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This study was carried out to explore the significance of morphology-based selection for breeding, using a case study of indigenous buckwheat landrace from Korea. Although many studies have utilized molecular methods to investigate germplasm diversity, this research underscores the often-overlooked importance of morphology-based selection in breeding strategies. Furthermore, it highlights the necessity of considering geographical information only when it accurately reflects the actual environment. The study collected and analyzed five traits from 96 samples across 27 varieties. The hypothesis was that these 27 samples might have been grouped by region due to similar environmental conditions. This hypothesis was tested using a dendrogram, clustering, correlation analysis of each trait, and PCA analysis. The results showed that morphological traits are a crucial factor in germplasm or landrace selection and that they should be considered the basic information for breeding purposes. Morphological traits were found to be random regardless of the locations where they were collected, indicating that they should be used to determine breeding targets rather than geographic locations. The study suggested that understanding the physiological traits of specific germplasm groups can accelerate breeding efforts. Overall, this study provided important insights into the selection of germplasm or landrace for breeding purposes, highlighting the importance of morphology-based selection and the need to consider environmental factors.

Abstract A digital twin is a digital representation that closely resembles or replicates a real world object by combining interdisciplinary knowledge and advanced technologies. Digital twins have been applied to various fields, including to the agricultural field. Given big data and systematic data management, digital twins can be used for predicting future outcomes. In this study, we endeavor to create an agricultural digital twin using mandarins as a model crop. We employ an Open API to aggregate data from various sources across Jeju Island, covering an area of approximately 185,000 hectares. The collected data are visualized and analyzed at regional, inter-orchard, and intra-orchard scales. We observe that the intra-orchard analysis explains the variation of fruit quality substantially more than the inter-orchard analysis. Our data visualization and analysis, incorporating statistical models and machine learning algorithms, demonstrate the potential use of agricultural digital twins in the future, particularly in the context of micro-precision and individualized agriculture. This concept extends the current management practices based on data-driven decisions, and it offers a glimpse into the future of individualized agriculture by enabling customized treatment for plants, akin to personalized medicine for humans.

Abstract Background Managing and investigating all available genetic resources are challenging. As an alternative, breeders and researchers use core collection—a representative subset of the entire collection. A good core is characterized by high genetic diversity and low repetitiveness. Among the several available software, GenoCore uses a coverage criterion that does not require computationally expensive distance-based metrics. Results ShinyCore is a new method to select a core collection through two phases. The first phase uses the coverage criterion to quickly attain a fixed coverage, and the second phase uses a newly devised score (referred to as the rarity score) to further enhance diversity. It can attain a fixed coverage faster than a currently available algorithm devised for the coverage criterion, so it will benefit users who have big data. ShinyCore attains the minimum coverage specified by a user faster than GenoCore, and it then seeks to add entries with the rarest allele for each marker. Therefore, measures of genetic diversity and distance can be improved. Conclusion Although GenoCore is a fast algorithm, its implementation is difficult for those unfamiliar with R, ShinyCore can be easily implemented in Shiny with RStudio and an interactive web applet is available for those who are not familiar with programming languages.

This study investigated the potential of petal colorimetric data to classify vase life stages in cut lisianthus flowers (Eustoma grandiflorum). We analyzed the changes in the petal color space over time, focusing on the b* value as an indicator of senescence due to increasing yellowing caused by copigmentation. A comparative analysis was conducted between two cultivation methods: soil (S) and hydroponic (H) cultivation. The objective was to evaluate the performance of machine learning models trained to classify vase life stages based on petal color data. Automated machine learning models exhibited better performance in H-cultivated cut flowers, effectively distinguishing days within the vase life stages from Days 1 to 14 for H cultivation. Cut flowers cultivated under S conditions showed less variation in the color space from Days 1 to 9, maintaining a relatively uniform color range. This made it more difficult to distinguish the vase life stages compared to H cultivation. These findings demonstrate that petal color metrics can serve as reliable indicators of cut flower senescence and potentially facilitate nondestructive methods for classifying vase life stages. This technology holds promise for wider applications in the floriculture industry, improving quality control, and extending the vase life of various cut-flower crops.

