Research on the Influence of Small-Scale Terrain on Precipitation

Terrain plays an important role in the formation, development and distribution of local precipitation and is a major factor leading to locally abnormal weather in weather systems. Although small-scale topography has little influence on the spatial distribution of precipitation, it interferes with precipitation fitting. Due to the arbitrary combination of small, medium and large-scale terrain, complex terrain distribution is formed, and small-scale terrain cannot be clearly defined and removed. Based on the idea of bidimensional empirical mode decomposition (BEMD), this paper extracts small-scale terrain data layer by layer to smooth the terrain and constructs a macroterrain model for different scales in Central China. Based on the precipitation distribution model using multiple regression, precipitation models (B0, B1, B2 and B3) of different scales are constructed. The 18-year monthly average precipitation data of each station are compared with the precipitation simulation results under different scales of terrain and TRMM precipitation data, and the influence of different levels of small-scale terrain on the precipitation distribution is analysed. The results show that (1) in Central China, the accuracy of model B2 is much higher than that of TRMM model A and monthly precipitation model B0. The comprehensive evaluation indexes are increased by 3.31% and 1.92%, respectively. (2) The influence of different levels of small-scale terrain on the precipitation distribution is different. The first- and second-order small-scale terrain has interference effects on precipitation fitting, and the third-order small-scale terrain has an enhancement effect on precipitation. However, the effect of small-scale topography on the precipitation distribution is generally reflected as interference

Brain MR image segmentation based on an improved active contour model.

It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%

DNA damage response and repair gene mutations are associated with tumor mutational burden and outcomes to platinum-based chemotherapy/immunotherapy in advanced NSCLC patients

Abstract Background DNA damage response and repair (DDR) genes are crucial for maintaining the integrity of the genome. This study aims to explore the correlation of DDR gene mutations with TMB, clinical characteristics, and outcomes to platinum-based chemotherapy and platinum-based chemotherapy/immunotherapy in non-small cell lung cancer (NSCLC) without EGFR and ALK alterations. Methods Tumor tissue from 49 patients with stage III or IV NSCLC who were without EGFR and ALK alterations were analyzed using targeted next-generation sequencing (NGS). Among them, 13 patients received first-line platinum-based chemotherapy, 32 patients received first-line platinum-based chemotherapy/immunotherapy. Results In these NSCLC patients without EGFR and ALK alterations, the frequently mutated genes included TP53, KMT2D and KRAS, the most frequently mutated DDR gene was FANCG, DDR gene mutations were detected in 20 patients. The mutation frequency of homologous recombination (HR) pathway was significantly higher in lung squamous cell carcinoma (LUSC) than that in lung adenocarcinoma (LUAD) (30.8% vs. 5.7%). Among DDR positive patients, a lower percentage exhibited metastasis. Patients with DDR gene mutations, cell-cycle checkpoint pathway mutations, and BER pathway mutations had significantly higher TMB compared to those without corresponding mutations. In the patients receiving platinum-based chemotherapy/immunotherapy, the disease control rate was significantly lower in the DDR-positive group compared with that in the DDR-negative group (55.6% vs. 100.0%). Among LUAD patients receiving platinum-based chemotherapy/immunotherapy, we observed a worse overall survival (OS) in DDR-positive group, as well as poorer progression-free survival(PFS)and OS in BER-positive and FANCG mutated group. Conclusions DDR gene mutations are associated with tumor metastasis, TMB, and outcomes to platinum-based chemotherapy/immunotherapy in advanced NSCLC patients

Application of Unsupervised Feature Selection in Cashmere and Wool Fiber Recognition

ABSTRACTSuitable features are the key to identifying cashmere and wool fibers, and feature selection is an important step in classification. Existing supervised feature selection methods need to consider the information between fiber features and class labels. Aiming at making up for this deficiency, we propose an unsupervised feature selection method based on k-means clustering, which overcome the difficulty that fiber feature class labels are either unavailable or costly to obtain. Firstly, the subset of fiber features that have been normalized are clustered by the k-means clustering algorithm to obtain the total number of clusters, and the clustering effect is evaluated by the DB Index criterion. Next, the DB value of each feature subset, the correlation of features and the total number of the clustering are considered as the judgment criteria to select the optimal feature subset. Finally, the optimal subset of features obtained by unsupervised feature selection algorithms is fed into a support vector machine for automatic identification and classification of the two fibers. The experimental results show that the method achieves a high recognition rate of 97.25%. It is verified that the unsupervised feature selection method based on k-means clustering is effective for the recognition of cashmere and wool

Segmentation results on clinical brain MR images.

<p>(a) is the 18th image of 1_24♯, (b)-(g) show the ground truth, the segmentation results of improved LBF method, improved LGD method, Zhang's method, MICO and our method, respectively. (g) is the 30th image of 2_4♯. (i)-(n) are the ground truth, the segmentation results of improved LBF method, improved LGD method, Zhang's method, MICO and our method, respectively.</p

Details of the segmentation results on the 92th transaxial image of a synthetic image data set with parameters: noise level 5% and the intensity inhomogeneity level: 30%.

<p>The second column to the right column show the segmentation results of improved LBF method, improved LGD method, Zhang's method, MICO and our method, respectively.</p

Js values (mean ± standard deviation) for the segmentation results on simulated T1-weighted brain MR images. (%).

<p>Js values (mean ± standard deviation) for the segmentation results on simulated T1-weighted brain MR images. (%).</p

Illustration of two 3-T intensity inhomogeneity corrupted brain MR images and one 7-T brain MR images (1st column).

<p>The second column to right column show the results of improved LBF, improved LGD, Zhang's method, MICO and our method, respectively. The odd rows show the bias field corrected images. The even rows show the corresponding estimated bias fields.</p

3D segmentation results of WM, GM on T1-weighted clinical brain MR image.

<p>Left column shows the ground truth and the right column shows the results of our method.</p

CV values for the bias corrected images. (%).

<p>CV values for the bias corrected images. (%).</p