Concentration-Dependent Diversification Effects of Free Cholesterol Loading on Macrophage Viability and Polarization

Background/Aims: The accumulation of free cholesterol in atherosclerotic lesions has been well documented in both animals and humans. In studying the relevance of free cholesterol buildup in atherosclerosis, contradictory results have been generated, indicating that free cholesterol produces both pro- and anti-atherosclerosis effects in macrophages. This inconsistency might stem from the examination of only select concentrations of free cholesterol. In the present study, we sought to investigate the implication of excess free cholesterol loading in the pathophysiology of atherosclerosis across a broad concentration range from (in µg/ml) 0 to 60. Methods:Macrophage viability was determined by measuring formazan formation and flow cytometry viable cell counting. The polarization of M1 and M2 macrophages was differentiated by FACS (Fluorescence-Activated Cell Sorting) assay. The secretion of IL-1β in macrophage culture medium was measured by ELISA kit. Macrophage apoptosis was detected by flow cytometry using a TUNEL kit. Results: Macrophage viability was increased at the treatment of lower concentrations of free cholesterol from (in µg/ml) 0 to 20, but gradually decreased at higher concentrations from 20 to 60. Lower free cholesterol loading induced anti-inflammatory M2 macrophage polarization. The activation of the PPARγ (Peroxisome Proliferator-Activated Receptor gamma) nuclear factor underscored the stimulation of this M2 phenotype. Nevertheless, higher levels of free cholesterol resulted in pro-inflammatory M1 activation. Moreover, with the application of higher free cholesterol concentrations, macrophage apoptosis and secretion of the inflammatory cytokine IL-1β increased significantly. Conclusion: These results for the first time demonstrate that free cholesterol could render concentration-dependent diversification effects on macrophage viability, polarization, apoptosis and inflammatory cytokine secretions, thereby reconciling the pros and cons of free cholesterol buildup in macrophages to the pathophysiology of atherosclerosis

Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM

Federated learning (FL) enables distributed devices to jointly train a shared model while keeping the training data local. Different from the horizontal FL (HFL) setting where each client has partial data samples, vertical FL (VFL), which allows each client to collect partial features, has attracted intensive research efforts recently. In this paper, we identified two challenges that state-of-the-art VFL frameworks are facing: (1) some works directly average the learned feature embeddings and therefore might lose the unique properties of each local feature set; (2) server needs to communicate gradients with the clients for each training step, incurring high communication cost that leads to rapid consumption of privacy budgets. In this paper, we aim to address the above challenges and propose an efficient VFL with multiple linear heads (VIM) framework, where each head corresponds to local clients by taking the separate contribution of each client into account. In addition, we propose an Alternating Direction Method of Multipliers (ADMM)-based method to solve our optimization problem, which reduces the communication cost by allowing multiple local updates in each step, and thus leads to better performance under differential privacy. We consider various settings including VFL with model splitting and without model splitting. For both settings, we carefully analyze the differential privacy mechanism for our framework. Moreover, we show that a byproduct of our framework is that the weights of learned linear heads reflect the importance of local clients. We conduct extensive evaluations and show that on four real-world datasets, VIM achieves significantly higher performance and faster convergence compared with state-of-the-arts. We also explicitly evaluate the importance of local clients and show that VIM enables functionalities such as client-level explanation and client denoising

A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model

Stack autoencoder (SAE), as a representative deep network, has unique and excellent performance in feature learning, and has received extensive attention from researchers. However, existing deep SAEs focus on original samples without considering the hierarchical structural information between samples. To address this limitation, this paper proposes a new SAE model-neighbouring envelope embedded stack autoencoder ensemble (NE_ESAE). Firstly, the neighbouring sample envelope learning mechanism (NSELM) is proposed for preprocessing of input of SAE. NSELM constructs sample pairs by combining neighbouring samples. Besides, the NSELM constructs a multilayer sample spaces by multilayer iterative mean clustering, which considers the similar samples and generates layers of envelope samples with hierarchical structural information. Second, an embedded stack autoencoder (ESAE) is proposed and trained in each layer of sample space to consider the original samples during training and in the network structure, thereby better finding the relationship between original feature samples and deep feature samples. Third, feature reduction and base classifiers are conducted on the layers of envelope samples respectively, and output classification results of every layer of samples. Finally, the classification results of the layers of envelope sample space are fused through the ensemble mechanism. In the experimental section, the proposed algorithm is validated with over ten representative public datasets. The results show that our method significantly has better performance than existing traditional feature learning methods and the representative deep autoencoders.Comment: 17 pages,6 figure

Holographic p-wave superconductivity from higher derivative theory

We construct a holographic SU(2) p-wave superconductor model with Weyl corrections. The high derivative (HD) terms do not seem to spoil the generation of the p-wave superconducting phase. We mainly study the properties of AC conductivity, which is absent in holographic SU(2) p-wave superconductor with Weyl corrections. The conductivities in superconducting phase exhibit obvious anisotropic behaviors. Along $y$ direction, the conductivity $\sigma_{yy}$ is similar to that of holographic s-wave superconductor. The superconducting energy gap exhibits a wide extension. For the conductivity $\sigma_{xx}$ along $x$ direction, the behaviors of the real part in the normal state are closely similar to that of $\sigma_{yy}$. However, the anisotropy of the conductivity obviously shows up in the superconducting phase. A Drude-like peak at low frequency emerges in $Re\sigma_{xx}$ once the system enters into the superconducting phase, regardless of the behaviors in normal state.Comment: 19 pages, 7 figure

Activation of Nlrp3 Inflammasomes Enhances Macrophage Lipid-Deposition and Migration: Implication of a Novel Role of Inflammasome in Atherogenesis

