High miR-34a and miR-26b expressions inhibit prostate cancer cell OPCN-1 proliferation and enhances apoptosis

Purpose: To investigate the effects of miR-34a and miR-26b on the targeted genes, LEF1 and EphA2, and proliferation and apoptosis of OPCN-1.Methods: Sixty specimens of cancer tissue (CT) and equivalent tissue adjacent to tumors (TAT) were collected from prostate cancer patients. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to determine the mRNA expression levels of miR-34a, miR-26b, LEF1, and EphA2 in the above tissues, while protein expression levels of LEF1 and EphA2 were evaluated by Western blot.Results: Compared with TAT, the expression levels of miR-26b and miR-34a in CT decreased significantly (p < 0.05), whereas the mRNA and protein expression levels of EphA2 and LEF1 in CT significantly increased (p < 0.05). TargetScanHuman7.2 assay data revealed that miR-26b targeted EphA2, while miR-34a targeted LEF1. MiR-26b MG showed decreased EphA2 mRNA and protein levels when compared with miR-26b-NC group after overexpression. The miR-34a MG exhibited decreased expression levels of LEF1 mRNA and protein compared with the miR-34a-NC group. Between 48 and 72 h, miR-26b MG grew more slowly than miR-26b-NC group; miR-34a MG also showed significantly slower growth than miR-34a-NC group. The miR-26b MG and miR-34a MG groups displayed higher apoptosis rate than miR-26b-NC and miR-34a-NC groups, respectively.Conclusion: High expressions of miR-34a and miR-26b targeted the inhibition of LEF1 and EphA2, respectively, indicating that they inhibit the proliferation, and also control the increased apoptosis rate of OPCN-1 cells. Hence, miR-34a and miR-26b are probable molecular targets for the development of new prostate cancer drugs

Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing

Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a normal sample with some carefully crafted human imperceptible perturbations, even highly accurate DNN make wrong decisions. Multiple defense mechanisms have been proposed which aim to hinder the generation of such adversarial samples. However, a recent work show that most of them are ineffective. In this work, we propose an alternative approach to detect adversarial samples at runtime. Our main observation is that adversarial samples are much more sensitive than normal samples if we impose random mutations on the DNN. We thus first propose a measure of `sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity. We then integrate statistical hypothesis testing and model mutation testing to check whether an input sample is likely to be normal or adversarial at runtime by measuring its sensitivity. We evaluated our approach on the MNIST and CIFAR10 datasets. The results show that our approach detects adversarial samples generated by state-of-the-art attacking methods efficiently and accurately.Comment: Accepted by ICSE 201

A Case Study on Foamy Oil Characteristics of the Orinoco Belt, Venezuela

With a current recovery of less than 11%, the Orinoco Belt in Venezuela still contains potentially more than 1.3 trillion barrels of reserves of “three highs, one low” oil at a depth of 100 to 1500 m. 5 joint projects and one project of Petroleos de Venezuela SA are making plans to improve oil recovery in the area. So it is important for them to have a thorough knowledge of foamy oil characteristics. This reservoir has a peculiar behavior called as a foamy phenomenon. In order to characterize the properties of the foamy oil, this paper discussed unconventional test methodology and the detailed suite of laboratory procedures including PVT and pressure depletion tests used to examine the Orinoco heavy oil. The results showed substantial differences in characteristics of foamy oil and conventional oil studied, not only in terms of PVT behavior but also in terms of the production performance during pressure depletion tests. The foamy oil compressibility was between 10-120×10-4 mPa-1, which was obviously higher than that of conventional oil. Differential liberation experiments of the oil, with obvious high formation volume factor, stable GOR, and low density showed a strong tendency to foam below the bubble point. Other notable observations were that more efficient oil recovery was achieved at high depletion rates while less free gas was produced.Key words: Foamy oil; Unconventional tests; The Orinoco Belt; PVT; Pressure depletion test