Simvastatin reduces atherogenesis and promotes the expression of hepatic genes associated with reverse cholesterol transport in apoE-knockout mice fed high-fat diet

<p>Abstract</p> <p>Background</p> <p>Statins are first-line pharmacotherapeutic agents for hypercholesterolemia treatment in humans. However the effects of statins on atherosclerosis in mouse models are very paradoxical. In this work, we wanted to evaluate the effects of simvastatin on serum cholesterol, atherogenesis, and the expression of several factors playing important roles in reverse cholesterol transport (RCT) in apoE-/- mice fed a high-fat diet.</p> <p>Results</p> <p>The atherosclerotic lesion formation displayed by oil red O staining positive area was reduced significantly by 35% or 47% in either aortic root section or aortic arch en face in simvastatin administrated apoE-/- mice compared to the control. Plasma analysis by enzymatic method or ELISA showed that high-density lipoprotein-cholesterol (HDL-C) and apolipoprotein A-I (apoA-I) contents were remarkably increased by treatment with simvastatin. And plasma lecithin-cholesterol acyltransferase (LCAT) activity was markedly increased by simvastatin treatment. Real-time PCR detection disclosed that the expression of several transporters involved in reverse cholesterol transport, including macrophage scavenger receptor class B type I, hepatic ATP-binding cassette (ABC) transporters ABCG5, and ABCB4 were induced by simvastatin treatment, the expression of hepatic ABCA1 and apoA-I, which play roles in the maturation of HDL-C, were also elevated in simvastatin treated groups.</p> <p>Conclusions</p> <p>We demonstrated the anti-atherogenesis effects of simvastatin in apoE-/- mice fed a high-fat diet. We confirmed here for the first time simvastatin increased the expression of hepatic ABCB4 and ABCG5, which involved in secretion of cholesterol and bile acids into the bile, besides upregulated ABCA1 and apoA-I. The elevated HDL-C level, increased LCAT activity and the stimulation of several transporters involved in RCT may all contribute to the anti-atherosclerotic effect of simvastatin.</p

Affective Image Content Analysis: Two Decades Review and New Perspectives

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Energy Transmission and Equilibrium Scheme in Data Communication Opportunistic Networks

In data communication, a good communication scheme can improve the transmission of data packets among nodes. The opportunistic network is a convenient wireless communication network and its model is easily applied in data communication. Energy consumption among nodes in the opportunistic network is an important parameter. The over-consumption of energy may cause the nodes to be dead, and then many useful data packets would be lost. Especially in data communication, this tendency is obvious. However, many researchers rarely consider energy consumption in the opportunistic network. This paper suggests a scheme in which data packets are transmitted among nodes. Energy supply and equilibrium is found in opportunistic networks. This scheme not only supplies energy to active nodes, but also considers inactive nodes to energy supply objects. Then, this scheme accomplishes data packets transmission and improves energy utilization in the opportunistic network. With the evidence of simulation and comparison of the epidemic algorithm, the direct delivery algorithm, and spray and wait algorithm in the opportunistic network, this scheme can be an equilibrium for energy consumption, for improving the delivering ratio, and the size of the cache time

Integrating OpenStreetMap tags for efficient LiDAR point cloud classification using graph neural networks

ABSTRACTThe urban environment exhibits significant vertical variations, Light Detection and Ranging (LiDAR) point cloud classification can provide insights for the 3D morphology of the urban environment. Introducing the adjacency relationships between urban objects can enhance the accuracy of LiDAR point cloud classification. Graph Neural Network (GNN) is a popular architecture to infer the labels of urban objects by utilizing adjacency relationships. However, existing methods ignored the power of the known labels of urban objects, such as crowd-sourced tagged labels from OpenStreetMap (OSM) data, in the inferring process. Therefore, this study proposes a strategy introduces OSM data into GNN for LiDAR point cloud classification. First, we perform an over-segmentation of the LiDAR point cloud to obtain superpoints, which act as basic elements for constructing superpoint adjacency graphs. Second, PointNet is applied to embed superpoint features and edge features are generated using these superpoint features. Finally, OSM data is associated with some part of superpoints and incorporated into the GNN to update the embedded features of superpoints. The results demonstrate that the GNN with OSM data significantly improves the classification accuracy of original GNN. The improvement highlights taking advantage of crowd-sourced geoinformation in LiDAR point cloud classification for understanding 3D urban landscape

Highly Efficient Visible Light Photocatalytic Activities in Self-Assembled Metastable TiO2/Bi4MoO9Heterojunctions

Metastable TiO2/Bi4MoO9 heterojunctions are synthesized using a hydrothermal method. TiO2 nanoparticles, which are used as templates in the hydrothermal process, can induce the heterogeneous nucleation and 1D preferential growth of Bi4MoO9 to form TiO2/Bi4MoO9 heterojunctions. The TiO2/Bi4MoO9 heterojunctions exhibit very efficient visible-light photocatalytic activity, which is much higher than those of TiO2 nanoparticles, Bi4MoO9 microcubes, and TiO2/Bi2MoO6 composites. This could be mainly ascribed to the synergistic effects of the band structure matching and the unique microstructure between TiO2 nanoparticles and Bi4MoO9 nanobelts in the heterojunctions. The Bi4MoO9 nanobelts with ≈150 nm in width promote the production of photogenerated electron-hole pairs due to its narrow bandgap. The TiO2 nanoparticles embedded into Bi4MoO9 nanobelts still remain small size, large specific surface area, and high crystallinity. These not only facilitate the transfer of photogenerated holes from Bi4MoO9 nanobelts to TiO2 nanoparticles, but also improve the holes capture rate on the surface of the heterojunctions

A Novel CD48-Based Analysis of Sepsis-Induced Mouse Myeloid-Derived Suppressor Cell Compartments

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous subset of cells that expands dramatically in many disease states and can suppress T-cell responses. MDSCs mainly include monocytic and granulocytic subpopulations that can be distinguished in mice by the expression of Ly6G and Ly6C cell surface markers. This identification system has been validated in experimental tumor models, but not in models of inflammation-associated conditions such as sepsis. We challenged growth factor independent 1 transcription repressor green fluorescent protein (Gfi1:GFP) knock-in reporter mice with cecal ligation and puncture surgery and found that CD11b+Ly6GlowLy6Chigh MDSCs in this sepsis model comprised both monocytic and granulocytic MDSCs. The evidence that conventional Ly6G/Ly6C marker analysis may not be suited to study of inflammation-induced MDSCs led to the development of a novel strategy of distinguishing granulocytic MDSCs from monocytic MDSCs in septic mice by expression of CD48. Application of this novel model should help achieve a more accurate understanding of the inflammation-induced MDSC activity