Comparative Study on Chinese Network Imported Food Safety Supervision System

This paper aims to improve the efficiency of food safety supervision and the effective way to ensure food safety through the comparative study of the imported food safety supervision system of China\u27s network, and the comparative study of the main methods to ensure food safety. In this paper, according to the basic theory of food safety, for the world\u27s major developed countries government food safety regulatory system, the Chinese government\u27s food safety regulatory system research, China\u27s food regulatory measures related to a new model idea, several parts, and discover network imported food safety supervision system in our country\u27s own insufficiency, thus absorbing the precious experience of foreign management system, finally proposed consummates our country network imported food safety regulatory system

BOOST THE DSICOVERY OF MRP7/ABCC10 SUBSTRATES AND INHIBITORS: ESTABLISHMENT OF NEW IN VITRO AND IN SILICO MODELS

ATP-binding cassette (ABC) transporters are responsible for the efflux of structurally distinct endo- and xenobiotics energized by ATP hydrolysis. MRP7/ABCC10 belongs to the 10th member of subfamily C and responsible for mediating MDR against a series of chemotherapeutic drugs such as taxanes, epothilones, Vinca alkaloids, anthracyclines and epipodophyllotoxins. Establishment of new in silico and in vitro models for MRP7 substrates/inhibitors prediction Considering the limited knowledge of MRP7, we established a homology model based on bovine MRP1 cryo-EM models. The final model was used for protein global motion analysis and docking analysis. Before docking, potential drug binding pockets were identified and evaluated. Next, MRP7 substrates and inhibitors were docked into drug binding pockets. We found that docked inhibitors and substrates formed separate clusters, from which a substrate binding region and an inhibitor binding region were proposed. This homology protein model enables the docking analysis of potential MRP7 ligands for future studies. Moreover, we established a new SKOV3/MRP7 cell line which exhibits similar drug resistance profile as the previously established HEK/MRP7 cell line. This new cell line is valuable for MRP7 substrates and inhibitors discovery. Last but not the least, we established a novel machine learning model named Mrp7Pred for large-scale MRP7 substrates/inhibitors prediction. The model was also deployed as a web server and is freely available to users in http://www.mrp7pred.com. We successfully identified 2 substrates and 4 inhibitors from 70 FDA-approved drugs using Mrp7Pred. New synthetic agents targeting MRP7 and overcomes MRP7-medited MDR Previously, we identified two synthetic compounds, CMP25 and CP55, as potent ABCB1 and ABCG2 inhibitors. Here we found these two compounds also significantly reversed the MDR mediated by MRP7. Both compounds significantly sensitized MRP7- overexpressing HEK/MRP7 cells to paclitaxel and vincristine. Western blotting indicates that neither CMP25 nor CP55 alters MRP7 expression level. Immunofluorescence showed that the subcellular localization of MRP7 was not altered by these two compounds. However, intracellular accumulation of [3H]-paclitaxel and [3H]-vincristine were significantly increased while the efflux was significantly reduced when co- administered with CMP25 or CP55. Hydrophobic interactions were predicted as the major contributors in stabilizing the drug-protein complex via docking analysis

Preparation and characterization of cellulose nanoparticles and their application in biopolymeric nanocomposites

Regenerated cellulose nanoparticles (RCNs) including both elongated fiber and spherical structures were prepared from microcrystalline cellulose (MCC) and cotton using 1-butyl-3-methylimidazolium chloride followed by high-pressure homogenization. The RCN has a two-step pyrolysis, different from raw MCC and cotton that had a one-step process. The crystalline structure of RCNs was cellulose II in contrast to the cellulose I form of the starting materials. Also, the RCNs have decreased crystallinity and crystallite size. The elongated RCNs produced from cotton and MCC had average lengths of 123 ± 34 and 112 ± 42 nm, and mean widths of 12 ± 5 and 12 ± 3 nm, respectively. The average diameter of spherical RCNs from MCC was 118 ± 32 nm. Cellulose nanocrystals and cellulose nanofibers with I and II crystalline allomorphs (designated as CNC I, CNC II, CNF I, and CNF II) were isolated from bleached wood fibers by alkaline pretreatment and acid hydrolysis. The effects of concentration, particle size, surface charge, and crystal structure on the lyophilization-induced self-assembly of cellulose particles in aqueous suspensions were studied. Within the concentration range of 0.5 to 1.0 wt %, cellulose particles self-organized into lamellar structured foam composed of aligned membrane layers with widths between 0.5 and 3 ì m. At 0.05 wt %, CNC I, CNF I, CNC II, and CNF II self-assembled into oriented ultrafine fibers with mean diameters of 0.57, 1.02, 1.50, and 1.00 ì m, respectively. Cellulose nanoparticle (CNP) reinforced Polyvinyl alcohol-borax (PB) hydrogels were prepared through a facile approach in an aqueous medium. The obtained stiff, high-water-capacity (~96%), low-density (~1.1g/cm3), translucence hydrogels exhibited birefringence textures. These free-standing, high elasticity and mouldable hydrogels also exhibited self-recovery under continuous step strain and thermo-reversibility under temperature sweep. The rheological tests and compression measurements confirmed the incorporation of well-dispersed CNPs to PB system significantly enhanced the compressive strength, viscoelasticity and stiffness of the hydrogels. Highly-crystalline CNPs not only tangled with PVA chains though numerous hydrogen bonds, but formed chemically crosslinked complexes with borax ions as well, thus acting as multifunctional crosslinking agents and nanofillers to physically and chemically bridge the 3D network hydrogels

Self-Interpretable Time Series Prediction with Counterfactual Explanations

Interpretable time series prediction is crucial for safety-critical areas such as healthcare and autonomous driving. Most existing methods focus on interpreting predictions by assigning important scores to segments of time series. In this paper, we take a different and more challenging route and aim at developing a self-interpretable model, dubbed Counterfactual Time Series (CounTS), which generates counterfactual and actionable explanations for time series predictions. Specifically, we formalize the problem of time series counterfactual explanations, establish associated evaluation protocols, and propose a variational Bayesian deep learning model equipped with counterfactual inference capability of time series abduction, action, and prediction. Compared with state-of-the-art baselines, our self-interpretable model can generate better counterfactual explanations while maintaining comparable prediction accuracy

Recovering the original simplicity: succinct and deterministic quantum algorithm for the welded tree problem

This work revisits quantum algorithms for the well-known welded tree problem, proposing a very succinct quantum algorithm based on the simplest coined quantum walks. It simply iterates the naturally defined coined quantum walk operator for a predetermined time and finally measure, where the predetermined time can be efficiently computed on classical computers. Then, the algorithm returns the correct answer deterministically, and achieves exponential speedups over any classical algorithm. The significance of the results may be seen as follows. (i) Our algorithm is rather simple compared with the one in (Jeffery and Zur, STOC'2023), which not only breaks the stereotype that coined quantum walks can only achieve quadratic speedups over classical algorithms, but also demonstrates the power of the simplest quantum walk model. (ii) Our algorithm theoretically achieves zero-error, which is not possible with existing methods. Thus, it becomes one of the few examples that exhibit exponential separation between deterministic (exact) quantum and randomized query complexities, which may also change people's perception that since quantum mechanics is inherently probabilistic, it impossible to have a deterministic quantum algorithm with exponential speedups for the weled tree problem.Comment: The paper has been revised and been accepted by SODA202