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Additional file 3: Table S2. Identified protein within subsets of interests during chromosomal genome comparison between S. sonnei, S. flexneri, pathogenic and non-pathogenic E. coli.

Additional file 3: Table S2. Identified protein within subsets of interests during chromosomal genome comparison between S. sonnei, S. flexneri, pathogenic and non-pathogenic E. coli.

Additional file 4: Supplementary file 2. Script used to retrieve annotation and protein sequences from the input Shigella genomes.

Additional file 2: Table S1. List of Shigella and E. coli strains used for the bioinformatics genome comparison.

Additional file 9: Figure S3. Convalescent antibody responses to top reactive Shigella antigens by age and sex.

Additional file 6: Table S4. The recombinant expression of highly reactive Shigella proteins for in vivo immunogen testing.

Additional file 4: Supplementary file 2. Script used to retrieve annotation and protein sequences from the input Shigella genomes.

Additional file 1: Supplementary file 1. In-house script to retrieve complete Shigella and Escherichia coli chromosomal sequences from GenBank (accessed July 2014).

Additional file 6: Table S4. The recombinant expression of highly reactive Shigella proteins for in vivo immunogen testing.

Additional file 9: Figure S3. Convalescent antibody responses to top reactive Shigella antigens by age and sex.

Additional file 7: Figure S1. No statistically significant seroconversion to Shigella antigens detected in diarrheal patients during Salmonella infections.

Additional file 7: Figure S1. No statistically significant seroconversion to Shigella antigens detected in diarrheal patients during Salmonella infections.

Additional file 8: Figure S2. Antibody responses to natural Shigella infections are influenced by severity of disease.

Poster presented at AGU Fall Meeting 2012 The solid state deformation processes permitting convection of Earth's rocky mantle necessarily lead to strong feedbacks between the deformation history and the instantaneous flow field. Mechanisms leading to the history dependence include the alignment of mineral grains with the attendant generation of elastic and rheological anisotropy, as well as processes operating at larger and smaller length scales including phase sepa- ration, grain size reduction, changes to the defect chemistry and dislocation multiplication and entanglement. Despite their sophistication, current models of mantle dynamics frequently ignore history dependent rheologies, and the feedback between deformation, grain size, crystal orientation, chemistry and viscosity. These processes have huge effects on viscosity: in the crust, they lead to the development of shear-zones and highly localised deformation, whilst, in the mantle, they are nearly always ignored. Here, we describe an approach intended to introduce the consequences of history dependence into models of whole-mantle convection. We make use of existing technology that exists in several convection codes: the ability to track markers, or particles, through the evolving flow field. Tracers have previously been used to track attributes such as the bulk chemical composition or trace element ratios. Our modifica- tion is to use this technology to track a description of the current state of the texture and microstructure (encompassing an orientation distribution function, grain size parameters and dislocation density) such that we can advance models of polycrystalline deformation for many particles alongside and in sync with models of mantle convection. Our current implementation (called Theia coupling TERRA to DRex) is designed to allow the seismic anisotropy of the upper mantle to be studied, but in future we aim to extend the approach to allow a direct feedback between the development and evolution of microstructure, and the rheology used to advance the model of mantle convection. Abstract number T33G-2741.

Additional file 1. Age-fitted seroprevalence plots for seroconversion and reversion calculations. The average seroprevalence for each age group is plotted for the seroreactive Plasmodium proteins, denoted as P1 through P326. Their corresponding gene ID and protein description are found in Additional file 4.

Additional file 1. Age-fitted seroprevalence plots for seroconversion and reversion calculations. The average seroprevalence for each age group is plotted for the seroreactive Plasmodium proteins, denoted as P1 through P326. Their corresponding gene ID and protein description are found in Additional file 4.