Effects of a single administration of prostaglandin F2alpha, or a combination of prostaglandin F2alpha and prostaglandin E2, or placebo on fertility variables in dairy cows 3-5 weeks post partum, a randomized, double-blind clinical trial

BACKGROUND: Delayed uterine involution has negative effects on the fertility of cows; use of prostaglandin F2alpha alone as a single treatment has not been shown to consistently improve fertility. Combined administration of PGF2alpha and PGE2 increased uterine pressure in healthy cows. We hypothesized, that the combination of both prostaglandins would accelerate uterine involution and have, therefore, a positive effect on fertility variables. In commercial dairy farming, the benefit of a single post partum combined prostaglandin treatment should be demonstrated. METHODS: 383 cows from commercial dairy farms were included in this study. Uterine size and secretion were evaluated at treatment 21-35 days post partum and 14 days later. Cows were randomly allocated to one of three treatment groups: PGF2alpha and PGE2, PGF2alpha or placebo. For every animal participating in the study, the following reproduction variables were recorded: Interval from calving to first insemination, days open, number of artificial inseminations (AI) to conception; subsequent treatment of uterus, subsequent treatment of ovaries. Plasma progesterone level at time of treatment was used as a covariable. For continuous measurements, analysis of variance was performed. Fisher's exact test for categorical non-ordered data and exact Kruskal-Wallis test for ordered data were used; pairwise group comparisons with Bonferroni adjustment of significance level were performed. RESULTS: There was no significant difference among treatment groups in uterine size. Furthermore, there was no significant difference among treatments concerning days open, number of AI, and subsequent treatment of uterus and ovaries. Days from calving to first insemination tended to be shorter for cows with low progesterone level given PGF2alpha and PGE2 in combination than for the placebo-group (P = 0.024). CONCLUSION: The results of this study indicate that the administration of PGF2alpha or a combination of PGF2alpha and PGE2 21 to 35 days post partum had no beneficial effect upon measured fertility variables. The exception was a tendency for a shorter interval from calving to first insemination after administration of the combination of PGF2alpha and PGE2, as compared to the placebo group. Further research should be done in herds with reduced fertility and/or an increased incidence of postpartum vaginal discharge.

Continuous thoracic epidural anesthesia improves gut mucosal microcirculation in rats with sepsis

Microcirculatory dysfunction contributes significantly to tissue hypoxia and multiple organ failure in sepsis. Ischemia of the gut and intestinal hypoxia are especially relevant for the evolution of sepsis because the mucosal barrier function may be impaired, leading to translocation of bacteria and toxins. Because sympathetic blockade enhances intestinal perfusion under physiologic conditions, we hypothesized that thoracic epidural anesthesia (TEA) may attenuate microcirculatory perturbations during sepsis. The present study was designed as a prospective and controlled laboratory experiment to assess the effects of continuous TEA on the mucosal microcirculation in a cecal ligation and perforation model of sepsis in rats. Anesthetized Sprague-Dawley rats underwent laparotomy and cecal ligation and perforation to induce sepsis. Subsequently, either bupivacaine 0.125% (n = 10) or isotonic sodium chloride solution (n = 9) was continuously infused via the thoracic epidural catheter for 24 h. In addition, a sham laparotomy was carried out in eight animals. Intravital videomicroscopy was then performed on six to ten villi of ileum mucosa. The capillary density was measured as areas encircled by perfused capillaries, that is, intercapillary areas. The TEA accomplished recruitment of microcirculatory units in the intestinal mucosa by decreasing total intercapillary areas (1,317 +/- 403 vs. 1,001 +/- 236 microm2) and continuously perfused intercapillary areas (1,937 +/- 512 vs. 1,311 +/- 678 microm2, each P < 0.05). Notably, TEA did not impair systemic hemodynamic variables beyond the changes caused by sepsis itself. Therefore, sympathetic blockade may represent a therapeutic option to treat impaired microcirculation in the gut mucosa resulting from sepsis. Additional studies are warranted to assess the microcirculatory effects of sympathetic blockade on other splanchnic organs in systemic inflammation.

