Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling label relationships with graph and understanding object regions using class activation maps (CAM). However, these methods ignore the complex intra- and inter-category relationships among specific semantic features, and CAM is prone to generate noisy information. To this end, we propose a novel semantic-aware dual contrastive learning framework that incorporates sample-to-sample contrastive learning (SSCL) as well as prototype-to-sample contrastive learning (PSCL). Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes. Then based on SSCL, label-level visual representations of the same category are aggregated together, and features belonging to distinct categories are separated. Meanwhile, we construct a novel PSCL module to narrow the distance between positive samples and category prototypes and push negative samples away from the corresponding category prototypes. Finally, the discriminative label-level features related to the image content are accurately captured by the joint training of the above three parts. Experiments on five challenging large-scale public datasets demonstrate that our proposed method is effective and outperforms the state-of-the-art methods. Code and supplementary materials are released on https://github.com/yu-gi-oh-leilei/SADCL.Comment: 8 pages, 6 figures, accepted by European Conference on Artificial Intelligence (2023 ECAI

The Characteristics of Mechanical Grinding on Kaolinite Structure and Thermal Behavior

AbstractThe relationship between kaolinite structure and the temperature of thermal transformation of phase was discussed in this paper through grinding and heating treatment. The results show that the structure of kaolinite is destroyed rapidly with increasing mechanical grinding time, and the kaolinite structure collapses completely after 1h grinding. The temperature of thermal transformation of phase decreases with the destruction of kaolinite structure. This result has a great significance for the utilization of kaolinite associated with coal measures in China

SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification

Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for single-label images, ignoring the considerable discrepancies between single- and multi-label images, i.e., a multi-label image involves multiple co-occurred categories and fickle object scales. On the other hand, previous multi-label image classification (MLIC) methods tend to design elaborate models, bringing expensive computation. In this paper, we introduce a simple but effective augmentation strategy for multi-label image classification, namely SpliceMix. The "splice" in our method is two-fold: 1) Each mixed image is a splice of several downsampled images in the form of a grid, where the semantics of images attending to mixing are blended without object deficiencies for alleviating co-occurred bias; 2) We splice mixed images and the original mini-batch to form a new SpliceMixed mini-batch, which allows an image with different scales to contribute to training together. Furthermore, such splice in our SpliceMixed mini-batch enables interactions between mixed images and original regular images. We also offer a simple and non-parametric extension based on consistency learning (SpliceMix-CL) to show the flexible extensibility of our SpliceMix. Extensive experiments on various tasks demonstrate that only using SpliceMix with a baseline model (e.g., ResNet) achieves better performance than state-of-the-art methods. Moreover, the generalizability of our SpliceMix is further validated by the improvements in current MLIC methods when married with our SpliceMix. The code is available at https://github.com/zuiran/SpliceMix.Comment: 13 pages, 10 figure

UBXN3B Positively Regulates STING-Mediated Antiviral Immune Responses

The ubiquitin regulatory X domain-containing proteins (UBXNs) are likely involved in diverse biological processes. Their physiological functions, however, remain largely unknown. Here we present physiological evidence that UBXN3B positively regulates stimulator-of-interferon genes (STING) signaling. We employ a tamoxifen-inducible Cre-LoxP approach to generate systemic Ubxn3b knockout in adult mice as the Ubxn3b-null mutation is embryonically lethal. Ubxn3b(-/-), like Sting(-/-) mice, are highly susceptible to lethal herpes simplex virus 1 (HSV-1) and vesicular stomatitis virus (VSV) infection, which is correlated with deficient immune responses when compared to Ubxn3b(+/+) littermates. HSV-1 and STING agonist-induced immune responses are also reduced in several mouse and human Ubxn3b(-/-) primary cells. Mechanistic studies demonstrate that UBXN3B interacts with both STING and its E3 ligase TRIM56, and facilitates STING ubiquitination, dimerization, trafficking, and consequent recruitment and phosphorylation of TBK1. These results provide physiological evidence that links the UBXN family with antiviral immune responses

Sexually dimorphic genetic architecture of complex traits in a large-scale F2 cross in pigs

BACKGROUND: It is common for humans and model organisms to exhibit sexual dimorphism in a variety of complex traits. However, this phenomenon has rarely been explored in pigs. RESULTS: To investigate the genetic contribution to sexual dimorphism in complex traits in pigs, we conducted a sex-stratified analysis on 213 traits measured in 921 individuals produced by a White Duroc × Erhualian F(2) cross. Of the 213 traits examined, 102 differed significantly between the two sexes (q value <0.05), which indicates that sex is an important factor that influences a broad range of traits in pigs. We compared the estimated heritability of these 213 traits between males and females. In particular, we found that traits related to meat quality and fatty acid composition were significantly different between the two sexes, which shows that genetic factors contribute to variation in sexual dimorphic traits. Next, we performed a genome-wide association study (GWAS) in males and females separately; this approach allowed us to identify 13.6% more significant trait-SNP (single nucleotide polymorphism) associations compared to the number of associations identified in a GWAS that included both males and females. By comparing the allelic effects of SNPs in the two sexes, we identified 43 significant sexually dimorphic SNPs that were associated with 22 traits; 41 of these 43 loci were autosomal. The most significant sexually dimorphic loci were found to be associated with muscle hue angle and Minolta a* values (which are parameters that reflect the redness of meat) and were located between 9.3 and 10.7 Mb on chromosome 6. A nearby gene i.e. NUDT7 that plays an important role in heme synthesis is a strong candidate gene. CONCLUSIONS: This study illustrates that sex is an important factor that influences phenotypic values and modifies the effects of the genetic variants that underlie complex traits in pigs; it also emphasizes the importance of stratifying by sex when performing GWAS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-014-0076-2) contains supplementary material, which is available to authorized users

