Decentralized digital twins of complex dynamical systems.

In this article, we introduce a decentralized digital twin (DDT) modeling framework and its potential applications in computational science and engineering. The DDT methodology is based on the idea of federated learning, a subfield of machine learning that promotes knowledge exchange without disclosing actual data. Clients can learn an aggregated model cooperatively using this method while maintaining complete client-specific training data. We use a variety of dynamical systems, which are frequently used as prototypes for simulating complex transport processes in spatiotemporal systems, to show the viability of the DDT framework. Our findings suggest that constructing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems may be made possible by federated machine learning.

Dipyridamole and vascular healing following stent implantation.

Introduction: Patients undergoing coronary stent implantation incur a 2% annual rate of adverse events, largely driven by in-stent restenosis (ISR) due to neointimal (NI) tissue proliferation, a process in which smooth muscle cell (SMC) biology may play a central role. Dipyridamole (DP) is an approved therapeutic agent with data supporting improved vascular patency rates. Pre-clinical data supports that DP may enact its vasculoprotective effects via adenosine receptor-A2B (ADOR-A2B). We sought to evaluate the efficacy of DP to mitigate ISR in a pre-clinical rabbit stent model. Methods & Results: 24 New Zealand White Rabbits were divided into two cohorts-non-atherosclerosis and atherosclerosis (n = 12/cohort, 6 male and 6 female). Following stent implantation, rabbits were randomized 1:1 to control or oral dipyridamole therapy for 6 weeks followed by optical coherence tomography (OCT) and histology assessment of NI burden and stent strut healing. Compared to control, DP demonstrated a 16.6% relative reduction in NI volume (14.7 ± 0.8% vs. 12.5 ± 0.4%, p = 0.03) and a 36.2% relative increase in optimally healed stent struts (37.8 ± 2.8% vs. 54.6 ± 2.5%, p < 0.0001). Atherosclerosis demonstrated attenuated effect with no difference in NI burden (15.2 ± 1.0% vs. 16.9 ± 0.8%, p = 0.22) and only a 14.2% relative increase in strut healing (68.3 ± 4.1% vs. 78.7 ± 2.5%, p = 0.02). DP treated rabbits had a 44.6% (p = 0.045) relative reduction in NI SMC content. In vitro assessment of DP and coronary artery SMCs yielded dose-dependent reduction in SMC migration and proliferation. Selective small molecule antagonism of ADOR-A2B abrogated the effects of DP on SMC proliferation. DP modulated SMC phenotypic switching with ADOR-A2B siRNA knockdown supporting its role in the observed effects. Conclusion: Dipyridamole reduces NI proliferation and improves stent healing in a preclinical model of stent implantation with conventional antiplatelets. Atherosclerosis attenuates the observed effect. Clinical trials of DP as an adjunctive agent may be warranted to evaluate for clinical efficacy in stent outcomes.

Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions.

The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of multivariate time series that may capture normal conditions or anomalies. Multivariate Time Series Anomaly Detection (MTSAD), i.e., the ability to identify normal or irregular operative conditions of a system through the analysis of data from multiple sensors, is crucial in many fields. However, MTSAD is challenging due to the need for simultaneous analysis of temporal (intra-sensor) patterns and spatial (inter-sensor) dependencies. Unfortunately, labeling massive amounts of data is practically impossible in many real-world situations of interest (e.g., the reference ground truth may not be available or the amount of data may exceed labeling capabilities); therefore, robust unsupervised MTSAD is desirable. Recently, advanced techniques in machine learning and signal processing, including deep learning methods, have been developed for unsupervised MTSAD. In this article, we provide an extensive review of the current state of the art with a theoretical background about multivariate time-series anomaly detection. A detailed numerical evaluation of 13 promising algorithms on two publicly available multivariate time-series datasets is presented, with advantages and shortcomings highlighted.

