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May2017 ImageXpress backup
No full textThe ImageXpress MicroXl platform is a high content instrument from Molecular Devices. Incorporation of a large sensor scientific CMOS camera together with LED solid light source provides enhanced optical sensitivity and image quality over standard high content systems. New MetaXpressTM software solutions such as a “Digital Confocal Option” and “Custom Module Editor” provides increased capability and flexibility to customize image analysis routines for quantification of defined phenotypes. The AcuityXpressTM software facilitates quality control assessment across multiple plates and tissue slides and incorporates multivariate statistical and similarity profiling tools to exploit multiparametric phenotypic data. The ImageXpress platform represents a fully equipped high content solution integrated with plate handling robotics (PAA Scara 4 robot), barcode reader and an extensive image-informatics suite (MetaXpressTM and AcuityXpressTM software) that stream-lines; complex high-content analysis routines; data analysis; image storage and review
Jul2017 ImageXpress backup
No full textThe ImageXpress MicroXl platform is a high content instrument from Molecular Devices. Incorporation of a large sensor scientific CMOS camera together with LED solid light source provides enhanced optical sensitivity and image quality over standard high content systems. New MetaXpressTM software solutions such as a “Digital Confocal Option” and “Custom Module Editor” provides increased capability and flexibility to customize image analysis routines for quantification of defined phenotypes. The AcuityXpressTM software facilitates quality control assessment across multiple plates and tissue slides and incorporates multivariate statistical and similarity profiling tools to exploit multiparametric phenotypic data. The ImageXpress platform represents a fully equipped high content solution integrated with plate handling robotics (PAA Scara 4 robot), barcode reader and an extensive image-informatics suite (MetaXpressTM and AcuityXpressTM software) that stream-lines; complex high-content analysis routines; data analysis; image storage and review
Dec2017 ImageXpress backup
No full textThe ImageXpress MicroXl platform is a high content instrument from Molecular Devices. Incorporation of a large sensor scientific CMOS camera together with LED solid light source provides enhanced optical sensitivity and image quality over standard high content systems. New MetaXpressTM software solutions such as a “Digital Confocal Option” and “Custom Module Editor” provides increased capability and flexibility to customize image analysis routines for quantification of defined phenotypes. The AcuityXpressTM software facilitates quality control assessment across multiple plates and tissue slides and incorporates multivariate statistical and similarity profiling tools to exploit multiparametric phenotypic data. The ImageXpress platform represents a fully equipped high content solution integrated with plate handling robotics (PAA Scara 4 robot), barcode reader and an extensive image-informatics suite (MetaXpressTM and AcuityXpressTM software) that stream-lines; complex high-content analysis routines; data analysis; image storage and review
RESTART trial main results dataset
No full textAnalysis dataset used for the RESTART main results and imaging sub-study results papers in 2019Salman, Rustam Al-Shahi; Stephen, Jacqueline; Drever, Jonathan. (2021). RESTART trial main results dataset, 2013-2018 [dataset]. University of Edinburgh. Deanery of Clinical Sciences. Centre for Clinical Brain Sciences. https://doi.org/10.7488/ds/3097
Geographic origin and post-invasion genetic divergence of Mytilus galloprovincialis in China
Full text linkBiological invasions serve as natural experiments to investigate how species adapt to novel environments. The Mediterranean mussel (Mytilus galloprovincialis), a highly successful marine invader and strong biofouler, has formed dominant populations along the coast of China. However, its genetic origin, population structure, and the consequences for the genetic diversity of invasive and native populations remain unclear. To address this, we employed a 60 K SNP array to genotype 320 individuals sampled from seven geographic populations, along with 112 reference samples. The results indicate a clear genetic affinity between Chinese and Mediterranean populations (FST < 0.05), while moderate differentiation was observed from Atlantic populations (FST > 0.05), supporting a Mediterranean origin. Genetic differentiation was observed in Dalian, whereas Lianyungang served as a key gene flow sink from both Mediterranean origins and nearby invasive populations. Moreover, using just 20 SNPs with a Support Vector Machine (SVM) model yielded 84 % classification accuracy, closely matching the full array's performance. Our findings provide novel insights into the invasion history and post-invasion genetic dynamics of M. galloprovincialis in East Asia and highlight the utility of SNP tools for species identification, population monitoring, and pollution-related biomonitoring in marine ecosystems.</p
Learning disabilities and adolescent suicidal ideation:Findings from the z-proso cohort study
No full textObjectiveTo investigate suicidal ideation among adolescents with learning disabilities and examine whether learning disabilities and a range of risk and protective factors assessed at age 13 are associated with suicidal ideation at age 15.MethodsLongitudinal data were drawn from a youth population-based cohort (Zurich Project on the Social Development from Childhood to Adulthood [z-proso]; N = 1675). Modified Poisson regression was used to estimate the relative risk of suicidal ideation at age 15, with learning disabilities along with the other variables as predictors. An additional model included an interaction term between learning disabilities and anxiety/depression symptoms to test whether the association between anxiety/depression and suicidal ideation varied by learning disability status. Average marginal effects were used to estimate absolute differences in predicted probabilities between groups.ResultsAdolescents with learning disabilities reported significantly higher rates of suicidal ideation (32.5 %) and self-injury (18.4 %) compared to peers without learning disabilities. They showed elevated levels of most risk factors and lower levels of protective factors. Significant predictors of increased relative risk of suicidal ideation included female sex, anxiety/depression symptoms, bullying experiences, and learning disabilities, the latter associated with a 40.2 % higher risk (RR = 1.402, 95 % CI = [1.070, 1.387]). Average marginal effects indicated that anxiety/depression significantly increased suicidal ideation risk among adolescents without learning disabilities but not among those with learning disabilities.ConclusionsFindings suggest that learning disabilities are a significant risk factor for adolescent suicidal ideation, highlighting the need for early identification, tailored assessment, and targeted prevention strategies