A continuous-time MILP model for short-term scheduling of make-and-pack production processes

In process industries, make-and-pack production is used to produce food and beverages, chemicals, and metal products, among others. This type of production process allows the fabrication of a wide range of products in relatively small amounts using the same equipment. In this article, we consider a real-world production process (cf. Honkomp et al. 2000. The curse of reality – why process scheduling optimization problems are diffcult in practice. Computers & Chemical Engineering, 24, 323–328.) comprising sequence-dependent changeover times, multipurpose storage units with limited capacities, quarantine times, batch splitting, partial equipment connectivity, and transfer times. The planning problem consists of computing a production schedule such that a given demand of packed products is fulfilled, all technological constraints are satisfied, and the production makespan is minimised. None of the models in the literature covers all of the technological constraints that occur in such make-and-pack production processes. To close this gap, we develop an efficient mixed-integer linear programming model that is based on a continuous time domain and general-precedence variables. We propose novel types of symmetry-breaking constraints and a preprocessing procedure to improve the model performance. In an experimental analysis, we show that small- and moderate-sized instances can be solved to optimality within short CPU times.

Accuracy of continuous glucose monitoring during differing exercise conditions.

AIM Depending on intensity, exercise may induce a strong hormonal and metabolic response, including acid-base imbalances and changes in microcirculation, potentially interfering with the accuracy of continuous glucose monitoring (CGM). The present study aimed at comparing the accuracy of the Dexcom G4 Platinum (DG4P) CGM during continuous moderate and intermittent high-intensity exercise (IHE) in adults with type 1 diabetes (T1DM). METHODS Ten male individuals with well-controlled T1DM (HbA1c 7.0±0.6% [54±6mmol/mol]) inserted the DG4P sensor 2 days prior to a 90min cycling session (50% VO2peak) either with (IHE) or without (CONT) a 10s all-out sprint every 10min. Venous blood samples for reference glucose measurement were drawn every 10min and euglycemia (target 7mmol/l) was maintained using an oral glucose solution. Additionally, lactate and venous blood gas variables were determined. RESULTS Mean reference blood glucose was 7.6±0.2mmol/l during IHE and 6.7±0.2mmol/l during CONT (p<0.001). IHE resulted in significantly higher levels of lactate (7.3±0.5mmol/l vs. 2.6±0.3mmol/l, p<0.001), while pH values were significantly lower in the IHE group (7.27 vs. 7.38, p=0.001). Mean absolute relative difference (MARD) was 13.3±2.2% for IHE and 13.6±2.8% for CONT suggesting comparable accuracy (p=0.90). Using Clarke Error Grid Analysis, 100% of CGM values during both IHE and CONT were in zones A and B (IHE: 77% and 23%; CONT: 78% and 22%). CONCLUSIONS The present study revealed good and comparable accuracy of the DG4P CGM system during intermittent high intensity and continuous moderate intensity exercise, despite marked differences in metabolic conditions. This corroborates the clinical robustness of CGM under differing exercise conditions. CLINICAL TRIAL REGISTRATION NUMBER ClinicalTrials.gov NCT02068638.

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Longitudinal categorical data analysis: A continuous -time Markov chain approach

In this dissertation, we propose a continuous-time Markov chain model to examine the longitudinal data that have three categories in the outcome variable. The advantage of this model is that it permits a different number of measurements for each subject and the duration between two consecutive time points of measurements can be irregular. Using the maximum likelihood principle, we can estimate the transition probability between two time points. By using the information provided by the independent variables, this model can also estimate the transition probability for each subject. The Monte Carlo simulation method will be used to investigate the goodness of model fitting compared with that obtained from other models. A public health example will be used to demonstrate the application of this method. ^

Actividad microbiológica de un suelo sometido a distintos usos y su relación con variables físico- químicas

