Sax1 (previously CHox3) is a chicken homeobox gene belonging to the same homeobox gene family as the Drosophila NK1 and the honeybee HHO genes. Sax1 transcripts are present from stage 2 H&H until at least 5 days of embryonic development. However, specific localization of Sax1 transcripts could not be detected by in situ hybridization prior to stage 8-, when Sax1 transcripts are specifically localized in the neural plate, posterior to the hindbrain. From stages 8- to 15 H&H, Sax1 continues to be expressed only in the spinal part of the neural plate. The anterior border of Sax1 expression was found to be always in the transverse plane separating the youngest somite from the yet unsegmented mesodermal plate and to regress with similar dynamics to that of the segregation of the somites from the mesodermal plate. The posterior border of Sax1 expression coincides with the posterior end of the neural plate. In order to study a possible regulation of Sax1 expression by its neighboring tissues, several embryonic manipulation experiments were performed. These manipulations included: removal of somites, mesodermal plate or notochord and transplantation of a young ectopic notochord in the vicinity of the neural plate or transplantation of neural plate sections into the extraembryonic area. The results of these experiments revealed that the induction of the neural plate by the mesoderm has already occurred in full primitive streak embryos, after which Sax1 is autonomously regulated within the spinal part of the neural plate.
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http://dx.doi.org/10.1242/dev.120.7.1817 | DOI Listing |
Sci Rep
January 2025
Department of Structural Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
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January 2025
Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 2360001, Japan.
Subsurface seismic velocity structure is essential for earthquake source studies, including hypocenter determination. Conventional hypocenter determination methods ignore the inherent uncertainty in seismic velocity structure models, and the impact of this oversight has not been thoroughly investigated. Here, we address this issue by employing a physics-informed deep learning (PIDL) approach that quantifies uncertainty in two-dimensional seismic velocity structure modeling and its propagation to hypocenter determination by introducing neural network ensembles trained on active seismic survey data, earthquake observation data, and the physical equation of wavefront movement.
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December 2024
Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea.
In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures.
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