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Additional file 6. BLAST against B. glabrata: A reciprocal BLAST search against the recently published transcriptome of twelve pooled Biomphalaria glabrata tissues [3].

Additional file 2: FCMPass calibration data excel sheet.

Additional file 2: Table S1. Details of the libraries generated from the Mütter Museum vaccination kits and vaccinia virus mapping statistics.

Video showing sample trajectory for inhibi5on of thermotaxis by naringenin

Additional file 3: Table S3. Lineage-specific accessory genes.

Many anaerobic microbial parasites possess highly modified mitochondria known as mitochondrion-related organelles (MROs). The best-studied of these are the hydrogenosomes of Trichomonas vaginalis and Spironucleus salmonicida, which produce ATP anaerobically through substrate-level phosphorylation with concomitant hydrogen production; and the mitosomes of Giardia intestinalis, which are functionally reduced and lack any role in ATP production. However, to understand the metabolic specializations that these MROs underwent in adaptation to parasitism, data from their free-living relatives are needed. Here, we present a large-scale comparative transcriptomic study of MROs across a major eukaryotic group, Metamonada, examining lineage-specific gain and loss of metabolic functions in the MROs of Trichomonas, Giardia, Spironucleus and their free-living relatives. Our analyses uncover a complex history of ATP production machinery in diplomonads such as Giardia, and their closest relative, Dysnectes; and a correlation between the glycine cleavage machinery and lifestyles. Our data further suggest the existence of a previously undescribed biochemical class of MRO that generates hydrogen but is incapable of ATP synthesis. Protein alignmentsAlignments of organellar proteinsmito_alignments.zipPhylogenomic datasetSingle gene alignments used for phylogenomic analysisPhylogenomic.zipData SourcesFasta files with assembled transcriptomes of Carpediemonas-like organismsAssemblies.zip

The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose-response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose-response model for listeriosis. While descriptive models can account for such variation, they have limited power to extrapolate beyond the details of particular outbreaks. In contrast, this study exhibits dose-response relationships from a mechanistic basis, quantifying key biological factors involved in pathogen-host dynamics. An efficient computational algorithm and geometric interpretation of the infection pathway are developed to connect dose-response outcomes with the underlying bistable dynamics of the model. Relying on in-vitro experiments as well as outbreak data, we estimate plausible parameters for the human context. Despite the presence of uncertainty in such parameters, sensitivity analysis reveals that the host response is most influenced by the pathogen-immune system interaction. In particular, we show how variation in this interaction across a subgroup of the population dictates the shape of dose-response curves. Finally, in terms of future experimentation, our model results provide guidelines and highlight vital aspects of the interplay between immune cells and particular strains of L. monocytogenes that should be examined. dose_response_normal_distData for figure 8CSV file contains 12 data set together with mean, standard deviation and 95% confidence interval.Dataforfigure8.csvmatlab program/code to generate the figure 8The program uses data from the CSV file 'Dataforfigure8'shaded_region.m

Introduction The Surgical Planning Laboratory (SPL) and the National Center for Image Guided Therapy (NCIGT) are making this dataset available as a resource to aid in the development of algorithms and tools for deformable registration, segmentation and analysis of prostate magnetic resonance imaging (MRI) and ultrasound (US) images. Description This dataset contains anonymized images of the human prostate (N=3 patients) collected during two sessions for each patient: MRI examination of the prostate for the purposes of disease staging. US examination of the prostate for the purposes of volumetric examination in preparation to the brachytherapy implant. These are three-dimensional (multi-slice) scalar images. Image files are stored using NRRD file format (files with .nrrd extension), see details at http://teem.sourceforge.net/nrrd/format.html. Each image file includes a code for the case number (internal numbering at the research site) and the modality (US or MR). Image annotations were prepared by Dr. Fedorov (no professional training in radiology) and Dr. Tuncali (10+ in prostate imaging interpretation). Annotations include Manual contouring (segmentation) of the whole prostate gland, performed in 3D Slicer software. These segmentation images are coded in the same fashion as the image files, and saved in NRRD format, with "-label" suffix. Manually placed points (fiducials) corresponding to the location of urethra entry into the prostate at base (coded as UB), verumontanum (VM), urethra entry into the prostate at apex (UA), as well as centroids of cysts and calcifications. UB, UA and VM locations are annotated both in MR and US for all cases, while cysts and calcifications are annotated when applicable. Fiducial points are stored in comma-separated CSV-style format adopted by 3D Slicer software (.fcsv file extension). There is one row per point in these files, encoding the location of the point in RAS coordinate space relative to the image data, and the name of the point. Viewing the collection We tested visualization of images, segmentations and fiducials in 3D Slicer software, and thus recommend 3D Slicer as the platform for visualization. 3D Slicer is a free open source platform (see http://slicer.org), with the pre-compiled binaries available for all major operating systems. You can download 3D Slicer at http://download.slicer.org. Acknowledgments Preparation of this data collection was made possible thanks to the funding from the National Institutes of Health (NIH) through grants R01 CA111288 and P41 RR019703. If you use this dataset in a publication, please cite the following manuscript. You can also learn more about this dataset from the publication below. Fedorov, A., Khallaghi, S., Antonio Sánchez, C., Lasso, A., Fels, S., Tuncali, K., Sugar, E. N., Kapur, T., Zhang, C., Wells, W., Nguyen, P. L., Abolmaesumi, P. & Tempany, C. Open-source image registration for MRI–TRUS fusion-guided prostate interventions. Int J CARS 10, 925–934 (2015). https://pubmed.ncbi.nlm.nih.gov/25847666/ Contact Andrey Fedorov, fedorov@bwh.harvard.edu {"references": ["Fedorov, Andriy, Siavash Khallaghi, C Antonio S\u00e1nchez, Andras Lasso, Sidney Fels, Kemal Tuncali, Emily Neubauer Sugar, et al. 2015. \"Open-Source Image Registration for MRI\u2013TRUS Fusion-Guided Prostate Interventions.\" International Journal of Computer Assisted Radiology and Surgery, April. Springer Berlin Heidelberg, 1\u201310. doi:10.1007/s11548-015-1180-7.", "Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion -Robin, J. C., Pujol, S., ... & Kikinis, R. (2012). 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magnetic resonance imaging, 30(9), 1323-1341."]}