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  1. Abstract

    Spatial transcriptomics technologies have shed light on the complexities of tissue structures by accurately mapping spatial microenvironments. Nonetheless, a myriad of methods, especially those utilized in platforms like Visium, often relinquish spatial details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored in the Transformer architecture, which operates independently of references, thereby ensuring cost-efficiency by circumventing the need for single-cell RNA sequencing. TransformerST not only elevates Visium data from a multicellular level to a single-cell granularity but also showcases adaptability across diverse spatial transcriptomics platforms. By employing a vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced by spatial correlations, derived from an adaptive graph Transformer module. The sophisticated cross-scale graph network, utilized in super-resolution, significantly boosts the model’s accuracy, unveiling complex structure–functional relationships within histology images. Empirical evaluations validate its adeptness in revealing tissue subtleties at the single-cell scale. Crucially, TransformerST adeptly navigates through image-gene co-representation, maximizing the synergistic utility of gene expression and histology images, thereby emerging as a pioneering tool in spatial transcriptomics. It not only enhances resolution to a single-cell level but also introduces a novel approach that optimally utilizes histology images alongside gene expression, providing a refined lens for investigating spatial transcriptomics.

     
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  2. Abstract

    Mitochondrial features and activities vary in a cell type- and developmental stage-dependent manner to critically impact cell function and lineage development. Particularly in male germ cells, mitochondria are uniquely clustered into intermitochondrial cement (IMC), an electron-dense granule in the cytoplasm to support proper spermatogenesis. But it remains puzzling how mitochondria assemble into such a stable structure as IMC without limiting membrane during development. Here, we showed that GASZ (germ cell-specific, ankyrin repeat, SAM and basic leucine zipper domain containing protein), a mitochondrion-localized germ cell-specific protein, self-interacted with each other to cluster mitochondria and maintain protein stability for IMC assembling. When the self-interaction of GASZ was disrupted by either deleting its critical interaction motif or using a blocking peptide, the IMC structure was destabilized, which in turn led to impaired spermatogenesis. Notably, the blocked spermatogenesis was reversible once GASZ self-interaction was recovered. Our findings thus reveal a critical mechanism by which mitochondrion-based granules are properly assembled to support germ cell development while providing an alternative strategy for developing nonhormonal male contraceptives by targeting IMC protein interactions.

     
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  3. Joint photoelectron spectroscopy and first-principles theory investigations indicate that the Pb-doped PbB2(BO)nclusters (n= 0−2) undergo a transformation from σ + π doubly aromatic triangle PbB2to PbB4(BO)2−/0complexes with a B≡B triple bond.

     
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    Free, publicly-accessible full text available February 7, 2025
  4. Morphological (e.g. shape, size, and height) and function (e.g. working, living, and shopping) information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling. Due to the limited availability of socio-economic geospatial data, it is more challenging to map building functions than building morphological information, especially over large areas. In this study, we proposed an integrated framework to map building functions in 50 U.S. cities by integrating multi-source web-based geospatial data. First, a web crawler was developed to extract Points of Interest (POIs) from Tripadvisor.com, and a map crawler was developed to extract POIs and land use parcels from Google Maps. Second, an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints. Third, the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions (i.e. hospital, hotel, school, shop, restaurant, and office). The accuracy assessment indicates that the proposed framework performed well, with an average overall accuracy of 94% and a kappa coefficient of 0.63. With the worldwide coverage of Google Maps and Tripadvisor.com, the proposed framework is transferable to other cities over the world. The data products generated from this study are of great use for quantitative city-scale urban studies, such as building energy use modeling at the single building level over large areas. 
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    Free, publicly-accessible full text available October 31, 2024
  5. We report the experimental observation and spectroscopic characterization, and structure and bonding analyses of copper–borozene complexes.

