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Long non-coding RNAs (lncRNAs) play an important regulatory role in gene transcription and post-transcriptional modification, and lncRNA regulatory dysfunction leads to a variety of complex human diseases. Hence, it might be beneficial to detect the underlying biological pathways and functional categories of genes that encode lncRNA. This can be carried out by using gene set enrichment analysis, which is a pervasive bioinformatic technique that has been widely used. However, accurately performing gene set enrichment analysis of lncRNAs remains a challenge. Most conventional enrichment analysis methods have not exhaustively included the rich association information among genes, which usually affects the regulatory functions of genes. Here, we developed a novel tool for lncRNA set enrichment analysis (TLSEA) to improve the accuracy of the gene functional enrichment analysis, which extracted the low-dimensional vectors of lncRNAs in two functional annotation networks with the graph representation learning method. A novel lncRNA–lncRNA association network was constructed by merging lncRNA-related heterogeneous information obtained from multiple sources with the different lncRNA-related similarity networks. In addition, the random walk with restart method was adopted to effectively expand the lncRNAs submitted by users according to the lncRNA–lncRNA association network of TLSEA. In addition, a case study of breast cancer was performed, which demonstrated that TLSEA could detect breast cancer more accurately than conventional tools. The TLSEA can be accessed freely at http://www.lirmed.com:5003/tlsea.

The graph convolutional networks (GCNs) have been applied to model the physically connected and non-local relations among human joints for 3D human pose estimation (HPE). In addition, the purely Transformer-based models recently show promising results in video-based 3D HPE. However, the single-frame method still needs to model the physically connected relations among joints because the feature representations transformed only by global relations via the Transformer neglect information on the human skeleton. To deal with this problem, we propose a novel method in which the Transformer encoder and GCN blocks are alternately stacked, namely AMPose, to combine the global and physically connected relations among joints towards HPE. In the AMPose, the Transformer encoder is applied to connect each joint with all the other joints, while GCNs are applied to capture information on physically connected relations. The effectiveness of our proposed method is evaluated on the Human3.6M dataset. Our model also shows better generalization ability by testing on the MPI-INF-3DHP dataset. Code can be retrieved at https://github.com/erikervalid/AMPose., ICASSP 2023 Accepted Paper

Accurate simulation of walking load is of great significance in conducting human-induced vibration analyses. However, accurate pedestrian walking load data obtained from long-span footbridges is scarce and data reliability depends on the sensor used for measurement. In the current work, Yanluo Footbridge with 102 m span was adopted as test site and Xnode high-precision acceleration wireless sensor was applied for measurements. An experimental investigation was performed on walking loads according to bipedal walking force model. In experimental studies, single-person and multi-person walking tests were performed at Yanluo Footbridge to measure corresponding stride frequency and dynamic load factor. The acceleration time-histories of walking pedestrians were accurately recorded using three-axis wireless acceleration sensor Xnode. Furthermore, the equation of dynamic load factor was derived by analyzing time-histories and power spectra and the design models of pedestrian walking load and crowd load were developed based on a great number of experimental data. Time histories of pedestrian walking loads showed regular periodic changes and dynamic load factor increased by increasing stride frequency. Using the walking load model developed in this work, the reliable structure response of human-induced vibration analysis can be obtained.

The no-subgoal-worked examples and the subgoal-worked examples are “one problem and two solutions.” Previous studies have found that the effect of subgoal-worked examples learning is better than that of no-subgoal-worked examples. However, there is still ambiguity around the subgoal-worked examples learning effect and mechanism of the subgoal labeling form. To address this issue, the current study recruited a total of 130 Chinese children (Mage = 8.78 years) to mathematical online learning under different worked example forms. The results revealed that, as compared with the no-subgoal-worked example, the subgoal-worked example increased primary school students’ working memory resource depletion. However, it did not improve their scores. Moreover, as compared with the subgoal-worked example, the focused subgoal-labeled worked example did not promote primary school students’ learning effectiveness in mathematics. However, the individual choice subgoal-labeled worked example improved primary school students’ near-transfer scores. It also reduced their mental effort and working memory resource depletion. It, therefore, appears to be a more effective subgoal-labeled worked example learning approach.

The near-field electrospinning is considered as one of the most effective techniques to direct-write aligned fibers which can be applied to various high-tech areas, including energy harvester, tissue engineering, and wearable sensors. For large area aligned pattern printing, the multi-nozzle electrohydrodynamic print-ing is an efficient method to enhance productivity. As a branch of electrohydro-dynamic printing technology, the near-field electrospinning is a crucial concern to make an investigation for the formation of aligned nanofibers. Here we fabricated various nanostructures from beaded fibers to aligned fibers and crimped fibers by the double-nozzle near-field electrospinning process. We found three key parameters affecting the process, including the collector speed, the applied voltage, and the electrode-to-collector distance, and the collector speed is the key factor affecting the crimped frequency. This paper provides a reliable experi-mental basis and theoretical guidance for the multi-nozzle near-field electrospin-ning to accurately direct-write microfibers and nanofibers.

In this paper, we propose a simple yet effective method to endow deep 3D models with rotation invariance by expressing the coordinates in an intrinsic frame determined by the object shape itself. Key to our approach is to find such an intrinsic frame which should be unique to the identical object shape and consistent across different instances of the same category, e.g. the frame axes of desks should be all roughly along the edges. Interestingly, the principal component analysis exactly provides an effective way to define such a frame, i.e. setting the principal components as the frame axes. As the principal components have direction ambiguity caused by the sign-ambiguity of eigenvector computation, there exist several intrinsic frames for each object. In order to achieve absolute rotation invariance for a deep model, we adopt the coordinates expressed in all intrinsic frames as inputs to obtain multiple output features, which will be further aggregated as a final feature via a self-attention module. Our method is theoretically rotation-invariant and can be flexibly embedded into the current network architectures. Comprehensive experiments demonstrate that our approach can achieve near state-of-the-art performance on rotation-augmented dataset for ModelNet40 classification and outperform other models on SHREC'17 perturbed retrieval task., Comment: 8 pages,5 figures