98 research outputs found

    The study of renal function and toxicity using zebrafish (Danio rerio) larvae as a vertebrate model

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    Zebrafish (Danio rerio) is a powerful model in biomedical and pharmaceutical sciences. The zebrafish model was introduced to toxicological sciences in 1960, followed by its use in biomedical sciences to investigate vertebrate gene functions. As a consequence of many research projects in this field, the study of human genetic diseases became instantly feasible. Consequently, zebrafish have been intensively used in developmental biology and associated disciplines. Due to the simple administration of medicines and the high number of offspring, zebrafish larvae became widely more popular in pharmacological studies in the following years. In the past decade, zebrafish larvae were further established as a vertebrate model in the field of pharmacokinetics and nanomedicines. In this PhD thesis, zebrafish larvae were investigated as an earlystage in vivo vertebrate model to study renal function, toxicity, and were applied in drug-targeting projects using nanomedicines. The first part focused on the characterization of the renal function of three-to four-dayold zebrafish larvae. Non-renal elimination processes were additionally described. Moreover, injection techniques, imaging parameters, and post-image processing scripts were established to serve as a toolbox for follow-up projects. The second part analyzed the impact of gentamicin (a nephrotoxin) on the morphology of the pronephros of zebrafish larvae. Imaging methodologies such as fluorescent-based laser scanning microscopy and X-ray-based microtomography were applied. A profound comparison study of specimens acquired with different laboratory X-ray-based microtomography devices and a radiation facility was done to promote the use of X-ray-based microtomography for broader biomedical applications. In the third part, the toxicity of nephrotoxins on mitochondria in renal epithelial cells of proximal tubules was assessed using the zebrafish larva model. Findings were compared with other teleost models such as isolated renal tubules of killifish (Fundulus heteroclitus). In view of the usefulness and high predictability of the zebrafish model, it was applied to study the pharmacokinetics of novel nanoparticles in the fourth part. Various in vivo pharmacokinetic parameters such as drug release, transfection of mRNA/pDNA plasmids, macrophage clearance, and the characterization of novel drug carriers that were manipulated with ultrasound were assessed in multiple collaborative projects. Altogether, the presented zebrafish model showed to be a reliable in vivo vertebrate model to assess renal function, toxicity, and pharmacokinetics of nanoparticles. The application of the presented model will hopefully encourage others to reduce animal experiments in preliminary studies by fostering the use of zebrafish larvae

    Acoustic and X-ray Chacterisation of Lithium-Ion Battery Failure

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    Lithium-ion batteries have become synonymous with modern consumer electronics and potentially, are the cornerstone to development of integrated electrified infrastructure that can support a clean and renewable national energy grid. Despite the widespread applications due to the favourable performance parameters, recent events have elevated the safety concerns associated with lithium-ion batteries. However, there is great difficulty in rapid diagnostic analysis outside specialised laboratories which can hinder the review of functional safety- and novel energy dense- materials for lithium-ion energy storage. The dynamic evolution of internal architectures and novel active materials across multiple length scales are investigated in this thesis; with in-situ and operando acoustic spectroscopy (AS) via ultrasonic time of flight (ToF) probing, high speed synchrotron X-ray imaging, computed tomography and fractional thermal runaway calorimetry. The identification of characteristic precursor events such as gas-induced delamination in degradation mechanisms before eventual failure by AS; is correlated with X-ray imaging and post-mortem computed tomography (CT), highlighting the potential for battery management systems. Mitigation and prevention of failure with plasticized current collectors and thermally stable cellulose separators was also investigated at multiple length scales, with the transient mechanical structure compared with their commercial counterparts in cylindrical cells. Further work investigating the robustness of acoustic spectroscopy and polymer current collectors were applied to pure silicon nanowire negative electrodes. The studies reported in this thesis assess novel materials in lithium-ion batteries, and the potential impact of the work is highlighted. Development of AS via ToF probing offers another unique and field deployable insight allowing more complete and comprehensive understanding of batteries as they continue to evolve in complexity. Lithium-ion failure characterisation techniques and literature have evolved and provided insights into the function of polymer current collectors in different cell formats and chemistries. Findings presented in this thesis are anticipated to augment future inherently safer battery design and characterisation of lithium-ion energy storage thermal runaway

    Just-in-time deep learning for real-time X-ray computed tomography

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    Real-time X-ray tomography pipelines, such as implemented by RECAST3D, compute and visualize tomographic reconstructions in milliseconds, and enable the observation of dynamic experiments in synchrotron beamlines and laboratory scanners. For extending real-time reconstruction by image processing and analysis components, Deep Neural Networks (DNNs) are a promising technology, due to their strong performance and much faster run-times compared to conventional algorithms. DNNs may prevent experiment repetition by simplifying real-time steering and optimization of the ongoing experiment. The main challenge of integrating DNNs into real-time tomography pipelines, however, is that they need to learn their task from representative data before the start of the experiment. In scientific environments, such training data may not exist, and other uncertain and variable factors, such as the set-up configuration, reconstruction parameters, or user interaction, cannot easily be anticipated beforehand, either. To overcome these problems, we developed just-in-time learning, an online DNN training strategy that takes advantage of the spatio-temporal continuity of consecutive reconstructions in the tomographic pipeline. This allows training and deploying comparatively small DNNs during the experiment. We provide software implementations, and study the feasibility and challenges of the approach by training the self-supervised Noise2Inverse denoising task with X-ray data replayed from real-world dynamic experiments

