A Bayesian Approach to Manifold Topology Reconstruction

In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated

A Bayesian Approach to Manifold Topology Reconstruction

In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated

Structure and Function of ABCG2-Rich Extracellular Vesicles Mediating Multidrug Resistance

Multidrug resistance (MDR) is a major impediment to curative cancer chemotherapy. The ATP-Binding Cassette transporters ABCG2, ABCB1 and ABCC2 form a unique defense network against multiple structurally and functionally distinct chemotherapeutics, thereby resulting in MDR. Thus, deciphering novel mechanisms of MDR and their overcoming is a major goal of cancer research. Recently we have shown that overexpression of ABCG2 in the membrane of novel extracellular vesicles (EVs) in breast cancer cells results in mitoxantrone resistance due to its dramatic sequestration in EVs. However, nothing is known about EVs structure, biogenesis and their ability to concentrate multiple antitumor agents. To this end, we here found that EVs are structural and functional homologues of bile canaliculi, are apically localized, sealed structures reinforced by an actin-based cytoskeleton and secluded from the extracellular milieu by the tight junction proteins occludin and ZO-1. Apart from ABCG2, ABCB1 and ABCC2 were also selectively targeted to the membrane of EVs. Moreover, Ezrin-Radixin-Moesin protein complex selectively localized to the border of the EVs membrane, suggesting a key role for the tethering of MDR pumps to the actin cytoskeleton. The ability of EVs to concentrate and sequester different antitumor drugs was also explored. Taking advantage of the endogenous fluorescence of anticancer drugs, we found that EVs-forming breast cancer cells display high level resistance to topotecan, imidazoacridinones and methotrexate via efficient intravesicular drug concentration hence sequestering them away from their cellular targets. Thus, we identified a new modality of anticancer drug compartmentalization and resistance in which multiple chemotherapeutics are actively pumped from the cytoplasm and highly concentrated within the lumen of EVs via a network of MDR transporters differentially targeted to the EVs membrane. We propose a composite model for the structure and function of MDR pump-rich EVs in cancer cells and their ability to confer multiple anticancer drug resistance

Computational Optical Measurement and Display: Case Studies in Plenoptic Imaging and Projection

Advances in imaging technology have to a large extent shaped scientific progress in the last 200 years. While progress in imaging technology originated in, and forced the development of, the field of optics, the design paradigm for optical instruments has always placed the human observer at the center of its efforts. With the advent of electronic computation in the second half of the 20th century, optical design could be elevated to a new level by exploiting computer-aided design and automated optimization procedures. However, only in recent years have computers become so powerful, and at the same time so small and inexpensive, that imaging technology, storage and transmission have become completely digitized. This move has not yet reached its full potential since the human observer is still considered the target of optimization, whereas in fact, today's primary observers are computers. It is this insight that enables an entirely new approach to optics and measurement instrumentation. Images no longer have to mimic what the human brain is accustomed to interpret as an image of the world, i.e. integrals over ray bundles of a restricted subset of the electro-magnetic spectrum. Instead, sensing mechanisms can be designed that re-distribute directional, spatial, temporal and wavelength information to essentially agnostic sensor elements serving as simple photon collectors. The questions of how such redistribution can be arranged for, which performance characteristics are to be expected of such devices, and how these novel sensing means can be used for measurement purposes form the basis and contents of this thesis. In particular examples, investigations into these larger questions are explored in detail. Through these studies we arrive at a larger picture of the current state of affairs: We have glimpsed at the exciting possibilities of computational optical imaging, however, we have seen a mountain of formidable size and difficulty, the ascend of which will require significant effort. This is how the subtitle of this thesis is to be understood

Opacity

Conducía por Calidonia el auto que le debo al banco y la he visto. Se bajaba por el lado del conductor de un carro doble tracción color champaña, con rines de lujo y tres hileras de asientos forrados con piel. Iba dentro de una blusita rosa y un diablofuerte ceñido que, obsequioso, exponía al escrutinio público una carnalidad preciosa, firme y líquida a la vez, con unas joyas imperiales que hace treinta años no tenía. Se veía como una mujer hecha y derecha, de esas que no tienen prisas porque ya aprendieron a conjurar el incendio del volcán despierto que se agita en sus profundidades, y que sólo se desata en estropicio cuando ellas dan la voz de mando

Adaptive Grid Optical Tomography

Image-based modeling of semi-transparent, dynamic phenomena is a challenging task. We present an optical tomography method that uses an adaptive grid for the reconstruction of a three-dimensional density function from its projections. The proposed method is applied to reconstruct thin smoke and flames volumetrically from synchronized multi-video recordings. Our adaptive reconstruction algorithm computes a time-varying volumetric model, that enables the photorealistical rendering of the recorded phenomena from arbitrary viewpoints. In contrast to previous approaches we sample the underlying unknown, three-dimensional density function adaptively which enables us to achieve a higher effective resolution of the reconstructed models