ABSTRACT The objective of this study was to evaluate the effects of supra-nutritional level of selected B vitamins in different types of diet on broiler performance. Two experiments were conducted using male and female one-day-old chicks (n=288 each; initial body weights in experiment I and II was, respectively, 47.57 ± 0.43, and 47.98 ± 0.31) reared in batteries up to 18 days. In experiment I, the chicks were fed a corn and soybean meal-based diet and, in experiment II, a diet containing oxidized animal by-product meals and soybean oil was used. Both experiments followed a completely randomized design in a 3 × 2 factorial arrangement, consisting of the factors: i) supplementation levels of selected B vitamins (control, 3- or 6-times control of the vitamins riboflavin, pantothenic acid, niacin, folic acid and vitamin B12); ii) dietary nutritional density (low or high), totaling 6 treatments and 8 replicates of 6 birds each (3 males and 3 females). As result of this study, in Exp. I, chicks showed higher weight gain (741.1 g vs. 697.3 g) and feed intake (920.2 vs. 878.5 g) when fed low-nutritional density diet with supra-nutritional vitamin level 6-times higher than the control. However, this effect was not found in the performance of chickens fed high-nutritional density diet. Despite the poor quality of the ingredients used in Exp. II, no statistical effect was shown of the use of vitamin super-dose in rations with different dietary nutrient density. Feed conversion ratio (FCR) was significantly improved for chickens fed high-nutritional density diet (1.191 vs. 1.246 in experiment I, 1.244 vs. 1.275 in experiment II, p<0.01). We conclude that birds fed a vegetable diet formulated with low-dietary density improved body weight (BW) and feed intake (FI) when receiving supra-nutritional levels of vitamins 6-times higher than the control.

INTRODUÇÃO: Diferentes fontes de variação, tais como características do eletrogoniômetro e procedimentos podem afetar a acurácia e precisão das medidas do movimento durante a marcha. OBJETIVO: Quantificar a variação e comparar o efeito das diferentes fontes de variação nos registros eletrogoniométricos da marcha: os sensores, os procedimentos (coletas consecutivas) e a articulação do joelho. MÉTODOS: Movimentos de flexo-extensão e valgo-varo do joelho foram registrados durante a caminhada na esteira. Os registros foram divididos em passadas e normalizados no tempo usando uma rotina desenvolvida em MatLab. As curvas médias do joelho durante a marcha foram derivadas de 50 passadas, e sete condições foram avaliadas: uma para comparar dois sensores; duas para comparar duas diferentes coletas (incluindo a variação do sensor), e quatro para comparar os joelhos direito e esquerdo (incluindo a variação dos sensores e coletas). Os desvios-padrão médios das diferenças foram calculados. Para estimar as variações devido às coletas e à articulação do joelho, os desvios-padrão compostos foram transformados em variâncias e seus componentes isolados. RESULTADOS: A variação introduzida por dois sensores do mesmo modelo aplicados em uma coleta foi menor do que a variação introduzida pelo mesmo sensor usado em duas coletas consecutivas. Ainda, a variação introduzida pela diferença entre os joelhos direito e esquerdo foi maior do que a variação introduzida pela diferença entre os sensores (A e B) e as coletas (1 e 2). CONCLUSÕES: Assim, é preferível usar diferentes sensores na mesma coleta de dados (simultânea) do que usar o mesmo sensor em dois registros diferentes (consecutivos).

INTRODUCTION: Different sources of variations, such as electrogoniometer characteristics and procedures, may affect the accuracy and precision of movement measurements during gait. OBJECTIVE: To quantify the variations and compare the effects produced by different sources of variation in electrogoniometric gait recordings: the sensors, procedures (trials) and the knee joint. METHODS: Knee flexion/extension and valgus/varus movements were recorded during gait on the treadmill. The recordings were partitioned into strides and normalized in time using a routine developed in MatLab. Mean curves for the knee during gait were derived from 50 strides, and seven conditions were evaluated: one comparing pairs of sensors; two comparing pairs of different trials (including variations due to sensors); and four comparing the right and left knees (including variations due to sensors and trials). Mean standard deviations of the differences were calculated. To estimate the variations relating to the trial and the knee joint, the compound standard deviations were transformed into variances and split into their components. RESULTS: The variation introduced by pairs of sensors in the same model applied in one trial was smaller than the variation introduced by the same sensor used in two consecutive trials. Furthermore, the variation introduced by the difference between the right and left knees was greater than the variation introduced by the difference between sensors (A and B) and trials (1 and 2). CONCLUSIONS: It is, therefore, preferable to use different sensors in the same data recording (simultaneous) than use the same sensor in two different recordings (consecutive).