Salidroside Reduces Cell Mobility via NF- κ

The unregulated activation of microglia following stroke results in the production of toxic factors that propagate secondary neuronal injury. Salidroside has been shown to exhibit protective effects against neuronal death induced by different insults. However, the molecular mechanisms responsible for the anti-inflammatory activity of salidroside have not been elucidated clearly in microglia. In the present study, we investigated the molecular mechanism underlying inhibiting LPS-stimulated BV2 microglial cell mobility of salidroside. The protective effect of salidroside was investigated in microglial BV2 cell, subjected to stretch injury. Moreover, transwell migration assay demonstrated that salidroside significantly reduced cell motility. Our results also indicated that salidroside suppressed LPS-induced chemokines production in a dose-dependent manner, without causing cytotoxicity in BV2 microglial cells. Moreover, salidroside suppressed LPS-induced activation of nuclear factor kappa B (NF-κB) by blocking degradation of IκBα and phosphorylation of MAPK (p38, JNK, ERK1/2), which resulted in inhibition of chemokine expression. These results suggest that salidroside possesses a potent suppressive effect on cell migration of BV2 microglia and this compound may offer substantial therapeutic potential for treatment of ischemic strokes that are accompanied by microglial activation

Seed- and solvent-free synthesis of ZSM-5 with tuneable Si/Al ratios for biomass hydrogenation

A novel method for the synthesis of MFI zeolites has been developed, which does not require any crystal seeds or solvent. The adaptability of this method was also evidenced; a series of ZSM-5 zeolites with differing Si/Al ratios (18∼∞) were synthesized, which to date, has been a challenge in the field of solvent-free synthesis. The materials were probed by in situ DRIFTS and 2D 27Al–19F HETCOR NMR spectroscopy, the results from which indicated that fluorine-containing species play a crucial role in the crystallization of ZSM-5. During the crystallization process F− anions coordinate with Al3+ cations, resulting in the formation of 6-coordinated “F–Al–O–Si” species. It is these intermediate species which drive the formation of tetrahedral [AlO4]− units in the zeolitic framework. The effectiveness of these materials as catalyst supports was subsequently assessed in the hydrogenation of levulinic acid and glucose, which exhibited a comparable performance to commercial ZSM-5. The simple, efficient and low-cost method presented herein provides an alternative approach for the green scaled-up synthesis of zeolites

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

vSimilar: A High-Adaptive VM Scheduler Based on the CPU Pool Mechanism

In a virtualized system, the virtual machine (VM) scheduler plays a key role to the performance promotion of the virtual machine monitor (VMM), a.k.a. hypervisor. The scheduler is responsible for assigning adequate system resources to each VM according to the demands of the VM tenants, which is quite challenging as VM tenants’ demands are quite dynamic and unpredictable. To this end, CPU pool mechanism has been widely adopted as an adaptive solution. However, the CPU pool mechanism still has defficiency in terms of VM classification model and time-slice allocation strategy, as the two strategies have to be effectively utilized for realizing a high-adaptive VM scheduler. In this paper, we thus explore opportunities to improve the CPU pool mechanism and develop a new VM scheduling solution, called vSimilar, which uses VM multi-classification model to more effectively adaptive to the VMs of running different types of tasks at different time. Moreover, by a dynamic time-slice function, vSimilar manages to provide a more efficient resource allocation. The experimental evaluation shows that vSimilar can significantly improve the performance of a VMM, such as Xen. The improvements include 1) a VM server hosted by Xen with vSimilar can reduce nearly 95% of a client\u27s Ping round-trip time (Ping RTT), 2) vSimilar can help increase about 40% the TCP throughput, and about 20% the UDP throughput, between a Xen-hosted VM server and a client, and 3) vSimilar also increases the page operation rate by nearly 50% for a Xen-hosted VM Web server

Plant Disease Recognition Model Based on Improved YOLOv5

To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases