Inclusión de Ensilado de Residuos de Trucha en el Alimento de Cerdos y su Efecto en el Rendimiento Productivo y Sabor de la Carne

The study aimed to determine the effect of the level of inclusion of silage from trout residues (ERT) in concentrate feed on body weight, feed intake, feed conversion of pigs and taste of pig meat. Three treatments were applied: a control (T1, 0%) and two treated groups with 5% (T2) and 10% (T3) of ERT in the ration, with five replicates per treatment. Thirty non-castrated male pigs (York-Duroc-Landrace) of 45 days of age were used. Feeds for the growing and finishing phase were formulated to be similar in protein (CP) and energy (ME) content (growing: 16.0% CP and 3.20 Mcal ME/kg and finishing: 13.0% CP and 3.20 Mcal ME/kg) and were administered ad libitum during 98 days. Body weight, feed intake and feed conversion rate were determined at 56 and 98 days. In addition, a sensory test of meat was carried out at the end of the study to evaluate the taste of the meat. The results at day 98 showed no differences between treatments on body weight (69.8, 81.3 and 86.4 kg for T1, T2 and T3 respectively) and feed intake (1.30, 1.50 and 1.54 kg/day for T1, T2 and T3 respectively). However, feed conversion improved when ERT was added to the ration (2.25, 2.15 and 2.04 for T1, T2 and T3 respectively, p=0.012). The sensorial evaluation indicated that ERT inclusion in feed did not affect the taste of the meat. In conclusion, the inclusion up to 10% of ERT in pig feed improves feed conversion and do not affect the taste of meat.Con la finalidad de determinar el efecto del nivel de inclusión de ensilado de residuos de trucha (ERT) en el alimento sobre el peso vivo, consumo de alimento y conversión alimenticia en cerdos, así como en el sabor de su carne, se plantearon tres tratamientos: un testigo (T1, 0%) y dos con niveles de 5% (T2) y 10% (T3) de inclusión de ERT en la ración, donde cada tratamiento se hizo con cinco réplicas. Se utilizaron 30 cerdos machos enteros de 45 días de edad (York-Duroc-Landrace). Los alimentos de crecimiento y engorde fueron similares en proteína (PB) y energía metabolizable (EM) (crecimiento: 16.0% PB y 3.20 Mcal/kg EM y engorde: 13.0% PB y 3.20 Mcal/kg EM) y fueron administrados ad libitum durante 98 días. Se determinó el peso vivo, el consumo de alimento y la conversión alimenticia a los 56 y 98 días. Al final se realizó una prueba sensorial para evaluar el sabor de la carne. A los 98 días, los resultados indicaron que los tratamientos T1, T2 y T3 no afectaron el peso vivo (69.8, 81.3 y 86.4 kg, respectivamente), ni el consumo de alimento (1.30, 1.50 y 1.54 kg/día, respectivamente). Sin embargo, la conversión alimenticia fue mejor con el incremento del ERT en la ración (2.25, 2.15 y 2.04 para T1, T2 y T3, respectivamente, p=0.012). Las calificaciones de la prueba sensorial indicaron que la inclusión de ERT en el alimento no afectó el sabor de la carne. Se concluye que hasta 10% de inclusión de ERT en el alimento mejora la conversión alimenticia y no afecta el sabor de la carne de los cerdos

The Effects of City Streets on an Urban Disease Vector.

With increasing urbanization vector-borne diseases are quickly developing in cities, and urban control strategies are needed. If streets are shown to be barriers to disease vectors, city blocks could be used as a convenient and relevant spatial unit of study and control. Unfortunately, existing spatial analysis tools do not allow for assessment of the impact of an urban grid on the presence of disease agents. Here, we first propose a method to test for the significance of the impact of streets on vector infestation based on a decomposition of Moran’s spatial autocorrelation index; and second, develop a Gaussian Field Latent Class model to finely describe the effect of streets while controlling for cofactors and imperfect detection of vectors. We apply these methods to cross-sectional data of infestation by the Chagas disease vector Triatoma infestans in the city of Arequipa, Peru. Our Moran’s decomposition test reveals that the distribution of T. infestans in this urban environment is significantly constrained by streets (p,0.05). With the Gaussian Field Latent Class model we confirm that streets provide a barrier against infestation and further show that greater than 90% of the spatial component of the probability of vector presence is explained by the correlation among houses within city blocks. The city block is thus likely to be an appropriate spatial unit to describe and control T. infestans in an urban context. Characteristics of the urban grid can influence the spatial dynamics of vector borne disease and should be considered when designing public health policies

