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Although deep convolutional neural networks have achieved remarkable success in removing synthetic fog, it is essential to be able to process images taken in complex foggy conditions, such as dense or non-homogeneous fog, in the real world. However, the haze distribution in the real world is complex, and downsampling can lead to color distortion or loss of detail in the output results as the resolution of a feature map or image resolution decreases. Moreover, the over-stacking of convolutional blocks might increase the model complexity. In addition to the challenges of obtaining sufficient training data, overfitting can also arise in deep learning techniques for foggy image processing, which can limit the generalization abilities of the model, posing challenges for its practical applications in real-world scenarios. Considering these issues, this paper proposes a Transformer-based wavelet network (WaveletFormerNet) for real-world foggy image recovery. We embed the discrete wavelet transform into the Vision Transformer by proposing the WaveletFormer and IWaveletFormer blocks, aiming to alleviate texture detail loss and color distortion in the image due to downsampling. We introduce parallel convolution in the Transformer block, which allows for the capture of multi-frequency information in a lightweight mechanism. Such a structure reduces computational expenses and improves the effectiveness of the network. Additionally, we have implemented a feature aggregation module (FAM) to maintain image resolution and enhance the feature extraction capacity of our model, further contributing to its impressive performance in real-world foggy image recovery tasks. Through extensive experiments on real-world fog datasets, we have demonstrated that our WaveletFormerNet achieves superior performance compared to state-of-the-art methods, as shown through quantitative and qualitative evaluations of minor model complexity. Additionally, our satisfactory results on real-world dust removal and application tests showcase the superior generalization ability and improved performance of WaveletFormerNet in computer vision-related applications compared to existing state-of-the-art methods, further confirming our proposed approach's effectiveness and robustness. Our code is available at https://github.com/shengli666666/WaveletFormerNet. [Display omitted] • Combining Vision Transformer and wavelet transform alleviates image detail loss. • Parallel convolution captures frequency information in a lightweight mechanism. • The feature aggregation module enhances the model feature extraction capability. • Experimental results demonstrate WaveletFormerNet's superior dehazing performance. [ABSTRACT FROM AUTHOR]

• A novel predictor called iPro-GAN to identify promoters and their strength. • Moran-based spatial auto-cross correlation considering physicochemical properties of dinucleotide is used to extract sequence features. • A generative adversarial network based on deep convolution is established to solve the binary classification problem in bioinformatics for the first time. Promoter is a component of the gene, which can specifically bind with RNA polymerase and determine where transcription starts, and also determine the transcription efficiency of the gene. Promoters can be divided into strong promoters and weak promoters because their structures and the interaction time interval are quite different. The functional variation of the promoter can lead to a variety of diseases. Therefore, identifying promoters and their strength is necessary and has important biological significance. A novel and promising model based on deep learning is proposed to achieve it. In this work, we build a power model named iPro-GAN for identification of promoters and their strength. First, we collect benchmark datasets and independent datasets for training and testing. Then, Moran-based spatial auto-cross correlation method is used as feature extraction method. Finally, deep convolution generative adversarial network with 10-fold cross validation is applied for classifying. The first layer of the model is used to identify the promoter and the second layer is used to determine its type. On the benchmark data set, the accuracy of the first layer predictor is 93.15%, and the accuracy of the second layer predictor is 92.30%. On the independent data set, the accuracy of the first layer predictor is 86.77%, and the accuracy of the second layer predictor is 91.66%. In particular, breakthrough progress has been made in the identification of promoters' strength. These results are far higher than the existing best predictor, which indicate that our model is serviceable and practicable to identify promoters and their strength. Furthermore, the datasets and source codes are available from this link: https://github.com/Bovbene/iPro-GAN. [ABSTRACT FROM AUTHOR]

Therapeutic peptides, as active substances involved in a variety of cell functions in the organism, are essential participants to complete complex physiological activities of the body. Therefo r e, the prediction of therapeutic peptides is essential for researching on peptide-based therapies. The method of using biological experiments is considered to be time-consuming and labor-intensive. As a fast and accurate method, deep learning can process massive amounts of data on therapeutic peptides. In this research, we raise a deep learning model called Pep-CNN to accurately predict therapeutic peptides. Firstly, we represent the features of the peptide sequence based on the sequence position, the physicochemical property, and the evolutionary-derived feature and use the vectors to represent the sequence. After fusing the features, we use the improved classifier of Convolutional Neural Network (imCNN) to classify and predict eight kinds of peptides. The results show that, compared with other models, Pep-CNN can identify peptides more accurately, which is more conductive to the further research of therapeutic peptides by biomedical scientists. The codes and benchmark datasets are accessible at https://github.com/alivelxj/Pep-CNN. • A new model called Pep-CNN was proposed to predict therapeutic peptides. • The different methods are applied to extract features from the dataset. • An improved convolutional neural network is used to classify the model. [ABSTRACT FROM AUTHOR]

RNA 5-hydroxymethylcytosine (5hmC) modification is the basis of the translation of genetic information and the biological evolution. The study of its distribution in transcriptome is fundamentally crucial to reveal the biological significance of 5hmC. Biochemical experiments can use a variety of sequencing-based technologies to achieve high-throughput identification of 5hmC; however, they are labor-intensive, time-consuming, as well as expensive. Therefore, it is urgent to develop more effective and feasible computational methods. In this paper, a novel and powerful model called iR5hmcSC is designed for identifying 5hmC. Firstly, we extract the different features by K-mer, Pseudo Structure Status Composition and One-Hot encoding. Subsequently, the combination of chi-square test and logistic regression is utilized as the feature selection method to select the optimal feature sets. And then stacking learning, an ensemble learning method including random forest (RF), extra trees (EX), AdaBoost (Ada), gradient boosting decision tree (GBDT), and support vector machine (SVM), is used to recognize 5hmC and non-5hmC. Finally, 10-fold cross-validation test is performed to evaluate the model. The accuracy reaches 85.27% and 79.92% on benchmark dataset and independent dataset, respectively. The result is better than the state-of-the-art methods, which indicates that our model is a feasible tool to identify 5hmC. The datasets and source code are freely available at https://github.com/HongyanShi026/iR5hmcSC. [Display omitted] • A new model named iR5hmcSC was proposed to predict RNA 5hmC sites. • K-mer, Pseudo Structure Status Composition and One-Hot encoding are applied to extract features from the dataset. • A new method combining chi-square test and logistic regression is used to reduce the dimensions of data. • The stacking learning is adopted to classify the model. [ABSTRACT FROM AUTHOR]