In the past forty years advancements in fluorescence-based methods including imaging (e.g. confocal and multi-photon) and quantitative spectroscopies (e.g. Fluorescence Correlation Spectroscopy) have been applied to systems ranging from solutions to in vivo models: such methods possess the ability to monitor fluorescence intensity fluctuations and offer the potential to unravel biophysical and biochemical phenomena. A major disadvantage associated with these methods is their ever-increasing cost resulting in the development of image analysis tools that offer the potential to exploit hidden information contained in confocal images.The hypothesis pertaining to this thesis is that image analysis tools developed in recent years exemplified by Raster Image Correlation Spectroscopy (RICS), Spatial Intensity Distribution Analysis (SpIDA) and Fluorescence Intensity Gaussian Mixture Model Analysis (FIGMMA) will provide a new insight into current pharmaceutical problems. The application of these methods to the quantification of protein aggregation, monomer/dimer equilibria, p-glycoprotein efflux activity and transcytosis are presented in this thesis.Protein aggregation poses a major challenge to the biotechnology industry which currently lacks analytical capabilities to profile broad particle size ranges. An in-house RICS (ManICS) software was validated against Dynamic Light Scattering and Fluorescence Correlation Spectroscopy (FCS) to determine Bovine Serum Albumin (BSA) aggregate population distributions under accelerated stability conditions. Initial stages implicated in the growth of aggregates are vital to the mechanistic assessment of protein aggregation. Hence, real-time in situ examination of monomer loss and aggregation of BSA was performed at 50 °C to enable continuous assessment with imaging and subsequent SpIDA analysis. Results obtained from this study suggested reversible fluctuation between monomers and dimers for up to four hours.To correlate membrane receptor and transporter expression with activity and enable the comparison of expression in multiple cell lines, population densities of p-glycoprotein transporters and transferrin receptors were determined using SpIDA in samples subjected to immunofluorescence labelling.The Calcein retention assay is a routine approach to determining multidrug resistance associated with p-glcoprotein efflux and the traditional plate reader approach omits microscopic aspects of p-glycoprotein Calcein-AM uptake and efflux. Confocal microscopy and data obtained from image analyses supported the subcellular and intercellular assessment of Calcein accumulation in MDR1-transfected and control cell lines as a function of time and verapamil concentration. Finally, live cell imaging of transferrin vesicular transport and Cell TraceTM Calcein red-orange AM internalisation in combination with traditional Transwell® assays were assessed to compare their transcellular transport and intracellular concentrations in multiple cell lines. Images obtained enabled visualisation of internalisation and following analysis using SpIDA, RICS and FIGMMA the number of intracellular vesicles and dynamic parameters of Cell TraceTM Calcein red-orange diffusion and intracellular concentration were determined.In conclusion, image analysis tools were applied to providing new parametric insights into a number of pharmaceutically-relevant processes and in some instances this is the first example of such studies. Despite current phenomenal advances in image acquisition capabilities, there remains a broad scope for the validation of image analysis tools and their application to a multitude of areas of interest to pharmaceutical and biomolecular research.

An NMR spectroscopy study of trimethylsilylmethyllilthium, TMSM-Li, indicates that TMSM-Li exists as two different aggregates in cyclopentane solution. Using previously reported colligative properties of TMSM-Li in different solutions in connection with new 13C and 6Li NMR data collected in this study, aggregation states were assigned as octamer and hexamer. Low temperature 13C and 6Li NMR peak intensities indicated an equilibrium exists between the two aggregates that shifts toward the octamer as the temperature decreases. ΔH was calculated to be 5.23 + 0.15 kcal/mol and ΔS was calculated to be 17.9 + 0.6 eu for the hexamer/octamer equilibrium system. Samples of TMSM-Li were mixed with TMSM-OH in attempts to form mixed alkyllithium/lithium alkoxide aggregates. 13C NMR data for these mixtures gave inconclusive results whether or not these compounds formed, which is different from other primary alkyllithium compounds studied in the past. A study of neopentyllithium, NpLi, indicates only one aggregate in solution with the aggregation state unknown using low temperature 13C NMR spectroscopy.