Data Stories dataset 2020-2022
No full textFictional stories in text and image form, submitted anonymously through a research project web site and inspired by prompts designed to explore possible surveillance futures in higher education. The stories include educational settings, characters, technologies, processes and wider social and political contexts
Efficient Tactile Perception with Soft Electrical Impedance Tomography and Pre-trained Transformer
No full textThis dataset is for publication "Efficient Tactile Perception with Soft Electrical Impedance Tomography and Pre-trained Transformer". ABSTRACT: Tactile sensing is fundamental to robotic systems, enabling interactions through physical contact in multiple tasks. Despite its importance, achieving high-resolution, large-area tactile sensing remains challenging. Electrical Impedance Tomography (EIT) has emerged as a promising approach for large-area, distributed tactile sensing with minimal electrode requirements which can lend itself to addressing complex contact problems in robotics. However, existing EIT-based tactile reconstruction methods often suffer from high computational costs or depend on extensive annotated simulation datasets, hindering its viability in real-world settings. To address this shortcoming, here we propose a Pre-trained Transformer for EIT-based Tactile Reconstruction (PTET), a learning-based framework that bridges the simulation-to-reality gap by leveraging self-supervised pretraining on simulation data and fine-tuning with limited real-world data. In simulations, PTET requires 99.44% fewer annotated samples than equivalent state-of-the-art approaches (2,500 vs. 450,000 samples) while achieving reconstruction performance improvements of up to 43.57% under identical data conditions. Fine-tuning with real-world data further enables PTET to overcome discrepancies between simulated and experimental datasets, achieving superior reconstruction and detail recovery in practical scenarios. PTET’s improved reconstruction accuracy, data efficiency, and robustness in real-world tasks establish it as a scalable and practical solution for tactile sensing systems in robotics, especially for object handling and adaptive grasping under varying pressure conditions
Abradable DEM: A Novel Framework to Capture the Mechanistic Evolution of Particle Shape
No full textAlthough various methods exist for modelling non-spherical particles in DEM, particles’ shapes are usually treated as immutable. However, particles often change shape gradually, e.g., due to abrasion or accrued plastic deformation. This manner of shape evolution has largely been neglected in DEM even though it can significantly influence bulk-scale behaviour. The following introduces an extendable framework for modelling the gradual and permanent evolution of particle shapes in DEM, focusing on abrasion/wear as an exemplar. By extending the existing LAMMPS rigid-body implementation, a comprehensive novel wear model is employed to simulate the abrasion of arbitrarily shaped dynamic particles. These abradable particles are represented as hollow shells of discrete spheres collated into a series of triangular facets. Following an impact exceeding a material yield criterion, spheres are displaced inwards along their normals. The result is a reduction in volume and a permanent change in shape. Following this, each abraded particle’s moment of inertia is recomputed and used to resolve future rigid-body dynamics. Thus, particle-level changes in shape affect the bulk dynamics of the system, which in turn informs all subsequent abrasion. Results exhibit particle shape evolution in agreement with a variety of abrasion scenarios in literature and showcase the resulting effect on the bulk dynamics of such systems. This research provides a versatile methodology for linking microscale abrasion mechanisms to macroscale system behaviour, with widespread applications in both natural processes and industrial particle-handling systems. Furthermore, the outlined framework can be readily adapted to other sources of mechanistic particle shape evolution in DEM
Convergence rates of non-stationary and deep Gaussian process regression
Full text linkThe focus of this work is the convergence of non-stationary and deep Gaussian process regression. More precisely, we follow a Bayesian approach to regression or interpolation, where the prior placed on the unknown function f is a non-stationary or deep Gaussian process, and we derive convergence rates of the posterior mean to the true function f in terms of the number of observed training points. In some cases, we also show convergence of the posterior variance to zero. The only assumption imposed on the function f is that it is an element of a certain reproducing kernel Hilbert space, which we in particular cases show to be norm-equivalent to a Sobolev space. Our analysis includes the case of estimated hyper-parameters in the covariance kernels employed, both in an empirical Bayes setting and the particular hierarchical setting constructed through deep Gaussian processes. We consider the settings of noise-free or noisy observations on deterministic or random training points. We establish general assumptions sufficient for the convergence of deep Gaussian process regression, along with explicit examples demonstrating the fulfilment of these assumptions. Specifically, our examples require that the Hölder or Sobolev norms of the penultimate layer are bounded almost surely.</p
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