Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides.

Prostate cancer is the second most frequent malignancy in men worldwide, and its incidence is increasing. Therefore, it is urgently required to clarify the underlying mechanisms of prostate cancer. Although the long non‐coding RNA LINC00115 was identified as an oncogene in several cancers, the expression and function of LINC00115 in prostate cancer have not been explored. Our results showed that LINC00115 was significantly up‐regulated in prostate cancer tissues, which was significantly associated with a poor prognosis for prostate cancer patients. Functional studies showed that knockdown LINC00115 inhibited cell proliferation and invasion. In addition, LINC00115 served as a competing endogenous RNA (ceRNA) through sponging miR‐212‐5p to release Frizzled Family Receptor 5 (FZD5) expression. The expression of miR‐212‐5p was noticeably low in tumour tissues, and FZD5 expression level was down‐regulated with the knockdown of LINC00115. Knockdown LINC00115 inhibited the Wnt/β‑catenin signalling pathway by inhibiting the expression of FZD5. Rescue experiments further showed that LINC00115 inhibits prostate cancer cell proliferation and invasion via targeting miR‐212‐5p/ FZD5/ Wnt/β‐catenin axis. The present study provided clues that LINC00115 may be a promising novel therapeutic target for prostate cancer patients.

Objective The purpose of this study was to explore serum miR-135a-5p expression in colorectal cancer and examine the potential usefulness of this molecule as a biomarker for diagnosis in colorectal cancer. Methods Serum samples were collected from 60 patients with primary colorectal cancer, 40 patients with colorectal polyps and 50 healthy controls. Serum miR-135a-5p expression levels were detected by reverse transcription quantitative real-time quantitative polymerase chain reaction. Serum carcinoembryonic antigen and carbohydrate antigen 199 concentrations were detected by MODULAR ANALYTICS E170. Results The relative expression level of serum miR-135a-5p in colorectal cancer patients, colorectal polyps patients and healthy controls was 2.451 (1.107, 4.413), 0.946 (0.401, 1.942) and 0.949 (0.194, 1.415), respectively, indicating that it was significantly higher in colorectal cancer patients than that in the other two groups ( U = 351.0, 313.0, both P < 0.001). Additionally, it was significantly correlated with different degrees of tumour differentiation ( U = 215.0, P = 0.029) and different tumour stages ( U = 202.0, P = 0.013). There was no significant correlation between the relative expression of serum miR-135a-5p and carcinoembryonic antigen ( r= 0.023, P = 0.293) or carbohydrate antigen 199 ( r= 0.067, P = 0.068) in colorectal cancer patients. Compared with colorectal polyps group, AUC of serum miR-135a-5p in colorectal cancer group was 0.832 with 95% CI 0.73-0.93; compared with healthy control group, AUC was 0.875 with 95% CI 0.80-0.95. Conclusion Serum miR-135a-5p expression in colorectal cancer patients was higher than that in patients with colorectal polyps and healthy controls, suggesting that serum miR-135a-5p may prove to be an important biomarker for auxiliary diagnosis of colorectal cancer.

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Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

A serious earthquake could trigger thousands of landslides and produce some slopes more sensitive to slide in future. Landslides could threaten human’s lives and properties, and thus mapping the post-earthquake landslide susceptibility is very valuable for a rapid response to landslide disasters in terms of relief resource allocation and posterior earthquake reconstruction. Previous researchers have proposed many methods to map landslide susceptibility but seldom considered the spatial structure information of the factors that influence a slide. In this study, we first developed a U-net like model suitable for mapping post-earthquake landslide susceptibility. The post-earthquake high spatial airborne images were used for producing a landslide inventory. Pre-earthquake Landsat TM (Thematic Mapper) images and the influencing factors such as digital elevation model (DEM), slope, aspect, multi-scale topographic position index (mTPI), lithology, fault, road network, streams network, and macroseismic intensity (MI) were prepared as the input layers of the model. Application of the model to the heavy-hit area of the destructive 2008 Wenchuan earthquake resulted in a high validation accuracy (precision 0.77, recall 0.90, F1 score 0.83, and AUC 0.90). The performance of this U-net like model was also compared with those of traditional logistic regression (LR) and support vector machine (SVM) models on both the model area and independent testing area with the former being stronger than the two traditional models. The U-net like model introduced in this paper provides us the inspiration that balancing the environmental influence of a pixel itself and its surrounding pixels to perform a better landslide susceptibility mapping (LSM) task is useful and feasible when using remote sensing and GIS technology.

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