Nanoscience and nanotechnology offer potential routes towards addressing critical issues such as clean and sustainable energy, environmental protection and human health. Specifically, metal and metal oxide nanomaterials are found in a wide range of applications and therefore hold a greater potential of possible release into the environment or for the human to be exposed. Understanding the aqueous phase behavior of metal and metal oxide nanomaterials is a key factor in the safe design of these materials because their interactions with living systems are always mediated through the aqueous phase. Broadly the transformations in the aqueous phase can be classified as dissolution, aggregation and adsorption which are dependent and linked processes to one another. The complexity of these processes at the liquid-solid interface has therefore been one of the grand challenges that has persisted since the beginning of nanotechnology. Although classical models provide guidance for understanding dissolution and aggregation of nanoparticles in water, there are many uncertainties associated with the recent findings. This is often due to a lack of fundamental knowledge of the surface structure and surface energetics for very small particles. Therefore currently the environmental health and safety studies related to nanomaterials are more focused on understanding the surface chemistry that governs the overall processes in the liquid-solid interfacial region at the molecular level. The metal based nanomaterials focused on in this dissertation include TiO2, ZnO, Cu and CuO. These are among the most heavily used in a number of applications ranging from uses in the construction industry to cosmetic formulation. Therefore they are produced in large scale and have been detected in the environment. There is debate within the scientific community related to their safety as a result of the lack of understanding on the surface interactions that arise from the detailed nature of the surfaces. Specifically, the interactions of these metal and metal oxide nanoparticles with environmental and biological ligands in the solutions have demonstrated dramatic alterations in their aqueous phase behavior in terms of dissolution and aggregation. Dissolution and aggregation are among the determining factors of nanoparticle uptake and toxicity. Furthermore, solution conditions such as ionic strength and pH can act as controlling parameters for surface ligand adsorption while adsorbed ligands themselves undergo surface induced structural and conformational changes. Because, nanomaterials in both the environment and in biological systems are subjected to a wide range of matrix conditions they are in fact dynamic and not static entities. Thus monitoring and tracking these nanomaterials in real systems can be extremely challenging which requires a thorough understanding of the surface chemistry governing their transformations. The work presented in this dissertation attempts to bridge the gap between the dynamic processing of these nanomaterials, the details of the molecular level processes that occur at the liquid-solid interfacial region and potential environmental and biological interactions. Extensive nanomaterial characterization is an integral part of these investigations and all the materials presented here are thoroughly analyzed for particle size, shape, surface area, bulk and surface compositions. Detailed spectroscopic analysis was used to acquire molecular information of the processes in the liquid-solid interfacial region and the outcomes are linked with the macroscopic analysis with the aid of dynamic and static light scattering techniques. Furthermore, emphasis is given to the size dependent behavior and theoretical modeling is adapted giving careful consideration to the details of the physicochemical characterization and molecular information unique to the nanomaterials.

The objectives of this research were to identify the individual ionization constants (pKa values) of lipopeptide (daptomycin), evaluate the factors of pH, concentration, temperature, and calcium ions on daptomycin aggregation in aqueous solutions, and elucidate the effects of conformation and aggregation on ionization and the interaction mechanism between polyamidoamine (PAMAM) dendrimers and daptomycin. Daptomycin is a cyclic anionic lipopeptide antibiotic. It is composed of 13 amino acids with six ionizable groups, four side-chain carboxylic acids and two side-chain amine residues. The pKa values for individual daptomycin residues have not been elucidated. The sequence-specific pKa values for the four acidic residues and one aromatic amine (Kyn-13) in daptomycin were determined in the monomeric state by TOCSY 2D 1H NMR. From the NMR pH titration, the estimated pKa values for Asp-3, Asp-9, and mGlu-12 were determined to be 4.15, 3.85, and 4.55 in the absence of salt, and 4.07, 3.83, and 4.39 in the presence of 150 mM NaCl, respectively. The pKa value for Asp-7 is estimated to be ~1.01 in the absence of salt and 1.31 in the presence of salt. The estimated Hill coefficients for Asp-7 were 0.72 and 1.31 in the absence and presence of salt, respectively. The increase in Hill coefficients from 0.72 to 1.31 with increasing salt concentration is consistent with the estimated lower pKa in the absence of salt and suggests that a salt bridge is formed in solution possibly between Asp-7 acidic group and the neighboring Orn-6 basic group. The pKa value of the aromatic amine (Kyn-13) was confirmed using UV and fluorescence spectroscopic titrations. Aggregation behavior and critical aggregation concentration (CAC) values of daptomycin were evaluated in the different pH aqueous solutions by using the complementary analytical techniques, fluorescence, dynamic and static light scattering, and NMR spectroscopy. Based on fluorescence resonance energy transfer (FRET) from donor Trp-1 to acceptor Kyn-13, the CAC values were determined by an upward inflection of the intrinsic fluorescence emission from Kyn-13 at 460 nm as a function of increasing daptomycin concentration. The pH-dependent CAC values were determined to be 0.14 mM at pH 3.0, 0.12 mM 4.0, and 0.20 mM at pH 2.5 and 5.0. The CAC values obtained by fluorescence spectroscopy were confirmed by dynamic light scattering and NMR spectroscopy. The effects of temperature and calcium ion on daptomycin aggregation were also discussed. The interaction mechanism between daptomycin and PAMAM dendrimers generation 5 and 6 was studied using fluorescence spectroscopy. The shapes of binding isotherms daptomycin were quantitatively described by one- and two-site binding models to estimate binding capacity and dissociation constants. Both solvent pH values and PAMAM generation size were shown to affect the binding model and parameters. The interaction between daptomycin and PAMAM dendrimer was proposed wherein the ionized Asp-3 and Asp-9 residues of daptomycin interact with PAMAM cationic surface amine.