Gran parte de los procesos microbianos que contribuyen a la fertilidad de los agroecosistemas y el ciclado de nutrientes ocurren en el suelo. Este ciclado de nutrientes depende críticamente de la actividad microbiológica de los suelos, la cual a su vez está mediada por la estructura y funcionamiento de la microbiota edáfica. En este contexto, el objetivo de este trabajo, fue determinar si la actividad microbiana puede ser buena indicadora de la intensidad de uso del suelo, analizando: 1- si las diferencias en la intensidad de uso del suelo se relacionan con diferencias en la actividad microbiológica estimada a través de la respiración edáfica y la actividad enzimática; y 2- las posibles relaciones entre estas variables microbiológicas y las variables físico-químicas. Entre 2008 y 2010 se realizaron muestreos trimestrales en campos de la provincia de Buenos Aires en suelos Argiudoles bajo diferentes usos: 1- Agricultura intensiva continua, 2- Agricultura reciente, y 3- Pastizales naturalizados. Tres sitios de muestreo se seleccionaron como réplicas para cada uso de suelo, con 5 muestras por fecha y réplica. La actividad microbiana se evaluó midiendo la respiración edáfica y la actividad de las enzimas nitrogenasas y se analizaron variables físico- químicas. Tanto las variables microbiológicas como las físico-químicas se analizaron mediante Kruskall-Wallis (P < 0,05). Se exploró la asociación entre las variables físico-químicas y microbiológicas aplicando el coeficiente de correlación no paramétrico (Spearman). Los distintos usos de un mismo suelo presentaron diferencias en la actividad microbiológica. La respiración edáfica fue significativamente mayor en los pastizales naturalizados que en los sistemas con agricultura. La actividad nitrogenasa resultó significativamente mayor en los pastizales naturalizados respecto de la agricultura continua y no se diferenció significativamente de la agricultura reciente. Las variables físico- químicas resultaron menos consistentes en detectar diferencias entre usos. Se detectaron correlaciones significativas entre la actividad microbiológica y algunas de las variables físico-químicas. Los resultados muestran que la actividad microbiológica puede resultar útil para diferenciar intensidades de usos de suelo.

Climate and total tree cover reconstructions based on the 43.7-kyr fossil pollen record from Khoe (NW Sakhalin Island) and modern climate variables of 236 modern surface pollen sampling sites

A late Quaternary pollen record from northern Sakhalin Island (51.34°N, 142.14°E, 15 m a.s.l.) spanning the last 43.7 ka was used to reconstruct regional climate dynamics and vegetation distribution by using the modern analogue technique (MAT). The long-term trends of the reconstructed mean annual temperature (TANN) and precipitation (PANN), and total tree cover are generally in line with key palaeoclimate records from the North Atlantic region and the Asian monsoon domain. TANN largely follows the fluctuations in solar summer insolation at 55°N. During Marine Isotope Stage (MIS) 3, TANN and PANN were on average 0.2 °C and 700 mm, respectively, thus very similar to late Holocene/modern conditions. Full glacial climate deterioration (TANN = -3.3 °C, PANN = 550 mm) was relatively weak as suggested by the MAT-inferred average climate parameters and tree cover densities. However, error ranges of the climate reconstructions during this interval are relatively large and the last glacial environments in northern Sakhalin could be much colder and drier than suggested by the weighted average values. An anti-phase relationship between mean temperature of the coldest (MTCO) and warmest (MTWA) month is documented during the last glacial period, i.e. MIS 2 and 3, suggesting more continental climate due to sea levels that were lower than present. Warmest and wettest climate conditions have prevailed since the end of the last glaciation with an optimum (TANN = 1.5 °C, PANN = 800 mm) in the middle Holocene interval (ca 8.7-5.2 cal. ka BP). This lags behind the solar insolation peak during the early Holocene. We propose that this is due to continuous Holocene sea level transgression and regional influence of the Tsushima Warm Current, which reached maximum intensity during the middle Holocene. Several short-term climate oscillations are suggested by our reconstruction results and correspond to Northern Hemisphere Heinrich and Dansgaard-Oeschger events, the Bølling-Allerød and the Younger Dryas. The most prominent fluctuation is registered during Heinrich 4 event, which is marked by noticeably colder and drier conditions and the spread of herbaceous taxa.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

