Conditional Graphical Lasso for Multi-label Image Classification

© 2016 IEEE. Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when exploiting label correlations or lack the ability to learn image-dependent conditional label structures. In this paper, we develop conditional graphical Lasso (CGL) to handle these challenges. CGL provides a unified Bayesian framework for structure and parameter learning conditioned on image features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient due to a tailored proximal gradient procedure by applying the maximum a posterior (MAP) methodology. CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms

DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation

Unmanned aerial vehicles (UAVs) are frequently used for inspecting power lines and capturing high-resolution aerial images. However, detecting power lines in aerial images is difficult,as the foreground data(i.e, power lines) is small and the background information is abundant.To tackle this problem, we introduce DUFormer, a semantic segmentation algorithm explicitly designed to detect power lines in aerial images. We presuppose that it is advantageous to train an efficient Transformer model with sufficient feature extraction using a convolutional neural network(CNN) with a strong inductive bias.With this goal in mind, we introduce a heavy token encoder that performs overlapping feature remodeling and tokenization. The encoder comprises a pyramid CNN feature extraction module and a power line feature enhancement module.After successful local feature extraction for power lines, feature fusion is conducted.Then,the Transformer block is used for global modeling. The final segmentation result is achieved by amalgamating local and global features in the decode head.Moreover, we demonstrate the importance of the joint multi-weight loss function in power line segmentation. Our experimental results show that our proposed method outperforms all state-of-the-art methods in power line segmentation on the publicly accessible TTPLA dataset

Contrastive Graph Pooling for Explainable Classification of Brain Networks

Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson's, Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a graph and extracts features by graph neural networks (GNNs). However, the unique characteristics of fMRI data require a special design of GNN. Tailoring GNN to generate effective and domain-explainable features remains challenging. In this paper, we propose a contrastive dual-attention block and a differentiable graph pooling method called ContrastPool to better utilize GNN for brain networks, meeting fMRI-specific requirements. We apply our method to 5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its superiority over state-of-the-art baselines. Our case study confirms that the patterns extracted by our method match the domain knowledge in neuroscience literature, and disclose direct and interesting insights. Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions

The profiles and clinical significance of extraocular muscle-expressed lncRNAs and mRNAs in oculomotor nerve palsy

IntroductionOculomotor nerve palsy (ONP) arises from primary abnormalities in the central neural pathways that control the extraocular muscles (EOMs). Long non-coding RNAs (lncRNAs) have been found to be involved in the pathogenesis of various neuroparalytic diseases. However, little is known about the role of lncRNAs in ONP.MethodsWe collected medial rectus muscle tissue from ONP and constant exotropia (CXT) patients during strabismus surgeries for RNA sequencing analysis. Differentially expressed mRNAs and lncRNAs were revealed and included in the functional enrichment analysis. Co-expression analysis was conducted between these differentially expressed mRNAs and lncRNAs, followed by target gene prediction of differentially expressed lncRNAs. In addition, lncRNA-microRNA and lncRNA-transcription factor-mRNA interaction networks were constructed to further elaborate the pathological changes in medial rectus muscle of ONP. Furthermore, RT-qPCR was applied to further validate the expression levels of important lncRNAs and mRNAs, whose clinical significance was examined by receiver operating characteristic (ROC) curve analysis.ResultsA total of 618 differentially expressed lncRNAs and 322 differentially expressed mRNAs were identified. The up-regulated mRNAs were significantly related to cholinergic synaptic transmission (such as CHRM3 and CHRND) and the components and metabolism of extracellular matrix (such as CHI3L1 and COL19A1), while the down-regulated mRNAs were significantly correlated with the composition (such as MYH7 and MYL3) and contraction force (such as MYH7 and TNNT1) of muscle fibers. Co-expression analysis and target gene prediction revealed the strong correlation between MYH7 and NR_126491.1 as well as MYOD1 and ENST00000524479. Moreover, the differential expressions of lncRNAs (XR_001739409.1, NR_024160.1 and XR_001738373.1) and mRNAs (CDKN1A, MYOG, MYOD1, MYBPH, TMEM64, STATH, and MYL3) were validated by RT-qPCR. ROC curve analysis showed that lncRNAs (XR_001739409.1, NR_024160.1, and NR_002766.2) and mRNAs (CDKN1A, MYOG, MYOD1, MYBPH, TMEM64, and STATH) might be promising biomarkers of ONP.ConclusionsThese results may shed light on the molecular biology of EOMs of ONP, as well as the possible correlation of lncRNAs and mRNAs with clinical practice

Identification and molecular characterization of novel duck reoviruses in Henan Province, China

Novel Duck reovirus (NDRV) is an ongoing non-enveloped virus with ten double-stranded RNA genome segments that belong to the genus Orthoreovirus, in the family Reoviridae. NDRV-associated spleen swelling, and necrosis disease have caused considerable economic losses to the waterfowl industry worldwide. Since 2017, a significant number of NDRV outbreaks have emerged in China. Herein, we described two cases of duck spleen necrosis disease among ducklings on duck farms in Henan province, central China. Other potential causative agent, including Muscovy duck reovirus (MDRV), Duck hepatitis A virus type 1 (DHAV-1), Duck hepatitis A virus type 3 (DHAV-3), Newcastle disease virus (NDV), and Duck tembusu virus (DTMUV), were excluded by reverse transcription-polymerase chain reaction (RT-PCR), and two NDRV strains, HeNXX-1/2021 and HNJZ-2/2021, were isolated. Sequencing and phylogenetic analysis of the σC genes revealed that both newly identified NDRV isolates were closely related to DRV/SDHZ17/Shandong/2017. The results further showed that Chinese NDRVs had formed two distinct clades, with late 2017 as the turning point, suggesting that Chinese NDRVs have been evolving in different directions. This study identified and genetic characteristics of two NDRV strains in Henan province, China, indicating NDRVs have evolved in different directions in China. This study provides an insight into the ongoing emerged duck spleen necrosis disease and enriches our understanding of the genetic diversity and evolution of NDRVs