Inflammation is a key driver of atherosclerosis, yet the mechanisms sustaining inflammation in human plaques remain poorly understood. This study uses a network-based approach to identify immune gene programs involved in the transition from low- to high-risk (rupture-prone) human atherosclerotic plaques. Expression data from human carotid artery plaques, both stable (low-risk, n = 16) and unstable (high-risk, n = 27), were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA). Bayesian network inference, operated on the eigengene values from the WGCNA, further extended the WGCNA analysis, and similarity to the signature of T cell subsets was validated in single-cell RNA sequencing data of human plaques, and a loss-of-function study in a mouse model of atherosclerosis. In silico drug repurposing was performed to identify potential therapeutic targets. Our analysis revealed a distinct gene module with a prominent T cell signature, particularly in unstable plaques. Key regulatory factors, RUNX3, IRF7 and in particular PRDM1, were significantly downregulated in plaque T cells from symptomatic versus asymptomatic patients, indicating a protective role. Additionally, as PRDM1 is downstream of IRF7, we opted for PRDM1 as a key target. T cell-specific Prdm1 deficiency in Western-type diet fed Ldlr knockout mice featured accelerated plaque progression. Finally, as PRDM1 targeting drugs are not yet available, we performed in silico drug repurposing, identifying EGFR inhibitors as promising therapeutic candidates. This study highlights a PRDM1-regulated T cell network that distinguishes high-risk from low-risk plaques and demonstrates the regulatory role of T cell PRDM1 in controlling atherosclerosis, positioning this pathway as a promising therapeutic target.

Background and aims: Dendritic cells (DCs), professional antigen-presenting cells, play an important role in pathologies by controlling adaptive immune responses. However, their adaptation to and functionality in hypercholesterolemia, a driving factor in disease onset and progression of atherosclerosis remains to be established.Methods: In this study, we addressed the immediate impact of high fat diet-induced hypercholesterolemia in low-density lipoprotein receptor deficient (Ldlr-/-) mice on separate DC subsets, their compartmentalization and functionality.Results: While hypercholesterolemia induced a significant rise in bone marrow myeloid and dendritic cell pro-genitor (MDP) frequency and proliferation rate after high fat diet feeding, it did not affect DC subset numbers in lymphoid tissue. Hypercholesterolemia led to almost immediate and persistent augmentation in granularity of conventional DCs (cDCs), in particular cDC2, reflecting progressive lipid accumulation by these subsets. Plasmacytoid DCs were only marginally and transiently affected. Lipid loading increased co-stimulatory molecule expression and ROS accumulation by cDC2. Despite this hyperactivation, lipid-laden cDC2 displayed a pro-foundly reduced capacity to stimulate nave CD4+ T cells.Conclusion: Our data provide evidence that in hypercholesterolemic conditions, peripheral cDC2 subsets engulf lipids in situ, leading to a more activated status characterized by cellular ROS accumulation while, paradoxically, compromising their T cell priming ability. These findings will have repercussions not only for lipid driven cardiometabolic disorders like atherosclerosis, but also for adaptive immune responses to pathogens and/or endogenous (neo) antigens under conditions of hyperlipidemia.

IntroductionThe impact of cardiovascular disease (CVD) risk factors, encompassing various biological determinants and unhealthy lifestyles, on the functional dynamics of circulating monocytes—a pivotal cell type in CVD pathophysiology remains elusive. In this study, we aimed to elucidate the influence of CVD risk factors on monocyte transcriptional responses to an infectious stimulus.MethodsWe conducted a comparative analysis of monocyte gene expression profiles from the CTMM – CIRCULATING CELLS Cohort of coronary artery disease (CAD) patients, at baseline and after lipopolysaccharide (LPS) stimulation. Gene co-expression analysis was used to identify gene modules and their correlations with CVD risk factors, while pivotal transcription factors controlling the hub genes in these modules were identified by regulatory network analyses. The identified gene module was subjected to a drug repurposing screen, utilizing the LINCS L1000 database.ResultsMonocyte responsiveness to LPS showed a highly significant, negative correlation with blood pressure levels (ρ< -0.4; P<10-80). We identified a ZNF12/ZBTB43-driven gene module closely linked to diastolic blood pressure, suggesting that monocyte responses to infectious stimuli, such as LPS, are attenuated in CAD patients with elevated diastolic blood pressure. This attenuation appears associated with a dampening of the LPS-induced suppression of oxidative phosphorylation. Finally, we identified the serine-threonine inhibitor MW-STK33-97 as a drug candidate capable of reversing this aberrant LPS response. ConclusionsMonocyte responses to infectious stimuli may be hampered in CAD patients with high diastolic blood pressure and this attenuated inflammatory response may be reversed by the serine-threonine inhibitor MW-STK33-97. Whether the identified gene module is a mere indicator of, or causal factor in diastolic blood pressure and the associated dampened LPS responses remains to be determined.

