Crossbred weanling pigs (an equal number of barrows and gilts) with an average initial weight of 7.4 (Exp. 1) or 9.6 kg (Exp. 2) were used in two 4-wk experiments (Exp. 1, n = 96; Exp. 2, n = 96) to investigate the effects of added phytase or citric acid on performance, rib mineralization, gastric pH, and digestibility measurements. A corn-soybean meal-based diet low in Ca and P was used in both experiments. In Exp. 1, three citric acid levels (0, 1.5, or 3.0%) and four phytase levels (0, 250, 500, or 750 U/kg) were used in a 3 x 4 factorial arrangement of treatments. In Exp. 2, two citric acid levels (0 or 2.0%) and three phytase levels (0, 250, or 500 U/kg) were used in a 2 x 3 factorial arrangement of treatments. Phosphorus was maintained at .33 and .34% in Exp. 1 and 2, respectively. Calcium was maintained at a 2.5:1 ratio with total available P (available P plus the estimated released phytate P by phytase) in Exp. 1 and at a level of .44% in Exp. 2. In both experiments, BW and feed consumption were measured weekly, and pen fecal samples were collected twice daily for 5 d during wk 4. At the end of wk 4, the barrow in each pen was killed following a fast-refeed-fast (22-1-2 h) regimen for collection of 10th ribs and stomach digesta. In Exp. 1 and 2, phytase addition did not affect (P > .05) performance but linearly increased (P < .05) rib shear force, shear energy, dry bone weight, ash weight, ash percentage, and Ca and P digestibilities. Addition of citric acid in both experiments reduced dietary pH and stomach digesta pH (P < .05). The addition of citric acid improved (P < .05) ADG, feed efficiency, and Ca digestibility in Exp. 1, but it had no effect on performance and Ca digestibility in Exp. 2. In summary, the additions of citric acid and phytase to weanling pig diets were each beneficial, but no synergistic effects were observed.

Biological sequence alignment using computational power has received increasing attention as technology develops. It is important to predict if a novel DNA sequence is potentially dangerous by determining its taxonomic identity and functional characteristics through sequence identification. This task can be facilitated by the rapidly increasing amounts of biological data in DNA and protein databases thanks to the corresponding increase in computational and storage costs. Unfortunately, the growth in biological databases has caused difficulty in exploiting this information. EnTrance presents an approach that can expedite the analysis of this large database by employing entropy scaling. This allows scaling with the amount of entropy in the database instead of scaling with the absolute size of the database. Since DNA and protein sequences are biologically meaningful, the space of biological sequences demonstrates the structure exploited by entropy scaling. As biological sequence databases grow, taking advantage of this structure can be extremely beneficial for reducing query times. EnTrance, the entropy scaling search algorithm introduced here, accelerates the biological sequence search exemplified by tools such as BLAST. EnTrance does this by utilizing a two step search approach. In this fashion, EnTrance quickly reduces the number of potential matches before more exhaustively searching the remaining sequences. Tests of EnTrance show that this approach can lead to improved query times. However, constructing the required entropy scaling indices beforehand can be challenging. To improve performance, EnTrance investigates several ideas for accelerating index build time that supports entropy scaling searches. In particular, EnTrance makes full use of the concurrency features of Go language greatly reducing the index build time. Our results identify key tradeoffs and demonstrate that there is potential in using these techniques for sequence similarity searches. Finally, EnTrance returns more matches and higher percentage identity matches when compared with existing tools.