Business Models in OER, a Contingency Approach

We will present an analysis of data from a literature review and semi-structured interviews with experts on OER, to identify different aspects of OER business models and to establish how the success of the OER initiatives is measured. The results collected thus far show that two different business models for OER initiatives exist, but no data on their success or failure is published. We propose a framework for measuring success of OER initiatives.

A simple approach improving the performance of urine reagent strips for rapid diagnosis of urinary schistosomiasis in Nigerian schoolchildren

In Nigeria, schistosomiasis, caused predominantly by the species Schistosoma haematobium, is highly endemic in resource-poor communities. We performed a school-based survey in two rural communities in Osun State (Southwestern Nigeria) and assessed macrohaematuria, microhaematuria and proteinuria as indirect indicators for the presence of disease. Urine samples were inspected macroscopically for haematuria and screened for microhaematuria and proteinuria using urine reagent strips. The microscopic examination of schistosome eggs was used as the gold standard for diagnosis. In total, 447 schoolchildren were included in this study and had a 51% prevalence of urinary schistosomiasis. The sensitivity of microhaematuria (68%) and proteinuria (53%) for infection with S. haematobium was relatively low. In patients with a heavy infection (>500 eggs/10 mL), the sensitivity of microhaematuria was high (95%). When the presence of macrohaematuria and the concomitant presence of microhaematuria and proteinuria were combined, it revealed a sensitivity of 63%, a specificity of 93% and a positive predictive value of 91%. Macrohaematuria also showed high specificity (96%) and a positive predictive value of 92%, while sensitivity was < 50%. These data show that combining urine reagent strip tests (presence of proteinuria and microhaematuria) and information on macrohaematuria increased the accuracy of the rapid diagnosis of urinary schistosomiasis in an endemic rural West African setting. This simple approach can be used to increase the quality of monitoring of schistosomiasis in schoolchildren.

Predicting random level and seasonality of hotel prices. A structural equation growth curve approach

This article examines the effect on price of different characteristics of holiday hotels in the sun-and-beach segment, under the hedonic function perspective. Monthly prices of the majority of hotels in the Spanish continental Mediterranean coast are gathered from May to October 1999 from the tour operator catalogues. Hedonic functions are specified as random-effect models and parametrized as structural equation models with two latent variables, a random peak season price and a random width of seasonal fluctuations. Characteristics of the hotel and the region where they are located are used as predictors of both latent variables. Besides hotel category, region, distance to the beach, availability of parking place and room equipment have an effect on peak price and also on seasonality. 3- star hotels have the highest seasonality and hotels located in the southern regions the lowest, which could be explained by a warmer climate in autumn

Compositional amalgamations and balances: a critical approach

The amalgamation operation is frequently used to reduce the number of parts of compositional data but it is a non-linear operation in the simplex with the usual geometry,the Aitchison geometry. The concept of balances between groups, a particular coordinate system designed over binary partitions of the parts, could be an alternative to theamalgamation in some cases. In this work we discuss the proper application of bothconcepts using a real data set corresponding to behavioral measures of pregnant sows

Dilatation pyélocalicielle chez le nouveau-né: attitude pratique pour le pédiatre [Antenatally detected hydronephrosis: practical approach for the pediatrician]

Approximately 1% of the fetuses present some dilatation of their urinary tract in utero. More than 50% of these antenatally detected hydronephrosis will disappear spontaneously after birth. The other 50% comprises ureteropelvic junction obstruction, vesico-ureteral reflux and primary megaureters. Postnatal radiological evaluation (renal ultrasonography and VCUG) is performed in every infant with a significantly dilated renal pelvis (> 8 mm between 20 and 30 weeks or > 10 mm after 30 weeks in utero). Renal nuclear scan should be done in every child with significant/worsening post-natal hydronephrosis. Antibioprophylaxis will be started from birth to prevent urinary tract infection. Medical or surgical approach will be chosen in the light of the uroradiological exam results and the clinical progress.

