The bacterial community in partially purified water, which is prepared by ion exchange from tap water and is used in pharmaceutical manufacturing processes, was analyzed by denaturing gradient gel electrophoresis (DGGE). 16S ribosomal DNA fragments, including V6, -7, and -8 regions, were amplified with universal primers and analyzed by DGGE. The bacterial diversity in purified water determined by PCR-DGGE banding patterns was significantly lower than that of other aquatic environments. The bacterial populations with esterase activity sorted by flow cytometry and isolated on soybean casein digest (SCD) and R2A media were also analyzed by DGGE. The dominant bacterium in purified water possessed esterase activity but could not be detected on SCD or R2A media. DNA sequence analysis of the main bands on the DGGE gel revealed that culturable bacteria on these media were Bradyrhizobium sp., Xanthomonas sp., and Stenotrophomonas sp., while the dominant bacterium was not closely related to previously characterized bacteria. These data suggest the importance of culture-independent methods of quality control for pharmaceutical water.

Abstract-A continuous vocal imitation system was developed using a computational model that explains the process of phoneme acquisition by infants. Human infants perceive speech sounds not as discrete phoneme sequences but as continuous acoustic signals. One of critical problems in phoneme acquisition is the design for segmenting these continuous speech sounds. The key idea to solve this problem is that articulatory mechanisms such as the vocal tract help human beings to perceive speech sound units corresponding to phonemes. To segment acoustic signal with articulatory movement, we apply the segmenting method to our system by Recurrent Neural Network with Parametric Bias (RNNPB). This method determines the multiple segmentation boundaries in a temporal sequence using the prediction error of the RNNPB model, and the PB values obtained by the method can be encoded as kind of phonemes. Our system was implemented by using a physical vocal tract model, called the Maeda model. Experimental results demonstrated that our system can self-organize the same phonemes in different continuous sounds, and can imitate vocal sound involving arbitrary numbers of vowels using the vowel space in the RNNPB. This suggests that our model reflects the process of phoneme acquisition.

No abstract

No abstract