Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
190 Research products, page 1 of 19

  • Research data
  • Other research products
  • 2013-2022
  • Open Access
  • Wellcome Trust
  • English

10
arrow_drop_down
Date (most recent)
arrow_drop_down
  • Open Access English
    Authors: 
    Menger, Katja E.; Chapman, James; Diaz-Maldonado, Hector; Khazeem, Mushtaq M.; Deen, Dasha; Erdinc, Direnis; Casement, John W.; Di Leo, Valeria; Pyle, Angela; Rodriguez-Luis, Alejandro; +4 more
    Publisher: Zenodo
    Project: WT | Mitochondrial DNA mainten... (213464)

    Raw image data from the article "Two type I topoisomerases maintain DNA topology in human mitochondria" by Katja E. Menger et al. Funding also from the Rosetrees and Stoneygate Trust, ref. M811.

  • Open Access English
    Authors: 
    Lucinde, Ruth Khadembu; Abdi, Abdirahman; Orindi, Benedict; Mwakio, Stella; Gathuri, Henry; Onyango, Edwin; Chira, Salome; Ogero, Morris; Isaaka, Lynda; Shangala, Jimmy; +32 more
    Publisher: Zenodo
    Project: WT | Core Support for the East... (203077)

    These files contain the SPIRIT checklist and study flow diagram for the SONIA Trial. This trial is a pragmatic randomized controlled trial of standard care versus steroids plus standard care for treatment of pneumonia in adults admitted to Kenyan hospitals.

  • Open Access English
    Authors: 
    Thiyagarajan, Sathish; Shuyuan Wang; Chew, Ting Gang; Junqi Huang; Kumar, Lokesh; Balasubramanian, Mohan K.; O'Shaughnessy, Ben;
    Publisher: Zenodo
    Project: WT | Using a permeabilized cel... (101885), NIH | Modeling Contractile Ring... (5R01GM086731-08), WT | Using a permeabilized cel... (101885), NIH | Modeling Contractile Ring... (5R01GM086731-08)

    This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM086731 to B.O'S. M.K.B was supported by a senior investigator award from the Wellcome Trust (WT101885MA). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Wellcome Trust, UK. We acknowledge computing resources from Columbia University's Shared Research Computing Facility project. Simulation and experimental data related to the preprint "Myosin turnover controls actomyosin contractile instability" (https://www.biorxiv.org/content/10.1101/2021.03.18.436017).

  • Open Access English
    Authors: 
    Vincent, Crystal; Beckwith, Esteban J.; Dionne, Marc; Gilestro, Giorgio F.;
    Publisher: Zenodo
    Project: UKRI | Sickness behavior: causes... (BB/L020122/2), UKRI | The ninna nanna gene: an ... (BB/R018839/1), EC | SEX_FIGHT_SLEEP (705930), UKRI | MEF2 targets and their fu... (MR/L018802/2), UKRI | Innate immune regulation ... (MR/R00997X/1), WT | Connecting causes and imm... (207467)

    Ethoscope dataset for Vicent et al 2022, PLoS Pathogens Original preprint available at: https://www.biorxiv.org/content/10.1101/2021.08.24.457493v1

  • Open Access English
    Authors: 
    Simpson, Charles; Brousse, Oscar; Mohajeri, Nahid; Davies, Michael; Heaviside, Clare;
    Publisher: Zenodo
    Project: WT | Health and economic impac... (216035), WT | Health and economic impac... (216035)