Macrophages (MΦ) are commonly cultured in vitro as a model of their biology and functions in tissues. Recent evidence suggests MΦ to engage in quorum sensing, adapting their functions in response to cues about the proximity of neighboring cells. However, culture density is frequently overlooked in the standardization of culture protocols as well as the interpretation of results obtained in vitro . In this study, we investigated how the functional phenotype of MΦ was influenced by culture density. We assessed 10 core functions of human MΦ derived from the THP-1 cell line as well as primary monocyte-derived MΦ. THP-1 MΦ showed increasing phagocytic activity and proliferation with increasing density but decreasing lipid uptake, inflammasome activation, mitochondrial stress, and secretion of cytokines IL-10, IL-6, IL-1β, IL-8, and TNF-α. For THP-1 MΦ, the functional profile displayed a consistent trajectory with increasing density when exceeding a threshold (of 0.2 x 10 3 cells/mm 2 ), as visualized by principal component analysis. Culture density was also found to affect monocyte-derived MΦ, with functional implications that were distinct from those observed in THP-1 MΦ, suggesting particular relevance of density effects for cell lines. With increasing density, monocyte-derived MΦ exhibited progressively increased phagocytosis, increased inflammasome activation, and decreased mitochondrial stress, whereas lipid uptake was unaffected. These different findings in THP-1 MΦ and monocyte-derived MΦ could be attributed to the colony-forming growth pattern of THP-1 MΦ. At the lowest density, the distance to the closest neighboring cells showed greater influence on THP-1 MΦ than monocyte-derived MΦ. In addition, functional differences between monocyte-derived MΦ from different donors could at least partly be attributed to differences in culture density. Our findings demonstrate the importance of culture density for MΦ function and demand for awareness of culture density when conducting and interpreting in vitro experiments.

Recent technical advances, such as single-cell RNA sequencing and mass cytometry, improve identification of cell types and subsets in a range of healthy and diseased tissues at the expense of their cellular and molecular context. Here, we present a protocol for in situ multispectral imaging to map myeloid cell heterogeneity in tissue cryosections, describing steps for cutting sequential sections, antibody titration, and building a spectral library. We then detail procedures for multispectral imaging and preparing data for downstream analysis. For complete details on the use and execution of this protocol, please refer to Goossens et al. (2022).1

Background and aims: This study aims to identify sex-specific transcriptional differences and signaling pathways in circulating monocytes contributing to cardiovascular disease. Methods and results: We generated sex-biased gene expression signatures by comparing male versus female monocytes of coronary artery disease (CAD) patients (n = 450) from the Center for Translational Molecular Medicine-Circulating Cells Cohort. Gene set enrichment analysis demonstrated that monocytes from female CAD patients carry stronger chemotaxis and migratory signature than those from males. We then inferred cytokine signaling activities based on CytoSig database of 51 cytokine and growth factor regulation profiles. Monocytes from females feature a higher activation level of EGF, IFN1, VEGF, GM-CSF, and CD40L pathways, whereas IL-4, INS, and HMGB1 signaling was seen to be more activated in males. These sex differences were not observed in healthy subjects, as shown for an independent monocyte cohort of healthy subjects (GSE56034, n = 485). More pronounced GM-CSF signaling in monocytes of female CAD patients was confirmed by the significant enrichment of GM-CSF-activated monocyte signature in females. As we show these effects were not due to increased plasma levels of the corresponding ligands, sex-intrinsic differences in monocyte signaling regulation are suggested. Consistently, regulatory network analysis revealed jun-B as a shared transcription factor activated in all femalespecific pathways except IFN1 but suppressed in male-activated IL-4. Conclusions: We observed overt CAD-specific sex differences in monocyte transcriptional profiles and cytokine- or growth factor-induced responses, which provide insights into underlying mechanisms of sex differences in CVD.

Cells often adopt different phenotypes, dictated by tissue-specific or local signals such as cell-cell and cell-matrix contacts or molecular micro-environment. This holds in extremis for macrophages with their high phenotypic plasticity. Their broad range of functions, some even opposing, reflects their heterogeneity, and a multitude of subsets has been described in different tissues and diseases. Such micro-environmental imprint cannot be adequately studied by single-cell applications as cells are detached from their context, while histology-based assessment lacks the phenotypic depth due to limitations in marker combination. Here, we present a novel, integrative approach in which 15-color multispectral imaging allows comprehensive cell classification based on multi-marker expression patterns, followed by downstream analysis pipelines to link their phenotypes to contextual, micro-environmental cues such as their cellular (“community”) and metabolic (“local lipidome”) niches in complex tissue. The power of this approach is illustrated for myeloid subsets and associated lipid signatures in murine atherosclerotic plaque.

