Integrating Neural-Symbolic Reasoning With Variational Causal Inference Network for Explanatory Visual Question Answering.

Recently, a novel multimodal reasoning task named Explanatory Visual Question Answering (EVQA) has been introduced, which combines answering visual questions with multimodal explanation generation to expound upon the underlying reasoning processes. In contrast to conventional Visual Question Answering (VQA) that merely concentrates on providing answers, EVQA aims to improve the explainability and verifiability of reasoning by providing user-friendly explanations. Despite the improved explainability of inferred results, the existing EVQA models still adopt black-box neural networks to infer results, lacking the explainability of the reasoning process. Moreover, existing EVQA models commonly predict answers and explanations in isolation, overlooking the inherent causal correlation between them. To handle these challenges, we propose a Program-guided Variational Causal Inference Network (Pro-VCIN) that integrates neural-symbolic reasoning with variational causal inference and constructs causal correlations between the predicted answers and explanations. First, we utilize pretrained models to extract visual features and convert questions into the corresponding programs. Secondly, we propose a multimodal program Transformer to translate programs and the related visual features into coherent and rational explanations of the reasoning processes Finally, we propose a variational causal inference to construct the target structural causal model and predict answers based on the causal correlation to explanations. Comprehensive experiments conducted on EVQA benchmark datasets reveal the superiority of Pro-VCIN in terms of both performance and explainability over state-of-the-art EVQA methods.

Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition.

Audio-visual video recognition (AVVR) aims to integrate audio and visual clues to categorize videos accurately. While existing methods train AVVR models using provided datasets and achieve satisfactory results, they struggle to retain historical class knowledge when confronted with new classes in real-world situations. Currently, there are no dedicated methods for addressing this problem, so this paper concentrates on exploring Class Incremental Audio-Visual Video Recognition (CIAVVR). For CIAVVR, since both stored data and learned model of past classes contain historical knowledge, the core challenge is how to capture past data knowledge and past model knowledge to prevent catastrophic forgetting. As audio-visual data and model inherently contain hierarchical structures, i.e., model embodies low-level and high-level semantic information, and data comprises snippet-level, video-level, and distribution-level spatial information, it is essential to fully exploit the hierarchical data structure for data knowledge preservation and hierarchical model structure for model knowledge preservation. However, current image class incremental learning methods do not explicitly consider these hierarchical structures in model and data. Consequently, we introduce Hierarchical Augmentation and Distillation (HAD), which comprises the Hierarchical Augmentation Module (HAM) and Hierarchical Distillation Module (HDM) to efficiently utilize the hierarchical structure of data and models, respectively. Specifically, HAM implements a novel augmentation strategy, segmental feature augmentation, to preserve hierarchical model knowledge. Meanwhile, HDM introduces newly designed hierarchical (video-distribution) logical distillation and hierarchical (snippet-video) correlative distillation to capture and maintain the hierarchical intra-sample knowledge of each data and the hierarchical inter-sample knowledge between data, respectively. Evaluations on four benchmarks (AVE, AVK-100, AVK-200, and AVK-400) demonstrate that the proposed HAD effectively captures hierarchical information in both data and models, resulting in better preservation of historical class knowledge and improved performance. Furthermore, we provide a theoretical analysis to support the necessity of the segmental feature augmentation strategy.

Source-Guided Target Feature Reconstruction for Cross-Domain Classification and Detection.

Existing cross-domain classification and detection methods usually apply a consistency constraint between the target sample and its self-augmentation for unsupervised learning without considering the essential source knowledge. In this paper, we propose a Source-guided Target Feature Reconstruction (STFR) module for cross-domain visual tasks, which applies source visual words to reconstruct the target features. Since the reconstructed target features contain the source knowledge, they can be treated as a bridge to connect the source and target domains. Therefore, using them for consistency learning can enhance the target representation and reduce the domain bias. Technically, source visual words are selected and updated according to the source feature distribution, and applied to reconstruct the given target feature via a weighted combination strategy. After that, consistency constraints are built between the reconstructed and original target features for domain alignment. Furthermore, STFR is connected with the optimal transportation algorithm theoretically, which explains the rationality of the proposed module. Extensive experiments on nine benchmarks and two cross-domain visual tasks prove the effectiveness of the proposed STFR module, e.g., 1) cross-domain image classification: obtaining average accuracy of 91.0%, 73.9%, and 87.4% on Office-31, Office-Home, and VisDA-2017, respectively; 2) cross-domain object detection: obtaining mAP of 44.50% on Cityscapes → Foggy Cityscapes, AP on car of 78.10% on Cityscapes → KITTI, MR -2 of 8.63%, 12.27%, 22.10%, and 40.58% on COCOPersons → Caltech, CityPersons → Caltech, COCOPersons → CityPersons, and Caltech → CityPersons, respectively.

