Traffic flow prediction is crucial to intelligent transportation systems (ITSs). However, the existing methods usually ignore the problem that the prediction difficulty of nodes in the traffic network is actually different. Besides, they fail to effectively handle the dilution of the original semantic information passed layer by layer when capturing the global dependency. To address these issues, this article proposes a curriculum learning guided spatial–temporal network (CurST-Net). Inspired by the human learning process, CurST-Net introduces a curriculum learning (CL) module that defines four metrics from multiple views to evaluate node difficulty and uses a training scheduler (TNS) to gradually introduce easy-to-difficult training nodes to the model to improve prediction ability. Moreover, we design a global spatial–temporal encoder that uses a multihead spatial–temporal attention mechanism and performs interlayer residual scaling on the original semantic information to efficiently capture the global spatial–temporal correlation of nodes. To the best of our knowledge, this is the first work that uses CL to solve the varying prediction difficulty of nodes in traffic flow prediction. The effectiveness of our model is validated through extensive experiments with 12 baseline models on three real-world traffic datasets. For the 1-h prediction horizon, the MAE values of our model on the three datasets are 15.19, 19.22, and 15.50, respectively. Our method yields an overall reduction of 6.51% in MAE across all three datasets compared to DSTAGNN, an advanced traffic flow prediction method. Additionally, the inference time on the PEMSD8 dataset is 5.44 s, requiring only 45% of the inference time of DSTAGNN, which demonstrates a significant improvement in inference speed.

Multi-modality image fusion (MMIF) integrates features from distinct modalities to enhance visual quality and improve downstream task performance. However, existing methods often overlook the sparsity variations and dynamic correlations between infrared and visible images, potentially limiting the utilization of both modalities. To address these challenges, we propose the Progressive Modality-Adaptive Interactive Network (PoMAI), a novel framework that not only dynamically adapts to the sparsity and structural disparities of each modality but also enhances inter-modal correlations, thereby optimizing fusion quality. The training process consists of two stages: in the first stage, the Neighbor-Group Matching Model (NGMM) models the high sparsity of infrared features, while the Context-Aware Modeling Network (CAMN) captures rich structural details in visible features, jointly refining modality-specific characteristics for fusion. In the second stage, the Modality-Interactive Compensation Module (MICM) refines inter-modal correlations via dynamic compensation mechanism, while freezing the first-stage modules to focus MICM solely on the compensation task. Extensive experiments on benchmark datasets demonstrate that PoMAI surpasses state-of-the-art methods in fusion quality and excels in downstream tasks.

Alternative polyadenylation (APA) has been implicated to play an important role in post-transcriptional regulation by regulating mRNA abundance, stability, localization and translation, which contributes considerably to transcriptome diversity and gene expression regulation. RNA-seq has become a routine approach for transcriptome profiling, generating unprecedented data that could be used to identify and quantify APA site usage. A number of computational approaches for identifying APA sites and/or dynamic APA events from RNA-seq data have emerged in the literature, which provide valuable yet preliminary results that should be refined to yield credible guidelines for the scientific community. In this review, we provided a comprehensive overview of the status of currently available computational approaches. We also conducted objective benchmarking analysis using RNA-seq data sets from different species (human, mouse and Arabidopsis) and simulated data sets to present a systematic evaluation of 11 representative methods. Our benchmarking study showed that the overall performance of all tools investigated is moderate, reflecting that there is still lot of scope to improve the prediction of APA site or dynamic APA events from RNA-seq data. Particularly, prediction results from individual tools differ considerably, and only a limited number of predicted APA sites or genes are common among different tools. Accordingly, we attempted to give some advice on how to assess the reliability of the obtained results. We also proposed practical recommendations on the appropriate method applicable to diverse scenarios and discussed implications and future directions relevant to profiling APA from RNA-seq data.

SUMMARY: Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. AVAILABILITY AND IMPLEMENTATION: AStrap is available for download at https://github.com/BMILAB/AStrap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we propose an approach to estimate and quantify the degree of extraversion for crowd motion based on individual trajectories. Extraversion is a typical personality that is often observed in human behaviors. We present a composite motion descriptor, which integrates the basic motion information and social metrics, to describe the extraversion of each individual in a crowd. In order to train a universal scoring function that can measure the degrees of extraversion, we incorporate the active learning technique with the relative attribute approach based on the social grouping behavior in crowd motions. In addition, we demonstrate the performance of the proposed method by measuring the degree of extraversion for real individual trajectories in a crowd and analyzing crowd scenes from a real-world dataset.

Summary: Alternative polyadenylation (APA) is now emerging as a widespread mechanism modulated tissue-specifically, which highlights the need to define tissue-specific poly(A) sites for profiling APA dynamics across tissues. We have developed an R package called TSAPA based on the machine learning model for identifying tissue-specific poly(A) sites in plants. A feature space including more than 200 features was assembled to specifically characterize poly(A) sites in plants. The classification model in TSAPA can be customized by selecting desirable features or classifiers. TSAPA is also capable of predicting tissue-specific poly(A) sites in unannotated intergenic regions. TSAPA will be a valuable addition to the community for studying dynamics of APA in plants. Availability and implementation: https://github.com/BMILAB/TSAPA. Supplementary information: Supplementary data are available at Bioinformatics online.

Dimension reduction methods including feature selection and feature extraction have played an important role in data mining and pattern recognition. In this study, we propose a novel unsupervised feature selection approach based on sparse representation theory, namely Sparsity Score (SS). Due to the sparse representation procedure, SS not only owns the global property of Variance Score (VS) and the local property of Laplacian Score (LS), but also possesses the discriminating nature. Experimental results, based on three well-known face datasets (Yale, ORL and CMU PIE), reveal that SS performs well in the evaluation of the feature significance, and it significantly outperforms VS and LS.