Correlation of wear in vivo and six laboratory wear methods.

OBJECTIVE: We examined the correlation between clinical wear rates of restorative materials and enamel (TRAC Research Foundation, Provo, USA) and the results of six laboratory test methods (ACTA, Alabama (generalized, localized), Ivoclar (vertical, volumetric), Munich, OHSU (abrasion, attrition), Zurich). METHODS: Individual clinical wear data were available from clinical trials that were conducted by TRAC Research Foundation (formerly CRA) together with general practitioners. For each of the n=28 materials (21 composite resins for intra-coronal restorations [20 direct and 1 indirect], 5 resin materials for crowns, 1 amalgam, enamel) a minimum of 30 restorations had been placed in posterior teeth, mainly molars. The recall intervals were up to 5 years with the majority of materials (n=27) being monitored, however, only for up to 2 years. For the laboratory data, the databases MEDLINE and IADR abstracts were searched for wear data on materials which were also clinically tested by TRAC Research Foundation. Only those data for which the same test parameters (e.g. number of cycles, loading force, type of antagonist) had been published were included in the study. A different quantity of data was available for each laboratory method: Ivoclar (n=22), Zurich (n=20), Alabama (n=17), OHSU and ACTA (n=12), Munich (n=7). The clinical results were summed up in an index and a linear mixed model was fitted to the log wear measurements including the following factors: material, time (0.5, 1, 2 and 3 years), tooth (premolar/molar) and gender (male/female) as fixed effects, and patient as random effect. Relative ranks were created for each material and method; the same was performed with the clinical results. RESULTS: The mean age of the subjects was 40 (±12) years. The materials had been mostly applied in molars (81%) and 95% of the intracoronal restorations were Class II restorations. The mean number of individual wear data per material was 25 (range 14-42). The mean coefficient of variation of clinical wear data was 53%. The only significant correlation was reached by OHSU (abrasion) with a Spearman r of 0.86 (p=0.001). Zurich, ACTA, Alabama generalized wear and Ivoclar (volume) had correlation coefficients between 0.3 and 0.4. For Zurich, Alabama generalized wear and Munich, the correlation coefficient improved if only composites for direct use were taken into consideration. The combination of different laboratory methods did not significantly improve the correlation. SIGNIFICANCE: The clinical wear of composite resins is mainly dependent on differences between patients and less on the differences between materials. Laboratory methods to test conventional resins for wear are therefore less important, especially since most of them do not reflect the clinical wear.

Pricing of Liquidity Risks in London Stock Exchange

This thesis aims to investigate pricing of liquidity risks in London Stock Exchange. Liquidity Adjusted Capital Asset Pricing Model i.e. LCAPM developed by Acharya and Pedersen (2005) is being applied to test the influence of various liquidity risks on stock returns in London Stock Exchange. The Liquidity Adjusted Capital Asset Pricing model provides a unified framework for the testing of liquidity risks. All the common stocks listed and delisted for the period of 2000 to 2014 are included in the data sample. The study has incorporated three different measures of liquidity – Percent Quoted Spread, Amihud (2002) and Turnover. The reason behind the application of three different liquidity measures is the multi-dimensional nature of liquidity. Firm fixed effects panel regression is applied for the estimation of LCAPM. However, the results are robust according to Fama-Macbeth regressions. The results of the study indicates that liquidity risks in the form of (i) level of liquidity, (ii) commonality in liquidity (iii) flight to liquidity, (iv) depressed wealth effect and market return as well as aggregate liquidity risk are priced at London Stock Exchange. However, the results are sensitive to the choice of liquidity measures.

Approximation de la distribution a posteriori d'un modèle Gamma-Poisson hiérarchique à effets mixtes

