• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.4147-4151
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• 2012

The current study was conducted to explore the inhibitory effects of a small interfering RNA (siRNA) on 3-phosphoinositide-dependent protein kinase 1 (PDK1) expression in esophageal cancer 9706 (EC9706) cells and the influence on their biological behavior. After transfection of a synthesized PDK1 siRNA, PDK1 mRNA and protein expression and the phosphorylation level of the downstream Akt protein were assessed using RT-PCR and Western blot analysis. Proliferation, apoptosis, cell invasion and in vivo tumor formation capacity were also investigated using MTT, flow cytometry, Transwell invasion trials, and nude mouse tumor transplantion, respectively. PDK1 siRNA effectively suppressed PDK1 mRNA and protein expression, and down-regulated the phosphorylation level of the Akt protein in the EC9706 cells (P < 0.05). It also inhibited cell proliferation and invasion, and promoted apoptosis; such effects were particularly obvious at 48 h and 72 h after transfection (P < 0.05). Growth of transplanted tumors was inhibited in nude mice, with decreased PDK1 expression in tumor tissues. PDK1 may be closely correlated with proliferation, apoptosis and invasion of esophageal cancer cells and thus may serve as an effective target for gene therapy.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.1803-1808
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• 2012

This study aimed to investigate the effects of Ser/Cys polymorphism in hOGG1 gene, Arg/Pro polymorphism in p53 gene, smoking and their interactions on the development of lung cancer. Ser/Cys polymorphism in hOGG1 and Arg/Pro polymorphism in p53 among 124 patients with lung cancer and 128 normal people were detected using PCR-RFLP. At the same time, smoking status was investigated between the two groups. Logistic regression was used to estimate the effects of Ser/Cys polymorphism and Arg/Pro polymorphisms, smoking and their interactions on the development of lung cancer. ORs (95% CI) of smoking, hOGG1 Cys/Cys and p53 Pro/Pro genotypes were 2.34 (1.41-3.88), 2.12 (1.03-4.39), and 2.12 (1.15-3.94), respectively. The interaction model of smoking and Cys/Cys was super-multiplicative or multiplicative, and the OR (95% CI) for their interaction item was 1.67 (0.36 -7.78). The interaction model of smoking and Pro/Pro was super-multiplicative with an OR (95%CI) of their interaction item of 5.03 (1.26-20.1). The interaction model of Pro/Pro and Cys/Cys was multiplicative and the OR (95%CI) of their interaction item was 0.99 (0.19-5.28). Smoking, hOGG1 Cys/Cys, p53 Pro/Pro and their interactions may be the important factors leading to the development of lung cancer.

• Structural Engineering and Mechanics
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• v.59 no.2
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• pp.261-275
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• 2016

Effect of silica fume on fresh properties, compressive strength at 28 days and fracture behavior of fly ash concrete composite were studied in this paper. Test results indicated that the fluidity and flowability of fly ash concrete composites decreased and fly ash concrete composite are more cohesive and appear to be sticky with the addition of silica fume. Addition of silica fume was very effective in improving the compressive strength at 28 days of fly ash concrete composite, and the compressive strength of fly ash concrete composite has a trend of increase with the increase of silica fume content. Results also indicated that all the fracture parameters of effective crack length, fracture toughness, fracture energy, the critical crack opening displacement and the maximum crack opening displacement of fly ash concrete composite decreased with the addition of silica fume. When the content of silica fume increased from 3% to 12%, these fracture parameters decreased gradually with the increase of silica fume content. Furthermore, silica fume had great effect on the relational curves of the three-point bending beam specimen. As the silica fume content increased from 3% to 12%, the areas surrounded by the three relational curves and the axes were becoming smaller and smaller, which indicated that the capability of concrete composite containing fly ash to resist crack propagation was becoming weaker and weaker.