    This archive contains geospatial data, as well as the code used to generate the geospatial data. The geospatial data consists of georeferenced polygons identifying areas which are covered by green roofs in London (GBR) generated from 2019 aerial imagery. The data is described in detail in the manuscript *An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery*. See abstract below. Archive contents: `geospatial_data/green_roofs_220719.geojson` is the main result, which can be opened in any GIS program. `segmentation_code` contains the Python code used to produce the segmentation from the aerial imagery. `analysis_code` contains the Python code used to produce the plots and tables for the paper, as well as the OS intersection postprocessing step. GeoJSON format: GeoJSON is a format for encoding geospatial data, see https://geojson.org/. GeoJSON can be read using GIS programs including ArcGIS, QGIS, OGR. Input data availability: Unfortunately the aerial imagery and building footprint data cannot be shared directly, as you will require the proper license. Both can be found at [Digimap](https://digimap.edina.ac.uk) provided your institution has the license. Abstract: Green roofs are roofs incorporating a deliberate layer of growing substrate and vegetation. They can reduce both indoor and outdoor temperatures, so are often presented as a strategy to reduce urban overheating, which is expected to increase due to climate change. In addition, they could help decrease the cooling energy demand of buildings thereby contributing to energy and emissions reductions and provide benefits to biodiversity and human well-being. To guide the design of more sustainable and climate resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is required to estimate any effect of green roofs on temperatures (or other phenomena), but this information is currently lacking. Using a machine-learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset. We estimate that there was 0.19 km^2 of green roof in the Central Activities Zone (CAZ) of London, (0.81 km^2) in Inner London, and (1.25 km^2) in Greater London in the year 2019. This corresponds to 1.6% of the total building footprint area in the CAZ, and 1.0% in Inner London. There is a relatively higher concentration of green roofs in the City of London (the historic financial district), covering 3.1% of the total building footprint area. The survey covers 1463 km^2 of Greater London, making this the largest open automatic survey of green roofs in any city. We improve on previous studies by including more negative examples in the training data, by experimenting with different data augmentation methods, and by requiring coincidence between vector building footprints and green roof patches. This dataset will enable future work examining the distribution and potential of green roofs in London and on urban climate modelling.

  • Open Access English
    Authors: 
    O'Toole, John M.; Mathieson, Sean R.; Magarelli, Fabio; Marnane, William P.; Lightbody, Gordon; Boylan, Geraldine B.;
    Publisher: Zenodo
    Project: WT | Development of a Neonatal... (209325), WT | Multicentre Clinical eval... (098983), WT | Development of a Neonatal... (209325), WT | Multicentre Clinical eval... (098983)

    The dataset consists of 169 multichannel EEG files of 1-hour in duration, recorded from 53 full-term newborns in the neonatal intensive care unit of the Cork University Maternity Hospital, Ireland. All 53 infants had received a diagnosis of hypoxic-ischaemic encephalopathy. The study to record the EEG was approved by the Cork Research Ethics Committee of the Cork Teaching Hospitals. Neonates were enrolled in the study after obtaining written and informed consent from a guardian or parent. The Cork Research Ethics Committee approved the publication of this fully-anonymised data set. Each 1-hour EEG was graded for severity of background abnormalities. Two experts in neonatal EEG graded each epoch independently. When grades differed between the experts, they jointly reviewed the EEG and agreed on a consensus grade. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Four grades were used: normal or mildly abnormal (grade 1), moderately abnormal (grade 2), severely abnormal (grade 3), and inactive (grade 4). The EEG data could be used to develop automated grading algorithms or to assist in training for the review of background neonatal EEG.

  • Open Access English
    Authors: 
    Souza, S.O.; Raposo, B.L.; Sarmento-Neto, J.F.; Rebouças, J.S.; Macêdo, D.P.C.; Figueiredo, R.C.B.Q.; Santos, B.S.; Freitas, A.Z.; Cabral Filho, P.E.; Ribeiro, M.S.; +1 more
    Publisher: Zenodo
    Project: WT | Using light to treat fung... (219677), WT | Using light to treat fung... (219677)

