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1. Bioprinted microvasculature: progressing from structure to function

   https://doi.org/10.1088/1758-5090/ac4fb5  

   Seymour, Alexis J; Westerfield, Ashley D; Cornelius, Vincent C; Skylar-Scott, Mark A; Heilshorn, Sarah C (February 2022, Biofabrication)

   Abstract Three-dimensional (3D) bioprinting seeks to unlock the rapid generation of complex tissue constructs, but long-standing challenges with efficient in vitro microvascularization must be solved before this can become a reality. Microvasculature is particularly challenging to biofabricate due to the presence of a hollow lumen, a hierarchically branched network topology, and a complex signaling milieu. All of these characteristics are required for proper microvascular—and, thus, tissue—function. While several techniques have been developed to address distinct portions of this microvascularization challenge, no single approach is capable of simultaneously recreating all three microvascular characteristics. In this review, we present a three-part framework that proposes integration of existing techniques to generate mature microvascular constructs. First, extrusion-based 3D bioprinting creates a mesoscale foundation of hollow, endothelialized channels. Second, biochemical and biophysical cues induce endothelial sprouting to create a capillary-mimetic network. Third, the construct is conditioned to enhance network maturity. Across all three of these stages, we highlight the potential for extrusion-based bioprinting to become a central technique for engineering hierarchical microvasculature. We envision that the successful biofabrication of functionally engineered microvasculature will address a critical need in tissue engineering, and propel further advances in regenerative medicine and ex vivo human tissue modeling. 

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2. Predicting capillary vessel network hemodynamics in silico by machine learning

   https://doi.org/10.1093/pnasnexus/pgae043  

   Ebrahimi, Saman; Bagchi, Prosenjit (January 2024, PNAS Nexus)

   Abstract Blood velocity and red blood cell (RBC) distribution profiles in a capillary vessel cross-section in the microcirculation are generally complex and do not follow Poiseuille’s parabolic or uniform pattern. Existing imaging techniques used to map large microvascular networks in vivo do not allow a direct measurement of full 3D velocity and RBC concentration profiles, although such information is needed for accurate evaluation of the physiological variables, such as the wall shear stress (WSS) and near-wall cell-free layer (CFL), that play critical roles in blood flow regulation, disease progression, angiogenesis, and hemostasis. Theoretical network flow models, often used for hemodynamic predictions in experimentally acquired images of the microvascular network, cannot provide the full 3D profiles either. In contrast, such information can be readily obtained from high-fidelity computational models that treat blood as a suspension of deformable RBCs. These models, however, are computationally expensive and not feasible for extension to the microvascular network at large spatial scales up to an organ level. To overcome such limitations, here we present machine learning (ML) models that bypass such expensive computations but provide highly accurate and full 3D profiles of the blood velocity, RBC concentration, WSS, and CFL in every vessel in the microvascular network. The ML models, which are based on artificial neural network and convolution-based U-net models, predict hemodynamic quantities that compare very well against the true data but reduce the prediction time by several orders. This study therefore paves the way for ML to make detailed and accurate hemodynamic predictions in spatially large microvascular networks at an organ-scale. 

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3. Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow

   https://doi.org/10.1039/C8AY01798K  

   Guo, Jiaming; Keller, Keely A.; Govyadinov, Pavel; Ruchhoeft, Paul; Slater, John H.; Mayerich, David (January 2019, Analytical Methods)

   In vivo , microvasculature provides oxygen, nutrients, and soluble factors necessary for cell survival and function. The highly tortuous, densely-packed, and interconnected three-dimensional (3D) architecture of microvasculature ensures that cells receive these crucial components. The ability to duplicate microvascular architecture in tissue-engineered models could provide a means to generate large-volume constructs as well as advanced microphysiological systems. Similarly, the ability to induce realistic flow in engineered microvasculature is crucial to recapitulating in vivo -like flow and transport. Advanced biofabrication techniques are capable of generating 3D, biomimetic microfluidic networks in hydrogels, however, these models can exhibit systemic aberrations in flow due to incorrect boundary conditions. To overcome this problem, we developed an automated method for generating synthetic augmented channels that induce the desired flow properties within three-dimensional microfluidic networks. These augmented inlets and outlets enforce the appropriate boundary conditions for achieving specified flow properties and create a three-dimensional output useful for image-guided fabrication techniques to create biomimetic microvascular networks. 

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4. An Information-Theoretic Framework for Evaluating Edge Bundling Visualization

   https://doi.org/10.3390/e20090625  

   Wu, Jieting; Zhu, Feiyu; Liu, Xin; Yu, Hongfeng (September 2018, Entropy)

   Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented. 

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5. Adaptive Deep Neural Network Inference Optimization with EENet

   https://doi.org/10.1109/WACV57701.2024.00140  

   Ilhan, Fatih; Chow, Ka-Ho; Hu, Sihao; Huang, Tiansheng; Tekin, Selim; Wei, Wenqi; Wu, Yanzhao; Lee, Myungjin; Kompella, Ramana; Latapie, Hugo; et al (January 2024, IEEE/CVFWinter Conference on Applications of Computer Vision (WACV))

   Well-trained deep neural networks (DNNs) treat all test samples equally during prediction. Adaptive DNN inference with early exiting leverages the observation that some test examples can be easier to predict than others. This paper presents EENet, a novel early-exiting scheduling framework for multi-exit DNN models. Instead of having every sam- ple go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently ter- minate the inference earlier for certain predictions, which the model has high confidence of early exit. As opposed to previous early-exiting solutions with heuristics-based meth- ods, our EENet framework optimizes an early-exiting policy to maximize model accuracy while satisfying the given per- sample average inference budget. Extensive experiments are conducted on four computer vision datasets (CIFAR-10, CIFAR-100, ImageNet, Cityscapes) and two NLP datasets (SST-2, AgNews). The results demonstrate that the adap- tive inference by EENet can outperform the representative existing early exit techniques. We also perform a detailed visualization analysis of the comparison results to interpret the benefits of EENet. 

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