Electrocardiogram (ECG) has been widely used for emotion recognition. This paper presents a deep neural network based on convolutional layers and a transformer mechanism to detect stress using ECG signals. We perform leave-one-subject-out experiments on two publicly available datasets, WESAD and SWELL-KW, to evaluate our method. Our experiments show that the proposed model achieves strong results, comparable or better than the state-of-theart models for ECG-based stress detection on these two datasets. Moreover, our method is end-to-end, does not require handcrafted features, and can learn robust representations with only a few convolutional blocks and the transformer component. CCS CONCEPTS• Computing methodologies → Neural networks; Machine learning.

We propose cross-modal attentive connections, a new dynamic and effective technique for multimodal representation learning from wearable data. Our solution can be integrated into any stage of the pipeline, i.e., after any convolutional layer or block, to create intermediate connections between individual streams responsible for processing each modality. Additionally, our method benefits from two properties. First, it can share information uni-directionally (from one modality to the other) or bi-directionally. Second, it can be integrated into multiple stages at the same time to further allow network gradients to be exchanged in several touch-points. We perform extensive experiments on three public multimodal wearable datasets, WE-SAD, SWELL-KW, and CASE, and demonstrate that our method can effectively regulate and share information between different modalities to learn better representations. Our experiments further demonstrate that once integrated into simple CNN-based multimodal solutions (2, 3, or 4 modalities), our method can result in superior or competitive performance to state-of-the-art and outperform a variety of baseline uni-modal and classical multimodal methods.

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Epidermal Growth Factor Receptor (EGFR) signaling to the Ras-MAPK pathway is implicated in the development and progression of cancer and is a major focus of targeted combination therapies. Physiochemical models have been used for identifying and testing the signal-inhibiting potential of targeted therapies, however, their application to larger multi-pathway networks is limited by the availability of experimentally-determined rate and concentration parameters. An alternate strategy for identifying and evaluating drug-targetable nodes is proposed. A physiochemical model of EGFR-Ras-MAPK signaling is implemented and calibrated to experimental data. Essential topological features of the model are converted into a Petri net and nodes that behave as siphons-a structural property of Petri nets-are identified. Siphons represent potential drug-targets since they are unrecoverable if their values fall below a threshold. Centrality measures are then used to prioritize siphons identified as candidate drug-targets. Single and multiple drug-target combinations are identified which correspond to clinically relevant drug targets and exhibit inhibition synergy in physiochemical simulations of EGF-induced EGFR-Ras-MAPK signaling. Taken together, these studies suggest that siphons and centrality analyses are a promising computational strategy to identify and rank drug-targetable nodes in larger networks as they do not require knowledge of the dynamics of the system, but rely solely on topology.