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Lacticaseibacillus paracasei strain PS23 (PS23) exhibits some probiotic properties. In this study, a genomic analysis of PS23 revealed no genes related to virulence or antibiotic resistance. Moreover, ornithine decarboxylase activity was not detected in vitro. In addition, PS23 was sensitive to the tested antibiotics. Genotoxicity tests for PS23 including the Ames test and chromosomal aberrations in vitro using Chinese hamster ovary cells and micronuclei in immature erythrocytes of ICR mice were all negative. Moreover, following a 28-day study involving repeated oral dose toxicity tests (40, 400, and 4000 mg/kg equal 1.28 × 1010, 1.28 × 1011, and 1.28 × 1012 CFU/kg body weight, respectively) using an ICR mouse model, no adverse effects were observed from any doses. In addition, supplementation with live or heat-killed PS23 ameliorates DSS-induced colonic inflammation in mice. Our findings suggest that PS23 is safe and has anti-inflammatory effects and may therefore have therapeutic implications. [ABSTRACT FROM AUTHOR]

Abstract Background The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. Results There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. Conclusion The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart.

The age and growth of Kwangtung skate, Dipturus kwangtungensis, in the waters off northern Taiwan were estimated from 422 specimens collected between July 2006 and July 2008 at the Tashi fishing market in north-eastern Taiwan. The sexes-combined relationship between total length (TL) and centrum diameter (D) was estimated as follows: TL = 14.11D0.888 (N = 411, r2 = 0.94, P < 0.001). Growth band pairs (comprised of translucent and opaque bands) in vertebrae were determined to form once annually, based on the centrum edge analysis. Up to 14 band pairs were found for both sexes. The von Bertalanffy growth function (VBGF), two-parameter VBGF, the Robertson function, and the Gompertz function were used to fit the observed length-at-age data. The Akaike information criterion corrected indicated that the Gompertz function best fit the observed length at age data. Sex-specific growth functions were not significantly different; the sexes-combined growth parameters were estimated as follows: asymptotic length (L∞) = 96.7 cm TL, growth coefficient (kG) = 0.144 year−1 and constant (t0) = 5.45 year (N = 364, P < 0.01). [ABSTRACT FROM PUBLISHER]

The evolutionary relationships of organisms are traditionally delineated by the alignment-based methods using some DNA or protein sequences. In the post-genome era, the phylogenetics of life could be inferred from many sources such as genomic features, not just from comparison of one or several genes. To investigate the possibility that the physicochemical properties of protein sequences might reflect the phylogenetic ones, an alignment-free method using a support vector machine (SVM) classifier is implemented to establish the phylogenetic relationships between some protein sequences. There are two types of datasets, namely, the 'Enzymatic' (assigned by an EC accession) and 'Proteins' used to train the SVM classifiers. By computing the F-score for feature selection, we find that the classification accuracies of trained SVM classifiers could be significantly enhanced to 84% and 80%, respectively, for the enzymatic and 'proteins' datasets classified if the protein sequences are represented with some top 255 features selected. These show that some physicochemical features of amino acid sequences selected are sufficient for inferring the phylogenetic properties of the protein sequences. Moreover, we find that the selected physicochemical features appear to correlate with the physiological characteristic of the taxonomic classes classified. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 [ABSTRACT FROM AUTHOR]