MOTIVATION: The rapid development of spatial multi-omics technology enables the simultaneous measurement of gene and protein expression alongside spatial location, providing valuable insights into tissue heterogeneity. However, challenges such as low spatial resolution and high feature dimensionality complicate data integration and biological interpretation. RESULTS: To address these issues, we propose SpaMFG, an innovative feature-group-level framework for interpretable spatial multi-omics integration. SpaMFG leverages spatial location information and introduces a spatial proximity weighting method to improve feature grouping accuracy. Additionally, it employs a new cross-omics feature group matching method that combines spatial location and Jaccard similarity to construct a weighted cost matrix, which is optimized using the Hungarian algorithm. This approach enhances the biological interpretability of cross-omics feature relationships. We evaluated SpaMFG's performance through comparative analysis on the human lymph node dataset, demonstrating its effectiveness. Further applications on human tonsils, mouse spleens, and mouse thymus datasets confirmed the robustness of SpaMFG in various biological contexts. AVAILABILITY AND IMPLEMENTATION: The source code for SpaMFG is available at https://github.com/LiangYu-Xidian/SpaMFG.

Power converter-based resources are susceptible to control interactions, particularly under weak grid conditions. Increasing the DC-link capacitance can help mitigate unstable oscillations. However, in engineering practices, there is a preference for reducing DC-link capacitance to decrease the size and cost of power converters. This study proposes the use of a low-pass filter (LPF)-based virtual impedance in the outer DC-link voltage control loop of a grid-tied voltage source converter (VSC) to enhance its stability in weak grids without using a large DC-link capacitor. Moreover, theoretical calculation results indicate that the virtual impedance can reduce the required DC-link capacitance, and the reduction in weak grid condition is more significant.

Spatial transcriptomics (STs) has emerged as a transformative approach to elucidate cellular heterogeneity and spatial organization within complex tissue microenvironments. However, the analysis of ST data is challenged by limited spatial resolution, resulting in mixed expression profiles at each spatial location. Moreover, the precious spatial information is rarely exploited, and noise issues in spatial transcriptomes (STs) are often overlooked by computational deconvolution methods. In this study, a novel computational framework for STs deconvolution (DeCoST), called DeCoST, is presented. DeCoST capitalizes on the valuable spatial context information by integrating a Gaussian kernel-based conditional autoregressive model. Additionally, the method employs domain adaptation techniques to address platform effects between single-cell and ST data, enabling robust cell type identification. Evaluations on simulated datasets under diverse spatial configurations, as well as real-world case studies on human pancreatic ductal adenocarcinoma, mouse olfactory bulb, and mouse brain samples, demonstrate the superior performance of DeCoST compared to existing deconvolution approaches. The method's ability to accurately map region-specific cell types and uncover spatial interactions advances our understanding of complex tissue organization and function, with broad applications in disease research and developmental biology.

Flow visualization is an essential tool for domain experts to understand and analyze flow fields intuitively. In the past decades, various interactive techniques were developed to customize flow visualization for exploration. However, these techniques usually use specifically designed graphical interfaces, requiring considerable learning and usage effort. Recently, FlowNL Huang et al., (2023) introduces a natural language interface to reduce the effort, but it still struggles with natural language ambiguities due to the lack of domain knowledge and provides limited ability to understand the context in dialogues. To address these issues, we propose an explorative flow visualization powered by a large language model that interacts with users. Our approach leverages an extensive dataset of flow-related queries to train the model, enhancing its ability to interpret a wide range of natural language expressions and maintain context over multi-turn interactions. Additionally, we introduce an advanced dialogue management system that supports interactive continuous communication between users and the system. Our empirical evaluations demonstrate significant improvements in user engagement and accuracy of flow structure extraction. These enhancements are crucial for expanding the applicability of flow visualization systems in real-world scenarios, where effective and intuitive user interfaces are paramount.

SUMMARY: We developed the variant-Set Test for Association using Annotation infoRmation (STAAR) workflow description language (WDL) workflow to facilitate the analysis of rare variants in whole genome sequencing association studies. The open-access STAAR workflow written in the WDL allows a user to perform rare variant testing for both gene-centric and genetic region approaches, enabling genome-wide, candidate and conditional analyses. It incorporates functional annotations into the workflow as introduced in the STAAR method in order to boost the rare variant analysis power. This tool was specifically developed and optimized to be implemented on cloud-based platforms such as BioData Catalyst Powered by Terra. It provides easy-to-use functionality for rare variant analysis that can be incorporated into an exhaustive whole genome sequencing analysis pipeline. AVAILABILITY AND IMPLEMENTATION: The workflow is freely available from https://dockstore.org/workflows/github.com/sheilagaynor/STAAR_workflow. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.