La méthode que nous présentons pour modéliser des données dites de "comptage" ou données de Poisson est basée sur la procédure nommée Modélisation multi-niveau et interactive de la régression de Poisson (PRIMM) développée par Christiansen et Morris (1997). Dans la méthode PRIMM, la régression de Poisson ne comprend que des effets fixes tandis que notre modèle intègre en plus des effets aléatoires. De même que Christiansen et Morris (1997), le modèle étudié consiste à faire de l'inférence basée sur des approximations analytiques des distributions a posteriori des paramètres, évitant ainsi d'utiliser des méthodes computationnelles comme les méthodes de Monte Carlo par chaînes de Markov (MCMC). Les approximations sont basées sur la méthode de Laplace et la théorie asymptotique liée à l'approximation normale pour les lois a posteriori. L'estimation des paramètres de la régression de Poisson est faite par la maximisation de leur densité a posteriori via l'algorithme de Newton-Raphson. Cette étude détermine également les deux premiers moments a posteriori des paramètres de la loi de Poisson dont la distribution a posteriori de chacun d'eux est approximativement une loi gamma. Des applications sur deux exemples de données ont permis de vérifier que ce modèle peut être considéré dans une certaine mesure comme une généralisation de la méthode PRIMM. En effet, le modèle s'applique aussi bien aux données de Poisson non stratifiées qu'aux données stratifiées; et dans ce dernier cas, il comporte non seulement des effets fixes mais aussi des effets aléatoires liés aux strates. Enfin, le modèle est appliqué aux données relatives à plusieurs types d'effets indésirables observés chez les participants d'un essai clinique impliquant un vaccin quadrivalent contre la rougeole, les oreillons, la rub\'eole et la varicelle. La régression de Poisson comprend l'effet fixe correspondant à la variable traitement/contrôle, ainsi que des effets aléatoires liés aux systèmes biologiques du corps humain auxquels sont attribués les effets indésirables considérés.

Efectividad y seguridad de las emulsiones de pescado en la colestasis asociada a nutrición parenteral en neonatos

El daño hepático asociado a Nutrición Parenteral es una complicación letal en pacientes con nutrición parenteral prolongada. Su etiología no es clara aún, y se cree que las emulsiones lipídicas tradicionales con en base aceite de soya pueden contribuir a su génesis. Recientemente la modificación del contenido de ácidos grasos de las emulsiones lipídicas tradicionalmente de ácidos grasos omega-6 a ricas en omega-3 se postula como terapia prometedora en niños con PNALD. Se realizó una búsqueda sistemática de la literatura en Cochrane y Pubmed, se seleccionaron ensayos clínicos controlados y estudios de cohorte prospectiva. Se utilizó le metodología SIGN para la calificación de los estudios y evaluación del grado de recomendación basada en la evidencia. El meta-análisis que se llevó a cabo incluyó dos artículos de cohorte prospectiva del grupo de Boston para la variable principal de desenlace considerada: tiempo de reverso de la colestasis. Los pacientes que recibieron emulsiones de lípidos de pescado experimentaron regresión de la colestasis significativamente más rápido que los que recibieron emulsiones de soya (HR=10.3, IC 95%:3.7,29,0, efectos fijos), menor mortalidad y menor necesidad de trasplante hepático. Su utilización no se asocia con deficiencia de ácidos grasos, hipertrigliceridemia, coagulopatía o retraso en el crecimiento. Se evidencio una correlación entre la disminución de los niveles de triglicéridos y aumento de los niveles de albúmina, sugiriendo un beneficio nutricional en los pacientes bajo infusiones de lípidos de pescado.

The environmental impact of civil conflict : the deforestation effect of paramilitary expansion in Colombia

Despite a growing body of literature on how environmental degradation can fuel civil war, the reverse effect, namely that of conflict on environmental outcomes, is relatively understudied. From a theoretical point of view this effect is ambiguous, with some forces pointing to pressures for environmental degradation and some pointing in the opposite direction. Hence, the overall effect of conflict on the environment is an empirical question. We study this relationship in the case of Colombia. We combine a detailed satellite-based longitudinal dataset on forest cover across municipalities over the period 1990-2010 with a comprehensive panel of conflict-related violent actions by paramilitary militias. We first provide evidence that paramilitary activity significantly reduces the share of forest cover in a panel specification that includes municipal and time fixed effects. Then we confirm these findings by taking advantage of a quasi-experiment that provides us with an exogenous source of variation for the expansion of the paramilitary. Using the distance to the region of Urab´a, the epicenter of such expansion, we instrument paramilitary activity in each cross-section for which data on forest cover is available. As a falsification exercise, we show that the instrument ceases to be relevant after the paramilitaries largely demobilized following peace negotiations with the government. Further, after the demobilization the deforestation effect of the paramilitaries disappears. We explore a number of potential mechanisms that may explain the conflict-driven deforestation, and show evidence suggesting that paramilitary violence generates large outflows of people in order to secure areas for growing illegal crops, exploit mineral resources, and engage in extensive agriculture. In turn, these activities are associated with deforestation.

The crime kuznets curve

We document the existence of a Crime Kuznets Curve in US states since the 1970s. As income levels have risen, crime has followed an inverted U-shaped pattern, first increasing and then dropping. The Crime Kuznets Curve is not explained by income inequality. In fact, we show that during the sample period inequality has risen monotonically with income, ruling out the traditional Kuznets Curve. Our finding is robust to adding a large set of controls that are used in the literature to explain the incidence of crime, as well as to controlling for state and year fixed effects. The Curve is also revealed in nonparametric specifications. The Crime Kuznets Curve exists for property crime and for some categories of violent crime.

