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Primary data for manuscript on a homologue of lymphostatin in Chlamydia pecorum
No full textPathogens frequently produce proteins to evade or inhibit host immune responses. One such protein is lymphostatin from attaching and effacing Escherichia coli (also known as lymphocyte inhibitory factor A; LifA), which influences intestinal colonization and inhibits mitogen- and antigen-activated proliferation of T lymphocytes and pro-inflammatory cytokine synthesis. Here, we report the cloning, purification and characterization of a LifA homologue from Chlamydia pecorum. The predicted 382 KDa protein (CPE2_0552) exhibited 36 % identity and 55 % similarity over 3171 amino acids to lymphostatin from enteropathogenic E. coli strain E2348/69. CPE2_0552 shares glycosyltransferase and cysteine protease motifs required for lymphostatin activity, including similarity in the tertiary structure of these domains predicted by AlphaFold 3. Purified CPE2_0552 exhibited a surface envelope similar to that of lymphostatin when analyzed by electron microscopy. CPE2_0552 inhibited concanavalin A-stimulated proliferation of bovine T cells in a concentration-dependent manner, with an inhibitory dose 50 (ID50) of 990 pg/mL. This was 70-fold higher than the ID50 of E. coli E2348/69 lymphostatin tested in parallel on T cells from the same donors (14 ± 4 pg/mL), but was similar to another LifA homologue from E. coli O157:H7 (ToxB). Moreover, CPE2_0552 inhibited the secretion of interferon gamma (IFNgamma), a key cytokine that influences the outcome of Chlamydia infections. At the concentrations at which CPE2_0552 inhibited T lymphocyte proliferation and IFN? secretion, negligible cytotoxicity was observed after 72 h of stimulation. Our study indicates that E. coli lymphostatin belongs to a wider family of lymphocyte-inhibitory molecules that exist in distantly related bacterial pathogens.Primary data for findings presented in the published article
DNS Kolmogorov flow data for Re 50
No full textThe dataset contains a three dimensional (3-D) Direct Numerical Simulation (DNS) of a Kolmogorov flow with the unit Reynolds number defined as Re=1/nu and a sinusoidal forcing on streamwise axis and one wavenumber. It is an extended shear flow simulation from 2-D simulation into 3-D with periodic boundary on the side walls, input on the left and output on the right walls. It was generated in python using the spectral solver Dedalus. The data can be used to train machine learning algorithm such as an autoencoder for reduce-order modelling of turbulent flow. This is a subset of three increasing Reynolds numbers of Kolmogorov flow set to Re= 30, 50 and 90. You can find the other dataset on the Datashare
High Resolution sections of eMouseAtlas Models: EMA49, Theiler Stage 17 TS17(10.5 dpc)
No full textHigh resolution images of each section used for the Mouse Atlas 3D models. Images are sub-sampled for the 3D models to provide approximately iso-tropic voxel dimensions, here the images are at the full resolution of the original digitisation
High Resolution sections of eMouseAtlas Models: EMA27, Theiler Stage 14 TS14(9 dpc)
No full textHigh resolution images of each section used for the Mouse Atlas 3D models. Images are sub-sampled for the 3D models to provide approximately iso-tropic voxel dimensions, here the images are at the full resolution of the original digitisation
Coexisting commensurate and incommensurate magnetic orders in the double double perovskite CaMnCoWO6
No full textA new double double perovskite of ideal composition CaMnCoWO6 has been synthesised at high pressure. The structure is tetragonal (P42/n, a = 7.6651(3), c = 7.6822(3) Å) and disorder between A-site Mn2+ and B-site Co2+ leads to a Co-rich off-stoichiometric composition CaMn0.8Co1.2WO6 [Ca(Mn0.65Co0.35)(Co0.86Mn0.14)WO6]. An unusual combination of commensurate and incommensurate spin orders (Mn-site spins with propagation vector (0.5 0.5 0.5) and Co-site spins with (0.5 0.42 0.5)) is observed below a magnetic transition at TC = 18 K. This is the first discovery of complex magnetism in the double double perovskite family. Data relates to the publication Data for publication K. Ji, R. Chen, P. Manuel, J.P. Attfield (2023). "Coexisting commensurate and incommensurate magnetic orders in the double double perovskite CaMnCoWO6", ZAAC ( https://doi.org/10.1002/zaac.202300047 ).See the Readme.txt fil
SUPERSEDED - WeightGait Dataset
No full text## This item has been replaced by the one which can be found at https://doi.org/10.7488/ds/7897 ##
Introduction:
Here we introduce the WeightGait dataset: a dataset developed for the purposes of facilitating vision-based gait assessment methodologies with more realistic conditions comparable to real world use.
