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The development of Chinese cabbage (Brassica rapa subsp. pekinensis) varieties that are resilient to climate change is becoming increasingly critical. Our study focused on developing stress-tolerant Chinese cabbage during the summer season to minimize the impacts of global climate change. We evaluated 52 Chinese cabbage accessions grown in the field during the late spring–summer season in Korea. Various phenotypic data of Chinese cabbage in adverse environments were collected from field data. In addition to field screening, we used a controlled-environment chamber to observe the direct impact of heat on young plants, particularly in the root area. A genome-wide association study was conducted using two sets of phenotypic data collected from both chamber and field studies and genotype data acquired from GBS analyses. A total of 57 SNPs distributed across all 10 B. rapa chromosomes were selected to be highly related to the target traits. Several candidate genes were annotated using the flanking sequences of these SNPs. The study revealed that most of the annotated genes seemed to be highly connected with the function of the heat stress response. Other genes were also found to be related to the environmental stress response and flowering. These candidate SNPs and genes can provide valuable tools for breeders to develop summer stress-tolerant Chinese cabbage varieties.

Cannabis sativa L. is an industrially valuable plant known for its cannabinoids, such as cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), renowned for its therapeutic and psychoactive properties. Despite its significance, the cannabis industry has encountered difficulties in guaranteeing consistent product quality throughout the drying process. Hyperspectral imaging (HSI), combined with advanced machine learning technology, has been used to predict phytochemicals that presents a promising solution for maintaining cannabis quality control. We examined the dynamic changes in cannabinoid compositions under diverse drying conditions and developed a non-destructive method to appraise the quality of cannabis flowers using HSI and machine learning. Even when the relative weight and water content remained constant throughout the drying process, drying conditions significantly influenced the levels of CBD, THC, and their precursors. These results emphasize the importance of determining the exact drying endpoint. To develop HSI-based models for predicting cannabis quality indicators, including dryness, precursor conversion of CBD and THC, and CBD : THC ratio, we employed various spectral preprocessing methods and machine learning algorithms, including logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB). The LR model demonstrated the highest accuracy at 94.7–99.7% when used in conjunction with spectral pre-processing techniques such as multiplicative scatter correction (MSC) or Savitzky–Golay filter. We propose that the HSI-based model holds the potential to serve as a valuable tool for monitoring cannabinoid composition and determining optimal drying endpoint. This tool offers the means to achieve uniform cannabis quality and optimize the drying process in the industry.

The integration of hyperspectral imaging with machine learning algorithms has presented a promising strategy for the non-invasive and rapid detection of plant metabolites. For this study, we developed prediction models using partial least squares regression (PLSR) and boosting algo-rithms (such as AdaBoost, XGBoost, and LightGBM) for five metabolites in Brassica juncea leaves: total chlorophyll, phenolics, flavonoids, glucosinolates, and anthocyanins. To enhance the model performance, we employed several spectral data preprocessing methods and feature-selection al-gorithms. Our results showed that the boosting algorithms generally outperformed the PLSR models in terms of prediction accuracy. In particular, the LightGBM model for chlorophyll and the AdaBoost model for flavonoids improved the prediction performance, with R2p = 0.71–0.74, com-pared to the PLSR models (R2p = 0.53–0.58). The final models for the glucosinolates and anthocya-nins performed sufficiently for practical uses such as screening, with R2p = 0.82–0.85 and RPD = 2.4–2.6. Our findings indicate that the application of a single preprocessing method is more effective than utilizing multiple techniques. Additionally, the boosting algorithms with feature selection ex-hibited superior performance compared to the PLSR models in the majority of cases. These results highlight the potential of hyperspectral imaging and machine learning algorithms for the non-destructive and rapid detection of plant metabolites, which could have significant implications for the field of smart agriculture.

