Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Periodic solutions in next generation neural field models
    (Springer Nature, 2023-10) Laing CR; Omel'chenko O
    We consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location are described by a complex-valued Riccati equation we derive a self-consistency equation that such periodic solutions must satisfy. We determine the stability of these solutions, and present numerical results to illustrate the usefulness of this technique. The generality of this approach is demonstrated through its application to several other systems involving delays, two-population architecture and networks of Winfree oscillators
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    Ankyrin2 is essential for neuronal morphogenesis and long-term courtship memory in Drosophila.
    (BioMed Central Ltd, 2023-05-16) Schwartz S; Wilson SJ; Hale TK; Fitzsimons HL
    Dysregulation of HDAC4 expression and/or nucleocytoplasmic shuttling results in impaired neuronal morphogenesis and long-term memory in Drosophila melanogaster. A recent genetic screen for genes that interact in the same molecular pathway as HDAC4 identified the cytoskeletal adapter Ankyrin2 (Ank2). Here we sought to investigate the role of Ank2 in neuronal morphogenesis, learning and memory. We found that Ank2 is expressed widely throughout the Drosophila brain where it localizes predominantly to axon tracts. Pan-neuronal knockdown of Ank2 in the mushroom body, a region critical for memory formation, resulted in defects in axon morphogenesis. Similarly, reduction of Ank2 in lobular plate tangential neurons of the optic lobe disrupted dendritic branching and arborization. Conditional knockdown of Ank2 in the mushroom body of adult Drosophila significantly impaired long-term memory (LTM) of courtship suppression, and its expression was essential in the γ neurons of the mushroom body for normal LTM. In summary, we provide the first characterization of the expression pattern of Ank2 in the adult Drosophila brain and demonstrate that Ank2 is critical for morphogenesis of the mushroom body and for the molecular processes required in the adult brain for the formation of long-term memories.
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    Deciphering the roles of subcellular distribution and interactions involving the MEF2 binding region, the ankyrin repeat binding motif and the catalytic site of HDAC4 in Drosophila neuronal morphogenesis
    (BioMed Central Ltd, 2024-12) Tan WJ; Hawley HR; Wilson SJ; Fitzsimons HL
    BACKGROUND: Dysregulation of nucleocytoplasmic shuttling of histone deacetylase 4 (HDAC4) is associated with several neurodevelopmental and neurodegenerative disorders. Consequently, understanding the roles of nuclear and cytoplasmic HDAC4 along with the mechanisms that regulate nuclear entry and exit is an area of concerted effort. Efficient nuclear entry is dependent on binding of the transcription factor MEF2, as mutations in the MEF2 binding region result in cytoplasmic accumulation of HDAC4. It is well established that nuclear exit and cytoplasmic retention are dependent on 14-3-3-binding, and mutations that affect binding are widely used to induce nuclear accumulation of HDAC4. While regulation of HDAC4 shuttling is clearly important, there is a gap in understanding of how the nuclear and cytoplasmic distribution of HDAC4 impacts its function. Furthermore, it is unclear whether other features of the protein including the catalytic site, the MEF2-binding region and/or the ankyrin repeat binding motif influence the distribution and/or activity of HDAC4 in neurons. Since HDAC4 functions are conserved in Drosophila, and increased nuclear accumulation of HDAC4 also results in impaired neurodevelopment, we used Drosophila as a genetic model for investigation of HDAC4 function. RESULTS: Here we have generated a series of mutants for functional dissection of HDAC4 via in-depth examination of the resulting subcellular distribution and nuclear aggregation, and correlate these with developmental phenotypes resulting from their expression in well-established models of neuronal morphogenesis of the Drosophila mushroom body and eye. We found that in the mushroom body, forced sequestration of HDAC4 in the nucleus or the cytoplasm resulted in defects in axon morphogenesis. The actions of HDAC4 that resulted in impaired development were dependent on the MEF2 binding region, modulated by the ankyrin repeat binding motif, and largely independent of an intact catalytic site. In contrast, disruption to eye development was largely independent of MEF2 binding but mutation of the catalytic site significantly reduced the phenotype, indicating that HDAC4 acts in a neuronal-subtype-specific manner. CONCLUSIONS: We found that the impairments to mushroom body and eye development resulting from nuclear accumulation of HDAC4 were exacerbated by mutation of the ankyrin repeat binding motif, whereas there was a differing requirement for the MEF2 binding site and an intact catalytic site. It will be of importance to determine the binding partners of HDAC4 in nuclear aggregates and in the cytoplasm of these tissues to further understand its mechanisms of action.
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    Learning emergent partial differential equations in a learned emergent space
    (Springer Nature Limited, 2022-06-09) Kemeth FP; Bertalan T; Thiem T; Dietrich F; Moon SJ; Laing CR; Kevrekidis IG
    We propose an approach to learn effective evolution equations for large systems of interacting agents. This is demonstrated on two examples, a well-studied system of coupled normal form oscillators and a biologically motivated example of coupled Hodgkin-Huxley-like neurons. For such types of systems there is no obvious space coordinate in which to learn effective evolution laws in the form of partial differential equations. In our approach, we accomplish this by learning embedding coordinates from the time series data of the system using manifold learning as a first step. In these emergent coordinates, we then show how one can learn effective partial differential equations, using neural networks, that do not only reproduce the dynamics of the oscillator ensemble, but also capture the collective bifurcations when system parameters vary. The proposed approach thus integrates the automatic, data-driven extraction of emergent space coordinates parametrizing the agent dynamics, with machine-learning assisted identification of an emergent PDE description of the dynamics in this parametrization.
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    AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification
    (IEEE, 2021-10-27) Wei Y; Jang-Jaccard J; Sabrina F; Singh A; Xu W; Camtepe S; Oliva D
    Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carry malicious DDoS payloads, increase exponentially as they cannot extract high importance features automatically. To address this concern, we propose a hybrid approach named AE-MLP that combines two deep learning-based models for effective DDoS attack detection and classification. The Autoencoder (AE) part of our proposed model provides an effective feature extraction that finds the most relevant feature sets automatically without human intervention (e.g., knowledge of cybersecurity professionals). The Multi-layer Perceptron Network (MLP) part of our proposed model uses the compressed and reduced feature sets produced by the AE as inputs and classifies the attacks into different DDoS attack types to overcome the performance overhead and bias associated with processing large feature sets with noise (i.e., unnecessary feature values). Our experimental results, obtained through comprehensive and extensive experiments on different aspects of performance on the CICDDoS2019 dataset, demonstrate both a very high and robust accuracy rate and F1-score that exceed 98% which also outperformed the performance of many similar methods. This shows that our proposed model can be used as an effective DDoS defense tool against the growing number of DDoS attacks.