Priority queuing models for hospital intensive care units and impacts to severe case patients.

This paper examines several different queuing models for intensive care units (ICU) and the effects on wait times, utilization, return rates, mortalities, and number of patients served. Five separate intensive care units at an urban hospital are analyzed and distributions are fitted for arrivals and service durations. A system-based simulation model is built to capture all possible cases of patient flow after ICU admission. These include mortalities and returns before and after hospital exits. Patients are grouped into 9 different classes that are categorized by severity and length of stay (LOS). Each queuing model varies by the policies that are permitted and by the order the patients are admitted. The first set of models does not prioritize patients, but examines the advantages of smoothing the operating schedule for elective surgeries. The second set analyzes the differences between prioritizing admissions by expected LOS or patient severity. The last set permits early ICU discharges and conservative and aggressive bumping policies are contrasted. It was found that prioritizing patients by severity considerably reduced delays for critical cases, but also increased the average waiting time for all patients. Aggressive bumping significantly raised the return and mortality rates, but more conservative methods balance quality and efficiency with lowered wait times without serious consequences.

A computational genomics pipeline for prokaryotic sequencing projects.

MOTIVATION: New sequencing technologies have accelerated research on prokaryotic genomes and have made genome sequencing operations outside major genome sequencing centers routine. However, no off-the-shelf solution exists for the combined assembly, gene prediction, genome annotation and data presentation necessary to interpret sequencing data. The resulting requirement to invest significant resources into custom informatics support for genome sequencing projects remains a major impediment to the accessibility of high-throughput sequence data. RESULTS: We present a self-contained, automated high-throughput open source genome sequencing and computational genomics pipeline suitable for prokaryotic sequencing projects. The pipeline has been used at the Georgia Institute of Technology and the Centers for Disease Control and Prevention for the analysis of Neisseria meningitidis and Bordetella bronchiseptica genomes. The pipeline is capable of enhanced or manually assisted reference-based assembly using multiple assemblers and modes; gene predictor combining; and functional annotation of genes and gene products. Because every component of the pipeline is executed on a local machine with no need to access resources over the Internet, the pipeline is suitable for projects of a sensitive nature. Annotation of virulence-related features makes the pipeline particularly useful for projects working with pathogenic prokaryotes. AVAILABILITY AND IMPLEMENTATION: The pipeline is licensed under the open-source GNU General Public License and available at the Georgia Tech Neisseria Base (http://nbase.biology.gatech.edu/). The pipeline is implemented with a combination of Perl, Bourne Shell and MySQL and is compatible with Linux and other Unix systems.

BIOSPIDA: A Relational Database Translator for NCBI.

As the volume and availability of biological databases continue widespread growth, it has become increasingly difficult for research scientists to identify all relevant information for biological entities of interest. Details of nucleotide sequences, gene expression, molecular interactions, and three-dimensional structures are maintained across many different databases. To retrieve all necessary information requires an integrated system that can query multiple databases with minimized overhead. This paper introduces a universal parser and relational schema translator that can be utilized for all NCBI databases in Abstract Syntax Notation (ASN.1). The data models for OMIM, Entrez-Gene, Pubmed, MMDB and GenBank have been successfully converted into relational databases and all are easily linkable helping to answer complex biological questions. These tools facilitate research scientists to locally integrate databases from NCBI without significant workload or development time.