Browsing by Author "Department of Computer and Information sciences, School of Science"

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    Direction Selectivity in Drosophila Proprioceptors Requires the Mechanosensory Channel Tmc
    (Elsevier, 2019-03-18) He, Liping; Gulyanon, Sarun; Mihovilovic Skanata, Mirna; Karagyozov, Doycho; Heckscher, Ellie S.; Krieg, Michael; Tsechpenakis, Gavriil; Gershow, Marc; Tracey, W. Daniel; Department of Computer and Information sciences, School of Science
    Summary Drosophila Transmembrane channel-like (Tmc) is a protein that functions in larval proprioception. The closely related TMC1 protein is required for mammalian hearing and is a pore-forming subunit of the hair cell mechanotransduction channel. In hair cells, TMC1 is gated by small deflections of microvilli that produce tension on extracellular tip-links that connect adjacent villi. How Tmc might be gated in larval proprioceptors, which are neurons having a morphology that is completely distinct from hair cells, is unknown. Here, we have used high-speed confocal microscopy both to measure displacements of proprioceptive sensory dendrites during larval movement and to optically measure neural activity of the moving proprioceptors. Unexpectedly, the pattern of dendrite deformation for distinct neurons was unique and differed depending on the direction of locomotion: ddaE neuron dendrites were strongly curved by forward locomotion, while the dendrites of ddaD were more strongly deformed by backward locomotion. Furthermore, GCaMP6f calcium signals recorded in the proprioceptive neurons during locomotion indicated tuning to the direction of movement. ddaE showed strong activation during forward locomotion, while ddaD showed responses that were strongest during backward locomotion. Peripheral proprioceptive neurons in animals mutant for Tmc showed a near-complete loss of movement related calcium signals. As the strength of the responses of wild-type animals was correlated with dendrite curvature, we propose that Tmc channels may be activated by membrane curvature in dendrites that are exposed to strain. Our findings begin to explain how distinct cellular systems rely on a common molecular pathway for mechanosensory responses.
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    Representing Graphs as Bag of Vertices and Partitions for Graph Classification
    (Springer, 2018-06-01) Bhuiyan, Mansurul; Al Hasan, Mohammad; Department of Computer and Information sciences, School of Science
    Graph classification is a difficult task because finding a good feature representation for graphs is challenging. Existing methods use topological metrics or local subgraphs as features, but the time complexity for finding discriminatory subgraphs or computing some of the crucial topological metrics (such as diameter and shortest path) is high, so existing methods do not scale well when the graphs to be classified are large. Another issue of graph classification is that the number of distinct graphs for each class that are available for training a classification model is generally limited. Such scarcity of graph data resources yields models that have much fewer instances than the model parameters, which leads to poor classification performance. In this work, we propose a novel approach for solving graph classification by using two alternative graph representations: the bag of vertices and the bag of partitions. For the first representation, we use representation learning-based node features and for the second, we use traditional metric-based features. Our experiments with 43 real-life graphs from seven different domains show that the bag representation of a graph improves the performance of graph classification significantly. We have shown 4–75% improvement on the vertex-based and 4–36% improvement on partition-based approach over the existing best methods. Besides, our vertex and partition multi-instance methods are on average 75 and 11 times faster in feature construction time than the current best, respectively.