Plasminogen Activator Inhibitor-1-Positive Platelet-Derived Extracellular Vesicles Predicts MACE and the Proinflammatory SMC Phenotype.

Patients with established coronary artery disease remain at elevated risk of major adverse cardiac events. The goal of this study was to evaluate the utility of plasminogen activator inhibitor-1-positive platelet-derived extracellular vesicles as a biomarker for major adverse cardiac events and to explore potential underlying mechanisms. Our study suggests these extracellular vesicles as a potential biomarker to identify and a therapeutic target to ameliorate neointimal formation of high-risk patients.

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems.

A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

Physics guided neural networks for modelling of non-linear dynamics.

The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention. However, it is difficult to train these models on complex dynamical systems from data alone due to their low data efficiency and sensitivity to hyperparameters and initialisation. This work demonstrates that injection of partially known information at an intermediate layer in a DNN can improve model accuracy, reduce model uncertainty, and yield improved convergence during the training. The value of these physics-guided neural networks has been demonstrated by learning the dynamics of a wide variety of nonlinear dynamical systems represented by five well-known equations in nonlinear systems theory: the Lotka-Volterra, Duffing, Van der Pol, Lorenz, and Henon-Heiles systems.

Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning.

Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector. Since the electrification of transport is happening simultaneously, autonomous marine vessels can reduce environmental impact, lower costs, and increase efficiency. Although close monitoring is still required to ensure safety, the ultimate goal is full autonomy. One major milestone is to develop a control system that is versatile enough to handle any weather and encounter that is also robust and reliable. Additionally, the control system must adhere to the International Regulations for Preventing Collisions at Sea (COLREGs) for successful interaction with human sailors. Since the COLREGs were written for the human mind to interpret, they are written in ambiguous prose and therefore not machine-readable or verifiable. Due to these challenges and the wide variety of situations to be tackled, classical model-based approaches prove complicated to implement and computationally heavy. Within machine learning (ML), deep reinforcement learning (DRL) has shown great potential for a wide range of applications. The model-free and self-learning properties of DRL make it a promising candidate for autonomous vessels. In this work, a subset of the COLREGs is incorporated into a DRL-based path following and obstacle avoidance system using collision risk theory. The resulting autonomous agent dynamically interpolates between path following and COLREG-compliant collision avoidance in the training scenario, isolated encounter situations, and AIS-based simulations of real-world scenarios.

miR-223 Exerts Translational Control of Proatherogenic Genes in Macrophages.

BACKGROUND: A significant burden of atherosclerotic disease is driven by inflammation. Recently, microRNAs (miRNAs) have emerged as important factors driving and protecting from atherosclerosis. miR-223 regulates cholesterol metabolism and inflammation via targeting both cholesterol biosynthesis pathway and NFkB signaling pathways; however, its role in atherosclerosis has not been investigated. We hypothesize that miR-223 globally regulates core inflammatory pathways in macrophages in response to inflammatory and atherogenic stimuli thus limiting the progression of atherosclerosis. METHODS AND RESULTS: Loss of miR-223 in macrophages decreases Abca1 gene and protein expression as well as cholesterol efflux to apoA1 (Apolipoprotein A1) and enhances proinflammatory gene expression. In contrast, overexpression of miR-223 promotes the efflux of cholesterol and macrophage polarization toward an anti-inflammatory phenotype. These beneficial effects of miR-223 are dependent on its target gene, the transcription factor Sp3. Consistent with the antiatherogenic effects of miR-223 in vitro, mice receiving miR223-/- bone marrow exhibit increased plaque size, lipid content, and circulating inflammatory cytokines (ie, IL-1ß). Deficiency of miR-223 in bone marrow-derived cells also results in an increase in circulating pro-atherogenic cells (total monocytes and neutrophils) compared with control mice. Furthermore, the expression of miR-223 target gene (Sp3) and pro-inflammatory marker (Il-6) are enhanced whereas the expression of Abca1 and anti-inflammatory marker (Retnla) are reduced in aortic arches from mice lacking miR-223 in bone marrow-derived cells. In mice fed a high-cholesterol diet and in humans with unstable carotid atherosclerosis, the expression of miR-223 is increased. To further understand the molecular mechanisms underlying the effect of miR-223 on atherosclerosis in vivo, we characterized global RNA translation profile of macrophages isolated from mice receiving wild-type or miR223-/- bone marrow. Using ribosome profiling, we reveal a notable upregulation of inflammatory signaling and lipid metabolism at the translation level but less significant at the transcription level. Analysis of upregulated genes at the translation level reveal an enrichment of miR-223-binding sites, confirming that miR-223 exerts significant changes in target genes in atherogenic macrophages via altering their translation. CONCLUSIONS: Our study demonstrates that miR-223 can protect against atherosclerosis by acting as a global regulator of RNA translation of cholesterol efflux and inflammation pathways.

