Quantification of microvascular remodeling as a meaningful discovery tool requires mapping and measurement of site-specific changes within vascular trees and networks. Vessel density and other critical vascular parameters are often modulated by molecular regulators as determined by local vascular architecture. For example, enlargement of vessel diameter by vascular endothelial growth factor (VEGF) is restricted to specific generations of vessel branching (Parsons-Wingerter et al., Microvascular Research72: 91, 2006). The averaging of vessel diameter over many successively smaller generations is therefore not particularly useful. The newly automated, user-interactive software VESsel GENeration Analysis (VESGEN) quantifies major vessel parameters within two-dimensional (2D) vascular trees, networks, and tree-network composites. This report reviews application of VESGEN 2D to angiogenic and lymphangiogenic tissues that includes the human and murine retina, embryonic coronary vessels, and avian chorioallantoic membrane. Software output includes colorized image maps with quantification of local vessel diameter, fractal dimension, tortuosity, and avascular spacing. The density of parameters such as vessel area, length, number, and branch point are quantified according to site-specific generational branching within vascular trees. The sole user input requirement is a binary (black/white) vascular image. Future applications of VESGEN will include analysis of 3D vascular architecture and bioinformatic dimensions such as blood flow and receptor localization. Branching analysis by VESGEN has demonstrated that numerous regulators including VEGF 165 , basic fibroblast growth factor, transforming growth factor b-1, angiostatin and the clinical steroid tri- Microvascular remodeling is now widely acknowledged as fundamental to normal physiological processes that include embryonic development, reproductive biology and healthy wound-healing, and the progressive pathologies of neovascular diseases such as cancer, diabetes, and heart disease (Folkman, 2007). However, angiogenesis, lymphangiogenesis, and other microvascular remodeling processes are difficult to map and quantify because of the morphological complexity of branching vascular trees and their associated capillary networks. Furthermore, the architecture of a vascular tree or network is locally adapted to specific needs of the host tissue or organ. In general, blood and lymphatic vascular structures can be classified as (1) heterogeneous, asymmetric trees of vessels that branch and taper, (2) relatively homogeneous, symmetric networks or plexuses, or (3) tree-network composites. Mature vascular trees typically develop from immature, capillary-like vasculogenic networks. Within a mature organ or tissue, capillary networks are necessarily continuous with their arterial and venous trees.The computer software VESsel GENeration Analysis (VESGEN) maps and quantifies major parameters of angiogenesis and lymphangiogenesis in vascular trees and networks. This review descr...
Pseudocolor view of vascular branching generations in the chorioallantoic membrane (CAM) of quail. Vascular architecture was analyzed using the automated, user‐interactive software, VESsel GENeration Analysis (VESGEN). See Vickerman, et al., on page 320, in this issue.
By VESGEN analysis, TA selectively inhibited the angiogenesis of smaller blood vessels, but decreased the vessel diameter of all vessels within the vascular tree.
This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Qrouting and Naïve Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN protocols is discussed. Finally, initial simulation setup and results are given.
The representations learned by deep neural networks are difficult to interpret in part due to their large parameter space and the complexities introduced by their multi-layer structure. We introduce a method for computing persistent homology over the graphical activation structure of neural networks, which provides access to the task-relevant substructures activated throughout the network for a given input. This topological perspective provides unique insights into the distributed representations encoded by neural networks in terms of the shape of their activation structures. We demonstrate the value of this approach by showing an alternative explanation for the existence of adversarial examples. By studying the topology of network activations across multiple architectures and datasets, we find that adversarial perturbations do not add activations that target the semantic structure of the adversarial class as previously hypothesized. Rather, adversarial examples are explainable as alterations to the dominant activation structures induced by the original image, suggesting the class representations learned by deep networks are problematically sparse on the input space.Preprint. Under review.
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