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This paper suggests a variation of a well-known probabilistic matrix factorization algorithm which is commonly used in data analysis and scientific computing, and which has been considered recently to serve natural language processing. The proposed variation is meant to take benefit from the fact that matrices processed in natural language processing tasks are normally sparse rectangular matrices with one dimension much larger than the other, and this can be used to ensure adequate accuracy with acceptable computation time. Preliminary experiments on real-world textual corpora show that the proposed algorithm achieves relevant improvements compared to the original one.
The aim of the present study was to test in vivo the effectiveness of an experimental green tea extract in reducing levels of mutans streptococci and lactobacilli in saliva by means of selective culture medium. Sixty-six healthy patients ranging in age from 12 to 18 years were recruited and randomly divided into two groups: Group A (n=33) and group B (n=33). Group A subjects were asked to rinse their mouths with 40 mL of an experimental green tea extract, for 1 minute, three times a day for a week, whereas Group B subjects were asked to rinse with 40 mL of a placebo mouth rinse. Saliva samples were obtained at baseline, 4 days, and 7 days. The counts of mutans streptococci and lactobacilli were investigated by chair-side kits. Data were statistically processed. A regression binary logistic analysis was done. The statistical significance level was established at P<.05. The experimental group showed a statistically significant reduction in colony counts of mutans streptococci and lactobacilli relative to the control group. These findings showed the efficacy of a green tea extract against cariogenic oral flora, opening a promising avenue of clinical applications in the preparation of specific and natural anticariogenic remedies.