Multi-fidelity information fusion with concatenated neural networks.

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

Niche Regulation of Hematopoiesis: The Environment Is "Micro," but the Influence Is Large.

Immune cell production is governed by a process known as hematopoiesis, where hematopoietic stem cells (HSCs) differentiate through progenitor cells and ultimately to the mature blood and immune cells found in circulation. While HSCs are capable of cell-autonomous regulation, they also rely on extrinsic factors to balance their state of quiescence and activation. These cues can, in part, be derived from the niche in which HSCs are found. Under steady-state conditions, HSCs are found in the bone marrow. This niche is designed to support HSCs but also to respond to external factors, which allows hematopoiesis to be a finely tuned and coordinated process. However, the niche, and its regulation, can become dysregulated to potentiate inflammation during disease. This review will highlight the architecture of the bone marrow and key regulators of hematopoiesis within this niche. Emphasis will be placed on how these mechanisms go awry to exacerbate hematopoietic contributions that drive cardiovascular disease.

Autophagy Is Differentially Regulated in Leukocyte and Nonleukocyte Foam Cells During Atherosclerosis.

RATIONALE: Atherosclerosis is characterized by an accumulation of foam cells within the arterial wall, resulting from excess cholesterol uptake and buildup of cytosolic lipid droplets (LDs). Autophagy promotes LD clearance by freeing stored cholesterol for efflux, a process that has been shown to be atheroprotective. While the role of autophagy in LD catabolism has been studied in macrophage-derived foam cells, this has remained unexplored in vascular smooth muscle cell (VSMC)-derived foam cells that constitute a large fraction of foam cells within atherosclerotic lesions. OBJECTIVE: We performed a comparative analysis of autophagy flux in lipid-rich aortic intimal populations to determine whether VSMC-derived foam cells metabolize LDs similarly to their macrophage counterparts. METHODS AND RESULTS: Atherosclerosis was induced in GFP-LC3 (microtubule-associated proteins 1A/1B light chain 3) transgenic mice by PCSK9 (proprotein convertase subtilisin/kexin type 9)-adeno-associated viral injection and Western diet feeding. Using flow cytometry of aortic digests, we observed a significant increase in dysfunctional autophagy of VSMC-derived foam cells during atherogenesis relative to macrophage-derived foam cells. Using cell culture models of lipid-loaded VSMCs and macrophages, we show that autophagy-mediated cholesterol efflux from VSMC foam cells was poor relative to macrophage foam cells, and largely occurs when HDL (high-density lipoprotein) was used as a cholesterol acceptor, as opposed to apoA-1 (apolipoproteinA-1). This was associated with the predominant expression of ABCG1 in VSMC foam cells. Using metformin, an autophagy activator, cholesterol efflux to HDL was significantly increased in VSMC, but not in macrophage, foam cells. CONCLUSIONS: These data demonstrate that VSMC and macrophage foam cells perform cholesterol efflux by distinct mechanisms, and that autophagy flux is highly impaired in VSMC foam cells, but can be induced by pharmacological means. Further investigation is warranted into targeting autophagy specifically in VSMC foam cells, the predominant foam cell subtype of advanced atherosclerotic plaques, to promote reverse cholesterol transport and resolution of the atherosclerotic plaque.

