Influence of glycerin and lecithin inclusion in the diet on liver characteristics and lipid fraction in the serum of brownegg laying hens at 55 week of age

The effects of the inclusion of raw glycerin (GLYC) and raw lecithin, in the diet (23 to 55 wk) on liver characteristics and various serum lipid fractions were studied in brown egg-laying hens at 55 wk of age. The control diets were based on corn, soybean meal, and 4% supplemental fat and contained 2,750 kcal AMEn/kg, 16.5% CP, and 0.73% digestible Lys. The diets were arranged as a 2 × 3 factorial with 2 levels of GLYC (0 and 7%) and 3 animal fat to lecithin ratios (4:0, 2:2, and 0:4%). Each treatment was replicated 8 times and the experimental unit was a cage with 10 hens. At 55 wk of age, 2 hens per cage replicate were randomly selected, weighed individually, and slaughtered by CO2 inhalation. Liver was immediately removed and weighed and the color recorded by spectrophotometry. In addition, blood samples from one bird per replicate were collected from the wing vein and the concentration of total cholesterol, low and high density lipoprotein cholesterol, and triglycerides were determined. The data were analyzed as a completely randomized design and the main effects of GLYC and lecithin content of the diet and the interactions were determined. No interactions between GLYC and lecithin content of the diets were detected for any of the variables studied. Liver characteristics and serum lipid traits were not affected by the inclusion of GLYC in the diet. The substitution of animal fat by lecithin, however, reduced the redness (a* 14.9 to 13.8) and yellowness (b* 8.60 to 7.20) values of the liver (P < 0.05) but did not affect the content of serum lipid fractions. It is concluded that the inclusion of GLYC and lecithin in the diet did not affect liver size or serum lipid fraction. However, the inclusion of lecithin reduced the a* and b* value of the liver

Aerodynamic database error filtering via high order singular value decomposition

Esta Tesis presenta un nuevo método para filtrar errores en bases de datos multidimensionales. Este método no precisa ninguna información a priori sobre la naturaleza de los errores. En concreto, los errrores no deben ser necesariamente pequeños, ni de distribución aleatoria ni tener media cero. El único requerimiento es que no estén correlados con la información limpia propia de la base de datos. Este nuevo método se basa en una extensión mejorada del método básico de reconstrucción de huecos (capaz de reconstruir la información que falta de una base de datos multidimensional en posiciones conocidas) inventado por Everson y Sirovich (1995). El método de reconstrucción de huecos mejorado ha evolucionado como un método de filtrado de errores de dos pasos: en primer lugar, (a) identifica las posiciones en la base de datos afectadas por los errores y después, (b) reconstruye la información en dichas posiciones tratando la información de éstas como información desconocida. El método resultante filtra errores O(1) de forma eficiente, tanto si son errores aleatorios como sistemáticos e incluso si su distribución en la base de datos está concentrada o esparcida por ella. Primero, se ilustra el funcionamiento delmétodo con una base de datosmodelo bidimensional, que resulta de la dicretización de una función transcendental. Posteriormente, se presentan algunos casos prácticos de aplicación del método a dos bases de datos tridimensionales aerodinámicas que contienen la distribución de presiones sobre un ala a varios ángulos de ataque. Estas bases de datos resultan de modelos numéricos calculados en CFD. ABSTRACT A method is presented to filter errors out in multidimensional databases. The method does not require any a priori information about the nature the errors. In particular, the errors need not to be small, neither random, nor exhibit zero mean. Instead, they are only required to be relatively uncorrelated to the clean information contained in the database. The method is based on an improved extension of a seminal iterative gappy reconstruction method (able to reconstruct lost information at known positions in the database) due to Everson and Sirovich (1995). The improved gappy reconstruction method is evolved as an error filtering method in two steps, since it is adapted to first (a) identify the error locations in the database and then (b) reconstruct the information in these locations by treating the associated data as gappy data. The resultingmethod filters out O(1) errors in an efficient fashion, both when these are random and when they are systematic, and also both when they concentrated and when they are spread along the database. The performance of the method is first illustrated using a two-dimensional toymodel database resulting fromdiscretizing a transcendental function and then tested on two CFD-calculated, three-dimensional aerodynamic databases containing the pressure coefficient on the surface of a wing for varying values of the angle of attack. A more general performance analysis of the method is presented with the intention of quantifying the randomness factor the method admits maintaining a correct performance and secondly, quantifying the size of error the method can detect. Lastly, some improvements of the method are proposed with their respective verification.