Fuzheng Huayu recipe prevents nutritional fibrosing steatohepatitis in mice

<p>Abstract</p> <p>Background</p> <p>Fuzheng Huayu recipe (FZHY), a compound of Chinese herbal medicine, was reported to improve liver function and fibrosis in patients with hepatitis B virus infection. However, its effect on nutritional fibrosing steatohepatitis is unclear. We aimed to elucidate the role and molecular mechanism of FZHY on this disorder in mice.</p> <p>Methods</p> <p>C57BL/6 J mice were fed with methionine-choline deficient (MCD) diet for 8 weeks to induce fibrosing steatohepatitis. FZHY and/or heme oxygenase-1 (HO-1) chemical inducer (hemin) were administered to mice, respectively. The effect of FZHY was assessed by comparing the severity of hepatic injury, levels of hepatic lipid peroxides, activation of hepatic stellate cells (HSCs) and the expression of oxidative stress, inflammatory and fibrogenic related genes.</p> <p>Results</p> <p>Mice fed with MCD diet for 8 weeks showed severe hepatic injury including hepatic steatosis, necro-inflammation and fibrosis. Administration of FZHY or hemin significantly lowered serum levels of alanine aminotransferase, aspartate aminotransferase, reduced hepatic oxidative stress and ameliorated hepatic inflammation and fibrosis. An additive effect was observed in mice fed MCD supplemented with FZHY or/and hemin. These effects were associated with down-regulation of pro-oxidative stress gene cytochrome P450 2E1, up-regulation of anti-oxidative gene HO-1; suppression of pro-inflammation genes tumor necrosis factor alpha and interleukin-6; and inhibition of pro-fibrotic genes including α-smooth muscle actin, transforming growth factor beta 1, collagen type I (Col-1) and Col-3.</p> <p>Conclusions</p> <p>Our study demonstrated the protective role of FZHY in ameliorating nutritional fibrosing steatohepatitis. The effect was mediated through regulating key genes related to oxidative stress, inflammation and fibrogenesis.</p

Haplotype-resolved genome assembly and allele-specific gene expression in cultivated ginger

Ginger (Zingiber officinale) is one of the most valued spice plants worldwide; it is prized for its culinary and folk medicinal applications and is therefore of high economic and cultural importance. Here, we present a haplotype-resolved, chromosome-scale assembly for diploid ginger anchored to 11 pseudochromosome pairs with a total length of 3.1 Gb. Remarkable structural variation was identified between haplotypes, and two inversions larger than 15 Mb on chromosome 4 may be associated with ginger infertility. We performed a comprehensive, spatiotemporal, genome-wide analysis of allelic expression patterns, revealing that most alleles are coordinately expressed. The alleles that exhibited the largest differences in expression showed closer proximity to transposable elements, greater coding sequence divergence, more relaxed selection pressure, and more transcription factor binding site differences. We also predicted the transcription factors potentially regulating 6-gingerol biosynthesis. Our allele-aware assembly provides a powerful platform for future functional genomics, molecular breeding, and genome editing in ginger.https://www.nature.com/hortreshj2022BiochemistryGeneticsMicrobiology and Plant Patholog

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

Centrality Measures in Directed Fuzzy Social Networks

AbstractThe centrality of actors has been a key issue in undirected fuzzy social network (UFSN) analysis. For undirected fuzzy social networks (UFSNs) where edges are just a present or absent undirected fuzzy relation with no more information attached. There has been a growing need to design centrality measures for directed fuzzy social networks (DFSNs), because DFSNs where edges are attached with directed fuzzy relation would contain rich information. In this paper, we propose some new centrality measure called fuzzy in-degree centrality, fuzzy out-degree centrality, fuzzy in-closeness centrality and fuzzy out-closeness centrality which are applicable to the DFSNs. It unveils more structural information about directed fuzzy relation and connectivity of DFSNs. Furthermore, by investigating the validness and robustness of this new centrality measure by illustrating this method to some cases and we obtain reliable results, which provide strong evidences of the new measures’ utility

Deep Learning Optimized Dictionary Learning and Its Application in Eliminating Strong Magnetotelluric Noise

The noise suppression method based on dictionary learning has shown great potential in magnetotelluric (MT) data processing. However, the constraints used in the existing algorithm&rsquo;s method need to set manually, which significantly limits its application. To solve this problem, we propose a deep learning optimized dictionary learning denoising method. We use a deep convolutional network to learn the characteristic parameters of high-quality MT data independently and then use them as the constraints for dictionary learning so as to achieve fully adaptive sparse decomposition. The method uses unified parameters for all data and completely eliminates subjective bias, which makes it possible to batch-process MT data using sparse decomposition. The processing results of simulated and field data examples show that the new method has good adaptability and can achieve recognition with high accuracy. After processing with our method, the apparent resistivity and phase curves became smoother and more continuous, and the results were validated by the remote reference method. Our method can be an effective alternative method when no remote reference station is set up or the remote reference processing is not effective