The scent of atherosclerosis.

Vascular macrophages sense an odorant to induce atherosclerotic plaque formation.

Deep neural network enabled corrective source term approach to hybrid analysis and modeling.

In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA)-a novel approach to Hybrid Analysis and Modeling (HAM). The objective of HAM is to combine physics-based modeling (PBM) and data-driven modeling (DDM) to create generalizable, trustworthy, accurate, computationally efficient and self-evolving models. CoSTA achieves this objective by augmenting the governing equation of a PBM model with a corrective source term generated using a deep neural network. In a series of numerical experiments on one-dimensional heat diffusion, CoSTA is found to outperform comparable DDM and PBM models in terms of accuracy - often reducing predictive errors by several orders of magnitude - while also generalizing better than pure DDM. Due to its flexible but solid theoretical foundation, CoSTA provides a modular framework for leveraging novel developments within both PBM and DDM. Its theoretical foundation also ensures that CoSTA can be used to model any system governed by (deterministic) partial differential equations. Moreover, CoSTA facilitates interpretation of the DNN-generated source term within the context of PBM, which results in improved explainability of the DNN. These factors make CoSTA a potential door-opener for data-driven techniques to enter high-stakes applications previously reserved for pure PBM.

Self-Organising Map Based Framework for Investigating Accounts Suspected of Money Laundering.

There has been an emerging interest by financial institutions to develop advanced systems that can help enhance their anti-money laundering (AML) programmes. In this study, we present a self-organising map (SOM) based approach to predict which bank accounts are possibly involved in money laundering cases, given their financial transaction histories. Our method takes advantage of the competitive and adaptive properties of SOM to represent the accounts in a lower-dimensional space. Subsequently, categorising the SOM and the accounts into money laundering risk levels and proposing investigative strategies enables us to measure the classification performance. Our results indicate that our framework is well capable of identifying suspicious accounts already investigated by our partner bank, using both proposed investigation strategies. We further validate our model by analysing the performance when modifying different parameters in our dataset.

Comparing Deep Reinforcement Learning Algorithms' Ability to Safely Navigate Challenging Waters.

Reinforcement Learning (RL) controllers have proved to effectively tackle the dual objectives of path following and collision avoidance. However, finding which RL algorithm setup optimally trades off these two tasks is not necessarily easy. This work proposes a methodology to explore this that leverages analyzing the performance and task-specific behavioral characteristics for a range of RL algorithms applied to path-following and collision-avoidance for underactuated surface vehicles in environments of increasing complexity. Compared to the introduced RL algorithms, the results show that the Proximal Policy Optimization (PPO) algorithm exhibits superior robustness to changes in the environment complexity, the reward function, and when generalized to environments with a considerable domain gap from the training environment. Whereas the proposed reward function significantly improves the competing algorithms' ability to solve the training environment, an unexpected consequence of the dimensionality reduction in the sensor suite, combined with the domain gap, is identified as the source of their impaired generalization performance.

Revisiting the Evidence for Dipyridamole in Reducing Restenosis: A Systematic Review and Meta-analysis.

