Hydrogels are widely used as three-dimensional (3D) tissue engineering scaffolds due to their tissue-like water content, as well as their tunable physical and chemical properties. Hydrogel-based scaffolds are generally associated with nanoscale porosity, whereas macroporosity is highly desirable to facilitate nutrient transfer, vascularization, cell proliferation and matrix deposition. Diverse techniques have been developed for introducing macroporosity into hydrogel-based scaffolds. However, most of these methods involve harsh fabrication conditions that are not cell friendly, result in spherical pore structure, and are not amenable for dynamic pore formation. Human tissues contain abundant microchannel-like structures, such as microvascular network and nerve bundles, yet fabricating hydrogels containing microchannel-like pore structures remains a great challenge. To overcome these limitations, here we aim to develop a facile, cell-friendly method for engineering hydrogels with microchannel-like porosity using stimuli-responsive microfibers as porogens. Microfibers with sizes ranging 150-200 μm were fabricated using a coaxial flow of alginate and calcium chloride solution. Microfibers containing human embryonic kidney (HEK) cells were encapsulated within a 3D gelatin hydrogel, and then exposed to ethylenediaminetetraacetic acid (EDTA) solution at varying doses and duration. Scanning electron microscopy confirmed effective dissolution of alginate microfibers after EDTA treatment, leaving well-defined, interconnected microchannel structures within the 3D hydrogels. Upon release from the alginate fibers, HEK cells showed high viability and enhanced colony formation along the luminal surfaces of the microchannels. In contrast, HEK cells in non-EDTA treated control exhibited isolated cells, which remained entrapped in alginate microfibers. Together, our results showed a facile, cell-friendly process for dynamic microchannel formation within hydrogels, which may simultaneously release cells in 3D hydrogels in a spatiotemporally controlled manner. This platform may be adapted to include other cell-friendly stimuli for porogen removal, such as Matrix metalloproteinase-sensitive peptides or photodegradable gels. While we used HEK cells in this study as proof of principle, the concept described in this study may also be used for releasing clinically relevant cell types, such as smooth muscle and endothelial cells that are useful for repairing tissues involving tubular structures.
Battery-free Internet-of-Things devices equipped with energy harvesting hold the promise of extended operational lifetime, reduced maintenance costs, and lower environmental impact. Despite this clear potential, it remains complex to develop applications that deliver sustainable operation in the face of variable energy availability and dynamic energy demands. This article aims to reduce this complexity by introducing AsTAR, an energy-aware task scheduler that automatically adapts task execution rates to match available environmental energy. AsTAR enables the developer to prioritize tasks based upon their importance, energy consumption, or a weighted combination thereof. In contrast to prior approaches, AsTAR is autonomous and self-adaptive, requiring no a priori modeling of the environment or hardware platforms. We evaluate AsTAR based on its capability to efficiently deliver sustainable operation for multiple tasks on heterogeneous platforms under dynamic environmental conditions. Our evaluation shows that (1) comparing to conventional approaches, AsTAR guarantees Sustainability by maintaining a user-defined optimum level of charge, and (2) AsTAR reacts quickly to environmental and platform changes, and achieves Efficiency by allocating all the surplus resources following the developer-specified task priorities. (3) Last, the benefits of AsTAR are achieved with minimal performance overhead in terms of memory, computation, and energy.
Network traces are considered a primary source of information to researchers, who use them to investigate research problems such as identifying user behavior, analyzing network hierarchy, maintaining network security, classifying packet flows, and much more. However, most organizations are reluctant to share their data with a third party or the public due to privacy concerns. Therefore, data anonymization prior to sharing becomes a convenient solution to both organizations and researchers. Although several anonymization algorithms are available, few of them allow sufficient privacy (organization need), acceptable data utility (researcher need), and efficient data analysis at the same time. This article introduces a condensation-based differential privacy anonymization approach that achieves an improved tradeoff between privacy and utility compared to existing techniques and produces anonymized network trace data that can be shared publicly without lowering its utility value. Our solution also does not incur extra computation overhead for the data analyzer. A prototype system has been implemented, and experiments have shown that the proposed approach preserves privacy and allows data analysis without revealing the original data even when injection attacks are launched against it. When anonymized datasets are given as input to graph-based intrusion detection techniques, they yield almost identical intrusion detection rates as the original datasets with only a negligible impact.
Betweenness centrality (BC) is an important metrics in graph analysis which indicates critical vertices in large-scale networks based on shortest path enumeration. Typically, a BC algorithm constructs a shortest-path DAG for each vertex to calculate its BC score. However, for emerging real-world graphs, even the state-of-the-art BC algorithm will introduce a number of redundancies, as suggested by the existence of articulation points. Articulation points imply some common sub-DAGs in the DAGs for different vertices, but existing algorithms do not leverage such information and miss the optimization opportunity. We propose a redundancy elimination approach, which identifies the common sub-DAGs shared between the DAGs for different vertices. Our approach leverages the articulation points and reuses the results of the common sub-DAGs in calculating the BC scores, which eliminates redundant computations. We implemented the approach as an algorithm with two-level parallelism and evaluated it on a multicore platform. Compared to the state-of-the-art implementation using shared memory, our approach achieves an average speedup of 4.6x across a variety of real-world graphs, with the traversal rates up to 45 ~ 2400 MTEPS (Millions of Traversed Edges per Second).
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