Prevalence and clinical aspects of respiratory syncytial virus A and B groups in children seen at Hospital de Clínicas of Uberlândia, MG, Brazil

Respiratory syncytial virus (RSV) is well recognized as the most important pathogen causing acute respiratory disease in infants and young children, mainly in the form of bronchiolitis and pneumonia. Two major antigenic groups, A and B, have been identified; however, there is disagreement about the severity of the diseases caused by these two types. This study investigated a possible association between RSV groups and severity of disease. Reverse transcription-polymerase chain reaction was used to characterize 128 RSV nasopharyngeal specimens from children less than five years old experiencing acute respiratory disease. A total of 82 of 128 samples (64.1%) could be typed, and, of these, 78% were group A, and 22% were group B. Severity was measured by clinical evaluation associated with demographic factors: for RSV A-infected patients, 53.1% were hospitalized, whereas for RSV B patients, 27.8% were hospitalized (p = 0.07). Around 35.0% of the patients presented risk factors for severity (e.g., prematurity). For those without risk factors, the hospitalization occurred in 47.6% of patients infected with RSV A and in 18.2% infected with RSV B. There was a trend for RSV B infections to be milder than those of RSV A. Even though RSV A-infected patients, including cases without underlying condition and prematurity, were more likely to require hospitalization than those infected by RSV B, the disease severity could not to be attributed to the RSV groups.

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

Learning of courtship components in Drosophila mercatorum (Paterson & Wheller) (Diptera, Drosophilidae)

In Drosophila, courtship is an elaborate sequence of behavioural patterns that enables the flies to identify conspecific mates from those of closely related species. This is important because drosophilids usually gather in feeding sites, where males of various species court females vigorously. We investigated the effects of previous experience on D. mercatorum courtship, by testing if virgin males learn to improve their courtship by observing other flies (social learning), or by adjusting their pre-existent behaviour based on previous experiences (facilitation). Behaviours recorded in a controlled environment were courtship latency, courtship (orientation, tapping and wing vibration), mating and other behaviours not related to sexual activities. This study demonstrated that males of D. mercatorum were capable of improving their mating ability based on prior experiences, but they had no social learning on the development of courtship.

Statistical Brief on the Agricultural Research of Cape Verde

The primary purpose of this brief is to provide various statistical and institutional details on the development and current status of the public agricultural research system in Cape Verde. This information has been collected and presented in a systematic way in order to inform and thereby improve research policy formulation with regard to the Cape Verdean NARS. Most importantly, these data are assembled and reported in a way that makes them directly comparable with the data presented in the other country briefs in this series. And because institutions take time to develop and there are often considerable lags in the agricultural research process, it is necessary for many analytical and policy purposes to have access to longer-run series of data. NARSs vary markedly in their institutional structure and these institutional aspects can have a substantial and direct effect on their research performance. To provide a basis for analysis and cross-country, over-time comparisons, the various research agencies in a country have been grouped into five general categories; government, semi-public, private, academic, and supranational. A description of these categories is provided in table 1.

Statistical Brief on the Agricultural Research of Cape Verde

The primary purpose of this brief is to provide various statistical and institutional details on the development and current status of the public agricultural research system in Cape Verde. This information has been collected and presented in a systematic way in order to inform and thereby improve research policy formulation with regard to the Cape Verdean NARS. Most importantly, these data are assembled and reported in a way that makes them directly comparable with the data presented in the other country briefs in this series. And because institutions take time to develop and there are often considerable lags in the agricultural research process, it is necessary for many analytical and policy purposes to have access to longer-run series of data. NARSs vary markedly in their institutional structure and these institutional aspects can have a substantial and direct effect on their research performance. To provide a basis for analysis and cross-country, over-time comparisons, the various research agencies in a country have been grouped into five general categories; government, semi-public, private, academic, and supranational. A description of these categories is provided in table 1.

Olfactory cues potentiate learning of distant visuospatial information

The influence of proximal olfactory cues on place learning and memory was tested in two different spatial tasks. Rats were trained to find a hole leading to their home cage or a single food source in an array of petri dishes. The two apparatuses differed both by the type of reinforcement (return to the home cage or food reward) and the local characteristics of the goal (masked holes or salient dishes). In both cases, the goal was in a fixed location relative to distant visual landmarks and could be marked by a local olfactory cue. Thus, the position of the goal was defined by two sets of redundant cues, each of which was sufficient to allow the discrimination of the goal location. These experiments were conducted with two strains of hooded rats (Long-Evans and PVG), which show different speeds of acquisition in place learning tasks. They revealed that the presence of an olfactory cue marking the goal facilitated learning of its location and that the facilitation persisted after the removal of the cue. Thus, the proximal olfactory cue appeared to potentiate learning and memory of the goal location relative to distant environmental cues. This facilitating effect was only detected when the expression of spatial memory was not already optimal, i.e., during the early phase of acquisition. It was not limited to a particular strain.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

Assessment of driving capability through the use of clinical and psychomotor tests in relation to blood cannabinoids levels following oral administration of 20 mg dronabinol or of a cannabis decoction made with 20 or 60 mg Delta9-THC.

