Temporal Knowledge Graph Completion (TKGC) aims to predict missing parts of quadruples, which is crucial for real-life knowledge graphs. Compared with methods that only use graph neural networks, the emergence of pre-trained model has introduced a trend of simultaneously leveraging text and graph structure information. However, most current methods based on pre-trained models struggle to effectively utilize both text and multi-hop graph structure information concurrently, resulting in insufficient association mining of relations. To address the challenge, we propose a novel model: Temporal Closing Path for Pre-trained Language Model-based TKGC (TCP-PLM). We obtain the temporal closing relation path of the target relation through sampling, and use the relation path as a bridge to simultaneously utilize text and multi-hop graph structure information. Moreover, the relation path serves as a tool for mining associations between relations. At the same time, due to the design of entity-independent relation paths, our model can also handle the inductive setting. Our experiments on three benchmarks, along with extensive analysis, demonstrate that our model not only achieves substantial performance enhancements across four metrics compared to other models but also adeptly handles inductive settings.

This paper reports the first robotic system for vitrification of mammalian embryos. Vitrification is a technique for preserving oocytes and embryos in clinical IVF (in vitro fertilization). The procedure involves multiple steps of stringently timed pick-and-place operation for processing an oocyte/embryo in vitrification media. In IVF clinics, vitrification is conducted manually by highly skilled embryologists. Processing one oocyte/embryo occupies the embryologist 15–20 minutes, depending on protocols chosen to implement. Due to poor reproducibility and inconsistency across operators, success rates and survival rates also vary significantly. Through collaboration with IVF clinics, we are in process to realize robotic vitrification and aim ultimately to standardize clinical vitrification from manual operation to fully automated robotic operation. Our robotic system is embedded with two contact detection methods to determine the relative Z positions of the vitrification micropipette, embryo, and vitrification straw. A 3D tracking algorithm is developed for visually servoed embryo transfer and real-time monitoring of embryo volume changes during vitrification. Excess medium is automatically removed from around the vitrified embryo on the vitrification straw to achieve a high cooling rate. Tests on mouse embryos demonstrate that the system is capable of performing vitrification with a throughput at least three times that of manual operation and achieved a high survival rate (88.9%) and development rate (93.8%).

Mechanical force is an integral part of tissue morphogenesis and patterning. We have developed an automated micro-aspiration system to investigate how mouse limb bud tissue responds to extrinsic forces in order to understand whether tissue-generated forces can be a part of the mechanism causing oriented cell behaviors observed in mouse limb bud morphogenesis. The system is capable of performing automated micropipette tracking, tissue-tip contact detection, pressure control, and prolonged application of constant pressure. A three-dimensional tissue tracking algorithm is developed based on the processing of time-lapsed confocal Z-stack images. 3D visual feedback from confocal microscopy imaging, for the first time, is used to realize 3D visual servoing to control the micropipette position to compensate for tissue movement. This enables stable force application in a biologically relevant time scale (e.g., 60 minutes) during which cell remodeling occurs. Experimental results demonstrate that micro-aspiration on mouse limb bud is capable of creating tension anisotropy which causes force-responsive cells to dynamically remodel through polarized cell division and rosette resolution.