Abundance of mesozooplankton in the western part of the Black Sea in summer 1991 during the NATO Programme Hydroblack

The "Hydroblack91" dataset is based on samples collected in the summer of 1991 and covers part of North-Western in front of Romanian coast and Western Black Sea (Bulgarian coasts) (between 43°30' - 42°10' N latitude and 28°40'- 31°45' E longitude). Mesozooplankton sampling was undertaken at 20 stations. The whole dataset is composed of 72 samples with data of zooplankton species composition, abundance and biomass. Samples were collected in discrete layers 0-10, 0-20, 0-50, 10-25, 25-50, 50-100 and from bottom up to the surface at depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36cm, mesh size 150 µm. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length Mesozooplankton abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). Taxon-specific abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972).

Abundance of mesozooplankton in the western part of the Black Sea in spring 2002 during the National Monitoring Programme of the Bulgarian Institute of Oceanography

The dataset is based on samples collected in the spring of 2002 in the Western Black Sea in front of Bulgaria coast. The whole dataset is composed of 76 samples (from 27 stations of National Monitoring Grid) with data of mesozooplankton species composition abundance and biomass. Sampling on zooplankton was performed from bottom up to the surface at depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36cm, mesh size 150 µm. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length. Mesozooplankton abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Kremena Stefanova using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). Taxon-specific abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Kremena Stefanova using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972).

Abundance of zooplankton based on a long-term study at the Galata transect and covers the spring-summer periods from 1967 till 2005

The dataset is based on a long-term study (38 years) at the Galata transect and covers the spring-summer periods from 1967 till 2005. The whole dataset is composed of 360 data of total zooplankton biomass and abundance . Samples were collected in discrete layers 0-10m, 10-20m, 10-25m, 25-50m, 50-70m, 50-100m, 100-150. Mesozooplankton abundance: the collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Fishery Resource by Prof. Asen Konsulov and Institute of Oceanology by Prof. Asen Konsulov, Lyudmila Kamburska and Kremena Stefanova using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). Taxon-specific mesozooplankton abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber for taxomomic identification and count. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Fishery Resource by prof. Asen Konsulov and Institute of Oceanology by Prof. Asen Konsulov, Lyudmila Kamburska and Kremena Stefanova using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972).

Abundance of mesozooplankton in the western part of the Black Sea during the 2001 National Monitoring Programme of the Bulgarian Institute of Oceanography

The dataset is based on samples collected in the autumn of 2001 in the Western Black Sea in front of Bulgaria coast. The whole dataset is composed of 42 samples (from 19 stations of National Monitoring Grid) with data of mesozooplankton species composition abundance and biomass. Samples were collected in the layers 0-10, 0-20, 0-50, 10-25, 25-50, 50-100 and from bottom up to the surface at depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36cm, mesh size 150 µm. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length. Mesozooplankton abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Kremena Stefanova using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). Taxon-specific abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Kremena Stefanova using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972).

Biomass of mesozooplankton in the western part of the Black Sea in 1992 during the Cooperative Marine Science Program for the Black Sea (CoMSBlack)

The "CoMSBlack92" dataset is based on samples collected in the summer of 1992 along the Bulgarian coast including coastal and open sea areas. The whole dataset is composed of 79 samples (28 stations) with data of zooplankton species composition, abundance and biomass. Sampling for zooplankton was performed from bottom up to the surface at standard depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36cm, mesh size 150 ?m. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length. Sampling volume was estimated by multiplying the mouth area with the wire length. The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972 ). The biomass was estimated as wet weight by Petipa, 1959 (based on species specific wet weight). Wet weight values were transformed to dry weight using the equation DW=0.16*WW as suggested by Vinogradov & Shushkina, 1987. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. The biomass was estimated as wet weight by Petipa, 1959 ussing standard average weight of each species in mg/m**3.

Biomass of mesozooplankton in the western part of the Black Sea in summer 1991 during the NATO Programme Hydroblack

The "Hydroblack91" dataset is based on samples collected in the summer of 1991 and covers part of North-Western in front of Romanian coast and Western Black Sea (Bulgarian coasts) (between 43°30' - 42°10' N latitude and 28°40'- 31°45' E longitude). Mesozooplankton sampling was undertaken at 20 stations. The whole dataset is composed of 72 samples with data of zooplankton species composition, abundance and biomass. Samples were collected in discrete layers 0-10, 0-20, 0-50, 10-25, 25-50, 50-100 and from bottom up to the surface at depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36cm, mesh size 150 µm. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length. Mesozooplankton abundance: The collected materia was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). The biomass was estimated as wet weight by Petipa, 1959 (based on species specific wet weight). Wet weight values were transformed to dry weight using the equation DW=0.16*WW as suggested by Vinogradov & Shushkina, 1987. Taxon-specific abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). The biomass was estimated as wet weight by Petipa, 1959 ussing standard average weight of each species in mg/m3. WW were converted to DW by equation DW=0.16*WW (Vinogradov ME, Sushkina EA, 1987).

