We demonstrate ensemble three-dimensional cell cultures and quantitative analysis of angiogenic growth from uniform endothelial monolayers. Our approach combines two key elements: a micro-fluidic assay that enables parallelized angiogenic growth instances subject to common extracellular conditions, and an automated image acquisition and processing scheme enabling high-throughput, unbiased quantification of angiogenic growth. Because of the increased throughput of the assay in comparison to existing three-dimensional morphogenic assays, statistical properties of angiogenic growth can be reliably estimated. We used the assay to evaluate the combined effects of vascular endothelial growth factor (VEGF) and the signaling lipid sphingoshine-1-phosphate (S1P). Our results show the importance of S1P in amplifying the angiogenic response in the presence of VEGF gradients. Furthermore, the application of S1P with VEGF gradients resulted in angiogenic sprouts with higher aspect ratio than S1P with background levels of VEGF, despite reduced total migratory activity. This implies a synergistic effect between the growth factors in promoting angiogenic activity. Finally, the variance in the computed angiogenic metrics (as measured by ensemble standard deviation) was found to increase linearly with the ensemble mean. This finding is consistent with stochastic agent-based mathematical models of angiogenesis that represent angiogenic growth as a series of independent stochastic cell-level decisions.

The majority of muscles, nerves, and tendons are composed of fiber-like fascicle morphology. Each fascicle has a) elongated cells highly aligned with the length of the construct, b) a high volumetric cell density, and c) a high length-to-width ratio with a diameter small enough to facilitate perfusion. Fiber-like fascicles are important building blocks for forming tissues of various sizes and cross-sectional shapes, yet no effective technology is currently available for producing long and thin fascicle-like constructs with aligned, high-density cells. Here we present a method for molding cell-laden hydrogels that generate cylindrical tissue structures that are ~100 μm in diameter with an extremely high length to diameter ratio (>100 : 1). Using this method we have successfully created skeletal muscle tissue with a high volumetric density (~50%) and perfect cell alignment along the axis. A new molding technique, sacrificial outer molding, allows us to i) create a long and thin cylindrical cavity of the desired size in a sacrificial mold that is solid at a low temperature, ii) release gelling agents from the sacrificial mold material after the cell-laden hydrogel is injected into fiber cavities, iii) generate a uniform axial tension between anchor points at both ends that promotes cell alignment and maturation, and iv) perfuse the tissue effectively by exposing it to media after melting the sacrificial outer mold at 37 °C. The effects of key parameters and conditions, including initial cavity diameter, axial tension, and concentrations of the hydrogel and gelling agent upon tissue compaction, volumetric cell density, and cell alignment are presented.

Delta-like 4 (Dll4), a membrane-bound Notch ligand, plays a fundamental role in vascular development and angiogenesis. Dll4 is highly expressed in capillary endothelial tip cells and is involved in suppressing neighboring stalk cells to become tip cells during angiogenesis. Dll4-Notch signaling is mediated either by direct cell-cell contact or by Dll4-containing exosomes from a distance. However, whether Dll4-containing exosomes influence tip cells of existing capillaries is unknown. Using a 3D microfluidic device and time-lapse confocal microscopy, we show here for the first time that Dll4-containing exosomes causes tip cells to lose their filopodia and trigger capillary sprout retraction in collagen matrix. We demonstrate that Dll4 exosomes can freely travel through 3D collagen matrix and transfer Dll4 protein to distant tip cells. Upon reaching endothelial sprout, it causes filopodia and tip cell retraction. Continuous application of Dll4 exosomes from a distance lead to significant reduction of sprout formation. This effect correlates with Notch signaling activation upon Dll4-containing exosome interaction with recipient endothelial cells. Furthermore, we show that Dll4-containing exosomes increase endothelial cell motility while suppressing their proliferation. These data revealed novel functions of Dll4 in angiogenesis through exosomes.

No abstract

Abstract-The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

This paper presents a decentralised particle filtering algorithm that enables multiple vehicles to jointly track 3D features under limited communication bandwidth. This algorithm, applied within a decentralised data fusion (DDF) framework, deals with correlated estimation errors due to common past information when fusing two discrete particle sets. Our solution is to transform the particles into Gaussian mixture models (GMMs) for communication and fusion. Not only can decentralised fusion be approximated by GMMs, but this representation also provides summaries of the particle set. Less bandwidth per communication step is required to communicate a GMM than the particle set itself hence conversion to GMMs for communication is an advantage. Real airborne data is used to demonstrate the accuracy of our decentralised particle filtering algorithm for airborne tracking and mapping.

To compare the performance of a convolutional neural network (CNN) to 11 radiologists in detecting scaphoid fractures on conventional radiographs of the hand, wrist, and scaphoid.

Abstract-This paper presents two solutions for performing decentralised particle filtering in view of non-linear, nonGaussian tracking in sensor networks. The issue is that no known methods exist to deal with correlated estimation errors due to common past information between two discrete particle sets.The first method transforms the particles to a Gaussian Mixture Model, the second approximates the set by a Parzen density estimate. Both of these representations accommodate consistent fusion and maintain accurate summaries of the particles. Requiring less bandwidth than particle representations, transformations to GMMs or Parzen representations for communication provide an added advantage.The accuracy in which the algorithms summarise the particle set, fusion methods and bandwidth requirements of each representation will be compared. Our results show that whilst less GMM components are required to summarise the sample statistics, the decentralised fusion solution using Parzen representations yields a more accurate result.