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Kale is a nutrient-dense leafy vegetable associated with wide-ranging health benefits. It is tolerant of drought and temperature fluctuations, and could thus serve an increasingly important role in providing a safe and nutritious food supply during the climate crisis, while kale's ease of cultivation and ability to be grown in a wide range of soils make it a good fit for urban agriculture. In this pilot study we explored potential differences between kale grown at urban versus rural farms. We planted kale seedlings (Darkibor variety) at three urban and four rural farms in and around Baltimore City, Maryland, instructed farmers to cultivate them using their usual growing practices, harvested the kale from fields and points of distribution, and analyzed it for concentrations of carotenoids, vitamins C and K1, ten nutritional elements, and eight non-essential metals. Although sample sizes for some analyses were in some cases too small to produce statistically significant results, we identified potentially meaningful differences in concentrations of several components between urban and rural kale samples. Compared to urban samples, mean concentrations of carotenoids and vitamins were 22-38% higher in rural field samples. By contrast, mean concentrations for eight nutritional elements were higher in urban field samples by as much as 413% for iron. Compared to rural field samples, mean concentrations of nine non-essential metals were higher in urban samples, although lead and cadmium concentrations for all samples were below public health guidelines. Some urban-rural differences were more pronounced than those identified in prior research. For six elements, variance within urban and rural farms was greater than variance between urban and rural farms, suggesting urbanicity may not be the primary driver of some observed differences. For some nutrients, mean concentrations were higher than upper ranges reported in prior estimates, suggesting kale may have the potential to be more nutrient-dense than previously estimated. The nutritive and metals composition of this important crop, and the factors that influence it, merit continued investigation given its growing popularity.

IntroductionSignal transducer and activator of transcription 3 (STAT3) is ubiquitously hyper-activated in numerous cancers, rendering it an appealing target for therapeutic intervention.Methods and resultsIn this study, using structure-based virtual screening complemented by molecular dynamics simulations, we identified ten potential STAT3 inhibitors. The simulations pinpointed compounds 8, 9, and 10 as forming distinct hydrogen bonds with the SH2 domain of STAT3. In vitro cytotoxicity assays highlighted compound 4 as a potent inhibitor of gastric cancer cell proliferation across MGC803, KATO III, and NCI-N87 cell lines. Further cellular assays substantiated the ability of compound 4 to attenuate IL-6-mediated STAT3 phosphorylation at Tyr475. Additionally, oxygen consumption rate assays corroborated compound 4's deleterious effects on mitochondrial function.DiscussionCollectively, our findings position compound 4 as a promising lead candidate warranting further exploration in the development of anti-gastric cancer therapeutics.

We studied the effect of freeze–thaw on isoflavone composition in germinated soybeans, particularly the conversion of aglycones, the isoflavone monomers with high biological activity. Germinated soybeans were frozen at −20 °C, −80 °C, and −196 °C respectively, and then the frozen samples (-20 °C) were thawed at 4 °C, 10 °C, and 25 °C respectively. Results showed total isoflavone increased after germination. Aglycones content increased most at −20 °C, which increased about 24 times. The effect of thaw temperature and time indicated there were approximately 89 % glucosides forms converted to aglycones during freeze–thaw. Isoflavone conjugate-hydrolyzing β-glucosidase (ICHG) activity increased by 65.78 % (25 °C) and 59.14 % (48 h) with freeze–thaw. The cells of germinated soybeans were broken, promoting ICHG contact with glucosides and malonyl-glucosides. These results indicated that freeze–thaw greatly changed the content and profile of isoflavones, resulting in a sharp increase in the content of aglycones.

