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Visual positioning plays a pivotal role in enabling robotic disc cutter replacement for the shield machine. However, underground operational challenges—including low illumination, high dust concentrations, and irregular sand deposition on the surface of the disc cutter and its holder—severely compromise recognition accuracy. To address this, we propose a multi-mechanism enhanced UNet model for robust segmentation of the disc cutter holder under heterogeneous surface conditions. Experimental comparisons with mainstream semantic segmentation models demonstrate that the Res-UNet achieves superior training efficiency and segmentation accuracy. Ablation studies further reveal optimal performance when utilizing a hybrid loss function (dice loss + cross-entropy loss) paired with the Adam optimizer. By integrating attention mechanisms, we develop the Res-UNet-CA architecture, which achieves state-of-the-art metrics on independent test sets: accuracy (99.45%), precision (98.9%), recall (99.11%), F1-score (99%), and mIoU (98.63%). The Res-UNet-CA model significantly outperforms other semantic segmentation models in prediction quality, offering an innovative solution for shield machine disc cutter holder detection.

The rapidly self-renewing epithelium in the mammalian intestine is maintained by multipotent intestinal stem cells (ISCs) located at the bottom of the intestinal crypt that are interspersed with Paneth cells in the small intestine and Paneth-like cells in the colon. The ISC compartment is also closely associated with a sub-epithelial compartment that contains multiple types of mesenchymal stromal cells. With the advances in single cell and gene editing technologies, rapid progress has been made for the identification and characterization of the cellular components of the niche microenvironment that is essential for self-renewal and differentiation of ISCs. It has become increasingly clear that a heterogeneous population of mesenchymal cells as well as the Paneth cells collectively provide multiple secreted niche signals to promote ISC self-renewal. Here we review and summarize recent advances in the regulation of ISCs with a main focus on the definition of niche cells that sustain ISCs.

The blood-brain barrier (BBB) restricts the uptake of many neuro-therapeutic molecules, presenting a formidable hurdle to drug development in brain diseases. We proposed a new and dynamic in vivo -like three-dimensional microfluidic system that replicates the key structural, functional and mechanical properties of the blood-brain barrier in vivo . Multiple factors in this system work synergistically to accentuate BBB-specific attributes–permitting the analysis of complex organ-level responses in both normal and pathological microenvironments in brain tumors. The complex BBB microenvironment is reproduced in this system via physical cell-cell interaction, vascular mechanical cues and cell migration. This model possesses the unique capability to examine brain metastasis of human lung, breast and melanoma cells and their therapeutic responses to chemotherapy. The results suggest that the interactions between cancer cells and astrocytes in BBB microenvironment might affect the ability of malignant brain tumors to traverse between brain and vascular compartments. Furthermore, quantification of spatially resolved barrier functions exists within a single assay, providing a versatile and valuable platform for pharmaceutical development, drug testing and neuroscientific research.

In a tunneling boring machine (TBM), to obtain the attitude in real time is very important for a driver. However, the current laser targeting system has a large delay before obtaining the attitude. So, an adaptive-neuro-fuzzy-based information fusion method is proposed to predict the attitude of a laser targeting system in real time. In the proposed method, a dual-rate information fusion is used to fuse the information of a laser targeting system and a two-axis inclinometer, and then obtain roll and pitch angles with a higher rate and provide a smoother attitude prediction. Considering that a measurement error exists, the adaptive neuro-fuzzy inference system (ANFIS) is proposed to model the measurement error, and then the ANFIS-based model is combined with the dual-rate information fusion to achieve high performance. Experimental results show the ANFIS-based information fusion can provide higher real-time performance and accuracy of the attitude prediction. Experimental results also verify that the ANFIS-based information fusion can solve the problem of the laser targeting system losing signals.

To achieve automatic disc cutter replacement of shield machines, measuring the accurate pose of the disc cutter holder by machine vision is crucial. However, under polluted and restricted illumination conditions, achieving pose estimation by vision is a great challenge. This paper proposes a line-features-based pose estimation method for the disc cutter holder of the shield machine by using a monocular camera. For the blurring effect of rounded corners on the image edge, a rounded edge model is established to obtain edge points that better match the 3D model of the workpiece. To obtain the edge search box corresponding to each edge, a contour separation method based on an adaptive threshold region growing method is proposed. By preprocesses on the edge points of each edge, the efficiency and the accuracy of RANSAC linear fitting are improved. The experimental result shows that the proposed pose estimation method is highly reliable and can meet the measurement accuracy requirements in practical engineering applications.