Validation and aplication of Bioelectrical Impedance (BIA) method to determine body and carcass composition and nutrient retention of growing rabbits = Validación y aplicación de la técnica de la Impedancia Bioeléctrica (BIA) para determinar la composición corporal, de la canal y la retención de nutrientes de conejos en crecimiento

Los objetivos de esta tesis fueron 1) obtener y validar ecuaciones de predicción para determinar in vivo la composición corporal y de la canal de conejos en crecimiento de 25 a 77 días de vida utilizando la técnica de la Impedancia Bioeléctrica (BIA), y 2) evaluar su aplicación para determinar diferencias en la composición corporal y de la canal, así como la retención de nutrientes de animales alimentados con diferentes fuentes y niveles de grasa. El primer estudio se realizó para determinar y después validar, usando datos independientes, las ecuaciones de predicción obtenidas para determinar in vivo la composición corporal de los conejos en crecimiento. Se utilizaron 150 conejos a 5 edades distintas (25, 35, 49, 63 y 77 días de vida), con un rango de pesos entre 231 y 3138 g. Para determinar los valores de resistencia (Rs,) and reactancia (Xc,) se usó un terminal (Model BIA-101, RJL Systems, Detroit, MI USA) con cuatro electrodos. Igualmente se registró la distancia entre electrodos internos (D), la longitud corporal (L) y el peso vivo (PV) de cada animal. En cada edad, los animales fueron molidos y congelados (-20 ºC) para su posterior análisis químico (MS, grasa, proteína, cenizas y EB). El contenido en grasa y energía de los animales se incrementó, mientras que los contenidos en proteína, cenizas y agua de los animales disminuyeron con la edad. Los valores medios de Rs, Xc, impedancia (Z), L y D fueron 83.5 ± 23.1 , 18.2 ± 3.8 , 85.6 ± 22.9 , 30.6 ± 6.9 cm y 10.8 ± 3.1 cm. Se realizó un análisis de regresión lineal múltiple para determinar las ecuaciones de predicción, utilizando los valores de PV, L and Z como variables independientes. Las ecuaciones obtenidas para estimar los contenidos en agua (g), PB (g), grasa (g), cenizas (g) and EB (MJ) tuvieron un coeficiente de determinación de (R2) de 0.99, 0.99, 0.97, 0.98 y 0.99, y los errores medios de predicción relativos (EMPR) fueron: 2.79, 6.15, 24.3, 15.2 y 10.6%, respectivamente. Cuando el contenido en agua se expresó como porcentaje, los valores de R2 y EMPR fueron 0.85 and 2.30%, respectivamente. Al predecir los contenidos en proteína (%MS), grasa (%MS), cenizas (%MS) y energía (kJ/100 g MS), se obtuvieron valores de 0.79, 0.83, 0.71 y 0.86 para R2, y 5.04, 18.9, 12.0 y 3.19% para EMPR. La reactancia estuvo negativamente correlacionada con el contenido en agua, cenizas y PB (r = -0.32, P < 0.0001; r = -0.20, P < 0.05; r = -0.26, P < 0.01) y positivamente correlacionada con la grasa y la energía (r = 0.23 y r = 0.24; P < 0.01). Sin embargo, Rs estuvo positivamente correlacionada con el agua, las cenizas y la PB (r = 0.31, P < 0.001; r = 0.28, P < 0.001; r = 0.37, P < 0.0001) y negativamente con la grasa y la energía (r = -0.36 y r = -0.35; P < 0.0001). Igualmente la edad estuvo negativamente correlacionada con el contenido en agua, cenizas y proteína (r = -0.79; r = -0.68 y r = -0.80; P < 0.0001) y positivamente con la grasa y la energía (r = 0.78 y r = 0.81; P < 0.0001). Se puede concluir que el método BIA es una técnica buena y no invasiva para estimar in vivo la composición corporal de conejos en crecimiento de 25 a 77 días de vida. El objetivo del segundo estudio fue determinar y validar con datos independientes las ecuaciones de predicción obtenidas para estimar in vivo la composición de la canal eviscerada mediante el uso de BIA en un grupo de conejos de 25 a 77 días, así como testar su aplicación para predecir la retención de nutrientes y calcular las eficacias de retención de la energía y del nitrógeno. Se utilizaron 75 conejos agrupados en 5 edades (25, 35, 49, 63 y 77 días de vida) con unos pesos que variaron entre 196 y 3260 g. Para determinar los valores de resistencia (Rs, ) y reactancia (Xc, ) se usó un terminal (Model BIA-101, RJL Systems, Detroit, MI USA) con cuatro electrodos. Igualmente se registró la distancia entre electrodos internos (D), la longitud corporal (L) y el peso vivo (PV) del cada animal. En cada edad, los animales fueron aturdidos y desangrados. Su piel, vísceras y contenido digestivo fueron retirados, y la canal oreada fue pesada y molida para posteriores análisis (MS, grasa, PB, cenizas y EB). Los contenidos en energía y grasa aumentaron mientras que los de agua, cenizas y proteína disminuyeron con la edad. Los valores medios de Rs, Xc, impedancia (Z), L y D fueron 95.9±23.9 , 19.5±4.7 , 98.0±23.8 , 20.6±6.3 cm y 13.7±3.1 cm. Se realizó un análisis de regresión linear múltiple para determinar las ecuaciones de predicción, utilizando los valores de PV, L and Z como variables independientes. Los coeficientes de determinación (R2) de las ecuaciones obtenidas para estimar los contenidos en agua (g), PB (g), grasa (g), cenizas (g) and EB (MJ) fueron: 0.99, 0.99, 0.95, 0.96 y 0.98, mientras que los errores medios de predicción relativos (EMPR) fueron: 