     
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    Free, publicly-accessible full text available January 1, 2025
  6. Abstract

    Shape morphing that transforms morphologies in response to stimuli is crucial for future multifunctional systems. While kirigami holds great promise in enhancing shape-morphing, existing designs primarily focus on kinematics and overlook the underlying physics. This study introduces a differentiable inverse design framework that considers the physical interplay between geometry, materials, and stimuli of active kirigami, made by soft material embedded with magnetic particles, to realize target shape-morphing upon magnetic excitation. We achieve this by combining differentiable kinematics and energy models into a constrained optimization, simultaneously designing the cuts and magnetization orientations to ensure kinematic and physical feasibility. Complex kirigami designs are obtained automatically with unparalleled efficiency, which can be remotely controlled to morph into intricate target shapes and even multiple states. The proposed framework can be extended to accommodate various active systems, bridging geometry and physics to push the frontiers in shape-morphing applications, like flexible electronics and minimally invasive surgery.

     
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  7. Abstract

    Multifunctional metamaterials (MMM) bear promise as next‐generation material platforms supporting miniaturization and customization. Despite many proof‐of‐concept demonstrations and the proliferation of deep learning assisted design, grand challenges of inverse design for MMM, especially those involving heterogeneous fields possibly subject to either mutual meta‐atom coupling or long‐range interactions, remain largely under‐explored. To this end, a data‐driven design framework is presented, which streamlines the inverse design of MMMs involving heterogeneous fields. A core enabler is implicit Fourier neural operator (IFNO), which predicts heterogeneous fields distributed across a metamaterial array, thus in general at odds with homogenization assumptions. Additionally, a standard formulation of inverse problem covering a broad class of MMMs is presented, together with gradient‐based multitask concurrent optimization identifying a set of Pareto‐optimal architecture‐stimulus (A‐S) pairs. Fourier multiclass blending is proposed to synthesize inter‐class meta‐atoms anchored on a set of geometric motifs, while enjoying training‐free dimension reduction and built‐it reconstruction. Interlocking the three pillars, the framework is validated for light‐by‐light programmable nanoantenna, whose design involves vast space jointly spanned by quasi‐freeform supercells, maneuverable incident phase distributions, and conflicting figure‐of‐merits (FoM) involving on‐demand localization patterns. Accommodating all the challenges, the framework can propel future advancements of MMM.

     
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  8. Abstract

    Hedgehog (Hh) signaling relies on the primary cilium, a cell surface organelle that serves as a signaling hub for the cell. Using proximity labeling and quantitative proteomics, we identify Numb as a ciliary protein that positively regulates Hh signaling. Numb localizes to the ciliary pocket and acts as an endocytic adaptor to incorporate Ptch1 into clathrin-coated vesicles, thereby promoting Ptch1 exit from the cilium, a key step in Hh signaling activation. Numb loss impedes Sonic hedgehog (Shh)-induced Ptch1 exit from the cilium, resulting in reduced Hh signaling. Numb loss in spinal neural progenitors reduces Shh-induced differentiation into cell fates reliant on high Hh activity. Genetic ablation of Numb in the developing cerebellum impairs the proliferation of granule cell precursors, a Hh-dependent process, resulting in reduced cerebellar size. This study highlights Numb as a regulator of ciliary Ptch1 levels during Hh signal activation and demonstrates the key role of ciliary pocket-mediated endocytosis in cell signaling.

     
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  9. Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies.

     
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  10. We present an investigation on the structures and chemical bonding of two Bi-doped boron clusters BiBn− (n = 4, 5) using photoelectron spectroscopy and theoretical calculations. The electron affinities of BiB4 and BiB5 are measured to be 2.22(2) eV and 2.61(2) eV, respectively. Well-resolved photoelectron spectra are obtained and used to compare with theoretical calculations to verify the structures of BiB4− and BiB5−. Both clusters adopt planar structures with the Bi atom bonded to the periphery of the planar Bn moiety. Chemical bonding analyses reveal that the Bn moiety maintains σ and π double-aromaticity. The Bi atom is found to induce relatively small structural changes to the Bn moiety, very different from transition metal-doped boron clusters.

     
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    Free, publicly-accessible full text available October 1, 2024