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Satellite remote sensing can operationalise the IUCN Global Ecosystem Typology in the biome-diverse north-east of Brazil
No full textAccurate biome delineation is difficult where biomes occupy the same climatic space, as is the case for tropical dry forest and savanna. The resulting confusion limits our ability to understand and manage impacts of global change on these biomes. To address this, we developed an unsupervised, repeatable method to delineate biomes and their component functional ecosystems, based on landscape-level vegetation structure measured using remote sensing and an understanding of the ecology of the region. This approach contrasts with previous definitions, based on climate differences amongst savanna, dry forest and rain forest. Using the heterogeneous north-east Brazil, where several biomes interdigitate, as a case study, a hierarchical functional ecosystem classification is proposed that aligns with both the IUCN Global Ecosystem Typology (GET) and previous work. Based on fuzzy clustering of remotely sensed vegetation attributes, seven groups were found, identified as rain forest, cerrado (savanna) and five caatinga vegetation groups. These groups broadly align with the literature, for example, sedimentary and arboreal caatinga. These groups align with three ‘Ecosystem Functional Groups’ (EFGs) described by the IUCN GET and, additionally, suggest there is a new, fourth EFG in the region: non-pyric shrublands. Random Forest models showed soil pH was the most important environmental variable distinguishing these vegetation groups. These results suggest a remotely sensed structure-based approach is an effective method for operationalising the IUCN GET. North-East Brazil - where many EFGs are interdigitated - serves as a challenging case study and, therefore, we hope our approach will have generality for other regions globally.The data and scripts are outlined in the README.txt file. All original data sources adapted for use in this study are acknowledged here
Obesity-induced mesenteric PVAT remodelling is sexually dimorphic, but not driven by ovarian hormones
No full textThese are the source data underlying the RNA sequencing figures (Figure 2 and 3) presented in a manuscript entitled "Obesity-induced mesenteric PVAT remodelling is sexually dimorphic, but not driven by ovarian hormones", which has been accepted for publication in Cardiovascular Diabetology. Once published, the metadata for this dataset will be updated with the DOI of the published paper.
Background: Obesity, a major risk factor for cardiovascular disease (CVD), is associated with hypertension and vascular dysfunction. Perivascular adipose tissue (PVAT), a metabolically active tissue surrounding blood vessels, plays a key role in regulating vascular tone. In obesity, PVAT becomes dysregulated which may contribute to vascular dysfunction; how sex impacts the remodelling of PVAT and thus the altered vascular contractility during obesity is unclear.
Objective: To investigate sex-specific PVAT dysregulation in the setting of obesity as a potential driver of sex differences in vascular pathologies and CVD risk.
Methods: Adult male and female C57Bl/6J mice were fed an obesogenic high-fat diet (HFD) or regular chow for 16 weeks. Mesenteric PVAT (mPVAT) was isolated for RNA-sequencing and histological analysis, and mesenteric arteries were isolated for assessment of vascular function by wire myography. In a separate study, female mice were subjected to bilateral ovariectomy prior to dietary intervention to determine the contribution of ovarian hormones to PVAT dysregulation.
