Detection of marine oil spills from radar satellite images for the coastal ecological risk assessment.

Coastal ecosystems offer substantial support and space for the sustainable development of human society, and hence the ecological risk evaluation of coastal ecosystems is of great significance. In this article, we propose an innovative framework for evaluating coastal ecological risk by considering oil spill risk information and environmental vulnerability information. Specifically, a deep learning based marine oil spill monitoring method is presented to obtain the oil spill risk information from Sentinel-1 polarimetric synthetic aperture radar (PolSAR) images. The environmental vulnerability information is then obtained from biological sample data and habitat information. Finally, a weighted probability model is introduced to utilize the oil spill risk and environmental vulnerability information, to evaluate the coastal ecological risk. In the experimental part, the proposed oil spill monitoring method shows its reliability in global ocean areas, and the proposed model is adopted to evaluate the ecological risk in Jiaozhou Bay, China. The results show that the ecological situation of more than half of the areas in Jiaozhou Bay is unstable, and the areas with high risk are mainly concentrated in the ports, shipping channels, and those areas with high biodiversity. This study provides some new perspectives on ecological risk assessment for coastal ecosystems, facilitating the planning process and the actions to be taken in response to the accidents that occur in the ocean, especially oil spill accidents.

Polarimetric SAR Speckle Filtering Using a Nonlocal Weighted LMMSE Filter.

Despeckling is a key preprocessing step for applications using PolSAR data in most cases. In this paper, a technique based on a nonlocal weighted linear minimum mean-squared error (NWLMMSE) filter is proposed for polarimetric synthetic aperture radar (PolSAR) speckle filtering. In the process of filtering a pixel by the LMMSE estimator, the idea of nonlocal means is employed to evaluate the weights of the samples in the estimator, based on the statistical equalities between the neighborhoods of the sample pixels and the processed pixel. The NWLMMSE estimator is then derived. In the preliminary processing, an effective step is taken to preclassify the pixels, aiming at preserving point targets and considering the similarity of the scattering mechanisms between pixels in the subsequent filter. A simulated image and two real-world PolSAR images are used for illustration, and the experiments show that this filter is effective in speckle reduction, while effectively preserving strong point targets, edges, and the polarimetric scattering mechanism.

Multiple Classifiers Based Semi-Supervised Polarimetric SAR Image Classification Method.

Polarimetric synthetic aperture radar (PolSAR) image classification has played an important role in PolSAR data application. Deep learning has achieved great success in PolSAR image classification over the past years. However, when the labeled training dataset is insufficient, the classification results are usually unsatisfactory. Furthermore, the deep learning approach is based on hierarchical features, which is an approach that cannot take full advantage of the scattering characteristics in PolSAR data. Hence, it is worthwhile to make full use of scattering characteristics to obtain a high classification accuracy based on limited labeled samples. In this paper, we propose a novel semi-supervised classification method for PolSAR images, which combines the deep learning technique with the traditional scattering trait-based classifiers. Firstly, based on only a small number of training samples, the classification results of the Wishart classifier, support vector machine (SVM) classifier, and a complex-valued convolutional neural network (CV-CNN) are used to conduct majority voting, thus generating a strong dataset and a weak dataset. The strong training set are then used as pseudo-labels to reclassify the weak dataset by CV-CNN. The final classification results are obtained by combining the strong training set and the reclassification results. Experiments on two real PolSAR images on agricultural and forest areas indicate that, in most cases, significant improvements can be achieved with the proposed method, compared to the base classifiers, and the improvement is approximately 3-5%. When the number of labeled samples was small, the superiority of the proposed method is even more apparent. The improvement for built-up areas or infrastructure objects is not as significant as forests.

[miR-218-5p Targeting TPX2 Regulates p53 Pathway and Inhibits Malignant Progression of Lung Adenocarcinoma].