Although Nlrp3 inflammasome activation in macrophages has been shown to be critical for the development of atherosclerosis upon atherogenic stimuli, it remains unknown whether activated Nlrp3 inflammasomes by other non-atherogenic stimuli induce alterations in macrophages that may contribute in the concert with other factors to atherogenesis. Thus, the present study tested the hypothesis that activation of Nlrp3 inflammasomes by ATP, which is a classical non-lipid danger stimulus, enhances the migration of macrophage and increases lipids deposition in macrophages accelerating foam cell formation. We first demonstrated that extracellular ATP (2.5 mM) markedly increased the formation and activation of Nlrp3 inflammasomes in bone marrow macrophages (BMMs) from wild type (Asc+/+) mice resulting in activation of caspase-1 and IL-1β production. In these Asc+/+ macrophages, such stimulation of inflammasomes by non-lipid ATP was similar to those induced by atherogenic stimuli such as cholesterol crystals or 7-ketocholesterol. Both non-lipid and lipid forms of stimuli induced formation and activation of Nlrp3 inflammasomes, which were prevented by Asc gene deletion. Interestingly, Asc+/+ BMMs had dramatic lipids accumulation after stimulation with ATP. Further, we demonstrated that large amount of cholesterol was accumulated in lysosomes of Asc+/+ BMMs when inflammasomes were activated by ATP. Such intracellular and lysosomal lipids deposition was not observed in Asc−/− BMMs and also prevented by caspase-1 inhibitor WEHD. In addition, in vitro and in vivo experiments revealed that migration of Asc+/+ BMMs increased due to stimulation of Nlrp3 inflammasomes, which was markedly attenuated in Asc−/− BMMs. Together, these results suggest that activation of Nlrp3 inflammasomes remarkably increases the susceptibility of macrophages to lipid deposition and their migration ability. Such novel action of inflammasomes may facilitate entry or retention of macrophages into the arterial wall, where they form foam cells and ultimately induce atherosclerosis

Quasinormal modes of quantum-corrected black holes

In this paper, we investigate the quasinormal mode (QNM) spectra for scalar perturbation over a quantum-corrected black hole (BH). The fundamental modes of this quantum-corrected BH exhibit two key properties. Firstly, there is a non-monotonic behavior concerning the quantum-corrected parameter for zero multipole number. Secondly, the quantum gravity effects result in slower decay modes. For higher overtones, a significant deviation becomes evident between the quasinormal frequencies (QNFs) of the quantum-corrected and Schwarzschild BHs. The intervention of quantum gravity corrections induces a significant outburst of overtones. This outburst of these overtones can be attributed to the distinctions near the event horizons between the Schwarzschild and quantum-corrected BHs. Therefore, overtones can serve as a means to probe physical phenomena or disparities in the vicinity of the event horizon.Comment: 29 pages, 9 figure

Macrophage migration inhibitory factor (MIF) family in arthropods : Cloning and expression analysis of two MIF and one D-dopachrome tautomerase (DDT) homologues in Mud crabs, Scylla paramamosain

Acknowledgements This research was supported by grants from the National Natural Science Foundation of China (Nos. 31172438 and U1205123), the Natural Science Foundation of Fujian Province (No. 2012J06008 and 201311180002) and the projects-sponsored by SRF. TW received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.Peer reviewedPostprin

LEFormer: A Hybrid CNN-Transformer Architecture for Accurate Lake Extraction from Remote Sensing Imagery

Lake extraction from remote sensing images is challenging due to the complex lake shapes and inherent data noises. Existing methods suffer from blurred segmentation boundaries and poor foreground modeling. This paper proposes a hybrid CNN-Transformer architecture, called LEFormer, for accurate lake extraction. LEFormer contains three main modules: CNN encoder, Transformer encoder, and cross-encoder fusion. The CNN encoder effectively recovers local spatial information and improves fine-scale details. Simultaneously, the Transformer encoder captures long-range dependencies between sequences of any length, allowing them to obtain global features and context information. The cross-encoder fusion module integrates the local and global features to improve mask prediction. Experimental results show that LEFormer consistently achieves state-of-the-art performance and efficiency on the Surface Water and the Qinghai-Tibet Plateau Lake datasets. Specifically, LEFormer achieves 90.86% and 97.42% mIoU on two datasets with a parameter count of 3.61M, respectively, while being 20 minor than the previous best lake extraction method. The source code is available at https://github.com/BastianChen/LEFormer.Comment: Accepted by ICASSP 202

High-Fidelity Lake Extraction via Two-Stage Prompt Enhancement: Establishing a Novel Baseline and Benchmark

The extraction of lakes from remote sensing images is a complex challenge due to the varied lake shapes and data noise. Current methods rely on multispectral image datasets, making it challenging to learn lake features accurately from pixel arrangements. This, in turn, affects model learning and the creation of accurate segmentation masks. This paper introduces a unified prompt-based dataset construction approach that provides approximate lake locations using point, box, and mask prompts. We also propose a two-stage prompt enhancement framework, LEPrompter, which involves prompt-based and prompt-free stages during training. The prompt-based stage employs a prompt encoder to extract prior information, integrating prompt tokens and image embeddings through self- and cross-attention in the prompt decoder. Prompts are deactivated once the model is trained to ensure independence during inference, enabling automated lake extraction. Evaluations on Surface Water and Qinghai-Tibet Plateau Lake datasets show consistent performance improvements compared to the previous state-of-the-art method. LEPrompter achieves mIoU scores of 91.48% and 97.43% on the respective datasets without introducing additional parameters or GFLOPs. Supplementary materials provide the source code, pre-trained models, and detailed user studies.Comment: 8 pages, 7 figure