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.

Symbolic music generation relies on the contextual representation capabilities of the generative model, where the most prevalent approach is the Transformer-based model. Learning contextual representations are also related to the structural elements in music, i.e., intro, verse, and chorus, which have not received much attention of scientific publications. In this paper, we propose a hierarchical Transformer model to learn multiscale contexts in music. In the encoding phase, we first design a fragment scope localization module to separate the music parts into chords and sections. Then, we use a multiscale attention mechanism to learn note-, chord-, and section-level contexts. In the decoding phase, we propose a hierarchical Transformer model that uses fine decoders to generate sections in parallel and a coarse decoder to decode the combined music. We also designed a music style normalization layer to achieve a consistent music style between the generated sections. Our model is evaluated on two open MIDI datasets. Experiments show that our model outperforms other comparative models in 50% (6 out of 12 metrics) and 83.3% (10 out of 12 metrics) of the quantitative metrics for short- and long-term music generation, respectively. Preliminary visual analysis also suggests its potential in following compositional rules, such as reuse of rhythmic patterns and critical melodies, which are associated with improved music quality.

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.

The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of complete RNA sequences (848 nt). The accuracy of the assembled structures was higher than that of the structures predicted directly using the state-of-the-art RNA secondary structure prediction methods. This proposed model could serve as a preprocessing step for RNA secondary structure prediction for enhancing the predictive performance (especially for long RNA sequences) and reducing the computational cost. In the future, predicting the secondary structure of long-sequence RNA with high accuracy can be enabled by developing a framework combining RNA-par with various existing RNA secondary structure prediction algorithms. Our models, test codes and test data are provided at https://github.com/mianfei71/RNAPar.

Objective The aim of this study was to develop a feedback system to assist gait rehabilitation of cerebral palsy (CP) toe walkers with dynamic equinus. Design Plantar pressure of the forefoot and the heel was collected by sensorized insoles embedded in custom-built shoes and transmitted to a smartphone via Bluetooth. Dynamic foot pressure index of each stride was calculated by purpose-designed software running in the smartphone to distinguish toe-walking strides from normal strides in real time. An auditory signal would be produced to alert the patient each time a toe-walking stride was detected. Results For CP toe walkers, the one-way analysis of variance indicated a significant difference (F_1,14 = 19.492, P = 0.001) in dynamic foot pressure index between the affected side (31.4 ± 12.0) and the unaffected side (58.6 ± 2.5). In addition, the validation test showed that this system can distinguish toe-walking strides from normal strides of children with CP with an accuracy of 95.3%. Conclusions This system was able to monitor the toe-walking strides of children with CP in real time and had the potential to enhance rehabilitation training efficiency and correct toe-walking gait in children with CP with dynamic equinus.