BACKGROUND: Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, and its treatment and diagnosis remain a hot research topic. Targeting protein for Xenopus kinesin-like protein 2 (TPX2) is highly expressed in a variety of cancer cells and may be associated with the progression of LUAD. This study aimed to investigate the effect of TPX2 on the malignant progression of LUAD cells and the regulatory mechanisms. METHODS: The expression of gene TPX2 in LUAD tissues from The Cancer Genome Atlas (TCGA) database was analyzed by bioinformatics analysis techniques. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of TPX2 and miR-218-5p in human lung normal cell lines and human LUAD cell lines. Western blot was used to detect TPX2 protein expression in cell lines and its effect on the expression of key proteins in the p53 signaling pathway. The relationship between TPX2 and miR-218-5p was predicted using bioinformatics and verified by dual luciferase reporter gene assay. Cell counting kit-8 (CCK-8) assay, cell clone formation, cell scratching, Transwell assay, and flow cytometry were used to detect the effects of miR-218-5p and TPX2 on LUAD cell function. RESULTS: TPX2 was significantly overexpressed in LUAD cells, and knockdown of TPX2 inhibited LUAD cell proliferation, migration, and invasion, promoted apoptosis and induced G2/M phase block, and promoted the expression of key proteins in the p53 signaling pathway. miR-218-5p, an upstream regulator of TPX2, could inhibit its expression. Overexpression of miR-218-5p eliminated the malignant development caused by high expression of TPX2, inhibited the malignant processes of LUAD cells such as proliferation and migration as well as promoted the p53 signaling pathway. CONCLUSIONS: miR-218-5p targets and inhibits TPX2 expression and exerts an inhibitory effect on the malignant progression of LUAD cells via p53.

Clinical significance of detecting circulating tumor cells in patients with esophageal squamous cell carcinoma by EpCAM­independent enrichment and immunostaining­fluorescence in situ hybridization.

Circulating tumor cells (CTCs) are tumor cells present in the bloodstream, which originate from tumor sites, and are ultimately responsible for metastasis or relapse in several types of cancer. However, to the best of our knowledge, only a few studies have investigated these extremely rare cells in esophageal squamous cell carcinoma (ESCC). In the present study, 63 patients with ESCC and 50 healthy donors were recruited, and the potential clinical significance of CTCs was assessed using subtraction enrichment and immunostaining­fluorescence in situ hybridization. Blood samples were collected at the following times: At first diagnosis, following neoadjuvant chemoradiotherapy, 24 h and 13 days post­surgery, and every 3 months during follow­up. Cytokeratin (CK)­positive and clustered CTCs only accounted for 1% of total CTCs detected, whereas most CTCs were CK­negative aneuploid cells. Patients with ESCC (n=63) had higher CTC counts compared with healthy donors (control group; n=50) (area under curve=0.807, median CTC count, 2 vs. 0). However, there was no statistical association between CTC counts and sex, age, pathological stage, tumor location, tumor depth or lymph node involvement (P>0.05). The association of tumor development with CTC status and other circulating biomarkers was monitored in patients for a further 2 years. The results revealed that a change in CTC counts between first diagnosis and 13 days post­surgery (ΔCTC) of ≥2/7.5 ml peripheral blood could be applied for predicting progression­free survival (hazard ratio, 3.922; 95% confidence interval, 0.907­16.951; P<0.05) in patients with ESCC. In conclusion, ΔCTC evaluation may be a promising indicator for predicting tumor prognosis and the clinical efficacy of treatment in patients with ESCC.

[Protective effects of ligustrazine against photoreceptor cell injury induced by N-methyl-N-nitrosourea and its mechanism].

AIM: To study the protective effect of ligustrazine against photoreceptor cell injury induced by N-methyl-N-nitrosourea (MNU) in Sprague-Dawley (SD) rats. METHODS: Ligustrazine injections of different doses were injected intraperitoneally into 47-day female SD rats once a day and a single intraperitoneal injection of MNU 60 mg x kg(-1) was given to 50-day rats. At different intervals after MNU treatment,the animals were sacrificed. The apoptotic index of photoreceptor cells was calculated by TUNEL labeling at 24 h following MNU treatment; peripheral retinal damage was evaluated based on retinal thickness at the d 7 after MNU treatment, and the expression of c-jun and c-fos genes was detected by RT-PCR technique. RESULTS: Ligustrazine injection could remarkably increase total thickness of peripheral retina and decrease apoptotic index of photoreceptor cells induced by MNU in a dose-dependent manner. Compared with MNU-treated rats, the gene expression of c-jun and c-fos was time-dependently down-regulated in ligustrazine-treated group. CONCLUSION: Ligustrazine injection partially protects against MNU-induced retinal damage by down-modulating the expression of c-jun and c-fos genes to inhibit apoptosis of photoreceptor cells.