Mohammad Khan

A single genotype of Acinetobacter baumannii expresses multiple antibiotic susceptibility phenotypes

It is common practice to define a bacterium with unequal antibiotic susceptibility phenotypes as a different genotype. Here, we describe the utility of this practice for Acinetobacter baumannii. We determined antibiotic susceptibility for 19 clinical isolates of A baumannii. Repetitive sequence polymerase chain reaction method was used to determine genotypes. Although all isolates were of the same genotype, 79% were predicted to be different based on susceptibility patterns. The antibiotic susceptibility phenotype does not accurately predict the genotype of A baumannii.

Migrating to the Cloud: The Sullivan University Experience

The growth in data traffic within higher institutions has always been accompanied with the adoption of a more scalable IT infrastructure. With the increasing budgetary constraints, cloud computing a disruptive technology presents an attractive means to address the scalability challenges while realizing the potential of ubiquitous IT services. The on-demand business proposition of the cloud provides virtualized resources software, platform, and infrastructure with far-reaching efficiencies that have lowered the initial and operating IT costs for both small and large organizations. In migrating to the cloud environments, various organizations encounter challenges and experiences which are worthy of considerations by new entrants and established users. In this paper, the authors present the experiences of Sullivan University in transitioning to the complex cloud environment, in which a mixture of pilot and phased conversion steps were deployed in the migration process.

Visual grids for managing data completeness in clinical research datasets

Missing data arise in clinical research datasets for reasons ranging from incomplete electronic health records to incorrect trial data collection. This has an adverse effect on analysis performed with the data, but it can also affect the management of a clinical trial itself. We propose two graphical visualization schemes to aid in managing the completeness of a clinical research dataset: the binary completeness grid (BCG) for single patient observation, and the gradient completeness grid (GCG) for an entire dataset. We use these tools to manage three clinical trials. Two are ongoing observational trials, while the other is a cohort study that is complete. The completeness grids revealed unexpected patterns in our data and enabled us to identify records that should have been purged and identify missing follow-up data from sets of observations thought to be complete. Binary and gradient completeness grids provide a rapid, convenient way to visualize missing data in clinical datasets.

Cloud Infrastructure for Higher Education: The Sullivan Experience

The growth in data traffic within higher institutions has always been accompanied with the adoption of a more scalable IT infrastructure. With the increasing budgetary constraints, cloud computing (a disruptive technology) presents an attractive means to address the scalability challenges while realizing the potential of ubiquitous IT services. The on-demand business proposition of the cloud provides virtualized resources (software, platform, and infrastructure) with far-reaching efficiencies that have lowered the initial and operating IT costs for both small and large organizations. In migrating to the cloud environments, various organizations encounter challenges and experiences which are worthy of considerations by new entrants and established users. In this paper, we present the experiences of Sullivan University in transitioning to the complex cloud environment, in which a mixture of pilot and phased conversion steps were deployed in the migration process.