Discovering web services to specify more complete system requirements

Service-centric systems pose new challenges and opportunities for requirements processes and techniques. This paper reports new techniques developed by the EU-funded SeCSE Integrated Project that enable service discovery during early requirements processes and exploit discovered services to enhance requirements specifications. The paper describes the algorithm for discovering services from requirements expressed using structured natural language, and demonstrates it using an automotive example. The paper also reports a first evaluation of the utility of the environment that implements this algorithm when improving the specification of requirements with retrieved services

Household Contamination with Salmonella enterica1

Household contamination with Salmonella enterica increases when occupational exposure exists (cattle farms with known salmonellosis in cattle, a salmonella research laboratory, or a veterinary clinic experiencing an outbreak of salmonellosis). Fifteen of 55 (27.2%) vacuum cleaner bags from households with occupational exposure to S. enterica were positive versus 1 of 24 (4.2% without known exposure. Use of a carpet cleaner and several cleaners/disinfectants reduced, but failed to eliminate, S. enterica from artificially contaminated carpet

A study on using genetic niching for query optimisation in document retrieval

International audienceThis paper presents a new genetic approach for query optimisation in document retrieval. The main contribution of the paper is to show the effectiveness of the genetic niching technique to reach multiple relevant regions of the document space. Moreover, suitable merging procedures have been proposed in order to improve the retrieval evaluation. Experimental results obtained using a TREC sub-collection indicate that the proposed approach is promising for applications

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

An adolescent with both Wegener's Granulomatosis and chronic blastomycosis

We report a case of Wegener's Granulomatosis (WG) associated with blastomycosis. This appears to be the first case report of WG co-existing with a tissue proven blastomycosis infection. The temporal correlation of the two conditions suggests that blastomycosis infection (and therefore possibly other fungal infections), may trigger the systemic granulomatous vasculitis in a predisposed individual; a provocative supposition warranting further study

Vaccine delivery by penetratin: mechanism of antigen presentation by dendritic cells

Cell-penetrating peptides (CPP) or membrane-translocating peptides such as penetratin from Antennapedia homeodomain or TAT from human immunodeficiency virus are useful vectors for the delivery of protein antigens or their cytotoxic (Tc) or helper (Th) T cell epitopes to antigen-presenting cells. Mice immunized with CPP containing immunogens elicit antigen-specific Tc and/or Th responses and could be protected from tumor challenges. In the present paper, we investigate the mechanism of class I and class II antigen presentation of ovalbumin covalently linked to penetratin (AntpOVA) by bone marrow-derived dendritic cells with the use of biochemical inhibitors of various pathways of antigen processing and presentation. Results from our study suggested that uptake of AntpOVA is via a combination of energy-independent (membrane fusion) and energy-dependent pathways (endocytosis). Once internalized by either mechanism, multiple tap-dependent or independent antigen presentation pathways are accessed while not completely dependent on proteasomal processing but involving proteolytic trimming in the ER and Golgi compartments. Our study provides an understanding on the mechanism of antigen presentation mediated by CPP and leads to greater insights into future development of vaccine formulations

Linking a dermal permeation and an inhalation model to a simple pharmacokinetic model to study airborne exposure to di(n-butyl) phthalate

Six males clad only in shorts were exposed to high levels of airborne di(n-butyl) phthalate (DnBP) and diethyl phthalate (DEP) in chamber experiments conducted in 2014. In two 6 h sessions, the subjects were exposed only dermally while breathing clean air from a hood, and both dermally and via inhalation when exposed without a hood. Full urine samples were taken before, during, and for 48 h after leaving the chamber and measured for key DnBP and DEP metabolites. The data clearly demonstrated high levels of DnBP and DEP metabolite excretions while in the chamber and during the first 24 h once leaving the chamber under both conditions. The data for DnBP were used in a modeling exercise linking dose models for inhalation and transdermal permeation with a simple pharmacokinetic model that predicted timing and mass of metabolite excretions. These models were developed and calibrated independent of these experiments. Tests included modeling of the “hood-on” (transdermal penetration only), “hood-off” (both inhalation and transdermal) scenarios, and a derived “inhalation-only” scenario. Results showed that the linked model tended to duplicate the pattern of excretion with regard to timing of peaks, decline of concentrations over time, and the ratio of DnBP metabolites. However, the transdermal model tended to overpredict penetration of DnBP such that predictions of metabolite excretions were between 1.1 and 4.5 times higher than the cumulative excretion of DnBP metabolites over the 54 h of the simulation. A similar overprediction was not seen for the “inhalation-only” simulations. Possible explanations and model refinements for these overpredictions are discussed. In a demonstration of the linked model designed to characterize general population exposures to typical airborne indoor concentrations of DnBP in the United States, it was estimated that up to one-quarter of total exposures could be due to inhalation and dermal uptake

An empirical Bayesian approach for model-based inference of cellular signaling networks

Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF) signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements

The Shine-Through Masking Paradigm Is a Potential Endophenotype of Schizophrenia

BACKGROUND: To understand the genetics of schizophrenia, a hunt for so-called intermediate phenotypes or endophenotypes is ongoing. Visual masking has been proposed to be such an endophenotype. However, no systematic study has been conducted yet to prove this claim. Here, we present the first study showing that masking meets the most important criteria for an endophenotype. METHODOLOGY/PRINCIPAL FINDINGS: We tested 62 schizophrenic patients, 39 non-affected first-degree relatives, and 38 healthy controls in the shine-through masking paradigm and, in addition, in the Continuous Performance Test (CPT) and the Wisconsin Card Sorting Test (WCST). Most importantly, masking performance of relatives was significantly in between the one of patients and controls in the shine-through paradigm. Moreover, deficits were stable throughout one year. Using receiver operating characteristics (ROC) methods, we show that the shine-through paradigm distinguishes with high sensitivity and specificity between schizophrenic patients, first-order relatives and healthy controls. CONCLUSIONS/SIGNIFICANCE: The shine-through paradigm is a potential endophenotype

Is there scope for community health nurses to address lifestyle risk factors? the community nursing SNAP trial

<p>Abstract</p> <p>Background</p> <p>This paper examines the opportunity and need for lifestyle interventions for patients attending generalist community nursing services in Australia. This will help determine the scope for risk factor management within community health care by generalist community nurses (GCNs).</p> <p>Methods</p> <p>This was a quasi-experimental study conducted in four generalist community nursing services in NSW, Australia. Prior to service contacts, clients were offered a computer-assisted telephone interview to collect baseline data on socio-demographics, health conditions, smoking status, physical activity levels, alcohol consumption, height and weight, fruit and vegetable intake and 'readiness-to-change' for lifestyle risk factors.</p> <p>Results</p> <p>804 clients participated (a response rate of 34.1%). Participants had higher rates of obesity (40.5% vs 32.1%) and higher prevalence of multiple risk factors (40.4% vs 29.5%) than in the general population. Few with a SNAPW (Smoking-Nutrition-Alcohol-Physical-Activity-Weight) risk factor had received advice or referral in the previous 3 months. The proportion of clients identified as at risk and who were open to change (i.e. contemplative, in preparation or in action phase) were 65.0% for obese/overweight; 73.8% for smokers; 48.2% for individuals with high alcohol intake; 83.5% for the physically inactive and 59.0% for those with poor nutrition.</p> <p>Conclusions</p> <p>There was high prevalence of lifestyle risk factors. Although most were ready to change, few clients recalled having received any recent lifestyle advice. This suggests that there is considerable scope for intervention by GCNs. The results of this trial will shed light on how best to implement the lifestyle risk factor management in routine practice.</p