• Molecules and Cells
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• v.41 no.2
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• pp.119-126
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• 2018

microRNA (miR)-612 shows anticancer activity in several types of cancers, yet its function in melanoma is still unclear. This study was undertaken to investigate the expression of miR-612 and its biological relevance in melanoma cell growth, invasion, and tumorigenesis. The expression and prognostic significance of miR-612 in melanoma were examined. The effects of miR-612 overexpression on cell proliferation, colony formation, tumorigenesis, and invasion were determined. Rescue experiments were conducted to identify the functional target gene(s) of miR-612. miR-612 was significantly downregulated in melanoma tissues compared to adjacent normal tissues. Low miR-612 expression was significantly associated with melanoma thickness, lymph node metastasis, and shorter overall, and disease-free survival of patients. Overexpression of miR-612 significantly decreased cell proliferation, colony formation, and invasion of SK-MEL-28 and A375 melanoma cells. In vivo tumorigenic studies confirmed that miR-612 overexpression retarded the growth of A375 xenograft tumors, which was coupled with a decline in the percentage of Ki-67-positive proliferating cells. Mechanistically, miR-612 targeted Espin in melanoma cells. Overexpression of Espin counteracted the suppressive effects of miR-612 on melanoma cell proliferation, invasion, and tumorigenesis. A significant inverse correlation (r = -0.376, P = 0.018) was observed between miR-612 and Espin protein expression in melanoma tissues. In addition, overexpression of miR-612 and knockdown of Espin significantly increased the sensitivity of melanoma cells to doxorubicin. Collectively, miR-612 suppresses the aggressive phenotype of melanoma cells through downregulation of Espin. Delivery of miR-612 may represent a novel therapeutic strategy against melanoma.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.17-27
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• 2023

Taking a subway shield tunnel in a certain section of Zhengzhou Metro Line 5 as an example, the field tests of shield cutting cement-soil monopile composite foundation were carried out. The load and internal force of the tunnel lining under the action of composite foundation were tested on-site and the distribution characteristics and variation laws of earth pressure around the tunnel under the load holding state of the composite foundation were analyzed. Five different load combinations (i.e., overburden load theory + q₀, Terzaghi's theory + q₀, Bierbaumer's theory + q₀, Xie's theory + q₀, and the proposed method (the combination of compound weight method and Terzaghi's theory) + q₀) were used to calculate the internal force of the tunnel structure and the obtained results were compared with the measured internal force results. The action mode of earth pressure on the tunnel lining structure was evaluated. Research results show that the earth pressure obtained by the calculation method proposed in this paper was more consistent with the measured value and the deviation between the two was within 5%. The distribution of the calculated internal force of the tunnel structure was more in line with the distribution law of field test data and the deviation between the calculated and measured values was small. This effectively verified the rationality and applicability of the proposed calculation method. Research results provided references for the design and evaluation of shield tunnels under the action of composite foundations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.840-855
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• 2011

In the context of additive noise steganography model, we propose a method to detect least significant bit (LSB) matching steganography in grayscale images. Images are decomposed into detail sub-bands with local linear transform (LLT) masks which are sensitive to embedding. Novel normalized characteristic function features weighted by a bank of band-pass filters are extracted from the detail sub-bands. A suboptimal feature set is searched by using a threshold selection algorithm. Extensive experiments are performed on four diverse uncompressed image databases. In comparison with other well-known feature sets, the proposed feature set performs the best under most circumstances.

• Journal of Computing Science and Engineering
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• v.8 no.3
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• pp.137-148
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• 2014

Named entity classification of Wikipedia articles is a fundamental research area that can be used to automatically build large-scale corpora of named entity recognition or to support other entity processing, such as entity linking, as auxiliary tasks. This paper describes a method of classifying named entities in Chinese Wikipedia with fine-grained types. We considered multi-faceted information in Chinese Wikipedia to construct four feature sets, designed different feature selection methods for each feature, and fused different features with a vector space using different strategies. Experimental results show that the explored feature sets and their combination can effectively improve the performance of named entity classification.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.73-82
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• 2015

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.