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The intent of this document is to highlight practical recommendations in a concise format designed to assist acute-care hospitals in implementing and prioritizing their surgical-site infection (SSI) prevention efforts. This document updates the Strategies to Prevent Surgical Site Infections in Acute Care Hospitals published in 2014. This expert guidance document is sponsored by the Society for Healthcare Epidemiology of America (SHEA). It is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America (IDSA), the Association for Professionals in Infection Control and Epidemiology (APIC), the American Hospital Association (AHA), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise.

BackgroundIn 2015, an international outbreak of Mycobacterium chimaera infections among patients undergoing cardiothoracic surgeries was associated with exposure to contaminated LivaNova 3T heater-cooler devices (HCDs). From June 2017 to October 2020, the Centers for Disease Control and Prevention was notified of 18 patients with M. chimaera infections who had undergone cardiothoracic surgeries at 2 hospitals in Kansas (14 patients) and California (4 patients); 17 had exposure to 3T HCDs. Whole-genome sequencing of the clinical and environmental isolates matched the global outbreak strain identified in 2015.MethodsInvestigations were conducted at each hospital to determine the cause of ongoing infections. Investigative methods included query of microbiologic records to identify additional cases, medical chart review, observations of operating room setup, HCD use and maintenance practices, and collection of HCD and environmental samples.ResultsOnsite observations identified deviations in the positioning and maintenance of the 3T HCDs from the US Food and Drug Administration (FDA) recommendations and the manufacturer's updated cleaning and disinfection protocols. Additionally, most 3T HCDs had not undergone the recommended vacuum and sealing upgrades by the manufacturer to decrease the dispersal of M. chimaera-containing aerosols into the operating room, despite hospital requests to the manufacturer.ConclusionsThese findings highlight the need for continued awareness of the risk of M. chimaera infections associated with 3T HCDs, even if the devices are newly manufactured. Hospitals should maintain vigilance in adhering to FDA recommendations and the manufacturer's protocols and in identifying patients with potential M. chimaera infections with exposure to these devices.

Growth in large-scale experiments using high capacity reliable networking as part of their design is creating a need for better monitoring and analysis of observed traffic. Network providers need intelligent solutions that can help quickly identify and understand anomalous behaviors at the network edge, allowing reactions to unexpected traffic or attacks on facilities and their peerings. However, due to lack of labeled data in network traffic analysis and user diversity, we introduce novel methods that process very large network datasets quickly for outlier identification. In this paper, we leverage artificial intelligence (AI), network research, and edge computing to collect and train unsupervised classification algorithms using streaming data pipelines from multiple months of network flow records. Once trained, individual classifiers quickly observe and flag alerts in hourly behaviors. Our work describes building the data pipeline as well as addressing issues of false positives and workflow integration.

As deep reinforcement learning (DRL) continues to gain interest in the smart building research community, there is a transition from simulation-based evaluations to deploying DRL control strategies in actual buildings. While the efficacy of a solution could depend on a particular implementation, there are common obstacles that developers have to overcome to deliver an effective controller. Additionally, a deployment in a physical building can invalidate some of the assumptions made during the controller development. Assumptions on the sensor placement or on the equipment behavior can quickly come undone. This paper presents some of the significant assumptions made during the development of DRL based controllers that could affect their operations in a physical building. Furthermore, a preliminary evaluation revealed that controllers developed with some of these assumptions can incur twice the expected costs when they are deployed in a building.

As data sets from DOE user science facilities grow in both size and complexity there is an urgent need for new capabilities to transfer, analyze and manage the data underlying scientific discoveries. LBNL's Superfacility project brings together experimental and observational research instruments with computational and network facilities at the National Energy Research Scientific Computing Center (NERSC) and the Energy Sciences Network (ESnet) with the goal of enabling user science. Here, we report on recent innovations in the Superfacility project, including advanced data management, API-based automation, real-Time interactive user interfaces, and supported infrastructure for 'edge' services.

Geothermal energy has traditionally been viewed as a baseload energy source, but the rapid growth of intermittent renewable energy has led to a need for more flexibility in power generation to avoid mandatory curtailment imposed by grid operators. This study of curtailment at The Geysers provides insights into the magnitude, duration, frequency, temporal and spatial distribution, and potential causes of curtailment events between 2013 and 2018. Annual levels of curtailment range during this period from 9 to 47 GW h, representing 0.15 to 0.81 % of the net generation. Most curtailments occurred at the power plants connected to a lower capacity transmission line and may result from transmission constriction. There is a clear link between negative pricing and economic curtailment, especially when solar production is higher. Economic curtailment events tend to be only a few hours and vary in magnitude up to almost 300 MW, whereas transmission-related curtailment events can be up to several weeks in duration. It is likely that curtailment of geothermal power will be an increasing concern, and could be mitigated by flexible generation strategies and increases in energy storage. It is critical to know the nature of curtailment events so that flexible generation options can be assessed properly.

We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. We present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.

Failure Prediction has long known to be a challenging problem. With the evolving trend of technology and growing complexity of high-performance cloud data centre infrastructure, focusing on failure becomes very vital particularly when designing systems for the next generation. The traditional runtime fault-tolerance (FT) techniques such as data replication and periodic check-pointing are not very effective to handle the current state of the art emerging computing systems. This has necessitated the urgent need for a robust system with an in-depth understanding of system and component failures as well as the ability to predict accurate potential future system failures. In this paper, we studied data in-production-faults recorded within a five years period from the National Energy Research Scientific computing centre (NERSC). Using the data collected from the Computer Failure Data Repository (CFDR), we developed an effective failure prediction model focusing on high-performance cloud data centre infrastructure. Using the Auto-Regressive Moving Average(ARMA), our model was able to predict potential future failures in the system. Our results also show a failure prediction accuracy of 95%, which is good. We thus believe that our strategy is practical and can be adapted to use in existing real-time systems.

