Electrostatic atomized water particles (EAWPs) treatment was applied to investigate its effect on chlorophyll (Chl) degradation and ripening delay in ‘Namwa’ bananas. Banana fruits were pretreated with EAWPs generated from a device (Panasonic F-GMK01) for 0 (control), 0.5, 1.0, 1.5, and 3.0 h in a closed 50 L container, and then kept in perforated polypropylene plastic bags and stored at ambient temperature (25 ± 2°C) under dark conditions. The results showed that 1.0 h-EAWPs treatment best retained peel greenness with a significantly higher hue angle and lower L* value than other treatments on day 6. Also, the 1.0 h-EAWPs treatment maintained the total Chl content, firmness, total soluble solids (TSS), and delayed the ripening index (RI) of fruit accompanied by a delayed climacteric rise in ethylene and respiration rate compared to the control. It was found that the 1.0 h-EAWP treatment induced accumulations of nitric oxide (NO) in peel tissues and suppressed the activities of Chl-degrading enzymes (chlorophyllase, Mg-dechelatase, Chl-degrading peroxidase, and pheophytinase) in the peel. Furthermore, Chl derivatives levels (chlorophyllide a, pheophobide a, 13²-hydroxychlorophyll a, and pheophytin a) were higher in fruits treated with EAWPs than the control fruits. The results suggest that EAWPs technology could be an alternative approach to delay Chl degradation and ripening in ‘Namwa’ bananas.
The classical microbubble generator of the venturi nozzle type is still used in a wide range of industries despite its relatively low performance. In this study, for overcoming the relatively low performances and maintaining the existing advantages, the cylindrical venturi type of microbubble generators with screw structures and flared diffusers are proposed using conventional 3D printers. In the experimental facility, flow rates and pressure drop were measured. Using high speed images, the performances were deduced for prepared microbubble generators. Based on experimental data, the effect of screw structures for performances of microbubble generators was identified. Furthermore, the flared diffuser to the diffuser of regular venturi nozzle with constant diverging angle were compared. Additionally, synergies are obtained by the screw structure in the flare diffuser. In conclusion, the performances of the venturi type microbubble generator through optimal designed screw structures by 39.4 % were improved.
Philosamia ricini (Eri silkworm) pupa protein isolate (EPI) was utilized to prepare pupa protein hydrolysate (EPIH) through enzymatic hydrolysis. Additionally, the isolate underwent ultrasonic treatment at 20 kHz to become ultrasound pretreated EPI (EPIU), which was then enzymatically hydrolyzed to obtain ultrasound pretreated protein hydrolysate (EPIUH). The physicochemical properties of these samples were investigated, including molecular weight, solubility, foaming and emulsion properties, water- and oil-holding capacity, antioxidant activity, and color. When compared to EPI (used as the control), EPIU exhibited a high degree of hydrolysis at 20 minutes ( DH = 29.24 % ). At a total process time of 20 minutes, the degree of hydrolysis for EPIH, EPIU, and EPIUH was found to be 13%, 29%, and 41%, respectively. SDS-PAGE analysis indicated no difference in molecular weight between EPI and EPIU (11–75 kDa). However, the molecular weight profiles of EPIH and EPIUH were reduced (8–45 kDa), resulting in changes in protein functionalities. The high DH value contributed to the enhancement of antioxidant activity, solubility, emulsion capacity, emulsion stability, and foam capacity of the protein isolate at pH 7. Furthermore, the ultrasonic pretreatment of the protein hydrolysate increased the lightness of the protein powder by reducing the enzyme activity of the polyphenol oxidase (PPO). These results suggest that ultrasonic pretreatment of the protein hydrolysate could be applied to improve the properties of Eri silkworm pupa protein for use in the food and beverage industry, such as protein-rich beverages or salad dressings.
This paper proposes, in the framework of Hadamard manifolds, two iterative schemes for approximating a solution of a variational inequality problem involving a nonexpansive mapping with a fixed point set of another nonexpansive mapping as constraint. The first scheme is a modified Halpern iteration and the second is a viscosity-type iteration with a weakly contraction mapping. We also discuss some special cases of the mentioned problem. Numerical examples are provided to illustrate the algorithm’s numerical behavior.
The relationship between soil radon and meteorological parameters in a region can provide insight into natural processes occurring between the lithosphere and the atmosphere. Understanding this relationship can help models establish more realistic results, rather than depending on theoretical consequences. Radon variation can be complicated to model due to the various physical variables which can affect it, posing a limitation in atmospheric studies. To predict Rn variation from meteorological parameters, a hybrid mod el called multiANN, which is a combination of multi-regression and artificial neural network (ANN) models, is established. The model was trained with 70% of the data and tested on the remaining 30%, and its robustness was tested using the Monte-Carlo method. The regions with low performance are identified and possibly related to seismic events. This model can be a good candidate for predicting Rn concentrations from meteorological parameters and establishing the lower boundary conditions in seismo-ionospheric coupling models.
To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19 infection and calculate the total severity score (TSS). The entire dataset consisted of 124 COVID-19 patients acquired from Chulabhorn Hospital, divided into 28 cases without lung lesions and 96 cases with lung lesions categorized severity by radiologists regarding TSS. The model used a 3D-UNet along with DenseNet and ResNet models that had already been trained to separate the lobes of the lungs and figure out the percentage of lung involvement due to COVID-19 infection. It also used the Dice similarity coefficient (DSC) to measure TSS. Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with the Dice similarity coefficients of 91.52% and 76.89%, respectively. The calculated TSS values were similar to those evaluated by radiologists, with an R2 of 0.842. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.890 and 0.709, respectively.
The discovery of novel bioactive compounds produced by microorganisms holds significant potential for the development of therapeutics and agrochemicals. In this study, we conducted genome mining to explore the biosynthetic potential of entomopathogenic bacteria belonging to the genera Xenorhabdus and Photorhabdus. By utilizing next-generation sequencing and bioinformatics tools, we identified novel biosynthetic gene clusters (BGCs) in the genomes of the bacteria, specifically plu00736 and plu00747. These clusters were identified as unidentified non-ribosomal peptide synthetase (NRPS) and unidentified type I polyketide synthase (T1PKS) clusters. These BGCs exhibited unique genetic architecture and encoded several putative enzymes and regulatory elements, suggesting its involvement in the synthesis of bioactive secondary metabolites. Furthermore, comparative genome analysis revealed that these BGCs were distinct from previously characterized gene clusters, indicating the potential for the production of novel compounds. Our findings highlighted the importance of genome mining as a powerful approach for the discovery of biosynthetic gene clusters and the identification of novel bioactive compounds. Further investigations involving expression studies and functional characterization of the identified BGCs will provide valuable insights into the biosynthesis and potential applications of these bioactive compounds.
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