4.20, 5.48, 21.9, 9.10 y 6.77%, respectivamente. Cuando el contenido en agua se expresó como porcentaje, los valores de R2 y EMPR fueron 0.79 y 1.62%, respectivamente. Cuando se realizó la predicción de los contenidos en proteína (%MS), grasa (%MS), cenizas (%MS) y energía (kJ/100 g MS), los valores de R2 fueron 0.68, 0.76, 0.66 and 0.82, y los de RMPE: 3.22, 10.5, 5.82 and 2.54%, respectivamente. La reactancia estuvo directamente correlacionada con el contenido en grasa (r = 0.24, P < 0.05), mientras que la resistencia guardó una correlación positiva con los contenidos en agua, cenizas y proteína (r = 0.55, P < 0.001; r = 0.54, P < 0.001; r = 0.40, P < 0.005) y negativa con la grasa y la energía (r = -0.44 y r = -0.55; P < 0.001). Igualmente la edad estuvo negativamente correlacionada con los contenidos en agua, cenizas y PB (r = -0.94; r = -0.85 y r = -0.75; P < 0.0001) y positivamente con la grasa y la energía (r = 0.89 y r = 0.90; P < 0.0001). Se estudió la eficacia global de retención de la energía (ERE) y del nitrógeno (ERN) durante todo el periodo de cebo (35-63 d), Los valores de ERE fueron 20.4±7.29%, 21.0±4.18% and 20.8±2.79% en los periodos 35 a 49, 49 a 63 y 35 a 63 d, respectivamente. ERN fue 46.9±11.7%, 34.5±7.32% y 39.1±3.23% para los mismos periodos. La energía fue retenida en los tejidos para crecimiento con una eficiencia del 52.5% y la eficiencia de retención de la energía como proteína y grasa fue de 33.3 y 69.9% respectivamente. La eficiencia de utilización del nitrógeno para crecimiento fue cercana al 77%. Este trabajo muestra como el método BIA es técnica buena y no invasiva para determinar in vivo la composición de la canal y la retención de nutrientes en conejos en crecimiento de 25 a 77 días de vida. En el tercer estudio, se llevaron a cabo dos experimentos con el fin de investigar los efectos del nivel de inclusión y de la fuente de grasa, sobre los rendimientos productivos, la mortalidad, la retención de nutrientes y la composición corporal total y de la canal eviscerada de conejos en crecimiento de 34 a 63 d de vida. En el Exp. 1 se formularon 3 dietas con un diseño experimental factorial 3 x 2 con el tipo de grasa utilizada: Aceite de Soja (SBO), Lecitinas de Soja (SLO) y Manteca (L) y el nivel de inclusión (1.5 y 4%) como factores principales. El Exp. 2 también fue diseñado con una estructura factorial 3 x 2, pero usando SBO, Aceite de Pescado (FO) y Aceite de Palmiste como fuentes de grasa, incluidas a los mismos niveles que en el Exp. 1. En ambos experimentos 180 animales fueron alojados en jaulas individuales (n=30) y 600 en jaulas colectivas en grupos de 5 animales (n=20). Los animales alimentados con un 4% de grasa añadida tuvieron unos consumos diarios y unos índices de conversión más bajos que aquellos alimentados con las dietas con un 1.5% de grasa. En los animales alojados en colectivo del Exp. 1, el consumo fue un 4.8% más alto en los que consumieron las dietas que contenían manteca que en los animales alimentados con las dietas SBO (P = 0.036). La inclusión de manteca tendió a reducir la mortalidad (P = 0.067) en torno al 60% y al 25% con respecto a las dietas con SBO y SLO, respectivamente. La mortalidad aumentó con el nivel máximo de inclusión de SLO (14% vs. 1%, P < 0.01), sin observarse un efecto negativo sobre la mortalidad con el nivel más alto de inclusión de las demás fuentes de grasa utilizadas. En los animales alojados colectivo del Exp. 2 se encontró una disminución del consumo (11%), peso vivo a 63 d (4.8%) y de la ganancia diaria de peso (7.8%) con la inclusión de aceite de pescado con respecto a otras dietas (P < 0.01). Los dos últimos parámetros se vieron especialmente más reducidos cuando en las dietas se incluyó el nivel más alto de FO (5.6 y 9.5%, respectivamente, (P < 0.01)). Los animales alojados individualmente mostraron unos resultados productivos muy similares. La inclusión de aceite pescado tendió (P = 0.078) a aumentar la mortalidad (13.2%) con respecto al aceite de palmiste (6.45%), siendo intermedia para las dietas que contenían SBO (8.10%). La fuente o el nivel de grasa no afectaron la composición corporal total o de la canal eviscerada de los animales. Un incremento en el nivel de grasa dio lugar a una disminución de la ingesta de nitrógeno digestible (DNi) (1.83 vs. 1.92 g/d; P = 0.068 en Exp. 1 y 1.79 vs. 1.95 g/d; P = 0.014 en Exp. 2). Debido a que el nitrógeno retenido (NR) en la canal fue similar para ambos niveles (0.68 g/d (Exp. 1) y 0.71 g/d (Exp. 2)), la eficacia total de retención del nitrógeno (ERN) aumentó con el nivel máximo de inclusión de grasa, pero de forma significativa únicamente en el Exp. 1 (34.9 vs. 37.8%; P < 0.0001), mientras que en el Exp. 2 se encontró una tendencia (36.2 vs. 38.0% en Exp. 2; P < 0.064). Como consecuencia, la excreción de nitrógeno en heces fue menor en los animales alimentados con el nivel más alto de grasa (0.782 vs. 0.868 g/d; P = 0.0001 en Exp. 1, y 0.745 vs. 0.865 g/d; P < 0.0001 en Exp.2) al igual que el nitrógeno excretado en orina (0.702 vs. 0.822 g/d; P < 0.0001 en Exp. 1 y 0.694 vs. 0.7999 g/d; P = 0.014 en Exp.2). Aunque no hubo