Study of the plastic metal conforming of brass with bismut

El material latón al bismuto (I), cuya microestructura está formado por cristales α + β, presenta inclusiones de bismuto en los límites de grano y en el grano. El material latón al bismuto-selenio (II), cuya microestructura está formado por cristales α + β, presenta inclusiones de bismuto en los límites de grano y en el grano; el selenio se presenta en los límites de grano. Las propiedades mecánicas encontradas en promedio, I y II son: Esfuerzo a la rotura, Mpa 385 387 Esfuerzo a la fluencia, Mpa 180 175 % de elongación 38,8 40,5 Dureza, BHN 110 112 La velocidad de corrosión electroquímica en milésimo de pulgada por año (mpy), según Tafel es como sigue: Material I En agua de caño: mpy = 0,998 En solución de NaCl al 3.56%: mpy = 1,065 Material II En agua de caño: mpy = 0,970 En solución de NaCl al 3.56%: mpy = 1,105The Bismuth Brass Material which microstructure is formed by α and β crystals presents Bismuth inclusions in the grain boun- daries and in the grain. The Bismuth-Selenium Brass Material which microstructure is formed by α and β crystals presents Bismuth inclusions in the grain boundaries and in the grain; Selenium is presented in the grain boundaries. The mechanical properties are the following: Mean Properties I II Tensile Strength, Mpa 385 387 Yield Strength, Mpa 180 175 Elongation % 38,8 40,5 Hardness, BHN 110 112 The speed of electrochemistry corrosion in mpy, according to Tafel is the following: Material I Tap Water: mpy = 0.998 NaCl Solution at 3.56% mpy = 1.065 Material II Tap Water: mpy = 0.970 NaCl solution at 3.56%: mpy = 1.10

Spatial distribution of <i>Triatoma infestans</i> presence in households of Paucarpata, Arequipa, Peru.

<p>Map of the study area. Black indicates infested households, white non-infested households, and grey non-inspected households. The area encircled by dashes was used to fit the Gaussian Field Latent Class model; the remaining area was used as a validation dataset. The close-up shows the urban grid underneath and the aggregation of vectors within city blocks.</p

Spatial autocorrelation of data simulated with the Gaussian Field Latent Class model of <i>Triatoma infestans</i> distribution.

<p>The autocorrelation of infestation in the generated data is compared to the autocorrelation in observed data. Infestation data were generated on the validation map using the estimated parameters for each of the kernels: exponential (first column), Cauchy (second column), Gaussian (third column), and geometric (fourth column). We calculated the standard Moran's I (first row) and the difference between within block and across street autocorrelation (second row) as a function of distance. The solid line indicates the values for the observed data. Box plots indicate the values obtained from generated data. The boxes indicate the , and quantiles, and the whiskers depict the 95% CrI.</p

General structure of the Gaussian Field Latent Class model.

<p>Working backward, we consider the infestation data to be the result of a latent infestation status , observed by imperfect inspectors of sensitivity . The true infestation is a binary manifestation of an underlying continuous infestation predictor . Cofactors and a local error term, , form the local component. The spatial component is modeled as a Gaussian field. The fit parameters, and , respectively tune how distances between neighbors and the streets define the spatial dependency between households in the spatial component.</p

Spatial autocorrelation of <i>Triatoma infestans</i> presence in Paucarpata, Arequipa, Peru and the effects of streets.

<p>Left: autocorrelation of the infestation status as a function of the distance. Solid line: Global Moran's index. Dot-Dashed line: Moran's Index for within blocks household pairs. Dashed line: Moran's Index for household pairs across streets. All Moran's I values are significantly different from the expected value under hypothesis of no spatial autocorrelation (). Right: significance of the difference between the correlation within city blocks and the correlation across streets. Box plots indicate the expected values under the null hypothesis using a permutation test. The boxes indicate the , and quantiles, and the whiskers depict the 95% CrI.</p

Spatial kernels and corresponding fitted parameters.

a<p>The shape factor is indicated in meters.</p>b<p>Same Block Index: Percent of the spatial component of infestation explained by same city block neighbors (see Section 2 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002801#pcbi.1002801.s003" target="_blank">Text S1</a>). In parentheses are the 95% Credible Intervals (CrI) according to the MCMC sampling. The probability of having no barrier effect of streets is indicated with the values of : ;</p>***<p>.</p

TIII - Arquitectura y Entorno - AR307 - 202101

Descripción: El curso TIII - Arquitectura y Entorno, es un curso de especialidad en la carrera de Arquitectura; parte del estudio del patrimonio edificado y la ciudad histórica, y propone el adiestramiento en el diseño arquitectónico a partir de la transformación y/o reciclaje de un objeto arquitectónico preexistente, y/o la propuesta de edificaciones nuevas relacionadas con el espacio urbano, desde un enfoque contemporáneo. Propósito: El TIII - Arquitectura y Entorno busca que el futuro arquitecto tome conciencia que todo proyecto arquitectónico está destinado a relacionarse con el contexto urbano. A través de la identificación y el análisis, el alumno adquiere las herramientas para diseñar respondiendo al entorno. El curso contribuye directamente al desarrollo de las competencias generales de Ciudadanía y Pensamiento Innovador y la competencia específica de Diseño Fundamentado (que corresponde a los criterios NAAB: PC2, PC3, PC5, PC8, SC3, SC5). Tiene como requisitos: Dibujo Arquitectónico (AR286) y TII - Arquitectura y Arte (AR306)