    Cost-effective non-destructive testing of biomedical components fabricated using additive manufacturing

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    Biocompatible titanium-alloys can be used to fabricate patient-specific medical components using additive manufacturing (AM). These novel components have the potential to improve clinical outcomes in various medical scenarios. However, AM introduces stability and repeatability concerns, which are potential roadblocks for its widespread use in the medical sector. Micro-CT imaging for non-destructive testing (NDT) is an effective solution for post-manufacturing quality control of these components. Unfortunately, current micro-CT NDT scanners require expensive infrastructure and hardware, which translates into prohibitively expensive routine NDT. Furthermore, the limited dynamic-range of these scanners can cause severe image artifacts that may compromise the diagnostic value of the non-destructive test. Finally, the cone-beam geometry of these scanners makes them susceptible to the adverse effects of scattered radiation, which is another source of artifacts in micro-CT imaging. In this work, we describe the design, fabrication, and implementation of a dedicated, cost-effective micro-CT scanner for NDT of AM-fabricated biomedical components. Our scanner reduces the limitations of costly image-based NDT by optimizing the scanner\u27s geometry and the image acquisition hardware (i.e., X-ray source and detector). Additionally, we describe two novel techniques to reduce image artifacts caused by photon-starvation and scatter radiation in cone-beam micro-CT imaging. Our cost-effective scanner was designed to match the image requirements of medium-size titanium-alloy medical components. We optimized the image acquisition hardware by using an 80 kVp low-cost portable X-ray unit and developing a low-cost lens-coupled X-ray detector. Image artifacts caused by photon-starvation were reduced by implementing dual-exposure high-dynamic-range radiography. For scatter mitigation, we describe the design, manufacturing, and testing of a large-area, highly-focused, two-dimensional, anti-scatter grid. Our results demonstrate that cost-effective NDT using low-cost equipment is feasible for medium-sized, titanium-alloy, AM-fabricated medical components. Our proposed high-dynamic-range strategy improved by 37% the penetration capabilities of an 80 kVp micro-CT imaging system for a total x-ray path length of 19.8 mm. Finally, our novel anti-scatter grid provided a 65% improvement in CT number accuracy and a 48% improvement in low-contrast visualization. Our proposed cost-effective scanner and artifact reduction strategies have the potential to improve patient care by accelerating the widespread use of patient-specific, bio-compatible, AM-manufactured, medical components

    Annals [...].

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    Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo SimĂŁo Diniz Dalmolin

    TRACEABLE GEOMETRIC QUALIFICATION OF ADDITIVELY MANUFACTURED LATTICE STRUCTURES

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    The manufacture of lattice geometries via Additive Manufacturing (AM) has the potential to impact the production of low-volume, high cost, complex components. However, the qualification of lattice geometries provides several challenges for traditional metrological techniques, limiting the use of these structures within industry. While recent studies in AM part qualification have improved its practice, the measurement of lattice structures unique to additive manufacturing is not well understood and methods to support traceability have yet to be developed. In this dissertation, a methodology to determine measurement uncertainty in the measurement of AM lattice components is developed. A refined sampling registration approach for lattice geometry based on spatially-dependent subsampling is derived and is shown to statistically decrease variation between measurement sources. The importance of sampling location in tactile measurements of components produced using additive manufacturing is investigated and recommends that definition of inspection locations/methods be integrated into the design cycle of AM parts. The substitution method is investigated to determine uncertainty in the measurement of AM lattice structures using X-ray Computed Tomography (XCT). A measurement artifact is developed and measured using the substitution method. The use of reporting measurement uncertainty using uncorrected bias is explored for strut diameter measurements. Components identical to the measurement artifact are manufactured using AM and measured to determine manufacturing variation. The uncertainty in XCT measurement of these AM components is determined using the substitution method and methods to report uncorrected bias. This study provides a methodology to design inspection routines for the qualification of a lattice component, furthers the scientific understanding XCT measurements of AM components, and lays the groundwork for further implementation of the presented method.Ph.D

    Sparsity-based algorithms for inverse problems

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    Inverse problems are problems where we want to estimate the values of certain parameters of a system given observations of the system. Such problems occur in several areas of science and engineering. Inverse problems are often ill-posed, which means that the observations of the system do not uniquely define the parameters we seek to estimate, or that the solution is highly sensitive to small changes in the observation. In order to solve such problems, therefore, we need to make use of additional knowledge about the system at hand. One such prior information is given by the notion of sparsity. Sparsity refers to the knowledge that the solution to the inverse problem can be expressed as a combination of a few terms. The sparsity of a solution can be controlled explicitly or implicitly. An explicit way to induce sparsity is to minimize the number of non-zero terms in the solution. Implicit use of sparsity can be made, for e.g., by making adjustments to the algorithm used to arrive at the solution.In this thesis we studied various inverse problems that arise in different application areas, such as tomographic imaging and equation learning for biology, and showed how ideas of sparsity can be used in each case to design effective algorithms to solve such problems.Financial support was provided by the European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska-Curie grant agreement no.~765604Number theory, Algebra and Geometr
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