diferencias en la eficacia total de retención de la energía (ERE), la energía excretada en heces disminuyó al aumentar el nivel de inclusión de grasa (142 vs. 156 Kcal/d; P = 0.0004 en Exp. 1 y 144 vs. 154 g/d; P = 0.050 en Exp. 2). Sin embargo, la energía excretada como orina y en forma de calor fue mayor en el los animales del Exp. 1 alimentados con el nivel más alto de grasa (216 vs. 204 Kcal/d; P < 0.017). Se puede concluir que la manteca y el aceite de palmiste pueden ser considerados como fuentes alternativas al aceite de soja debido a la reducción de la mortalidad, sin efectos negativos sobre los rendimientos productivos o la retención de nutrientes. La inclusión de aceite de pescado empeoró los rendimientos productivos y la mortalidad durante el periodo de crecimiento. Un aumento en el nivel de grasa mejoró el índice de conversión y la eficacia total de retención de nitrógeno. ABSTRACT The aim of this Thesis is: 1) to obtain and validate prediction equations to determine in vivo whole body and carcass composition using the Bioelectrical Impedance (BIA) method in growing rabbits from 25 to 77 days of age, and 2) to study its application to determine differences on whole body and carcass chemical composition, and nutrient retention of animals fed different fat levels and sources. The first study was conducted to determine and later validate, by using independent data, the prediction equations obtained to assess in vivo the whole body composition of growing rabbits. One hundred and fifty rabbits grouped at 5 different ages (25, 35, 49, 63 and 77 days) and weighing from 231 to 3138 g were used. A four terminal body composition analyser was used to obtain resistance (Rs, ) and reactance (Xc, ) values (Model BIA-101, RJL Systems, Detroit, MI USA). The distance between internal electrodes (D, cm), body length (L, cm) and live BW of each animal were also registered. At each selected age, animals were slaughtered, ground and frozen (-20 ºC) for later chemical analyses (DM, fat, CP, ash and GE). Fat and energy body content increased with the age, while protein, ash, and water decreased. Mean values of Rs, Xc, impedance (Z), L and D were 83.5 ± 23.1 , 18.2 ± 3.8 , 85.6 ± 22.9 , 30.6 ± 6.9 cm and 10.8 ± 3.1 cm. A multiple linear regression analysis was used to determine the prediction equations, using BW, L and Z data as independent variables. Equations obtained to estimate water (g), CP (g), fat (g), ash (g) and GE (MJ) content had, respectively, coefficient of determination (R2) values of 0.99, 0.99, 0.97, 0.98 and 0.99, and the relative mean prediction error (RMPE) was: 2.79, 6.15, 24.3, 15.2 and 10.6%, respectively. When water was expressed as percentage, the R2 and RMPE were 0.85 and 2.30%, respectively. When prediction of the content of protein (%DM), fat (%DM), ash (%DM) and energy (kJ/100 g DM) was done, values of 0.79, 0.83, 0.71 and 0.86 for R2, and 5.04, 18.9, 12.0 and 3.19% for RMPE, respectively, were obtained. Reactance was negatively correlated with water, ash and CP content (r = -0.32, P < 0.0001; r = -0.20, P < 0.05; r = -0.26, P < 0.01) and positively correlated with fat and GE (r = 0.23 and r = 0.24; P < 0.01). Otherwise, resistance was positively correlated with water, ash and CP (r = 0.31, P < 0.001; r = 0.28, P < 0.001; r = 0.37, P < 0.0001) and negatively correlated with fat and energy (r = -0.36 and r = -0.35; P < 0.0001). Moreover, age was negatively correlated with water, ash and CP content (r = -0.79; r = -0.68 and r = -0.80; P < 0.0001) and positively correlated with fat and energy (r = 0.78 and r = 0.81; P < 0.0001). It could be concluded that BIA is a non-invasive good method to estimate in vivo whole body composition of growing rabbits from 25 to 77 days of age. The aim of the second study was to determine and validate with independent data, the prediction equations obtained to estimate in vivo carcass composition of growing rabbits by using the results of carcass chemical composition and BIA values in a group of rabbits from 25 to 77 days. Also its potential application to predict nutrient retention and overall energy and nitrogen retention efficiencies was analysed. Seventy five rabbits grouped at 5 different ages (25, 35, 49, 63 and 77 days) with weights ranging from 196 to 3260 g were used. A four terminal body composition analyser (Model BIA-101, RJL Systems, Detroit, MI USA) was used to obtain resistance (Rs, ) and reactance (Xc, ) values. The distance between internal electrodes (D, cm), body length (L, cm) and live weight (BW, g) were also registered. At each selected age, all the animals were stunned and bled. The skin, organs and digestive content were removed, and the chilled carcass were weighed and processed for chemical analyses (DM, fat, CP, ash and GE). Energy and fat increased with the age, while CP, ash, and water decreased. Mean values of Rs, Xc, impedance (Z), L and D were 95.9±23.9 , 19.5±4.7 , 98.0±23.8 , 20.6±6.3 cm y 13.7±3.1 cm. A multiple linear regression analysis was done to determine the equations, using BW, L and Z data as parameters. Coefficient of determination (R2) of the equations obtained to estimate