Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Profile, cost and pattern of prescriptions for polymedicated patients in Catalonia (Spain).

OBJECTIVES: Polypharmacy is one of the main management issues in public health policies because of its financial impact and the increasing number of people involved. The polymedicated population according to their demographic and therapeutic profile and the cost for the public healthcare system were characterised. DESIGN: Cross-sectional study. SETTING: Primary healthcare in Barcelona Health Region, Catalonia, Spain (5 105 551 inhabitants registered). PARTICIPANTS: All insured polymedicated patients. Polymedicated patients were those with a consumption of ≥16 drugs/month. MAIN OUTCOMES MEASURES: The study variables were related to age, gender and medication intake obtained from the 2008 census and records of prescriptions dispensed in pharmacies and charged to the public health system. RESULTS: There were 36 880 polymedicated patients (women: 64.2%; average age: 74.5±10.9 years). The total number of prescriptions billed in 2008 was 2 266 830 (2 272 920 total package units). The most polymedicated group (up to 40% of the total prescriptions) was patients between 75 and 84 years old. The average number of prescriptions billed monthly per patient was 32±2, with an average cost of 452.7±27.5. The total cost of those prescriptions corresponded to 2% of the drug expenditure in Catalonia. The groups N, C, A, R and M represented 71.4% of the total number of drug package units dispensed to polymedicated patients. Great variability was found between the medication profiles of men and women, and between age groups; greater discrepancies were found in paediatric patients (5-14 years) and the elderly (≥65 years). CONCLUSIONS: This study provides essential information to take steps towards rational drug use and a structured approach in the polymedicated population in primary healthcare.

Major variables influencing correct antibiotic prescription by primary care physicians in acute pharyngitis: a cross-sectional study

Objectives:To analyse which are the main variables that influence primary care professionals, in the prescription of antibiotics in patients with acute pharyngitis.To analyse which is the diagnosis pattern used by primary care professionals towards cutepharyngitis. To recognize the clinical and analytical criteria that primary care professionals use, to determine antibiotic treatment in acute pharyngitis.To identify the main clinical variables related with the prescription of antibiotics by primary care professionals, in acute pharyngitis treatment. Design: Cross-­‐sectional study Participants:165 primary care professionals from the Sanitary Region of Girona not attending paediatric patients and randomly selected from 29 ABS managed by two of the main health care providers: Insitut Català de la Salut (ICS) and Institut d’Assistència Sanitària (IAS) Main outcome measures: Each participant will fill out a questionnaire with personal and workplace questions, as well as about knowledge and attitude in front of the acute pharyngitis caused by group A streptococci. They will also answer 4 clinical questions about correct treatment and diagnosis of acute pharyngitis caused by group A streptococci

VAMP-2 and cellubrevin are expressed in pancreatic beta-cells and are essential for Ca(2+)-but not for GTP gamma S-induced insulin secretion.

VAMP proteins are important components of the machinery controlling docking and/or fusion of secretory vesicles with their target membrane. We investigated the expression of VAMP proteins in pancreatic beta-cells and their implication in the exocytosis of insulin. cDNA cloning revealed that VAMP-2 and cellubrevin, but not VAMP-1, are expressed in rat pancreatic islets and that their sequence is identical to that isolated from rat brain. Pancreatic beta-cells contain secretory granules that store and secrete insulin as well as synaptic-like microvesicles carrying gamma-aminobutyric acid. After subcellular fractionation on continuous sucrose gradients, VAMP-2 and cellubrevin were found to be associated with both types of secretory vesicle. The association of VAMP-2 with insulin-containing granules was confirmed by confocal microscopy of primary cultures of rat pancreatic beta-cells. Pretreatment of streptolysin-O permeabilized insulin-secreting cells with tetanus and botulinum B neurotoxins selectively cleaved VAMP-2 and cellubrevin and abolished Ca(2+)-induced insulin release (IC50 approximately 15 nM). By contrast, the pretreatment with tetanus and botulinum B neurotoxins did not prevent GTP gamma S-stimulated insulin secretion. Taken together, our results show that pancreatic beta-cells express VAMP-2 and cellubrevin and that one or both of these proteins selectively control Ca(2+)-mediated insulin secretion.

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

Variables físicas que explican la variabilidad de suelo aluvial y su comportamiento espacial

El objetivo de este trabajo fue utilizar el análisis de componentes principales y de semivarianza para seleccionar variables físicas que explicaran la variabilidad de un suelo aluvial, y establecer el comportamiento espacial de las variables seleccionadas, con el fin de definir técnicamente la localización de parcelas experimentales para estudiar los efectos de la abrasividad del suelo sobre el desgaste de herramientas agrícolas. Las pruebas de campo se realizaron en 2008, en un lote plano de 6.000 m² con suelos de textura media a pesada (Vertic Haplustepts). Se hizo un muestreo intensivo en cuadrícula de 10x14 m. Las variables que mayor peso tuvieron en los tres primeros componentes principales fueron los contenidos de limo, arena fina y media, gravilla media, la humedad a capacidad de campo y el coeficiente higroscópico. A excepción de la arena media y la capacidad de campo, las demás propiedades presentaron alta dependencia espacial y su distribución mostró que en el lote experimental se encuentran tres sectores de acumulación diferencial de limo y de arena fina. La combinación de los análisis de componentes principales y geoestadística permitió definir las propiedades del suelo involucradas en el desgaste de herramientas, su patrón espacial y la manera más adecuada de distribuir parcelas experimentales, para estudiar la abrasividad del suelo.

