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Backgrounds The role of O-GlcNAc transferase (OGT)-induced O-linked N-acetylglucosaminylation (O-GlcNAcylation) has been reported in multiple human diseases. However, its specific functions in osteoarthritis (OA) progression remain undetermined.Objective This study focused on the target proteins of OGT-induced O-GlcNAcylation in OA and the specific functional mechanism.Methods The levels of total O-GlcNAc and OGT were measured in both in vitro and in vivo OA models using western blot. The effects of OGT knockout on OA progression were detected through Safranin O staining, immunohistochemical staining and OARSI score evaluation. The effects of OGT silencing on LPS-induced chondrocyte injury were assessed by performing loss-of function assays. Co-immunoprecipitation (co-IP) was conducted to verify the effect of OGT-induced O-GlcNAcylation on the interaction between NEK7 and NLRP3. The role of OGT in modulating the O-GlcNAcylation and phosphorylation levels of NEK7 was analysed using western blot.Results The OGT-indued O-GlcNAcylation level was increased in both in vitro and in vivo OA models. Knockout of OGT mitigated OA progression in model mice. Additionally, silencing of OGT suppressed LPS-induced chondrocyte pyroptosis. Moreover, silencing of OGT inhibited the O-GlcNAcylation and enhanced the phosphorylation of NEK7 at S260 site, thereby blocking the binding of NEK7 with NLRP3.Conclusion OGT-induced NEK7 O-GlcNAcylation promotes OA progression by promoting chondrocyte pyroptosis via the suppressing interaction between NEK7 and NLRP3.

Abstract Purpose Smoking is a risk factor for sarcopenia. Nevertheless, few studies analyzed the independent effects of various smoking dimensions (duration, intensity, cumulative dose) on sarcopenia risk. This is a cross-sectional study based on an older population in Zhejiang Province to determine which smoking dimensions are mainly important for sarcopenia risk and to explore the dose–response relationship between them. Methods Our study included 783 patients with sarcopenia and 4918 non-sarcopenic individuals. Logistic regression and restricted cubic with logistic regression (for nonlinear dose effects) were used to obtain odds ratios (ORs) and 95% confidence intervals as well as restricted cubic splines (RCS) curves. Results Compared with never-smokers, current smokers had an increased risk of sarcopenia (OR = 1.786; 95% CI 1.387–2.301) after adjusting for confounders such as age, sex, education, alcohol consumption, disease history, etc. There was no significant association between smoking intensity and sarcopenia after more than 20 cigarettes per day (OR = 1.484; 95% CI 0.886–2.487), whereas the risk of sarcopenia increased significantly with increasing duration of smoking after more than 40 years (OR = 1.733; 95% CI 1.214–2.473). Meanwhile, there was a significant non-linear dose–response relationship between smoking duration or intensity and the risk of sarcopenia. However, the risk of sarcopenia increased linearly with the number of pack-years of smoking, which is not a significant nonlinear dose–response relationship. Conclusions This study indicated the association between smoking and sarcopenia. Both smoking duration and cumulative dose were significantly and positively associated with sarcopenia. These findings reflect the important role of the number of years of smoking in increasing the risk of sarcopenia and provide scientific evidence that different smoking dimensions may influence the risk of the sarcopenia.

Abstract Pulmonary arterial hypertension (PAH) is associated with aberrant pulmonary vascular smooth muscle cell (PASMC) function and vascular remodeling. MiR‐30d plays an important role in the pathogenesis of several cardiovascular disorders. However, the function of miR‐30d in PAH progression remained unknown. Our study shows that circulating miR‐30d level is significantly reduced in the plasma from PAH patients. In miR‐30d transgenic (TG) rats, overexpressing miR‐30d attenuates monocrotaline (MCT)‐induced pulmonary hypertension (PH) and pulmonary vascular remodeling. Increasing miR‐30d also inhibits platelet‐derived growth factor‐bb (PDGF‐bb)‐induced proliferation and migration of human PASMC. Metadherin (MTDH) and phosphodiesterase 5A (PDE5A) are identified as direct target genes of miR‐30d. Meanwhile, nuclear respiratory factor 1 (NRF1) acts as a positive upstream regulator of miR‐30d. Using miR‐30d knockout (KO) rats treated with sildenafil, a PDE5A inhibitor that is used in clinical PAH therapies, it is further found that suppressing miR‐30d partially attenuates the beneficial effect of sildenafil against MCT‐induced PH and vascular remodeling. The present study shows a protective effect of miR‐30d against PAH and pulmonary vascular remodeling through targeting MTDH and PDE5A and reveals that miR‐30d modulates the beneficial effect of sildenafil in treating PAH. MiR‐30d should be a prospective target to treat PAH and pulmonary vascular remodeling.