water (g), CP (g), fat (g), ash (g) and GE (MJ) content were: 0.99, 0.99, 0.95, 0.96 and 0.98, and relative mean prediction error (RMPE) were: 4.20, 5.48, 21.9, 9.10 and 6.77%, respectively. When water content was expressed as percentage, the R2 and RMPE were 0.79 and 1.62%, respectively. When prediction of protein (%DM), fat (%DM), ash (%DM) and energy (kJ/100 g DM) content was done, R2 values were 0.68, 0.76, 0.66 and 0.82, and RMPE: 3.22, 10.5, 5.82 and 2.54%, respectively. Reactance was positively correlated with fat content (r = 0.24, P < 0.05) while resistance was positively correlated with water, ash and protein carcass content (r = 0.55, P < 0.001; r = 0.54, P < 0.001; r = 0.40, P < 0.005) and negatively correlated with fat and energy (r = -0.44 and r = -0.55; P < 0.001). Moreover, age was negatively correlated with water, ash and CP content (r = -0.97, r = -0.95 and r = -0.89, P < 0.0001) and positively correlated with fat and GE (r = 0.95 and r = 0.97; P < 0.0001). In the whole growing period (35-63 d), overall energy retention efficiency (ERE) and nitrogen retention efficiency (NRE) were studied. The ERE values were 20.4±7.29%, 21.0±4.18% and 20.8±2.79%, from 35 to 49, 49 to 63 and from 35 to 63 d, respectively. NRE was 46.9±11.7%, 34.5±7.32% and 39.1±3.23% for the same periods. Energy was retained in body tissues for growth with an efficiency of approximately 52.5% and efficiency of the energy for protein and fat retention was 33.3 and 69.9%, respectively. Efficiency of utilization of nitrogen for growth was near to 77%. This work shows that BIA it’s a non-invasive and good method to estimate in vivo carcass composition and nutrient retention of growing rabbits from 25 to 77 days of age. In the third study, two experiments were conducted to investigate the effect of the fat addition and source, on performance, mortality, nutrient retention, and the whole body and carcass chemical composition of growing rabbits from 34 to 63 d. In Exp. 1 three diets were arranged in a 3 x 2 factorial structure with the source of fat: Soybean oil (SBO), Soya Lecithin Oil (SLO) and Lard (L) and the dietary fat inclusion level (1.5 and 4%) as the main factors. Exp. 2 had also arranged as a 3 x 2 factorial design, but using SBO, Fish Oil (FO) and Palmkernel Oil (PKO) as fat sources, and included at the same levels than in Exp. 1. In both experiments 180 animals were allocated in individual cages (n=30) and 600 in collectives cages, in groups of 5 animals (n=20). Animals fed with 4% dietary fat level showed lower DFI and FCR than those fed diets with 1.5%. In collective housing of Exp. 1, DFI was a 4.8% higher in animals fed with diets containing lard than SBO (P = 0.036), being intermediate for diet with SLO. Inclusion of lard also tended to reduce mortality (P = 0.067) around 60% and 25% with respect SBO and SLO diets, respectively. Mortality increased with the greatest level of soya lecithin (14% vs. 1%, P < 0.01). In Exp. 2 a decrease of DFI (11%), BW at 63 d (4.8%) and DWG (7.8%) were observed with the inclusion of fish oil with respect the other two diets (P < 0.01). These last two traits impaired with the highest level of fish oil (5.6 and 9.5%, respectively, (P < 0.01)). Animals housed individually showed similar performance results. The inclusion of fish oil also tended to increase (P = 0.078) mortality (13.2%) with respect palmkernel oil (6.45%), being mortality of SBO intermediate (8.10%). Fat source and level did not affect the whole body or carcass chemical composition. An increase of the fat sources addition led to a decrease of the digestible nitrogen intake (DNi) (1.83 vs. 1.92 g/d; P = 0.068 in Exp. 1 and 1.79 vs. 1.95 g/d; P = 0.014 in Exp. 2). As the nitrogen retained (NR) in the carcass was similar for both fat levels (0.68 g/d (Exp. 1) and 0.71 g/d (Exp. 2)), the overall efficiency of N retention (NRE) increased with the highest level of fat, but only reached significant level in Exp. 1 (34.9 vs. 37.8%; P < 0.0001), while in Exp. 2 a tendency was found (36.2 vs. 38.0% in Exp. 2; P < 0.064). Consequently, nitrogen excretion in faeces was lower in animals fed with the highest level of fat (0.782 vs. 0.868 g/d; P = 0.0001 in Exp. 1, and 0.745 vs. 0.865 g/d; P < 0.0001 in Exp.2). The same effect was observed with the nitrogen excreted as urine (0.702 vs. 0.822 g/d; P < 0.0001 in Exp. 1 and 0.694 vs. 0.7999 g/d; P = 0.014 in Exp.2). Although there were not differences in ERE, the energy excreted in faeces decreased as fat level increased (142 vs. 156 Kcal/d; P = 0.0004 in Exp. 1 and 144 vs. 154 g/d; P = 0.050 in Exp. 2). In Exp. 1 the energy excreted as urine and heat production was significantly higher when animals were fed with the highest level of dietary fat (216 vs. 204 Kcal/d; P < 0.017). It can be concluded that lard and palmkernel oil can be considered as alternative sources to soybean oil due to the reduction of the mortality, without negative effects on performances or nutrient retention. Inclusion of fish impaired animals´ productivity and mortality. An increase of the dietary fat level improved FCR and overall protein efficiency retention.

