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  1. Portable smartphone-based fluorescent microscopes are becoming popular owing to their ability to provide major functionalities offered by regular benchtop microscopes at a fraction of the cost. However, smartphone-based microscopes are still limited to a single fluorophore, fixed magnification, the inability to work with a different smartphones, and limited usability to either glass slides or cover slips. To overcome these challenges, here we present a modular smartphone-based microscopic attachment. The modular design allows the user to easily swap between different sets of filters and lenses, thereby enabling utility of multiple fluorophores and magnification levels. Our microscopic smartphone attachment can also bemore »used with different smartphones and was tested with Nokia Lumia 1020, Samsung Galaxy S9+, and an iPhone XS. Further, we showed imaging results of samples on glass slides, cover slips, and microfluidic devices. A 1951 USAF resolution test target was used to quantify the maximum resolution of the microscope which was found to be 3.9 μm. The performance of the smartphone-based microscope was compared with a benchtop microscope and we found an R 2 value of 0.99 using polystyrene beads and blood cells isolated from human blood samples collected from Robert Wood Johnson Medical Hospital. Additionally, to count the particles (cells and beads) imaged from the smartphone-based fluorescent microscope, we developed artificial neural networks (ANNs) using multiple training algorithms, and evaluated their performances compared to the control (ImageJ). Finally, we did ANOVA and Tukey's post-hoc analysis and found a p -value of 0.97 which shows that no statistical significant difference exists between the performance of the trained ANN and control (ImageJ).« less
  2. We present a fast dynamic graph data structure for the GPU. Our dynamic graph structure uses one hash table per vertex to store adjacency lists and achieves 3.4–14.8x faster insertion rates over the state of the art across a diverse set of large datasets, as well as deletion speedups up to 7.8x. The data structure supports queries and dynamic updates through both edge and vertex insertion and deletion. In addition, we define a comprehensive evaluation strategy based on operations, workloads, and applications that we believe better characterize and evaluate dynamic graph data structures.
  3. We engineer a GPU implementation of a B-Tree that supports concurrent queries (point, range, and successor) and updates (insertions and deletions). Our B-tree outperforms the state of the art, a GPU log-structured merge tree (LSM) and a GPU sorted array. In particular, point and range queries are significantly faster than in a GPU LSM (the GPU LSM does not implement successor queries). Furthermore, B-Tree insertions are also faster than LSM and sorted array insertions unless insertions come in batches of more than roughly 100k. Because we cache the upper levels of the tree, we achieve lookup throughput that exceeds themore »DRAM bandwidth of the GPU. We demonstrate that the key limiter of performance on a GPU is contention and describe the design choices that allow us to achieve this high performance.« less
  4. Successful rehabilitation of oropharyngeal swallowing disorders (i.e., dysphagia) requires frequent performance of head/neck exercises that primarily rely on expensive biofeedback devices, often only available in large medical centers. This directly affects treatment compliance and outcomes, and highlights the need to develop a portable and inexpensive remote monitoring system for the telerehabilitation of dysphagia. Here, we present the development and preliminarily validation of a skin-mountable sensor patch that can fit on the curvature of the submental (under the chin) area noninvasively and provide simultaneous remote monitoring of muscle activity and laryngeal movement during swallowing tasks and maneuvers. This sensor patch incorporatesmore »an optimal design that allows for the accurate recording of submental muscle activity during swallowing and is characterized by ease of use, accessibility, reusability, and cost-effectiveness. Preliminary studies on a patient with Parkinson’s disease and dysphagia, and on a healthy control participant demonstrate the feasibility and effectiveness of this system.« less