Enhancing Fresh-Cut Apple Preservation: Impact of Slightly Acidic Electrolyzed Water and Chitosan-Apple Essence Microencapsulation Coating on Browning and Flavor.

Fresh-cut apple preservation is a critical concern in the food industry due to the rapid deterioration of texture, color, and flavor. While our previous study introduced apple essence microencapsulation (AEM) to enhance flavor during storage, its impact on overall storage quality was minimal. Thus, this study explores the application of two preservation techniques, namely, slightly acidic electrolyzed water (SAEW) and chitosan-apple essence microencapsulation (CH-AEM) coating, to enhance the quality of fresh-cut apples. Our findings reveal that SAEW treatment significantly reduces the browning index (from 65.38 to 57.36) and respiratory rate (from 5.10% to 4.30% of CO2), and maintains a desirable aroma profile compared to uncoated treatment during 10 days of storage. Additionally, the CH-AEM coating acts as a protective barrier, further preserving the sensory characteristics of fresh-cut apples. Notably, the SAEW-CH-AEM group exhibits superior performance in firmness (8.14 N), respiratory rate (3.37% of CO2), ion leakage (34.86%), and juice yield (47.52%) after 10 days. Our research highlights the synergistic effect of combining these preservation strategies, providing a promising approach for extending the shelf life of fresh-cut apples while maintaining their visual appeal and aromatic quality. These results offer valuable insights for the fresh-cut produce industry, contributing to improved apple product preservation and consumer satisfaction.

Drug response prediction using graph representation learning and Laplacian feature selection.

BACKGROUND: Knowing the responses of a patient to drugs is essential to make personalized medicine practical. Since the current clinical drug response experiments are time-consuming and expensive, utilizing human genomic information and drug molecular characteristics to predict drug responses is of urgent importance. Although a variety of computational drug response prediction methods have been proposed, their effectiveness is still not satisfying. RESULTS: In this study, we propose a method called LGRDRP (Learning Graph Representation for Drug Response Prediction) to predict cell line-drug responses. At first, LGRDRP constructs a heterogeneous network integrating multiple kinds of information: cell line miRNA expression profiles, drug chemical structure similarity, gene-gene interaction, cell line-gene interaction and known cell line-drug responses. Then, for each cell line, learning graph representation and Laplacian feature selection are combined to obtain network topology features related to the cell line. The learning graph representation method learns network topology structure features, and the Laplacian feature selection method further selects out some most important ones from them. Finally, LGRDRP trains an SVM model to predict drug responses based on the selected features of the known cell line-drug responses. Our five-fold cross-validation results show that LGRDRP is significantly superior to the art-of-the-state methods in the measures of the average area under the receiver operating characteristics curve, the average area under the precision-recall curve and the recall rate of top-k predicted sensitive cell lines. CONCLUSIONS: Our results demonstrated that the usage of multiple types of information about cell lines and drugs, the learning graph representation method, and the Laplacian feature selection is useful to the improvement of performance in predicting drug responses. We believe that such an approach would be easily extended to similar problems such as miRNA-disease relationship inference.

Evaluation of simultaneous-multislice diffusion-weighted imaging of liver at 3.0 T with different breathing schemes.

PURPOSE: To obtain the optimal simultaneous-multislice (SMS)-accelerated diffusion-weighted imaging (DWI) of the liver at 3.0 T MRI by systematically estimating the repeatability of apparent diffusion coefficient (ADC), signal-to-noise ratio (SNR) and image quality of different breathing schemes in comparison to standard DWI (STD) and other SMS sequences. METHODS: In this institutional review board-approved prospective study, hepatic DWIs (b = 50, 300, 600 s/mm2) were performed in 23 volunteers on 3.0 T MRI using SMS and STD with breath-hold (BH-SMS, BH-STD), free-breathing (FB-SMS, FB-STD) and respiratory-triggered (RT-SMS, RT-STD). Reduction of scan time with SMS-acceleration was calculated. ADC and SNR were measured in nine anatomic locations and image quality was assessed on all SMS and STD sequences. An optimal SMS-DWI was decided by systematically comparing the ADC repeatability, SNR and image quality among above DWIs. RESULTS: SMS-DWI reduced scan time significantly by comparison with corresponding STD-DWI (27 vs. 42 s for BH, 54 vs. 78 s for FB and 42 vs. 97 s for RT). In all DWIs, BH-SMS had the greatest intraobserver agreement (intraclass correlation coefficient (ICC): 0.920-0.944) and good interobserver agreement (ICC: 0.831-0.886) for ADC measurements, and had the best ADC repeatability (mean ADC absolute differences: 0.046-0.058 × 10-3mm2/s, limits of agreement (LOA): 0.010-0.013 × 10-3mm2/s) in nine locations. BH-SMS had the highest SNR in three representative sections except for RT-STD. There were no significant differences in image quality between BH-SMS and other DWI sequences (median BH-SMS: 4.75, other DWI: 4.5-5.0; P > 0.0.5). CONCLUSION: BH-SMS provides considerable scan time reduction with good image quality, sufficient SNR and highest ADC repeatability on 3.0 T MRI, which is thus recommended as the optimal hepatic DWI sequence for those subjects with adequate breath-holding capability.

Monolithic Au/CeO2 nanorod framework catalyst prepared by dealloying for low-temperature CO oxidation.

Monolithic Au/CeO2 nanorod frameworks (NFs) with porous structure were prepared by dealloying melt-spun Al89.7Ce10Au0.3 ribbons. After calcination in O2, a 3D Au/CeO2 NF catalyst with large surface area was obtained and used for low-temperature CO oxidation. The small Au clusters/nanoparticles (NPs) were in situ supported and highly dispersed on the nanorod surface, creating many nanoscale contact interfaces. XPS results demonstrated that high-concentration oxygen vacancy and Au δ+/Au0 co-existed in the calcined sample. The Au/CeO2 nanorod catalyst calcined at 400 °C exhibited much higher catalytic activity for CO oxidation compared with the dealloyed sample and bare CeO2 nanorods. Moreover, its complete reaction temperature was as low as 91 °C. The designed Au/CeO2 NF catalyst not only possessed extreme sintering resistance but also exhibited high performance owing to the enhanced interaction between the Au clusters/NPs and CeO2 nanorod during calcination.