Changes in body water distribution during treatment with inhaled steroid in pre-school children

Primary objective: The study aimed to examine the changes in water distribution in the soft tissue during systemic steroid activity. Research design: A three-way cross-over, randomized, placebo-controlled, double-blind trial was used, including 4 weeks of fluticasone propionate pMDI 200 mug b.i.d. delivered via Babyhaler(R), budesonide pressurized metered dose inhaler (pMDI) 200 mug b.i.d. delivered via Nebuchamber(R) and placebo. Spacers were primed before use. In total, 40 children aged 1-3 years, with mild intermittent asthma were included. Twenty-five of the children completed all three treatments. At the end of each treatment period body impedance and skin ultrasonography were measured. Methods and procedures: We measured changes in water content of the soft tissues by two methods. Skin ultrasonography was used to detect small changes in dermal water content, and bioelectrical impedance was used to assess body water content and distribution. Main outcomes and results: We found an increase in skin density of the shin from fluticasone as measured by ultrasonography (p = 0.01). There was a tendency for a consistent elevation of impedance parameters from active treatments compared to placebo although overall this effect was not statistically significant (0.1< p <0.2). However, sub-analyses indicated a significant effect on whole-body and leg impedance from budesonide treatment (p <0.05). Conclusion: Decreased growth during inhaled steroid treatment seems to partly reflect generalized changes in body water.

