Considerable advancements have been made in robotic technology to mimic human behavior but implementation of intelligence of human perception still lacks significantly in the area of texture recognition. Texture Recognition is key to improving the grasping capabilities of robotic hands. Given the limited knowledge of the vast variety of texture samples, machine learning is used to develop a robust predictive model to recognize textures of materials using the knowledge of haptic components, which will significantly improve grasping strategies, building intelligent and adaptive robots and thereby improving the path towards advancing robotic manipulation and human-robot interactions.
The advancement of accurate microglia models is essential for developing potential treatments against incurable neurodegenerative diseases. Existing models, including primary, murine, and iPSC-derived microglia, confront limitations due to transcriptomic and phenotypic alterations. The objective of this study was to conduct gene expression comparisons in microglia ex vivo and iPSC-derived microglia to identify differentially expressed genes, transcription factors, and signaling pathways to improve the existing in vitro model. SALL1 gene, a critical transcriptional regulator of microglia identity, and NR4A1, a transcription factor that activates the SALL1 gene, exhibited significant expression differences. The findings suggest the need to target NR4A1 to increase SALL1 levels in in vitro models.
Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths, primarily due to its low early detection rate of approximately 19%. The circular RNA (circRNA) - microRNA (miRNA) - messenger RNA (mRNA) pathway impacts NSCLC and could provide an optimal biomarker/target. This study revealed that CircFNDC3B, by targeting miR-885-3p, significantly impacts NSCLC survival (p=0.011). miR-885-3p influences the gene MCM5, which impacts NSCLC survival (p=0.0018). MCM5 regulates Ras protein signaling and insulin-like growth factor transport via insulin-like growth factor binding proteins in NSCLC. This research established a correlation between the CircFNDC3B-miR-885-3p-MCM5 axis and NSCLC survival, potentially improving early detection methods in the future.