Towards closing the gap between hygroscopic growth and activation for secondary organic aerosol: Part 1 – Evidence from measurements

Secondary Organic Aerosols (SOA) studied in previous laboratory experiments generally showed only slight hygroscopic growth, but a much better activity as a CCN (Cloud Condensation Nucleus) than indicated by the hygroscopic growth. This discrepancy was examined at LACIS (Leipzig Aerosol Cloud Interaction Simulator), using a portable generator that produced SOA particles from the ozonolysis of <i>α</i>-pinene, and adding butanol or butanol and water vapor during some of the experiments. The light scattering signal of dry SOA-particles was measured by the LACIS optical particle spectrometer and was used to derive a refractive index for SOA of 1.45. LACIS also measured the hygroscopic growth of SOA particles up to 99.6% relative humidity (RH), and a CCN counter was used to measure the particle activation. SOA-particles were CCN active with critical diameters of e.g. 100 nm and 55 nm at super-saturations of 0.4% and 1.1%, respectively. But only slight hygroscopic growth with hygroscopic growth factors ≤1.05 was observed at RH<98% RH. At RH>98%, the hygroscopic growth increased stronger than would be expected if a constant hygroscopicity parameter for the particle/droplet solution was assumed. An increase of the hygroscopicity parameter by a factor of 4–6 was observed in the RH-range from below 90% to 99.6%, and this increase continued for increasingly diluted particle solutions for activating particles. This explains an observation already made in the past: that the relation between critical super-saturation and dry diameter for activation is steeper than what would be expected for a constant value of the hygroscopicity. Combining measurements of hygroscopic growth and activation, it was found that the surface tension that has to be assumed to interpret the measurements consistently is greater than 55 mN/m, possibly close to that of pure water, depending on the different SOA-types produced, and therefore only in part accounts for the discrepancy between hygroscopic growth and CCN activity observed for SOA particles in the past

Designing the Arriving Refugee Informatics Surveillance and Epidemiology (ARIVE) System: A Web-based Electronic Database for Epidemiological Surveillance

Objectives: We design and implement the Arriving Refugee Informatics surVeillance and Epidemiology (ARIVE) system to improve the health of refugees undergoing resettlement and enhance existing health surveillance networks. Materials and Methods: Using the REDCap electronic data capture software as a basis we create a refugee health database incorporating data from the Center for Disease Control and Prevention’s Electronic Disease Notification (EDN) system and domestic screening data from refugee health care providers. Results: Domestic screening and EDN refugee health data have been integrated for 13,824 refugees resettled from 35 different countries into the state of Kentucky from the years 2013-2016. Discussion: A flexible software solution like REDCap provides a way to implement the core of a health surveillance network in a way that is sustainable and cost-effective and REDCap’s data dictionary standard provides an easy way to share and improve the database structure of a health surveillance network

OPTN/SRTR 2016 Annual Data Report: Heart

In 2016, 3209 heart transplants were performed in the United States. New, active listings increased 57% since 2005. The number of adult heart transplant survivors continued to increase, and in 2016, 30,622 recipients were living with heart transplants. Patient mortality following transplant has declined. The number of pediatric candidates and transplants performed also increased. New listings for pediatric heart transplants increased from 454 in 2005 to 624 in 2016. The number of pediatric heart transplants performed each year increased from 319 in 2005 to 445 in 2016. Among pediatric patients who underwent transplant in 2015, death occurred in 5.9% at 6 months and 7.2% at 1 year.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141946/1/ajt14561.pd

Strategies to prevent Clostridium difficile infections in acute care hospitals: 2014 update

Previously published guidelines are available that provide comprehensive recommendations for detecting and preventing healthcare-associated infections (HAIs). 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 Clostridium difficile infection (CDI) prevention efforts. This document updates “Strategies to Prevent Clostridium difficile Infections in Acute Care Hospitals,” published in 2008. This expert guidance document is sponsored by the Society for Healthcare Epidemiology of America (SHEA) and is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America (IDSA), the American Hospital Association (AHA), the Association for Professionals in Infection Control and Epidemiology (APIC), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise. The list of endorsing and supporting organizations is presented in the introduction to the 2014 updates

