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Primary failure of eruption (PFE) is a rare disorder that is characterized by the inability of a molar tooth/teeth to erupt to the occlusal plane or to normally react to orthodontic force. This condition is related to hereditary factors and has been extensively researched over many years. However, the etiological mechanisms of pathogenesis are still not fully understood. Evidence from studies on PFE cases has shown that PFE patients may carry parathyroid hormone 1 receptor (PTH1R) gene mutations, and genetic detection can be used to diagnose PFE at an early stage. PTH1R variants can lead to altered protein structure, impaired protein function, and abnormal biological activities of the cells, which may ultimately impact the behavior of teeth, as observed in PFE. Dental follicle cells play a critical role in tooth eruption and root development and are regulated by parathyroid hormone-related peptide (PTHrP)–PTH1R signaling in their differentiation and other activities. PTHrP–PTH1R signaling also regulates the activity of osteoblasts, osteoclasts and odontoclasts during tooth development and eruption. When interference occurs in the PTHrP–PTH1R signaling pathway, the normal function of dental follicles and bone remodeling are impaired. This review provides an overview of PTH1R variants and their correlation with PFE, and highlights that a disruption of PTHrP–PTH1R signaling impairs the normal process of tooth development and eruption, thus providing insight into the underlying mechanisms related to PTH1R and its role in driving PFE. [ABSTRACT FROM AUTHOR]

Segmentation involves separating an object from the background in a given image. Image segmentation has a variety of applications and has received considerable attention in multimedia application and computer vision. Although numerous approaches have been introduced, image segmentation is still far from being solved due to most of image segmentation algorithms are often so complicated and some unsatisfactory results appear frequently. Therefore, developing a suitable technique of image segmentation is still a challenging problem. In this article, a novel color image segmentation will be introduced based on quaternion radial harmonic Fourier moments (QRHFMs) and proximal classifier. Firstly, the image feature of pixel-level is represented by the accurate and invariant QRHFMs holistically as a vector field, which can describe sufficiently the image pixel information due to take into account the relationship among different color channels. Secondly, the image feature from pixel-level is utilized as the input of the proximal classifier with consistency (PCC), which not only has lower computation time but also has better generalization compared to traditional support vector machines classifiers. Then, we choose the training samples by Tsallis entropy thresholding to train PCC classification model. Finally, the color image is classified by the trained PCC classification model. Our algorithm can make full use of the accurate and robust local image feature, as well the quickness and generalization ability of PCC classifier. A series of experimental results shows that this algorithm has better segmentation performance than the state-of-the-art method from the literature. [ABSTRACT FROM AUTHOR]

Cold-rolled steel sheets in automotive applications require an excellent deep draw ability, which is characterized by the Lankford value ( r-value). In this study, a correlation was identified between r-value and pancake-shaped grain flatness which is indicated as the ratio of grain diameter in the rolling direction (RD) and normal direction (ND) of sheets ( d/ d). A mathematical model $$\left( {r = e^{0.345\left( {d_n^{ - 1/2} - d_r^{ - 1/2} } \right)} } \right)$$ was developed to calculate r-value by the microstructure of steel sheets hot-rolled by compact strip production (CSP). It is shown that the r-value is higher, if the microstructure of steel sheet is of pancake-shaped grains elongated in the rolling direction. The calculated r-value is confirmed to fit exactly to the measured one from the large-scale production. [ABSTRACT FROM AUTHOR]