Ultraviolet-B (UV-B, 280–315 nm) radiation has been known as an elicitor to enhance bioactive compound contents in plants. However, unpredictable yield is an obstacle to the application of UV-B radiation to controlled environments such as plant factories. A typical three-dimensional (3D) plant structure causes uneven UV-B exposure with leaf position and age-dependent sensitivity to UV-B radiation. The purpose of this study was to develop a model for predicting phenolic accumulation in kale (Brassica oleracea L. var. acephala) according to UV-B radiation interception and growth stage. The plants grown under a plant factory module were exposed to UV-B radiation from UV-B light-emitting diodes with a peak at 310 nm for 6 or 12 h at 23, 30, and 38 days after transplanting. The spatial distribution of UV-B radiation interception in the plants was quantified using ray-tracing simulation with a 3D-scanned plant model. Total phenolic content (TPC), total flavonoid content (TFC), total anthocyanin content (TAC), UV-B absorbing pigment content (UAPC), and the antioxidant capacity were significantly higher in UV-B-exposed leaves. Daily UV-B energy absorbed by leaves and developmental age was used to develop stepwise multiple linear regression models for the TPC, TFC, TAC, and UAPC at each growth stage. The newly developed models accurately predicted the TPC, TFC, TAC, and UAPC in individual leaves with R2 > 0.78 and normalized root mean squared errors of approximately 30% in test data, across the three growth stages. The UV-B energy yields for TPC, TFC, and TAC were the highest in the intermediate leaves, while those for UAPC were the highest in young leaves at the last stage. To the best of our knowledge, this study proposed the first statistical models for estimating UV-B-induced phenolic contents in plant structure. These results provided the fundamental data and models required for the optimization process. This approach can save the experimental time and cost required to optimize the control of UV-B radiation.

UV-B (280–315 nm) radiation has been used as an effective tool to improve bioactive compound contents in controlled environments, such as plant factories. However, plant structure changes with growth progress induce different positional distributions of UV-B radiation interception, which cause difficulty in accurately evaluating the effects of UV-B on biosynthesis of bioactive compounds. The objective of this study was to quantitatively analyze the positional distributions of UV-B radiation interception and bioactive compound contents of kales (Brassica oleracea L. var. acephala) with growth progress and their relationships. Short-term moderate UV-B levels did not affect the plant growth and photosynthetic parameters. Spatial UV-B radiation interception was analyzed quantitatively by using 3D-scanned plant models and ray-tracing simulations. As growth progressed, the differences in absorbed UV-B energy between leaf positions were more pronounced. The concentrations of total phenolic compound (TPC) and total flavonoid compound (TFC) were higher with more cumulative absorbed UV-B energy. The cumulative UV energy yields for TFC were highest for the upper leaves of the older plants, while those for TPC were highest in the middle leaves of the younger plants. Despite the same UV-B levels, the UV-B radiation interception and UV-B susceptibility in the plants varied with leaf position and growth stage, which induced the different biosynthesis of TFC and TPC. This attempt to quantify the relationship between UV-B radiation interception and bioactive compound contents will contribute to the estimation and production of bioactive compounds in plant factories.

Ultraviolet-B (UV-B) acts as a regulatory stimulus, inducing the dose-dependent biosynthesis of phenolic compounds such as flavonoids at the leaf level. However, the heterogeneity of biosynthesis activation generated within a whole plant is not fully understood until now and cannot be interpreted without quantification of UV-B radiation interception. In this study, we analyzed the spatial UV-B radiation interception of kales (Brassica oleracea L. var. Acephala) grown under supplemental UV-B LED using ray-tracing simulation with 3-dimension-scanned models and leaf optical properties. The UV-B-induced phenolic compounds and flavonoids accumulated more, with higher UV-B interception and younger leaves. To distinguish the effects of UV-B energy and leaf developmental age, the contents were regressed separately and simultaneously. The effect of intercepted UV-B on flavonoid content was 4.9-fold that of leaf age, but the effects on phenolic compound biosynthesis were similar. This study confirmed the feasibility and relevance of UV-B radiation interception analysis and paves the way to explore the physical and physiological base determining the intraindividual distribution of phenolic compound in controlled environments.

Although plant responses to artificial lighting spectra often produce abnormal morphogenesis and reduced productivity, no quantification method to determine how plants perceive and respond to light has been available. Our objective in this study was to test whether a plant’s spectral perception can be quantified using the light absorption of its major photoreceptors, phytochrome, cryptochrome, and phototropin. We developed an artificial solar lamp and three different light sources, based on a high-pressure sodium lamp, a fluorescent lamp, and red and blue light-emitting diodes, whose absorption by photoreceptors was equal to that of the standard solar spectrum. Cucumber plants grown under the artificial solar and developed light sources showed normal photomorphogenesis and were indistinguishable from each other. Plants grown under unmodified commercial light sources had abnormal photomorphogenesis that made them short and small. The photosynthetic rate was higher under the unmodified light sources; however, dry masses were highest under the artificial solar and modified light sources, indicating that the cucumber plants are optimized to the solar spectrum. Our results clearly demonstrate that the spectral perceptions of plants can be quantified using the light absorption of their photoreceptors, not visual color or spectra. We expect that our findings will contribute to a better understanding of plant perceptions of and responses to light quality, and improve the productivity of plants cultivated under artificial light.

