1,358 research outputs found
Establishment of in vitro multi-organ system using 3D bioprinting strategies for understanding molecular pathogenesis of enteric hyperoxaluria
No full textEnteric hyperoxaluria (a.k.a. secondary hyperoxaluria; SH) can occur as a complication of inflammatory bowel disease, causing oxalate malabsorption. The SH patients often have an increased risk of having recurrent kidney stones and loss of kidney function from oxalate nephropathy. Current therapeutic options are simply limited to correcting the underlying gastrointestinal disorders. Therefore developing SH model is needed to better define the precise factors that influence risk of having SH. In this study, in vitro SH model was successfully designed and constructed by utilizing 3D co-axial cell printing technique and transwell systems with integrated intestinal barrier and proximal tubule into a single platform. The hallmarks in SH pathogenesis have been successfully recapitulated on in vitro SH model, and the overall performance of this platform is being measured by multiple biochemical methods.1
Establishment of in vitro multi-organ system using 3D bioprinting strategies for understanding molecular pathogenesis of enteric hyperoxaluria
No full text1
Erratum: 3D bioprinted in vitro secondary hyperoxaluria model by mimicking intestinal-oxalatemalabsorption-related kidney stone disease (Applied Physics Reviews (2022) 9 (041408) DOI: 10.1063/5.0087345)
No full text© 2023 Author(s).This article was originally published online on 21 November 2022 with an incorrect affiliation identifier for author Dong-Woo Cho. It is correct as it appears above. All online versions of this article were corrected on 23 November 2022. AIP Publishing apologizes for this error.11Nsciescopu
Establishment of in vitro multi-organ system using 3D bioprinting strategies for understanding molecular pathogenesis of enteric hyperoxaluria
No full textEnteric hyperoxaluria (a.k.a. secondary hyperoxaluria; SH) can occur as a complication of inflammatory bowel disease, causing oxalate malabsorption. The SH patients often have an increased risk of having recurrent kidney stones and loss of kidney function from oxalate nephropathy. Current therapeutic options are simply limited to correcting the underlying gastrointestinal disorders. Therefore developing SH model is needed to better define the precise factors that influence risk of having SH. In this study, in vitro SH model was successfully designed and constructed by utilizing 3D co-axial cell printing technique and transwell systems with integrated intestinal barrier and proximal tubule into a single platform. The hallmarks in SH pathogenesis have been successfully recapitulated on in vitro SH model, and the overall performance of this platform is being measured by multiple biochemical methods.1
Establishment of An Integrative 3D Bioprinted In Vitro Disease Model by Recapitulating Pathophysiological Features of Secondary Hyperoxaluria
No full text2
Moxifloxacin-Based Extended Depth-of-Field Fluorescence Microscopy for Real-Time Conjunctival Goblet Cell Examination
No full textConjunctival goblet cells (CGCs) are mucin-secreting cells in the eye and play essential roles for ocular surface homeostasis. Since various ocular surface pathologies are related to CGC dysfunction, CGC examination is important for the evaluation of ocular surface conditions. Recently we introduced moxifloxacin-based fluorescence microscopy (MBFM) for non-invasive CGC imaging. However, the imaging speed was up to 1 frame per second (fps) and needed to be improved for clinical applications. In this study, we developed a high-speed moxifloxacin-based, extended depth-of-field (EDOF) microscopy system that operates at a maximum imaging speed of 15 fps. The system used a deformable mirror for the high-speed axial sweeping of focal plane during single-frame acquisitions. The acquired images contained both in-focus and out-of-focus information, and deconvolution was used to filter the in-focus information. The system had a DOF of 800 μm, field-of-view of 1.2 mm × 1.2 mm, and resolution of 2.3 μm. Its performance was demonstrated by real-time, breathing-motion-insensitive CGC imaging of mouse and rabbit models, in vivo. High-speed EDOF microscopy has potentials for non-invasive, real-time CGC examinations of human subjects.11Nsciescopu
Decentralized Approximate Bayesian Inference for Distributed Sensor Network
No full textBayesian models provide a framework for probabilistic modelling of complex datasets. Many such models are computationally demanding, especially in the presence of large datasets. In sensor network applications, statistical (Bayesian) parameter estimation usually relies on decentralized algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a framework for decentralized Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM).We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed affine structure from motion (SfM).Peer reviewe
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty
No full textWe propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates the speed of convergence by automatically deciding the constraint penalty needed for parameter consensus in each iteration. In addition, we also propose an extension of the method that adaptively determines the maximum number of iterations to update the penalty. We show that this approach effectively leads to an adaptive, dynamic network topology underlying the distributed optimization. The utility of the new penalty update schemes is demonstrated on both synthetic and real data, including an instance of the probabilistic matrix factorization task known as the structure-from-motion problem.Peer reviewe
A Knowledge Distribution Model to Support an Author in Narrative Creation
No full textAdjusting the knowledge of characters and the reader is a critical task for an author in narrative creation. Throughout a narrative, both characters and the reader experience events according to their own timelines and perspectives. They interpret information accumulated through their experience and update knowledge to the narrative-world which the author constructed. In this paper, we present a Knowledge Distribution Model which supports an author in finely controlling the knowledge of characters and the reader. Within the model, the Knowledge Structure is constructed by connecting event, information, and knowledge. The Knowledge State is evaluated as the degree of belief under the knowledge structure. We adopted a probabilistic reasoning model to calculate the knowledge state. The change in knowledge state, defined as Knowledge Flow, is visually presented to the author. We designed a GUI prototype to implement the proposed modeling process, and demonstrated the knowledge flow with an actual cinematic narrative
Distributed Probabilistic Learning for Camera Networks
No full textProbabilistic approaches to computer vision typically assume a centralized setting, with the algorithm granted access to all observed data points. However, many problems in wide-area surveillance can benefit from distributed modeling, either because of physical or computations constraints. In this work we present an approach to estimation and learning of generative probabilistic models in a distributed context. In particular, we show how traditional centralized models, such as probabilistic principal component analysis (PPCA), can be learned when the data is distributed across a network of sensors. We demonstrate the utility of this approach on the problem of distributed affine structure from motion (SfM). Our experiments suggest that the accuracy of the accuracy of the learned probabilistic structure and motion models rivals that of traditional centralized factorization methods.Technical report DCS-TR-69
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