{'fraction_non_alphanumeric': 0.031192660550458717, 'fraction_numerical': 0.024587155963302753, 'mean_word_length': 4.964989059080962, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evaluation of broiler growth velocity and acceleration in relation to pulmonary hypertension syndrome.

An evaluation was made of the relationship between individual daily growth patterns and susceptibility of broiler chickens to pulmonary hypertension syndrome (PHS). In the first experiment, 46 male broilers were weighed for each of 50 d, during which time 13 developed PHS. Three temporal phases (0 to 15, 16 to 35, and 36 to 50 d) of broiler growth velocity and acceleration were examined. Correlation dimensions and Lyapunov exponents suggested evidence of chaos in growth velocity and acceleration, but the absence of detectable differences between broilers in the normal and PHS categories led us to reject the hypotheses that growth is more chaotic in normal broilers than in broilers susceptible to PHS. Growth velocity and acceleration values for mean and SD were statistically evaluated as response variables for each growth phase. Mean values for velocity during the third phase were different between broilers in the normal and PHS categories (velocity: 68.8 vs 48.9 g/d, P = 0.03, respectively) and (acceleration: 0.3 vs -1.4 g/d2, P = 0.07, respectively). The third phase SD (reflecting oscillation for velocity and acceleration) was greater for normal than for PHS birds (velocity: 26.1 vs 21.3 g/d, P = 0.13, respectively; acceleration: 39.7 vs 28.2 g/d2, P = 0.03, respectively). The hypothesis was accepted that normal birds have greater oscillations in growth velocity and acceleration than birds susceptible to PHS. A general regression neural network (GRNN) with genetic adaptive calibration was trained to predict PHS based on individual growth phases and their combinations. Data representing the first, first two, and all three phases of growth were determined to have potential for computerized diagnostic weighing. With the GRNN, birds in all three data sets were successfully classified (100%) with or without PHS. A third hypothesis, therefore, was accepted that artificial neural networks could be used to distinguish the difference between normal broilers and those susceptible to PHS. In the second experiment, only one bird was diagnosed with PHS. Velocity and acceleration neural networks from Phase 1 and Phases 1 and 2 in the first experiment were applied to the growth velocity and acceleration data of Experiment 2. The Phase 1 neural networks were the most promising in that they correctly identified 71.6 and 72.4% of the birds as normal for velocity and acceleration data, respectively. In general, data in the second experiment exceeded the neural network range of training for both velocity and acceleration, which reflected increased oscillation during the second phase of growth.