Model-based Curvilinear Network Extraction and Tracking toward Quantitative Analysis of Biopolymer Networks

Curvilinear biopolymer networks pervade living systems. They are routinely imaged by fluorescence microscopy to gain insight into their structural, mechanical, and dynamic properties. Image analysis can facilitate understanding the mechanisms of their formation and their biological functions from a quantitative viewpoint. Due to the variability in network geometry, topology and dynamics as well as often low resolution and low signal-to-noise ratio in images, segmentation and tracking networks from these images is challenging. In this dissertation, we propose a complete framework for extracting the geometry and topology of curvilinear biopolymer networks, and also tracking their dynamics from multi-dimensional images. The proposed multiple Stretching Open Active Contours (SOACs) can identify network centerlines and junctions, and infer plausible network topology. Combined with a $k$-partite matching algorithm, temporal correspondences among all the detected filaments can be established. This work enables statistical analysis of structural parameters of biopolymer networks as well as their dynamics. Quantitative evaluation using simulated and experimental images demonstrate its effectiveness and efficiency. Moreover, a principled method of optimizing key parameters without ground truth is proposed for attaining the best extraction result for any type of images. The proposed methods are implemented into a usable open source software ``SOAX\u27\u27. Besides network extraction and tracking, SOAX provides a user-friendly cross-platform GUI for interactive visualization, manual editing and quantitative analysis. Using SOAX to analyze several types of biopolymer networks demonstrates the potential of the proposed methods to help answer key questions in cell biology and biophysics from a quantitative viewpoint

3D Bioprinting Hydrogel for Tissue Engineering an Ascending Aortic Scaffold

The gold standard in 2016 for thoracic aortic grafts is Dacron®, polyethylene terephthalate, due to the durability over time, the low immune response elicited and the propensity for endothelialization of the graft lumen over time. These synthetic grafts provide reliable materials that show remarkable long term patency. Despite the acceptable performance of Dacron® grafts, it is noted that autographs still outperform other types of vascular grafts when available due to recognition of the host\u27s cells and adaptive mechanical properties of a living graft. 3-D bioprinting patient-specific scaffolds for tissue engineering (TE) brings the benefits of non-degrading synthetic grafts and autologous grafts together by constructing a synthetic scaffold that supports cell infiltration, adhesion, and development in order to promote the cells to build the native extracellular matrix in response to biochemical and physical cues. Using the BioBots 3-D bioprinter, scaffold materials we tested non-Newtonian photosensitive hydrogel that formed a crosslinked matrix under 365 nm UV light with appropriate water content and mechanical properties for cell infiltration and adhesion to the bioprinted scaffold. Viscometry data on the PEGDA-HPMC 15%-2% w/v hydrogel (non-Newtonian behavior) informed CFD simulation of the extrusion system in order to exact the pressure-flow rate relationship for every hydrogel and geometry combination. Surface tension data and mechanical properties were obtained from material testing and provide content to further characterize each hydrogel and resulting crosslinked scaffold. The goal of this work was to create a basis to build a database of hydrogels with corresponding print settings and resulting mechanical properties in order to progress the field of tissue engineered vascular grafts fabricated by nozzle-based rapid prototyping

AI-based design methodologies for hot form quench (HFQ®)

This thesis aims to develop advanced design methodologies that fully exploit the capabilities of the Hot Form Quench (HFQ®) stamping process in stamping complex geometric features in high-strength aluminium alloy structural components. While previous research has focused on material models for FE simulations, these simulations are not suitable for early-phase design due to their high computational cost and expertise requirements. This project has two main objectives: first, to develop design guidelines for the early-stage design phase; and second, to create a machine learning-based platform that can optimise 3D geometries under hot stamping constraints, for both early and late-stage design. With these methodologies, the aim is to facilitate the incorporation of HFQ capabilities into component geometry design, enabling the full realisation of its benefits. To achieve the objectives of this project, two main efforts were undertaken. Firstly, the analysis of aluminium alloys for stamping deep corners was simplified by identifying the effects of corner geometry and material characteristics on post-form thinning distribution. New equation sets were proposed to model trends and design maps were created to guide component design at early stages. Secondly, a platform was developed to optimise 3D geometries for stamping, using deep learning technologies to incorporate manufacturing capabilities. This platform combined two neural networks: a geometry generator based on Signed Distance Functions (SDFs), and an image-based manufacturability surrogate model. The platform used gradient-based techniques to update the inputs to the geometry generator based on the surrogate model's manufacturability information. The effectiveness of the platform was demonstrated on two geometry classes, Corners and Bulkheads, with five case studies conducted to optimise under post-stamped thinning constraints. Results showed that the platform allowed for free morphing of complex geometries, leading to significant improvements in component quality. The research outcomes represent a significant contribution to the field of technologically advanced manufacturing methods and offer promising avenues for future research. The developed methodologies provide practical solutions for designers to identify optimal component geometries, ensuring manufacturing feasibility and reducing design development time and costs. The potential applications of these methodologies extend to real-world industrial settings and can significantly contribute to the continued advancement of the manufacturing sector.Open Acces