Low-density lipoproteins (LDL) are heterogeneous nanoparticles containing one copy of apolipoprotein B (~550 kDa) and thousands of lipids. LDL are the main plasma carriers of cholesterol and the major risk factor for atherosclerosis, the number one cause of death in the developed world. In atherosclerosis, LDL lipids are deposited in the arterial intima. Fusion of modified LDL in the arterial wall is an important underexplored triggering event in early atherosclerosis. Previous studies from our laboratory showed that thermal denaturation mimics LDL remodeling and fusion, and revealed the kinetic origin of LDL stability. Here, we report the first quantitative kinetic analysis of LDL stability. We show that LDL denaturation monitored by turbidity follows a sigmoidal time course that is unique among lipoproteins, suggesting that slow conformational changes in apoB precede lipoprotein fusion. High activation energy of LDL denaturation, Ea~100 kcal/mol, indicates disruption of extensive protein-protein and protein-lipid interactions involving large apoB domains. Next, we combined size-exclusion chromatography, gel electrophoresis and electron microscopy to show that dimerization is a common early step preceding LDL fusion. Monoclonal antibody binding studies indicated that α-helices in the N-terminal βα1 domain of apoB undergo conformational changes at early stages of LDL aggregation and fusion. Better understanding of these structural changes that prime LDL for fusion is important, as it may help control this pathogenic process before it occurs. We applied the kinetic approach to test how selected factors that are expected to contribute to LDL fusion in vivo affect the rate of LDL fusion and rupture in vitro. The results show that LDL fusion accelerates at pH<7, which may contribute to LDL retention in acidic atherosclerotic lesions. Fusion also accelerates upon increasing LDL concentration in near-physiologic range, which likely contributes to atherogenesis. Further, we showed that thermal stability of LDL decreases with increasing particle size, indicating that the pro-atherogenic properties of small dense LDL do not result from their enhanced fusion. Our work provides the first kinetic approach to measuring LDL stability and suggests that lipid-lowering therapies that reduce LDL concentration but increase the particle size may have opposite effects on LDL fusion.

Streaming analytics deploy Kleene pattern queries to detect and aggregate event trends against high-rate data streams. Despite increasing workloads, most state-of-the-art systems process each query independently, thus missing cost-saving sharing opportunities. Sharing complex event trend aggregation poses several technical challenges. First, the execution of nested and diverse Kleene patterns is difficult to share. Second, we must share aggregate computation without the exponential costs of constructing the event trends. Third, not all sharing opportunities are beneficial because sharing aggregation introduces overhead. We propose a novel framework, Muse (Multi-query Snapshot Execution), that shares aggregation queries with Kleene patterns while avoiding expensive trend construction. It adopts an online sharing strategy that eliminates re-computations for shared sub-patterns. To determine the beneficial sharing plan, we introduce a cost model to estimate the sharing benefit and design the Muse refinement algorithm to efficiently select robust sharing candidates from the search space. Finally, we explore optimization decisions to further improve performance. Our experiments over a wide range of scenarios demonstrate that Muse increases throughput by 4 orders of magnitude compared to state-of-the-art approaches with negligible memory requirements.

