Hypothetical model representing the distinct strategies of epithelial barrier disruption by Oki strains. by Tetsuya Kakita (4922584)

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<i>Leptospira interrogans</i> strains isolated from patients in Okinawa, Japan, in 2023. by Tetsuya Kakita (4922584)

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<i>L.</i><i>interrogans</i> induces afadin degradation in infected RPTECs through different strain-dependent mechanisms. by Tetsuya Kakita (4922584)

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Immunofluorescence analysis for F-actin in RPTECs infected with Oki53 and Oki65 at 24 h post-infection. by Tetsuya Kakita (4922584)

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Uncropped blots corresponding to Fig 2B, provided for assessment of band specificity and loading consistency. by Tetsuya Kakita (4922584)

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Representative merged immunofluorescence images of afadin with TO-PRO-3, and nectin-2 with TO-PRO-3. by Tetsuya Kakita (4922584)

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<i>Leptospira interrogans</i> from Okinawa induces epithelial barrier disruption with different kinetics. by Tetsuya Kakita (4922584)

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Representative merged immunofluorescence images of E-cadherin, afadin, and TO-PRO-3. by Tetsuya Kakita (4922584)

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Proteosomal and pan-caspase inhibitors differentially prevent the disruption of the epithelial cell barrier by the clinical <i>Leptospira</i> isolates. by Tetsuya Kakita (4922584)

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Representative merged immunofluorescence images of F-actin and TO-PRO-3. by Tetsuya Kakita (4922584)

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Representative merged immunofluorescence images of E-cadherin, α-catenin, and TO-PRO-3. by Tetsuya Kakita (4922584)

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Uncropped blots corresponding to Fig 3A, provided for assessment of band specificity and loading consistency. by Tetsuya Kakita (4922584)

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Immunofluorescence analysis using dual staining for E-cadherin (E-cad) and afadin and F-actin in RPTECs infected with Oki53 and Oki65 at 24 h post-infection. by Tetsuya Kakita (4922584)

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AI KRegression by CHIN-CHENG CHU (20718575)

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. This research proposes a new RNN architecture that decreases the prediction error up to 50% for cargo vessels when compared to the OU model. …”

Presents the bibliometric search results. by Yan She (8639796)

S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism. by Yan She (8639796)

Modeling results of 8-week-old female C57BL/6J mice. by Yan She (8639796)

Sexual maturity status of female C57BL/6J Mice at different ages. by Yan She (8639796)

Flow chart of data screening and analysis. by Yan She (8639796)