Results: Transcriptomic analysis of mPVAT revealed sexually dimorphic responses to HFD, with upregulation of extracellular matrix (ECM) remodelling pathways in male but not female mice. Histological and RT-qPCR approaches demonstrated increased collagen deposition and ECM remodelling in mPVAT from obese male compared with obese female mice. Assessment of vascular function in mesenteric arteries -/+ PVAT revealed that in obesity, mPVAT impaired endothelium-mediated vasodilation in male but not female mice. Ovariectomy of female mice prior to HFD administration did not alter ECM transcript expression or collagen deposition in mPVAT compared to sham-operated female mice
Large-Scale Multi-Disciplinary Mass Optimization in the Auto Industry
Get PDFAt the MOPTA 2008 conference, the plenary speaker Don Jones gave a presentation entitled Large-Scale Multi-Disciplinary Mass Optimization in the Auto Industry. As part of that presentation, he set a new multi-disciplinary optimization (MDO) benchmark problem as a challenge to the optimization community. The slides for Jones' talk, as well as the files for the test problem, are available
Contaminant removal using vibrating surfaces: nanoscale insights and a universal scaling law
No full textThe development of active self-cleaning surfaces, i.e. surfaces that remove nanoscale contaminants using external forces such as electric or magnetic fields, is critical to many engineering applications. The use of surface vibrations represents a promising alternative, but the underlying nanoscale physics - in the absence of an intermediate liquid medium - is poorly-understood. We use molecular dynamics simulations to explore the use of ultrahigh-frequency surface acoustic wave devices for contaminant removal. Our simulations reveal that there exists a critical vibrational energy threshold, determined by the amplitude and frequency of the surface vibrations, that must be surpassed to effectively dislodge contaminant particles. We derive a universal scaling law that links the characteristic size of particles to the optimal vibrational parameters required for their removal. This provides a theoretical framework to aid the development of advanced, scalable self-cleaning surfaces, with applications ranging from semiconductors to large-scale industrial systems.
The dataset is related to the publication by Rohit Pillai, David Neilan, Cameron Handel, and Saikat Datta (2025), "Contaminant removal using vibrating surfaces: nanoscale insights and a universal scaling law", Nano Letters
High Resolution sections of eMouseAtlas Models: EMA76, Theiler Stage 20 TS20(12 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: EMA65, Theiler Stage 19 TS19(11.5 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: EMA102, Theiler Stage 26 TS26(17.5 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
Image and Other Data Available for each Mouse Developmental (Theiler) Stage
No full textStage specific data, including 3D reconstructions, delineated anatomy, 2D high resolution section images and associated movie and video sequences that is available from the University of Edinburgh DataShare. The data available for each stage can be accessed via each stage xlsx file. Data is hyperlinked witin each file and can be accessed directly be selecting the stage of interest or by downloading the full dataset of spreadsheets and using them off-line
IfA Satellite Camera example observations from night of 4th May 2025
No full textDataset consists of 6 images taken by the satellite camera at the Institute for Astronomy, University of Edinburgh. There are three images each of two known satellites, HAIYANG 2C (46469) and SWOT (54754). Each image includes a full frame NEF version, a smaller PNG image containing just the satellite trail, and a FITS file that contains the astrometric calibration appropriate for the PNG image.Files associated with HAIYANG 2C (46469):
005_2025-05-04_221914_A_DSC_0359.NEF
005_2025-05-04_221914_A_DSC_0359_streak_1.png
005_2025-05-04_221914_A_DSC_0359_wcs_1.fits
005_2025-05-04_221929_A_DSC_0360.NEF
005_2025-05-04_221929_A_DSC_0360_streak_1.png
005_2025-05-04_221929_A_DSC_0360_wcs_1.fits
005_2025-05-04_221944_A_DSC_0361.NEF
005_2025-05-04_221944_A_DSC_0361_streak_1.png
005_2025-05-04_221944_A_DSC_0361_wcs_1.fits
Files associated with SWOT (54754):
005_2025-05-05_013644_A_DSC_1144.NEF
005_2025-05-05_013644_A_DSC_1144_streak_1.png
005_2025-05-05_013644_A_DSC_1144_wcs_1.fits
005_2025-05-05_013659_A_DSC_1145.NEF
005_2025-05-05_013659_A_DSC_1145_streak_2.png
005_2025-05-05_013659_A_DSC_1145_wcs_2.fits
005_2025-05-05_013714_A_DSC_1146.NEF
005_2025-05-05_013714_A_DSC_1146_streak_2.png
005_2025-05-05_013714_A_DSC_1146_wcs_2.fit
Klebsiella pneumoniae dataset
No full textThis dataset of Klebsiella pneumonia contains 1000 genome files randomly selected from NCBI. This dataset was built for comparative analysis with the Klebsiella variicola dataset