The repurposing of approved drugs is an appealing method to fast-track the development of novel therapies for neglected diseases. Amiodarone and dronedarone, two approved antiarrhythmic agents, have been reported to have potential for the management of Chagas disease patients displaying symptomatic heart pathology. More recently, it has been suggested that both molecules not only have an antiarrhythmic effect, but also have trypanocidal activity against Trypanosoma cruzi, the causative agent of Chagas disease. In this work, we assessed the in vitro activity of these compounds against T. cruzi, the in vivo pharmacokinetics, and pharmacodynamics, to determine the potential for repurposing these drugs as therapies for Chagas disease. Based on these results, we were unable to reproduce the in vitro potencies of amiodarone and dronedarone described in the literature, and both drugs were found to be inactive or cytotoxic against a variety of different mammalian cell lines. The evaluation of in vivo efficacy in a bioluminescent murine model of T. cruzi did not show antiparasitic activity at the highest tolerated dose tested. While the potential of amiodarone and dronedarone as antiarrhythmic agents in Chagas cardiomyopathic patients cannot be completely excluded, a trypanocidal effect in patients treated with these two drugs appears unlikely.

Objective To evaluate the associations between serum uric acid (SUA) levels and cardiovascular disease (CVD) risk factors, focusing on potential sex-specific differences.Design A retrospective cohort study.Setting A large community-based survey was conducted every two years from 2010 to 2018 in Hangzhou, Zhejiang Province, outheastern China.Participants 6119 participants aged 40 years and above who underwent at least three times of physical examinations were enrolled.Methods Participants were categorised into four groups (Q1–Q4) based on baseline SUA quartiles within the normal range, with hyperuricaemia (HUA) as the fifth group. The Q1 was the reference. By stratifying participants by gender, the relationships between SUA levels and systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG) and total cholesterol (TC) were investigated using linear regression models in the generalised estimating equation. Additionally, the associations of elevated SUA levels and HUA with hypertension, hyperglycaemia and dyslipidaemia were correspondingly examined using multivariate logistic regression models.Results After adjusting for confounding variables, we found positive associations between SUA levels and SBP, DBP, FBG and TC in women, and with TC in men (p

Abstract Circulating circular RNAs (circRNAs) are emerging as novel biomarkers for cardiovascular diseases (CVDs). Machine learning can provide optimal predictions on the diagnosis of diseases. Here we performed a proof‐of‐concept study to determine if combining circRNAs with an artificial intelligence approach works in diagnosing CVD. We used acute myocardial infarction (AMI) as a model setup to prove the claim. We determined the expression level of five hypoxia‐induced circRNAs, including cZNF292, cAFF1, cDENND4C, cTHSD1, and cSRSF4, in the whole blood of coronary angiography positive AMI and negative non‐AMI patients. Based on feature selection by using lasso with 10‐fold cross validation, prediction model by logistic regression, and ROC curve analysis, we found that cZNF292 combined with clinical information (CM), including age, gender, body mass index, heart rate, and diastolic blood pressure, can predict AMI effectively. In a validation cohort, CM + cZNF292 can separate AMI and non‐AMI patients, unstable angina and AMI patients, acute coronary syndromes (ACS), and non‐ACS patients. RNA stability study demonstrated that cZNF292 was stable. Knockdown of cZNF292 in endothelial cells or cardiomyocytes showed anti‐apoptosis effects in oxygen glucose deprivation/reoxygenation. Thus, we identify circulating cZNF292 as a potential biomarker for AMI and construct a prediction model “CM + cZNF292.”