Cross-Modal Federated Human Activity Recognition.

Federated human activity recognition (FHAR) has attracted much attention due to its great potential in privacy protection. Existing FHAR methods can collaboratively learn a global activity recognition model based on unimodal or multimodal data distributed on different local clients. However, it is still questionable whether existing methods can work well in a more common scenario where local data are from different modalities, e.g., some local clients may provide motion signals while others can only provide visual data. In this paper, we study a new problem of cross-modal federated human activity recognition (CM-FHAR), which is conducive to promote the large-scale use of the HAR model on more local devices. CM-FHAR has at least three dedicated challenges: (1) distributive common cross-modal feature learning, (2) modality-dependent discriminate feature learning, (3) modality imbalance issue. To address these challenges, we propose a modality-collaborative activity recognition network (MCARN), which can comprehensively learn a global activity classifier shared across all clients and multiple modality-dependent private activity classifiers. To produce modality-agnostic and modality-specific features, we learn an altruistic encoder and an egocentric encoder under the constraint of a separation loss and an adversarial modality discriminator collaboratively learned in hyper-sphere. To address the modality imbalance issue, we propose an angular margin adjustment scheme to improve the modality discriminator on modality-imbalanced data by enhancing the intra-modality compactness of the dominant modality and increase the inter-modality discrepancy. Moreover, we propose a relation-aware global-local calibration mechanism to constrain class-level pairwise relationships for the parameters of the private classifier. Finally, through decentralized optimization with alternative steps of adversarial local updating and modality-aware global aggregation, the proposed MCARN obtains state-of-the-art performance on both modality-balanced and modality-imbalanced data.

DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization.

Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into a stylized one according to textual descriptions of the target style provided by the user. Unlike the previous image-to-image transfer approaches, text-guided stylization progress provides users with a more precise and intuitive way to express the desired style. However, the huge discrepancy between cross-modal inputs/outputs makes it challenging to conduct text-driven image stylization in a typical feed-forward CNN pipeline. In this article, we present DiffStyler, a dual diffusion processing architecture to control the balance between the content and style of the diffused results. The cross-modal style information can be easily integrated as guidance during the diffusion process step-by-step. Furthermore, we propose a content image-based learnable noise on which the reverse denoising process is based, enabling the stylization results to better preserve the structure information of the content image. We validate the proposed DiffStyler beyond the baseline methods through extensive qualitative and quantitative experiments. The code is available at https://github.com/haha-lisa/Diffstyler.

Accurate detection and grading of pterygium through smartphone by a fusion training model.

BACKGROUND/AIMS: To improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data. METHOD: Two datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea. RESULTS: The detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F1 score of 0.8981, sensitivity of 0.8709, specificity of 0.9668 and area under the curve (AUC) of 0.9295. Compared with the same group of patients' smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F1 score of 0.9313, sensitivity of 0.9360, specificity of 0.9613, AUC of 0.9426, accuracy of 92.38%) is close to the model (trained by slit-lamp images) in slit-lamp images (F1 score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%). CONCLUSION: Our fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands.

NExT-OOD: Overcoming Dual Multiple-Choice VQA Biases.

