SHEEP AS ANIMAL MODEL IN MINIMALLY INVASIVE NEUROSURGERY IN EDEN2020

Glioblastomas (GBMs) is a malignant type of central nervous system tumours and its presentation is almost 80% of all malignant primary brain neoplasia. This kind of tumour is highly invasive infiltrating the white matter area and is confined to the central nervous with a very poor patient outcome survival around 10 months. Of the existing treatment approaches, Convection Enhanced drug Delivery (CED) offers several advantages for the patient but still suffers from significant shortcomings. Enhanced Delivery Ecosystem for Neurosurgery in 2020 (EDEN2020) is a European project supported with a new catheter development as the key project point in an integrated technology platform for minimally invasive neurosurgery. Due to the particular anatomy and size, sheep (Ovis aries) have been selected as experimental large animal model and a new Head Frame system MRI/CT compatible has been made and validated ad hoc for the project. In order to understand experimentally the best target point for the catheter introduction a sheep brain DTI atlas has been created. Corticospinal tract (CST), corpus callosum (CC), fornix (FX), visual pathway (VP) and occipitofrontal fascicle (OF), have been identified bilaterally for all the animals. Three of these white matter tracts, the corpus callosum, the fornix and the corona radiata, have been selected to understand the drugs diffusion properties and create a computational model of diffusivity inside the white matter substance. The analysis have been conducted via Focused Ion Beam using scanning Electron Microscopy combined with focused ion beam milling and a 2D analysis and 3D reconstruction made. The results showed homogeneous myelination via detection of ~40% content of lipids in all the different fibre tracts and the fibrous organisation of the tissue described as composite material presenting elliptical tubular fibres with an average cross-sectional area of circa 0.52\u3bcm2 and an estimated mean diameter of 1.15\u3bcm. Finally, as the project is currently ongoing, we provided an overview on the future experimental steps focalised on the brain tissue damage after the rigid catheter introduction

Design, Synthesis and Analysis of Self-Assembling Triangulated Wireframe DNA Structures

The field of DNA nanotechnology offers a wide range of design strategies with which nanometer-sized structures with a desired shape, size and aspect ratio can be built. The most established techniques in the field rely on close-packed 'solid' DNA nanostructures produced with either the DNA origami or the single-stranded tile techniques. These structures depend on high-salt buffer solutions and require more material than comparable size hollow wireframe structures. This dissertation explores the construction of hollow wireframe DNA nanostructures composed of equilateral triangles. To achieve maximal material efficiency the design is restricted to use a single DNA double helix per triangle edge. As a proof of principle, the DNA origami technique is extended to produce a series of truss structures including the flat, tetrahedral, octahedral, or irregular dodecahedral truss designs. In contrast to close packed DNA origami designs these structures fold at low-salt buffer conditions. These structures have defined cavities that may in the future be used to precisely position functional elements such as metallic nanoparticles or enzymes. The design process of these structures is simplified by a custom design software. Next, the triangulated construction motif is extended to the single-stranded DNA tile technique. A collection of finite structures, as well as one-dimensional crystalline assemblies is explored. The ideal assembly conditions are determined experimentally and using molecular dynamics simulations. A custom design software is presented to simplify the design and handling of these structures. At last, the cost-effective prototyping of triangulated wireframe DNA origami structures is explored. This is achieved through the introduction of single-stranded “gap” regions along the triangle edges. These gap regions are then filled using a DNA polymerase rather than by synthetic oligonucleotides. This technique also allows the mechanical transformation of these structures, which is exemplified by the transition of a bent into a straight structure upon completion of the gap filling.:Abstract v Publications vii Acknowledgements ix Contents xi Chapter 1 A short introduction into DNA nanotechnology 1 1.1 Nanotechnology 1 1.1.1 Top down 1 1.1.2 Bottom up 3 1.2 Deoxyribonucleic acid (DNA) 4 1.3 DNA Nanotechnology 6 1.3.1 Tile based assembly 9 1.3.2 DNA origami and single-stranded tiles 10 1.3.3 Some applications of DNA nanotechnology 12 1.3.4 Wireframe structures 15 1.3.5 Computational tools and DNA nanotechnology. 17 Chapter 2 Motivation and objectives 19 Chapter 3 Design and Synthesis of Triangulated DNA Origami Trusses 20 3.1 Introduction 20 3.2 Results and Discussion 21 3.2.1 Design 21 3.2.2 Nomenclature and parameters of the tube structures 23 3.2.3 Gel electrophoreses analysis 25 3.2.4 Imaging of the purified structures 26 3.2.5 Optimizing the folding conditions 28 3.2.6 Comparison to vHelix 29 3.3 Conclusions 29 3.4 Methods 30 3.4.1 Standard DNA origami assembly reaction. 30 3.4.2 Gel purification. 30 3.4.3 AFM sample preparation. 31 3.4.4 TEM sample preparation. 31 3.4.5 Instructions for mixing the staple sets. 31 Chapter 4 Triangulated wireframe structures assembled using single-stranded DNA tiles 33 4.1 Introduction 33 4.2 Results and Discussion 35 4.2.1 Designing the structures 35 4.2.2 Synthesis of test structures 37 4.2.3 Molecular dynamics simulations of 6-arm junctions 38 4.2.4 Assembly of the finite structures 40 4.2.5 Influence of salt concentration and folding times 42 4.2.6 Molecular dynamics simulations of the rhombus structure 43 4.2.7 1D SST crystals 44 4.2.8 Controlling the crystal growth 46 4.3 Conclusions 48 4.4 Methods 49 4.4.1 SST Folding 49 4.4.2 Agarose Gel Electrophoresis 49 4.4.3 tSEM Characterization 49 4.4.4 AFM Imaging 49 4.4.5 AGE-Based Folding-Yield Estimation 49 4.4.6 Molecular Dynamics Simulations 50 Chapter 5 Structural transformation of wireframe DNA origami via DNA polymerase assisted gap-filling 52 5.1 Introduction 52 5.2 Results and Discussion 54 5.2.1 Design of the Structures 54 5.2.2 Folding of Gap-Structures 56 5.2.3 Inactivation of Polymerase. 57 5.2.4 Secondary Structures. 58 5.2.5 Folding Kinetics of Gap Origami. 60 5.3 Conclusions 61 5.4 Methods 62 5.4.1 DNA origami folding 62 5.4.2 Gap filling of the wireframe DNA origami structures 63 5.4.3 Agarose gel electrophoresis 63 5.4.4 PAGE gel analysis 63 5.4.5 tSEM characterization 64 5.4.6 AFM imaging 64 5.4.7 AGE based folding-yield estimation 64 5.4.8 Gibbs free energy simulation using mfold 65 5.4.9 List of sequence for folding the DNA origami triangulated structures 65 Chapter 6 Summary and outlook 67 Appendix 69 A.1 Additional figures from chapter 369 A.2 Additional figures from chapter 4 77 A.3 Additional figures from chapter 5 111 Bibliography 127 Erklärung 13

