The completion of the sequencing of the human genome and the concurrent, rapid development of high-throughput proteomic methods have resulted in an increasing need for automated approaches to archive proteomic data in a repository that enables the exchange of data among researchers and also accurate integration with genomic data. PeptideAtlas () addresses these needs by identifying peptides by tandem mass spectrometry (MS/MS), statistically validating those identifications and then mapping identified sequences to the genomes of eukaryotic organisms. A meaningful comparison of data across different experiments generated by different groups using different types of instruments is enabled by the implementation of a uniform analytic process. This uniform statistical validation ensures a consistent and high-quality set of peptide and protein identifications. The raw data from many diverse proteomic experiments are made available in the associated PeptideAtlas repository in several formats. Here we present a summary of our process and details about the Human, Drosophila and Yeast PeptideAtlas builds.

A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information.

A prospective population-based study was conducted in Australia and New Zealand during 1994-1997 to elucidate the epidemiology of cryptococcosis due to Cryptococcus neoformans var. neoformans (CNVN) and C. neoformans var. gattii (CNVG) and to relate clinical manifestations to host immune status and cryptococcal variety. The mean annual incidence per 10(6) population was 6.6 in Australia and 2.2 in New Zealand. Of 312 episodes, CNVN caused 265 (85%; 98% of the episodes in immunocompromised hosts) and CNVG caused 47 (15%; 44% of the episodes in immunocompetent hosts). The incidence of AIDS-associated cases in Australia declined annually (P<.001). Aborigines in rural or semirural locations (P<.001) and immunocompetent males (P<.001) were at increased risk of CNVG infection. Cryptococcomas in lung or brain were more common in immunocompetent hosts (P< or =.03) in whom there was an association only between lung cryptococcomas and CNVG. An AIDS-associated genetic profile of CNVN serotype A was confirmed by random amplification of polymorphic DNA analysis. Resistance to antifungal drugs was uncommon. The epidemiology of CNVN infection has changed substantially. Clinical manifestations of disease are influenced more strongly by host immune status than by cryptococcal variety.

The study highlighted a high burden of candidemia in Indian ICUs, early onset after ICU admission, higher risk despite less severe physiology score at admission and a vast spectrum of agents causing the disease with predominance of C. tropicalis.