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Intercellular friction and motility drive orientational order in cell monolayers
No full textSpatiotemporal patterns in multicellular systems are important to understanding tissue dynamics, for instance, during embryonic development and disease. Here, we use a multiphase field model to study numerically the behavior of a near-confluent monolayer of deformable cells with intercellular friction. Varying friction and cell motility drives a solid-liquid transition, and near the transition boundary, we find the emergence of local nematic order of cell deformation driven by shear-aligning cellular flows. Intercellular friction contributes to the monolayer's viscosity, which significantly increases the spatial correlation in the flow and, concomitantly, the extent of nematic order. We also show that local hexatic and nematic order are tightly coupled and propose a mechanical-geometric model for the colocalization of +1/2 nematic defects and 5-7 disclination pairs, which are the structural defects in the hexatic phase. Such topological defects coincide with regions of high cell-cell overlap, suggesting that they may mediate cellular extrusion from the monolayer, as found experimentally. Our results delineate a mechanical basis for the recent observation of nematic and hexatic order in multicellular collectives in experiments and simulations and pinpoint a generic pathway to couple topological and physical effects in these systems. This dataset contains the simulation code and data underlying the figures of the associated paper.Please see the README file for the file descriptions
Oviposition time, interval and cuticle deposition
No full text1) The cuticle acts as a barrier to prevent microbial penetration of the eggshell. The reduction in the oviposition interval with selection for egg production and the activity of clock genes in the oviduct led to testing the hypothesis that the interval between successive oviposition of eggs would be related to the deposition of the cuticle.
2) There was oviposition interval and cuticle deposition data from 2140 eggs from 5 White Leghorn pure lines for over 7 days. The association between oviposition interval and cuticle deposition was assessed using a random slopes model for each hen.
3) The time of oviposition was 05:37 h:m, about 2.5 hours after lights on. Differences in oviposition time between lines were significant (P=0.025). Oviposition interval was slightly greater than 24 hours at 24:06 h:m with significant differences between lines (P=0.003). The variance was low and the maximum difference between lines for oviposition interval was only 11 minutes. Cuticle deposition was 28.87ΔE∗ab with no differences between lines.
4) The number of eggs a hen laid had an effect on the oviposition interval (P=0.004), being shortest in hens laying 7 eggs (24:01) than those laying 6 (24:08) or 5 eggs (24:14).
5) There was a significant positive association between cuticle deposition and oviposition interval (p=0.0073) with a with a modest increase of 0.79 ΔE∗ab in cuticle deposition for each additional hour of oviposition interval. Heritability for cuticle deposition in this study was 0.48 but heritability was not measurable for oviposition time.
6) Combined with a difference between the top and tail of the distribution for cuticle deposition, there was evidence for a significant but relatively small relationship between oviposition interval and deposition of cuticle on the egg. This may have contributed to some reduction in cuticle coverage as the oviposition interval approached 24 hours, but it seems unlikely that it was a major component
Velocity time-series data from a hybrid finite element Large Eddy Simulation of flow over a backward-facing step with associated geometry and mesh files
No full textA Large Eddy Simulation turbulence (LES) algorithm has been developed for finite element-based computational fluid dynamics, using a hybrid continuous-discontinuous Galerkin scheme. The test case for this was a backward-facing step, which is a well-known example with published experimental results for validation. This dataset contains output data from the validation testcase as well as mesh files for grid generation of the associated test case. It also contains the GMSH geometry file for the problem, and the GMSH mesh file with the final adapted mesh
LADDIE
No full text180 pairs of depth and ambient images captured with a Xenomatix XenoLidar Xact LiDAR sensor.
Images were captured in various locations in and around Edinburgh (UK).
Depth images are 16-bit with each pixel value corresponding to depth in centimeters.