AFBC of coal with tyre rubber. Influence of the co-combustion variables on the mineral matter of solid by-products and on Zn lixiviation

The study focuses on the generation and distribution of mineral species in fly and bottom ashes. These were formed during a fluidised co-combustion of a fossil fuel (coal) and a non-fossil fuel (tyre rubber) in a small fluidised bed combustor (7cm x 70cm). The pilot plant had continuous fuel feed using varying ratios of coal and rubber. The study also focuses on the lixiviation behaviour of metallic elements with the assessement of zinc recovering.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Generalización de variables medioambientales mediante interpolación GIS

La investigación de esta tesis se centra en el estudio de técnicas geoestadísticas y su contribución a una mayor caracterización del binomio factores climáticos-rendimiento de un cultivo agrícola. El inexorable vínculo entre la variabilidad climática y la producción agrícola cobra especial relevancia en estudios sobre el cambio climático o en la modelización de cultivos para dar respuesta a escenarios futuros de producción mundial. Es información especialmente valiosa en sistemas operacionales de monitoreo y predicción de rendimientos de cultivos Los cuales son actualmente uno de los pilares operacionales en los que se sustenta la agricultura y seguridad alimentaria mundial; ya que su objetivo final es el de proporcionar información imparcial y fiable para la regularización de mercados. Es en este contexto, donde se quiso dar un enfoque alternativo a estudios, que con distintos planteamientos, analizan la relación inter-anual clima vs producción. Así, se sustituyó la dimensión tiempo por la espacio, re-orientando el análisis estadístico de correlación interanual entre rendimiento y factores climáticos, por el estudio de la correlación inter-regional entre ambas variables. Se utilizó para ello una técnica estadística relativamente nueva y no muy aplicada en investigaciones similares, llamada regresión ponderada geográficamente (GWR, siglas en inglés de “Geographically weighted regression”). Se obtuvieron superficies continuas de las variables climáticas acumuladas en determinados periodos fenológicos, que fueron seleccionados por ser factores clave en el desarrollo vegetativo de un cultivo. Por ello, la primera parte de la tesis, consistió en un análisis exploratorio sobre comparación de Métodos de Interpolación Espacial (MIE). Partiendo de la hipótesis de que existe la variabilidad espacial de la relación entre factores climáticos y rendimiento, el objetivo principal de esta tesis, fue el de establecer en qué medida los MIE y otros métodos geoestadísticos de regresión local, pueden ayudar por un lado, a alcanzar un mayor entendimiento del binomio clima-rendimiento del trigo blando (Triticum aestivum L.) al incorporar en dicha relación el componente espacial; y por otro, a caracterizar la variación de los principales factores climáticos limitantes en el crecimiento del trigo blando, acumulados éstos en cuatro periodos fenológicos. Para lleva a cabo esto, una gran carga operacional en la investigación de la tesis consistió en homogeneizar y hacer los datos fenológicos, climáticos y estadísticas agrícolas comparables tanto a escala espacial como a escala temporal. Para España y los Bálticos se recolectaron y calcularon datos diarios de precipitación, temperatura máxima y mínima, evapotranspiración y radiación solar en las estaciones meteorológicas disponibles. Se dispuso de una serie temporal que coincidía con los mismos años recolectados en las estadísticas agrícolas, es decir, 14 años contados desde 2000 a 2013 (hasta 2011 en los Bálticos). Se superpuso la malla de información fenológica de cuadrícula 25 km con la ubicación de las estaciones meteorológicas con el fin de conocer los valores fenológicos en cada una de las estaciones disponibles. Hecho esto, para cada año de la serie temporal disponible se calcularon los valores climáticos diarios acumulados en cada uno de los cuatro periodos fenológicos seleccionados P1 (ciclo completo), P2 (emergencia-madurez), P3 (floración) y P4 (floraciónmadurez). Se calculó la superficie interpolada por el conjunto de métodos seleccionados en la comparación: técnicas deterministas convencionales, kriging ordinario y cokriging ordinario ponderado por la altitud. Seleccionados los métodos más eficaces, se calculó a nivel de provincias las variables climatológicas interpoladas. Y se realizaron las regresiones locales GWR para cuantificar, explorar y modelar las relaciones espaciales entre el rendimiento del trigo y las variables climáticas acumuladas en los cuatro periodos fenológicos. Al comparar la eficiencia de los MIE no destaca una técnica por encima del resto como la que proporcione el menor error en su predicción. Ahora bien, considerando los tres indicadores de calidad de los MIE estudiados se han identificado los métodos más efectivos. En el caso de la precipitación, es la técnica geoestadística