Browsing by Subject "Shape"

Now showing 1 - 4 of 4

Effect of shape on cell internalization of polymeric hydrogel nanoparticles
(2013-05) Agarwal, Rachit, Ph. D.; Roy, Krishnendu
Recent progress in drug discovery has enabled us to target specific intracellular molecules to achieve therapeutic effects. These next generation therapeutics are often biologics which cannot enter cells by mere diffusion. Therefore it is imperative that drug carriers are efficiently internalized by cells before releasing their cargo. Nanoscale polymeric carriers are particularly suitable for such intra-cellular delivery. Although size and surface-charge has been the most studied parameters for nanocarriers, it is now well appreciated that particle shape also plays a critical role in their transport across physiological barriers. Hence there is increasing interest in fabricating shape-specific polymeric nano and microparticles for efficient delivery of drugs and imaging agents. Nanoimprint lithography methods, such as Jet-and-flash imprint lithography (J-FIL), provide versatile top-down processes to fabricate shape-specific, biocompatible nanoscale hydrogels that can deliver therapeutic and diagnostic molecules in response to disease-specific cues. However, the key challenges in top-down fabrication of such nanocarriers are scalable imprinting with biological and biocompatible materials, ease of particle-surface modification using both aqueous and organic chemistry as well as simple yet biocompatible harvesting. Here we report that a biopolymer-based sacrificial release layer in combination with improved nanocarrier-material formulation can address these challenges. The sacrificial layer improves scalability and ease of imprint-surface modification due to its switchable solubility through simple ion exchange between monovalent and divalent cations. This process enables large-scale bio-nanoimprinting and efficient, one-step harvesting of hydrogel nanoparticles in both water- and organic-based imprint solutions. We also show that when shape is decoupled from volume, charge and composition, mammalian cells preferentially internalize disc-shaped nanohydrogels of higher aspect ratios over nanorods. Interestingly, unlike nanospheres, larger-sized hydrogel nanodiscs and nanorods are internalized more efficiently. Uptake kinetics, efficiency and internalization mechanisms are all shape-dependent and cell-type specific. Although macropinocytosis is used by all cells, epithelial cells uniquely internalize nanodiscs using caveolae pathway. On the other hand, endothelial cells use clathrin-mediated uptake along with macropinocytosis for all shapes and show significantly higher uptake efficiency compared to epithelial cells. We also study the effect of shape and surface properties for their tissue uptake and penetration using spheroids as a 3D tumor model and show that hydrophobic particles show no difference in penetration inside such models even after 125 fold reduction in volume. These results provide a fundamental understanding of how cell and tissue behavior is influenced by nanoscale shape and surface properties and are critical for designing improved nanocarriers and predicting nanomaterial toxicity.
The (fe)male shifts shame : androgyny and transformation in Marie de France, Gerald of Wales, and the Volsungasaga
(2013-05) Gutierrez-Neal, Paula Christina; Wojciehowski, Hannah Chapelle, 1957-; Blockley, Mary
Transformation is inherently entwined with the transgression of borders; for male shifters, there is an acquittance of this transhuman breach, but not so for female shifters. Gerald of Wale's History and Topography of Ireland depicts two werewolves: the male's shapeshifting is all but disregarded, while the female's own transformation is depicted in detail and effectively shames her into silence. In addition, the Volsungasaga also contains werewolves: Sigmund and Sinfjotli don wolfskins, but soon regret their transformations. However, neither is shamed for the shapeshifting, and indeed, Sinfjotli successfully twists the experience to his advantage. The female werewolf, King Siggeir's mother, however, is killed and her identity as a "foul" witch exposed. There are also the human-to-human transformations of Signy/a witch and Sigurd/Gunnar. Signy expresses shame for the incident; Sigurd and Gunnar's plot is revealed, but neither is condemned: the tale passes over the shapeshifting in favor of the narrative drama. Furthermore, Marie de France's Bisclavret perpetuates the same pattern: the male werewolf is praised and exonerated for his transhuman nature while the wife's pseudo-shapeshifting is met with condemnation and shame. However, Marie de France's Yonec attempts to break this pattern, with the shapeshifter Muldumarec transgressing not only the animal/human binary but that of the male/female. His androgyny is conferred onto his beloved, who also undergoes transformations but is spared the shaming consequences via Muldumarec. While this sharing of androgyny breaks the pattern and keeps the beloved from condemnation, it ultimately fails in breaking the patriarchal underpinnings of the pattern itself.
Quantifying the characteristics of fine aggregate using direct and indirect test methods
(2013-12) Alqarni, Ali Saeed; Fowler, David W.