Cardiomyocyte apoptosis has been characterized as one of the major mechanisms underlying doxorubicin (DOX)-induced cardiomyopathy. MicroRNA-21-5p (miR-21-5p) was reported to mitigate ischemia-induced cardiomyocyte apoptosis and cardiac injury. However, to our knowledge, the functional role of miR-21-5p in DOX-induced cardiomyopathy is unclear. In this study, we explored the role of miR-21-5p in DOX-induced cardiac injury. The expression level of miR-21-5p was detected by quantitative real-time polymerase chain reaction (qRT-PCR). Dual luciferase reporter assay was used to verify the potential target gene of miR-21-5p. The apoptosis rate of NRCMs was detected by TUNEL staining assay. Western blot analysis was used to detect the protein expression levels of Bax, Bcl-2, Caspase3, cleaved-Caspase3 and BTG2. For animal studies, mice were injected with AAV9-miR-21-5p or AAV9-Empty viruses, and treated with DOX at a dose of 5 mg/kg per week through intraperitoneally administration. After 4 weeks of DOX treatment, mice were subjected to echocardiography to measure the left ventricular ejection fraction (EF) and fractional shortening (FS). Results showed that miR-21-5p was upregulated in both DOX-treated primary cardiomyocytes and mouse heart tissues. Interestingly, enhanced miR-21-5p expression inhibited DOX-induced cardiomyocyte apoptosis and oxidative stress, while decreased miR-21-5p expression promoted cardiomyocyte apoptosis and oxidative stress. Furthermore, cardiac overexpression of miR-21-5p protected against DOX-induced cardiac injury. The mechanistic study indicated that BTG2 was a target gene of miR-21-5p. The anti-apoptotic effect of miR-21-5p could be inhibited by BTG2 overexpression. Conversely, inhibition of BTG2 rescued the pro-apoptotic effect of miR-21-5p inhibitor. Taken together, our study showed that miR-21-5p could prevent DOX-induced cardiomyopathy by downregulating BTG2.

Background: Peripheral blood mononuclear cells (PBMNCs) and purified CD34+ cells (PCCs) are increasingly being used at treating no-option critical limb ischaemia (NO-CLI). We aimed to compare the efficacies and uncover the advantages associated with each treatment approach. Methods: A randomised single-blinded non-inferiority trial (Number: NCT 02089828) was performed. NO-CLI patients were 1:1 randomised to the PBMNCs and PCCs groups, and compared in relation to safety and efficacy outcomes. The primary efficacy outcomes included major amputation and total amputation over 12 months. The major amputation-free survival (MAFS) and total amputation-free survival (TAFS) rates were calculated. Findings: Fifty patients (25 per group, 47 with thromboangiitis obliterans and 3 with other angiitis) were enrolled, with a median follow-up period of 24.5 months (interquartile range: 17–34 months). One patient in the PCCs group was lost at 2 months and one major amputation occurred in the PBMNCs group at 3 months post-transplantation. The total amputation rates at 6 months post-transplantation were 28.0% in the PCCs group and 16.0% in the PBMNCs group (p = 0.343), and remained unchanged at 12 months. The groups did not differ regarding the MAFS and TAFS (Breslow-Wilcoxon test: p = 0.3014 and p = 0.3414). The PCCs group had a significantly higher probability of rest pain relief than the PBMNCs group (Breslow-Wilcoxon test: p = 0.0454). Interpretation: PCCs was not inferior to PBMNCs at limb salvage in the treatment of angiitis-induced NO-CLI and appeared to induce earlier ischaemia relief. Each cell type had specific advantages. These outcomes require verification from longer-term trials involving larger numbers of patients. Fund: Training program for outstanding academic leaders of Shanghai health and family planning system (Hundred Talent Program，Grant No. 2018BR40); China National Natural Science Funds (Grant No. 30801122); The excellent core member training programme at Zhongshan Hospital, Fudan University, China (Grant No. 2015ZSYXGG02); and Zhongshan Funds for the Institute of Vascular Surgery, Fudan University, China. Clinical trial registration: This study is registered with ClinicalTrials.gov (NCT 02089828). Keywords: Critical limb ischaemia, Cell therapy, Purified CD34+ cells, Peripheral blood mononuclear cells, Limb salvage

