Introduction of snow and geomorphic disturbance variables into predictive models of alpine plant distribution in the Western Swiss Alps

Indirect topographic variables have been used successfully as surrogates for disturbance processes in plant species distribution models (SDM) in mountain environments. However, no SDM studies have directly tested the performance of disturbance variables. In this study, we developed two disturbance variables: a geomorphic index (GEO) and an index of snow redistribution by wind (SNOW). These were developed in order to assess how they improved both the fit and predictive power of presenceabsence SDM based on commonly used topoclimatic (TC) variables for 91 plants in the Western Swiss Alps. The individual contribution of the disturbance variables was compared to TC variables. Maps of models were prepared to spatially test the effect of disturbance variables. On average, disturbance variables significantly improved the fit but not the predictive power of the TC models and their individual contribution was weak (5.6% for GEO and 3.3% for SNOW). However their maximum individual contribution was important (24.7% and 20.7%). Finally, maps including disturbance variables (i) were significantly divergent from TC models in terms of predicted suitable surfaces and connectivity between potential habitats, and (ii) were interpreted as more ecologically relevant. Disturbance variables did not improve the transferability of models at the local scale in a complex mountain system, and the performance and contribution of these variables were highly species-specific. However, improved spatial projections and change in connectivity are important issues when preparing projections under climate change because the future range size of the species will determine the sensitivity to changing conditions.

Variation in biomass related variables of reed canary grass

Selostus: Ruokohelven biomassan tuotantoon vaikuttavien ominaisuuksien vaihtelu

Mining the human phenome using allelic scores that index biological intermediates.

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

Summer ozone cycles in the Lisbon metropolitan area and its relation to some meteorological variables

The aim of this work is to study the tropospheric ozone concentrations and daily peak cycles in the Lisbon MetropolitanArea (LMA) during the summer season (June, July and August, JJA) covering the 4-yr study period 2002-2005. Theresults show that all the stations have the same pattern: a minimum in the early morning followed by an increase at 1000UTC reaching to a peak at 1300-1400 UTC, dropped again to minimum values 1800 UTC but with different concentrationsdue to regional and local wind circulations and complex dynamic interactions. We identified in Lisbon city the ozone “weekendeffect”. Finally, we studied an episode of very high levels of tropospheric ozone and related daily ozone concentrationswith some meteorological variables.

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

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

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.

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

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.

Clinical chemistry variables in normal elderly and healthy ambulatory populations: comparison with reference values.

Laboratory values of the most commonly assayed clinical chemistry variables were determined in selected elderly and healthy ambulatory populations. The upper and lower limits (2.5 and 97.5 fractiles) were compared with the adult reference values in use in university hospitals of Switzerland. The results suggest that conventional adult reference values can be used for most variables in the elderly and that these values are also useful in an ambulatory population.

The Persistence of Finnish Equity Fund Performance

Tutkimuksen tavoitteena on selvittää, esiintyykö suomeen sijoittavilla osakerahastoilla menestyksen pysyvyyttä. Tutkimusaineisto koostuu kaikista suomalaisista osakerahastoista, jotka toimivat ajanjaksolla 15.1.1998-13.1.2005. Aineisto on vapaa selviytymisvinoumasta. Suorituskyvyn mittareina käytetään CAPM-alfaa sekä kolmi- ja nelifaktori-alfaa. Empiirisessä osassa osakerahastojen menestyksen pysyvyyttä testataan Spearmanin järjestyskorrelaatiotestillä. Evidenssi menestyksen pysyvyydestä jäi vähäiseksi, vaikkakin sitä esiintyi satunnaisesti kaikilla menestysmittareilla joillakin ranking- ja sijoitusperiodin yhdistelmillä. CAPM-alfalla tarkasteltuna tilastollisesti merkitsevää menestyksen pysyvyyttä esiintyi selvästi useammin kuin muilla menestysmittareilla. Tulokset tukevat viimeaikaisia kansainvälisiä tutkimuksia, joiden mukaan menestyksen pysyvyys riippuu usein mittaustavasta. Menestysmittareina käytettyjen regressiomallien merkitsevyystestit osoittavat multifaktorimallien selittävän osakerahastojen tuottoja CAPM:a paremmin. Lisätyt muuttujat parantavat merkittävästi CAPM:n selitysvoimaa.