The motivation for this dataset is to create a testing environment for gait assessment algorithms that is closer to the realities of application. To accomplish this, unlike other similar datasets, we do two main things uniquely:
We simulate overlapping abnormalities, for a total of 9 different combinations of abnormality detailed below.
The background and equipment used are imperfect and noisy to simulate the similar hardship experienced when trying to install a gait monitor into someone's home. This means cheap recording equipment for scalability resulting in relatively low-frames per recording. It also means slight feet/head clipping at times, only a single camera view to detect depth and no curation to the background or the clothing/walking speed of the participants.
In order to preserve privacy, all original faces in the videos have been replaced by a deep-fake variation of the original created by the algorithm given in the paper 'DeepPrivacy'. Each frame has an independent new face and as a consequence, there is some flickering on the faces in the videos. The original 2D joint positions are estimated on the original videos using a lightweight implementation of the algorithm given in the paper 'HigherHRNet'.Readme.md : Description of the file and data structure and best practice
Machine learning-based approaches for functional variant classification across mammals
No full textThe source code for the variant annotation pipeline mentioned in Chapter 2 of the PhD thesis titled "Machine learning-based approaches for functional variant classification across mammals". The variant annotation pipeline, developed using Nextflow, can be utilised for annotating variants in various mammalian species. The pipeline encompasses three sub-workflows: the main annotation sub-workflow, the Enformer sub-workflow, and the mammalian conservation score sub-workflow. These sub-workflows offer annotations based on different variant properties, including the distance from the variants to various genomic elements and conservation scores
Systemic Carbon Cycle Analyses for the Southern African Woodlands ecoregion from 2006-2017
Get PDFThis dataset contains systemic carbon (C) cycle analyses for the Southern African Woodlands ecoregion from 2006-2017, mapped at 0.5 degree spatial resolution and monthly temporal resolution. There are five netcdf files: DRIVERS_OBS_2006_2017.nc : This file contains the environmental drivers, the assimilated observations, and their uncertainties; PARS_2006_2017.nc : This file contains maps of the parameter ensembles for the DALEC.4 ecosystem C cycle model, as retrieved by the Bayesian calibration; CFLUX_2006_2017.nc : This file contains the gross and net C fluxes for all C fluxes simulated by DALEC.4 at monthly temporal resolution; CSTOCK_2006_2017.nc : This file contains the C stocks in the live and dead organic matter pools simulated by DALEC.4 at monthly temporal resolution; NPP_MRT_2006_2017.nc : This file contains the monthly Net Primary Productivity (NPP), the monthly NPP allocation to different live C pools, the fractional NPP allocation to different live C pools, and the residence times for the live and dead organic matter C pools. We also include a readme file describing the datasets in more detail, alongside a set of python scripts used to generate the diagnostic analyses described in the manuscript. This dataset was produced using the CARDAMOM model-data fusion framework, and accompanies the manuscript: Williams, M., Milodowski, D. T., Smallman, T. L., Dexter, K. G., Hegerl, G. C., McNicol, I. M., O'Sullivan, M., Roesch, C. M., Ryan, C. M., Sitch, S., and Valade, A.: Precipitation–fire functional interactions control biomass stocks and carbon exchanges across the world's largest savanna, Biogeosciences, 22, 1597–1614,https://doi.org/10.5194/bg-22-1597-2025
SV2A is expressed in synapse subpopulations in mouse and human brain: implications for PET radiotracer studies
No full textSynapse pathology is a feature of most brain diseases and there is a pressing need to monitor the onset and progression of this pathology using brain imaging in living patients. A major step toward this goal has been the development of small-molecule radiotracers that bind to synaptic vesicle glycoprotein 2A (SV2A) for use in positron emission tomography (PET). Changes in SV2A radiotracer binding in PET are widely interpreted to report differences in the density of all synapses throughout brain regions.
Here, we analyse the expression of SV2A at single-synapse resolution across regions of adult mouse and human brain. We find that SV2A is expressed in fewer than 50% of excitatory and inhibitory synapses and that the density of SV2A-positive synapses differs between brain regions. Furthermore, individual synapses differ in their amounts of SV2A. These findings have important implications for the interpretation of PET imaging studies in a clinical setting and point to the need for a detailed understanding of SV2A synaptome architecture in both healthy brain and disease cases where PET imaging is being applied
Array tomography image analysis macros 2024
No full textThe Matlab tool in this entry allows the processing and analysis of array tomography microscopy images.
To use it, follow the included protocol described in the "Array tomography Matlab tool quick protocol.docx" file.
What you will find... - Processing tools; - Alignment of consecutive images (registration); Selection of objects of interest (3D local threshold segmentation). Analysis tools: Density of objects in the 3D space; Colocalization between n channels (by distance between centroids or overlapping area); Distance of the objects from the biggest object (created for distance of objects from amyloid plaques)