175 research outputs found
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Design of ROMP-based Protein Mimics for siRNA Delivery
Designing delivery agents for therapeutics is an ongoing challenge. As treatments and desired cargoes become more complex, the need for improved delivery vehicles becomes critical. Excellent delivery vehicles must ensure the stability of the cargo, maintain the cargo’s solubility, and promote efficient delivery and release. In order to address these issues, many research groups have looked to nature for design inspiration. Proteins, such as HIV-1 TAT and Antennapedia homeodomain protein, are capable of crossing cellular membranes. However, due to the complexities of their structures, they are synthetically challenging to reproduce in the laboratory setting. Being able to incorporate the key features of these proteins that enable cell entry into simpler scaffolds opens up a wide range of opportunities for the development of new delivery reagents with improved performance.
Herein we report the development of guanidinium-rich polymeric protein mimics using a ring-opening metathesis polymerization (ROMP)-based scaffold capable of interacting with cell membranes and facilitating the internalization of small interfering ribonucleic acids (siRNAs). These materials are referred to interchangeably as cell-penetrating peptide mimics (CPPMs) or protein transduction domain mimics (PTDMs), and derive inspiration from proteins and peptides with cellular internalization capabilities, capturing key features of these materials necessary for intracellular delivery, including cationic charge content in the form of guanidinium moieties and a segregated, hydrophobic component. This thesis documents the development of design principles for PTDMs with optimal membrane interactions and siRNA internalization and delivery.
Chapter 2 documents the development of homopolymer CPPMs that contain aromatic rings with varying π-electronics. This study demonstrated that a wide range of functional groups could be incorporated into CPPMs without negatively impacting their ability to interact with cellular membranes. It is also suggested that other design parameters, such as cationic charge content and overall hydrophobic content, play more dominant roles in membrane interactions. This finding ultimately influenced the PTDM optimization performed in later chapters.
Chapter 3 documents the development of homopolymer and block copolymer PTDMs with varying numbers of guanidinium moieties that were tested to assess the affect cationic charge content and the addition of a segregated, hydrophobic block had on siRNA delivery. This study demonstrated that there was a critical charge content necessary for internalization and established the importance of incorporating a hydrophobic block into PTDM structures. Furthermore, this platform demonstrated that bioactive siRNA could successfully be delivered into cells and affect the target gene.
Chapter 4 documents the exploration of hydrophobic block incorporated into copolymer PTDMs in order to determine how the length of the hydrophobic block of the PTDMs as well as the hydrophobic block composition of the PTDMs impacted siRNA internalization. This study demonstrated that there was a critical hydrophobic content necessary for efficient siRNA internalization and that incorporation of additional hydrophobicity did not guarantee improved efficiencies
Sand dynamics along the Belgian coast based on airborne hyperspectral data and lidar data
The goal of this project was to explore the possibilities of airborne hyperspectral data and airborne lidar data to study sand dynamics on the Belgian backshore and foreshore. The Belgian coast is formed by a sandy strip at the southern edge of the North Sea Basin which is commonly known as the Southern Bight. Since the beach is prone to structural and occasional erosion, it is very important to obtain a better understanding of the processes controlling it. The combination of multi-temporal hyperspectral data and lidar data provides a suitable tool for follow-up of the Belgian coastline, and sandy coastlines in general. Hyperspectral imagery generates a reflectance spectrum for each pixel in the image. The shape of this spectrum is influenced by the composition of the topsoil of the beach, being mainly the mineralogical composition and the grain size. A Spectral Angle Mapper (SAM) algorithm was used to perform a supervised classification of the hyperspectral images in order to distinguish between different sand types. Digital terrain models (DTM’s) with a mean vertical accuracy of 5 cm were generated from lidar data. By differencing a DTM from September 2000 and one from September 2001 a map with sedimentation and erosion zones was generated. By combining the erosion/sedimentation map with the classified hyperspectral images, dating from August 2000 and August 2001, an appropriate and cost-effective method was found for studying the processes of sand transport along the Belgian coastline
Remote sensing of coastal vegetation in the Netherlands and Belgium
Vegetation maps are frequently used in conservation planning and evaluation. Monitoring commitments, a.o. in relation to the European Habitat Directive, increase the need for efficient mapping tools. This paper explores methods of vegetation mapping with particular attention to automated classification of remotely sensed images. Characteristics of two main types of imagery are discussed, very high spatial resolution false colour images on the one hand and hyperspectral images on the other. The first type has proved its qualities for mapping of - mainly - vegetation structure in dunes and salt marshes. Hyperspectral imagery enables thematic detail but encounters more technical problems
Classifying hyperspectral airborne imagery for vegetation survey along coastlines
This paper studies the potential of airborne hyperspectral imagery for classifying vegetation along the Belgian coastlines. Here, the aim is to build vegetation maps using automatic classification. Besides a general linear multiclass classifier (Linear Discriminant Analysis), several strategies for combining binary classifiers are proposed: one based on a hierarchical decision tree, one based on the Hamming distance between the codewords obtained by binary classifiers and one based on the coupling of posterior probabilities. In addition, a new procedure is proposed for spatial classification smoothing. This procedure takes into account spatial information by letting the decision for classification of a pixel depend on the classification probabilities of neighboring pixels. This is shown to render smoother classification images
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