Here we describe characterization of chicken neuronal Per-Arnt-Sim domain 3 (NPAS3) gene during embryogenesis including examinations of expression pattern and function of the gene. RTPCR assay showed that the primary tissue of expression for this gene is the central nervous system (CNS) while RNA in situ hybridization assay confirmed that NPAS3 was expressed in the ventricular zone of developing neural tube as early as Hamburger-Hamilton (HH) stage 20. Ectopic over-expression of the gene in ovo in the developing chicken neural tube by electroporation had little effect on stem cell population, overall neurogenesis, and motor neuron differentiation. We discuss the implications of our observation.
Central Nervous System ; Chickens ; Electroporation ; Embryonic Development ; Female ; In Situ Hybridization ; Motor Neurons ; Neural Tube ; Neurogenesis ; Neurons ; Pregnancy ; RNA ; Stem Cells

PURPOSE: Although previous studies on the screw-home movement (SHM) for autopsy specimen and walking of living persons conducted, the possibility of acquiring SHM based on inertial measurement units received little attention. This study aimed to investigate the possibility of measuring SHM for the non-weighted bearing using a micro-electro-mechanical system-based wearable motion capture system (MEMSS). METHODS: MEMSS and camera-based motion analysis systems were used to obtain kinematic data of the knee joint. The knee joint moved from the flexion position to a fully extended position and then back to the start point. The coefficient of multiple correlation and the difference in the range of motion were used to assess the waveform similarity in the movement measured by two measurement systems. RESULTS: The waveform similarity in the sagittal plane was excellent and the in the transverse plane was good. Significant differences were found in the sagittal plane between the two systems (p<0.05). However, there was no significant difference in the transverse plane between the two systems (p>0.05). CONCLUSION The SHM during the passive motion without muscle contraction in the non-weighted bearing appeared in the entire range. We thought that the MEMSS could be easily applied to the acquisition of biomechanical data on the knee related to physical therapy.

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.