Antibacterial activity of essential oils on Xanthomonas vesicatoria and control of bacterial spot in tomato

The objective of this work was to evaluate the effects of plant essential oils (EOs) on the growth of Xanthomonas vesicatoria, on bacterial morphology and ultrastructure, and on the severity of tomato bacterial spot. EOs from citronella, clove, cinnamon, lemongrass, eucalyptus, thyme, and tea tree were evaluated in vitro at concentrations of 0.1, 1.0, 10, and 100% in 1.0% powdered milk. The effect of EOs, at 0.1%, on the severity of tomato bacterial spot was evaluated in tomato seedlings under greenhouse conditions. The effects of citronella, lemongrass, clove, and tea tree EOs, at 0.1%, on X. vesicatoria cells were evaluated by transmission electron microscopy. All EOs showed direct toxic effect on the bacteria at a 10%-concentration in vitro. Under greenhouse conditions, the EOs of clove, citronella, tea tree, and lemongrass reduced disease severity. EOs of clove and tea tree, and streptomycin sulfate promoted loss of electron-dense material and alterations in the cytoplasm, whereas EO of tea tree promoted cytoplasm vacuolation, and those of citronella, lemongrass, clove, and tea tree caused damage to the bacterial cell wall. The EOs at a concentration of 0.1% reduce the severity of the disease.

Dry matter production of Tanzania grass as a function of agrometeorological variables

The objective of this work was to develop and validate linear regression models to estimate the production of dry matter by Tanzania grass (Megathyrsus maximus, cultivar Tanzania) as a function of agrometeorological variables. For this purpose, data on the growth of this forage grass from 2000 to 2005, under dry‑field conditions in São Carlos, SP, Brazil, were correlated to the following climatic parameters: minimum and mean temperatures, degree‑days, and potential and actual evapotranspiration. Simple linear regressions were performed between agrometeorological variables (independent) and the dry matter accumulation rate (dependent). The estimates were validated with independent data obtained in São Carlos and Piracicaba, SP, Brazil. The best statistical results in the development and validation of the models were obtained with the agrometeorological parameters that consider thermal and water availability effects together, such as actual evapotranspiration, accumulation of degree‑days corrected by water availability, and the climatic growth index, based on average temperature, solar radiation, and water availability. These variables can be used in simulations and models to predict the production of Tanzania grass.

Stratified estimation of forest inventory variables using spatially summarized stratifications

Abstract

Análisis de la eficacia de los saques de esquina en la copa del mundo de fútbol 2010. Un intento de identificación de variables explicativas

In soccer, dead-ball moves are those in which the ball is returned to play from a stationary position following an interruption of play. The aim of this study was to analyse the effectiveness of one such dead-ball move, namely corner kicks, and to identify the key variables that determine the success of a shot or header following a corner, thereby enabling a model of successful corner kicks to be proposed. We recorded 554 corner kicks performed during the 2010 World Cup in South Africa and carried out a univariate, bivariate and multivariate analysis of the data. The results indicated that corners were of limited effectiveness in terms of the success of subsequent shots or headers. The analysis also revealed a series of variables that were significantly related to one another, and this enabled us to propose an explanatory model. Although this model had limited explanatory power, it nonetheless helps to understand the execution of corner kicks in practical terms.

Genetic diversity between improved banana diploids using canonical variables and the Ward-MLM method

The objective of this work was to estimate the genetic diversity of improved banana diploids using data from quantitative analysis and from simple sequence repeats (SSR) marker, simultaneously. The experiment was carried out with 33 diploids, in an augmented block design with 30 regular treatments and three common ones. Eighteen agronomic characteristics and 20 SSR primers were used. The agronomic characteristics and the SSR were analyzed simultaneously by the Ward-MLM, cluster, and IML procedures. The Ward clustering method considered the combined matrix obtained by the Gower algorithm. The Ward-MLM procedure identified three ideal groups (G1, G2, and G3) based on pseudo-F and pseudo-t² statistics. The dendrogram showed relative similarity between the G1 genotypes, justified by genealogy. In G2, 'Calcutta 4' appears in 62% of the genealogies. Similar behavior was observed in G3, in which the 028003-01 diploid is the male parent of the 086079-10 and 042079-06 genotypes. The method with canonical variables had greater discriminatory power than Ward-MLM. Although reduced, the genetic variability available is sufficient to be used in the development of new hybrids.

Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.