Ambient images are 8-bit grey-scale
Music Creativity and Wellbeing Phase 1
No full textThis data set contains research material from Phase 1 of a British Academy postdoctoral fellowship. It contains interviews with two practitioners from Limelight Music who delivered a 6 week creative music workshop program with 7 children
Sexually dimorphic murine brain uptake of the 18 kDa translocator protein PET radiotracer [18F]LW223
No full textThe 18 kDa translocator protein is a well-known biomarker of neuroinflammation, but also plays a role in homeostasis. PET with
18 kDa translocator protein radiotracers [11C]PBR28 in humans and [18F]GE180 in mice has demonstrated sex-dependent uptake patterns in the healthy brain, suggesting sex-dependent 18 kDa translocator protein expression, although humans and mice had differing results. This study aimed to assess whether the 18 kDa translocator protein PET radiotracer [18F]LW223 exhibited sexually dimorphic uptake in healthy murine brain and peripheral organs. Male and female C57Bl6/J mice (13.6 ± 5.4 weeks, 26.8 ± 5.4 g, mean ± SD) underwent 2 h PET scanning post-administration of [18F]LW223 (6.7 ± 3.6 MBq). Volume of interest and parametric analyses were performed using standard uptake values (90–120 min). Statistical differences were assessed by unpaired t-test or two-way ANOVA with Šidak’s test (alpha = 0.05). The uptake of [18F]LW223 was significantly higher across multiple regions of the male mouse brain, with the most pronounced difference detected in hypothalamus (P < 0.0001). Males also exhibited significantly higher [18F]LW223 uptake in the heart when compared to females (P = 0.0107). Data support previous findings on sexually dimorphic 18 kDa translocator protein radiotracer uptake patterns in mice and highlight the need to conduct sex-controlled comparisons in 18 kDa translocator protein PET imaging studies.File descriptions are included in a Readme.txt file
Impact of random nanoscale roughness on gas scattering dynamics
No full textIn this work, we develop a scattering kernel for surfaces having nanoscale roughness that distinctly characterises the two major types of interactions between gas molecules and rough surfaces, namely (a) the weak perturbations arising from the thermal motion of wall atoms, essentially gas–phonon collisions, which are captured by the well-established Cercignani–Lampis model, and (b) the hard collisions owing to the irregularities of the rough, static potential energy surface, which are generally described by the fully diffuse model. Drawing an analogy between wave–surface and gas–surface scattering, a pseudo Debye–Waller factor is incorporated into modelling as a weighting coefficient to allow the transition between smooth and rough surface conditions. The proposed scattering kernel is validated through high-fidelity molecular dynamics simulations that are performed for systems with varying roughness, temperature, and gas-surface combinations. The results indicate that the model well captures the scattering dynamics of gas molecular beams impinging on surfaces at different velocities, specifically for the accommodation coefficients and reflection patterns. Additionally, it accurately predicts macroscopic quantities such as velocity slip and temperature jumps across the range of tested conditions. The dataset contains molecular dynamics LAMMPS files and post-processing scripts to reproduce results from this work
Windows on the Past: Digital Analysis of Window Design in Later Medieval England (1200-1300)
No full textThe dataset consists of a group of point cloud models of the South Transept at Ely Cathedral in Cambridgeshire, UK. These were generated from a laser scanning (LIDAR) survey conducted on 22nd June 2024. The survey was undertaken as part of the Windows on the Past: Digital Analysis of Window Design in Later Medieval England (1200-1300) project, funded by the Paul Mellon Centre. Its Principal Investigator was James Hillson during the time was a Lecturer in Architectural History at the University of Edinburgh (2023-24).
The aim of the project was to use laser scanning data to analyse window design processes, focusing on the critical period of the introduction of bar tracery into medieval England (c. 1200-1300). This resulted in both RAW scanning data and processed point clouds for two key sites in East Anglia: Binham Priory (Norfolk, UK) and the South Transept at Ely Cathedral (Cambridgeshire, UK). The dataset provided here consists of the RAW scanning data and the processed point cloud data for Ely Cathedral, made publicly available in .e57 format for any non-commercial purpose.
Further details regarding the dataset and the surveying process can be found in the accompanying .xls spreadsheet.
The dataset for the South Transept at Ely Cathedral can also be found on Edinburgh DataShare.The files are organised as follows:
Group 1 - Finding Aids and Metadata [2 files, .xls and .jpg format]
• WotP_Ely_Cathedral_point_cloud_metadata.xls - Spreadsheet of metadata for scanning process and point clouds.
• WotP_Ely_Cathedral_Site_Plan_Bay_and_Window_Key.jpg - Plan identifying bay positions for point clouds.
Group 2 - Interior Point Cloud Models [7 files, .e57 format]
• WotP_Ely_Cathedral_point_cloud_interior_S10_3.e57 - Processed point cloud model for interior of South Transept at Ely Cathedral, Bay S10.3.
• WotP_Ely_Cathedral_point_cloud_interior_S11_1.e57 - Processed point cloud model for interior of South Transept at Ely Cathedral, Bay S11.1.
• WotP_Ely_Cathedral_point_cloud_interior_S11_2.e57 - Processed point cloud model for interior of South Transept at Ely Cathedral, Bay S11.2.
• WotP_Ely_Cathedral_point_cloud_interior_S11_3.e57 - Processed point cloud model for interior of South Transept at Ely Cathedral, Bay S11.3.
• WotP_Ely_Cathedral_point_cloud_interior_S12_1.e57 - Processed point cloud model for interior of South Transept at Ely Cathedral, Bay S12.1.
• WotP_Ely_Cathedral_point_cloud_interior_S12_2.e57 - Processed point cloud model for interior of South Transept at Ely Cathedral, Bay S12.2.
• WotP_Ely_Cathedral_point_cloud_interior_S12_3.e57 - Processed point cloud model for interior of South Transept at Ely Cathedral, Bay S12.3.
Group 3 - Exterior Point Cloud Models [1 file, .e57 format]
• WotP_Ely_Cathedral_point_cloud_exterior_S10_1-3.e57 - Processed point cloud model for exterior of South Transept at Ely Cathedral, east wall, Bays S10.1, S10.2 and S10.3.
