2,385 research outputs found

    Bioactive Self-Assembled Protein Nanosheets for Stem Cell-Based Biotechnologies

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    Tissue and stem cell culture methods have been dominated by glass and plastic substrates such as Tissue culture plastic. These solid substrates, although widely used, are associated with poor scalability for adherent stem cell expansion in systems such as 3D bioreactors and the design of parallel culture systems. Therefore, investigating strategies to bypass these obstacles in stem cell expansion is essential to enable the wider translation of stem cell technologies. An alternative strategy recently proposed consists in using a liquid surface instead, such as an oil, and associated oil droplets. Indeed, emulsions can be formed using protein nanosheets to stabilise oil/water interfaces to promote the adhesion of stem cells and enable their proliferation. These nanosheets exhibit enhanced interfacial mechanics and allow the introduction of bioactive components via recombinant protein expression to promote bioactivity. Beyond the application of resulting bioemulsions for the expansion of Mesenchymal stem cells, the impact of these bioactive interfaces on the differentiation of iPSCs and the development of cerebral organoids will be presented. The Bovine serum albumin protein was recombinantly modified to attach an N-terminal Avi-Tag, this was expressed and purified from the yeast P. pastoris expression system. The Avi-tag was then biotinylated in vitro by recombinantly expressed BirA. Emulsions of a specific size were formed using the newly biotinylated Bt-BSA protein and functionalized with a cascade of components to mimic cell-cell ligands, this resulted in bioemulsions with a bioactive surface that can interact with surrounding cells. These functionalised droplets were integrated into developing cerebral organoids and their impact on phenotype was studied. The droplets were found not to deform sufficiently to allow mechanical forces to be measured, yet the many of these droplets were retained within the organoids which led to an interesting phenotype within the organoids. The developing rosettes were found to develop enlarged lumens shown by an increase in area, this phenotype did not impact the differentiation into the cerebral lineage depicted by immunohistochemistry of hallmark marker of neuronal differentiation within organoids retaining droplets. The interfacial mechanics of fibrinogen nanosheets treated with varying concentrations of thrombin was studied using interfacial shear rheology. The effect of thrombin significantly altered the interfacial mechanics with the lower concentration of thrombin significantly increasing the toughness multiple folds and decreasing the elasticity of the nanosheets. Additionally, the nanostructure of nanosheets was studied using SEM and TEM and traditional fibrin fibres were found to not form at these interfaces, but local rearrangements and retractions in the thrombin treated nanosheets were observed. Finally, these enhanced mechanical properties promoted the proliferation and expansion of Mesenchymal stem cells on quasi-2D and 3D interfaces

    The development of liquid crystal lasers for application in fluorescence microscopy

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    Lasers can be found in many areas of optical medical imaging and their properties have enabled the rapid advancement of many imaging techniques and modalities. Their narrow linewidth, relative brightness and coherence are advantageous in obtaining high quality images of biological samples. This is particularly beneficial in fluorescence microscopy. However, commercial imaging systems depend on the combination of multiple independent laser sources or use tuneable sources, both of which are expensive and have large footprints. This thesis demonstrates the use of liquid crystal (LC) laser technology, a compact and portable alternative, as an exciting candidate to provide a tailorable light source for fluorescence microscopy. Firstly, to improve the laser performance parameters such that high power and high specification lasers could be realised; device fabrication improvements were presented. Studies exploring the effect of alignment layer rubbing depth and the device cell gap spacing on laser performance were conducted. The results were the first of their kind and produced advances in fabrication that were critical to repeatedly realising stable, single-mode LC laser outputs with sufficient power to conduct microscopy. These investigations also aided with the realisation of laser diode pumping of LC lasers. Secondly, the identification of optimum dye concentrations for single and multi-dye systems were used to optimise the LC laser mixtures for optimal performance. These investigations resulted in novel results relating to the gain media in LC laser systems. Collectively, these advancements yielded lasers of extremely low threshold, comparable to the lowest reported thresholds in the literature. A portable LC laser system was integrated into a microscope and used to perform fluorescence microscopy. Successful two-colour imaging and multi-wavelength switching ability of LC lasers were exhibited for the first time. The wavelength selectivity of LC lasers was shown to allow lower incident average powers to be used for comparable image quality. Lastly, wavelength selectivity enabled the LC laser fluorescence microscope to achieve high enough sensitivity to conduct quantitative fluorescence measurements. The development of LC lasers and their suitability to fluorescence microscopy demonstrated in this thesis is hoped to push towards the realisation of commercialisation and application for the technology

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Stimuli-responsive hydrogels: smart state of-the-art platforms for cardiac tissue engineering

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    Biomedicine and tissue regeneration have made significant advancements recently, positively affecting the whole healthcare spectrum. This opened the way for them to develop their applications for revitalizing damaged tissues. Thus, their functionality will be restored. Cardiac tissue engineering (CTE) using curative procedures that combine biomolecules, biomimetic scaffolds, and cells plays a critical part in this path. Stimuli-responsive hydrogels (SRHs) are excellent three-dimensional (3D) biomaterials for tissue engineering (TE) and various biomedical applications. They can mimic the intrinsic tissues’ physicochemical, mechanical, and biological characteristics in a variety of ways. They also provide for 3D setup, adequate aqueous conditions, and the mechanical consistency required for cell development. Furthermore, they function as competent delivery platforms for various biomolecules. Many natural and synthetic polymers were used to fabricate these intelligent platforms with innovative enhanced features and specialized capabilities that are appropriate for CTE applications. In the present review, different strategies employed for CTE were outlined. The light was shed on the limitations of the use of conventional hydrogels in CTE. Moreover, diverse types of SRHs, their characteristics, assembly and exploitation for CTE were discussed. To summarize, recent development in the construction of SRHs increases their potential to operate as intelligent, sophisticated systems in the reconstruction of degenerated cardiac tissues

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

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    According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

    University of Windsor Graduate Calendar 2023 Winter

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp

    Laser cladding and its potential to reduce particulate matter emissions from the automotive and locomotive sector

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    Laser Cladding (LC) is an emerging technology which is used both for coating applications as well as near-net shape fabrication. Despite its significant advantages, such as low dilution and metallurgical bond with the substrate, it still faces issues such as process control and repeatability, which restricts the extension to its applications. The following thesis evaluates the LC technology and tests its potential to be applied to reduce particulate matter emissions from the automotive and locomotive sector. The evaluation of LC technology was carried out for the deposition of multi-layer and multi-track coatings. 316L stainless steel coatings were deposited to study the minimisation of geometric distortions in thin-walled samples. Laser power, as well as scan strategy, were the main variables to achieve this goal. The use of constant power, reduction at successive layers, a control loop control system, and two different scan strategies were studied. The closed-loop control system was found to be practical only when coupled with the correct scan strategy for the deposition of thin walls. Three overlapped layers of aluminium bronze were deposited onto a structural steel pipe for multitrack coatings. The effect of laser power, scan speed and hatch distance on the final geometry of coating were studied independently, and a combined parameter was established to effectively control each geometrical characteristic (clad width, clad height and percentage of dilution). LC was then applied to coat commercial GCI brake discs with tool steel. The optical micrography showed that even with preheating, the cracks that originated from the substrate towards the coating were still present. The commercial brake discs emitted airborne particles whose concentration and size depended on the test conditions used for simulation in the laboratory. The contact of LC cladded wheel with rail emitted significantly less ultra-fine particles while maintaining the acceptable values of coefficient of friction
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