No description available.

Urbanization has considerably altered natural hydrology of urban watersheds by increasing runoff volume, producing higher and faster peak flows, and reducing water quality. Efforts to minimize or avoid these impacts, for example by implementing low impact development (LID) practices, are gaining momentum. Designing effective and economical stormwater management practices at a watershed scale is challenging; LIDs are commonly designed at site scales, considering local hydrologic conditions (i.e., one LID at a time). A number of empirical studies have documented hydrologic and water quality improvements achieved by LIDs. However, watershed scale effectiveness of LIDs has not been well studied. Considering cost, effort, and practicality, computer modeling is the only viable approach to assess LID performance at a watershed scale. As such, the United States Environmental Protection Agency’s Stormwater Management Model (SWMM) was selected for this study. It is well recognized that model predictions are plagued by uncertainties that arise from the lack of quality data and inadequacy of the model to accurately simulate the watershed. To scrutinize sensitivity of prediction accuracies to spatial resolution, four SWMM models of different spatial detail were developed for the Ballona Creek watershed, a highly urbanized watershed in the Los Angeles Basin, as a case study. Detailed uncertainty analyses were carried out for each model to quantify their prediction uncertainties and to examine if a detailed model improves prediction accuracy. Results show that there is a limit to the prediction accuracy achieved by using detailed models. Three of the four models (i.e., all but the least detailed model) produced comparable prediction accuracy. This implies that devoting substantial resources on collecting very detailed data and building fine resolution watershed models may not be necessary, as models of moderate detail could suffice. If confirmed using other urban watersheds, this result could benefit stormwater managers and modelers. All four SWMM models were then used to evaluate hydrologic effectiveness of implementing bioretention cells at a watershed scale. Event based analyses, 1-year, 2-year, 5-year and 10-year storms of 24-hours were considered, as well as data from October 2005 to March 2010 for a continuous simulation. The runoff volume reductions achieved by implementing bioretention cells were not substantial for the event storms. For the continuous simulation analysis, however, about twenty percent reductions in runoff volume were predicted. These results are in-line with previous studies that have reported ineffectiveness of LIDs to reduce runoff volume and peak for less frequent but high intensity storm events.

A vertical-style data structure and operations on data in that structure are explored and tested in the domain of sliding window average algorithms for geographical information systems (GIS) data. The approach allows working with data of arbitrary precision, which is centrally important for very large GIS data sets. The novel data structure can be constructed from existing multi-channel image data, and data in the structure can be converted back to image data. While in the new structure, operations such as addition, division, and bit-level shifting can be performed in a parallelized manner. It is shown that the computation of averages for sliding windows on this data structure can be performed faster than using traditional computation techniques, and the approach scales to larger sliding window sizes.

Large scale semantic segmentation is considered as one of the fundamental tasks in 3D scene understanding. Point clouds provide a basic and rich geometric representation of scenes and tangible objects. Convolutional Neural Networks (CNNs) have demonstrated an impressive success in processing regular discrete data such as 2D images and 1D audio. However, CNNs do not directly generalize to point cloud processing due to their irregular and un-ordered nature. One way to extend CNNs to point cloud understanding is to derive an intermediate euclidean representation of a point cloud by projecting onto image domain, voxelizing, or treating points as vertices of an un-directed graph. Graph-CNNs (GCNs) have demonstrated to be a very promising solution for deep learning on irregular data such as social networks, biological systems, and recently point clouds. Early works in literature for graph based point networks relied on constructing dynamic graphs in the node feature space to define a convolution kernel. Later works constructed hierarchical static graphs in 3D space for an encoder-decoder framework inspired from image segmentation. This thesis takes a closer look at both dynamic and static graph neighborhoods of graph- based point networks for the task of semantic segmentation in order to: 1) discuss a potential cause for why going deep in dynamic GCNs does not necessarily lead to an improved performance, and 2) propose a new approach in treating points in a static graph neighborhood for an improved information aggregation. The proposed method leads to an efficient graph based 3D semantic segmentation network that is on par with current state-of-the-art methods on both indoor and outdoor scene semantic segmentation benchmarks such as S3DIS and Semantic3D.