Cloud based intelligent system for delivering health care as a service

The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity.     

Serotyping of Streptococcus pneumoniae by coagglutination with 12 pooled antisera

We report on the performance of a recently introduced commercial chessboard method using 12 antisera, in comparison with that of the 55-antiserum panel used in determining the serogroups and types (SGTs) of Streptococcus pneumoniae, both of which were carried out by a coagglutination technique. Of a total of 150 strains of S. pneumoniae studied, 135 (90%) belonged to the SGTs represented in the 23-valent pneumococcal vaccine; of these, 130 (96.3%) were identified as the same SGTs by both typing methods. The remaining five strains showed cross-reactivity with more than two pools by the chessboard method, but could be assigned to a single SGT by the Quellung test. The 96.3% concordance of the chessboard method suggests it can be adopted for determination of the SGTs of S. pneumoniae in laboratories.     

Sequence database searches via de novo peptide sequencing by tandem mass spectrometry

A method is described for searching protein sequence databases using tandem mass spectra of tryptic peptides. The approach uses a de novo sequencing algorithm to derive a short list of possible sequence candidates which serve as query sequences in a subsequent homology-based database search routine. The sequencing algorithm employs a graph theory approach similar to previously described sequencing programs. In addition, amino acid composition, peptide sequence tags and incomplete or ambiguous Edman sequence data can be used to aid in the sequence determinations. Although sequencing of peptides from tandem mass spectra is possible, one of the frequently encountered difficulties is that several alternative sequences can be deduced from one spectrum. Most of the alternative sequences, however, are sufficiently similar for a homology-based sequence database search to be possible. Unfortunately, the available protein sequence database search algorithms (e.g. Blast or FASTA) require a single unambiguous sequence as input. Here we describe how the publicly available FASTA computer program was modified in order to search protein databases more effectively in spite of the ambiguities intrinsic in de novo peptide sequencing algorithms.