Uncertainty visualization in 3D scalar data

One problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.

Nanowires for 3d silicon interconnection – low temperature compliant nanowire-polymer film for z-axis interconnect

Semiconductor chip packaging has evolved from single chip packaging to 3D heterogeneous system integration using multichip stacking in a single module. One of the key challenges in 3D integration is the high density interconnects that need to be formed between the chips with through-silicon-vias (TSVs) and inter-chip interconnects. Anisotropic Conductive Film (ACF) technology is one of the low-temperature, fine-pitch interconnect method, which has been considered as a potential replacement for solder interconnects in line with continuous scaling of the interconnects in the IC industry. However, the conventional ACF materials are facing challenges to accommodate the reduced pad and pitch size due to the micro-size particles and the particle agglomeration issue. A new interconnect material - Nanowire Anisotropic Conductive Film (NW-ACF), composed of high density copper nanowires of ~ 200 nm diameter and 10-30 µm length that are vertically distributed in a polymeric template, is developed in this work to tackle the constrains of the conventional ACFs and serves as an inter-chip interconnect solution for potential three-dimensional (3D) applications.

Gene delivery for the treatment of prostate cancer

Prostate cancer is one of the most common cancers diagnosed in men. Whilst treatments for early-stage disease are largely effective, current therapies for metastatic prostate cancer, particularly for bone metastasis, offer only a few months increased lifespan at best. Hence new treatments are urgently required. Small interfering RNA (siRNA) has been investigated for the treatment of prostate cancer where it can ‘silence’ specific cancer-related genes. However the clinical application of siRNA-based gene therapy is limited due to the absence of an optimised gene delivery vector. The optimisation of such gene delivery vectors is routinely undertaken in vitro using 2D cell culture on plastic dishes which does not accurately simulate the in vivo bone cancer metastasis microenvironment. The goal of this thesis was to assess the potential of two different targeted delivery vectors (gold or modified β-cyclodextrin derivatives) to facilitate siRNA receptor-mediated uptake into prostate cancer cells. Furthermore, this project aimed to develop a more physiologically relevant 3D in vitro cell culture model, to mimic prostate cancer bone metastasis, which is suitable for evaluating the delivery of nanoparticulate gene therapeutics. In the first instance, cationic derivatives of gold and β-cyclodextrin were synthesized to complex anionic siRNA. The delivery vectors were targeted to prostate cancer cells using the anisamide ligand which has high affinity for the sigma receptor that is overexpressed by prostate cancer cells. The gold nanoparticle demonstrated high levels of uptake into prostate cancer PC3 cells and efficient gene silencing when transfection was performed in serum-free media. However, due to the absence of a poly(ethylene glycol) (PEG) stabilising group, the formulation was unsuitable for use in serum-containing conditions. Conversely, the modified β-cyclodextrin formulation demonstrated enhanced stability in the presence of serum due to the inclusion of a PEG chain onto which the anisamide ligand was conjugated. However, the maximum level of gene silencing efficacy from three different prostate cancer cell lines (DU145, VCaP and PC3 cells) was 30 %, suggesting that further optimisation of the formulation would be required prior to application in vivo. In order to develop a more physiologically-relevant in vitro model of prostate cancer bone metastasis, prostate cancer cells (PC3 and LNCaP cells) were cultured in 3D on collagenbased scaffolds engineered to mimic the bone microenvironment. While the model was suitable for assessing nanoparticle-mediated gene knockdown, prostate cancer cells demonstrated a phenotype with lower invasive potential when grown on the scaffolds relative to standard 2D cell culture. Hence, prostate cancer cells (PC3 and LNCaP cells) were subsequently co-cultured with bone osteoblast cells (hFOB 1.19 cells) to enhance the physiological relevance of the model. Co-cultures secreted elevated levels of the MMP9 enzyme, a marker of prostate cancer metastasis, relative to prostate cancer cell monocultures (2D and 3D) indicating enhanced physiological relevance of the model. Furthermore, the coculture model proved suitable for investigating nanoparticle-mediated gene silencing. In conclusion, the work outlined in this thesis identified two different sigma receptor-targeted gene delivery vectors with potential for the treatment of prostate cancer. In addition, a more physiologically relevant model of prostate cancer bone metastasis was developed with the capacity to help optimise gene delivery vectors for the treatment of prostate cancer.