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  1. Abstract Motivation

    The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data.


    We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments.

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  4. Abstract

    The Internet of Everything (IoE), which aims to realize information exchange and communications for anything with the Internet, has revolutionized our modern world. Serving as the driving force for devices in the IoE network, power supply systems play a fundamental role in the development of the IoE. However, due to the complexity, multifunctionality and wide‐scale deployment of diverse applications, power supply systems face great challenges, including distribution, connection, charging technologies, and management. In this review, some challenges and advances in the development of both power supply systems and their units are presented. In the overall system‐level field, establishing sustainable and maintenance‐free power supply systems through wireless connections, efficient power management and integrated energy harvesting and storage systems is highlighted. Additionally, the main performance metrics of power supply units are discussed, including energy density, service life, and self‐power ability. In addition, some directions of power quality assessment for both the system and unit levels of power supply systems are presented, aiming to provide insight into the future development of high‐performance power supply systems for the IoE.

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