Computational modelling of angiogenesis: The importance of cell rearrangements during vascular growth

Angiogenesis is the process wherein endothelial cells (ECs) form sprouts that elongate from the pre-existing vasculature to create new vascular networks. In addition to its essential role in normal development, angiogenesis plays a vital role in pathologies such as cancer, diabetes and atherosclerosis. Mathematical and computational modelling has contributed to unravelling its complexity. Many existing theoretical models of angiogenic sprouting are based on the 'snail-trail' hypothesis. This framework assumes that leading ECs positioned at sprout tips migrate towards low-oxygen regions while other ECs in the sprout passively follow the leaders' trails and proliferate to maintain sprout integrity. However, experimental results indicate that, contrary to the snail-trail assumption, ECs exchange positions within developing vessels, and the elongation of sprouts is primarily driven by directed migration of ECs. The functional role of cell rearrangements remains unclear. This review of the theoretical modelling of angiogenesis is the first to focus on the phenomenon of cell mixing during early sprouting. We start by describing the biological processes that occur during early angiogenesis, such as phenotype specification, cell rearrangements and cell interactions with the microenvironment. Next, we provide an overview of various theoretical approaches that have been employed to model angiogenesis, with particular emphasis on recent in silico models that account for the phenomenon of cell mixing. Finally, we discuss when cell mixing should be incorporated into theoretical models and what essential modelling components such models should include in order to investigate its functional role.Comment: 26 pages, 9 figures, 1 table. Submitted for publication to WIREs Mechanisms of Diseas