Lisianthus, a globally popular ornamental plant, has a variable vase life (5–28 days). This study investigated biomass-related characteristics of four cultivars grown in soil or hydroponic cultivation with different treatment timings (vegetative and reproductive stage) and concentrations (0, 0.1, 0.3, and 0.5 mM) of salicylic acid (SA) in order to explain vase life. The results show that the SA treatment effects varied depending on cultivar, SA treatment timing, concentration, and cultivation method. Principle component analysis revealed that Blue Picote cultivar cultivated hydroponically with 0.5 mM SA at the reproductive stage had the longest vase life. Furthermore, vase life demonstrated a high positive correlation with dry weight, SPAD, Mg content, and flowering day. We developed a model using automated machine learning algorithms to estimate postharvest vase life, based on biomass-related characteristics measured during the pre-harvest period. Similar to the PCA results, this model also identified dry weight as the most influential predictor of vase life. This model proposes the possibility of estimating vase life by setting characteristics highly correlated with vase life as features for machine learning. It is anticipated that this model will be widely utilized in the floriculture industry for standardizing cut flower quality assessments in the future.

Bunium persicum is a valuable medicinal plant with limited production but high market demand. It thrives predominantly in high-altitude regions. The main challenges hindering its widespread cultivation are seed dormancy and a lengthy seed-to-seed cycle, making its large-scale cultivation difficult. Six genotypes of Bunium persicum were collected from different altitudes to evaluate its germination behavior and seed dormancy. The study was conducted during 2020–23 and comprised three experiments (viz., seed germination under an open field, controlled conditions, and micro-tuberization). Under open field conditions, germination percent was genotype dependent, and the highest germination percentage, root length, and shoot length were recorded in Shalimar Kalazeera-1. Germination behavior assessment of the Bunium persicum revealed that treatment T9 (GA3 (25 ppm) + TDZ (9 µM/L)) is effective in breaking the dormancy of Bunium persicum as well as in obtaining a higher germination percent for early development of the tubers. Similarly, with regard to the effect of temperature and moisture conditions, stratification under moist chilling conditions showed effectiveness in breaking seed dormancy as the germination percentage in stratified seeds was at par with the most efficient growth hormone. With regard to the in vitro micro-propagation, direct regeneration showed multiple shoot primordia at the base of the tubers without intervening callus phase from the MS medium supplemented with BA (22.2 µM) and NAA (13.95 µM) 4 weeks after sub-culturing. Similarly, medium supplemented with JA (8.0 mg/L) and BA (22.2 µM) produced well-organized somatic embryos with shiny surfaces, which appeared at the swelled basal portion of apical stems. Further, the combination of JA (6.0 mg/L) and BA (22.2 M) was effective in developing the micro-tubers and also enhanced the weight and length of Bunium persicum micro-tubers.

Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.

Facility cultivation has been evolved from greenhouses to smart farms using artificial intelligence (AI) that simulates big data to maximize production. However, the big data for AI in smart farm is not studied well; the effect of differences among varieties within a crop remains unclear. Therefore, the response of two varieties of blueberry, 'Suziblue' and 'Star', to light was tested using SAPD meter in order to demonstrate the environmental responses could be different among varieties within the same species. The results showed that those two varieties had significant differences in SPAD values based on the leaf's position and time, whereas 'Star' did not. This indicates that the effect of light depends on the variety, which implies that other traits and other crops may show similar differences. These results are based on a simple experiment. However, it is enough to elucidate that it is extremely important to characterize responses to the environment not only for each crop but also for each variety to collect data for smart farming to increase accuracy for modeling; consequently, to maximize the efficiency of these facilities.

