptions for LTOT released in 2009 in the provinces of Modena, Parma, Piacenza and Reggio Emilia. A specific questionnaire with data collection through an electronic database allowed to analyze both organizational / administrative and clinical variables related to LTOT prescription. We analyzed a total of 364 prescriptions: 62 from Modena, 96 from Parma, 73 from Reggio Emilia, and 133 from Piacenza. The data collected highlighted several differences in the prescribing process between the four provinces. Among the most frequently omitted information in the prescription we identified: a) values of PaO2 and PaCO2 (absent in more than 90% of prescriptions dispensed at the AUSL Modena, while present in about 70% of prescriptions in Parma and in almost all of those of Piacenza and Reggio Emilia); b) differential information on oxygen flow at night or during exercise (absent in the prescriptions from Parma, Modena and Piacenza and present in more than 60% of prescriptions from Reggio Emilia); c) indications concerning the follow-up (not covered in the prescriptions from Modena and Parma). Finally, definition of the diagnosis that justifies the need for prescription of LTOT is often missing or incomplete. Therefore the results of the study have allowed the identification of some factors of inappropriateness both on the clinical side and on the management side. This analysis indicates the need for interventions aimed at improving and standardizing the process of LTOT prescription