MOTIVATION: Spatial clustering is a critical analytical task in spatial transcriptomics (ST) that aids in uncovering the spatial molecular mechanisms underlying biological phenotypes. Along with the numerous spatial clustering methods, there comes the imperative need for an effective metric to evaluate their performance. An ideal metric should consider three factors: label agreement, spatial organization, and error severity. However, existing evaluation metrics focus solely on either label agreement or spatial organization, leading to biased and misleading evaluations. RESULTS: To fill this gap, we propose CEMUSA, a novel graph-based metric that integrates these factors into a unified evaluation framework. Extensive testing on both simulated and real datasets demonstrate CEMUSA's superiority over conventional metrics in differentiating clustering results with subtle differences in topology and error severity, while maintaining computational efficiency. AVAILABILITY AND IMPLEMENTATION: The source code and data are freely available at https://github.com/YihDu/CEMUSA. CEMUSA is implemented as an R package at https://yihdu.github.io/CEMUSA.

Contact sensing along the soft catheter robots (SCRs) is crucial for enhancing surgical performance and intraoperative safety during minimally invasive procedures. Nevertheless, current approaches relying on medical imaging devices or optical fiber sensors are noticeably costly and pose challenges for clinical applications. This study develops a more implementable and cost-effective contact sensing method based on magnetic signals for SCR-based surgery. Specifically, several miniature Hall sensors are integrated into the SCR, and a heterogeneous magnetic field is applied. When the SCR comes into contact with the external environment, its deformation leads to changes in the magnetic signal detected by the Hall sensors. A contact sensing deep learning (ContactSenseDL) model is then developed to map the magnetic signal variation to contact position and force along the SCR. The proposed approach is validated on a catheter robot system prototype and achieves remarkable performance. In contact position prediction, the average error of axial arc length is as low as 1.97 mm (2.81% of the maximum insert length of the SCR), and the prediction of radial position exhibits high consistency with actual values. In contact force prediction, the average errors of friction and pressure are 2.43 (2.59% of the maximum friction) and 1.61 mN (2.45% of the maximum pressure), respectively. Additionally, contact sensing experiments are conducted on a knee model to demonstrate the potential application of this method. Overall, the proposed contact sensing strategy can effectively sense the contact position and force along SCRs in 3D space, holding promise for enhancing safety in SCR-based surgery.

Spatial labeling assigns labels to specific spatial locations to characterize their spatial properties and relationships, with broad applications in scientific research and practice. Measuring the similarity between two spatial labelings is essential for understanding their differences and the contributing factors, such as changes in location properties or labeling methods. An adequate and unbiased measurement of spatial labeling similarity should consider the number of matched labels (label agreement), the topology of spatial label distribution, and the heterogeneous impacts of mismatched labels. However, existing methods often fail to account for all these aspects. To address this gap, we propose a methodological framework to guide the development of methods that meet these requirements. Given two spatial labelings, the framework transforms them into graphs based on location organization, labels, and attributes (e.g., location significance). The distributions of their graph attributes are then extracted, enabling an efficient computation of distributional discrepancy to reflect the dissimilarity level between the two labelings. We further provide a concrete implementation of this framework, termed Spatial Labeling Analogy Metric (SLAM), along with an analysis of its theoretical foundation, for evaluating spatial labeling results in spatial transcriptomics (ST) as per their similarity with ground truth labeling. Through a series of carefully designed experimental cases involving both simulated and real ST data, we demonstrate that SLAM provides a comprehensive and accurate reflection of labeling quality compared to other well-established evaluation metrics. Our code is available at https://github.com/YihDu/ SLAM.

Dialogue state tracker is responsible for inferring user intentions through dialogue history.Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information.We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information's interference and improve long dialogue context tracking.Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue and then integrate them using a Slot Information Sharing.The sharing improve the models ability to deduce value from related slots.Our model yields a significantly improved performance compared to previous state-of-the-art models on the Multi-WOZ dataset.