Generation, mechanisms, kinetics, and effects of gaseous chlorine dioxide in food preservation.

Food preservation is a critical issue in ensuring food safety and quality. Growing concern around industrial pollution of food and demand for environmentally sustainable food has led to increased interest in developing effective and eco-friendly preservation techniques. Gaseous ClO2 has gained attention for its strong oxidizing properties, high efficacy in microorganism inactivation, and potential for preserving the attributes and nutritional quality of fresh food while avoiding the formation of toxic byproducts or unacceptable levels of residues. However, the widespread use of gaseous ClO2 in the food industry is limited by several challenges. These include large-scale generation, high cost and environmental considerations, a lack of understanding of its mechanism of action, and the need for mathematical models to predict inactivation kinetics. This review aims to provide an overview of the up-to-date research and application of gaseous ClO2 . It covers preparation methods, preservation mechanisms, and kinetic models that predict the sterilizing efficacy of gaseous ClO2 under different conditions. The impacts of gaseous ClO2 on the quality attributes of fresh produce and low-moisture foods, such as seeds, sprouts, and spices, are also summarized. Overall, gaseous ClO2 is a promising preservation approach, and future studies are needed to address the challenges in large-scale generation and environmental considerations and to develop standardized protocols and databases for safe and effective use in the food industry.

Enrichment of grape berries and tomato fruit with health-promoting tartaric acid by expression of the Vitis vinifera transketolase VvTK2 gene.

Tartaric acid (TA) is a major non-fermentable plant soluble acid that abundantly occur in grapes and wines, imparting low pH and tart flavour to berries thereby regulating numerous quality attributes of wine, such as flavour, microbial stability, and aging potential. Evaluation of acidity in mature fruits of 21 wine grape (Vitis vinifera) varieties revealed significant variation between 'Beichun' and 'Gewürztraminer', which was correlated with TA content. RNA-seq analysis of fruits from the two cultivars at different developmental stages revealed that a transketolase gene, VvTK2, was significantly dominantly expressed in the high TA phenotype 'Beichun' variety. Subcellular localization assay showed that VvTK2 protein was located in the chloroplast. Virus-induced VvTK2 gene silencing significantly decreased the expression of 2-keto-L-gulonic acid reductase (Vv2-KGR) as well as L-idonate dehydrogenase (VvL-IdnDH3) and inhibited TA accumulation, while its transient over-expression in grape showed the opposite results. Heterologous VvTK2 over-expression in tomato demonstrated its obvious capacity to induce TA synthesis. Overall, these results highlights a novel role of VvTK2 in modulating TA biosynthesis, which could be an excellent strategy for future genetic improvement of grape flavour.

ULECGNet: An Ultra-Lightweight End-to-End ECG Classification Neural Network.

ECG classification is a key technology in intelligent electrocardiogram (ECG) monitoring. In the past, traditional machine learning methods such as support vector machine (SVM) and K-nearest neighbor (KNN) have been used for ECG classification, but with limited classification accuracy. Recently, the end-to-end neural network has been used for ECG classification and shows high classification accuracy. However, the end-to-end neural network has large computational complexity including a large number of parameters and operations. Although dedicated hardware such as field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) can be developed to accelerate the neural network, they result in large power consumption, large design cost, or limited flexibility. In this work, we have proposed an ultra-lightweight end-to-end ECG classification neural network that has extremely low computational complexity (∼8.2k parameters & ∼227k multiplication/addition operations) and can be squeezed into a low-cost microcontroller (MCU) such as MSP432 while achieving 99.1% overall classification accuracy. This outperforms the state-of-the-art ECG classification neural network. Implemented on MSP432, the proposed design consumes only 0.4 mJ and 3.1 mJ per heartbeat classification for normal and abnormal heartbeats respectively for real-time ECG classification.