To understand the formation process of dough with different hydration levels upon mixing and the response of dough rheology, the dynamic evolution of gluten protein was tracked and quantified at morphological, structural, and molecular levels. Both macroscopical and microscopic distribution images showed that partial and full hydration induced quick formation of a more compact gluten network compared with limited hydration. Gluten network in highly hydrated samples was more susceptible to the formation and collapse induced by mechanical force. SE-HPLC results indicated significant depolymerization of glutenin macropolymer (GMP) in fully and partially hydrated samples. Sufficient mixing was accompanied by the increase of ionic and hydrogen bonds, while excessive mixing increased exposure of free -SH. Higher hydration level induced more ordered secondary structure. Correlation and principal component analysis revealed the patterns and dynamics of gluten evolution during dough formation with different hydration levels, and their contribution to the changes in dough modulus.

Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.

BackgroundThe counts of examined lymph nodes (ELNs) in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC) is a controversial issue. We conducted a retrospective study to develop an ELNs-based model to individualize ESCC prognosis.MethodsPatients with ESCC from the SEER database and our center were strictly screened. The optimal threshold value was determine by the X-tile software. A prognostic model for ESCC patients was developed and validated with R. The model’s efficacy was evaluated by C-index, ROC curve, and decision curve analysis (DCA).Results3,629 cases and 286 cases were screened from the SEER database and our center, respectively. The optimal cut-off value of ELNs was 10. Based on this, we constructed a model with a favorable C-index (training group: 0.708; external group 1: 0.687; external group 2: 0.652). The model performance evaluated with ROC curve is still reliable among the groups. 1-year AUC for nomogram in three groups (i.e., 0.753, 0.761, and 0.686) were superior to that of the TNM stage (P

The effects of ultraviolet-B (UV-B) radiation on the physiological properties, phenolic acid accumulation, and dough-making quality of wheat during germination were investigated. UV-B radiation inhibited the wheat sprout length and reduced the dry matter loss. As phenolic acids were principally present in the kernels' bran, UV-B radiation could promote their accumulation in the interior of germinated wheat (GW). The total phenolic compounds, ascorbic acid, and antioxidant activity were also enhanced significantly during germination with UV-B. UV-B improved the development time, stability time, rheological properties, and viscosity of GW, and inhibited the α-amylase activity, the destruction of the amorphous region of starch particles, and the proteins degradation process during germination, and thus the deterioration of dough-making quality caused by germination was inhibited. Therefore, UV-B radiation could be a potential approach to enhance the nutritional and dough-making quality of germinated whole wheat flour.

The development of green and efficient methods for hydrogenation of nitroarenes is still highly demanding in organic synthesis. Herein, we report an industrial Cunninghamia lanceolata carbon supported FeO(OH) nanoparticles process for the synthesis of aryl amines with good yields via hydrogenation of nitroarenes. Nine key anti-cancer drug intermediates were successfully achieved with protocol. And Osimertinib intermediate 4m can be smoothly synthesized at a 2.67 kg-scale with >99.5% HPLC purity. This protocol features cheap carbon source, highly catalytic activity, simple operation, kilogram-scalable and recyclable catalysts (eight times without observable losing activity).

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.

Selenium (Se) is an essential mineral in multiple human metabolic pathways with immune modulatory effects on viral diseases including the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and HIV. Plant-based foods contain Se metabolites with unique functionalities for the human metabolism. In order to assess the value of common salad greens as Se source, we conducted a survey of lettuce commercially grown in 15 locations across the USA and Canada and found a tendency for Se to accumulate higher (up to 10 times) in lettuce grown along the Colorado river basin region, where the highest amount of annual solar radiation of the country is recorded. In the same area, we evaluated the effect of sunlight reduction on the Se content of two species of arugula [Eruca sativa (E. sativa) cv. “Astro” and Diplotaxis tenuifolia (D. tenuifolia) cv. “Sylvetta”]. A 90% light reduction during the 7 days before harvest resulted in over one-third Se decline in D. tenuifolia. The effect of light intensity on yield and Se uptake of arugula microgreens was also examined under indoor controlled conditions. This included high intensity (HI) (160 μ mol−2 s−1 for 12 h/12 h light/dark); low intensity (LI) (70 μ mol m−2 s−1 for 12 h/12 h light/dark); and HI-UVA (12 h light of 160 μ mol m−2 s−1, 2 h UVA of 40 μ mol m−2 s−1, and 10 h dark) treatments in a factorial design with 0, 1, 5, and 10 ppm Se in the growing medium. HI and HI-UVA produced D. tenuifolia plants with 25–100% higher Se content than LI, particularly with the two higher Se doses. The addition of Se produced a marked increase in fresh matter (>35% in E. sativa and >45% in D. tenuifolia). This study (i) identifies evidence to suggest the revision of food composition databases to account for large Se variability, (ii) demonstrates the potential of introducing preharvest Se to optimize microgreen yields, and (iii) provides the controlled environment industry with key information to deliver salad greens with targeted Se contents.

