Role of macrophage 11β-HSD1 in inflammation mediated angiogenesis, arthritis and obesity

11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1, encoded by Hsd11b1) is an enzyme that predominantly converts inactive glucocorticoids (cortisone in human and most mammals, 11dehydro-corticosterone in mice and rats) into their active forms (cortisol and corticosterone, respectively). Thus 11β-HSD1 amplifies intracellular levels of glucocorticoids. Studies in globally 11β-HSD1 deficient mice have revealed changes in glucocorticoid-regulated physiological and pathological processes, including metabolism, aging, arthritis and angiogenesis. The function of macrophages, which play an important role in inflammation, is also altered. For example, 11β-HSD1 deficiency in macrophages causes a delay in their acquisition of phagocytic capacity. To dissect the role of macrophage 11β-HSD1 in angiogenesis, arthritis and obesity, both in vitro macrophage stimulation and in vivo functional assays in macrophage-specific 11β-HSD1 knockout mice, were conducted. Thioglycollate-elicited peritoneal macrophages from globally 11β-HSD1 deficient and control C57BL/6 mice were used for in vitro studies. In M1/M2 macrophage polarisation experiments, 11β-HSD1 deficient macrophages showed increased expression of mRNAs encoding pro-inflammatory factors upon lipopolysaccharide and interferon-ϒ treatment and decreased expression of pro-resolution genes with interleukin-4 stimulation. However, at cytokine or protein levels, there was little difference between the genotypes except for decrease IL12 p40 levels in 11β-HSD1 deficient macrophages. Hypoxic stress failed to show differences between genotypes in hypoxia-regulated gene expression. These data do not support a strong role for macrophage 11β-HSD1 in inflammation regulation, nor in response to hypoxia, at least when measured in vitro. The discrepancy between transcriptional and translational responses is currently unexplained, but may reflect altered posttranscriptional activity. To investigate the role of macrophage 11β-HSD1 in vivo, macrophage-specific Hsd11b1 knockout mice, LysM-Cre Hsd11b1 flox/flox (MKO) mice and Hsd11b1flox/flox littermate controls were generated. In MKO mice, 11β-HSD1 protein levels and enzyme activity were reduced by >80% in resident peritoneal macrophages. However, 11β-HSD1 protein and enzyme activity levels were unchanged or only modestly reduced in thioglycocollate-elicited peritoneal neutrophils, monocytes/macrophages, or in bone marrow-derived macrophages, despite >80% decrease in Hsd11b1 mRNA levels in these cells. A relatively long half-life of 11β-HSD1 protein compared to that of circulating myeloid cells may underlie this mismatch between transcriptional and translational expression. Furthermore, following 12 days of inflammatory arthritis induced by K/BxN serum transfer, the reduction in 11β-HSD1 protein levels in circulating neutrophils of MKO mice is consistently around 50%, which corroborates the above explanation. MKO mice and littermate controls were subjected to inflammatory models which may involve resident macrophages. First, to address the role of 11β-HSD1 in macrophages in angiogenesis, sponge implants were inserted subcutaneously into the flanks of adult male mice and harvested after 21 days. Chalkley counting on hematoxylin and eosin stained sponge sections showed significantly increased angiogenesis in MKO mice (scores: 5.2±1.0 versus 4.3±0.7; p<0.05, n=9-11). Cdh5 expression (encoding VE-cadherin, a marker of endothelial cells) was higher in sponges from MKO mice (relative expression: 1.5±0.9 versus 0.8±0.6; p<0.05), as was Il1b (encoding IL-1 beta, a marker of inflammation, relative expression: 6.5±6.4 versus 1.5±0.9; p<0.05). Vegfa mRNA (encoding vascular endothelial growth factor alpha) was unchanged, with a trend for higher Angpt1 (encoding angiopoietin 1, p=0.09) expression levels in the MKO group. These results suggest that lack of 11β- HSD1 in resident macrophages increases their pro-angiogenic activity, independently of VEGF-. The K/BxN serum transfer model of arthritis was used to investigate the role of macrophage 11β-HSD1 in arthritis. Adult male MKO and control mice received a single i.p. injection of 125μl K/BxN serum per mouse, followed by 21 days of clinical scoring to assess joint inflammation. The onset of inflammation (d1-8) was similar between MKO and control mice, but MKO mice exhibited greater clinical inflammation scores in the resolution phase of arthritis (d13-21; area-under-the-curve: 86.6±14.7 versus 60.1±13.4; p<0.005), indistinguishable from globally 11β-HSD1- deficient mice. Hematoxylin and eosin staining revealed pronounced fibroplasia predominantly in the supporting mesenchyme associated with the tenosynovium, with new bone and blood vessel formation. These results suggest that macrophage 11β-HSD1 deficiency is fully accountable for the worse arthritis resolution phenotype in the globally 11β-HSD1 deficient mice, but not the earlier onset of inflammation with global 11β-HSD1 deficiency. Macrophage activation states are closely linked with adipose insulin sensitivity. Globally 11β-HSD1 deficient mice are protected from high fat diet induced insulin resistance and adipose tissue hypoxia and fibrosis. To study the effect of macrophage 11β-HSD1 deficiency on insulin sensitivity, adult male MKO and control mice were given a 14 week high fat diet, which typically causes insulin resistance in control but not globally 11β-HSD1 KO mice. The level of fibrosis in subcutaneous adipose tissues was reduced as indicated by quantification of picrosirius red staining of collagen, though GTT data so far does not support protection from insulin resistance in MKO mice. In summary, in vitro macrophage polarisation experiments do not support a strong role of 11β-HSD in M1/M2 macrophage polarisations or response to hypoxia. However, MKO mice reveal, for the first time, an important in vivo role of macrophage 11β-HSD1 to promote angiogenesis and facilitate resolution of K/BxN serum transfer induced arthritis. Modulation of fibrosis is context dependent. Reduced adipose fibrosis may be one of the mechanisms that improve insulin sensitivity. Meanwhile, these findings suggest caution regarding the potential side effects of 11β-HSD1 inhibitors in treating metabolic disease in patients with inflammation-related co-morbidities, such as rheumatoid arthritis

