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Synthetic Aperture Radar (SAR) plays a crucial role in all-weather and all-day Earth observation owing to its distinctive imaging mechanism. However, interpreting SAR images is not as intuitive as optical images. Therefore, to make SAR images consistent with human cognitive habits and assist inexperienced people in interpreting SAR images, a generative model is needed to realize the translation from SAR images to optical ones. In this work, inspired by the processing of the human brain in painting, a novel conditional image-to-image translation framework is proposed for SAR to optical image translation based on the diffusion model. Firstly, considering the limited performance of existing CNN-based feature extraction modules, the model draws insights from the self-attention and long-skip connection mechanisms to enhance feature extraction capabilities, which are aligned more closely with the memory paradigm observed in the functioning of human brain neurons. Secondly, addressing the scarcity of SAR-optical image pairs, data augmentation that does not leak the augmented mode into the generated mode is designed to optimize data efficiency. The proposed SAR-to-optical image translation method is thoroughly evaluated using the SAR2Opt dataset. Experimental results demonstrate its capacity to synthesize high-fidelity optical images without introducing blurriness.

BackgroundInsulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) has been reported to exhibit an oncogenic effect as an RNA-binding protein (RBP) by promoting tumor cell proliferation, migration and invasion in several tumor types. However, a pan-cancer analysis of IGF2BP3 is not currently available, and the exact roles of IGF2BP3 in prognosis and immunology in cancer patients remain enigmatic. The main aim of this study was to provide visualization of the systemic prognostic landscape of IGF2BP3 in pan-cancer and to uncover the potential relationship between IGF2BP3 expression in the tumor microenvironment and immune infiltration profile.MethodsRaw data on IGF2BP3 expression were obtained from GTEx, CCLE, TCGA, and HPA data portals. We have investigated the expression patterns, diagnostic and prognostic significance, mutation landscapes, functional analysis, and functional states of IGF2BP3 utilizing multiple databases, including HPA, TISIDB, cBioPortal, GeneMANIA, GESA, and CancerSEA. Moreover, the relationship of IGF2BP3 expression with immune infiltrates, TMB, MSI and immune-related genes was evaluated in pan-cancer. IGF2BP3 with drug sensitivity analysis was performed from the CellMiner database. Furthermore, the expression of IGF2BP3 in different grades of glioma was detected by immunohistochemical staining and western blot.ResultsWe found that IGF2BP3 was ubiquitously highly expressed in pan-cancer and significantly correlated with diagnosis, prognosis, TMB, MSI, and drug sensitivity in various types of cancer. Besides, IGF2BP3 was involved in many cancer pathways and varied in different immune and molecular subtypes of cancers. Additionally, IGF2BP3 is critically associated with genetic markers of immunomodulators in various cancers. Finally, we validated that IGF2BP3 protein expression was significantly higher in glioma than in normal tissue, especially in GBM.ConclusionsIGF2BP3 may be a potential molecular biomarker for diagnosis and prognosis in pan-cancer, especially for glioma. It could become a novel therapeutic target for various cancers.

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.