School of Mathematics and Statistics - Research Publications
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ItemNo Preview AvailableHBO1 (KAT7) Does Not Have an Essential Role in Cell Proliferation, DNA Replication, or Histone 4 Acetylation in Human Cells(American Society for Microbiology, 2020-02-01)HBO1 (MYST2/KAT7) is essential for histone 3 lysine 14 acetylation (H3K14ac) but is dispensable for H4 acetylation and DNA replication in mouse tissues. In contrast, previous studies using small interfering RNA (siRNA) knockdown in human cell lines have suggested that HBO1 is essential for DNA replication. To determine if HBO1 has distinctly different roles in immortalized human cell lines and normal mouse cells, we performed siRNA knockdown of HBO1. In addition, we used CRISPR/Cas9 to generate 293T, MCF7, and HeLa cell lines lacking HBO1. Using both techniques, we show that HBO1 is essential for all H3K14ac in human cells and is unlikely to have a direct effect on H4 acetylation and only has minor effects on cell proliferation. Surprisingly, the loss of HBO1 and H3K14ac in HeLa cells led to the secondary loss of almost all H4 acetylation after 4 weeks. Thus, HBO1 is dispensable for DNA replication and cell proliferation in immortalized human cells. However, while cell proliferation proceeded without HBO1 and H3K14ac, HBO1 gene deletion led to profound changes in cell adhesion, particularly in 293T cells. Consistent with this phenotype, the loss of HBO1 in both 293T and HeLa principally affected genes mediating cell adhesion, with comparatively minor effects on other cellular processes.
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ItemEffect of SiC Reinforcement and Its Variation on the Mechanical Characteristics of AZ91 Composites.(MDPI AG, 2020-10-31)In this study, the processing of SiC particulate-strengthened magnesium alloy metal matrix composites via vacuum supported inert atmosphere stir casting process is presented. The effects of small variations in the SiC particulate (average size 20 µm) reinforcement in magnesium alloy AZ91 were examined. It was found that with the addition of SiC particulate reinforcement, the hardness improved considerably, while the ultimate tensile and yield strength improved slightly. The density and porosity of the magnesium alloy-based composites increased with the increase in the wt.% of SiC particulates. The tensile and compressive fracture study of the fabricated composites was also performed. The tensile fractures were shown to be mixed-mode fractures (i.e., ductile and cleavage). The fractured surface also disclosed tiny dimples, micro-crack, and cleavage fractures which increases with increasing reinforcement. For the compression fracture, the surface microstructural studies of AZ91 displayed major shear failure and demonstrated the greater shear bands when compared to AZ91/SiC composites, which instead revealed rough fracture surfaces with mixed-mode brittle and shear features.
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ItemStrategies to enable large-scale proteomics for reproducible research(NATURE PORTFOLIO, 2020-07-30)Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.
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ItemControlling technical variation amongst 6693 patient microarrays of the randomized MINDACT trial(NATURE PUBLISHING GROUP, 2020-07-27)Gene expression data obtained in large studies hold great promises for discovering disease signatures or subtypes through data analysis. It is also prone to technical variation, whose removal is essential to avoid spurious discoveries. Because this variation is not always known and can be confounded with biological signals, its removal is a challenging task. Here we provide a step-wise procedure and comprehensive analysis of the MINDACT microarray dataset. The MINDACT trial enrolled 6693 breast cancer patients and prospectively validated the gene expression signature MammaPrint for outcome prediction. The study also yielded a full-transcriptome microarray for each tumor. We show for the first time in such a large dataset how technical variation can be removed while retaining expected biological signals. Because of its unprecedented size, we hope the resulting adjusted dataset will be an invaluable tool to discover or test gene expression signatures and to advance our understanding of breast cancer.
