Estimating and Analyzing Demographic Models Using the popbio Package in R

A complete assessment of population growth and viability from field census data often requires complex data manipulations, statistical routines, mathematical tools, programming environments, and graphical capabilities. We therefore designed an R package called popbio to facilitate both the construction and analysis of projection matrix models. The package consists primarily of the R translation of MATLAB code found in Caswell (2001) and Morris and Doak (2002) for the analysis of projection matrix models. The package also includes methods to estimate vital rates and construct projection matrix models from census data typically collected in plant demography studies. In these studies, vital rates can often be estimated directly from annual censuses of tagged individuals using transition frequency tables. Because the construction of projection matrix models requires careful management of census data, we describe the steps to construct a projection matrix in detail.

Estimating and Analyzing Demographic Models Using the popbio Package in R

A complete assessment of population growth and viability from field census data often requires complex data manipulations, statistical routines, mathematical tools, programming environments, and graphical capabilities. We therefore designed an R package called popbio to facilitate both the construction and analysis of projection matrix models. The package consists primarily of the R translation of MATLAB code found in Caswell (2001) and Morris and Doak (2002) for the analysis of projection matrix models. The package also includes methods to estimate vital rates and construct projection matrix models from census data typically collected in plant demography studies. In these studies, vital rates can often be estimated directly from annual censuses of tagged individuals using transition frequency tables. Because the construction of projection matrix models requires careful management of census data, we describe the steps to construct a projection matrix in detail

The peripheral blood transcriptome in septic cardiomyopathy: an observational, pilot study.

BACKGROUND:Septic cardiomyopathy (SCM) is common in sepsis and associated with increased morbidity and mortality. Left ventricular global longitudinal strain (LV GLS), measured by speckle tracking echocardiography, allows improved identification of impaired cardiac contractility. The peripheral blood transcriptome may be an important window into SCM pathophysiology. We therefore studied the peripheral blood transcriptome and LV GLS in a prospective cohort of patients with sepsis. RESULTS:In this single-center observational pilot study, we enrolled adult patients (age > 18) with sepsis within 48 h of admission to the ICU. SCM was defined as LV GLS > - 17% based on echocardiograms performed within 72 h of admission. We enrolled 27 patients, 24 of whom had high-quality RNA results; 18 (75%) of 24 had SCM. The group was 50% female and had a median (IQR) age of 59.5 (48.5-67.0) years and admission APACHE II score of 21.0 (16.0-32.3). Forty-six percent had septic shock. After filtering for low-expression and non-coding genes, 15,418 protein coding genes were expressed and 73 had significantly different expression between patients with vs. without SCM. In patients with SCM, 43 genes were upregulated and 30 were downregulated. Pathway analysis identified enrichment in type 1 interferon signaling (adjusted p < 10-5). CONCLUSIONS:In this hypothesis-generating study, SCM was associated with upregulation of genes in the type 1 interferon signaling pathway. Interferons are cytokines that stimulate the innate and adaptive immune response and are implicated in the early proinflammatory and delayed immunosuppression phases of sepsis. While type 1 interferons have not been implicated previously in SCM, interferon therapy (for viral hepatitis and Kaposi sarcoma) has been associated with reversible cardiomyopathy, perhaps suggesting a role for interferon signaling in SCM

Steps toward broad-spectrum therapeutics: discovering virulence-associated genes present in diverse human pathogens.

© 2009 Stubben et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: New and improved antimicrobial countermeasures are urgently needed to counteract increased resistance to existing antimicrobial treatments and to combat currently untreatable or new emerging infectious diseases. We demonstrate that computational comparative genomics, together with experimental screening, can identify potential generic (i.e., conserved across multiple pathogen species) and novel virulence-associated genes that may serve as targets for broad-spectrum countermeasures. Results: Using phylogenetic profiles of protein clusters from completed microbial genome sequences, we identified seventeen protein candidates that are common to diverse human pathogens and absent or uncommon in non-pathogens. Mutants of 13 of these candidates were successfully generated in Yersinia pseudotuberculosis and the potential role of the proteins in virulence was assayed in an animal model. Six candidate proteins are suggested to be involved in the virulence of Y. pseudotuberculosis, none of which have previously been implicated in the virulence of Y. pseudotuberculosis and three have no record of involvement in the virulence of any bacteria. Conclusion: This work demonstrates a strategy for the identification of potential virulence factors that are conserved across a number of human pathogenic bacterial species, confirming the usefulness of this tool