Background: Arsenic trioxide (As2O3), an ancient drug used in traditional Chinese medicine, has substantial anticancer activities, especially in the treatment of patients suffering from acute promyelocytic leukemia (APL); however the underlying mechanisms are not well understood. Methods: MTT assay was used to detect the cell viability. Flow Cytometry analysis and caspase-3 activity assay were used to measure apoptosis of APL cells. Caspase-3 and Bax levels were analyzed by western blot and let-7d and miR-766 levels were determined by real-time RT-PCR. Results: As2O3 significantly inhibited cell viability and induced apoptosis in APL cells. Several microRNAs, including let-7d and miR-766, were dysregulated in APL cells treated with As2O3. The expression of caspase-3 and Bax, which are targets of let-7d and miR-766, respectively, were up-regulated in As2O3 treated cells. Transfection of let-7d and miR-766 into NB4 cells decreased the expression of caspase-3 and Bax, respectively. Correspondingly, transfection of these microRNAs increased NB4 cell viability. As2O3 induced degradation of promyelocytic leukemia (PML), and then induced the down-regulation of both let-7d and miR-766 in NB4 cells. Conclusions: We construct a dysregulated microRNA network involved in As2O3-induced apoptosis in APL. Targeting this network may be a new strategy for the prevention of side effects associated with APL treatment with As2O3.

Circulating circular RNAs (circRNAs) are emerging as novel biomarkers for cardiovascular diseases (CVDs). Machine learning can provide optimal predictions on the diagnosis of diseases. Here we performed a proof-of-concept study to determine if combining circRNAs with an artificial intelligence approach works in diagnosing CVD. We used acutemyocardial infarction (AMI) as a model setup to prove the claim. We determined the expression level of five hypoxia-induced circRNAs, including cZNF292, cAFF1, cDENND4C, cTHSD1, and cSRSF4, in the whole blood of coronary angiography positive AMI and negative non-AMI patients. Based on feature selection by using lasso with 10-fold cross validation, prediction model by logistic regression, and ROC curve analysis, we found that cZNF292 combined with clinical information (CM), including age, gender, body mass index, heart rate, and diastolic blood pressure, can predict AMI effectively. In a validation cohort, CM + cZNF292 can separate AMI and non-AMI patients, unstable angina and AMI patients, acute coronary syndromes (ACS), and non-ACS patients. RNA stability study demonstrated that cZNF292 was stable. Knockdown of cZNF292 in endothelial cells or cardiomyocytes showed anti-apoptosis effects in oxygen glucose deprivation/reoxygenation. Thus, we identify circulating cZNF292 as a potential biomarker for AMI and construct a prediction model "CM + cZNF292.". [ABSTRACT FROM AUTHOR]

Abstract C-to-G base editors have been successfully constructed recently, but limited work has been done on concurrent C-to-G and A-to-G base editing. In addition, there is also limited data on how chromatin-associated factors affect the base editing. Here, we test a series of chromatin-associated factors, and chromosomal protein HMGN1 was found to enhance the efficiency of both C-to-G and A-to-G base editing. By fusing HMGN1, GBE and ABE to Cas9, we develop a CRISPR-based dual-function A-to-G and C-to-G base editor (GGBE) which is capable of converting simultaneous A and C to G conversion with substantial editing efficiency. Accordingly, the HMGN1 role shown in this work and the resulting GGBE tool further broaden the genome manipulation capacity of CRISPR-directed base editors.

