A Novel Steganography Method for Character-Level Text Image Based on Adversarial Attacks.

The Internet has become the main channel of information communication, which contains a large amount of secret information. Although network communication provides a convenient channel for human communication, there is also a risk of information leakage. Traditional image steganography algorithms use manually crafted steganographic algorithms or custom models for steganography, while our approach uses ordinary OCR models for information embedding and extraction. Even if our OCR models for steganography are intercepted, it is difficult to find their relevance to steganography. We propose a novel steganography method for character-level text images based on adversarial attacks. We exploit the complexity and uniqueness of neural network boundaries and use neural networks as a tool for information embedding and extraction. We use an adversarial attack to embed the steganographic information into the character region of the image. To avoid detection by other OCR models, we optimize the generation of the adversarial samples and use a verification model to filter the generated steganographic images, which, in turn, ensures that the embedded information can only be recognized by our local model. The decoupling experiments show that the strategies we adopt to weaken the transferability can reduce the possibility of other OCR models recognizing the embedded information while ensuring the success rate of information embedding. Meanwhile, the perturbations we add to embed the information are acceptable. Finally, we explored the impact of different parameters on the algorithm with the potential of our steganography algorithm through parameter selection experiments. We also verify the effectiveness of our validation model to select the best steganographic images. The experiments show that our algorithm can achieve a 100% information embedding rate and more than 95% steganography success rate under the set condition of 3 samples per group. In addition, our embedded information can be hardly detected by other OCR models.

Celastrol Loaded Nanoparticles With ROS-Response and ROS-Inducer for the Treatment of Ovarian Cancer.

Ovarian cancer is a gynecological cancer from which it is difficult to be completely cured. It is common to use regimens as an effective treatment for ovarian cancer, but these inevitably bring serious side effects. New treatment strategies and special drugs are needed to improve the prognosis of patients. Celastrol is a natural product, isolated from traditional medicine, that has been proven to be curative for inflammation and cancers. However, the non-targeting and low solubility of celastrol limit its clinical application. We prepared celastrol-loaded nanoparticles for the efficient treatment of ovarian cancer via oxidative stress amplification. In this work, a tumor-targeted, ROS-sensitive nanoparticle was designed, synthesized, and assembled into a drug delivery system that used celastrol. Folic acid (FA) groups on the surface of nanoparticles guide them to actively target the surface of the tumor cell membrane. Thioketal (TK) bonds in nanoparticles can be oxidized and broken into -SH within the ROS level of tumor tissues, which causes the breaking of the PEG hydrophilic shell layer of nanoparticles and promotes the release of celastrol. The released celastrol further stimulated the production of ROS and amplified the intracellular ROS level to promote the apoptosis of tumor cells, thus achieving a therapeutic effect on the celastrol treated ovarian cancer.

OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning.

Due to the openness of an Android system, many Internet of Things (IoT) devices are running the Android system and Android devices have become a common control terminal for IoT devices because of various sensors on them. With the popularity of IoT devices, malware on Android-based IoT devices is also increasing. People's lives and privacy security are threatened. To reduce such threat, many researchers have proposed new methods to detect Android malware. Currently, most malware detection products on the market are based on malware signatures, which have a fast detection speed and normally a low false alarm rate for known malware families. However, they cannot detect unknown malware and are easily evaded by malware that is confused or packaged. Many new solutions use syntactic features and machine learning techniques to classify Android malware. It has been known that analysis of the Function Call Graph (FCG) can capture behavioral features of malware well. This paper presents a new approach to classifying Android malware based on deep learning and OpCode-level FCG. The FCG is obtained through static analysis of Operation Code (OpCode), and the deep learning model we used is the Long Short-Term Memory (LSTM). We conducted experiments on a dataset with 1796 Android malware samples classified into two categories (obtained from Virusshare and AndroZoo) and 1000 benign Android apps. Our experimental results showed that our proposed approach with an accuracy of 97 % outperforms the state-of-the-art methods such as those proposed by Nikola et al. and Hou et al. (IJCAI-18) with the accuracy of 97 % and 91 % , respectively. The time consumption of our proposed approach is less than the other two methods.

Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine.

Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

Downregulation of MicroRNA-147 Inhibits Cell Proliferation and Increases the Chemosensitivity of Gastric Cancer Cells to 5-Fluorouracil by Directly Targeting PTEN.

Gastric cancer is the fourth most common malignancy and the third leading cause of cancer-related deaths worldwide. This study aimed to investigate the expression patterns, biological roles, and underlying mechanisms of microRNA-147 (miR-147) in gastric cancer. The present study demonstrated that miR-147 was significantly upregulated in gastric cancer tissues and cell lines. Downregulation of miR-147 decreased cell proliferation and enhanced the chemosensitivity of gastric cancer cells to 5-fluorouracil (5-FU) through the cell apoptosis pathway. In addition, phosphatase and tensin homolog (PTEN) was mechanically identified as the direct target of miR-147 in gastric cancer. PTEN knockdown reversed the effects of miR-147 downregulation on the proliferation, chemosensitivity, and 5-FU-induced apoptosis of gastric cancer cells. Moreover, miR-147 regulated the PI3K/AKT signaling pathway in gastric cancer by targeting PTEN. In conclusion, miR-147 suppressed the proliferation and enhanced the chemosensitivity of gastric cancer cells to 5-FU by promoting cell apoptosis through directly targeting PTEN and regulating the PI3K/AKT signaling pathway. This study provides important insight into the molecular mechanism that underlies the chemoresistance of gastric cancer cells. The results of this study could aid the development of a novel therapeutic strategy for gastric cancer.

Association of Epstein Barr virus A73 gene polymorphism with nasopharyngeal carcinoma.

OBJECTIVE: To study the association between Epstein Barr (EB) virus A73 gene polymorphism and nasopharyngeal carcinoma (NPC) in a Chinese Han population. METHODS: A case-control study was designed, including 510 nasopharyngeal cancer patients and 520 healthy controls, A157154C genotypes of the A73 gene in EB virus were detected, genotype and allele frequency distribution between the two groups were compared. RESULTS: The C allele frequency in the NPC group was significantly higher than that in the control group (68.4% vs. 61.2%; p<0.001). The CC genotype frequency in the NPC group was significantly higher than that in the control group, the difference was significant (47.4% vs. 41.2%; p<0.001). The CC genotype frequency in male patients was significantly higher than that in female patients in the NPC group, the difference was significant (50.3% vs. 34.7%; p<0.001). CONCLUSION: A157154C polymorphism of the A73 gene in EB virus was associated with NPC susceptibility.

MicroRNA-410 suppresses migration and invasion by targeting MDM2 in gastric cancer.

Gastric cancer is one of the most frequent malignancies in tumors in the East Asian countries. Identifying precise prognostic markers and effective therapeutic targets is important in the treatment of gastric cancer. microRNAs (miRNAs) play important roles in tumorigenesis. However, the mechanisms by which miRNAs regulate gastric cancer metastasis remain poorly understood. In this study, we found that the levels of miR-410 in gastric cancer and cell lines were much lower than that in the normal control, respectively, and the lower level of miR-410 was significantly associated with lymph-node metastasis. Transfection of miR-410 mimics could significantly inhibit the cell proliferation, migration and invasion in the HGC-27 gastric cancer cell lines. In contrast, knockdown of miR-410 had the opposite effect on the cell proliferation, migration and invasion. Moreover, we also found that MDM2 was negatively regulated by miR-410 at the post-transcriptional level, via a specific target site with the 3'UTR by luciferase reporter assay. The expression of MDM2 was inversely correlated with miR-410 expression in gastric cancer tissues, and overexpression of MDM2 in miR-410-transfected gastric cancer cells effectively rescued the inhibition of cell proliferation and invasion caused by miR-410. Thus, our findings suggested that miR-410 acted as a new tumor suppressor by targeting the MDM2 gene and inhibiting gastric cancer cells proliferation, migration and invasion. The findings of this study contributed to the current understanding of these functions of miR-410 in gastric cancer.