In recent years, multiple-choice Visual Question Answering (VQA) has become topical and achieved remarkable progress. However, most pioneer multiple-choice VQA models are heavily driven by statistical correlations in datasets, which cannot perform well on multimodal understanding and suffer from poor generalization. In this paper, we identify two kinds of spurious correlations, i.e., a Vision-Answer bias (VA bias) and a Question-Answer bias (QA bias). To systematically and scientifically study these biases, we construct a new video question answering (videoQA) benchmark NExT-OOD in OOD setting and propose a graph-based cross-sample method for bias reduction. Specifically, the NExT-OOD is designed to quantify models' generalizability and measure their reasoning ability comprehensively. It contains three sub-datasets including NExT-OOD-VA, NExT-OOD-QA, and NExT-OOD-VQA, which are designed for the VA bias, QA bias, and VA&QA bias, respectively. We evaluate several existing multiple-choice VQA models on our NExT-OOD, and illustrate that their performance degrades significantly compared with the results obtained on the original multiple-choice VQA dataset. Besides, to mitigate the VA bias and QA bias, we explicitly consider the cross-sample information and design a contrastive graph matching loss in our approach, which provides adequate debiasing guidance from the perspective of whole dataset, and encourages the model to focus on multimodal contents instead of spurious statistical regularities. Extensive experimental results illustrate that our method significantly outperforms other bias reduction strategies, demonstrating the effectiveness and generalizability of the proposed approach.

Honokiol ameliorates angiotensin II-induced cardiac hypertrophy by promoting dissociation of the Nur77-LKB1 complex and activating the AMPK pathway.

Pathological cardiac hypertrophy is a key contributor to heart failure, and the molecular mechanisms underlying honokiol (HNK)-mediated cardioprotection against this condition remain worth further exploring. This study aims to investigate the effect of HNK on angiotensin II (Ang II)-induced myocardial hypertrophy and elucidate the underlying mechanisms. Sprague-Dawley rats were exposed to Ang II infusion, followed by HNK or vehicle treatment for 4 weeks. Our results showed that HNK treatment protected against Ang II-induced myocardial hypertrophy, fibrosis and dysfunction in vivo and inhibited Ang II-induced hypertrophy in neonatal rat ventricular myocytes in vitro. Mechanistically, HNK suppressed the Ang II-induced Nur77 expression at the transcriptional level and promoted ubiquitination-mediated degradation of Nur77, leading to dissociation of the Nur77-LKB1 complex. This facilitated the translocation of LKB1 into the cytoplasm and activated the LKB1-AMPK pathway. Our findings suggest that HNK attenuates pathological remodelling and cardiac dysfunction induced by Ang II by promoting dissociation of the Nur77-LKB1 complex and subsequent activation of AMPK signalling. This study uncovers a novel role of HNK on the LKB1-AMPK pathway to protect against cardiac hypertrophy.

LncRNA SNHG8 regulates the migration and angiogenesis of pHUVECs induced by high glucose via the TRPM7/ERK1/2 signaling axis.

This study aimed to evaluate the regulatory effect and molecular mechanism of long noncoding RNA small nucleolus RNA host gene 8 (LncRNA SNHG8) in the migration and angiogenesis of primary human umbilical vein endothelial cells (pHUVECs) under high-glucose (HG) conditions. The HG-induced endothelial injury model was established in vitro.The cell model of silencing SNHG8, overexpressing SNHG8, and silencing TRPM7 was established by transfecting SNHG8-siRNA, SNHG8 plasmid and TRPM7-siRNA into cells with liposomes.The SNHG8 level was determined through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The expression levels of transient receptor potential melastatin 7 (TRPM7), endothelial nitric oxide synthase (eNOS), p-eNOS, extracellular signal-regulated kinase 1/2(ERK1/2), and p-ERK1/2 were assessed through western blot. Nitric oxide (NO) levels were measured with DAF-FM. pHUVEC migration was examined through wound healing and Transwell assay, and pHUVEC angiogenesis was observed through a tube formation assay. Results showed that HG promoted the expression of lncRNA SNHG8 and TRPM7 and decreased the ratio of p-eNOS/eNOS and p-ERK1/2/ERK1/2 in pHUVECs . NO production, migration , and angiogenesis were inhibited in pHUVECs under HG conditions. Silencing lncRNA SNHG8 and TRPM7 could significantly reverse the HG-induced decrease in eNOS activation, NO production , migration, and angiogenesis . SNHG8 and U0126 (ERK pathway inhibitor) overexpression enhanced the HG effects, whereas using U0126 did not affect the TRPM7 expression. In conclusion, lncRNA SNHG8 participates in HG-induced endothelial cell injury and likely regulates NO production, migration, and angiogenesis of pHUVECs via the TRPM7/ERK1/2 signaling axis.

SPA2Net: Structure-Preserved Attention Activated Network for Weakly Supervised Object Localization.

