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Food safety evaluation, which aims to reflect food safety status, is an important part of food safety management. Traditional food evaluation methods often consider limited data, and the evaluation process is subjective, time-consuming, and difficult to popularize. We developed a new food safety evaluation system that incorporates simple qualification degrees, food consumption, project hazard degrees, sales channels, food production regions, and other information obtained from food safety sampling and inspection to reflect the food safety situation accurately, objectively, and comprehensively. This evaluation model combined the statistical method and the machine learning method. The optimal distance method was used to calculate the basic qualification degree, and then expert elicitation via a questionnaire and the factor analysis of mixed data method (FADM) was applied to modify the basic qualification degree so as to obtain the food safety index, which indicates food safety status. Then, the effectiveness of this new method was verified by calculating and analyzing of the food safety index in region X. The results show that this model can clearly distinguish food safety levels in different cities and food categories and identify food safety trends in different years. Thus, this food safety evaluation system based on the FADM quantifies the real food safety level, screens out cities and food categories with high food safety risks, and, finally, helps to optimize the allocation of regulatory resources and provide technical and theoretical support for government decision-making. [ABSTRACT FROM AUTHOR]

As a high-sensitivity and non-invasive technique, optical molecular tomography (OMT) can locate and visualize tissue quantitatively in three dimensions by collecting surface photons generated from luminescent biomarkers. It has great potential for tumor detection, surgery guidance, and pharmacokinetics research studies. However, due to the limited measurable surface photons and the highly scattered feature of photons, the reconstruction of OMT is highly ill-posed and ill-conditioned, which limits the performance in practice. To improve the accuracy of OMT, plenty of studies focus on precisely modeling photon propagation and accurately reconstructing light source. Since these methods are carried out based on different theories, we review famous solutions for the reconstruction in terms of the photon propagation models and inverse reconstruction algorithms to make an overall understanding for the methods of OMT reconstruction. Additionally, some prospects are listed to provide possible research orientation that may benefit future research. [ABSTRACT FROM AUTHOR]

Objective. Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion method is proposed to address these problems. Approach. Firstly, Taylor expansion framework is introduced to reduce time and computational cost. Secondly, group sparsity based on structural prior is introduced to improve reconstruction accuracy. Thirdly, alternating iterative solution based on accelerated gradient descent algorithm is introduced to solve the problem. Main results. Numerical simulation and in vivo experimental results demonstrate that, in comparison to existing methods, the proposed approach significantly enhances reconstruction speed without a degradation of quality, particularly when confronted with background fluorescence interference from other organs. Significance. Our research greatly reduces time and computational cost, providing strong support for real-time monitoring of liver metabolism. [ABSTRACT FROM AUTHOR]

Objectives We aimed to investigate the value of deep learning (DL) models based on multimodal ultrasonographic (US) images to quantify RA activity. Methods Static greyscale (SGS), dynamic greyscale (DGS), static power Doppler (SPD) and dynamic power Doppler (DPD) US images were collected and evaluated by two expert radiologists according to the EULAR–OMERACT Synovitis Scoring system. Four DL models were developed based on the ResNet-type structure, evaluated on two separate test cohorts, and finally compared with the performance of 12 radiologists with different levels of experience. Results In total, 1244 images were used for the model training, and 152 and 354 for testing (cohort 1 and 2, respectively). The best-performing models for the scores of 0/1/2/3 were the DPD, SGS, DGS and SPD models, respectively (Area Under the receiver operating characteristic Curve [AUC] = 0.87/0.95/0.74/0.95; no significant differences). All the DL models provided results comparable to the experienced radiologists on a per-image basis (intraclass correlation coefficient: 0.239–0.756, P  < 0.05). The SPD model performed better than the SGS one on test cohort 1 (score of 0/2/3: AUC = 0.82/0.67/0.95 vs 0.66/0.66/0.75, respectively) and test cohort 2 (score of 0: AUC = 0.89 vs 0.81). The dynamic DL models performed better than the static ones in most of the scoring processes and were more accurate than the most of senior radiologists, especially the DPD model. Conclusion DL models based on multimodal US images allow a quantitative and objective assessment of RA activity. Dynamic DL models in particular have potential value in assisting radiologists to improve the accuracy of RA US-based grading. [ABSTRACT FROM AUTHOR]

Tunable hydrogel that contained well-defined poly(vinyl alcohol) (PVA), labile lactate groups, and hydrophilic poly(ethylene glycol) (PEG) segments was prepared through a combination of reversible addition-fragmentation chain transfer (RAFT) polymerization and esterification reaction. A diol was prepared via the esterification between lactic acid (LA) and PEG. Then the diol was allowed to react with maleic anhydride to produce a diacid. Meanwhile, well-defined PVA was synthesized by the alcoholysis of poly(vinyl acetate) (PVAc) obtained by RAFT polymerization of vinyl acetate. The hydrogels with tailor-made structure were generated by crosslinking PVA with LA-based diacid. The structures and properties of LA-based intermediates and the hydrogels were characterized with Fourier transform infrared spectroscopy, gel permeation chromatography, differential scanning calorimetry, and thermogravimetric analysis. Both LA-based diol and diacid were semicrystalline and water-soluble, their melting temperature and glass transition temperature were 52 and -51, 54 and -41°C, respectively. The polydispersity indexes of the precursor of PVA samples were within the range of 1.03-1.10. It was found that the thermal stability of hydrogel was higher than that of LA-based diacid. Both the swelling and release properties of the hydrogels depend on the feeding ratio of PVA/LEM and the chain length of PVA, which reflected that the structure and properties of the hydrogels were controllable., INTRODUCTION Owing to the advantages such as nontoxic, biocompatible, excellent mechanical and physicochemical properties, biodegradable hydrogels are regarded as suitable materials for biomedical, pharmaceutical, environmental, and other applications [1-3]. The [...]