Familial tooth agenesis (FTA), distinguished by developmental failure of selected teeth, is one of the most prevalent craniofacial anomalies in humans. Mutations in genes involved in WNT/β-catenin signaling, including AXIN2 WNT10A, WNT10B, LRP6, and KREMEN1, are known to cause FTA. However, mutational interactions among these genes have not been fully explored. In this study, we characterized four FTA kindreds with LRP6 pathogenic mutations: p.(Gln1252*), p.(Met168Arg), p.(Ala754Pro), and p.(Asn1075Ser). The three missense mutations were predicted to cause structural destabilization of the LRP6 protein. Two probands carrying both an LRP6 mutant allele and a WNT10A variant exhibited more severe phenotypes, suggesting mutational synergism or digenic inheritance. Biallelic LRP6 mutations in a patient with many missing teeth further supported the dose-dependence of LRP6-associated FTA. Analysis of 21 FTA cases with 15 different LRP6 loss-of-function mutations revealed high heterogeneity of disease severity and a distinctive pattern of missing teeth, with maxillary canines being frequently affected. We hypothesized that various combinations of sequence variants in WNT-related genes can modulate WNT signaling activities during tooth development and cause a wide spectrum of tooth agenesis severity, which highlights the importance of exome/genome analysis for the genetic diagnosis of FTA in this era of precision medicine.
Spectrum sensing is one of the major elements of cognitive radio applications. A single secondary user (SU) usually cannot provide robust sensing capability due to channel effects. In order to overcome this problem, cooperative spectrum sensing techniques have been proposed. Because conventional cooperative spectrum sensing schemes assumed that all sensing nodes have the same sensing reliability, the SUs were assigned identical falsealarm probability even though some of them suffer deep fading, which may degrade their detection performance. In this study, we propose an adaptive credibility-based cooperative spectrum sensing technique, which evaluates the sensing reliability of SUs based on previous sensing performance, and adjusts the probability of false alarm of each SU individually. Assigning a higher false alarm probability for a more reliable SU can result in better detection performance. To verify the effectiveness of the proposed cooperative spectrum sensing technique, both the false alarm probability and the detection probability are simulated under Rayleigh fading channels, and the results show that the proposed approach outperforms any conventional detection method in terms of detection performance.
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.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.