The rapid advancement of spatial transcriptomics (ST) sequencing technology has made it possible to capture gene expression with spatial coordinate information at the cellular level. Although many methods in ST data analysis can detect spatially variable genes (SVGs), these methods often fail to identify genes with explicit spatial expression patterns due to the lack of consideration for spatial domains. Considering spatial domains is crucial for identifying SVGs as it focuses the analysis of gene expression changes on biologically relevant regions, aiding in the more accurate identification of SVGs associated with specific cell types. Existing methods for identifying SVGs based on spatial domains predefine spot similarity before training, which prevents adaptive learning and limits generalizability across different tissues or samples. This limitation may also lead to inaccurate identification of specific genes at boundary regions. To address these issues, we present GAADE, an unsupervised neural network architecture based on graph-structured data representation learning. GAADE stacks encoder/decoder layers and integrates a self-attention mechanism to reconstruct node attributes and graph structure, effectively capturing spatial domain structures of different sections. Consequently, we confine the identification of SVGs within spatial domains. By performing differential expression analysis on spots within the target spatial domain and their multi-order neighbors, GAADE detects genes with enriched expression patterns within defined domains. Comparative evaluations with five other popular methods on ST datasets across four different species, regions and tissues demonstrate that GAADE exhibits superior performance in detecting SVGs and capturing the extent of spatial gene expression variation.

Enhancer-promoter interaction (EPI) is a critical component of gene regulation. Accurately predicting EPIs across diverse cell types can advance our understanding of the molecular mechanisms behind transcriptional regulation and provide valuable insights into the onset and progression of related diseases. At present, large-scale genome-wide EPI predictions typically rely on computational approaches. However, most of these methods focus on predicting EPIs within a single cell line and lack a global perspective encompassing multiple cell lines. Furthermore, they often fail to fully account for the nonlinear relationships between features, leading to suboptimal prediction accuracy. In this study, we propose KansformerEPI, a global EPI prediction model designed for multiple cell lines. The model is built on Kansformer, an encoder that integrates KAN and Transformer, effectively capturing the nonlinear relationships among various epigenetic and sequence features. We utilized KansformerEPI to achieve cross-tissue prediction of EPIs across different cell types. This approach enhances the model's scalability, eliminating the complexity of designing separate prediction models for individual tissues. As a result, our model is applicable to various tissues, thereby reducing dependency on extensive datasets. Experimental results demonstrate that KansformerEPI surpasses existing methods such as TransEPI, TargetFinder, and SPEID in both accuracy and stability of EPI predictions across datasets including HMEC, IMR90, K562, and NHEK.

This article proposes a generalized compact polarimetry (GCP) mode along with two optimal polarization mode selection parameters to address the challenges of polarization mode selection in classification tasks across diverse scenarios. Theoretically, we conduct an in-depth analysis of the differences between circular and linear transmit polarizations, demonstrating their fundamental equivalence in terms of information content. For the first time, we propose that different classification tasks require different optimal polarization modes, and the optimal transmit polarization mode may lie in the elliptic polarization domain of synthetic aperture radar (SAR) systems rather than traditional circular compact polarimetric (CP) or linear dual-polarization (DP) modes. The proposed approach is validated using full-polarimetric SAR data from San Francisco and Hainan, showing that the optimal elliptical polarization mode achieves classification accuracies that are 2% to 42% higher than those of traditional CP or DP modes for certain categories, and performs comparably to full polarization. This improvement in accuracy stems from the interaction between the transmit polarization and the target scene, rather than advancements in classification algorithms. Using the two proposed parameters, the overall and category-specific classification performance of GCP modes can be effectively evaluated, enabling the identification of the optimal polarization mode for a given task. These findings provide significant insight into the design of future polarimetric SAR systems and offer new perspectives and directions for mission planning and mode selection for on-orbit satellites.

The accuracy of the polarimetric evaluation of low frequency polarimetric synthetic aperture radar (PolSAR) systems based on distributed targets is uncertain when the ionospheric information is unknown. Therefore, in this paper we investigate the potential of polarimetric evaluation of LT-1 images based on a set of calibrators arranged for L-band LT-1 PolSAR images. First, reasonable assumptions are made for common calibration models for LT-1 images. The ideal backscatter matrix of the set of calibrators is subsequently used in conjunction with the polarimetric distortion matrices (PDMs) to construct constraints for solving the PDMs. Finally, for possible ambiguous values of the solution, we use phase consistency as well as the Frobenius norm to remove the ambiguous values of the PDMs and obtain the final evaluated results. In the experimental part, we verify the feasibility of the method based on simulation data. Thus, the method contributes to accurate evaluation of LT-1 images.

The dual-frequency (DF) synthetic aperture radar (SAR) in lunar orbit, using L and S bands, is the only full-polarization (FP) SAR. It explores different layers of the moon, relying on the unusual rotational symmetry for polarimetric calibration. Addressing prior issues in polarimetric evaluation, this study tackles the challenges of an expanding dataset (now exceeding 900 scenes) and the necessity to evaluate polarimetric distortion (PD) values in the range direction. A novel polarimetric evaluation framework is introduced, enhancing existing methods. First, a scheme is proposed to eliminate low-quality areas in the range direction based on antenna isolation specifications, validated theoretically with simulated and real data. Second, more than 900 DFSAR scenes, downloaded prior to April 25, 2023, are utilized for evaluation, surpassing previous limitations and enhancing accessibility for users. By evaluating the L-band data, the image calibration results acquired by DFSAR between September 19, 2019, and December 4, 2020, have high accuracy and can be prioritized for lunar applications. In addition, we recommend that researchers pay attention to the issue of changing data quality for releases around March 2021.The amount of data in the S-band with the L-S-joint mode is small, and we provide the availability results directly. Third, the study applies the evaluated results to lunar polarization research, contributing to lunar exploration through polarimetric parameters. This comprehensive framework ensures accurate data utilization, addressing the evolving needs of lunar exploration with improved methodologies and expanded datasets.