Research Support Infrastructure: Implementing a Clinical Research Coordinating Center

Insufficient infrastructure is one of the challenges facing investigators in the field of clinical research. At the University of Louisville (UofL) Division of Infectious Diseases, we developed a multidisciplinary coordinating center with the aim to support investigators in all aspects of the clinical research process. The objective of this article is to describe the composition and the role of the different units of the UofL Clinical Research Coordinating Center. The different components of the Center can serve as a template for institutions interested in developing a clinical research support infrastructure

Communication Gaps Associated With Donor‐Derived Infections

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109830/1/ajt12978.pd

Success Factors of European Syndromic Surveillance Systems: A Worked Example of Applying Qualitative Comparative Analysis

Introduction: Syndromic surveillance aims at augmenting traditional public health surveillance with timely information. To gain a head start, it mainly analyses existing data such as from web searches or patient records. Despite the setup of many syndromic surveillance systems, there is still much doubt about the benefit of the approach. There are diverse interactions between performance indicators such as timeliness and various system characteristics. This makes the performance assessment of syndromic surveillance systems a complex endeavour. We assessed if the comparison of several syndromic surveillance systems through Qualitative Comparative Analysis helps to evaluate performance and identify key success factors. Materials and Methods: We compiled case-based, mixed data on performance and characteristics of 19 syndromic surveillance systems in Europe from scientific and grey literature and from site visits. We identified success factors by applying crisp-set Qualitative Comparative Analysis. We focused on two main areas of syndromic surveillance application: seasonal influenza surveillance and situational awareness during different types of potentially health threatening events. Results: We found that syndromic surveillance systems might detect the onset or peak of seasonal influenza earlier if they analyse non-clinical data sources. Timely situational awareness during different types of events is supported by an automated syndromic surveillance system capable of analysing multiple syndromes. To our surprise, the analysis of multiple data sources was no key success factor for situational awareness. Conclusions: We suggest to consider these key success factors when designing or further developing syndromic surveillance systems. Qualitative Comparative Analysis helped interpreting complex, mixed data on small-N cases and resulted in concrete and practically relevant findings

Ranked Adjusted Rand: integrating distance and partition information in a measure of clustering agreement

BACKGROUND: Biological information is commonly used to cluster or classify entities of interest such as genes, conditions, species or samples. However, different sources of data can be used to classify the same set of entities and methods allowing the comparison of the performance of two data sources or the determination of how well a given classification agrees with another are frequently needed, especially in the absence of a universally accepted "gold standard" classification. RESULTS: Here, we describe a novel measure – the Ranked Adjusted Rand (RAR) index. RAR differs from existing methods by evaluating the extent of agreement between any two groupings, taking into account the intercluster distances. This characteristic is relevant to evaluate cases of pairs of entities grouped in the same cluster by one method and separated by another. The latter method may assign them to close neighbour clusters or, on the contrary, to clusters that are far apart from each other. RAR is applicable even when intercluster distance information is absent for both or one of the groupings. In the first case, RAR is equal to its predecessor, Adjusted Rand (HA) index. Artificially designed clusterings were used to demonstrate situations in which only RAR was able to detect differences in the grouping patterns. A study with larger simulated clusterings ensured that in realistic conditions, RAR is effectively integrating distance and partition information. The new method was applied to biological examples to compare 1) two microbial typing methods, 2) two gene regulatory network distances and 3) microarray gene expression data with pathway information. In the first application, one of the methods does not provide intercluster distances while the other originated a hierarchical clustering. RAR proved to be more sensitive than HA in the choice of a threshold for defining clusters in the hierarchical method that maximizes agreement between the results of both methods. CONCLUSION: RAR has its major advantage in combining cluster distance and partition information, while the previously available methods used only the latter. RAR should be used in the research problems were HA was previously used, because in the absence of inter cluster distance effects it is an equally effective measure, and in the presence of distance effects it is a more complete one