RESUMEN
Background: Pyroptosis is a form of cell death triggered by proinflammatory signals. Recent studies have reported that oxidized phospholipids function as caspase-11 agonists to induce noncanonical inflammasome activation in immune cells. As the levels of oxidized phospholipids derived from ox-LDL are largely elevated in atherosclerotic lesions, this study sought to determine whether oxidized lipids trigger pyroptosis and subsequent inflammation in the pathogenesis of atherosclerosis. Methods and Results: In our current study, after integrating transcriptomic data available from the Gene Expression Omnibus with data from hyperlipidemic mice and ox-LDL-treated peritoneal macrophages, we discovered that caspase-4/11-gasdermin D-associated inflammatory signaling was significantly activated. Consistently, the mRNA expression of caspase-4 and gasdermin D was upregulated in peripheral blood mononuclear cells from patients with coronary heart disease. In particular, the expression of caspase-4 was closely associated with the severity of lesions in the coronary arteries. An in vivo study showed that caspase-11-gasdermin D activation occurred in response to a high-fat/high-cholesterol (HFHC) diet in ApoE-/- mice, while caspase-11 deletion largely attenuated the volume and macrophage infiltration of atherosclerotic lesions. An in vitro mechanistic study showed that caspase-11-mediated inflammation occurred partly via gasdermin D-mediated pyroptosis in macrophages. Suppressing gasdermin D in HFHC-fed ApoE-/- mice via delivery of an adeno-associated virus markedly decreased lesion volume and infiltrating macrophage numbers. Conclusion: Caspase-11-gasdermin D-mediated pyroptosis and the subsequent proinflammatory response in macrophages are involved in the pathogenesis of atherosclerosis. Therefore, targeting the caspase 11-gasdermin D may serve as an alternative strategy for the treatment of atherosclerosis.
RESUMEN
Person re-identification (re-id), the process of matching pedestrian images across different camera views, is an important task in visual surveillance. Substantial development of re-id has recently been observed, and the majority of existing models are largely dependent on color appearance and assume that pedestrians do not change their clothes across camera views. This limitation, however, can be an issue for re-id when tracking a person at different places and at different time if that person (e.g., a criminal suspect) changes his/her clothes, causing most existing methods to fail, since they are heavily relying on color appearance, and thus, they are inclined to match a person to another person wearing similar clothes. In this work, we call the person re-id under clothing change the "cross-clothes person re-id." In particular, we consider the case when a person only changes his clothes moderately as a first attempt at solving this problem based on visible light images; that is, we assume that a person wears clothes of a similar thickness, and thus the shape of a person would not change significantly when the weather does not change substantially within a short period of time. We perform cross-clothes person re-id based on a contour sketch of person image to take advantage of the shape of the human body instead of color information for extracting features that are robust to moderate clothing change. To select/sample more reliable and discriminative curve patterns on a body contour sketch, we introduce a learning-based spatial polar transformation (SPT) layer in the deep neural network to transform contour sketch images for extracting reliable and discriminant convolutional neural network (CNN) features in a polar coordinate space. An angle-specific extractor (ASE) is applied in the following layers to extract more fine-grained discriminant angle-specific features. By varying the sampling range of the SPT, we develop a multistream network for aggregating multi-granularity features to better identify a person. Due to the lack of a large-scale dataset for cross-clothes person re-id, we contribute a new dataset that consists of 33,698 images from 221 identities. Our experiments illustrate the challenges of cross-clothes person re-id and demonstrate the effectiveness of our proposed method.
