ABSTRACT
A new hyperspectral images (HSIs) denoising method via Interpolated Block-Matching and 3D filtering and Guided Filtering (IBM3DGF) denoising method is proposed. First, inter-spectral correlation analysis is used to obtain inter-spectral correlation coefficients and divide the HSIs into several adjacent groups. Second, high-resolution HSIs are produced by using adjacent three images to interpolate. Third, Block-Matching and 3D filtering (BM3D) is conducted to reduce the noise level of each group; Fourth, the guided image filtering is utilized to denoise HSI of each group. Finally, the inverse interpolation is applied to retrieve HSI. Experimental results of synthetic and real HSIs showed that, comparing with other state-of-the-art denoising methods, the proposed IBM3DGF method shows superior performance according to spatial and spectral domain noise assessment. Therefore, the proposed method has a potential to effectively remove the spatial/spectral noise for HSIs.
ABSTRACT
This paper proposed a new MNF-BM4D denoising algorithm based on guided filtering to improve the denoising performance of the state-of-the-art Block-Matching and 4D filtering(BM4D) algorithm for hyperspectral images in the spatial and spectral domain. BM4D is firstly used to denoise hyperspectral images. Then Minimum Noise Fraction(MNF) algorithm is introduced to distinguish between the main component and the noisy component. Finally, the guided image filtering technology is utilized to further improve the denoising performance. A number of experiments on both simulated and real data are conducted to validate the effective denoising performance of the proposed method. Therefore, the proposed algorithm can be considered as a promising technique for hyperspectral imagery denoising.
ABSTRACT
@# Objective: To explore the molecular mechanism of miR-17-5p regulating the proliferation and invasion of nasopharyngeal carcinoma cells by regulating the expression of breast cancer metastasis suppressor 1 like (BRMS1-like or BRMS1L) gene. Methods:A total of 40 cases of nasopharyngeal carcinoma tissues and corresponding paracancerous tissues resected from nasopharyngeal carcinoma patients, who were admitted to the General Hospital of Pingdingshan Shenma Medical Group during January 2014 to December 2017, were included in this study; in addition, nasopharyngeal carcinoma cell lines CNE 2, HONE 1, C666-1 and nasopharyngeal immortalized epithelial cell line NP69 were also collected for this study. The expression of miR-17-5p in nasopharyngeal carcinoma tissues and cell lines was detected by qPCR. The targeted relationship between BRMS1L and miR-17-5p was predicted by the StarBase and verified by the Dual luciferase reporter gene assay. Effects of transfection of miR-17-5p mimics and inhibitors on the expression of BRMS1Lin CNE2 cells were detected by WB assay. CCK-8, Transwell and Flow cytometry were used to detect the effects of miR-17-5p/BRMS1L axis on the proliferation, migration, invasion and apoptosis of CNE 2 cells. Results: miR-17-5p was highly expressed in nasopharyngeal carcinoma tissues and cell lines (P<0.05 or P<0.01). Knockdown of miR-17-5p significantly inhibited proliferation, invasion and migration of CNE2 cells but promoted apoptosis (P<0.05 or P<0.01); miR-17-5p targeted BRMS1Land down-regulated its expression. Over-expression of BRMS1Lsignificantly inhibited the proliferation, invasion and migration of CNE2 cells but promoted apoptosis (all P<0.01); while simultaneous over-expression of miR-17-5p and BRMS1L reversed the above effects (all P<0.01). Conclusion: miR-17-5p promoted proliferation, invasion, migration and inhibited apoptosis of CNE 2 cells by down-regulating the expression of BRMS1L.
ABSTRACT
In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results-the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain-than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance.
ABSTRACT
This study was aimed to explore the frequency of PTPN11 mutation in children with leukemia and its clinical significance. Genomic DNAs were extracted from peripheral leukocytes of 131 patients with leukemia, including 101 cases of ALL, 26 cases of AML, 3 cases of CML and 1 case of juvenil myelomonocytic leukemia (JMML). The sequences of PTPN11 exons 3, 8, 13 were amplified by polymerase chain reaction (PCR), and the clinical characteristics of positive patients were analyzed. The results indicated that the PTPN11 mutation was found in 10 cases (9.9%) from newly diagnosed 101 cases of ALL. Grouping the newly diagnosed ALL children by various clinical features, it was found that the PTPN11 mutation did not show associations with sex, age, white blood cell (WBC) count, prednisone test sensitivity, clinical risk and disease recurrences at the first visit (P > 0.05). PTPN11 mutations were found in 2 cases out of 26 AML patients, including one AML-M(2) and one AML-M(4). No PTPN11 mutation in 3 CML patients was found. Exon 13 mutation of PTPN11 gene was found in 1 case of JMML. It is concluded that the E76 of exon 3 is the hot spot of PTPN11 mutation in children leukemia. The novel G503E (1508G > A) mutation is detected in one JMML patient. The PTPN11 mutation does not associate with the sex, age, WBC count, prednisone sensitive test and early recurrence.