Invariant Feature Learning for Generalized Long-Tailed Classification

Existing long-tailed classification (LT) methods only focus on tackling the class-wise imbalance that head classes have more samples than tail classes, but overlook the attribute-wise imbalance. In fact, even if the class is balanced, samples within each class may still be long-tailed due to the varying attributes. Note that the latter is fundamentally more ubiquitous and challenging than the former because attributes are not just implicit for most datasets, but also combinatorially complex, thus prohibitively expensive to be balanced. Therefore, we introduce a novel research problem: Generalized Long-Tailed classification (GLT), to jointly consider both kinds of imbalances. By "generalized", we mean that a GLT method should naturally solve the traditional LT, but not vice versa. Not surprisingly, we find that most class-wise LT methods degenerate in our proposed two benchmarks: ImageNet-GLT and MSCOCO-GLT. We argue that it is because they over-emphasize the adjustment of class distribution while neglecting to learn attribute-invariant features. To this end, we propose an Invariant Feature Learning (IFL) method as the first strong baseline for GLT. IFL first discovers environments with divergent intra-class distributions from the imperfect predictions and then learns invariant features across them. Promisingly, as an improved feature backbone, IFL boosts all the LT line-up: one/two-stage re-balance, augmentation, and ensemble. Codes and benchmarks are available on Github: https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorchComment: Accepted to ECCV 2022. Codes and benchmarks are available on Github: https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorc

TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models

Data augmentation has been established as an efficacious approach to supplement useful information for low-resource datasets. Traditional augmentation techniques such as noise injection and image transformations have been widely used. In addition, generative data augmentation (GDA) has been shown to produce more diverse and flexible data. While generative adversarial networks (GANs) have been frequently used for GDA, they lack diversity and controllability compared to text-to-image diffusion models. In this paper, we propose TTIDA (Text-to-Text-to-Image Data Augmentation) to leverage the capabilities of large-scale pre-trained Text-to-Text (T2T) and Text-to-Image (T2I) generative models for data augmentation. By conditioning the T2I model on detailed descriptions produced by T2T models, we are able to generate photo-realistic labeled images in a flexible and controllable manner. Experiments on in-domain classification, cross-domain classification, and image captioning tasks show consistent improvements over other data augmentation baselines. Analytical studies in varied settings, including few-shot, long-tail, and adversarial, further reinforce the effectiveness of TTIDA in enhancing performance and increasing robustness

Empirical Review of Smart Contract and DeFi Security: Vulnerability Detection and Automated Repair

Decentralized Finance (DeFi) is emerging as a peer-to-peer financial ecosystem, enabling participants to trade products on a permissionless blockchain. Built on blockchain and smart contracts, the DeFi ecosystem has experienced explosive growth in recent years. Unfortunately, smart contracts hold a massive amount of value, making them an attractive target for attacks. So far, attacks against smart contracts and DeFi protocols have resulted in billions of dollars in financial losses, severely threatening the security of the entire DeFi ecosystem. Researchers have proposed various security tools for smart contracts and DeFi protocols as countermeasures. However, a comprehensive investigation of these efforts is still lacking, leaving a crucial gap in our understanding of how to enhance the security posture of the smart contract and DeFi landscape. To fill the gap, this paper reviews the progress made in the field of smart contract and DeFi security from the perspective of both vulnerability detection and automated repair. First, we analyze the DeFi smart contract security issues and challenges. Specifically, we lucubrate various DeFi attack incidents and summarize the attacks into six categories. Then, we present an empirical study of 42 state-of-the-art techniques that can detect smart contract and DeFi vulnerabilities. In particular, we evaluate the effectiveness of traditional smart contract bug detection tools in analyzing complex DeFi protocols. Additionally, we investigate 8 existing automated repair tools for smart contracts and DeFi protocols, providing insight into their advantages and disadvantages. To make this work useful for as wide of an audience as possible, we also identify several open issues and challenges in the DeFi ecosystem that should be addressed in the future.Comment: This paper is submitted to the journal of Expert Systems with Applications (ESWA) for revie

Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization

The self-media era provides us tremendous high quality videos. Unfortunately, frequent video copyright infringements are now seriously damaging the interests and enthusiasm of video creators. Identifying infringing videos is therefore a compelling task. Current state-of-the-art methods tend to simply feed high-dimensional mixed video features into deep neural networks and count on the networks to extract useful representations. Despite its simplicity, this paradigm heavily relies on the original entangled features and lacks constraints guaranteeing that useful task-relevant semantics are extracted from the features. In this paper, we seek to tackle the above challenges from two aspects: (1) We propose to disentangle an original high-dimensional feature into multiple sub-features, explicitly disentangling the feature into exclusive lower-dimensional components. We expect the sub-features to encode non-overlapping semantics of the original feature and remove redundant information. (2) On top of the disentangled sub-features, we further learn an auxiliary feature to enhance the sub-features. We theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature. Extensive experiments on two large-scale benchmark datasets (i.e., SVD and VCSL) demonstrate that our method achieves 90.1% TOP-100 mAP on the large-scale SVD dataset and also sets the new state-of-the-art on the VCSL benchmark dataset. Our code and model have been released at https://github.com/yyyooooo/DMI/, hoping to contribute to the community.Comment: This paper is accepted by ACM MM 202