Two experiments were conducted to quantify the effects of protein intake on protein and fat deposition rates at two protein-free, energy intake levels in 90 preruminant Holstein Friesian x Dutch Friesian calves. The two experiments were similar in design, but were performed in two different weight ranges: 80 to 160 kg BW and 160 to 240 kg BW in Exp. 1 and 2, respectively. In each experiment, calves were allocated to either an initial slaughter group or to one of 12 treatments (three calves per treatment), which consisted of six protein intake levels at each of two protein-free energy intake levels. Calves were slaughtered and analyzed for body composition when they had reached the target weight. A balance study was conducted when calves reached 120 and 200 kg BW in Exp. 1 and 2, respectively. Protein digestibility increased with increasing protein intake in both experiments (P < .001). Average daily gain of the empty body varied between 640 and 1,340 g/d and between 420 and 1,370 g/d in Exp. 1 and 2, respectively, and was affected by protein (P < .001) and protein-free energy intake (P < .001). The calves responded to increased protein intake by increasing their protein (P < .001) and fat (P < .01) deposition rates. Maximum protein deposition was reached in the second experiment at 244 g/d. Extra protein-free energy intake resulted mainly in extra fat deposition (P < .001), but also increased the protein deposition (P < .01), even at low protein intake levels. In both experiments, the response of protein deposition rate to increased protein intakes was low: about 30% of the extra ingested protein was deposited. These results clearly demonstrate a low priority for partitioning dietary protein into protein gain in these calves.

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In a companion paper, a mechanistic model is described, integrating protein and energy metabolism in preruminant calves of 80-240 kg live weight. The model simulates the partitioning of nutrients from ingestion through intermediary metabolism to growth, consisting of accretions of protein, fat, ash and water. The model also includes a routine to check possible dietary amino acid imbalance and can be used to predict amino acid requirements. This paper describes a sensitivity and behavioral analysis of the model, as well as tests against independent data. Increasing the carbohydrate:fat ratio at equal gross energy intakes leads to higher simulated protein- and lower simulated fat-deposition rates. Simulation of two experiments, not used for the development of the model, showed that rates of gain of live weight, protein and fat were predicted satisfactorily. The representation of protein turnover enables the investigation of the quantitative importance of hide, bone and visceral protein in protein and energy metabolism. The model is highly sensitive to 25% changes in kinetic parameters describing muscle protein synthesis and amino acid oxidation. Comparing simulated with experimentally derived amino acid requirements shows agreement for most amino acids for calves of approximately 90 kg live weight. For calves of approximately 230 kg live weight, however, lower requirements for lysine and for methionine+cystine are suggested by the model. More attention has to be paid to the inevitable oxidative losses of amino acids. It is concluded that the model provides a useful tool for the development of feeding strategies for preruminant calves in this weight range.

A total of 539 clinical isolates belonging to 10 species of the Enterobacteriaceae family were identified by enzyme activity profiles within 30 min of test inoculation. Each isolate was grown at 37 degrees C for 18 h on Mueller-Hinton agar and suspended to an optical density of 200 Klett units on 0.85% saline. Enzyme activity profiles were obtained by inoculating 18 fluorogenic substrates with the standardized bacterial suspension and monitoring initial rates of hydrolysis over the first 30 min of analysis. Individual enzyme activity profiles were entered into a coded data bank, and identifications were based on the Bayesian theory of probabilities. At a confidence level of 95%, five species were identified with a greater than 90% efficiency, three species were identified between 83 and 88% efficiency, and two species demonstrated a 72 and 75% efficiency of identification. The enzyme activity profile method of bacterial identification is rapid, easily automated, and reproducible.

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