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In this work, a low-profile optical transparent wideband metamaterial absorber based on radiation–absorption reciprocal theory is proposed. The top layer and bottom layer are composed of indium tin oxide–polyethylene terephthalate films, and the middle layer is a transparent substrate polymethyl methacrylate. The method of transparent wideband absorption using reciprocal theory is proposed to guide the design. Based on characteristic mode analysis and calculated equivalent aperture efficiency, the absorber consisting of composite resonators is analyzed and designed. The equivalent circuit model and surface current distributions are studied to verify the design. The numerical results show that high absorption of greater than 90% in the frequency range of 5.7–16.4 GHz is obtained. A wideband absorption rate of greater than 80% at a 60° incident angle under TM polarization and an absorption rate of greater than 65% at a 60° incident angle under TE polarization are obtained. The proposed design has the advantages of broadband absorption, polarization insensitivity, light transmittance of more than 70%, and compact structure, which can be applied to special scenes that require optical transparency, such as anechoic chamber glass and cockpit glass. [ABSTRACT FROM AUTHOR]

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In this letter, a wideband folded reflectarray antenna with a novel circularly polarized converter is presented. The main reflectarray is composed of dual-polarized units, which can achieve good polarization and phase characteristics with a smaller size. Based on 3-D printing technology, the proposed dielectric unit can be used to design the low-cost linear-to-circular polarization converter (LCPC) with wider bandwidth. In order to validate the performance of the proposed circularly polarized folded reflectarray antenna (CPFRA), a prototype is manufactured and measured. The maximum gain of 25.3 dBic and the maximum aperture efficiency of 38 % are obtained. The measured 3 dB gain bandwidth is 32.7 % and the measured 3 dB AR bandwidth is 31.7 %, respectively. The simulated and measured results show great agreement. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications. [ABSTRACT FROM AUTHOR]

As we age, the ability to move is foundational to health. Life space is one measure of a person's ability to move and engage in activity beyond the home. A separate but related concept is activity space, a measurement of a person's spatial behaviors and visited locations that include social networks, neighborhoods, and institutions. In this article, we integrate the literature on life space and activity space, discussing how physical function is not only determined by individual capabilities, but also by the surrounding social and environmental factors, which may limit their agency. We show how structural racism contributes to inequities within this paradigm linking related concepts of movement, agency, belonging, and timing. We also explore implications for research and theory for mobility, social connection, and activity. [ABSTRACT FROM AUTHOR]

Genetic mouse models of skeletal abnormalities have demonstrated promise in the identification of phenotypes relevant to human skeletal diseases. Traditionally, phenotypes are assessed by manually examining radiographs, a tedious and potentially error-prone process. In response, this study developed a deep learning-based model that streamlines the measurement of murine bone lengths from radiographs in an accurate and reproducible manner. A bone detection and measurement pipeline utilizing the Keypoint R-CNN algorithm with an EfficientNet-B3 feature extraction backbone was developed to detect murine bone positions and measure their lengths. The pipeline was developed utilizing 94 X-ray images with expert annotations on the start and end position of each murine bone. The accuracy of our pipeline was evaluated on an independent dataset test with 592 images, and further validated on a previously published dataset of 21,300 mouse radiographs. The results showed that our model performed comparably to humans in measuring tibia and femur lengths (R2 > 0.92, p-value = 0) and significantly outperformed humans in measuring pelvic lengths in terms of precision and consistency. Furthermore, the model improved the precision and consistency of genetic association mapping results, identifying significant associations between genetic mutations and skeletal phenotypes with reduced variability. This study demonstrates the feasibility and efficiency of automated murine bone length measurement in the identification of mouse models of abnormal skeletal phenotypes. [ABSTRACT FROM AUTHOR]