    Data supporting the figures presented in the research article Photoinactivation of Yeast and Biofilm Communities of Candida albicans Mediated by ZnTnHex-2-PyP4+ Porphyrin. Candida albicans is the main cause of superficial candidiasis. While the antifungals available are defied by biofilm formation and resistance emergence, antimicrobial photodynamic inactivation (aPDI) arises as an alternative antifungal therapy. The tetracationic metalloporphyrin Zn(II) meso-tetrakis(N-n-hexylpyridinium-2-yl)porphyrin (ZnTnHex-2-PyP4+) has high photoefficiency and improved cellular interactions. We investigated the ZnTnHex-2-PyP4+ as a photosensitizer (PS) to photoinactivate yeasts and biofilms of C. albicans strains (ATCC 10231 and ATCC 90028) using a blue light-emitting diode. The photoinactivation of yeasts was evaluated by quantifying the colony forming units. The aPDI of ATCC 90028 biofilms was assessed by the MTT assays, propidium iodide (PI) labeling, and scanning electron microscopy. Mammalian cytotoxicity was investigated in Vero cells using MTT assay. The aPDI (4.3 J/cm2) promoted eradication of yeasts at 0.8 and 1.5 µM of PS for ATCC 10231 and ATCC 90028, respectively. At 0.8 µM and same light dose, aPDI-treated biofilms showed intense PI labeling, about 89% decrease in the cell viability, and structural alterations with reduced hyphae. No considerable toxicity was observed in mammalian cells. Our results introduce the ZnTnHex-2-PyP4+ as a promising PS to photoinactivate both yeasts and biofilms of C. albicans, stimulating studies with other Candida species and resistant isolates. This research was funded by the Wellcome Trust, grant 219677/Z/19/Z; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, 424159/2018-0 and 406450/2021-8); Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE, APQ-0573-2.09/18); and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, n° 2018/20226-7). This study is also associated with the Instituto Nacional de Ciência e Tecnologia em Fotônica (INCT-INFo).

  • Open Access English
    Authors: 
    Maria Kiourlappou; Stephen Taylor; Ilan Davis;
    Publisher: Zenodo
    Project: UKRI | The Oxford Interdisciplin... (BB/M011224/1), WT | Advanced Microscopy for C... (091911), UKRI | Machine learning-based au... (BB/S507623/1), WT | Micron Oxford: super-reso... (107457), WT | The mechanism of mRNA tra... (096144), WT | Regulated mRNA stability ... (209412), UKRI | The Oxford Interdisciplin... (BB/M011224/1), WT | Advanced Microscopy for C... (091911), UKRI | Machine learning-based au... (BB/S507623/1), WT | Micron Oxford: super-reso... (107457),...

    The explosion in biological data generation challenges the available technologies and methodologies for data interrogation. Moreover, highly rich and complex datasets together with diverse linked data are difficult to explore when provided in flat files. Here we provide a way to filter and analyse in a systematic way a dataset with more than 18 thousand data points using Zegami (link), a solution for interactive data visualisation and exploration. The primary data we use are derived from a systematic analysis of 200 YFP gene traps reveals common discordance between mRNA and protein across the nervous system which is submitted elsewhere. This manual provides the raw image data together with annotations and associated data and explains how to use Zegami for exploring all these data types together by providing specific examples. We also provide the open source python code (github link) used to annotate the figures.

  • Open Access English
    Authors: 
    Sammut, Stephen John;
    Publisher: Zenodo
    Project: WT | Wellcome Trust PhD Progra... (106566), WT | Wellcome Trust PhD Progra... (106566)

    H&E slides used in the training dataset described in "Multi-omic machine learning predictor of breast cancer therapy response" published in Nature: https://www.nature.com/articles/s41586-021-04278-5 Metadata associated with these images also included in file Slide metadata.xlsx {"references": ["Sammut, SJ. et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature 601, 623\u2013629 (2022)."]}

  • Open Access English
    Authors: 
    Dobbie, Ian;
    Publisher: Zenodo
    Project: WT | Micron Oxford: super-reso... (107457), WT | Micron Oxford: super-reso... (107457)

    This file contains instructions for setting up a simulated microscope environment using Microscope-Cockpit and Python-Microscope. This environment includes a large tiled image of which segments are returned to simulate stage movement and different colour channels returned to simulate changing an emission filter. This simulated microscope is then used to test the findNuclei script showing the ease of extending Cockpit functionality with Python libraries, Python-openCV is used in this case. {"references": ["Phillips et al. Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science. Wellcome Open Res 2021, 6:76"]}

Send a message
How can we help?
We usually respond in a few hours.