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Background Colorectal cancer (CRC) is prevalent worldwide and is often challenged by treatment failure and recurrence due to resistance to radiotherapy. Here, we aimed to identify the elusive underlying molecular mechanisms of radioresistance in CRC. Methods Weighted gene co-expression network analysis was used to identify potential radiation-related genes. Colony formation and comet assays and multi-target single-hit survival and xenograft animal models were used to validate the results obtained from the bioinformatic analysis. Immunohistochemistry was performed to examine the clinical characteristics of ALDH1L2. Co-immunoprecipitation, immunofluorescence and flow cytometry were used to understand the molecular mechanisms underlying radioresistance. Results Bioinformatic analysis, in vitro, and in vivo experiments revealed that ALDH1L2 is a radiation-related gene, and a decrease in its expression induces radioresistance in CRC cells by inhibiting ROS-mediated apoptosis. Patients with low ALDH1L2 expression exhibit resistance to radiotherapy. Mechanistically, ALDH1L2 interacts with thioredoxin (TXN) and regulates the downstream NF-κB signaling pathway. PX-12, the TXN inhibitor, overcomes radioresistance due to decreased ALDH1L2. Conclusions Our results provide valuable insights into the potential role of ALDH1L2 in CRC radiotherapy. We propose that the simultaneous application of TXN inhibitors and radiotherapy would significantly ameliorate the clinical outcomes of patients with CRC having low ALDH1L2.

Audio Sentiment Analysis is a popular research area which extends the text-based sentiment analysis to depend on effectiveness of acoustic features extracted from speech. However, current progress on audio sentiment analysis mainly focuses on extracting homogeneous acoustic features or doesn't fuse heterogeneous features effectively. In this paper, we propose an utterance-based deep neural network model, which has a parallel combination of CNN and LSTM based network, to obtain representative features termed Audio Sentiment Vector (ASV), that can maximally reflect sentiment information in an audio. Specifically, our model is trained by utterance-level labels and ASV can be extracted and fused creatively from two branches. In the CNN model branch, spectrum graphs produced by signals are fed as inputs while in the LSTM model branch, inputs include spectral centroid, MFCC and other recognized traditional acoustic features extracted from dependent utterances in an audio. Besides, BiLSTM with attention mechanism is used for feature fusion. Extensive experiments have been conducted to show our model can recognize audio sentiment precisely, and demonstrate our ASV are better than traditional acoustic features or vectors extracted from other deep learning models. Furthermore, experimental results indicate that the proposed model outperforms state-of-the-art approaches by 9.33% on MOSI.

Deep learning model trained by imbalanced data may not work satisfactorily since it could be determined by major classes and thus may ignore the classes with small amount of data. In this paper, we apply deep learning based imbalanced data classification for the first time to cellular macromolecular complexes captured by Cryo-electron tomography (Cryo-ET). We adopt a range of strategies to cope with imbalanced data, including data sampling, bagging, boosting, Genetic Programming based method and. Particularly, inspired from Inception 3D network, we propose a multi-path CNN model combining focal loss and mixup on the Cryo-ET dataset to expand the dataset, where each path had its best performance corresponding to each type of data and let the network learn the combinations of the paths to improve the classification performance. In addition, extensive experiments have been conducted to show our proposed method is flexible enough to cope with different number of classes by adjusting the number of paths in our multi-path model. To our knowledge, this work is the first application of deep learning methods of dealing with imbalanced data to the internal tissue classification of cell macromolecular complexes, which opened up a new path for cell classification in the field of computational biology.