The epithelial cell sheet functions as a barrier to prevent invasion of pathogens. It is necessary to eliminate intercellular gaps not only at bicellular junctions, but also at tricellular contacts, where three cells meet, to maintain epithelial barrier function. To that end, tight junctions between adjacent cells must associate as closely as possible, particularly at tricellular contacts. Tricellulin is an integral component of tricellular tight junctions (tTJs), but the molecular mechanism of its contribution to the epithelial barrier function remains unclear. In this study, we revealed that tricellulin contributes to barrier formation by regulating actomyosin organization at tricellular junctions. Furthermore, we identified α-catenin, which is thought to function only at adherens junctions, as a novel binding partner of tricellulin. α-catenin bridges tricellulin attachment to the bicellular actin cables that are anchored end-on at tricellular junctions. Thus, tricellulin mobilizes actomyosin contractility to close the lateral gap between the TJ strands of the three proximate cells that converge on tricellular junctions.

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When fonts are used on documents, they are intentionally selected by designers. For example, when designing a book cover, the typography of the text is an important factor in the overall feel of the book. In addition, it needs to be an appropriate font for the rest of the book cover. Thus, we propose a method of generating a book title image based on its context within a book cover. We propose an end-to-end neural network that inputs the book cover, a target location mask, and a desired book title and outputs stylized text suitable for the cover. The proposed network uses a combination of a multi-input encoder-decoder, a text skeleton prediction network, a perception network, and an adversarial discriminator. We demonstrate that the proposed method can effectively produce desirable and appropriate book cover text through quantitative and qualitative results. The code can be found at https://github.com/Taylister/FontFits.

We propose TrueType Transformer (T 3 ), which can perform character and font style recognition in an outline format. The outline format, such as TrueType, represents each character as a sequence of control points of stroke contours and is frequently used in born-digital documents. T 3 is organized by a deep neural network, so-called Transformer. Transformer is originally proposed for sequential data, such as text, and therefore appropriate for handling the outline data. In other words, T 3 directly accepts the outline data without converting it into a bitmap image. Consequently, T 3 realizes a resolution-independent classification. Moreover, since the locations of the control points represent the fine and local structures of the font style, T 3 is suitable for font style classification, where such structures are very important. In this paper, we experimentally show the applicability of T 3 in character and font style recognition tasks, while observing how the individual control points contribute to classification results.

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