Comparison of two field methods for estimating body fat in different Spanish Dance disciplines

The purpose of the present study was to investigate percentage body fat (%BF) differences in three Spanish dance disciplines and to compare skinfold and bioelectrical impedance predictions of body fat percentage in the same sample. Seventy-six female dancers, divided into three groups, Classical (n=23), Spanish (n=29) and Flamenco (n=24), were measured using skinfold measurements at four sites: triceps, subscapular, biceps and iliac crest, and whole body multi-frequency bioelectrical impedance (BIA). The skin-fold measures were used to predict body fat percentage via Durnin and Womersley's and Segal, Sun and Yannakoulia equations by BIA. Differences in percent fat mass between groups (Classical, Spanish and Flamenco) were tested by using repeated measures analysis (ANOVA). Also, Pearson's product-moment correlations were performed on the body fat percentage values obtained using both methods. In addition, Bland-Altman plots were used to assess agreement, between anthropometric and BIA methods. Repeated measures analysis of variance did not found differences in %BF between modalities (p<0.05). Fat percentage correlations ranged from r= 0.57 to r=0.97 (all, p<0.001). Bland-Altman analysis revealed differences between BIA Yannakoulia as a reference method with BIA Segal (-0.35 ± 2.32%, 95%CI: -0.89to 0.18, p=0.38), with BIA Sun (-0.73 ± 2.3%, 95%CI: -1.27 to -0.20, p=0.014) and Durnin-Womersley (-2.65 ± 2,48%, 95%CI: -3.22 to -2.07, p<0.0001). It was concluded that body fat percentage estimates by BIA compared with skinfold method were systematically different in young adult female ballet dancers, having a tendency to produce underestimations as %BF increased with Segal and Durnin-Womersley equations compared to Yannakoulia, concluding that these methods are not interchangeable.

Body composition changes assessed by bioelectrical impedance and their associations with functional class deterioration in stable heart failure patients

Background: Heart failure (HF) patients develop important changes in body composition, but only a small number of studies have evaluated the associations between these changes and functional class deterioration in a prospective manner. Objective: The aim of this study was to evaluate whether changes in bioimpedance parameters were associated with NYHA functional class deterioration over six months. Methods: A total of 275 chronic stable HF patients confirmed by echocardiography were recruited. Body composition measurements were obtained by whole body bioelectrical impedance with multiple frequency equipment (BodyStat QuadScan 4000). We evaluated functional class using the New York Heart Association (NYHA) classification at baseline and after six months. Results: According to our results, 66 (24%) subjects exhibited functional class deterioration, while 209 improved or exhibited no change. A greater proportion of patients exhibited higher extracellular water (> 5%), and these patients developed hypervolemia, according to location on the resistance/reactance graph. A 5% decrease in resistance/height was associated with functional class deterioration with an OR of 1.42 (95% CI 1.01-2.0, p = 0.04). Conclusions: Body composition assessment through bioelectrical impedance exhibited a valuable performance as a marker of functional class deterioration in stable HF patients.

Ikkε Is Key To Induction Of Insulin Resistance In The Hypothalamus, And Its Inhibition Reverses Obesity.

