Bacteriophage therapy for Staphylococcus aureus bacteremia in streptozotocin-induced diabetic mice

The protective effect of bacteriophage was assessed against experimental Staphylococcus aureus lethal bacteremia in streptozotocin (STZ) induced-diabetic and non-diabetic mice. Intraperitoneal administrations of S. aureus (RCS21) of 2 x 10(8) CFU caused lethal bacteremia in both diabetic and non-diabetic mice. A single administration of a newly isolated lytic phage strain (GRCS) significantly protected diabetic and nondiabetic mice from lethal bacteremia (survival rate 90% and 100% for diabetic and non-diabetic bacteremic groups versus 0% for saline-treated groups). Comparison of phage therapy to oxacillin treatment showed a significant decrease in RCS21 of 5 and 3 log units in diabetic and nondiabetic bacteremic mice, respectively. The same protection efficiency of phage GRCS was attained even when the treatment was delayed up to 4 h in both diabetic and non-diabetic bacteremic mice. Inoculation of mice with a high dose (10(10) PFU) of phage GRCS alone produced no adverse effects attributable to the phage per se. These results suggest that phages could constitute valuable prophylaxis against S. aureus infections, especially in immunocompromised patients. (C) 2010 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.