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Abstract The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data. 

The multiconfigurational reaction path features a small barrier for a pyridine-appended iminoguanidinium photoswitch from the Franck–Condon geometry of theEisomer in the π–π* state to the ground stateZphotoproductviathe conical intersection. 

Abstract The quality of solar images plays an important role in the analysis of small events in solar physics. Therefore, the improvement of image resolution based on super-resolution (SR) reconstruction technology has aroused the interest of many researchers. In this paper, an improved conditional denoising diffusion probability model (ICDDPM) based on the Markov chain is proposed for the SR reconstruction of solar images. This method reconstructs high-resolution (HR) images from low-resolution images by learning a reverse process that adds noise to HR images. To verify the effectiveness of the method, images from the Goode Solar Telescope at the Big Bear Solar Observatory and the Helioseismic and Magnetic Imager (HMI) on the Solar Dynamics Observatory are used to train a network, and the spatial resolution of reconstructed images is 4 times that of the original HMI images. The experimental results show that the performance based on ICDDPM is better than the previous work in subject judgment and object evaluation indexes. The reconstructed images of this method have higher subjective vision quality and better consistency with the HMI images. And the structural similarity and rms index results are also higher than the compared method, demonstrating the success of the resolution improvement using ICDDPM.