Among abiotic stresses, both drought and UV-B radiation effectively trigger the accumulation of secondary metabolites, and can be widely applied in plant factories. The objectives of this study were to investigate antioxidant accumulation under drought stress alone, or in combination with UV-B radiation near harvest, and to determine an optimal treatment time for maximum antioxidant production. Kale (Brassica oleracea L. var. acephala) plants were grown in a plant factory and harvested at 42 days after transplanting. The single and combination treatments lasted for 7 to 1 days and 4 to 2 days before harvest, respectively. The results of both Fv/Fm (maximal photochemical efficiency in photosystem II) and leaf water potential could ensure the function of photosynthesis and maintain normal leaf moisture in single drought treatments of less than 4 days. The total phenolic and flavonoid contents and antioxidant activities were significantly increased in both single and combination treatments for 3 to 4 days, compared to other treatments. The supplementary UV-B treatments showed no extra formation of antioxidants compared to the single drought treatments. As a result, drought for 3 days before harvest could achieve the highest potential value of kale as a source of natural antioxidants.

In an effort to discover novel scaffolds of non-nucleotide-derived Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) inhibitors to stimulate the Stimulator of Interferon Genes (STING) pathway, we designed and synthesised pyrrolopyrimidine and pyrrolopyridine derivatives and performed structure-activity relationship (SAR) study. We found 18p possessed high potency (IC50 = 25.0 nM) against ENPP1, and activated STING pathway in a concentration dependent manner. Also, in response to STING pathway activation, cytokines such as IFN-β and IP-10 were induced by 18p in a concentration dependent manner. Finally, we discovered that 18p causes inhibition of tumour growth in 4T1 syngeneic mouse model. This study provides new insight into the designing of novel ENPP1 inhibitors and warrants further development of small molecule immune modulators for cancer immunotherapy.

INTRODUCTION: There has been a rapid increase in the utilization of magnetic resonance imaging (MRI) for prostate cancer detection. The objective of this study was to measure the increase in utilization of MRI before prostate biopsy and the effects on the distribution of Prostate Imaging Reporting and Data System (PI-RAD) scores and Gleason grades over a 5-year interval in an integrated health system. METHODS: The authors conducted a retrospective analysis of prostate MRI studies prior to biopsy in the calendar years of 2017 and 2022. Peak PI-RADS score, peak Gleason grade of suspected prostatic lesions, and the number of biopsy cores were collected from radiology reports and pathology reports from patients’ electronic health records, respectively. All statistical tests were 2-tailed with a significance level set at p < 0.05. Categorical data analyses were performed using Mann–Whitney tests. Continuous data analyses were performed using t-tests. RESULTS: The total number of prostate MRIs and the number of MRIs with subsequent biopsy respectively increased by 178% and 215% over a 5-year interval (2017–2022). There was a higher proportion of MRI studies with an associated biopsy given a PIRADS score of ≥ 3 (91%) and a Gleason grade of ≥ 7 (61%) in 2022 than in 2017 (PI-RADS: 75%; Gleason: 28%). CONCLUSIONS: Increased utilization of prostate MRI has been associated with a higher proportion of biopsies with high PI-RADS and Gleason scores consistent with improved patient selection in this integrated health system. [ABSTRACT FROM AUTHOR]

Abstract Objectives The objective of our study was to investigate the relationship between catastrophic health expenditure (CHE) and health-related quality of life (HRQoL) in general population. Methods We used Korean Health Panel Survey data from 2011 to 2013, which included data from 8850 baseline participants of 19 years of age or older. We defined CHE as total annual out-of-pocket health payment that was 40% greater than the household’s capacity to pay. HRQoL was measured using the EuroQol-visual analogue scale (EQ-VAS). We used generalized estimating equations to perform a longitudinal regression analysis. Results A total of 4.5% of the participants (n = 398) experienced CHE. Those with CHE tended to have a lower EQ-VAS index score compared with those without CHE (β: − 1.34, p = 0.013). A subgroup analysis revealed that individuals experiencing CHE had significant decreases as the number of chronic diseases increased (three or more, β: − 1.85, p = 0.014). Conclusions Catastrophic health expenditure influences HRQoL, which was more pronounced in patient with chronic disease. The efforts should focus on people who suffer from excessive health expenditures and chronic diseases.