Behavioral differences in violence: the case of intra-group differences of paramilitaries and guerrillas in Colombia

In most studies on civil wars, determinants of conflict have been hitherto explored assuming that actors involved were either unitary or stable. However, if this intra-group homogeneity assumption does not hold, empirical econometric estimates may be biased. We use Fixed Effects Finite Mixture Model (FE-FMM) approach to address this issue that provides a representation of heterogeneity when data originate from different latent classes and the affiliation is unknown. It allows to identify sub-populations within a population as well as the determinants of their behaviors. By combining various data sources for the period 2000-2005, we apply this methodology to the Colombian conflict. Our results highlight a behavioral heterogeneity in guerrilla’s armed groups and their distinct economic correlates. By contrast paramilitaries behave as a rather homogenous group.

Network externalities across financial institutions

We propose and estimate a financial distress model that explicitly accounts for the interactions or spill-over effects between financial institutions, through the use of a spatial continuity matrix that is build from financial network data of inter bank transactions. Such setup of the financial distress model allows for the empirical validation of the importance of network externalities in determining financial distress, in addition to institution specific and macroeconomic covariates. The relevance of such specification is that it incorporates simultaneously micro-prudential factors (Basel 2) as well as macro-prudential and systemic factors (Basel 3) as determinants of financial distress. Results indicate network externalities are an important determinant of financial health of a financial institutions. The parameter that measures the effect of network externalities is both economically and statistical significant and its inclusion as a risk factor reduces the importance of the firm specific variables such as the size or degree of leverage of the financial institution. In addition we analyze the policy implications of the network factor model for capital requirements and deposit insurance pricing.

Métodos de estadística espacial para evaluar la influencia de factores medioambientales sobre la incidencia y mortalidad por cáncer

Los objetivos de la tesis son: 1.- Estudiar la relación entre la incidencia y mortalidad por cáncer y los factores medioambientales, en particular la contaminación atmosférica, controlando por factores socioeconómicos. 2.- Utilizar aquellos métodos de estadística espacial apropiados para cada tipo de diseño. 3.- Distinguir en los modelos las diferentes fuentes de extra-variabilidad espacial. 4.- Controlar el problema de exceso de ceros inherente a alguna de las neoplasias de interés medioambientales. Conclusiones: - Tanto la incidencia como la mortalidad de las neoplasias, presentaron dos fuentes de extravariación. La extravariaicón espacial, por la que unidades vecinas tienden a presentar razones de incidencia/mortalidad similares, y la heterogeneidad no espacial. En general la extravariabilidad espacial ha resultado ser mucho mayor que la no espacial. - Para suavizar las RIE/RME correspondientes a variables con un porcentaje de ceros superior al40-50% debe utilizarse un modelo que capture este comportamiento. - El mejor modelo en términos de ajuste para recoger el exceso de ceros en las variables de interés ha resultado ser el modelo mixto de riesgo relativo. - Las RIE/RME suavizadas presentan un patrón geográfico claro sólo en algunas neoplasias de interés medioambiental. - Parte de la variabilidad remanente en las RIE/RME suavizadas pudo ser explicada mediante la introducción de variables explicativas, en particular la contaminación atmosférica y variables socioeconómicas. -Como los contaminantes atmosféricos fueron observados en un diseño geoestadístico y las neoplasias de interés mediambiental lo fueron en un diseño en rejilla se modelizó la superficie de exposición. - El efecto del contaminante en cada municipio/sección censal se aproximó introduciendo en el modelo el valor promedio en cada área y la variabilidad intra-área. - El efecto del contaminante se consideró aleatorio, en el sentido de que podría ser diferente en cada una de las áreas. - Las condiciones socioeconómicas fueron otra de las variables que redujeron la variabilidad remanente en las RIE/RME suavizadas. -Las variables explicativas observadas con un diseño en rejilla, como el índice de privación, se introdujeron en el modelo como efectos fijos. - El efecto de la privación sobre la incidencia y/o mortalidad por cáncer de tráquea, bronquios y pulmón, controlando por contaminantes atmosféricos, fue mayor en las mujeres que en los hombres. -Altas concentraciones de contaminantes atmosféricos aumentan el riesgo de padecer neoplasias de interés medioambiental, controlando por condiciones socioeconómicas.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Input usage, output mix and industry deregulation: an analysis of the Australian dairy manufacturing industry

In this paper we estimate a Translog output distance function for a balanced panel of state level data for the Australian dairy processing sector. We estimate a fixed effects specification employing Bayesian methods, with and without the imposition of monotonicity and curvature restrictions. Our results indicate that Tasmania and Victoria are the most technically efficient states with New South Wales being the least efficient. The imposition of theoretical restrictions marginally affects the results especially with respect to estimates of technical change and industry deregulation. Importantly, our bias estimates show changes in both input use and output mix that result from deregulation. Specifically, we find that deregulation has positively biased the production of butter, cheese and powders.

