Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.

Cerebral palsy (CP) is a neurological disorder that severely limits physical movements in everyday life. For individuals with CP, precise movement patterns are critical to improving their physical abilities. However, because of the time and expense involved, obtaining feedback from qualified rehabilitation professionals could be difficult. To address this problem, we propose a multimodal human action recognition (HAR) framework that integrates time-series data on skeletons and angles for upper body exercise for individuals with CP. Previous, studies typically used skeleton data as input to HAR. However, given the repetitive motion sequence, angles also are a crucial factor. In this study, we create video data of upper body exercises. Using the data, we estimate the skeleton using BlazePose, a human pose estimation (HPE) algorithm, and calculate the critical angles from the estimated skeletons. Our proposed framework incorporates graph-based models for skeletons and recurrent-based models for angles with an attention mechanism, allowing identification of important temporal information. It is shown that our framework performs better than unimodal HAR models. By reducing the costs associated with exercise feedback, we expect our approach to ultimately improve the physical health of individuals with CP.

Remanufacturing has emerged as a way to solve production problems, as raw material costs increase and environmental pollution caused by discarded equipment occurs. The process can extend product lifetime and prevent waste of resources. In particular, it has economical efficiency for large equipment such as GIS (Gas Insulated Switchgear). The crucial points in remanufacturing are determining replaceable parts and economic valuation. To address these issues, we propose a framework for remanufacturing GIS with remaining lifetime prediction. We construct a regression model for remaining useful life (RUL) in the proposed framework using GIS sensor data. The cost of the replacement parts is estimated with the selected sensors. To validate the effectiveness of the proposed framework, we conducted accelerated life testing on a GIS for data acquisition and applied our framework. The experimental results demonstrate that the tree-based RUL regression model outperforms the others in prediction accuracy. In the simulation of part replacement, the important sensor-based decision-making improves RUL significantly.