Molecular epidemiological analysis of Escherichia coli sequence type ST131 (O25:H4) and bla CTX-M-15among extended-spectrum-β- lactamase-producing E. coli from the United States, 2000 to 2009

Escherichia coli sequence type ST131 (from phylogenetic group B2), often carrying the extended-spectrum-β-lactamase (ESBL) gene bla , is an emerging globally disseminated pathogen that has received comparatively little attention in the United States. Accordingly, a convenience sample of 351 ESBL-producing E. coli isolates from 15 U.S. centers (collected in 2000 to 2009) underwent PCR-based phylotyping and detection of ST131 and bla . A total of 200 isolates, comprising 4 groups of 50 isolates each that were (i) bla negative non-ST131, (ii) bla positive non-ST131, (iii) bla negative ST131, or (iv) bla positive ST131, also underwent virulence genotyping, antimicrobial susceptibility testing, and pulsed-field gel electrophoresis (PFGE). Overall, 201 (57%) isolates exhibited bla , whereas 165 (47%) were ST131. ST131 accounted for 56% of bla -positive-versus 35% of bla -negative isolates (

Learning and interaction in groups with computers: when do ability and gender matter?

In the research reported in this paper, we attempt to identify the background and process factors influencing the effectiveness of groupwork with computers in terms of mathematics learning. The research used a multi-site case study design in six schools and involved eight groups of six mixed-sex, mixed-ability pupils (aged 9-12) undertaking three research tasks – two using Logo and one a database. Our findings suggest that, contrary to other recent research, the pupil characteristics of gender and ability have no direct influence on progress in group tasks with computers. However, status effects – pupils' perceptions of gender and ability – do have an effect on the functioning of the group, which in turn can impede progress for all pupils concerned

Cardiac performance, biomarkers and gene expression studies in previously sedentary men participating in half-marathon training

BACKGROUND: The mechanisms through which exercise reduces cardiovascular disease are not fully understood. We used echocardiograms, cardiac biomarkers and gene expression to investigate cardiovascular effects associated with exercise training. METHODS: Nineteen sedentary men (22-37 years) completed a 17-week half-marathon training program. Serial measurements of resting heart rate, blood pressure, maximum oxygen consumption, lipids, C-reactive protein, cardiac troponin T, echocardiograms and blood for gene expression were obtained from baseline to peak training. Controls included 22 sedentary men who did not exercise. RESULTS: Among the training group, VO2 max increased from 37.1 to 42.0 ml/kg/min (p \u3c 0.001). Significant changes were seen in left ventricular wall thickness and mass, stroke volume, resting heart rate and blood pressure (p \u3c 0.001). The control group demonstrated no significant changes. Expression profiling in the training group identified 10 significantly over-expressed and 53 significantly under-expressed loci involved in inflammatory pathways. Dividing the training group into high and low responders based on percent change in VO2 max identified loci that differentiated these two groups at baseline and after training. CONCLUSION: Intensive exercise training leads to significant increase in cardiac and hemodynamic performance, and significant changes in expression of genes involved in immune and inflammatory response.

Using data-driven rules to predict mortality in severe community acquired pneumonia

Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available. © 2014 Wu et al

Improving on the Contingent Fee

Two basic fees--contingent and hourly--dominate the variety of fees that lawyers charge clients for pursuing damage claims. Each of these two types has its advantages; each is plagued with substantial disadvantages. This Article proposes a new type of fee, one that preserves the respective advantages of the two present fees while minimizing their distinct disadvantages. In essence, the proposed fee calls for the payment, on a contingent basis, of an amount computed by adding one component tied to hours worked and another component linked to amount recovered. The preferability and feasibility of this proposed fee argue for the abolishment, or at least for the severe restriction, of the contingent fee as it is now known; the hourly fee should continue as a client\u27s option

Multi-task deep learning with incomplete training samples for the image-based prediction of variables describing silk fabrics

This paper presents a method for the classification of images of silk fabrics with the aim to predict properties such as the placeand time of origin and the production technique. The proposed method was developed in the context of the EU project SILKNOW(http://silknow.eu/). In the context of classification, we address the problem of limited as well as not fully labelled data andinvestigate the connection between the distinct variables. A pre-trained Convolutional Neural Network (CNN) is used for thefeature extraction and a classification network realizing Multi-task learning (MTL) is trained based on these features. The trainingprocedure is adapted to enable the consideration of images that do not have a label for all tasks. Additionally, MTL with fullylabeled training data is investigated for the classification of silk fabrics. The impact of both MTL approaches is compared to singletask learning based on two different class structures. We achieve overall accuracies of 92-95% and average F1-scores of 88-90% inour best experiments. © 2019 Authors

Improving on the Contingent Fee

Two basic fees--contingent and hourly--dominate the variety of fees that lawyers charge clients for pursuing damage claims. Each of these two types has its advantages; each is plagued with substantial disadvantages. This Article proposes a new type of fee, one that preserves the respective advantages of the two present fees while minimizing their distinct disadvantages. In essence, the proposed fee calls for the payment, on a contingent basis, of an amount computed by adding one component tied to hours worked and another component linked to amount recovered. The preferability and feasibility of this proposed fee argue for the abolishment, or at least for the severe restriction, of the contingent fee as it is now known; the hourly fee should continue as a client\u27s option