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.

During tunneling processes, disc cutters of a tunnel boring machine (TBM) usually need to be frequently and unexpectedly replaced. Regular inspections are needed to check disc cutters’ status, which significantly reduces the work efficiency and increases the cost. This paper proposes a new prediction model based on TBM operational parameters and geological conditions that determines whether disc cutter replacement is needed. Firstly, an evaluation criterion for whether the cutters need to be replaced is constructed. Secondly, specific parameters related to the evaluation criterion are analyzed and 18 features are established on tunneling monitoring information. Then, the mapping model between the cutter replacement judgement and the established features is built based on a kernel support vector machine (KSVM). Finally, the data obtained from a Jilin water transport tunnel project is utilized to verify the performance of the proposed model. Test results show that the new model can obtain an average accuracy of 90.0% and an average F1 score of 86.2% on field data prediction based on data from past tunneling days. Therefore, the proposed data-predictive model can be used in tunneling to accurately predict whether disc cutters need to be replaced before human judgment, and thereby greatly improve tunneling safety and efficiency.

Increasing evidence suggest functional roles of subepithelial mesenchymal niche cells in maintaining intestinal stem cells and in modulating the pathogenesis of various intestinal diseases in mammals. A recent study reported the discovery of a new population of stromal cells in mice termed MAP3K2-Regulated Intestinal Stromal Cells (MRISCs); these cells reside at the base of colonic crypt and function to protect colonic stem cells during colonic inflammation by expressing the Wnt agonist R-spondin1 (Rspo1).

Coilia nasus (Japanese grenadier anchovy) undergoes spawning migration from the ocean to fresh water inland. Previous studies have suggested that anadromous fish use olfactory cues to perform successful migration to spawn. However, limited genomic information is available for C. nasus. To understand the molecular mechanisms of spawning migration, it is essential to identify the genes and pathways involved in the migratory behavior of C. nasus. Using de novo transcriptome sequencing and assembly, we constructed two transcriptomes of the olfactory epithelium from wild anadromous and non-anadromous C. nasus. Over 178 million high-quality clean reads were generated using Illumina sequencing technology and assembled into 176,510 unigenes (mean length: 843 bp). About 51% (89,456) of the unigenes were functionally annotated using protein databases. Gene ontology analysis of the transcriptomes indicated gene enrichment not only in signal detection and transduction, but also in regulation and enzymatic activity. The potential genes and pathways involved in the migratory behavior were identified. In addition, simple sequence repeats and single nucleotide polymorphisms were analyzed to identify potential molecular markers. We, for the first time, obtained high-quality de novo transcriptomes of C. nasus using a high-throughput sequencing approach. Our study lays the foundation for further investigation of C. nasus spawning migration and genome evolution.

Thermal‐responsive hydrogels are developed as ion‐conductive switchs for energy storage devices, however, the molecule mechanism of switch on/off remains unclear. Here, poly(N‐isopropylacrylamide‐co‐acrylamide) hydrogel is synthesized as a model material and nanodiamond (ND) based quantum sensing for phase change study is developed. First, micro‐scale phase separation with cross‐linked mesh structure after sol‐gel transition is visualized in situ and water molecules are trapped by polymer chains and on a chemically “frozen” state. Then, the nano‐scale inhomogeneous distributions of viscosity, thermal conductivity and ionic mobility in hydrogel at high temperature are observed by measuring the rotation, translation and zero‐field splitting of NDs. Besides, the ionic mobility of hydrogel is found to be dependent not only on temperature but also on polymer concentration. These observations suggested that the physical “wall” induced by inhomogeneous phase separation at microscopic scale blocked the ion conduction pathways, providing a potential intrinsic explanation for ion migration shut‐down of ionic hydrogels at high temperature. The study develops nanodiamond (ND) based quantum sensing for operando decoding phase behavior at microscopic scale in stimuli‐responsive hydrogel. The rotation, translation and zero‐field splitting of NDs at different phases reveal the spatial distribution of viscoelastic and thermodynamic properties within 3D polymer networks, disclosing the inhomogeneous phase transition at nano‐scale and accounting for the ion migration shut‐down at high temperatures.