Zika Virus Non-structural Protein 4A Blocks the RLR-MAVS Signaling

Flaviviruses have evolved complex mechanisms to evade the mammalian host immune systems including the RIG-I (retinoic acid-inducible gene I) like receptor (RLR) signaling. Zika virus (ZIKV) is a re-emerging flavivirus that is associated with severe neonatal microcephaly and adult Guillain-Barre syndrome. However, the molecular mechanisms underlying ZIKV pathogenesis remain poorly defined. Here we report that ZIKV non-structural protein 4A (NS4A) impairs the RLR-mitochondrial antiviral-signaling protein (MAVS) interaction and subsequent induction of antiviral immune responses. In human trophoblasts, both RIG-I and melanoma differentiation-associated protein 5 (MDA5) contribute to type I interferon (IFN) induction and control ZIKV replication. Type I IFN induction by ZIKV is almost completely abolished in MAVS-/- cells. NS4A represses RLR-, but not Toll-like receptor-mediated immune responses. NS4A specifically binds the N-terminal caspase activation and recruitment domain (CARD) of MAVS and thus blocks its accessibility by RLRs. Our study provides in-depth understanding of the molecular mechanisms of immune evasion by ZIKV and its pathogenesis

18F-FDG PET/CT for identifying the potential causes and extent of secondary hemophagocytic lymphohistiocytosis

PURPOSE:We aimed to evaluate the value of 18F-FDG positron emission tomography/computed tomography (PET/CT) for identifying the possible causes of secondary hemophagocytic lymphohistiocytosis (HLH).METHODS:Forty-five cases (17 female, 28 male; age, 17–79 years) with secondary HLH were included. The standard of reference for diagnosis in all patients was a combination of histology, clinical results (medical history, physical examination, and laboratory test results), and follow-up imaging for at least 12 months. All cases underwent 18F-FDG PET/CT to identify the possible trigger in HLH.RESULTS:Of 45 secondary HLH cases 10 (22.2%) were associated with infection, seven (15.6%) with rheumatic disease, and 28 (62.2%) with lymphoma. PET/CT images of 22 secondary HLH cases (48.9%) showed true positive results. PET/CT images demonstrated obvious tracer uptake in five of 10 secondary HLH cases with infection, one of three cases with lupus, two of two cases with rheumatoid arthritis, one of two cases with adult-onset Still disease, and 13 of 28 cases with lymphoma.CONCLUSION:PET/CT is helpful for identifying the possible trigger (infection or malignant disease) and extent of secondary HLH. However, PET/CT alone is not sufficient to make a correct differential diagnosis

Grasping force prediction based on sEMG signals

In order to realize the force control, when the prosthetic hand grasps the object, the forearm electromyography signal is collected by the multi-channel surface electromyography (sEMG) acquisition system. The grasping force information of the human hand is recorded by the six-dimensional force sensor. The root mean square (RMS) of the electromyography signal steady state is selected, which is an effective feature. The gene expression programming algorithm (GEP) and BP neural network are used to construct the prediction model and predict the grasping force. The force prediction accuracy of GEP algorithm and BP neural network algorithm are discussed under different grasping power levels and different grasping modes. The performance of the two algorithm models are evaluated by two measures of root mean square error (RMSE) and correlation coefficient (CC). The results show that the RMS eigenvalue extracted from the sEMG signal can better characterize the grasping force. The prediction model with GEP algorithm has smaller relative error and higher prediction effect

Lipid Transporter Activity-Related Genetic Polymorphisms Are Associated With Steroid-Induced Osteonecrosis of the Femoral Head: An Updated Meta-Analysis Based on the GRADE Guidelines

Aims: The purpose of this study was to assess the relationship between genetic variants and steroid-induced osteonecrosis of the femoral head (SONFH) in steroid use populations.Methods: We searched the public databases up to April 15, 2018. This study analyzed only the single-nucleotide polymorphisms (SNPs) that have appeared in more than three studies and assessed the level of evidence by classifying the outcomes according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.Results: The ABCB1 rs1045642 C&gt;T mutation had a protective effect against SONFH in the allelic model (I2 = 50.2%; OR: 0.74; 95% CI: 0.55–1.00; p = 0.046). The rs2032582 mutation in the ABCB1 gene showed no relationship to SONFH (allelic model: I2 = 63.4%; OR: 0.85; 95% CI: 0.58–1.23; p = 0.382). In ApoB rs693, four models showed that mutations can increase SONFH risk, but the allelic model did not. The ApoB rs1042031 mutation increased SONFH risk in the dominant model (I2 = 50.3%; OR: 2.90; 95% CI: 1.49–5.66; p = 0.002).Conclusion: An allelic model of ABCB1 rs1045642 showed that mutations have a protective effect against SONFH at a very low level of evidence. The mutations in ApoB rs693 and rs1042031 increase the SONFH risk with moderate levels of evidence