Akt signaling is an important regulator of neural development, but the distinctive function of Akt isoforms in brain development presents a challenge. Here we show Siah1 as an ubiquitin ligase that preferentially interacts with Akt3 and facilitates ubiquitination and degradation of Akt3. Akt3 is enriched in the axonal shaft and branches but not growth cone tips, where Siah1 is prominently present. Depletion of Siah1 enhanced Akt3 levels in the soma and axonal tips, eliciting multiple branching. Brain-specific somatic mutation in Akt3-E17K escapes from Siah1-mediated degradation and causes improper neural development with dysmorphic neurons. Remarkably, coexpression of Siah1 with Akt3-WT restricted disorganization of neural development is caused by Akt3 overexpression, whereas forced expression of Siah1 with the Akt3-E17K mutant fails to cope with malformation of neural development. These findings demonstrate that Siah1 limits Akt3 turnover during brain development and that this event is essential for normal organization of the neural network.
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http://dx.doi.org/10.1074/jbc.RA119.009618 | DOI Listing |
Sci Rep
December 2024
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan, 430070, China.
Urban rail transit systems, represented by subways, have significantly alleviated the traffic pressure brought by urbanization and have addressed issues such as traffic congestion. However, as a commonly used construction method for subway tunnels, shield tunneling inevitably disturbs the surrounding soil, leading to uneven ground surface settlement, which can impact the safety of nearby buildings. Therefore, it is crucial to promptly obtain and predict the ground surface settlement induced by shield tunneling construction to enable safety warnings and evaluations.
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December 2024
Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
Vertebral collapse (VC) following osteoporotic vertebral compression fracture (OVCF) often requires aggressive treatment, necessitating an accurate prediction for early intervention. This study aimed to develop a predictive model leveraging deep neural networks to predict VC progression after OVCF using magnetic resonance imaging (MRI) and clinical data. Among 245 enrolled patients with acute OVCF, data from 200 patients were used for the development dataset, and data from 45 patients were used for the test dataset.
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December 2024
Department of Architecture, Rafsanjan Branch, Islamic Azad University, Rafsanjan, Iran.
The advent of smart cities has brought about a paradigm shift in urban management and citizen engagement. By leveraging technological advancements, cities are now able to collect and analyze extensive data to optimize service delivery, allocate resources efficiently, and enhance the overall well-being of residents. However, as cities become increasingly interconnected and data-dependent, concerns related to data privacy and security, as well as citizen participation and representation, have surfaced.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Department of Computer Science and Digital Technologies, University of East London, London, UK.
Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.
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