Airplane detection in synthetic aperture radar (SAR) images has drawn much attention owing to the success of deep learning methods. However, the development of fine-grained airplane detection in SAR images is still in a dilemma due to the small interclass variance and the large intraclass variance in complex scenes with strong interference from the background. In addition, the class imbalance problem in multiclass fine-grained airplane recognition also significantly limits the direct application of general deep-learning-based airplane detectors. This article proposes two effective methods to tackle the above two problems, respectively. First, we propose a sparse attention-guided fine-grained pyramid module to simultaneously sample discriminative local features scattered in multiscale layers and adaptively aggregate them with fine-grained attention to better classify subordinate-level airplanes with multiple scales. Second, a simple class-balanced copy-paste data augmentation strategy, which randomly copies an airplane of one category and pastes it onto an image according to the classwise probability, is proposed for class balance. Finally, extensive experiments on one public dataset and three representative deep-learning-based detection benchmarks are conducted to show the effectiveness and generalization of the two proposed methods. The combination of these two methods based on the cascade R-CNN benchmark also won the fifth place in fine-grained airplane detection in SAR images in the 2021 GaoFen Challenge.

Non-line-of-sight imaging allows reconstruction and recognition of an obscured object, but external manipulation of the data can lead to inaccurate results. Here, the accuracy and robustness of passive non-line-of-sight recognition and its fragility to adversarial attacks are studied.

Numerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD); second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance. [ABSTRACT FROM AUTHOR]

Metric-learning-based methods, which attempt to learn a deep embedding space on extremely large episodes, have been successfully applied to few-shot classification problems. In this paper, we propose the adoption of large margin nearest center (LMNC) loss during episodic training to enhance metric-learning-based few-shot classification methods. Loss functions (such as cross-entropy and mean square error) commonly used in episodic training strive to achieve the strict goal that differently labeled examples in the embedding space are separated by an infinite distance. However, the learned embedding space cannot guarantee that this goal will be achieved for every episode sampled from a large number of classes. Instead of an infinite distance, LMNC loss requires only that differently labeled examples be separated by a large margin, which can well relax the strict constraint of the traditional loss functions, easily leading to a discriminative embedding space. Moreover, considering the multilevel similarity between various classes, we alleviate the constraint of a fixed large margin and extend LMNC loss to weighted LMNC (WLMNC) loss, which can effectively take advantage of interclass information, achieving a more separable embedding space with adaptive interclass margins. Experiments on state-of-the-art benchmarks demonstrate that the adoption of LMNC and WLMNC losses can strongly improve the embedding learning performance and classification accuracy of metric-based few-shot classification methods for various few-shot scenarios. In particular, LMNC and WLMNC losses can obtain 1.86% and 2.46% gains in prototypical network on miniImageNet for 5-way 1-shot scenario, respectively.

Deep learning methods have made significant progress in ship detection in synthetic aperture radar (SAR) images. The pretraining technique is usually adopted to support deep neural networks-based SAR ship detectors due to the scarce labeled SAR images. However, directly leveraging ImageNet pretraining is hard to obtain a good ship detector because of different imaging perspectives and geometry. In this article, to resolve the problem of inconsistent imaging perspectives between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique to transfer the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset. On the other hand, to handle the problem of different imaging geometry between optical and SAR images, we propose an optical-SAR matching (OSM) pretraining technique, which transfers plentiful texture features from optical images to SAR images by common representation learning on the OSM task. Finally, observing that the OSD pretraining-based SSD has a better recall on sea area while the OSM pretraining-based SSD can reduce false alarms on land area, we combine the predictions of the two detectors through weighted boxes fusion to further improve detection results. Extensive experiments on four SAR ship detection datasets and three representative convolutional neural network-based detection benchmarks are conducted to show the effectiveness and complementarity of the two proposed detectors, and the state-of-the-art performance of the combination of the two detectors. The proposed method won the sixth place of ship detection in SAR images in the 2020 Gaofen challenge.