cokriging la más idónea en la mayoría de los casos. De manera unánime, la interpolación determinista en función radial (spline regularizado) fue la técnica que mejor describía la superficie de precipitación acumulada en los cuatro periodos fenológicos. Los resultados son más heterogéneos para la evapotranspiración y radiación. Los métodos idóneos para estas se reparten entre el Inverse Distance Weighting (IDW), IDW ponderado por la altitud y el Ordinary Kriging (OK). También, se identificó que para la mayoría de los casos en que el error del Ordinary CoKriging (COK) era mayor que el del OK su eficacia es comparable a la del OK en términos de error y el requerimiento computacional de este último es mucho menor. Se pudo confirmar que existe la variabilidad espacial inter-regional entre factores climáticos y el rendimiento del trigo blando tanto en España como en los Bálticos. La herramienta estadística GWR fue capaz de reproducir esta variabilidad con un rendimiento lo suficientemente significativo como para considerarla una herramienta válida en futuros estudios. No obstante, se identificaron ciertas limitaciones en la misma respecto a la información que devuelve el programa a nivel local y que no permite desgranar todo el detalle sobre la ejecución del mismo. Los indicadores y periodos fenológicos que mejor pudieron reproducir la variabilidad espacial del rendimiento en España y Bálticos, arrojaron aún, una mayor credibilidad a los resultados obtenidos y a la eficacia del GWR, ya que estaban en línea con el conocimiento agronómico sobre el cultivo del trigo blando en sistemas agrícolas mediterráneos y norteuropeos. Así, en España, el indicador más robusto fue el balance climático hídrico Climatic Water Balance) acumulado éste, durante el periodo de crecimiento (entre la emergencia y madurez). Aunque se identificó la etapa clave de la floración como el periodo en el que las variables climáticas acumuladas proporcionaban un mayor poder explicativo del modelo GWR. Sin embargo, en los Bálticos, países donde el principal factor limitante en su agricultura es el bajo número de días de crecimiento efectivo, el indicador más efectivo fue la radiación acumulada a lo largo de todo el ciclo de crecimiento (entre la emergencia y madurez). Para el trigo en regadío no existe ninguna combinación que pueda explicar más allá del 30% de la variación del rendimiento en España. Poder demostrar que existe un comportamiento heterogéneo en la relación inter-regional entre el rendimiento y principales variables climáticas, podría contribuir a uno de los mayores desafíos a los que se enfrentan, a día de hoy, los sistemas operacionales de monitoreo y predicción de rendimientos de cultivos, y éste es el de poder reducir la escala espacial de predicción, de un nivel nacional a otro regional. ABSTRACT This thesis explores geostatistical techniques and their contribution to a better characterization of the relationship between climate factors and agricultural crop yields. The crucial link between climate variability and crop production plays a key role in climate change research as well as in crops modelling towards the future global production scenarios. This information is particularly important for monitoring and forecasting operational crop systems. These geostatistical techniques are currently one of the most fundamental operational systems on which global agriculture and food security rely on; with the final aim of providing neutral and reliable information for food market controls, thus avoiding financial speculation of nourishments of primary necessity. Within this context the present thesis aims to provide an alternative approach to the existing body of research examining the relationship between inter-annual climate and production. Therefore, the temporal dimension was replaced for the spatial dimension, re-orienting the statistical analysis of the inter-annual relationship between crops yields and climate factors to an inter-regional correlation between these two variables. Geographically weighted regression, which is a relatively new statistical technique and which has rarely been used in previous research on this topic was used in the current study. Continuous surface values of the climate accumulated variables in specific phenological periods were obtained. These specific periods were selected because they are key factors in the development of vegetative crop. Therefore, the first part of this thesis presents an exploratory analysis regarding the comparability of spatial interpolation methods (SIM) among diverse SIMs and alternative geostatistical methodologies. Given the premise that spatial variability of the relationship between climate factors and crop production exists, the primary aim of this thesis was to examine the extent to which the SIM and other geostatistical methods of local regression (which are integrated tools of the GIS software) are useful in relating crop production and climate variables. The usefulness of these methods was examined