ABSTRACT: Atherosclerosis remains a leading cause of morbidity and mortality, with revascularization remaining a cornerstone of management. Conventional revascularization modalities remain challenged by target vessel reocclusion-an event driven by mechanical, thrombotic, and proliferative processes. Despite considerable advancements, restenosis remains the focus of ongoing research. Adjunctive agents, including dipyridamole, offer a multitude of effects that may improve vascular homeostasis. We sought to quantify the potential therapeutic impact of dipyridamole on vascular occlusion. We performed a literature search (EMBASE and MEDLINE) examining studies that encompassed 3 areas: (1) one of the designated medical therapies applied in (2) the setting of a vascular intervention with (3) an outcome including vascular occlusion rates and/or quantification of neointimal proliferation/restenosis. The primary outcome was vascular occlusion rates. The secondary outcome was the degree of restenosis by neointimal quantification. Both human and animal studies were included in this translational analysis. There were 6,839 articles screened, from which 73 studies were included, encompassing 16,146 vessels followed up for a mean of 327.3 days (range 7-3650 days). Preclinical studies demonstrate that dipyridamole results in reduced vascular occlusion rates {24.9% vs. 48.8%, risk ratio 0.53 [95% confidence interval (CI) 0.40-0.70], I2 = 39%, P < 0.00001}, owing to diminished neointimal proliferation [standardized mean differences -1.13 (95% CI -1.74 to -0.53), I2 = 91%, P = 0.0002]. Clinical studies similarly demonstrated reduced occlusion rates with dipyridamole therapy [23.5% vs. 31.0%, risk ratio 0.77 (95% CI 0.67-0.88), I2 = 84%, P < 0.0001]. Dipyridamole may improve post-intervention vascular patency and mitigate restenosis. Dedicated studies are warranted to delineate its role as an adjunctive agent after revascularization.

Multifidelity computing for coupling full and reduced order models.

Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity descriptions toward predictive digital twin technologies. At the interface, we introduce a long short-term memory network to bridge these high and low-fidelity models in various forms of interfacial error correction or prolongation. The proposed interface learning approaches are tested as a new way to address ROM-FOM coupling problems solving nonlinear advection-diffusion flow situations with a bifidelity setup that captures the essence of a broad class of transport processes.

Macrophage Responses to Environmental Stimuli During Homeostasis and Disease.

Work over the last 40 years has described macrophages as a heterogeneous population that serve as the frontline surveyors of tissue immunity. As a class, macrophages are found in almost every tissue in the body and as distinct populations within discrete microenvironments in any given tissue. During homeostasis, macrophages protect these tissues by clearing invading foreign bodies and/or mounting immune responses. In addition to varying identities regulated by transcriptional programs shaped by their respective environments, macrophage metabolism serves as an additional regulator to temper responses to extracellular stimuli. The area of research known as "immunometabolism" has been established within the last decade, owing to an increase in studies focusing on the crosstalk between altered metabolism and the regulation of cellular immune processes. From this research, macrophages have emerged as a prime focus of immunometabolic studies, although macrophage metabolism and their immune responses have been studied for centuries. During disease, the metabolic profile of the tissue and/or systemic regulators, such as endocrine factors, become increasingly dysregulated. Owing to these changes, macrophage responses can become skewed to promote further pathophysiologic changes. For instance, during diabetes, obesity, and atherosclerosis, macrophages favor a proinflammatory phenotype; whereas in the tumor microenvironment, macrophages elicit an anti-inflammatory response to enhance tumor growth. Herein we have described how macrophages respond to extracellular cues including inflammatory stimuli, nutrient availability, and endocrine factors that occur during and further promote disease progression.

The secretome of liver X receptor agonist-treated early outgrowth cells decreases atherosclerosis in Ldlr-/- mice.