Climate change may alter tree species’ distribution, which could impact on forest biodiversity. However, frequent and continuous surveys of forests need intense labor and are time-consuming. The current study utilized SENTINEL-2 images of Geomunoreum to solve this problem as a case study. Acquired images were converted into various indices, such as the normalized difference vegetation index (NDVI), which could be an efficient method to examine the diversity in forests over time. In the current study, the images were obtained in March and April from 2017 to 2021. As a result of analysis using NDVI images of the study area taken from the satellite, vegetation groups were classified into evergreen trees and deciduous trees. This implies that NDVI using extracted data from SENTINEL-2 images could be used for surveying large-scale examinations for tree classification in order to observe variations caused by climate change in an efficient and cost-effective manner.

An assay for detecting Apple scar skin viroid (ASSVd) was developed based on nucleic acid sequence based amplification (NASBA) in combination with real-time detection during the amplification process using molecular beacon. The ASSVd specific primers for amplification of the viroid RNA and molecular beacon for detecting the viroid were designed based on highly conserved regions of several ASSVd sequences including Korean isolate. The assay had a detection range of 1 × 104 to 1 × 1012 ASSVd RNA copies/μl with reproducibility and precision. Following the construction of standard curves based on time to positive (TTP) value for the serial dilutions ranging from 1 × 107 to 1 × 1012 copies of the recombinant plasmid, a standard regression line was constructed by plotting the TTP values versus the logarithm of the starting ASSVd RNA copy number of 10-fold dilutions each. Compared to the established RT-PCR methods, our method was more sensitive for detecting ASSVd. The real-time quantitative NASBA method will be fast, sensitive, and reliable for routine diagnosis and selection of viroid-free stock materials. Furthermore, real-time quantitative NASBA may be especially useful for detecting low levels in apple trees with early viroid-infection stage and for monitoring the influence on tree growth.

Tangor (Citrus unshiu × C. sinensis) cultivars obtained through interspecific hybridization have gained popularity in various countries, including South Korea. However, tangor has a relatively short history of cultivation and thus has been less well researched in terms of physiology and opping technology compared to mandarin. In addition, although tangor grows more vigorously than mandarin and thus has high nutrient demands and longer harvest time, it is more prone to various physiological disorders. Furthermore, the demand for nutrients could vary depending on the cultivars even within the same species. Thus, the current study examined the effects of nitrogen deficiency and resupply on the uptake of mineral nutrients using a specific cultivar ‘Shiranuhi’ as a case study. In this study, the tangor cultivar ‘Shiranuhi’ was cultivated in a hydroponics system, which allows the control of nutrient contents, and changes in mineral nutrient contents under nitrogen deficiency and nitrogen resupply were examined. Based on this, the results show the straightforward relationship between nitrogen and other mineral nutrients under a hydroponic system applying the cultivation of tangor. This implies that the hydroponics system can be applied to tangor cultivation and efficiently and widely expanded in Northeast Asia, and the income of growers will increase through the production of high-quality fruits.

Silicon promotes nodule formation in legume roots which is crucial for nitrogen fixation. However, it is very time-consuming and laborious to count the number of nodules and to measure nodule size manually, which led nodule characterization not to be study as much as other agronomical characters. Thus, the current study incorporated various techniques including machine learning to determine the number and size of root nodules and identify various root phenotypes from root images that may be associated with nodule formation with and without silicon treatment. Among those techniques, the machine learning for characterizing nodule is the first attempt, which enabled us to find high correlations among root phenotypes including root length, number of forks, and average link angles, and nodule characters such as number of nodules and nodule size with silicon treatments. The methods here could greatly accelerate further investigation such as delineating the optimal concentration of silicon for nodule formation.