The characteristics of fine aggregates, such as shape, angularity, and surface texture, have been shown to influence the performance of concrete and asphalt mixtures and to play an important role in obtaining valuable properties of early age concrete such as workability, and compatibility. However, the measurement of fine aggregate characteristics is not easy. In the present study, 26 fine aggregates, covering a wide spectrum of mineralogy, were examined using direct and indirect test methods in order to evaluate the shape, angularity, and surface texture, as well as to analyze the gradation. The direct test methods, such as AIMS and Camsizer, which provide a digital image of the aggregates proved to be the best. However, the cost of such systems can limit the use of digital imagining systems in practice. The indirect test methods which provide an estimate of aggregate surface characteristics, such as uncompacted void test, mortar flow test, compressive strength test, and flakiness test gave variable results. The uncompacted void test (Method A) was shown to be the most accurate indirect test method. The Camsizer and the sieve analysis test produced identical gradation analysis results when an adequate sample was used. General correlations were developed between the direct and indirect test methods. The non-approved fine aggregates on the TxDOT’s list were analyzed and compared to those of the approved fine aggregates to see whether they could be successfully used. It was found that both LS-5 and LS-8 had good results—even better than the results of some of the approved fine aggregates. Thus, they could be successfully used.
Region detection and matching for object recognition
(2013-08) Kim, Jaechul; Grauman, Kristen Lorraine, 1979-
In this thesis, I explore region detection and consider its impact on image matching for exemplar-based object recognition. Detecting regions is important to provide semantically meaningful spatial cues in images. Matching establishes similarity between visual entities, which is crucial for recognition. My thesis starts by detecting regions in both local and object level. Then, I leverage geometric cues of the detected regions to improve image matching for the ultimate goal of object recognition. More specifically, my thesis considers four key questions: 1) how can we extract distinctively-shaped local regions that also ensure repeatability for robust matching? 2) how can object-level shape inform bottom-up image segmentation? 3) how should the spatial layout imposed by segmented regions influence image matching for exemplar-based recognition? and 4) how can we exploit regions to improve the accuracy and speed of dense image matching? I propose novel algorithms to tackle these issues, addressing region-based visual perception from low-level local region extraction, to mid-level object segmentation, to high-level region-based matching and recognition. First, I propose a Boundary Preserving Local Region (BPLR) detector to extract local shapes. My approach defines a novel spanning-tree based image representation whose structure reflects shape cues combined from multiple segmentations, which in turn provide multiple initial hypotheses of the object boundaries. Unlike traditional local region detectors that rely on local cues like color and texture, BPLRs explicitly exploit the segmentation that encodes global object shape. Thus, they respect object boundaries more robustly and reduce noisy regions that straddle object boundaries. The resulting detector yields a dense set of local regions that are both distinctive in shape as well as repeatable for robust matching. Second, building on the strength of the BPLR regions, I develop an approach for object-level segmentation. The key insight of the approach is that objects shapes are (at least partially) shared among different object categories--for example, among different animals, among different vehicles, or even among seemingly different objects. This shape sharing phenomenon allows us to use partial shape matching via BPLR-detected regions to predict global object shape of possibly unfamiliar objects in new images. Unlike existing top-down methods, my approach requires no category-specific knowledge on the object to be segmented. In addition, because it relies on exemplar-based matching to generate shape hypotheses, my approach overcomes the viewpoint sensitivity of existing methods by allowing shape exemplars to span arbitrary poses and classes. For the ultimate goal of region-based recognition, not only is it important to detect good regions, but we must also be able to match them reliably. A matching establishes similarity between visual entities (images, objects or scenes), which is fundamental for visual recognition. Thus, in the third major component of this thesis, I explore how to leverage geometric cues of the segmented regions for accurate image matching. To this end, I propose a segmentation-guided local feature matching strategy, in which segmentation suggests spatial layout among the matched local features within each region. To encode such spatial structures, I devise a string representation whose 1D nature enables efficient computation to enforce geometric constraints. The method is applied for exemplar-based object classification to demonstrate the impact of my segmentation-driven matching approach. Finally, building on the idea of regions for geometric regularization in image matching, I consider how a hierarchy of nested image regions can be used to constrain dense image feature matches at multiple scales simultaneously. Moving beyond individual regions, the last part of my thesis studies how to exploit regions' inherent hierarchical structure to improve the image matching. To this end, I propose a deformable spatial pyramid graphical model for image matching. The proposed model considers multiple spatial extents at once--from an entire image to grid cells to every single pixel. The proposed pyramid model strikes a balance between robust regularization by larger spatial supports on the one hand and accurate localization by finer regions on the other. Further, the pyramid model is suitable for fast coarse-to-fine hierarchical optimization. I apply the method to pixel label transfer tasks for semantic image segmentation, improving upon the state-of-the-art in both accuracy and speed. Throughout, I provide extensive evaluations on challenging benchmark datasets, validating the effectiveness of my approach. In contrast to traditional texture-based object recognition, my region-based approach enables to use strong geometric cues such as shape and spatial layout that advance the state-of-the-art of object recognition. Also, I show that regions' inherent hierarchical structure allows fast image matching for scalable recognition. The outcome realizes the promising potential of region-based visual perception. In addition, all my codes for local shape detector, object segmentation, and image matching are publicly available, which I hope will serve as useful new additions for vision researchers' toolbox.