Preventing respiratory viral transmission in long-term care: Knowledge, attitudes, and practices of healthcare personnel

OBJECTIVETo examine knowledge and attitudes about influenza vaccination and infection prevention practices among healthcare personnel (HCP) in a long-term-care (LTC) setting.DESIGNKnowledge, attitudes, and practices (KAP) survey.SETTINGAn LTC facility in St Louis, Missouri.PARTICIPANTSAll HCP working at the LTC facility were eligible to participate, regardless of department or position. Of 170 full- and part-time HCP working at the facility, 73 completed the survey, a 42.9% response rate.RESULTSMost HCP agreed that respiratory viral infections were serious and that hand hygiene and face mask use were protective. However, only 46% could describe the correct transmission-based precautions for an influenza patient. Correctly answering infection prevention knowledge questions did not vary by years of experience but did vary for HCP with more direct patient contact versus less patient contact. Furthermore, 42% of respondents reported working while sick, and 56% reported that their coworkers did. In addition, 54% reported that facility policies made staying home while ill difficult. Some respondents expressed concerns about the safety (22%) and effectiveness (27%) of the influenza vaccine, and 28% of respondents stated that they would not get the influenza vaccine if it was not required.CONCLUSIONSThis survey of staff in an LTC facility identified several areas for policy improvement, particularly sick leave, as well as potential targets for interventions to improve infection prevention knowledge and to address HCP concerns about influenza vaccination to improve HCP vaccination rates in LTCs.Infect Control Hosp Epidemiol 2017;38:1449–1456</jats:sec

Free Iron Distribution in Some Poorly Drained Prairie Soils in Iowa

In classification and mapping of soils an interpretation of the natural drainage characteristics of the soil types is usually made. Some standard natural drainage classes used are poorly drained, imperfectly drained, moderately well- drained, and well-drained (1). Interpretation of the natural drainage of the soils is important from the agronomic standpoint, and also is basic to the soil classification scheme in present use. The natural drainage of a soil is interpreted mainly by inferences from the color and mottling of hydrated iron oxides in the subsoil. Few studies have been made of the nature and quantity of these iron oxides in soils. Extractable iron or free iron has been determined in a few well-drained Brunizem and Gray Brown Podzolic soils, and in several poorly drained Forested Planosols (2) (3) (4) (5). The purpose of this paper is to report data on free iron in several poorly drained prairie (Wiesenboden) soils and to compare these data with available data of other great soil groups in Iowa

Differential expression analysis with global network adjustment

&lt;p&gt;Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.&lt;/p&gt; &lt;p&gt;Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.&lt;/p&gt; &lt;p&gt;Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.&lt;/p&gt

Differential Forms on Log Canonical Spaces

The present paper is concerned with differential forms on log canonical varieties. It is shown that any p-form defined on the smooth locus of a variety with canonical or klt singularities extends regularly to any resolution of singularities. In fact, a much more general theorem for log canonical pairs is established. The proof relies on vanishing theorems for log canonical varieties and on methods of the minimal model program. In addition, a theory of differential forms on dlt pairs is developed. It is shown that many of the fundamental theorems and techniques known for sheaves of logarithmic differentials on smooth varieties also hold in the dlt setting. Immediate applications include the existence of a pull-back map for reflexive differentials, generalisations of Bogomolov-Sommese type vanishing results, and a positive answer to the Lipman-Zariski conjecture for klt spaces.Comment: 72 pages, 6 figures. A shortened version of this paper has appeared in Publications math\'ematiques de l'IH\'ES. The final publication is available at http://www.springerlink.co

Biodistribution and inflammatory profiles of novel penton and hexon double-mutant serotype 5 adenoviruses