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.

Abstract Background Hydroxycinnamoyl-spermine conjugates (HCSpm) are a class of hydroxycinnamic acid amides (HCAAs), which not only are instrumental in plant development and stress response, but also benefit human health. However, HCSpm are not commonly produced in plants, and the mechanism of their biosynthesis remains unclear. In previous investigations of phenolics in Solanum fruits related to eggplant (Solanum melongena L.), we discovered that Solanum richardii, an African wild relative of eggplant, was rich in HCSpms in fruits. Results The putative spermine hydroxycinnamoyl transferase (HT) SpmHT was isolated from S. richardii and eggplant. SrSpmHT expression was high in flowers and fruit, and was associated with HCSpm accumulation in S. richardii; however, SpmHT was hardly detected in eggplant cultivars and other wild relatives. Recombinant SpmHT exclusively selected spermine as the acyl acceptor substrate, while showing donor substrate preference in the following order: caffeoyl-CoA, feruloyl-CoA, and p-coumaroyl-CoA. Molecular docking revealed that substrate binding pockets of SpmHT could properly accommodate spermine but not the shorter, more common spermidine. Conclusion SrSpmHT is a novel spermine hydroxycinnamoyl transferase that uses Spm exclusively as the acyl acceptor substrate to produce HCSpms. Our findings shed light on the HCSpm biosynthetic pathway that may allow an increase of health beneficial metabolites in Solanum crops via methods such as introgression or engineering HCAA metabolism.

ABSTRACT HIV-1 group M was transmitted to humans nearly one century ago. The virus has since evolved to form distinct clades, which spread to different regions of the world. The envelope glycoproteins (Envs) of HIV-1 have rapidly diversified in all infected populations. We examined whether key antigenic sites of Env and signatures of vaccine efficacy are evolving toward similar or distinct structural forms in different populations worldwide. Patterns of amino acid variants that emerged at each position of Env were compared between diverse HIV-1 clades and isolates from different geographic regions. Interestingly, at each Env position, the amino acid in the clade ancestral or regional-founder virus was replaced by a unique frequency distribution (FD) of amino acids. FDs are highly conserved in populations from different regions worldwide and in paraphyletic and monophyletic subclade groups. Remarkably, founder effects of Env mutations at the clade and regional levels have gradually decreased during the pandemic by evolution of each site toward the unique combination of variants. Therefore, HIV-1 Env is evolving at a population level toward well-defined “target” states; these states are not specific amino acids but rather specific distributions of amino acid frequencies. Our findings reveal the powerful nature of the forces that guide evolution of Env and their conservation across different populations. Such forces have caused a gradual decrease in the interpopulation diversity of Env despite an increasing intrapopulation diversity. IMPORTANCE The Env protein of HIV-1 is the primary target in AIDS vaccine design. Frequent mutations in the virus increase the number of Env forms in each population, limiting the efficacy of AIDS vaccines. Comparison of newly emerging forms in different populations showed that each position of Env is evolving toward a specific combination of amino acids. Similar changes are occurring in different HIV-1 subtypes and geographic regions toward the same position-specific combinations of amino acids, often from distinct ancestral sequences. The predictable nature of HIV-1 Env evolution, as shown here, provides a new framework for designing vaccines that are tailored to the unique combination of variants expected to emerge in each virus subtype and geographic region.

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.