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Abstract Although adamantinomatous craniopharyngioma (ACP) is a tumour with low histological malignancy, there are very few therapeutic options other than surgery. ACP has high histological complexity, and the unique features of the immunological microenvironment within ACP remain elusive. Further elucidation of the tumour microenvironment is particularly important to expand our knowledge of potential therapeutic targets. Here, we performed integrative analysis of 58,081 nuclei through single-nucleus RNA sequencing and spatial transcriptomics on ACP specimens to characterize the features and intercellular network within the microenvironment. The ACP environment is highly immunosuppressive with low levels of T-cell infiltration/cytotoxicity. Moreover, tumour-associated macrophages (TAMs), which originate from distinct sources, highly infiltrate the microenvironment. Using spatial transcriptomic data, we observed one kind of non-microglial derived TAM that highly expressed GPNMB close to the terminally differentiated epithelial cell characterized by RHCG, and this colocalization was verified by asmFISH. We also found the positive correlation of infiltration between these two cell types in datasets with larger cohort. According to intercellular communication analysis, we report a regulatory network that could facilitate the keratinization of RHCG+ epithelial cells, eventually causing tumour progression. Our findings provide a comprehensive analysis of the ACP immune microenvironment and reveal a potential therapeutic strategy base on interfering with these two types of cells.

Line structured light sensor has been applied in various three dimensional (3D) measurement scenes with the advantages of non-contact, low cost and high speed. Its accuracy, which is directly determined by the calibration method, needs to be further improved to fulfill the measuring tasks of precision parts. Here, we proposed a numerical method that can eliminate the errors of the model based methods through two strategies. One is the establishment of the numerical mapping relationship between stripe pixel coordinates and their world coordinates through piecewise cubic interpolation. Corner points of a checkerboard target are used to obtain sufficient interpolating nodes. This target can be manually aligned with the laser plane and the alignment error would be eliminated via the point projection. The other is the data-driven laser plane optimization. The data set of reference interval distance is computed based on the invariance of cross ratio. The optimization model is to minimize the root mean squared error of measured interval distances by adjusting the laser plane coefficients. After the optimization, a higher numerical mapping relationship can be achieved. It bypasses the camera and the distortion models and reaches a calibration error of only 0.005mm. The comparison studies and the measurement of the steps further validate the proposed method.

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.