With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.

Osteosarcoma (OS) is the most prevalent human bone malignancy, and presents a global annual morbidity of approximately five cases per million. Notably, precise and efficient targeted therapy has become the most promising strategy for the treatment of OS; however, there is still an urgent need for the identification of suitable therapeutic targets. Metastasis-associated in colon cancer 1 (MACC1) was first identified in colon tumors by differential display RT-PCR, and was shown to be involved in the regulation of colon tumor growth and metastasis through the hepatocyte growth factor (HGF)/c-Met signaling pathway. Additionally, MACC1 overexpression has been reported to induce the growth of several types of cancers, including glioblastoma multiforme and gastric cancer. However, whether MACC1 also plays a role in the progression of OS remains unclear. In this study, we found that MACC1 was highly expressed in human OS tissues, as well as in U-2OS and MG-63 cells, when compared with normal tissues and osteoblasts, respectively. Our data further indicated that MACC1 expression was correlated with several clinicopathological features of OS. Through in vitro assays, we found that MACC1 depletion markedly suppressed the proliferative ability of both OS cells and endothelial cells, and inhibited the angiogenic capacity of endothelial cells. Similarly, MACC1 depletion inhibited tumor growth, metastasis, and angiogenesis in mice. Mechanistically, we found that MACC1 could bind to the MET promoter, and enhanced the proliferation of both OS cells and endothelial cells through the HGF/c-Met signaling pathway. Furthermore, we show that MACC1 also promoted angiogenesis by regulating microtubule dynamics, thereby promoting the progression of OS. Our results indicate that MACC1 may be a new and promising therapeutic target for the treatment of OS.

As optical performance monitoring (OPM) requires accurate and robust solutions to tackle the increasing dynamic and complicated optical network architectures, we experimentally demonstrate an end-to-end optical signal-to-noise (OSNR) estimation method based on the convolutional neural network (CNN), named OptInception. The design principles of the proposed scheme are specified. The idea behind the combination of the Inception module and finite impulse response (FIR) filter is elaborated as well. We experimentally evaluate the mean absolute error (MAE) and root-mean-squared error (RMSE) of the OSNR monitored in PDM-QPSK and PDM-16QAM signals under various symbol rates. The results suggest that the MAE reaches as low as 0.125 dB and RMSE is 0.246 dB in general. OptInception is also proved to be insensitive to the symbol rate, modulation format, and chromatic dispersion. The investigation of kernels in CNN indicates that the proposed scheme helps convolutional layers learn much more than a lowpass filter or bandpass filter. Finally, a comparison in performance and complexity presents the advantages of OptInception.

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