Atherosclerotic calcification is an independent predictor for a cardiovascular event, and its development is driven by macrophage-led inflammation. PRG4 was previously shown to be the most upregulated gene in microarray data of carotid endarterectomy-derived, high vs low-calcified atherosclerotic plaques. It was shown to be a pro-calcifying factor in the disease, modulating the pro-calcifying switch of vascular smooth muscle cells (VSMC). PRG4’s role in calcified plaque is largely unknown, however, we are able to show a profound effect on macrophage immune response. Transcriptomic data of a highly calcified plaque cohort (N=18 patients) shows PRG4 expression is correlated with inflammatory gene pathways, including toll-like receptor signalling; and immunohistochemistry of this cohort displays co-localization with CD68 + cells. Despite this, PRG4 is not expressed in macrophages in plaque single-cell datasets or transcriptomics datasets, and plaque origin is likely fibroblast and VSMC. Human peripheral blood mononuclear cell (PBMC)-derived macrophage incubation with 100μg/ml recombinant human (rh)PRG4 shows strong NF-κB activation, and secretion of TNF, IL-6, IL-12p70 and IL-10. rhPRG4 stimulation also significantly enhances inflammasome activation, increasing ASC activation (p<0.003) and IL-1β secretion (p<0.001). Moreover, it suppressed macrophage phagocytosis of both zymosan particles (p>0.001), and hydroxyapatite particles (p=0.004). Subsequently, cells stimulated with rhPRG4 develop immune tolerance, a suppressed inflammatory response, to secondary stimulation with either rhPRG4 or lipopolysaccharide - with almost complete ablation of TNF secretion (p<0.001). This phenomenon is accompanied by a 5-fold A20 upregulation (p<0.001), a well-established key regulator of endotoxin tolerance. This immunosuppressive effect was coupled with increased resistance to mitochondrial stress (p<0.001). In conclusion, we see a PRG4-specific phenotype in macrophages; suppressed phagocytosis capability, and acute pro-inflammatory activation of NF-κB and inflammasome pathways, that over longer exposure develops into immunosuppression. This may have profound implications for the overall disease mechanism.

With the increasing penetration of distributed photovoltaics (PVs), the operation and control of distribution networks (DNs), especially voltage control, have become more complicated. To deal with the voltage violation problem caused by large-scale PV access, this paper presents a fully distributed optimization method that combines the alternative direction multiplier method (ADMM) with the branch and bound method (BBM) for regional DNs. The total cost of active power losses and PV generation losses is minimized by making full use of the voltage regulation resources, e.g., reactive power compensators, step voltage regulators (SVR), and PV inverters, and the ADMM is employed to realize the intra-regional optimization and inter-regional coordination. To overcome the non-convex problem that is introduced by the SVR, the constraints of real-value tap positions are reformulated as linear inequality constraints of boundary voltages and added to the original problem, then the integer optimal solutions of SVR tap positions are obtained by BBM. The effectiveness of the proposed method is verified via numerical simulations on a practical 32-bus DN in China and a modified IEEE123-bus system.

Dynamic Spectrum Access (DSA) is a promising solution to alleviate spectrum crowding. However, geolocation database-driven spectrum access system (SAS) presents privacy risks, as sensitive Incumbent User (IU) operation parameters are required to be stored by SAS in order to perform spectrum assignments properly. These sensitive operation parameters may potentially be compromised if SAS is the target of a cyber attack or SU inference attack. In this paper, we propose a novel privacy-preserving SAS-based DSA framework, Suspicion Zone SAS (SZ-SAS). This is the first framework which protects against both the scenario of inference attacks in an area with sparsely distributed IUs and the scenario of untrusted or compromised SAS. Evaluation results show SZ-SAS is capable of utilizing compatible obfuscation schemes to prevent the SU inference attack, while operating using only homomorphically encrypted IU operation parameters.

Database driven dynamic spectrum sharing is one of the most promising dynamic spectrum access (DSA) solution to address the spectrum scarcity issue. In such a database driven DSA system, the centralized spectrum management infrastructure, called spectrum access system (SAS), makes its spectrum allocation decisions to secondary users (SUs) according to sensitive operational data of incumbent users (IUs). Since both SAS and SUs are not necessarily fully trusted, privacy protection against untrusted SAS and SUs become critical for IUs that have high operational privacy requirements. To address this problem, many IU privacy preserving solutions emerge recently. However, there is a lack of understanding and comparison of capability in protecting IU operational privacy under these existing approaches. In this paper, thus, we fill in the void by providing a comparative study that investigates existing solutions and explores several existing metrics to evaluate the strength of privacy protection. Moreover, we propose two general metrics to evaluate privacy preserving level and evaluate existing works with them.