Bt cotton and production risk: Panel data evidence

The farm-level success of Bt-cotton in developing countries is well documented. However, the literature has only recently begun to recognise the importance of accounting for the effects of the technology on production risk, in addition to the mean effect estimated by previous studies. The risk effects of the technology are likely very important to smallholder farmers in the developing world due to their risk-aversion. We advance the emergent literature on Bt-cotton and production risk by using panel data methods to control for possible endogeneity of Bt-adoption. We estimate two models, the first a fixed-effects version of the Just and Pope model with additive individual and time effects, and the second a variation of the model in which inputs and variety choice are allowed to affect the variance of the time effect and its correlation with the idiosyncratic error. The models are applied to panel data on smallholder cotton production in India and South Africa. Our results suggest a risk-reducing effect of Bt-cotton in India, but an inconclusive picture in South Africa.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Spatial and management factors associated with exposure of smallholder dairy cattle in Tanzania to tick-borne pathogens

A cross-sectional study of serum antibody responses of cattle to tick-borne pathogens (Theileria parva, Theileria mutans, Anaplasma marginale, Babesia bigemina and Babesia bovis) was conducted on smallholder dairy farms in Tanga and Iringa Regions of Tanzania. Seroprevalence was highest for T. parva (48% in Iringa and 23% in Tanga) and B. bigemina (43% in Iringa and 27% in Tanga) and lowest for B. bovis (12% in Iringa and 6% in Tanga). We use spatial and non-spatial models, fitted using classical and Bayesian methods, to explore risk factors associated with seroprevalence. These include both fixed effects (age, grazing history and breeding status) and random effects (farm and local spatial effects). In both regions, seroprevalence for all tick-borne pathogens increased significantly with age. Animals pasture grazed in the 3 months prior to the start of the sampling period were significantly more likely to be seropositive for Theileria spp. and Babesia spp. Pasture grazed animals were more likely to be seropositive than zero-grazed animals for A. marginale, but the relationship was weaker than that observed for the other four pathogens. This study did not detect any significant differences in seroprevalence associated with other management-related variables, including the method or frequency of acaricide application. After adjusting for age, there was weak evidence of localised (< 5 km) spatial correlation in exposure to some of the tick borne diseases. However, this was small compared with the 'farm-effect', suggesting that risk factors specific to the farm were more important than those common to the local neighbourhood. Many animals were seropositive for more than one pathogen and the correlation between exposure to the different pathogens remained after adjusting for the identified risk factors. Identifying the determinants of exposure to multiple tick-borne pathogens and characterizing local variation in risk will assist in the development of more effective control strategies for smallholder dairy farms. (c) 2005 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Testing white line strength in the dairy cow

The tensile strength of 576 pieces of white line horn collected over 6 mo from 14 dairy cows restricted to parity 1 or 2 was tested. None of the cows had ever been lame. Seven cows were randomly assigned to receive 20 mg/d biotin supplementation, and 7 were not supplemented. Hoof horn samples were taken from zones 2 and 3 (the more proximal and distal sites of the abaxial white line) of the medial and lateral claws of both hind feet on d 1 and on 5 further occasions over 6 mo. The samples were analyzed at 100% water saturation. Hoof slivers were notched to ensure that tensile strength was measured specifically across the white line region. The tensile stress at failure was measured in MPa and was adjusted for the cross-sectional area of the notch site. Data were analyzed in a multilevel model, which accounted for the repeated measures within cows. All other variables were entered as fixed effects. In the final model, there was considerable variation in strength over time. Tensile strength was significantly higher in medial compared with lateral claws, and zone 2 was significantly stronger than zone 3. Where the white line was visibly damaged the tensile strength was low. Biotin supplementation did not affect the tensile strength of the white line. Results of this study indicate that damage to the white line impairs its tensile strength and that in horn with no visible abnormality the white line is weaker in the lateral hind claw than the medial and in zone 3 compared with zone 2. The biomechanical strength was lowest at zone 3 of the lateral hind claw, which is the most common site of white line disease lameness in cattle.