Video object segmentation and tracking.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2005One of the more complex video processing problems currently vexing researchers is that of object segmentation. This involves identifying semantically meaningful objects in a scene and separating them from the background. While the human visual system is capable of performing this task with minimal effort, development and research in machine vision is yet to yield techniques that perform the task as effectively and efficiently. The problem is not only difficult due to the complexity of the mechanisms involved but also because it is an ill-posed problem. No unique segmentation of a scene exists as what is of interest as a segmented object depends very much on the application and the scene content. In most situations a priori knowledge of the nature of the problem is required, often depending on the specific application in which the segmentation tool is to be used. This research presents an automatic method of segmenting objects from a video sequence. The intent is to extract and maintain both the shape and contour information as the object changes dynamically over time in the sequence. A priori information is incorporated by requesting the user to tune a set of input parameters prior to execution of the algorithm. Motion is used as a semantic for video object extraction subject to the assumption that there is only one moving object in the scene and the only motion in the video sequence is that of the object of interest. It is further assumed that there is constant illumination and no occlusion of the object. A change detection mask is used to detect the moving object followed by morphological operators to refine the result. The change detection mask yields a model of the moving components; this is then compared to a contour map of the frame to extract a more accurate contour of the moving object and this is then used to extract the object of interest itself. Since the video object is moving as the sequence progresses, it is necessary to update the object over time. To accomplish this, an object tracker has been implemented based on the Hausdorff objectmatching algorithm. The dissertation begins with an overview of segmentation techniques and a discussion of the approach used in this research. This is followed by a detailed description of the algorithm covering initial segmentation, object tracking across frames and video object extraction. Finally, the semantic object extraction results for a variety of video sequences are presented and evaluated

Non-Newtonian Microfluidics

Microfluidics has seen a remarkable growth over recent decades, with its extensive applications in engineering, medicine, biology, chemistry, etc. Many of these real applications of microfluidics involve the handling of complex fluids, such as whole blood, protein solutions, and polymeric solutions, which exhibit non-Newtonian characteristics—specifically viscoelasticity. The elasticity of the non-Newtonian fluids induces intriguing phenomena, such as elastic instability and turbulence, even at extremely low Reynolds numbers. This is the consequence of the nonlinear nature of the rheological constitutive equations. The nonlinear characteristic of non-Newtonian fluids can dramatically change the flow dynamics, and is useful to enhance mixing at the microscale. Electrokinetics in the context of non-Newtonian fluids are also of significant importance, with their potential applications in micromixing enhancement and bio-particles manipulation and separation. In this Special Issue, we welcomed research papers, and review articles related to the applications, fundamentals, design, and the underlying mechanisms of non-Newtonian microfluidics, including discussions, analytical papers, and numerical and/or experimental analyses

Inorganic micro/nanostructures-based high-performance flexible electronics for electronic skin application

Electronics in the future will be printed on diverse substrates, benefiting several emerging applications such as electronic skin (e-skin) for robotics/prosthetics, flexible displays, flexible/conformable biosensors, large area electronics, and implantable devices. For such applications, electronics based on inorganic micro/nanostructures (IMNSs) from high mobility materials such as single crystal silicon and compound semiconductors in the form of ultrathin chips, membranes, nanoribbons (NRs), nanowires (NWs) etc., offer promising high-performance solutions compared to conventional organic materials. This thesis presents an investigation of the various forms of IMNSs for high-performance electronics. Active components (from Silicon) and sensor components (from indium tin oxide (ITO), vanadium pentaoxide (V2O5), and zinc oxide (ZnO)) were realised based on the IMNS for application in artificial tactile skin for prosthetics/robotics. Inspired by human tactile sensing, a capacitive-piezoelectric tandem architecture was realised with indium tin oxide (ITO) on a flexible polymer sheet for achieving static (upto 0.25 kPa-1 sensitivity) and dynamic (2.28 kPa-1 sensitivity) tactile sensing. These passive tactile sensors were interfaced in extended gate mode with flexible high-performance metal oxide semiconductor field effect transistors (MOSFETs) fabricated through a scalable process. The developed process enabled wafer scale transfer of ultrathin chips (UTCs) of silicon with various devices (ultrathin chip resistive samples, metal oxide semiconductor (MOS) capacitors and n‐channel MOSFETs) on flexible substrates up to 4″ diameter. The devices were capable of bending upto 1.437 mm radius of curvature and exhibited surface mobility above 330 cm2/V-s, on-to-off current ratios above 4.32 decades, and a subthreshold slope above 0.98 V/decade, under various bending conditions. While UTCs are useful for realizing high-density high-performance micro-electronics on small areas, high-performance electronics on large area flexible substrates along with low-cost fabrication techniques are also important for realizing e-skin. In this regard, two other IMNS forms are investigated in this thesis, namely, NWs and NRs. The controlled selective source/drain doping needed to obtain transistors from such structure remains a bottleneck during post transfer printing. An attractive solution to address this challenge based on junctionless FETs (JLFETs), is investigated in this thesis via technology computer-aided design (TCAD) simulation and practical fabrication. The TCAD optimization implies a current of 3.36 mA for a 15 μm channel length, 40 μm channel width with an on-to-off ratio of 4.02x 107. Similar to the NRs, NWs are also suitable for realizing high performance e-skin. NWs of various sizes, distribution and length have been fabricated using various nano-patterning methods followed by metal assisted chemical etching (MACE). Synthesis of Si NWs of diameter as low as 10 nm and of aspect ratio more than 200:1 was achieved. Apart from Si NWs, V2O5 and ZnO NWs were also explored for sensor applications. Two approaches were investigated for printing NWs on flexible substrates namely (i) contact printing and (ii) large-area dielectrophoresis (DEP) assisted transfer printing. Both approaches were used to realize electronic layers with high NW density. The former approach resulted in 7 NWs/μm for bottom-up ZnO and 3 NWs/μm for top-down Si NWs while the latter approach resulted in 7 NWs/μm with simultaneous assembly on 30x30 electrode patterns in a 3 cm x 3 cm area. The contact-printing system was used to fabricate ZnO and Si NW-based ultraviolet (UV) photodetectors (PDs) with a Wheatstone bridge (WB) configuration. The assembled V2O5 NWs were used to realize temperature sensors with sensitivity of 0.03% /K. The sensor arrays are suitable for tactile e-skin application. While the above focuses on realizing conventional sensing and addressing elements for e-skin, processing of a large amount of data from e-skin has remained a challenge, especially in the case of large area skin. A Neural NW Field Effect Transistors (υ-NWFETs) based hardware-implementable neural network (HNN) approach for tactile data processing in e-skin is presented in the final part of this thesis. The concept is evaluated by interfacing with a fabricated kirigami-inspired e-skin. Apart from e-skin for prosthetics and robotics, the presented research will also be useful for obtaining high performance flexible circuits needed in many futuristic flexible electronics applications such as smart surgical tools, biosensors, implantable electronics/electroceuticals and flexible mobile phones

