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Postharvest ultraviolet B (UV-B) radiation has been used to control pathogen incidence on fresh produce, but little attention has been paid to preharvest UV-B effect on strawberry fruit quality and storage. In this study, strawberry (Fragaria × ananassa Duch. cv Albion) plants grown in greenhouse were irradiated with UV-B lamps with an intensity of 0.7362 J/s m2 for 1 and 2 h every day from 5–7 pm after flowering. Fruits were harvested at red stage for quality evaluation. The results indicated that the preharvest UV-B-treated fruits had lighter color (increased L*) than the control. Total soluble solids (TSS), total phenolics content (TPS) and total anthocyanin content (TAC) of UV-B-treated fruits were higher than for the control fruits. There were no significant differences in any of the quality traits between UV-B one-hour and two-hour treatments. We further evaluated the fruit quality when stored at 5 °C on day 7 and day 14. Compared to untreated control, UV-B-treated fruits retarded decrease in L*, TSS, TPS, TAC and firmness, and reduced fruit decay. Examination of expression of genes related to UV-B signaling indicated that HY5 was the major component of UV-B signaling during the green and white stages. However, anthocyanin genes were highly responsive to UV-B treatment during the red stage. Our results suggest that utilization of a low dose of UV-B radiation during the growth stage can improve strawberry fruit quality, and extend shelf life. This research facilitates the utilization of preharvest UV-B treatment for improving fruit quality in controlled environment agriculture.

In order to categorize feature classes by capturing subtle differences, hyperspectral images (HSIs) have been extensively used due to the rich spectral-spatial information. The 3D convolution-based neural networks (3DCNNs) have been widely used in HSI classification because of their powerful feature extraction capability. However, the 3DCNN-based HSI classification approach could only extract local features, and the feature maps it produces include a lot of spatial information redundancy, which lowers the classification accuracy. To solve the above problems, we proposed a spatial attention network (SATNet) by combining 3D OctConv and ViT. Firstly, 3D OctConv divided the feature maps into high-frequency maps and low-frequency maps to reduce spatial information redundancy. Secondly, the ViT model was used to obtain global features and effectively combine local-global features for classification. To verify the effectiveness of the method in the paper, a comparison with various mainstream methods on three publicly available datasets was performed, and the results showed the superiority of the proposed method in terms of classification evaluation performance.

Broccoli microgreens have shown potential health benefits due to their high glucosinolate (GL) levels. Previously, we observed that postharvest UVB treatment did not have much effect on increasing GLs in broccoli microgreens. In this study, we investigated the influence of preharvest UVB irradiation on GL levels in broccoli microgreens. UHPLC-ESI/ITMS analysis showed that preharvest UVB treatments with UVB 0.09 and 0.27 Wh/m2 significantly increased the glucoraphanin (GLR), glucoerucin (GLE), and total aliphatic GL levels by 13.7 and 16.9%, respectively, in broccoli microgreens when measured on harvest day. The nutritional qualities of UVB-treated microgreens were stable during 21-day storage, with only small changes in their GL levels. Broccoli microgreens treated before harvest with UVB 0.27 Wh/m2 and 10 mM CaCl2 spray maintained their overall quality, and had the lowest tissue electrolyte leakage and off-odor values during the storage. Furthermore, preharvest UVB 0.27 Wh/m2 treatment significantly increased GL biosynthesis genes when evaluated before harvest, and reduced the expression level of myrosinase, a gene responsible for GL breakdown during postharvest storage. Overall, preharvest UVB treatment, together with calcium chloride spray, can increase and maintain health-beneficial compound levels such as GLs and prolong the postharvest quality of broccoli microgreens.

N,N′-chelate organoboron compounds have been successfully applied in bioimaging, organic light-emitting diodes (OLEDs), functional polymer, photocatalyst, electroluminescent (EL) devices, and other science and technology areas. However, the concise and efficient synthetic methods become more and more significant for material science, biomedical research, or other practical science. Here, we summarized the organoboron-N,N′-chelate derivatives and showed the different routes of their syntheses. Traditional methods to synthesize N,N′-chelate organoboron compounds were mainly using bidentate ligand containing nitrogen reacting with trivalent boron reagents. In this review, we described a series of bidentate ligands, such as bipyridine, 2-(pyridin-2-yl)-1H-indole, 2-(5-methyl-1H-pyrrol-2-yl)quinoline, N-(quinolin-8-yl)acetamide, 1,10-phenanthroline, and diketopyrrolopyrrole (DPP).

Calmodulin, a ubiquitous calcium sensor, plays an important role in decoding stress-triggered intracellular calcium changes and regulates the functions of numerous target proteins involved in various plant physiological responses. To determine the functions of calmodulin in fleshy fruit, expression studies were performed on a family of six calmodulin genes (SlCaMs) in mature-green stage tomato fruit in response to mechanical injury and Botrytis cinerea infection. Both wounding and pathogen inoculation triggered expression of all those genes, with SlCaM2 being the most responsive one to both treatments. Furthermore, all calmodulin genes were upregulated by salicylic acid and methyl jasmonate, two signaling molecules involved in plant immunity. In addition to SlCaM2, SlCaM1 was highly responsive to salicylic acid and methyl jasmonate. However, SlCaM2 exhibited a more rapid and stronger response than SlCaM1. Overexpression of SlCaM2 in tomato fruit enhanced resistance to Botrytis-induced decay, whereas reducing its expression resulted in increased lesion development. These results indicate that calmodulin is a positive regulator of plant defense in fruit by activating defense pathways including salicylate- and jasmonate-signaling pathways, and SlCaM2 is the major calmodulin gene responsible for this event.

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter ( PM 2.5 ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration on the basis of meteorological data of previous days by formulating the prediction over 24 h as a multi-task learning (MTL) problem. This enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other and compare it with several typical regularizations for MTL, including standard Frobenius norm regularization, nuclear norm regularization, and ℓ 2 , 1 -norm regularization. Our experiments have showed that the proposed parameter-reducing formulations and consecutive-hour-related regularizations achieve better performance than existing standard regression models and existing regularizations.