Summary Background In preimplantation mouse embryos, the first cell fate specification to the trophectoderm or inner cell mass occurs by the early blastocyst stage. The cell fate is controlled by cell position-dependent Hippo signaling, although the mechanisms underlying position-dependent Hippo signaling are unknown. Results We showed that a combination of cell polarity and cell–cell adhesion establishes position-dependent Hippo signaling, where the outer and inner cells are polar and nonpolar, respectively. The junction-associated proteins angiomotin (Amot) and Amotl2 are essential for Hippo pathway activation and appropriate cell fate specification. In the nonpolar inner cells, Amot localizes to adherens junctions (AJs) and cell–cell adhesion activates the Hippo pathway. In the outer cells, the cell polarity sequesters Amot from basolateral AJs to apical domains, thereby suppressing Hippo signaling. The N-terminal domain of Amot is required for actin binding, Nf2/Merlin-mediated association with the E-cadherin complex, and interaction with Lats protein kinase. In AJs, Ser176 in the N-terminal domain of Amot is phosphorylated by Lats, which inhibits the actin-binding activity, thereby stabilizing the Amot–Lats interaction to activate the Hippo pathway. Conclusion We propose that the phosphorylation of S176 in Amot is a critical step for activation of the Hippo pathway in AJs and that cell polarity disconnects the Hippo pathway from cell–cell adhesion by sequestering Amot from AJ. This mechanism converts positional information into differential Hippo signaling, thereby leading to differential cell fates.

Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements.

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In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.