The use of adenovirus serotype 5 (Ad5) vectors in the clinical setting is severely hampered by the profound liver tropism observed after intravascular delivery coupled with the pronounced inflammatory and innate immune response elicited by these vectors. Liver transduction by circulating Ad5 virions is mediated by a high-affinity interaction between the capsid hexon protein and blood coagulation factor X (FX), whilst penton-α(v)integrin interactions are thought to contribute to the induction of anti-Ad5 inflammatory and innate immune responses. To overcome these limitations, we sought to develop and characterise for the first time novel Ad5 vectors possessing mutations ablating both hexon:FX and penton:integrin interactions. As expected, intravascular administration of the FX binding-ablated Ad5HVR5*HVR7*E451Q vector (AdT*) resulted in significantly reduced liver transduction in vivo compared to Ad5. In macrophage-depleted mice, increased spleen uptake of AdT* was accompanied by an elevation in the levels of several inflammatory mediators. However ablation of the penton RGD motif in the AdT* vector background (AdT*RGE) resulted in a significant 5-fold reduction in spleen uptake and attenuated the antiviral inflammatory response. A reduction in spleen uptake and inflammatory activation was also observed in animals after intravascular administration of Ad5RGE compared to the parental Ad5 vector, with reduced co-localisation of the viral beta-galactosidase transgene with MAdCAM-1+ sinus-lining endothelial cells. Our detailed assessment of these novel adenoviruses indicates that penton base RGE mutation in combination with FX binding-ablation may be a viable strategy to attenuate the undesired liver uptake and pro-inflammatory responses to Ad5 vectors after intravascular deliver

Respiratory viral surveillance of healthcare personnel and patients at an adult long-term care facility

We conducted active surveillance of acute respiratory viral infections (ARIs) among residents and healthcare personnel (HCP) at a long-term care facility during the 2015-2016 respiratory illness season. ARIs were observed among both HCP and patients, highlighting the importance of including HCP in surveillance programs

Network 'small-world-ness': a quantitative method for determining canonical network equivalence

Background: Many technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction ('small/not-small') rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model-the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified. Methodology/Principal Findings: We defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S. 1-an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process. Conclusions/Significance: We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing

Radon and Remedial Action in Spokane River Valley Residences: An Interim Report

Fifty-six percent of 46 residences monitored in the Spokane River Valley in eastern Washington/northern Idaho have indoor radon concentrations above the National Council for Radiation Protection (NCRP) guidelines of 8 pCi/1. Indoor levels were over 20 pCi/1 in eight homes, and ranged up to 132 pCi/1 in one house. Radon concentrations declined by factors of 4 to 38 during summer months. Measurements of soil emanation rates, domestic water supply concentrations, and building material flux rates indicate that diffusion of radon does not significantly contribute to the high concentrations observed. Rather, radon entry is dominated by pressure-driven bulk soil gas transport, aggravated by the local subsurface soil composition and structure. A variety of radon control strategies are being evaluated in 14 of these homes. Sub-surface ventilation by depressurization and overpressurization, basement overpressurization, and crawlspace ventilation are capable of successfully reducing radon levels below 5 pCi/1 in these homes. House ventilation is appropriate in buildings with low-moderate concentrations, while sealing of cracks has been relatively ineffective

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

Background: The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations. Methodology and Principal Findings: We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets. Conclusions: The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters

Phenotype Prediction Using Regularized Regression on Genetic Data in the DREAM5 Systems Genetics B Challenge

A major goal of large-scale genomics projects is to enable the use of data from high-throughput experimental methods to predict complex phenotypes such as disease susceptibility. The DREAM5 Systems Genetics B Challenge solicited algorithms to predict soybean plant resistance to the pathogen Phytophthora sojae from training sets including phenotype, genotype, and gene expression data. The challenge test set was divided into three subcategories, one requiring prediction based on only genotype data, another on only gene expression data, and the third on both genotype and gene expression data. Here we present our approach, primarily using regularized regression, which received the best-performer award for subchallenge B2 (gene expression only). We found that despite the availability of 941 genotype markers and 28,395 gene expression features, optimal models determined by cross-validation experiments typically used fewer than ten predictors, underscoring the importance of strong regularization in noisy datasets with far more features than samples. We also present substantial analysis of the training and test setup of the challenge, identifying high variance in performance on the gold standard test sets.National Science Foundation (U.S.). Graduate Research Fellowship ProgramNational Defense Science and Engineering Graduate Fellowshi