Effects of nutrition on digestión efficiency and gaseous emissions from slurry in growing pigs: III. Influence of varying the dietary level of calcium soap of palm fatty acids distillate with or without orange pulp supplemention

The aim of this study was to establish the relationships between faecal fat concentration and gaseous emissions from pig slurry. Five diets were designed to meet essential nutrient requirements: a control and four experimental feeds including two levels (35 or 70 g/kg) of calcium soap fatty acids distillate (CSP) and 0 or 200 g/kg of orange pulp (OP) combined in a 2 × 2 factorial structure. Thirty growing pigs (six per treatment) were used to measure dry matter (DM) and N balance, coefficients of total tract apparent digestibility (CTTAD) of nutrients, faecal and urine composition and potential emissions of ammonia (NH3) and methane (CH4). Increasing dietary CSP level decreased DM, ether extract (EE) and crude protein (CP) CTTAD (by 4.0, 11.1 and 3.5%, respectively, P < 0.05), but did not influence those of fibrous constituents. It also led to a decrease (from 475 to 412 g/kg DM, P < 0.001) of faecal concentration of neutral detergent fibre (aNDFom) and to an increment (from 138 to 204 g/kg, P < 0.001) of EE in faecal DM that was related to greater CH4 emissions, both per gram of organic matter (P = 0.021) or on a daily basis (P < 0.001). Level of CSP did not affect N content in faeces or urine, but increased daily DM (P < 0.001), and N (P = 0.031) faecal excretion with no effect on urine N excretion. This resulted in lesser (P = 0.036) NH3 potential emission per kg of slurry. Addition of OP decreased CTTAD of EE (by 7.9%, P = 0.044), but increased (P < 0.05) that of all the fibrous fractions. As a consequence, faecal EE content increased (from 165 to 177 g/kg DM; P = 0.012), and aNDFom decreased greatly (from 483 to 404 g/kg DM, P < 0.001), which in all resulted in a lack of effect of OP on CH4 potential emission. Inclusion of OP in the diet also led to a significant decrease of CP CTTAD (by 6.85%, P < 0.001), and to an increase of faecal CP concentration (from 174 to 226 g/kg DM, P < 0.001), with no significant influence on urine N content. These effects resulted in higher N faecal losses, especially those of the undigested dietary origin, without significant effects on potential NH3 emission. No significant interactions between CSP and OP supplementation were observed for the gaseous emissions measured.

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.

Treatment of tropical forages with exogenous fibrolytiic enzymes: effects on chemical composition and in vitro rumen fermentation

The effects of three treatments of fibrolytic enzymes (cellulase from Trichoderma longibrachiatum (CEL), xylanase from rumen micro-organisms (XYL) and a 1:1 mixture of CEL and XYL (MIX) on the in vitro fermentation of two samples of Pennisetum clandestinum (P1 and P2), two samples of Dichanthium aristatum (D1 and D2) and one sample of each Acacia decurrens and Acacia mangium (A1 and A2) were investigated. The first experiment compared the effects of two methods of applying the enzymes to forages, either at the time of incubation or 24 h before, on the in vitro gas production. In general, the 24 h pre-treatment resulted in higher values of gas production rate, and this application method was chosen for a second study investigating the effects of enzymes on chemical composition and in vitro fermentation of forages. The pre-treatment with CEL for 24 h reduced (p < 0.05) the content of neutral detergent fibre (NDF) of P1, P2, D1 and D2, and that of MIX reduced the NDF content of P1 and D1, but XYL had no effect on any forage. The CEL treatment increased (p < 0.05) total volatile fatty acid (VFA) production for all forages (ranging from 8.6% to 22.7%), but in general, no effects of MIX and XYL were observed. For both P. clandestinum samples, CEL treatment reduced (p < 0.05) the molar proportion of acetate and increased (p < 0.05) that of butyrate, but only subtle changes in VFA profile were observed for the rest of forages. Under the conditions of the present experiment, the treatment of tropical forages with CEL stimulated their in vitro ruminal fermentation, but XYL did not produce any positive effect. These results showed clearly that effectiveness of enzymes varied with the incubated forage and further study is warranted to investigate specific, optimal enzyme-substrate combinations.

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