in two ways; on one hand the way this information could help to achieve higher production of the white wheat binomial (Triticum aestivum L.) by incorporating the spatial component in the examination of the above-mentioned relationship. On the other hand, the way it helps with the characterization of the key limiting climate factors of soft wheat growth which were analysed in four phenological periods. To achieve this aim, an important operational workload of this thesis consisted in the homogenization and obtention of comparable phenological and climate data, as well as agricultural statistics, which made heavy operational demands. For Spain and the Baltic countries, data on precipitation, maximum and minimum temperature, evapotranspiration and solar radiation from the available meteorological stations were gathered and calculated. A temporal serial approach was taken. These temporal series aligned with the years that agriculture statistics had previously gathered, these being 14 years from 2000 to 2013 (until 2011 for the Baltic countries). This temporal series was mapped with a phenological 25 km grid that had the location of the meteorological stations with the objective of obtaining the phenological values in each of the available stations. Following this procedure, the daily accumulated climate values for each of the four selected phenological periods were calculated; namely P1 (complete cycle), P2 (emergency-maturity), P3 (flowering) and P4 (flowering- maturity). The interpolated surface was then calculated using the set of selected methodologies for the comparison: deterministic conventional techniques, ordinary kriging and ordinary cokriging weighted by height. Once the most effective methods had been selected, the level of the interpolated climate variables was calculated. Local GWR regressions were calculated to quantify, examine and model the spatial relationships between soft wheat production and the accumulated variables in each of the four selected phenological periods. Results from the comparison among the SIMs revealed that no particular technique seems more favourable in terms of accuracy of prediction. However, when the three quality indicators of the compared SIMs are considered, some methodologies appeared to be more efficient than others. Regarding precipitation results, cokriging was the most accurate geostatistical technique for the majority of the cases. Deterministic interpolation in its radial function (controlled spline) was the most accurate technique for describing the accumulated precipitation surface in all phenological periods. However, results are more heterogeneous for the evapotranspiration and radiation methodologies. The most appropriate technique for these forecasts are the Inverse Distance Weighting (IDW), weighted IDW by height and the Ordinary Kriging (OK). Furthermore, it was found that for the majority of the cases where the Ordinary CoKriging (COK) error was larger than that of the OK, its efficacy was comparable to that of the OK in terms of error while the computational demands of the latter was much lower. The existing spatial inter-regional variability between climate factors and soft wheat production was confirmed for both Spain and the Baltic countries. The GWR statistic tool reproduced this variability with an outcome significative enough as to be considered a valid tool for future studies. Nevertheless, this tool also had some limitations with regards to the information delivered by the programme because it did not allow for a detailed break-down of its procedure. The indicators and phenological periods that best reproduced the spatial variability of yields in Spain and the Baltic countries made the results and the efficiency of the GWR statistical tool even more reliable, despite the fact that these were already aligned with the agricultural knowledge about soft wheat crop under mediterranean and northeuropean agricultural systems. Thus, for Spain, the most robust indicator was the Climatic Water Balance outcome accumulated throughout the growing period (between emergency and maturity). Although the flowering period was the phase that best explained the accumulated climate variables in the GWR model. For the Baltic countries where the main limiting agricultural factor is the number of days of effective growth, the most effective indicator was the accumulated radiation throughout the entire growing cycle (between emergency and maturity). For the irrigated soft wheat there was no combination capable of explaining above the 30% of variation of the production in Spain. The fact that the pattern of the inter-regional relationship between the crop production and key climate variables is heterogeneous within a country could contribute to one is one of the greatest challenges that the monitoring and forecasting operational systems for crop production face nowadays. The present findings suggest that the solution may lay in downscaling the spatial target scale from a national to a regional level.