By exploring the localizable representations in deep CNN, weakly supervised object localization (WSOL) methods could determine the position of the object in each image just trained by the classification task. However, the partial activation problem caused by the discriminant function makes the network unable to locate objects accurately. To alleviate this problem, we propose Structure-Preserved Attention Activated Network (SPA2Net), a simple and effective one-stage WSOL framework to explore the ability of structure preservation of deep features. Different from traditional WSOL approaches, we decouple the object localization task from the classification branch to reduce their mutual influence by involving a localization branch which is online refined by a self-supervised structural-preserved localization mask. Specifically, we employ the high-order self-correlation as structural prior to enhance the perception of spatial interaction within convolutional features. By succinctly combining the structural prior with spatial attention, activations by SPA2Net will spread from part to the whole object during training. To avoid the structure-missing issue caused by the classification network, we furthermore utilize the restricted activation loss (RAL) to distinguish the difference between foreground and background in the channel dimension. In conjunction with the self-supervised localization branch, SPA2Net can directly predict the class-irrelevant localization map while prompting the network to pay more attention to the target region for accurate localization. Extensive experiments on two publicly available benchmarks, including CUB-200-2011 and ILSVRC, show that our SPA2Net achieves substantial and consistent performance gains compared with baseline approaches. The code and models are available at https://github.com/MsterDC/SPA2Net.

A Fully Automatic Estimation of Tear Meniscus Height Using Artificial Intelligence.

Purpose: Accurate quantification measurement of tear meniscus is vital for the precise diagnosis of dry eye. In current clinical practice, the measurement of tear meniscus height (TMH) relies on doctors' manual operation. This study aims to propose a novel automatic artificial intelligence (AI) system to evaluate TMH. Methods: A total of 510 photographs obtained by the oculus camera were labeled. Three thousand and five hundred images were finally attained by data enhancement to train the neural network model parameters, and 60 were used to evaluate the model performance in segmenting the cornea and tear meniscus region. One hundred images were used to test generalization ability of the model. We modified a segmentation model of the cornea and the tear meniscus based on the UNet-like network. The output of the segmentation model is followed by a calculation module that calculates and reports the TMH. Results: Compared with ground truth (GT) manually labeled by clinicians, our modified model achieved a Dice Similarity Coefficient (DSC) and Intersection over union (Iou) of 0.99/0.98 in the corneal segmentation task and 0.92/0.86 for the detection of tear meniscus on the validation set, respectively. On the test set, the TMH automatically measured by our AI system strongly correlates with the results manually calculated by the ophthalmologists. Conclusions: We developed a fully automated and reliable AI system to obtain TMH. After large-scale clinical testing, our method could be used for dry eye screening in clinical practice.

Vectorized Evidential Learning for Weakly-Supervised Temporal Action Localization.

With the explosive growth of videos, weakly-supervised temporal action localization (WS-TAL) task has become a promising research direction in pattern analysis and machine learning. WS-TAL aims to detect and localize action instances with only video-level labels during training. Modern approaches have achieved impressive progress via powerful deep neural networks. However, robust and reliable WS-TAL remains challenging and underexplored due to considerable uncertainty caused by weak supervision, noisy evaluation environment, and unknown categories in the open world. To this end, we propose a new paradigm, named vectorized evidential learning (VEL), to explore local-to-global evidence collection for facilitating model performance. Specifically, a series of learnable meta-action units (MAUs) are automatically constructed, which serve as fundamental elements constituting diverse action categories. Since the same meta-action unit can manifest as distinct action components within different action categories, we leverage MAUs and category representations to dynamically and adaptively learn action components and action-component relations. After performing uncertainty estimation at both category-level and unit-level, the local evidence from action components is accumulated and optimized under the Subject Logic theory. Extensive experiments on the regular, noisy, and open-set settings of three popular benchmarks show that VEL consistently obtains more robust and reliable action localization performance than state-of-the-arts.

Myxobacteria restrain Phytophthora invasion by scavenging thiamine in soybean rhizosphere via outer membrane vesicle-secreted thiaminase I.

