Mutations affecting the Rossman fold of isoleucyl-tRNA synthetase are correlated with low-level mupirocin resistance in Staphylococcus aureus.

The isoleucyl-tRNA synthetase (ileS) gene was sequenced in toto from 9 and in part from 31 Staphylococcus aureus strains with various degrees of susceptibility to mupirocin. All strains for which the mupirocin MIC was greater than 8 µg/ml contained point mutations affecting the Rossman fold via Val-to-Phe changes at either residue 588 (V588F) or residue 631 (V631F). The importance of the V588F mutation was confirmed by an allele-specific PCR survey of 32 additional strains. Additional mutations of uncertain significance were found in residues clustered on the surface of the IleS protein.

Plasma cell polyp of the vocal fold

Plasma cell polyps of the vocal fold (plasma cell granulomas) are rare inflammatory polyps of the larynx. They should be included in the clinical and histological differential diagnosis of laryngeal polyps. Histologically they are polyclonal aggregates of plasma cells. It is essential to distinguish them from monoclonal, neoplastic plasma cell proliferations. The treatment of choice is surgical resection, although radiotherapy, laser ablation, antibiotics and steroids have been used successfully. We present a case of plasma cell granuloma presenting as a vocal fold polyp, treated surgically.

k-fold Subsampling based Sequential Backward Feature Elimination

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid featureselection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimalfeature vector that well represents the shapes of the subjects in the images. In detail, the proposed featureselection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while thestandard linear support vector machine (SVM) is used as the classifier for human detection. We apply theproposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCALVOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approachcan improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy.Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach witharound 9% improvement in the detection accuracy