Objectives As one of the most life‐threatening malignancies, gastric cancer is the third contributor of cancer mortalities globally. Increasing studies have proven the regulatory roles of lncRNAs in the development of diverse malignant tumours. But little is known about its function and molecular mechanism in gastric carcinoma. Materials and methods RT‐qPCR was performed to measure the expression pattern of LOXL1‐AS1 in gastric cancer. To ascertain its definite role, CCK‐8, EdU, Western blot, transwell and sphere formation assays were adopted. RNA pull‐down, RIP, ChIP and luciferase reporter assays were carried out to investigate the molecular mechanism of LOXL1‐AS1 in gastric carcinoma. Results LOXL1‐AS1 was highly expressed in tissues and cells of gastric cancer. The upregulation of LOXL1‐AS1 predicted poor prognosis in gastric carcinoma. Our findings demonstrated that LOXL1‐AS1 accelerated the deterioration of gastric cancer by inducing cell proliferation, migration, EMT and stemness. Moreover, the expression of USF1 in gastric cancer was higher than in normal control and LOXL1‐AS1 negatively modulated USF1. Functionally, LOXL1‐AS1 acted as a ceRNA to upregulate USF1 via sponging miR‐708‐5p. Besides, we confirmed USF1 promoted the transcription of stemness marker SOX2. Rescue experiments testified the stimulative role of LOXL1‐AS1/miR‐708‐5p/USF1 pathway in gastric cancer progression. It was also validated that LOXL1‐AS1 facilitated cell growth of gastric carcinoma in vivo. Conclusions Our study unravelled that LOXL1‐AS1/miR‐708‐5p/USF1 pathway contributed to the development of gastric cancer.

Gastric cancer (GC) is one of the most frequent malignancies worldwide. Long noncoding RNAs (lncRNAs) are found to be largely implicated in various cancers, including GC. However, the function of lncRNA VCAN antisense RNA 1 (VCAN-AS1) in GC remains unclear. Herein, we observed a low level of VCAN-AS1 in normal gastric tissues through NCBI and UCSC, and that VCAN-AS1 upregulation in GC tissues was related to poor prognosis by TCGA. Furthermore, VCAN-AS1 was found markedly enhanced in GC tissues and cell lines, while its upregulation was related with clinical outcomes of GC patients. Besides this, silencing VCAN-AS1 represses cell proliferation, migration, and invasion but enhances apoptosis. More important, we discovered that VCAN-AS1 expression was negatively correlated with wild-type p53 levels in GC tissues and that p53 was negatively modulated by VCAN-AS1 in GC cells.Furthermore, p53 suppression reversed the repression of VCAN-AS1 silence on the biological processes of AGS cells. Intriguingly, we identified that both VCAN-AS1 and TP53 can bind with eIF4A3, one of the core proteins in the exon junction complex.Also, we confirmed that VCAN-AS1 negatively regulates TP53 expression by competitively binding with eIF4A3. Our findings disclosed that VCAN-AS1 contributes to GC progression through interacting with eIF4A3 to downregulate TP53 expression, indicating that VCAN-AS1 is a novel therapeutic strategy for GC treatment. K E Y W O R D S eIF4A3, EJC, GC progression, p53, VCAN-AS1

Automatic estimation of crowd density is very important for the safety management of the crowds. In particular, when the density of the crowds exceeds a critical level, the safety of people in the crowd may be compromised. This paper describes a novel method to estimate the crowd density based on the combination of multi-scale analysis and a support vector machine. The algorithm will first transform the crowd image into multi-scale formats using wavelet transform. The first-order and second-order statistical features at each scale of the transformed images are then extracted as density character vectors. Furthermore, a classifier based on a support vector machine is designed to classify the extracted density character vectors into different density levels. Compared with the conventional statistical techniques and wavelet energy techniques used in single-scale images, the test results of a set of 300 images show that the proposed algorithm can achieve much improved performance and more detailed information of the crowd density can be captured by the new feature extraction method.