Public metabolites such as vitamins play critical roles in maintaining the ecological functions of microbial community. However, the biochemical and physiological bases for fine-tuning of public metabolites in the microbiome remain poorly understood. Here, we examine the interactions between myxobacteria and Phytophthora sojae, an oomycete pathogen of soybean. We find that host plant and soil microbes complement P. sojae's auxotrophy for thiamine. Whereas, myxobacteria inhibits Phytophthora growth by a thiaminase I CcThi1 secreted into extracellular environment via outer membrane vesicles (OMVs). CcThi1 scavenges the required thiamine and thus arrests the thiamine sharing behavior of P. sojae from the supplier, which interferes with amino acid metabolism and expression of pathogenic effectors, probably leading to impairment of P. sojae growth and pathogenicity. Moreover, myxobacteria and CcThi1 are highly effective in regulating the thiamine levels in soil, which is correlated with the incidence of soybean Phytophthora root rot. Our findings unravel a novel ecological tactic employed by myxobacteria to maintain the interspecific equilibrium in soil microbial community.

Uncertainty-Aware Dual-Evidential Learning for Weakly-Supervised Temporal Action Localization.

Weakly-supervised temporal action localization (WTAL) aims to localize the action instances and recognize their categories with only video-level labels. Despite great progress, existing methods suffer from severe action-background ambiguity, which mainly arises from background noise and neglect of non-salient action snippets. To address this issue, we propose a generalized evidential deep learning (EDL) framework for WTAL, called Uncertainty-aware Dual-Evidential Learning (UDEL), which extends the traditional paradigm of EDL to adapt to the weakly-supervised multi-label classification goal with the guidance of epistemic and aleatoric uncertainties, of which the former comes from models lacking knowledge, while the latter comes from the inherent properties of samples themselves. Specifically, targeting excluding the undesirable background snippets, we fuse the video-level epistemic and aleatoric uncertainties to measure the interference of background noise to video-level prediction. Then, the snippet-level aleatoric uncertainty is further deduced for progressive mutual learning, which gradually focuses on the entire action instances in an "easy-to-hard" manner and encourages the snippet-level epistemic uncertainty to be complementary with the foreground attention scores. Extensive experiments show that UDEL achieves state-of-the-art performance on four public benchmarks. Our code is available in github/mengyuanchen2021/UDEL.

Reducing Vision-Answer Biases for Multiple-Choice VQA.

Multiple-choice visual question answering (VQA) is a challenging task due to the requirement of thorough multimodal understanding and complicated inter-modality relationship reasoning. To solve the challenge, previous approaches usually resort to different multimodal interaction modules. Despite their effectiveness, we find that existing methods may exploit a new discovered bias (vision-answer bias) to make answer prediction, leading to suboptimal VQA performances and poor generalization. To solve the issues, we propose a Causality-based Multimodal Interaction Enhancement (CMIE) method, which is model-agnostic and can be seamlessly incorporated into a wide range of VQA approaches in a plug-and-play manner. Specifically, our CMIE contains two key components: a causal intervention module and a counterfactual interaction learning module. The former devotes to removing the spurious correlation between the visual content and the answer caused by the vision-answer bias, and the latter helps capture discriminative inter-modality relationships by directly supervising multimodal interaction training via an interactive loss. Extensive experimental results on three public benchmarks and one reorganized dataset show that the proposed method can significantly improve seven representative VQA models, demonstrating the effectiveness and generalizability of the CMIE.

ACE2 inhibits proliferation of smooth muscle cell through AT1R and its downstream signaling pathway.

To investigate the effect of the angiotensin converting enzyme 2 (ACE2) on AT1R expression, ERK1/2 and STAT3 protein phosphorylation in rat vascular smooth muscle cells (VSMCs) was studied. VSMCs were transfected with a lentiviral vector including the ACE2 gene and with siRNA to regulate the level of ACE2 in VSMCs. The levels of mRNA and proteins of ACE2, AT1R, ERK1/2, p-ERK1/2, STAT3, and p-STAT3 in VSMCs were examined using real-time PCR and western blot. The proliferation of VSMCs was observed by CCK-8 assay and BrdU measurement. Upregulation of ACE2 inhibited the growth of cells elicited by angiotensin II (Ang II). ACE2 significantly suppressed the level of the AT1 receptor (AT1R) protein induced by Ang II and phosphorylated the ERK1/2 and STAT3 proteins in the downstream signaling pathway. The transcriptional and translational levels of ACE2 were significantly lower in the si-ACE2 group than in the control group. The level of AT1R mRNA and protein, both with the phosphorylation expression of ERK1/2 and STAT3 protein in the siACE2 group and the Ang II group, were significantly enhanced than those in the control group. ACE2 significantly inhibited the growth of VSMCs. ACE2 inhibited the proliferation of VSMCs by suppressing AT1R and the downstream ERK1/2 and STAT3 signaling axes. Also, Ang II enhanced the level of AT1R and phosphorylated ERK1/2 and STAT3 by inhibiting the level of the ACE2 mRNA and protein.

