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Ameloblastomas are benign tumors associated with recurrence and morbidity. Mutations in the V-Raf murine sarcoma viral oncogene homolog B1 (BRAF) and smoothened (SMO) genes have been identified in ameloblastomas. The mutation rate of BRAFin different regions and mutation sites of SMOremains controversial. This study assesses the relationship between these mutations and clinicopathological features of ameloblastoma in Chinese patients. Ameloblastomas were surgically removed from 30 patients and BRAFgene polymorphisms were detected using DNA sequencing analysis. We also examined the relationship between BRAFor SMOmutations and clinicopathological parameters. BRAFV600E mutation was detected in 18 of 30 ameloblastomas. No significant correlation was found between BRAFV600E mutation and age, sex, location, histologic type, capsular invasion, or tumor recurrence. Five cases carried an SMOT364N mutation in exon 5, whereas six carried an SMOS590 T mutation in exon 10. The SMOmutation rate in patients aged ≤ 20 years was significantly higher than that in patients aged > 20 years. SMOT364N and S590 T mutations were closely related to age and capsular invasion. These findings indicate that BRAFV600E, SMOT364N, and SMOS590 T mutations play important roles in the pathogenesis of ameloblastoma.Key highlightsThe BRAFV600E mutation was highly prevalent in Chinese patients with ameloblastoma.SMOT364N and S590 T mutations are closely related to age and capsular invasion.The BRAFV600E, SMOT364N, and SMOS590 T mutations play important roles in the pathogenesis of ameloblastoma.

A novel tri-band bandpass filter (BPF) is proposed by using cross-coupled stepped impedance resonators (SIRs), which can operate at 2.4 GHz, 3.5 GHz and 5.25 GHz respectively. In engineering application, it is suitable for wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX). In addition, the passband of this filter can be easily controlled by changing the electrical parameters of the resonators. So it is helpful to the selection of passband in engineering applications. For in-depth analysis, the equivalent circuit of the filter is also proposed. At last, it shows that the measured results agree well with the simulated results. The insertion loss of the filter within each passband is less than 1.9 dB, meanwhile two transmission zeros are obtained which can be able to meet the engineering requirements.

Person re-identification (re-id) involves matching a person across nonoverlapping views, with different poses, illuminations and conditions. Visual attributes are understandable semantic information to help improve the issues including illumination changes, viewpoint variations and occlusions. This paper proposes an end-to-end framework of deep learning for attribute-based person re-id. In the feature representation stage of framework, the improved convolutional neural network (CNN) model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features. Moreover, an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model. The coupled clusters loss function is used in the training stage of the framework, which enhances the discriminability of both types of features. The combined features are mapped into the Euclidean space. The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same. Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets. [ABSTRACT FROM AUTHOR]