Prevention of overweight and obesity: a Spanish approach

Background. Obesity is considered a major public health issue in most developed countries nowadays. This paper provides an overview of current population data available in Spain and the approach to develop preventive strategies in the country. Methods. Review of population data available is based on individually measured weight and height as well as determinants. On this basis, the approach used in the country to develop preventive strategies is discussed. Results. According to the DORICA study, the prevalence of obesity (BMI ≥30 kg m−2) is 15.5% in Spanish adults aged 25–60 years (13.2% in men and 17.5% in women). Obesity rates are higher among women aged 45 years and older, low social class, living in semi-urban places. Population estimates for the prevalence of obesity in Spanish children and young people based on the enKid study are 13.9% for the whole group. In this study, overweight and obesity is related to absence of breastfeeding, low consumption of fruit and vegetables, high consumption of cakes, buns, softdrinks and butchery products, low physical activity levels and a positive association with time spent watching TV. In 2005, the Spanish Ministry of Health jointly with the Spanish Agency for Food Safety and Nutrition launched the multifaceted NAOS strategy for nutrition, physical activity and the prevention of obesity. The important role of the family and the school setting as well as the responsibility of the Health Administration and Pediatric Care in the prevention of obesity is highlighted in the document. The need for environmental actions is recognised. The PERSEO programme, a multicomponent school-based intervention project is part of the strategy currently in place. Conclusion. Obesity is a public health issue in Spain. A national multifaceted strategy was launched to counteract the problem. Environmental and policy actions are a priority. Young children and their families are among the main target groups.

A new approach for potential drug target discovery through in silico metabolic pathway analysis using Trypanosoma cruzi genome information

The current drug options for the treatment of chronic Chagas disease have not been sufficient and high hopes have been placed on the use of genomic data from the human parasite Trypanosoma cruzi to identify new drug targets and develop appropriate treatments for both acute and chronic Chagas disease. However, the lack of a complete assembly of the genomic sequence and the presence of many predicted proteins with unknown or unsure functions has hampered our complete view of the parasite's metabolic pathways. Moreover, pinpointing new drug targets has proven to be more complex than anticipated and has revealed large holes in our understanding of metabolic pathways and their integrated regulation, not only for this parasite, but for many other similar pathogens. Using an in silicocomparative study on pathway annotation and searching for analogous and specific enzymes, we have been able to predict a considerable number of additional enzymatic functions in T. cruzi. Here we focus on the energetic pathways, such as glycolysis, the pentose phosphate shunt, the Krebs cycle and lipid metabolism. We point out many enzymes that are analogous to those of the human host, which could be potential new therapeutic targets.

Réduction de volume pulmonaire dans l'emphysème sévère: importance d'une prise en charge multidisciplinaire [Lung volume reduction (LVR) in severe emphysema: a multidisciplinary approach].

Most cases of emphysema are managed conservatively. However, in severe symptomatic emphysema associated with hyperinflation, lung volume reduction (LVR) may be proposed to improve dyspnea, exercice capacity, pulmonary functions, walk distance and to decrease long-term mortality. LVR may be achieved either surgically (LVRS) or endoscopically (EVLR by valves or coils) according to specific clinical criteria. Currently, the optimal approach is discussed in a multidisciplinary setting. The latter permits a personalized evaluation the patient's clinical status and allows the best possible therapeutic intervention to be proposed to the patient.

A Bayesian network approach to the database serch problem in criminal proceedings

Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.

Prévalence des troubles de l'humeur dans la maladie de Parkinson traitée par stimulation cérébrale profonde [Prevalence of mood disorders in Parkinson's disease patients treated with subthalamic nucleus deep brain stimulation].

Subthalamic nucleus deep brain stimulation (STN-DBS) is a recognized treatment for advanced and severe forms of Parkinson's Disease. The procedure improves motor signs and often allows a reduction of the medication. The impact of the procedure on cognitive and neuropsychiatric signs of the disease is more debated and there is an international consensus for the need of a multidisciplinary evaluation of patients undergoing such programs, including a neuropsychiatric assessment. We present a review of the literature as well as the experience at our centre focused on the short and long term outcome on mood following STN-DBS.

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

An approach to vision-based station keeping for an unmanned underwater vehicle

This paper presents an automatic vision-based system for UUV station keeping. The vehicle is equipped with a down-looking camera, which provides images of the sea-floor. The station keeping system is based on a feature-based motion detection algorithm, which exploits standard correlation and explicit textural analysis to solve the correspondence problem. A visual map of the area surveyed by the vehicle is constructed to increase the flexibility of the system, allowing the vehicle to position itself when it has lost the reference image. The testing platform is the URIS underwater vehicle. Experimental results demonstrating the behavior of the system on a real environment are presented