Background & Aims: Metabolic-dysfunction associated steatohepatitis (MASH) is associated with obesity and diabetes, and is linked to liver fibrosis and cardiovascular disease. Identification of patients who have MASH is challenging and the development of non-invasive strategies to diagnose and follow this condition is an important unmet need. Recent studies in mouse and humans have identified that significant changes occur in liver macrophage composition during MASH progression; namely, resident Kupffer cells decrease in number while recruited monocyte-derived macrophages increase. Methods: We developed peptide radiotracers targeted to C–C motif chemokine receptor 2 (CCR2) and CD163 to conduct positron emission tomography (PET) imaging of recruited vs. resident macrophages, respectively. Mice were placed on a MASH-inducing diet and non-invasive PET imaging of the liver was performed with tissue confirmation studies using flow cytometry and immunofluorescence. Statistical analyses were conducted using Student’s t tests, Pearson correlational analysis, and linear regression. Results: Using a mouse model of MASH, we found that the liver uptake of both CCR2 and CD163 radiotracers detected an increase in recruited cells and a decrease in resident macrophages. These findings correlated well with tissue macrophage content assessed by flow cytometry with an r value of 0.77 (p = 0.002) and 0.78 (p = 0.001) for CCR2 and CD163, respectively. Serial imaging with these radiotracers at several time points during MASH progression and regression revealed good correlation between liver macrophage composition and PET signal intensity. Conclusion: We demonstrate that novel PET radiotracers targeting CCR2 and CD163 can be used to image macrophage composition in MASH. Non-invasive molecular imaging of inflammation has the potential for diagnosis and monitoring of disease activity in humans with MASH. Impact and implications:: Macrophage-mediated inflammation contributes to MASH progression and fibrosis; however, liver biopsy is currently the only tool to assess this response. Thus, the development of non-invasive imaging modalities to identify and follow inflammatory activation is an area of need for patient care. In this study, we leverage molecular imaging using PET radiotracers to follow changes in macrophage composition that are related to MASH disease activity. The results in our preclinical model provide important proof of concept evidence that this approach can be used to diagnose MASH and to follow disease activity in response to intervention. Ongoing studies will evaluate the utility of this modality in humans with MASH.

Purpose: Pelvis tilting in sagittal plane influences the acetabular cup position. Majority of total hip arthroplasty (THA) are performed in lateral decubitus surgical position. This study is to assess whether there is any difference in sacral slope between standing and lateral decubitus position and influence of this variation in planning acetabular cup anteversion. Materials and Methods: This is a prospective study including 50 patients operated between January 2020 to March 2022. Preoperative radiograph included lumbosacral spine lateral X-ray in standing, supine and lateral decubitus positions to calculate the sacral slope for assessment of anterior or posterior pelvic tilting. In our study, we determined the position of the acetabular cup based on changes in sacral slope between standing and lateral decubitus postures. For patients whose sacral slope increased from lateral decubitus to standing, we implanted the acetabular component with a higher degree of anteversion. Conversely, for patients with reverse phenomenon, the cup was inserted at lower anteversion. Results: Twenty-four patients (48.0%) had increase in sacral slope from lateral decubitus to standing whereas 26 patients (52.0%) had decrease in sacral slope. There was linear correlation between difference in preoperative sacral slope and postoperative cross table lateral cup anteversion. Harris hip scores improved from 40.78 to 85.43. There was no subluxation or dislocation in any patient at minimum 2-year follow-up. Conclusion: Individualized acetabular cup placement is important for better functional outcome in THA. Evaluation of pelvic tilting in lateral decubitus position is necessary for better positioning of acetabular cup and avoid postoperative complications. [ABSTRACT FROM AUTHOR]

Complement component 3 glomerulonephritis (C3GN) is a rare kidney disease characterized by complement dysregulation that results in prominent complement component 3 (C3) deposition in the kidneys. The clinical course of C3GN varies from mild hematuria to progressive chronic kidney disease. In most patients, C3GN is driven by acquired factors, namely, autoantibodies that target C3 or C5 convertases. Genetic variations in complement-related genes are less frequent. We report the case of a 9-year-old Korean boy who presented with microscopic hematuria and a persistently low C3 level and had biopsy findings of C3GN, with the presence of a C3 gene mutation: a frameshift mutation associated with C3 deficiency. However, the patient did not exhibit any other symptoms of complement deficiency. Direct DNA sequencing of his family members revealed the same genetic mutation in his father and older brother. This case report is significant because there are very few such reports worldwide concerning gene mutations related to C3 deficiency to be discovered in patients with C3GN. Explaining C3GN pathogenesis is challenging; therefore, additional research is required in the future.