Reappraisal of transcallosal neuron organization in mice and evaluation of their dendritic remodeling and circuit integration following traumatic brain injury

Traumatic Brain Injury (TBI) is an enormous global socio-economic burden since, apart from its high death rate, it is the primary cause of coma worldwide and a prevalent cause of long-term disability. Until today there is no established treatment for dealing with the long-term outcomes of TBI despite many years of research. Although a lot is known about the pathophysiology of TBI in the damaged tissue and the surrounding area in case of focal lesion, only few studies have investigated the structural and functional integrity of the contralateral intact cortex. In order to explore this territory, this study employs a well-established and widely used animal model of focal open skull TBI known as the Controlled Cortical Impact (CCI) model. The first aim of this study was to systematically characterize a specific neuronal population, the transcallosal projection neurons, as they are the ones connecting the intact cortex with the lesioned cortex. The description of the organization of transcallosal neurons and their axonal projections at the contralateral hemisphere was carried out in healthy, non-injured C57Bl6 mice. Retrograde and anterograde tracing methods were implemented to label transcallosal cell bodies and their axonal projections, respectively. In addition, different injection coordinates were used in order to label transcallosal connections at distinct brain regions, including the motor cortex (M1), somatosensory cortex (S1), and barrel cortex, rostral and caudal to Bregma. In agreement with previous research, I observed that transcallosal projections are organized homotopically across the various brain regions, with the axonal terminals spanning the entire cortical column. Interestingly my study describes for the first time a non-negligible fraction of heterotopic transcallosal neurons that, in addition, display a slightly less strict layer distribution pattern compared to the homotopic ones. After the initial characterization of transcallosal neuron organization, I proceeded by investigating how these neurons with projections at the injury site are affected at various timepoints following focal TBI. I used GFPM mice to visualize dendrites and spines of transcallosal and non-transcallosal neurons, in order to examine their structural integrity at different timepoints post-injury. I detected significant differences in dendritic spine density and morphology between controls and injured mice, which were time-dependent. More specifically, the dendritic spine density in transcallosal neurons was strongly decreased as soon as 7days following injury. Interestingly, spine density in non-transcallosal neurons was not changed following TBI. In terms of spine shape, I found a morphological shift only for the apical tuft segments. These results point towards a general sensitivity of transcallosal spines to TBI-induced damage, where loss of spines (preferentially mature) seems to take place at 1-2 weeks post-injury and resolve at 3-6 weeks post-injury, indicative of late plasticity processes. As the anatomically connected neuronal population seems to recover overtime I then decided to further explore whether transcallosal circuit remodeling takes place after TBI. To do so I used the retrograde mono-trans-synaptic tracer SADΔG-GFP (EnvA) Rabies virus. In that way, I was able to distinctively label transcallosal neurons and their presynaptic partners and obtain an overview of the presynaptic population throughout the cortex across brain regions at different post-injury timepoints. This study demonstrates that spine plasticity did not result in adaptive circuit plasticity with the recruitment of other brain regions but rather that initial circuits were re-established. In brief, during this thesis I have demonstrated the adaptive plastic capacities of anatomically connected neurons to the brain injury. I believe that this knowledge may help in unraveling further compensatory plastic mechanisms that could then be therapeutically targeted to improve the outcome following brain injury