IKK epsilon (IKKε) is induced by the activation of nuclear factor-κB (NF-κB). Whole-body IKKε knockout mice on a high-fat diet (HFD) were protected from insulin resistance and showed altered energy balance. We demonstrate that IKKε is expressed in neurons and is upregulated in the hypothalamus of obese mice, contributing to insulin and leptin resistance. Blocking IKKε in the hypothalamus of obese mice with CAYMAN10576 or small interfering RNA decreased NF-κB activation in this tissue, relieving the inflammatory environment. Inhibition of IKKε activity, but not TBK1, reduced IRS-1(Ser307) phosphorylation and insulin and leptin resistance by an improvement of the IR/IRS-1/Akt and JAK2/STAT3 pathways in the hypothalamus. These improvements were independent of body weight and food intake. Increased insulin and leptin action/signaling in the hypothalamus may contribute to a decrease in adiposity and hypophagia and an enhancement of energy expenditure accompanied by lower NPY and increased POMC mRNA levels. Improvement of hypothalamic insulin action decreases fasting glycemia, glycemia after pyruvate injection, and PEPCK protein expression in the liver of HFD-fed and db/db mice, suggesting a reduction in hepatic glucose production. We suggest that IKKε may be a key inflammatory mediator in the hypothalamus of obese mice, and its hypothalamic inhibition improves energy and glucose metabolism.

Is Thyroid Stunning Clinically Relevant? A Retrospective Analysis Of 208 Patients.

Current guidelines have advised against the performance of (131)I-iodide diagnostic whole body scintigraphy (dxWBS) to minimize the occurrence of stunning, and to guarantee the efficiency of radioiodine therapy (RIT). The aim of the study was to evaluate the impact of stunning on the efficacy of RIT and disease outcome. This retrospective analysis included 208 patients with differentiated thyroid cancer managed according to a same protocol and followed up for 12-159 months (mean 30 ± 69 months). Patients received RIT in doses ranging from 3,700 to 11,100 MBq (100 mCi to 300 mCi). Post-RIT-whole body scintigraphy images were performed 10 days after RIT in all patients. In addition, images were also performed 24-48 hours after therapy in 22 patients. Outcome was classified as no evidence of disease (NED), stable disease (SD) and progressive disease (PD). Thyroid stunning occurred in 40 patients (19.2%), including 26 patients with NED and 14 patients with SD. A multivariate analysis showed no association between disease outcome and the occurrence of stunning (p = 0.3476). The efficacy of RIT and disease outcome do not seem to be related to thyroid stunning.

[health Protection For Rural Workers: The Need To Standardize Techniques For Quantifying Dermal Exposure To Pesticides].

Quantification of dermal exposure to pesticides in rural workers, used in risk assessment, can be performed with different techniques such as patches or whole body evaluation. However, the wide variety of methods can jeopardize the process by producing disparate results, depending on the principles in sample collection. A critical review was thus performed on the main techniques for quantifying dermal exposure, calling attention to this issue and the need to establish a single methodology for quantification of dermal exposure in rural workers. Such harmonization of different techniques should help achieve safer and healthier working conditions. Techniques that can provide reliable exposure data are an essential first step towards avoiding harm to workers' health.

Comparação preliminar entre ultrassonografia quantitativa de falanges e densitometria óssea na avaliação da massa óssea em adolescentes

OBJECTIVE: To evaluate the association between quantitative ultrasonography at hand phalanges (QUS) and dual energy X-ray absorptiometry (DXA), and between these methods with food intake and history of bone fractures. SUBJECTS AND METHODS:After two years of follow up of 270 schoolchildren, 10 of them, who showed bone mass below - 2 SD in QUS, were included in the present study. Laboratory results and DXA data were analyzed. RESULTS: Bone mass evaluated by DXA at L1-L4 ranged from -2.8 to -1.1 SDS, and whole body bone mass, from -2.9 to -1.2 SDS. Three children had history of non-pathological bone fractures. Dietary assessment showed low intake of calcium in 10 cases, of phosphorus in 6, and of vitamin D in 8 cases. There were no differences among the cases of bone mass below-2 SD in any of the three used methods. There was no association between history of bone fractures and food intake, and between these evaluations and bone mass. CONCLUSION: In this small group of schoolchildren there was an association between the methods QUS and DXA. However, there was no association between bone mass and the history of bone fractures, or calcium, phosphorus and vitamin D intake.

Percepção de esforço em exercício sob fadiga em normóxia e hipóxia

Universidade Estadual de Campinas . Faculdade de Educação Física