In dynamic spectrum sharing (DSS) ecosystem with non-informing incumbent users (IUs), Environmental Sensing Capability (ESC) system has been proposed to detect IU activity information for a geolocation database-driven spectrum access system (SAS). SAS then allocates unused spectrum left by IUs to secondary users (SUs). However, IU location information is often highly sensitive and thus it is preferable to avoid storing ESC sensing data on a SAS, especially if the SAS is a potential target of cyber attacks. None of existing works studied the IU location privacy protection problem in such an ESC-based dynamic spectrum access (DSA) system. In this paper, we fill in the void by proposing novel privacy-preserving ESC-based dynamic spectrum access (PriDSA) schemes. We designed two versions of PriDSA that preserve IU privacy to different extent under two different adversary models, namely non-colluding honest but curious SAS model and colluding malicious SAS model. Evaluation results show that PriDSA is efficient in terms of communication and computation overhead and accurately serves SU spectrum access requests while preserving location privacy of IUs.

Compressed sensing (CS) is used to estimate channel information in underwater acoustic OFDM system recently. However, there are no recognition criteria to specify the pilot patterns in channel estimation. In order to improve the accuracy of the estimation, this paper proposes a pilot routing algorithm based on minimizing the adjusted mutual coherence of the measurement matrix in the recovery algorithm of compressed sensing theory. Each pilot symbol location is determined by calculating the adjusted mutual coherence of the corresponding measurement matrix. The matrix is produced by fixing other pilot location indexes and replacing the index of the current pilot location with all optional indexes. The simulation results show that the BER is 2dB and 8dB lower when SNR is 5dB and 30dB respectively, and the MSE is 2dB lower compared with the results of algorithm 1. Comparing with the results of algorithm 2, the BER is 2dB better, and the MSE is 3dB lower when SNR is 30dB. The data confirms the efficiency of this pilot routing algorithm.

With the rapid development of information technology and digital communication, the data types are more abundant by integration of various technologies. In this paper, based on the analysis of a large number of historical data of oil and water wells, the changes of some important parameters of the wells can be monitored and then used in the trend prediction and the early warning system. Subsequently, we use 6 Sigma algorithm to process the historical data, and by the big data trend analysis combining with various parameters, we can diagnose six operating conditions, such as sand production, abnormal of moisture content etc. Through experiments, the algorithm is stable and reliable in practical application, and it has great significance to ensure the normal production of oil field and improve the management ability for oil field.

Oil is essential to our life mainly in transportation, and thus the productivity of oil well is very important. Classification of oil wells can make it easier to manage wells to ensure good oil productivity. Machine learning is an emerging technology of analyzing data in which cluster is a good way to do classification. The paper will apply two kinds of cluster method to the data from Dagang oil well and then do analysis on not only the classification results but also the choice of method for future analysis.

Dynamic early warning makes great sense for oil management to keep safety and stability of oil production. In this paper, we derive the production regression model, predict production with 10 oil parameters based on Least Absolute Shrinkage and Selection Operator (Lasso) and Least Angle Regression (LARS) methods. The 10 most relevant oil parameters are decided by the warning parameters selection method from kinds of different parameters, which makes the prediction more reliable. The accuracy of regression model achieves 97%. Then we get the warning thresholds based on 6σ. Oil parameters for warning threshold partition experiment are from the database of Tianjin oilfield. The experiment results show that our method is capable of warning both mild and severe situation, and the accuracy is 95%, which runs ahead in oil industry and has great popularization value.

We investigate the scalable image classification problem with a large number of categories. Hierarchical visual data structures are helpful for improving the efficiency and performance of large-scale multi-class classification. We propose a novel image classification method based on learning hierarchical inter-class structures. Specifically, we first design a fast algorithm to compute the similarity metric between categories, based on which a visual tree is constructed by hierarchical spectral clustering. Using the learned visual tree, a test sample label is efficiently predicted by searching for the best path over the entire tree. The proposed method is extensively evaluated on the ILSVRC2010 and Caltech 256 benchmark datasets. The experimental results show that our method obtains significantly better category hierarchies than other state-of-the-art visual tree-based methods and, therefore, much more accurate classification.

We propose a framework to integrate the large scale image data visualization with image classification. The Convolution Neural Network is used to learn the feature vector for an image. A fast algorithm is developed for inter-class similarity measurement. The spectral clustering is implemented to construct a hierarchical visual tree. Instead of the flat classification way, a hierarchical classification is designed according to the visual tree, which is transformed to a path search problem. The path with the maximum joint probability is the final solution. Experimental results on the ILSVRC2010 dataset demonstrate that our method achieves the highest top-1 and top-5 classification accuracy in comparison with 6 state-of-the-art methods.