Effects of replacing rye-grass (Lolium spp.) hay by banana (Musa acuminata L.) by-products on feed intake, growth, and feed conversion rate of Canary hair sheep breed (Pelibuey) lambs.

The objective of this study was to evaluate the effects of replacing rye-grass (Lolium spp.) hay with banana (Musa acuminata L.) by-products on feed intake, average daily gain (ADG), and feed conversion rate (FCR) in Pelibuey (hair sheep breed) lambs. The lambs (n = 22; initial BW = 14.8 ± 2.5 kg) were individually housed and fed on two different types of diet for 58 days: the first group (experimental diet) received as forage a mixture of fresh banana by-products composed of leaves and pseudostem. The second group received commercial rye-grass hay (conventional diet) as a fiber source. Both groups received a supplementary commercial concentrate food. The total daily forage intake and FCR were lower (P < 0.001) in animals fed on the conventional diet (rye-grass hay) than animals fed on the experimental diet (banana by-products). However, there were no significant differences in total dry matter intake, total digestible energy intake, total body weight gain, and ADG between the two groups. The results obtained in this study suggest that banana by-products could be used as alternative forage for Pelibuey lambs raised in subtropical conditions.

Symbolic dynamics marker of heart rate variability combined with clinical variables enhance obstructive sleep apnea screening.

Many sleep centres try to perform a reduced portable test in order to decrease the number of overnight polysomnographies that are expensive, time-consuming, and disturbing. With some limitations, heart rate variability (HRV) has been useful in this task. The aim of this investigation was to evaluate if inclusion of symbolic dynamics variables to a logistic regression model integrating clinical and physical variables, can improve the detection of subjects for further polysomnographies. To our knowledge, this is the first contribution that innovates in that strategy. A group of 133 patients has been referred to the sleep center for suspected sleep apnea. Clinical assessment of the patients consisted of a sleep related questionnaire and a physical examination. The clinical variables related to apnea and selected in the statistical model were age (p < 10(-3)), neck circumference (p < 10(-3)), score on a questionnaire scale intended to quantify daytime sleepiness (p < 10(-3)), and intensity of snoring (p < 10(-3)). The validation of this model demonstrated an increase in classification performance when a variable based on non-linear dynamics of HRV (p < 0.01) was used additionally to the other variables. For diagnostic rule based only on clinical and physical variables, the corresponding area under the receiver operating characteristic (ROC) curve was 0.907 (95% confidence interval (CI) = 0.848, 0.967), (sensitivity 87.10% and specificity 80%). For the model including the average of a symbolic dynamic variable, the area under the ROC curve was increased to 0.941 (95% = 0.897, 0.985), (sensitivity 88.71% and specificity 82.86%). In conclusion, symbolic dynamics, coupled with significant clinical and physical variables can help to prioritize polysomnographies in patients with a high probability of apnea. In addition, the processing of the HRV is a well established low cost and robust technique.