Due to the vast geometric and radiometric differences between SAR and optical images, SAR-optical image matching remains an intractable challenge. Despite the fact that the deep learning-based matching model has achieved great success, SAR feature embedding ability is not fully explored yet because of the lack of well-designed pre-training techniques. In this paper, we propose to employ the self-supervised learning method in the SAR-optical matching framework, in order to serve as a pre-training strategy for improving the representation learning ability of SAR images as well as optical images. We first use a state-of-the-art self-supervised learning method, Momentum Contrast (MoCo), to pre-train an optical feature encoder and an SAR feature encoder separately. Then, the pre-trained encoders are transferred to an advanced common representation learning model, Bridge Neural Network (BNN), to project the SAR and optical images into a more distinguishable common feature representation subspace, which leads to a high multi-modal image matching result. Experimental results on three SAR-optical matching benchmark datasets show that our proposed MoCo pre-training method achieves a high matching accuracy up to 0.873 even for the complex QXS-SAROPT SAR-optical matching dataset. BNN pre-trained with MoCo outperforms BNN with the most commonly used ImageNet pre-training, and achieves at most 4.4% gains in matching accuracy.

The current interpretation technology of remote sensing images is mainly focused on single-modal data, which cannot fully utilize the complementary and correlated information of multimodal data with heterogeneous characteristics, especially for synthetic aperture radar (SAR) data and optical imagery. To solve this problem, we propose a bridge neural network- (BNN-) based optical-SAR image joint intelligent interpretation framework, optimizing the feature correlation between optical and SAR images through optical-SAR matching tasks. It adopts BNN to effectively improve the capability of common feature extraction of optical and SAR images and thus improving the accuracy and application scenarios of specific intelligent interpretation tasks for optical-SAR/SAR/optical images. Specifically, BNN projects optical and SAR images into a common feature space and mines their correlation through pair matching. Further, to deeply exploit the correlation between optical and SAR images and ensure the great representation learning ability of BNN, we build the QXS-SAROPT dataset containing 20,000 pairs of perfectly aligned optical-SAR image patches with diverse scenes of high resolutions. Experimental results on optical-to-SAR crossmodal object detection demonstrate the effectiveness and superiority of our framework. In particular, based on the QXS-SAROPT dataset, our framework can achieve up to 96% high accuracy on four benchmark SAR ship detection datasets.

For the space-based remote sensing system, onboard intelligent processing based on deep learning has become an inevitable trend. To adapt to the dynamic changes of the observation scenes, there is an urgent need to perform distributed deep learning onboard to fully utilize the plentiful real-time sensing data of multiple satellites from a smart constellation. However, the network bandwidth of the smart constellation is very limited. Therefore, it is of great significance to carry out distributed training research in a low-bandwidth environment. This paper proposes a Randomized Decentralized Parallel Stochastic Gradient Descent (RD-PSGD) method for distributed training in a low-bandwidth network. To reduce the communication cost, each node in RD-PSGD just randomly transfers part of the information of the local intelligent model to its neighborhood. We further speed up the algorithm by optimizing the programming of random index generation and parameter extraction. For the first time, we theoretically analyze the convergence property of the proposed RD-PSGD and validate the advantage of this method by simulation experiments on various distributed training tasks for image classification on different benchmark datasets and deep learning network architectures. The results show that RD-PSGD can effectively save the time and bandwidth cost of distributed training and reduce the complexity of parameter selection compared with the TopK-based method. The method proposed in this paper provides a new perspective for the study of onboard intelligent processing, especially for online learning on a smart satellite constellation.

L-Ascorbate (Asc) plays important roles in plant development, hormone signaling, the cell cycle and cellular redox system, etc. The higher content of Asc in plant chloroplasts indicates its important role in the photosystem. The objective of this study was to study the roles of Asc in tall fescue leaves against heat stress. After a heat stress treatment, we observed a lower value of the maximum quantum yield for primary photochemistry (φPo), which reflects the inhibited activity of the photochemical phase of photosystem II (PSII). Moreover, we observed a higher value of efficiency of electron transfer from QB to photosystem I acceptors (δR0), which reflects elevated activity of the thermal phase of the photosystem of the tall fescue. The addition of Asc facilitate the behavior of the photochemical phase of the PSII by lowering the ROS content as well as that of the alternative electron donor to provide electron to the tyrosine residue of the D1 protein. Additionally, exogenous Asc reduces the activity of the thermal phase of the photosystem, which could contribute to the limitation of energy input into the photosystem in tall fescue against heat stress. Synthesis of the Asc increased under heat stress treatment. However, under heat stress this regulation does not occur at the transcription level and requires further study.