Electrical and Electronic Engineering - Research Publications

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    Display of Native Antigen on cDC1 That Have Spatial Access to Both T and B Cells Underlies Efficient Humoral Vaccination.
    Kato, Y ; Steiner, TM ; Park, H-Y ; Hitchcock, RO ; Zaid, A ; Hor, JL ; Devi, S ; Davey, GM ; Vremec, D ; Tullett, KM ; Tan, PS ; Ahmet, F ; Mueller, SN ; Alonso, S ; Tarlinton, DM ; Ploegh, HL ; Kaisho, T ; Beattie, L ; Manton, JH ; Fernandez-Ruiz, D ; Shortman, K ; Lahoud, MH ; Heath, WR ; Caminschi, I (American Association of Immunologists, 2020-10-01)
    Follicular dendritic cells and macrophages have been strongly implicated in presentation of native Ag to B cells. This property has also occasionally been attributed to conventional dendritic cells (cDC) but is generally masked by their essential role in T cell priming. cDC can be divided into two main subsets, cDC1 and cDC2, with recent evidence suggesting that cDC2 are primarily responsible for initiating B cell and T follicular helper responses. This conclusion is, however, at odds with evidence that targeting Ag to Clec9A (DNGR1), expressed by cDC1, induces strong humoral responses. In this study, we reveal that murine cDC1 interact extensively with B cells at the border of B cell follicles and, when Ag is targeted to Clec9A, can display native Ag for B cell activation. This leads to efficient induction of humoral immunity. Our findings indicate that surface display of native Ag on cDC with access to both T and B cells is key to efficient humoral vaccination.
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    Comparative Analysis of Power Ramp Rate Control Strategies for Photovoltaic Systems
    Yan, HW ; Liang, G ; Rodriguez, E ; Beniwal, N ; Farivar, GG ; Pou, J (IEEE, 2023-01-01)
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    Flexible Power Point Tracking Aided Power Ramp Rate Control for Photovoltaic Systems With Small Energy Storage Capacity
    Yan, HW ; Liang, G ; Beniwal, N ; Rodriguez, E ; Farivar, GG ; Pou, J (Institute of Electrical and Electronics Engineers (IEEE), 2024-02-01)
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    Learning the Vector Coding of Egocentric Boundary Cells from Visual Data
    Lian, Y ; Williams, S ; Alexander, AS ; Hasselmo, ME ; Burkitt, AN (SOC NEUROSCIENCE, 2023-07-12)
    The use of spatial maps to navigate through the world requires a complex ongoing transformation of egocentric views of the environment into position within the allocentric map. Recent research has discovered neurons in retrosplenial cortex and other structures that could mediate the transformation from egocentric views to allocentric views. These egocentric boundary cells respond to the egocentric direction and distance of barriers relative to an animal's point of view. This egocentric coding based on the visual features of barriers would seem to require complex dynamics of cortical interactions. However, computational models presented here show that egocentric boundary cells can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Simulation of this simple sparse synaptic modification generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. Furthermore, some egocentric boundary cells learnt by the model can still function in new environments without retraining. This provides a framework for understanding the properties of neuronal populations in the retrosplenial cortex that may be essential for interfacing egocentric sensory information with allocentric spatial maps of the world formed by neurons in downstream areas, including the grid cells in entorhinal cortex and place cells in the hippocampus.SIGNIFICANCE STATEMENT The computational model presented here demonstrates that the recently discovered egocentric boundary cells in retrosplenial cortex can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Additionally, our model generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. This transformation between sensory input and egocentric representation in the navigational system could have implications for the way in which egocentric and allocentric representations interface in other brain areas.
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    Direct-acting antiviral resistance of Hepatitis C virus is promoted by epistasis
    Zhang, H ; Quadeer, AA ; Mckay, MR (NATURE PORTFOLIO, 2023-11-17)
    Direct-acting antiviral agents (DAAs) provide efficacious therapeutic treatments for chronic Hepatitis C virus (HCV) infection. However, emergence of drug resistance mutations (DRMs) can greatly affect treatment outcomes and impede virological cure. While multiple DRMs have been observed for all currently used DAAs, the evolutionary determinants of such mutations are not currently well understood. Here, by considering DAAs targeting the nonstructural 3 (NS3) protein of HCV, we present results suggesting that epistasis plays an important role in the evolution of DRMs. Employing a sequence-based fitness landscape model whose predictions correlate highly with experimental data, we identify specific DRMs that are associated with strong epistatic interactions, and these are found to be enriched in multiple NS3-specific DAAs. Evolutionary modelling further supports that the identified DRMs involve compensatory mutational interactions that facilitate relatively easy escape from drug-induced selection pressures. Our results indicate that accounting for epistasis is important for designing future HCV NS3-targeting DAAs.
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    Evolving graph-based video crowd anomaly detection
    Yang, M ; Feng, Y ; Rao, AS ; Rajasegarar, S ; Tian, S ; Zhou, Z (Springer, 2024)
    Detecting anomalous crowd behavioral patterns from videos is an important task in video surveillance and maintaining public safety. In this work, we propose a novel architecture to detect anomalous patterns of crowd movements via graph networks. We represent individuals as nodes and individual movements with respect to other people as the node-edge relationship of an evolving graph network. We then extract the motion information of individuals using optical flow between video frames and represent their motion patterns using graph edge weights. In particular, we detect the anomalies in crowded videos by modeling pedestrian movements as graphs and then by identifying the network bottlenecks through a max-flow/min-cut pedestrian flow optimization scheme (MFMCPOS). The experiment demonstrates that the proposed framework achieves superior detection performance compared to other recently published state-of-the-art methods. Considering that our proposed approach has relatively low computational complexity and can be used in real-time environments, which is crucial for present day video analytics for automated surveillance.
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    An efficient deep neural model for detecting crowd anomalies in videos
    Yang, M ; Tian, S ; Rao, AS ; Rajasegarar, S ; Palaniswami, M ; Zhou, Z (Springer, 2023-06-01)
    Identifying unusual crowd events is highly challenging, laborious, and prone to errors in video surveillance applications. We propose a novel end-to-end deep learning architecture called Stacked Denoising Auto-Encoder (DeepSDAE) to address these challenges, comprising SDAE, VGG16 and Plane-based one-class Support Vector Machine (SVM), abbreviated as PSVM, to detect anomalies such as stationary people in an active scene or loitering activities in a crowded scene. The DeepSDAE framework is a hybrid deep learning architecture. It consists of a four-layered SDAE and an enhanced convolutional neural network (CNN) model. Our framework employs Reinforcement Learning to optimise the learning parameters to detect crowd anomalies. We use the Markov Decision Process (MDP) with Deep Q-learning to find the optimal Q value. We also present a late fusion procedure to combine individual decisions from the intermediate and final layers of the SDAE and VGG16 networks to detect different anomalies. Our experiments on four real-world datasets reveal the superior performance of our proposed framework in detecting (frame-level and pixel-level) anomalies.
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    Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review
    Mendis, L ; Palaniswami, M ; Brownfoot, F ; Keenan, E (MDPI, 2023-09)
    The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them.
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    Incentivizing Local Controllability in Optimal Trajectory Planning
    Skoraczynski, AZ ; Manzie, C ; Dower, PM (IEEE, 2023-01-01)
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    Min-max and stat game representations for nonlinear optimal control problems
    Dower, PM ; McEneaney, WM ; Zheng, Y (IEEE, 2023-01-01)