Background
Matrix metallopeptidase 20 (MMP20) is an evolutionarily conserved protease that is essential for processing enamel matrix proteins during dental enamel formation.
MMP20
mutations cause human autosomal recessive pigmented hypomaturation‐type amelogenesis imperfecta (AI2A2; OMIM #612529). MMP20 is expressed in both odontoblasts and ameloblasts, but its function during dentinogenesis is unclear.
Methods
We characterized 10 AI kindreds with
MMP20
defects, characterized human third molars and/or
Mmp20
−/−
mice by histology, Backscattered Scanning Electron Microscopy (bSEM), µCT, and nanohardness testing.
Results
We identified six novel
MMP20
disease‐causing mutations. Four pathogenic variants were associated with exons encoding the MMP20 hemopexin‐like (PEX) domain, suggesting a necessary regulatory function. Mutant human enamel hardness was softest (13% of normal) midway between the dentinoenamel junction (DEJ) and the enamel surface. bSEM and µCT analyses of the third molars revealed reduced mineral density in both enamel and dentin. Dentin close to the DEJ showed an average hardness number 62%–69% of control. Characterization of
Mmp20
−/−
mouse dentin revealed a significant reduction in dentin thickness and mineral density and a transient increase in predentin thickness, indicating disturbances in dentin matrix secretion and mineralization.
Conclusion
These results expand the spectrum of
MMP20
disease‐causing mutations and provide the first evidence for MMP20 function during dentin formation.
This paper presents a technique for reconstructing a high-quality high dynamic range (HDR) image from a set of differently exposed and possibly blurred images taken with a hand-held camera. Recovering an HDR image from differently exposed photographs has become very popular. However, it often requires a tripod to keep the camera still when taking photographs of different exposures. To ease the process, it is often preferred to use a hand-held camera. This, however, leads to two problems, misaligned photographs and blurred long-exposed photographs. To overcome these problems, this paper adapts an alignment method and proposes a method for HDR reconstruction from possibly blurred images. We use Bayesian framework to formulate the problem and apply a maximumlikelihood approach to iteratively perform blur kernel estimation, HDR image reconstruction and camera curve recovery. When convergence, we simultaneously obtain an HDR image with rich and clear structures, the camera response curve and blur kernels. To show the effectiveness of our method, we test our method on both synthetic and real photographs. The proposed method compares favorably to two other related methods in the experiments.
Advances in adhesive dentistry have led to increased use of indirect restorations. In some situations, indirect composite techniques are more advantageous than direct composite filling techniques, such as establishing proper occlusal and interproximal anatomy, reducing polymerization shrinkage stress, and promoting the degree of conversion. This article presents a case about restoring the lower right first molar with extensive loss of tooth structure by the composite onlay to achieve a proper anatomic form and rehabilitate chewing function. This one-year clinical case encourages clinicians to manage large decay of posterior tooth conservatively. The given functional and esthetic outcomes demonstrate the promising applicability of the indirect composite technique.
This paper presents a technique for reconstructing a high-quality high dynamic range (HDR) image from a set of differently exposed and possibly blurred images taken with a hand-held camera. Recovering an HDR image from differently exposed photographs has become very popular. However, it often requires a tripod to keep the camera still when taking photographs of different exposures. To ease the process, it is often preferred to use a hand-held camera. This, however, leads to two problems, misaligned photographs and blurred long-exposed photographs. To overcome these problems, this paper adapts an alignment method and proposes a method for HDR reconstruction from possibly blurred images. We use Bayesian framework to formulate the problem and apply a maximumlikelihood approach to iteratively perform blur kernel estimation, HDR image reconstruction and camera curve recovery. When convergence, we simultaneously obtain an HDR image with rich and clear structures, the camera response curve and blur kernels. To show the effectiveness of our method, we test our method on both synthetic and real photographs. The proposed method compares favorably to two other related methods in the experiments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.