Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals. Here, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs. Furthermore, a random forest model and a one-dimensional convolutional neural network model were trained using miRNA sequence features and raw miRNA sequences respectively and then employed to predict unlabeled plant miRNAs in miRBase. A total of 241 possible plant-derived xenomiRs were predicted by both models. Finally, the potential functions of these possible plant-derived xenomiRs along with our previously reported ones in human body were analyzed. Our study, for the first time, presents the systematic plant-derived xenomiR sequences analysis and provides evidence for selective absorption of plant miRNA by human body, which could facilitate the future investigation about the mechanisms underlying the transference of plant-derived xenomiR.

Sniffer attack has been a severe threat to network communication security. Traditional network usually uses static network configuration, which provides convenience to sniffer attack. In this paper, an SDN-based double hopping communication (DHC) approach is proposed to solve this problem. In DHC, ends in communication packets as well as the routing paths are changed dynamically. Therefore, the traffic will be distributed to multiple flows and transmitted along different paths. Moreover, the data from multiple users will be mixed, bringing difficulty for attackers in obtaining and recovering the communication data, so that sniffer attack will be prevented effectively. It is concluded that DHC is able to increase the overhead of sniffer attack, as well as the difficulty of communication data recovery.

The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively.

Deep sternal wound infection is a severe complication after open heart surgery. According to the different severity and dimensions of the deep sternal wound infection, the treatment method is different. In this study, we aimed to describe our experience with the rectus abdominis myocutaneous flap for large sternal wound management, especially when 1 or 2 internal mammary arteries were absent. Between October 2010 and January 2016, a retrospective review of 9 patients who suffered from the extensive thoracic defects after deep sternal wound infection was conducted. All of these sternal defects encompassed almost the full length of the sternum after debridement. Defect reconstruction was achieved by covering with a rectus abdominis myocutaneous flap. When the ipsilateral or bilateral internal mammary artery had been harvested previously, we took advantage of the inferior epigastric artery to provide additional blood supply to the rectus abdominis myocutaneous flap. Thus, this flap had a double blood supply. There was no recurrent infection in all 9 patients. Three patients received the rectus abdominis myocutaneous flap with a double blood supply. Flap complications occurred in 2 patients (22%). One patient who did not have the double blood supply flap suffered from necrosis on the distal part of the flap, which was then debrided and reconstructed with a split-skin graft. The other patient had a seroma at the abdomen donor site and was managed conservatively. None of the patients died during the hospital stay. This study suggests that the rectus abdominis myocutaneous flap may be a good choice to repair the entire length of sternal wound. When 1 or 2 internal mammary arteries have been harvested, the inferior epigastric artery can be anastomosed to the second intercostal artery or the internal mammary artery perforator to provide the rectus abdominis myocutaneous flap with a double blood supply.