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1. Calcium-Dependent Chemiluminescence Catalyzed by a Truncated c-MYC Promoter G-Triplex DNA

   https://doi.org/10.3390/molecules29184457  

   Das, Malay Kumar; Williams, Elizabeth P; Myhre, Mitchell W; David, Wendi M; Kerwin, Sean M (September 2024, Molecules)

   The dynamic landscape of non-canonical DNA G-quadruplex (G4) folding into G-triplex intermediates has led to the study of G-triplex structures and their ability to serve as peroxidase-mimetic DNAzymes. Here we report the formation, stability, and catalytic activity of a 5′-truncated c-MYC promoter region G-triplex, c-MYC-G3. Through circular dichroism, we demonstrated that c-MYC-G3 adopts a stable, parallel-stranded G-triplex conformation. The chemiluminescent oxidation of luminol by the peroxidase mimicking DNAzyme activity of c-MYC-G3 was increased in the presence of Ca2+ ions. We utilized surface plasmon resonance to characterize both c-MYC-G3 G-triplex formation and its interaction with hemin. The detailed study of c-MYC-G3 and its ability to form a G-triplex structure and its DNAzyme activity identifies issues that can be addressed in future G-triplex DNAzyme designs. 

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2. ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data

   https://doi.org/10.1609/aaai.v39i11.33310  

   Lei, Zhenyu; Dong, Yushun; Li, Jundong; Chen, Chen (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningful insights from this data. However, in real-world applications, most nodes may not possess any available temporal data during training. For example, the pandemic dynamics of most cities on a geographical graph may not be available due to the asynchronous nature of outbreaks. Such a phenomenon disagrees with the training requirements of most existing spatial-temporal forecasting methods, which jeopardizes their effectiveness and thus blocks broader deployment. In this paper, we propose to formulate a novel problem of inductive forecasting with limited training data. In particular, given a spatial-temporal graph, we aim to learn a spatial-temporal forecasting model that can be easily generalized onto those nodes without any available temporal training data. To handle this problem, we propose a principled framework named ST-FiT. ST-FiT consists of two key learning components: temporal data augmentation and spatial graph topology learning. With such a design, ST-FiT can be used on top of any existing STGNNs to achieve superior performance on the nodes without training data. Extensive experiments verify the effectiveness of ST-FiT in multiple key perspectives. 

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3. Event-Aware Multimodal Mobility Nowcasting

   https://doi.org/10.1609/aaai.v36i4.20342  

   Wang, Zhaonan; Jiang, Renhe; Xue, Hao; Salim, Flora D.; Song, Xuan; Shibasaki, Ryosuke (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes nor adaptive to unprecedented volatility brought by potential societal events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of societal events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/underdoc-wang/EAST-Net. 

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4. Spike-train level backpropagation for training deep recurrent spiking neural networks

   Zhang, Wenrui; Li, Peng (December 2019, Advances in neural information processing systems)

   Spiking neural networks (SNNs) well support spatio-temporal learning and energy-efficient event-driven hardware neuromorphic processors. As an important class of SNNs, recurrent spiking neural networks (RSNNs) possess great computational power. However, the practical application of RSNNs is severely limited by challenges in training. Biologically-inspired unsupervised learning has limited capability in boosting the performance of RSNNs. On the other hand, existing backpropagation (BP) methods suffer from high complexity of unfolding in time, vanishing and exploding gradients, and approximate differentiation of discontinuous spiking activities when applied to RSNNs. To enable supervised training of RSNNs under a well-defined loss function, we present a novel Spike-Train level RSNNs Backpropagation (ST-RSBP) algorithm for training deep RSNNs. The proposed ST-RSBP directly computes the gradient of a rate-coded loss function defined at the output layer of the network w.r.t tunable parameters. The scalability of ST-RSBP is achieved by the proposed spike-train level computation during which temporal effects of the SNN is captured in both the forward and backward pass of BP. Our ST-RSBP algorithm can be broadly applied to RSNNs with a single recurrent layer or deep RSNNs with multiple feedforward and recurrent layers. Based upon challenging speech and image datasets including TI46, N-TIDIGITS, Fashion-MNIST and MNIST, ST-RSBP is able to train SNNs with an accuracy surpassing that of the current state-of-the-art SNN BP algorithms and conventional non-spiking deep learning models. 

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5. Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids

   https://doi.org/10.1109/TSG.2022.3196943  

   Wu, Jiaqi; Yuan, Jingyi; Weng, Yang; Ayyanar, Raja (January 2023, IEEE Transactions on Smart Grid)

   The widespread use of distributed energy sources (DERs) raises significant challenges for power system design, planning, and operation, leading to wide adaptation of tools on hosting capacity analysis (HCA). Traditional HCA methods conduct extensive power flow analysis. Due to the computation burden, these time-consuming methods fail to provide online hosting capacity (HC) in large distribution systems. To solve the problem, we first propose a deep learning-based problem formulation for HCA, which conducts offline training and determines HC in real time. The used learning model, long short-term memory (LSTM), implements historical time-series data to capture periodical patterns in distribution systems. However, directly applying LSTMs suffers from low accuracy due to the lack of consideration on spatial information, where location information like feeder topology is critical in nodal HCA. Therefore, we modify the forget gate function to dual forget gates, to capture the spatial correlation within the grid. Such a design turns the LSTM into the Spatial-Temporal LSTM (ST-LSTM). Moreover, as voltage violations are the most vital constraints in HCA, we design a voltage sensitivity gate to increase accuracy further. The results of LSTMs and ST-LSTMs on feeders, such as IEEE 34-, 123-bus feeders, and utility feeders, validate our designs. 

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