Group 4 - Interior RAW Point Clouds [13 files, .e57 format]
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-516.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (1 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-517.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (2 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-518.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (3 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-519.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (4 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-521.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (5 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-522.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (6 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-523.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (7 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-524.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (8 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-525.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (9 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-526.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (10 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-527.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (11 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-528.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (12 of 13 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-529.e57 - RAW point cloud data for interior of South Transept at Ely Cathedral (13 of 13 setups).
Group 5 - Exterior RAW Point Clouds [9 files, .e57 format]
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-531.e57 - RAW point cloud data for exterior of South Transept at Ely Cathedral (1 of 9 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-532.e57 - RAW point cloud data for exterior of South Transept at Ely Cathedral (2 of 9 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-533.e57 - RAW point cloud data for exterior of South Transept at Ely Cathedral (3 of 9 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-535.e57 - RAW point cloud data for exterior of South Transept at Ely Cathedral (4 of 9 setups).
• WotP_Ely_Cathedral _RAW_point_cloud_WR3-18-537.e57 - RAW point cloud data for exterior of South Transept at Ely Cathedral (5 of 9 setups).
• WotP_Ely_Cathedral_RAW_point_cloud_WR3-18-538.e57 - RAW point cloud data for exterior of South Transept at Ely Cathedral (6 of 9 setups).
• WotP_Ely_Cathedral_RAW_point_cloud_WR3-18-539.e57 - RAW point cloud data for exterior of South Transept at Ely Cathedral (7 of 9 setups).
• WotP_Ely_Cathedral_RAW_point_cloud_WR3-18-540.e57 - RAW point cloud data for exterior of South Transept at Ely Cathedral (8 of 9 setups).
• WotP_Ely_Cathedral_RAW_point_cloud_WR3-18-541.e57 - RAW point cloud data for exterior of South Transept at Ely Cathedral (9 of 9 setups).
File structure and descriptions can be found in the following attached spreadsheet (.xls format):
• WotP_Ely_Cathedral_point_cloud_metadata.xls
The locations of individual models within the building can be identified using the following planimetric map:
• WotP_Ely_Cathedral_Site_Plan_Bay_and_Window_Key.jp
AInsectID Version 1.1 - a free to use species identification, image analysis and colour mapping software
No full textAInsectID Version 1.1 is a heavily updated and improved version of AInsectID. It is a GUI based software that with this new (v1.1) release, can be used to identify (currently 150) insect species. The software can also be used for color processing and for the analysis of insect body parts such as wings, and additionally includes a simple image analysis giving users the flexibility to both automatically and manually quantify the geometrical features within the image. In a world bustling with diverse insect species, accurate identification is important. This software uses AI to revolutionize the way we identify insects with up to 99.64% accuracy (determined through validation testing). AInsectID represents a significant leap forward in insect species identification. Traditional methods require any of: specialized taxonomical knowledge, time-consuming morphological and geographical characterization methods, genetic barcoding expertise, or even in some cases, destructive sampling. With AInsectID Version 1.1, machine learning and deep learning algorithms are used to identify and classify insect species based on their morphological features, This process is fast, highly accurate, and is now freely accessible to a broader audience. This is a software that we will continue to update over time, this is our second release, Version 1.1. The dataset contains free to use software developed at The University of Edinburgh entitled AInsectID Version 1.1. The software comes with the following functionality: (1) Insect species identification (2) image analysis and morphometric analysis tools (3) colour mapping and colour manipulation tools. This is the second release of this software. A user manual is provided with the software
Testing the evolutionary drivers of malaria parasite rhythms and their consequences for host-parasite interactions
Get PDFUndertaking certain activities at the time of day that maximises fitness is assumed to explain the evolution of circadian clocks. Organisms often use daily environmental cues such as light and food availability to set the timing of their clocks. These cues may be the environmental rhythms that ultimately determine fitness, act as proxies for the timing of less tractable ultimate drivers, or are used simply to maintain internal synchrony. While many pathogens/parasites undertake rhythmic activities, both the proximate and ultimate drivers of their rhythms are poorly understood. Explaining the roles of rhythms in infections offers avenues for novel interventions to interfere with parasite fitness and reduce the severity and spread of disease. Here, we perturb several rhythms in the hosts of malaria parasites to investigate why parasites align their rhythmic replication to the host’s feeding-fasting rhythm. We manipulated host rhythms governed by light, food, or both, and assessed the fitness implications for parasites, and the consequences for hosts, to test which host rhythms represent ultimate drivers of the parasite’s rhythm. We found that alignment with the host’s light-driven rhythms did not affect parasite fitness metrics. In contrast, aligning with the timing of feeding-fasting rhythms may be beneficial for the parasite, but only when the host possess a functional canonical circadian clock. Because parasites in clock-disrupted hosts align with the host’s feeding-fasting rhythms and yet derive no apparent benefit, our results suggest cue(s) from host food act as a proxy rather than being a key selective driver of the parasite’s rhythm. Alternatively, parasite rhythmicity may only be beneficial because it promotes synchrony between parasite cells and/or allows parasites to align to the biting rhythms of vectors. Our results also suggest that interventions can disrupt parasite rhythms by targeting the proxies or the selective factors driving them without impacting host health