Two new tools for assessing patients' knowledge and beliefs about obstructive sleep apnea and continuous positive airway pressure therapy

Background and purpose: Patients' knowledge and beliefs about their illnesses are known to influence a range of health related variables, including treatment compliance. It may, therefore, be important to quantify these variables to assess their impact on compliance, particularly in chronic illnesses such as Obstructive Sleep Apnea (OSA) that rely on self-administered treatments. The aim of this study was to develop two new tools, the Apnea Knowledge Test (AKT) and the Apnea Beliefs Scale (ABS), to assess illness knowledge and beliefs in OSA patients. Patients and methods: The systematic test construction process followed to develop the AKT and the ABS included consultation with sleep experts and OSA patients. The psychometric properties of the AKT and ABS were then investigated in a clinical sample of 81 OSA patients and 33 healthy, non-sleep disordered adults. Results: Results suggest both measures are easily understood by OSA patients, have adequate internal consistency, and are readily accepted by patients. A preliminary investigation of the validity of these tools, conducted by comparing patient data to that of the 33 healthy adults, revealed that apnea patients knew more about OSA, had more positive attitudes towards continuous positive airway pressure (CPAP) treatment, and attributed more importance to treating sleep disturbances than non-clinical groups. Conclusions: Overall, the results of psychometric analyses of these tests suggest these measures will be useful clinical tools with numerous beneficial applications, particularly in CPAP compliance studies and apnea education program evaluations. (C) 2004 Elsevier B.V. All rights reserved.

The effects of metallurgical variables on the mechanical properties of high-chromium cast irons

The.use of high-chromium cast irons for abrasive wear resistance is restricted due to their poor fracture toughness properties. An.attempt was made to improve the fracture characteristics by altering the distribution, size and.shape of the eutectic carbide phase without sacrificing their excellent wear resistance. This was achieved by additions of molybdenum or tungsten followed by high temperature heat treatments. The absence of these alloying elements or replacement of them with vanadium or manganese did not show any significant effect and the continuous eutectic carbide morphology remained the same after application of high temperature heat treatments. The fracture characteristics of the alloys with these metallurgical variables were evaluated for both sharp-cracks and blunt notches. The results were used in conjunction with metallographic and fractographic observations to establish possible failure mechanisms. The fracture mechanism of the austenitic alloys was found to be controlled not only by the volume percent but was also greatly influenced by the size and distribution of the eutectic carbides. On the other hand, the fracture mechanism of martensitic alloys was independent of the eutectic carbide morphology. The uniformity of the secondary carbide precipitation during hardening heat treatments was shown to be a reason for consistant fracture toughness results being obtained with this series of alloys although their eutectic carbide morphologies were different. The collected data were applied to a model which incorporated the microstructural parameters and correlated them with the experimentally obtained valid stress intensity factors. The stress intensity coefficients of different short-bar fracture toughness test specimens were evaluated from analytical and experimental compliance studies. The.validity and applicability of this non-standard testing technique for determination of the fracture toughness of high-chromium cast irons were investigated. The results obtained correlated well with the valid results obtained from standard fracture toughness tests.