Delta(9)-Tetrahydrocannabinol (THC) is frequently found in the blood of drivers suspected of driving under the influence of cannabis or involved in traffic crashes. The present study used a double-blind crossover design to compare the effects of medium (16.5 mg THC) and high doses (45.7 mg THC) of hemp milk decoctions or of a medium dose of dronabinol (20 mg synthetic THC, Marinol on several skills required for safe driving. Forensic interpretation of cannabinoids blood concentrations were attempted using the models proposed by Daldrup (cannabis influencing factor or CIF) and Huestis and coworkers. First, the time concentration-profiles of THC, 11-hydroxy-Delta(9)-tetrahydrocannabinol (11-OH-THC) (active metabolite of THC), and 11-nor-9-carboxy-Delta(9)-tetrahydrocannabinol (THCCOOH) in whole blood were determined by gas chromatography-mass spectrometry-negative ion chemical ionization. Compared to smoking studies, relatively low concentrations were measured in blood. The highest mean THC concentration (8.4 ng/mL) was achieved 1 h after ingestion of the strongest decoction. Mean maximum 11-OH-THC level (12.3 ng/mL) slightly exceeded that of THC. THCCOOH reached its highest mean concentration (66.2 ng/mL) 2.5-5.5 h after intake. Individual blood levels showed considerable intersubject variability. The willingness to drive was influenced by the importance of the requested task. Under significant cannabinoids influence, the participants refused to drive when they were asked whether they would agree to accomplish several unimportant tasks, (e.g., driving a friend to a party). Most of the participants reported a significant feeling of intoxication and did not appreciate the effects, notably those felt after drinking the strongest decoction. Road sign and tracking testing revealed obvious and statistically significant differences between placebo and treatments. A marked impairment was detected after ingestion of the strongest decoction. A CIF value, which relies on the molar ratio of main active to inactive cannabinoids, greater than 10 was found to correlate with a strong feeling of intoxication. It also matched with a significant decrease in the willingness to drive, and it matched also with a significant impairment in tracking performances. The mathematic model II proposed by Huestis et al. (1992) provided at best a rough estimate of the time of oral administration with 27% of actual values being out of range of the 95% confidence interval. The sum of THC and 11-OH-THC blood concentrations provided a better estimate of impairment than THC alone. This controlled clinical study points out the negative influence on fitness to drive after medium or high dose oral THC or dronabinol.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Soils in an agricultural landscape of Jokioinen, south-western Finland

Selostus: Jokioisten seudun maannokset

Enhanced degradation of metalaxyl in agricultural soils of São Paulo State, Brazil

This work investigated the effect of repeated applications on enhanced degradation of metalaxyl in two different agricultural soils used for cultivation of orange and lemon from Casa Branca and Itapetininga districts of São Paulo State, Brazil. Soil samples were collected from areas repeatedly treated with commercial ridomil 50GR for six successive years, and from other areas never exposed to this fungicide. At the laboratory, soil samples received a 14C-metalaxyl solution and its degradation was studied through radiometric techniques to measure biomineralization and recovery of extractable- and soil-bound products. Enhanced degradation was verified only in one soil, although partial degradation and mineralization of the fungicide were detected in both soils. The different rates and patterns of metalaxyl degradation in the soils were probably due to their different physical, chemical, and biological characteristics.

Do real books help the learning of english in P4?

The purpose of this study is to answer the principal question, Do Real Books help the learning of English in P4?, and create a useful document to English teachers in Infant Education that are interested in working through stories. Previous to the development of the work, it has been necessary a research work and a theoretical documentation, performed using a literature review. This process has served to establish the basis of the study and to develop the subsequent didactic intervention. This second part of the study, the didactic intervention, contains all the information about how it was planned, carried to term and evaluated. There are three different ‘real books’ used as a reference in which the reader can see how to choose the correct story, how to adapt the text to the children, the daily planning, and how to evaluate the activities.

Social and individual learning of helping in humans and other species.

Helping behaviors can be innate, learned by copying others (cultural transmission) or individually learned de novo. These three possibilities are often entangled in debates on the evolution of helping in humans. Here we discuss their similarities and differences, and argue that evolutionary biologists underestimate the role of individual learning in the expression of helping behaviors in humans.