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With the rapid development of 3D reconstruction, especially the emergence of algorithms such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent years. 3D reconstruction technology provides crucial support for training extensive computer vision models and advancing the development of general artificial intelligence. With the development of deep learning and GPU technology, the demand for high-precision and high-efficiency 3D reconstruction information is increasing, especially in the fields of unmanned systems, human-computer interaction, virtual reality, and medicine. The rapid development of 3D reconstruction is becoming inevitable. This survey categorizes the various methods and technologies used in 3D reconstruction. It explores and classifies them based on three aspects: traditional static, dynamic, and machine learning. Furthermore, it compares and discusses these methods. At the end of the survey, which includes a detailed analysis of the trends and challenges in 3D reconstruction development, we aim to provide a comprehensive introduction for individuals who are currently engaged in or planning to conduct research on 3D reconstruction. Our goal is to help them gain a comprehensive understanding of the relevant knowledge related to 3D reconstruction.
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Muscle growth stands as a pivotal economic trait within pig production, governed by a complex interplay of multiple genes, each playing a role in its quantitative manifestation. Understanding the intricate regulatory mechanisms of porcine muscle development is crucial for enhancing both pork yield and quality. This study used the GSE99749 dataset downloaded from the GEO database, conducting a detailed analysis of the RNA-seq results from the longissimus dorsi muscle (LD) of Tibetan pigs (TP), Wujin pigs (WJ) and large white pigs (LW) at 60 days of gestation, representing diverse body sizes and growth rates. Comparative analyses between TPvsWJ and TPvsLW, along with differential gene expression (DEG) analysis, functional enrichment analysis, and protein-protein interaction (PPI) network analysis, revealed 1048 and 1157 significantly differentially expressed genes (p < 0.001) in TPvsWJ and TPvsLW, respectively. With stricter screening criteria, 37 DEGs were found to overlap between the 2 groups. PPI analysis identified MYL5, MYL4, and ACTC1 as the three core genes. This article focuses on exploring the MYL4 gene. Molecular-level experimental validation, through overexpression and interference of the MYL4 gene combined with EDU staining experiments, demonstrated that overexpression of MYL4 significantly promoted the proliferation of porcine skeletal muscle satellite cells (PSMSC), while interference with MYL4 inhibited their proliferation. Furthermore, by examining the effects of overexpressing and interfering with the MYL4 gene on the muscle hypertrophy marker Fst gene and the muscle degradation marker FOXO3 gene, the pivotal role of the MYL4 gene in promoting muscle growth and preventing muscle degradation was further confirmed. These findings offer a new perspective on the molecular mechanisms behind porcine muscle growth and development, furnishing valuable data and insights for muscle biology research.
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Personalized medical diagnostics and monitoring have become increasingly important due to inefficient and delayed medical services of traditional centralized healthcare systems. To enhance the comfort and portability, flexible health monitoring systems have been developed in recent years. In particular, smart fiber/textile-based sensing devices show superiority for continuously monitoring personal health and vital physiological parameters owing to their light weight, good flexibility and inherent miniaturization. This review focuses on the recent advances in smart fiber/textile-based sensing devices for wearable electronic applications. First, fabrication strategies of smart sensing fibers/textiles are introduced in detail. In addition, sensing performances, working principles and applications of smart sensing fibers/textiles such as pressure sensing fibers/textiles, stretchable strain sensing fibers/textiles, temperature sensing fibers/textiles, and biofluid, gas and humidity sensing fibers/textiles in health monitoring are also reviewed systematically. Finally, we propose current challenges and future prospects in the area of fiber/textile-based sensors for wearable healthcare monitoring and diagnosis systems. In general, this review aims to give an overall perspective of the promising field by reviewing various fiber/textile-based sensing devices and highlighting the importance for researchers to keep up with the sequential exploration of soft sensing fibers/textiles for applications in wearable smart systems.