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

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

Succinct quantum testers for closeness and kk-wise uniformity of probability distributions

We explore potential quantum speedups for the fundamental problem of testing the properties of closeness and $k$-wise uniformity of probability distributions. \textit{Closeness testing} is the problem of distinguishing whether two $n$-dimensional distributions are identical or at least $\varepsilon$-far in $\ell^1$- or $\ell^2$-distance. We show that the quantum query complexities for $\ell^1$- and $\ell^2$-closeness testing are O\rbra{\sqrt{n}/\varepsilon} and O\rbra{1/\varepsilon}, respectively, both of which achieve optimal dependence on $\varepsilon$, improving the prior best results of \hyperlink{cite.gilyen2019distributional}{Gily{\'e}n and Li~(2019)}. \textit{$k$-wise uniformity testing} is the problem of distinguishing whether a distribution over \cbra{0, 1}^n is uniform when restricted to any $k$ coordinates or $\varepsilon$-far from any such distributions. We propose the first quantum algorithm for this problem with query complexity O\rbra{\sqrt{n^k}/\varepsilon}, achieving a quadratic speedup over the state-of-the-art classical algorithm with sample complexity O\rbra{n^k/\varepsilon^2} by \hyperlink{cite.o2018closeness}{O'Donnell and Zhao (2018)}. Moreover, when $k = 2$ our quantum algorithm outperforms any classical one because of the classical lower bound \Omega\rbra{n/\varepsilon^2}. All our quantum algorithms are fairly simple and time-efficient, using only basic quantum subroutines such as amplitude estimation.Comment: We have added the proof of lower bounds and have polished the languag

A review on regulation of DNA methylation during post-myocardial infarction

Myocardial infarction (MI) imposes a huge medical and economic burden on society, and cardiac repair after MI involves a complex series of processes. Understanding the key mechanisms (such as apoptosis, autophagy, inflammation, and fibrosis) will facilitate further drug development and patient treatment. Presently, a substantial body of evidence suggests that the regulation of epigenetic processes contributes to cardiac repair following MI, with DNA methylation being among the notable epigenetic factors involved. This article will review the research on the mechanism of DNA methylation regulation after MI to provide some insights for future research and development of related drugs

Radiofrequency catheter ablation for re-do procedure after single-shot pulmonary vein isolation with pulsed field ablation for paroxysmal atrial fibrillation: case report

BackgroundCatheter ablation is frequently used to manage recurrent atrial fibrillation (AF) resistant to drug therapy, with pulmonary vein isolation (PVI) as a key tactic. Pulsed field ablation (PFA) has emerged as an innovative technology for PVI but poses challenges for redo procedures.Case presentationWe report on a 73-year-old female patient who experienced recurrent AF after initial successful PVI using a novel PFA technology and subsequently underwent radiofrequency catheter ablation during a repeat intervention. The reconnection of pulmonary veins was discovered primarily in the anterior region of the right superior PV and the superior portion of the left superior PV. An anatomically-based segmental approach and larger circumferential PVI, followed by additional linear ablations at non-PV trigger sites, proved decisive in preventing further recurrence of atrial tachycardia.ConclusionWhile PFA exhibits promise as a secure and efficient modality for PVI, it necessitates excellent contact quality to ensure lasting results. For patients experiencing AF recurrences post-PFI, expanded strategies incorporating both comprehensive PVI and linear ablations at targeted non-PV sites might enhance treatment outcomes

Photo-Induced Depolymerisation: Recent Advances and Future Challenges

Facing the growing environmental issues provoked by the use of nondegradable polymers in many fields (for example, packing, building, and clothing), tremendous efforts have been made to explore photodegradable materials to alleviate the increase in plastic pollution. Photodegradable materials would exploit significant advantages presented by the use of light, such as abundance, safety and the ability to easily tune intensity and wavelength. In particular, photo-induced depolymerisation has received increasing attention, which could enable polymers to degrade to their original monomers or small molecules under certain photoirradiation conditions. Most importantly, the obtained molecules or monomers via photo-induced depolymerisation could be conveniently recycled or further transformed to other high-value-added products, which is of great benefit for environmental protection. This Review summarizes recent advances in the growing field of photo-induced depolymerisation and also considers future challenges that must be addressed. It aims to encourage new researchers to enter this flourishing area and presents a brief guide to the field

Iron deficiency: prevalence, mortality risk, and dietary relationships in general and heart failure populations

BackgroundIron deficiency (ID) is the most common nutritional deficiency, with little research on its prevalence and long-term outcomes in the general population and those with heart failure (HF). Both the relationships between dietary iron and ID, as well as dietary folate and ID, are understudied.MethodsWe used data from the National Health and Nutrition Examination Survey from 1999 to 2002 to investigate the prevalence, prognosis, and relationship between dietary and ID defined by different criteria in the general population (n = 6,660) and those with HF (n = 182).ResultsThere was no significant difference in the prevalence of ID between HF patients and the general population after propensity score matching. Transferrin saturation (TSAT) <20% was associated with higher 5-year all-cause mortality (HR: 3.49, CI: 1.40–8.72, P = 0.007), while ferritin <30 ng/ml was associated with higher 10-year (HR: 2.70, CI: 1.10–6.67, P = 0.031) and 15-year all-cause mortality (HR: 2.64, CI: 1.40–5.00, P = 0.003) in HF patients. Higher dietary total folate but dietary iron reduced the risk of ID (defined as ferritin <100 ng/ml) in HF patients (OR: 0.80; 95% CI: 0.65–1.00; P = 0.047).ConclusionsThe prevalence of ID was identical in HF and non-HF individuals. Ferritin <30 ng/ml was associated with long-term outcomes whereas TSAT <20% was associated with short-term prognosis in both the general population and HF patients. A diet rich in folate might have the potential for prevention and treatment of ID in HF patients

A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks

Tensorial Convolutional Neural Networks (TCNNs) have attracted much research attention for their power in reducing model parameters or enhancing the generalization ability. However, exploration of TCNNs is hindered even from weight initialization methods. To be specific, general initialization methods, such as Xavier or Kaiming initialization, usually fail to generate appropriate weights for TCNNs. Meanwhile, although there are ad-hoc approaches for specific architectures (e.g., Tensor Ring Nets), they are not applicable to TCNNs with other tensor decomposition methods (e.g., CP or Tucker decomposition). To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs. Specifically, we first present the Reproducing Transformation to convert the backward process in TCNNs to an equivalent convolution process. Then, based on the convolution operators in the forward and backward processes, we build a unified paradigm to control the variance of features and gradients in TCNNs. Thus, we can derive fan-in and fan-out initialization for various TCNNs. We demonstrate that our paradigm can stabilize the training of TCNNs, leading to faster convergence and better results.Comment: Accepted in ICML 202