Bioinformatics analysis of the immune cell infiltration characteristics and correlation with crucial diagnostic markers in pulmonary arterial hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a pathophysiological syndrome, characterized by pulmonary vascular remodeling. Immunity and inflammation are progressively recognized properties of PAH, which are crucial for the initiation and maintenance of pulmonary vascular remodeling. This study explored immune cell infiltration characteristics and potential biomarkers of PAH using comprehensive bioinformatics analysis. METHODS: Microarray data of GSE117261, GSE113439 and GSE53408 datasets were downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified in GSE117261 dataset. The proportions of infiltrated immune cells were evaluated by CIBERSORT algorithm. Feature genes of PAH were selected by least absolute shrinkage and selection operator (LASSO) regression analysis and validated by fivefold cross-validation, random forest and logistic regression. The GSE113439 and GSE53408 datasets were used as validation sets and logistic regression and receiver operating characteristic (ROC) curve analysis were performed to evaluate the prediction value of PAH. The PAH-associated module was identified by weighted gene association network analysis (WGCNA). The intersection of genes in the modules screened and DEGs was used to construct protein-protein interaction (PPI) network and the core genes were selected. After the intersection of feature genes and core genes, the hub genes were identified. The correlation between hub genes and immune cell infiltration was analyzed by Pearson correlation analysis. The expression level of LTBP1 in the lungs of monocrotaline-induced PAH rats was determined by Western blotting. The localization of LTBP1 and CD4 in lungs of PAH was assayed by immunofluorescence. RESULTS: A total of 419 DEGs were identified, including 223 upregulated genes and 196 downregulated genes. Functional enrichment analysis revealed that a significant enrichment in inflammation, immune response, and transforming growth factor ß (TGFß) signaling pathway. CIBERSORT analysis showed that ten significantly different types of immune cells were identified between PAH and control. Resting memory CD4+ T cells, CD8+ T cells, γδ T cells, M1 macrophages, and resting mast cells in the lungs of PAH patients were significantly higher than control. Seventeen feature genes were identified by LASSO regression for PAH prediction. WGCNA identified 15 co-expression modules. PPI network was constructed and 100 core genes were obtained. Complement C3b/C4b receptor 1 (CR1), thioredoxin reductase 1 (TXNRD1), latent TGFß binding protein 1 (LTBP1), and toll-like receptor 1 (TLR1) were identified as hub genes and LTBP1 has the highest diagnostic efficacy for PAH (AUC = 0.968). Pearson correlation analysis showed that LTBP1 was positively correlated with resting memory CD4+ T cells, but negatively correlated with monocytes and neutrophils. Western blotting showed that the protein level of LTBP1 was increased in the lungs of monocrotaline-induced PAH rats. Immunofluorescence of lung tissues from rats with PAH showed increased expression of LTBP1 in pulmonary arteries as compared to control and LTBP1 was partly colocalized with CD4+ cells in the lungs. CONCLUSION: LTBP1 was correlated with immune cell infiltration and identified as the critical diagnostic maker for PAH.

Retinoid X receptor agonists alleviate fibroblast activation and post-infarction cardiac remodeling via inhibition of TGF-ß1/Smad pathway.

Retinoid X receptor (RXR), particularly RXRα, has been implicated in cardiovascular diseases. However, the functional role of RXR activation in myocardial infarction (MI) remains unclear. This study aimed to determine the effects of RXR agonists on MI and to dissect the underlying mechanisms. Sprague-Dawley (SD) rats were subjected to MI and then treated (once daily for 4 weeks) with either RXR agonist bexarotene (10 or 30 mg/kg body weight) or vehicle. Heart function was determined using echocardiography and cardiac hemodynamic measurements. Four weeks post MI, myocardial tissues were collected to evaluate cardiac remodeling. Primary cardiac fibroblasts (CFs) were treated with or without RXR ligand 9-cis-RA followed by stimulation with TGF-ß1. Immunoblot, immunofluorescence, and co-immunoprecipitation were performed to elucidate the regulatory role of RXR agonists in TGF-ß1/Smad signaling. In vivo treatment with Bexarotene moderately affects systemic inflammation and apoptosis and ameliorated left ventricular dysfunction after MI in rat model. In contrast, bexarotene significantly inhibited post-MI myocardial fibrosis. Immunoblot analysis of heart tissue homogenates from MI rats revealed that bexarotene regulated the activation of the TGF-ß1/Smad signaling pathway. In vitro, 9-cis-RA inhibited the TGF-ß1-induced proliferation and collagen production of CFs. Importantly, upon activation by 9-cis-RA, RXRα interacted with p-Smad2 in cytoplasm, inhibiting the TGF-ß1-induced nuclear translocation of p-Smad2, thereby negatively regulating TGF-ß1/Smad signaling and attenuating the fibrotic response of CFs. These findings suggest that RXR agonists ameliorate post-infarction myocardial fibrosis, maladaptive remodeling, and heart dysfunction via attenuation of fibrotic response in CFs through inhibition of the TGF-ß1/Smad pathway activation.

Semantic and Temporal Contextual Correlation Learning for Weakly-Supervised Temporal Action Localization.

Weakly-supervised temporal action localization (WSTAL) aims to automatically identify and localize action instances in untrimmed videos with only video-level labels as supervision. In this task, there exist two challenges: (1) how to accurately discover the action categories in an untrimmed video (what to discover); (2) how to elaborately focus on the integral temporal interval of each action instance (where to focus). Empirically, to discover the action categories, discriminative semantic information should be extracted, while robust temporal contextual information is beneficial for complete action localization. However, most existing WSTAL methods ignore to explicitly and jointly model the semantic and temporal contextual correlation information for the above two challenges. In this article, a Semantic and Temporal Contextual Correlation Learning Network (STCL-Net) with the semantic (SCL) and temporal contextual correlation learning (TCL) modules is proposed, which achieves both accurate action discovery and complete action localization by modeling the semantic and temporal contextual correlation information for each snippet in the inter- and intra-video manners respectively. It is noteworthy that the two proposed modules are both designed in a unified dynamic correlation-embedding paradigm. Extensive experiments are performed on different benchmarks. On all the benchmarks, our proposed method exhibits superior or comparable performance in comparison to the existing state-of-the-art models, especially achieving gains as high as 7.2% in terms of the average mAP on THUMOS-14. In addition, comprehensive ablation studies also verify the effectiveness and robustness of each component in our model.

Zinc promotes cell proliferation via regulating metal-regulatory transcription factor 1 expression and transcriptional activity in pulmonary arterial hypertension.

Metal responsive transcription factor 1 (MTF-1) is a zinc-dependent transcription factor involved in the development of pulmonary arterial hypertension (PAH), which is a life-threatening disease characterized by elevated pulmonary artery pressure and pulmonary vascular remodeling. However, little is known about the role and regulatory signaling of MTF-1 in PAH. This study aimed to investigate the effect and mechanism of MTF-1 on the proliferation of pulmonary arterial smooth muscle cells (PASMCs). Several techniques including intracellular-free zinc detected by fluorescent indicator-fluozinc-3-AM, western blot, luciferase reporter, and cell proliferation assay were conducted to perform a comprehensive analysis of MTF-1 in proliferation of PASMCs in PAH. Increased cytosolic zinc was shown in monocrotaline (MCT)-PASMCs and ZnSO4-treated PASMCs, which led to overexpression and overactivation of MTF-1, followed by the up-regulation of placental growth factor (PlGF). Elevated MTF-1 and PlGF were observed in western blot, and high transcriptional activity of MTF-1 was confirmed by luciferase reporter in ZnSO4-treated cells. Further investigation of cell proliferation revealed a favorable impact of zinc ions on PASMCs proliferation, with the deletion of Mtf-1/Plgf attenuating ZnSO4-induced proliferation. Flow cytometry analysis showed that blockade of PKC signaling inhibited the cell cycle of MCT-PASMCs and ZnSO4-treated PASMCs. The Zinc/PKC/MTF-1/PlGF pathway is involved in the up-regulatory effect on the PASMCs proliferation in the process of PAH. This study provided novel insight into zinc homeostasis in the pathogenesis of PAHs, and the regulation of MTF-1 might be a potential target for therapeutic intervention in PAH.