Liquid Phase Electron Microscopy of Soft Specimens

In the last decade, liquid-phase electron microscopy (LPEM) has provided a new strategy for investigating samples immersed in their media at the nanoscale.1–8 The main focus of previous research have mainly revolved around inorganic matter (e.g. metallic nanoparticles);9 nonetheless, the field of soft materials, classified as organic synthetic (i.e. polymers and gels), and biological (i.e. membranes and protein) structures have rapidly grown interest in LPEM to study fundamental questions.2 Soft materials deform easily or undergo dynamic changes by thermal fluctuations and external forces. Despite the great advantages LPEM provides, electron beam damage and image contrast present still an issue, particularly in sensitive samples. New technological and methodological advances may attenuate these issues. There is a need to employ these advancements to develop strategies to image soft materials. This thesis focuses on the development of methodologies for the investigation of soft materials using LPEM. Amongst the different conducted studies, there are three main sections of focus: (i) the reconstruction of three-dimensional (3D) structures via Brownian tomography (BT) and Brownian particle analysis (BPA), enabling the investigation of the 3D conformational space of single unit of the specimen, via BT, and an average reconstruction of several specimens, via BPA; (ii) the dynamic studies of biological and synthetic soft materials, specifically oxidant-sensitive polymeric micelles and viruses, focusing on their disassembly via external factors, reactive-oxygen species (ROS) and virucidal nanoparticles respectively; and (iii) the imaging of intracellular ultrastructure via organometallic, cyclometalated complexes for intracellular targeting, particularly actin and nuclear DNA, via correlative light and liquid phase electron microscopy (CLLEM)

Multigranularity Representations for Human Inter-Actions: Pose, Motion and Intention

Tracking people and their body pose in videos is a central problem in computer vision. Standard tracking representations reason about temporal coherence of detected people and body parts. They have difficulty tracking targets under partial occlusions or rare body poses, where detectors often fail, since the number of training examples is often too small to deal with the exponential variability of such configurations. We propose tracking representations that track and segment people and their body pose in videos by exploiting information at multiple detection and segmentation granularities when available, whole body, parts or point trajectories. Detections and motion estimates provide contradictory information in case of false alarm detections or leaking motion affinities. We consolidate contradictory information via graph steering, an algorithm for simultaneous detection and co-clustering in a two-granularity graph of motion trajectories and detections, that corrects motion leakage between correctly detected objects, while being robust to false alarms or spatially inaccurate detections. We first present a motion segmentation framework that exploits long range motion of point trajectories and large spatial support of image regions. We show resulting video segments adapt to targets under partial occlusions and deformations. Second, we augment motion-based representations with object detection for dealing with motion leakage. We demonstrate how to combine dense optical flow trajectory affinities with repulsions from confident detections to reach a global consensus of detection and tracking in crowded scenes. Third, we study human motion and pose estimation. We segment hard to detect, fast moving body limbs from their surrounding clutter and match them against pose exemplars to detect body pose under fast motion. We employ on-the-fly human body kinematics to improve tracking of body joints under wide deformations. We use motion segmentability of body parts for re-ranking a set of body joint candidate trajectories and jointly infer multi-frame body pose and video segmentation. We show empirically that such multi-granularity tracking representation is worthwhile, obtaining significantly more accurate multi-object tracking and detailed body pose estimation in popular datasets

Homogeneity based segmentation and enhancement of Diffusion Tensor Images : a white matter processing framework

In diffusion magnetic resonance imaging (DMRI) the Brownian motion of the water molecules, within biological tissue, is measured through a series of images. In diffusion tensor imaging (DTI) this diffusion is represented using tensors. DTI describes, in a non-invasive way, the local anisotropy pattern enabling the reconstruction of the nervous fibers - dubbed tractography. DMRI constitutes a powerful tool to analyse the structure of the white matter within a voxel, but also to investigate the anatomy of the brain and its connectivity. DMRI has been proved useful to characterize brain disorders, to analyse the differences on white matter and consequences in brain function. These procedures usually involve the virtual dissection of white matters tracts of interest. The manual isolation of these bundles requires a great deal of neuroanatomical knowledge and can take up to several hours of work. This thesis focuses on the development of techniques able to automatically perform the identification of white matter structures. To segment such structures in a tensor field, the similarity of diffusion tensors must be assessed for partitioning data into regions, which are homogeneous in terms of tensor characteristics. This concept of tensor homogeneity is explored in order to achieve new methods for segmenting, filtering and enhancing diffusion images. First, this thesis presents a novel approach to semi-automatically define the similarity measures that better suit the data. Following, a multi-resolution watershed framework is presented, where the tensor field’s homogeneity is used to automatically achieve a hierarchical representation of white matter structures in the brain, allowing the simultaneous segmentation of different structures with different sizes. The stochastic process of water diffusion within tissues can be modeled, inferring the homogeneity characteristics of the diffusion field. This thesis presents an accelerated convolution method of diffusion images, where these models enable the contextual processing of diffusion images for noise reduction, regularization and enhancement of structures. These new methods are analysed and compared on the basis of their accuracy, robustness, speed and usability - key points for their application in a clinical setting. The described methods enrich the visualization and exploration of white matter structures, fostering the understanding of the human brain