Identification and antifungal susceptibility of a large collection of yeast strains isolated in Tunisian hospitals.

Abstract In this study, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used as a rapid method to identify yeasts isolated from patients in Tunisian hospitals. When identification could not be exstablished with this procedure, sequencing of the internal transcribed spacer with 5.8S ribosomal DNA (rDNA) (ITS1-5.8S-ITS2) and D1/D2 domain of large-subunit (LSU rDNA) were employed as a molecular approach for species differentiation. Candida albicans was the dominant species (43.37% of all cases), followed by C. glabrata (16.55%), C. parapsilosis (13.23%), C. tropicalis (11.34%), C. dubliniensis (4.96%), and other species more rarely encountered in human diseases such as C. krusei, C. metapsilosis, C. lusitaniae, C. kefyr, C. palmioleophila, C. guilliermondii, C. intermedia, C. orthopsilosis, and C. utilis. In addition, other yeast species were obtained including Saccharomyces cerevisiae, Debaryomyces hansenii (anamorph known as C. famata), Hanseniaspora opuntiae, Kodamaea ohmeri, Pichia caribbica (anamorph known as C. fermentati), Trichosporon spp. and finally a novel yeast species, C. tunisiensis. The in vitro antifungal activities of fluconazole and voriconazole were determined by the agar disk diffusion test and Etest, while the susceptibility to additional antifungal agents was determined with the Sensititre YeastOne system. Our results showed low incidence of azole resistance in C. albicans (0.54%), C. tropicalis (2.08%) and C. glabrata (4.28%). In addition, caspofungin was active against most isolates of the collection with the exception of two K. ohmeri isolates. This is the first report to describe caspofungin resistant isolates of this yeast.

Art painting Data Collection

The classification of Art painting images is a computer vision applications that isgrowing considerably. The goal of this technology, is to classify an art paintingimage automatically, in terms of artistic style, technique used, or its author. For thispurpose, the image is analyzed extracting some visual features. Many articlesrelated with these problems have been issued, but in general the proposed solutionsare focused in a very specific field. In particular, algorithms are tested using imagesat different resolutions, acquired under different illumination conditions. Thatmakes complicate the performance comparison of the different methods. In thiscontext, it will be very interesting to construct a public art image database, in orderto compare all the existing algorithms under the same conditions. This paperpresents a large art image database, with their corresponding labels according to thefollowing characteristics: title, author, style and technique. Furthermore, a tool thatmanages this database have been developed, and it can be used to extract differentvisual features for any selected image. This data can be exported to a file in CSVformat, allowing researchers to analyze the data with other tools. During the datacollection, the tool stores the elapsed time in the calculation. Thus, this tool alsoallows to compare the efficiency, in computation time, of different mathematicalprocedures for extracting image data.

Catalogue des dessins et estampes... composant la collection de M. L. P. M. Norblin... / [expert] Mannheim

[Vente. Estampes. 1855-02-05. Paris]

Catalogue d'une très-belle collection de tabatières... [collections Prince Demidoff, Comte Potocki] ... vente... 15 février 1861... / [expert] Mannheim

[Vente. Art. 1861-02-15 - 1861-02-16. Paris]

Catalogue d'une précieuse et rare collection de 124 miniatures anciennes... [Guérin-Laurens] / [expert] Mannheim

[Vente. Art. 1860-03-23 - 1860-03-24. Paris]

Monitoring of illicit pill distribution networks using an image collection exploration framework

This paper proposes a novel approach for the analysis of illicit tablets based on their visual characteristics. In particular, the paper concentrates on the problem of ecstasy pill seizure profiling and monitoring. The presented method extracts the visual information from pill images and builds a representation of it, i.e. it builds a pill profile based on the pill visual appearance. Different visual features are used to build different image similarity measures, which are the basis for a pill monitoring strategy based on both discriminative and clustering models. The discriminative model permits to infer whether two pills come from the same seizure, while the clustering models groups of pills that share similar visual characteristics. The resulting clustering structure allows to perform a visual identification of the relationships between different seizures. The proposed approach was evaluated using a data set of 621 Ecstasy pill pictures. The results demonstrate that this is a feasible and cost effective method for performing pill profiling and monitoring.