Globally distributed scientific experiments involve movement of massive data volumes and many collaborators performing distributed data analysis. With complex workloads and heterogeneous resources, each user may desire certain behavior characteristics for their network paths. In this paper, we present the iNDIRA tool, which interacts with SDN north-bound interfaces to enable intent-based networking. It provides reliable, simple, and technology-agnostic communication between users and networks. Focusing particularly on science applications, iNDIRA uses natural language processing to construct semantic RDF graphs to understand, interact, and create the required network services. The technical challenges addressed by iNDIRA are: (1) development of a high-level descriptive language to query network-application requirements, (2) provides keyword identification and condition checking based on user profiles and topology details, (3) allows user negotiation based on the current network state, and (4) integrates network provisioning and service tools used by the application. iNDIRA is implemented on the ESnet network, where it interacts with OpenNSA (aka the NSI client) and Globus data transfer tools, to build complex cross-domain network paths for heterogeneous science applications, and perform secure data transfer. We argue that iNDIRA's approach presents users with an alternative approach to interact and communicate their network demands, allowing seamless network service integration.

Cloud computing has emerged as a popular paradigm and an attractive model forproviding a reliable distributed computing model.it is increasing attractinghuge attention both in academic research and industrial initiatives. Clouddeployments are paramount for institution and organizations of all scales. Theavailability of a flexible, free open source cloud platform designed with nopropriety software and the ability of its integration with legacy systems andthird-party applications are fundamental. Open stack is a free and opensourcesoftware released under the terms of Apache license with a fragmented anddistributed architecture making it highly flexible. This project was initiatedand aimed at designing a secured cloud infrastructure called BradStack, whichis built on OpenStack in the Computing Laboratory at the University ofBradford. In this report, we present and discuss the steps required indeploying a secured BradStack Multi-node cloud infrastructure and conductingPenetration testing on OpenStack Services to validate the effectiveness of thesecurity controls on the BradStack platform. This report serves as a practicalguideline, focusing on security and practical infrastructure related issues. Italso serves as a reference for institutions looking at the possibilities ofimplementing a secured cloud solution.

Cloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault-tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution, and hardware performance, various fault-tolerant techniques exist for building self-autonomous cloud systems. In comparison with current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualized mechanisms, the proposed smart failover strategy (SFS) scheme uses components such as cloud fault manager, cloud controller, cloud load balancer, and a selection mechanism, providing fault tolerance via redundancy, optimized selection, and checkpointing. In our approach, the cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared with existing approaches, preliminary experiment of the SFS algorithm indicates an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison with other techniques, laying a foundation for a fully fault-tolerant infrastructure as a service cloud environment. Copyright © 2017 John Wiley & Sons, Ltd.

Sensor and smart phone technologies present opportunities for data explosion, streaming and collecting from heterogeneous devices every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, and help optimize approaches to city wide applications or societal use cases. However, collecting and handling of these massive datasets presents challenges in how to perform optimized online data analysis 'on-the-fly', as current approaches are often limited by capability, expense and resources. This presents a need for developing new methods for data management particularly using public clouds to minimize cost, network resources and on-demand availability. This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data.

Donor age has become the dominant donor factor used to predict graft failure (GF) after liver transplantation (LT) in hepatitis C virus (HCV) recipients. The purpose of this study was to develop and validate a model of corrected donor age (CDA) for HCV LT recipients that transforms the risk of other donor factors into the scale of donor age. We analyzed all first LT recipients with HCV in the United Network for Organ Sharing (UNOS) registry from January 1998 to December 2007 (development cohort, n = 14,538) and January 2008 to December 2011 (validation cohort, n = 7502) using Cox regression, excluding early GF ( 8 hours and -1 year/hour

In the United States, the peak hepatitis C virus (HCV) antibody prevalence of 4% occurred in persons born in the calendar years 1940-1965. The goal of this study was to examine observed and projected age-specific trends in the demand for liver transplantation (LT) among patients with HCV-associated liver disease stratified by concurrent hepatocellular carcinoma (HCC). All new adult LT candidates registered with the Organ Procurement and Transplantation Network for LT between 1995 and 2010 were identified. Patients who had primary, secondary, or text field diagnoses of HCV with or without HCC were identified. There were 126,862 new primary registrants for LT, and 52,540 (41%) had HCV. The number of new registrants with HCV dramatically differed by the age at calendar year, and this suggested a birth cohort effect. When the candidates were stratified by birth year in 5-year intervals, the birth cohorts with the highest frequency of HCV were as follows (in decreasing order): 1951-1955, 1956-1960, 1946-1950, and 1941-1945. These 4 birth cohorts, spanning from 1941 to 1960, accounted for 81% of all new registrants with HCV. A 4-fold increase in new registrants with HCV and HCC occurred between the calendar years 2000 and 2010 in the 1941-1960 birth cohorts. By 2015, we anticipate that an increasing proportion of new registrants with HCV will have HCC and be ≥60 years old (born in or before 1955). In conclusion, the greatest demand for LT due to HCV-associated liver disease is occurring among individuals born between 1941 and 1960. This demand appears to be driven by the development of HCC in patients with HCV. During the coming decade, the projected increase in the demand for LT from an aging HCV-infected population will challenge the transplant community to reconsider current treatment paradigms.

FLAME allows complex models to be automatically parallelised on High Performance Computing (HPC) grids enabling large number of agents to be simulated over short periods of time. Modellers are hindered by complexities of porting models on parallel platforms and time taken to run large simulations on a single machine, which FLAME overcomes. Three case studies from different disciplines were modelled using FLAME, and are presented along with their performance results on a grid.