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ItemOrthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex(NATURE PORTFOLIO, 2020-02-12)Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. While CNNs can accurately assign one image to potentially thousands of categories, network performance could be the result of layers that are tuned to represent the visual shape of objects, rather than object category, since both are often confounded in natural images. Using two stimulus sets that explicitly dissociate shape from category, we correlate these two types of information with each layer of multiple CNNs. We also compare CNN output with fMRI activation along the human visual ventral stream by correlating artificial with neural representations. We find that CNNs encode category information independently from shape, peaking at the final fully connected layer in all tested CNN architectures. Comparing CNNs with fMRI brain data, early visual cortex (V1) and early layers of CNNs encode shape information. Anterior ventral temporal cortex encodes category information, which correlates best with the final layer of CNNs. The interaction between shape and category that is found along the human visual ventral pathway is echoed in multiple deep networks. Our results suggest CNNs represent category information independently from shape, much like the human visual system.
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ItemNo Preview AvailableAcoustic flows in a slightly rarefied gas(AMER PHYSICAL SOC, 2020-04-06)
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ItemNo Preview AvailableField-only surface integral equations: Scattering from a perfect electric conductor(Optical Society of America, 2020-02-01)A field-only boundary integral formulation of electromagnetics is derived without the use of surface currents that appear in the Stratton–Chu formulation. For scattering by a perfect electrical conductor (PEC), the components of the electric field are obtained directly from surface integral equation solutions of three scalar Helmholtz equations for the field components. The divergence-free condition is enforced via a boundary condition on the normal component of the field and its normal derivative. Field values and their normal derivatives at the surface of the PEC are obtained directly from surface integral equations that do not contain divergent kernels. Consequently, high-order elements with fewer degrees of freedom can be used to represent surface features to a higher precision than the traditional planar elements. This theoretical framework is illustrated with numerical examples that provide further physical insight into the role of the surface curvature in scattering problems.
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ItemNo Preview AvailableField-only surface integral equations: Scattering from a dielectric body(Optical Society of America, 2020-02-01)An efficient field-only nonsingular surface integral method to solve Maxwell’s equations for the components of the electric field on the surface of a dielectric scatterer is introduced. In this method, both the vector wave equation and the divergence-free constraint are satisfied inside and outside the scatterer. The divergence-free condition is replaced by an equivalent boundary condition that relates the normal derivatives of the electric field across the surface of the scatterer. Also, the continuity and jump conditions on the electric and magnetic fields are expressed in terms of the electric field across the surface of the scatterer. Together with these boundary conditions, the scalar Helmholtz equation for the components of the electric field inside and outside the scatterer is solved by a fully desingularized surface integral method. Compared with the most popular surface integral methods based on the Stratton–Chu formulation or the Poggio–Miller–Chew–Harrington–Wu–Tsai (PMCHWT) formulation, our method is conceptually simpler and numerically straightforward because there is no need to introduce intermediate quantities such as surface currents, and the use of complicated vector basis functions can be avoided altogether. Also, our method is not affected by numerical issues such as the zero-frequency catastrophe and does not contain integrals with (strong) singularities. To illustrate the robustness and versatility of our method, we show examples in the Rayleigh, Mie, and geometrical optics scattering regimes. Given the symmetry between the electric field and the magnetic field, our theoretical framework can also be used to solve for the magnetic field.
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ItemCentral moment inequalities using Stein's method(UNIV WASHINGTON, DEPT MATHEMATICS, 2020-01-01)We derive explicit central moment inequalities for random variables that admit a Stein coupling, such as exchangeable pairs, size–bias couplings or local dependence, among others. The bounds are in terms of moments (not necessarily central) of variables in the Stein coupling, which are typically local in some sense, and therefore easier to bound. In cases where the Stein couplings have the kind of behaviour leading to good normal approximation, the central moments are closely bounded by those of a normal. We show how the bounds can be used to produce concentration inequalities, and compare them to those existing in related settings. Finally, we illustrate the power of the theory by bounding the central moments of sums of neighbourhood statistics in sparse Erdős–Rényi random graphs.
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ItemExponential and Laplace approximation for occupation statistics of branching random walk(UNIV WASHINGTON, DEPT MATHEMATICS, 2020)