Endothelial progenitor cells (EPCs) promote the maintenance of the endothelium by secreting vasoreparative factors. A population of EPCs known as early outgrowth cells (EOCs) is being investigated as novel cell-based therapies for the treatment of cardiovascular disease. We previously demonstrated that the absence of liver X receptors (LXRs) is detrimental to the formation and function of EOCs under hypercholesterolemic conditions. Here, we investigate whether LXR activation in EOCs is beneficial for the treatment of atherosclerosis. EOCs were differentiated from the bone marrow of wild-type (WT) and LXR-knockout (Lxrαß-/-) mice in the presence of vehicle or LXR agonist (GW3965). WT EOCs treated with GW3965 throughout differentiation showed reduced mRNA expression of endothelial lineage markers (Cd144, Vegfr2) compared with WT vehicle and Lxrαß-/- EOCs. GW3965-treated EOCs produced secreted factors that reduced monocyte adhesion to activated endothelial cells in culture. When injected into atherosclerosis-prone Ldlr-/- mice, GW3965-treated EOCs, or their corresponding conditioned media (CM) were both able to reduce aortic sinus plaque burden compared with controls. Furthermore, when human EOCs (obtained from patients with established CAD) were treated with GW3965 and the CM applied to endothelial cells, monocyte adhesion was decreased, indicating that our results in mice could be translated to patients. Ex vivo LXR agonist treatment of EOCs therefore produces a secretome that decreases early atherosclerosis in Ldlr-/- mice, and additionally, CM from human EOCs significantly inhibits monocyte to endothelial adhesion. Thus, active factor(s) within the GW3965-treated EOC secretome may have the potential to be useful for the treatment of atherosclerosis.

RIPK1 Expression Associates With Inflammation in Early Atherosclerosis in Humans and Can Be Therapeutically Silenced to Reduce NF-κB Activation and Atherogenesis in Mice.

BACKGROUND: Chronic activation of the innate immune system drives inflammation and contributes directly to atherosclerosis. We previously showed that macrophages in the atherogenic plaque undergo RIPK3 (receptor-interacting serine/threonine-protein kinase 3)-MLKL (mixed lineage kinase domain-like protein)-dependent programmed necroptosis in response to sterile ligands such as oxidized low-density lipoprotein and damage-associated molecular patterns and that necroptosis is active in advanced atherosclerotic plaques. Upstream of the RIPK3-MLKL necroptotic machinery lies RIPK1 (receptor-interacting serine/threonine-protein kinase 1), which acts as a master switch that controls whether the cell undergoes NF-κB (nuclear factor κ-light-chain-enhancer of activated B cells)-dependent inflammation, caspase-dependent apoptosis, or necroptosis in response to extracellular stimuli. We therefore set out to investigate the role of RIPK1 in the development of atherosclerosis, which is driven largely by NF-κB-dependent inflammation at early stages. We hypothesize that, unlike RIPK3 and MLKL, RIPK1 primarily drives NF-κB-dependent inflammation in early atherogenic lesions, and knocking down RIPK1 will reduce inflammatory cell activation and protect against the progression of atherosclerosis. METHODS: We examined expression of RIPK1 protein and mRNA in both human and mouse atherosclerotic lesions, and used loss-of-function approaches in vitro in macrophages and endothelial cells to measure inflammatory responses. We administered weekly injections of RIPK1 antisense oligonucleotides to Apoe-/- mice fed a cholesterol-rich (Western) diet for 8 weeks. RESULTS: We find that RIPK1 expression is abundant in early-stage atherosclerotic lesions in both humans and mice. Treatment with RIPK1 antisense oligonucleotides led to a reduction in aortic sinus and en face lesion areas (47.2% or 58.8% decrease relative to control, P<0.01) and plasma inflammatory cytokines (IL-1α [interleukin 1α], IL-17A [interleukin 17A], P<0.05) in comparison with controls. RIPK1 knockdown in macrophages decreased inflammatory genes (NF-κB, TNFα [tumor necrosis factor α], IL-1α) and in vivo lipopolysaccharide- and atherogenic diet-induced NF-κB activation. In endothelial cells, knockdown of RIPK1 prevented NF-κB translocation to the nucleus in response to TNFα, where accordingly there was a reduction in gene expression of IL1B, E-selectin, and monocyte attachment. CONCLUSIONS: We identify RIPK1 as a central driver of inflammation in atherosclerosis by its ability to activate the NF-κB pathway and promote inflammatory cytokine release. Given the high levels of RIPK1 expression in human atherosclerotic lesions, our study suggests RIPK1 as a future therapeutic target to reduce residual inflammation in patients at high risk of coronary artery disease.