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Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems.

The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in different geographical regions of South Korea. Spectra were obtained from the adaxial side of the leaves at 1.5 nm intervals in the Vis-NIR spectral range between 400 and 1075 nm. The obtained spectra were assessed with four different preprocessing methods in order to detect the optimum preprocessing method with high classification accuracy. Preprocessed spectra of six Amaranthus sp. were used as input for the machine learning-based chemometric analysis. All the classification results were validated using cross-validation to produce robust estimates of classification accuracies. The different combinations of preprocessing and modeling were shown to have a classification accuracy of between 71% and 99.7% after the cross-validation. The combination of Savitzky-Golay preprocessing and Support vector machine showed a maximum mean classification accuracy of 99.7% for the discrimination of Amaranthus sp. Considering the high number of spectra involved in this study, the growth stage of the plants, varying measurement locations, and the scanning position of leaves on the plant are all important. We conclude that Vis-NIR spectroscopy, in combination with appropriate preprocessing and machine learning methods, may be used in the field to effectively classify Amaranthus sp. for the effective management of the weedy species and/or for monitoring their food applications.

In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination.

Globally, the cultivation area of genetically modified (GM) crops is increasing dramatically. Despite their well-known benefits, they may also pose many risks to agriculture and the environment. Among the various GM crops, GM rapeseed (Brassica napus L.) is widely cultivated, mainly for oil production. At the same time, B. napus possesses a number of characteristics, including the ability to form feral populations and act as small-seeded weeds, and has a high potential for hybridization with other species. In this review, we provide an overview of the commercialization, approval status, and cultivation of GM rapeseed, as well as the status of the feral rapeseed populations. In addition, we highlight the case studies on the unintentional environmental release of GM rapeseed during transportation in several countries. Previous studies suggest that the main reason for the unintentional release is seed spillage during transport/importing of rapeseed in both GM rapeseed-cultivating and -non-cultivating countries. Despite the fact that incidents of unintentional release have been recorded often, there have been no reports of serious detrimental consequences. However, since rapeseed has a high potential for hybridization, the possibilities of gene flow within the genus, especially with B. rapa, are relatively significant, and considering their weedy properties, effective management methods are needed. Hence, we recommend that specific programs be used for the effective monitoring of environmental releases of GM rapeseed as well as management to avoid environmental and agricultural perturbations.

Isoflavones are ecophysiologically active secondary metabolites derived from the phenylpropanoid pathway. They were mostly found in leguminous plants, especially in the pea family. Isoflavones play a key role in plant–environment interactions and act as phytoalexins also having an array of health benefits to the humans. According to epidemiological studies, a high intake of isoflavones-rich diets linked to a lower risk of hormone-related cancers, osteoporosis, menopausal symptoms, and cardiovascular diseases. These characteristics lead to the significant advancement in the studies on genetic and metabolic engineering of isoflavones in plants. As a result, a number of structural and regulatory genes involved in isoflavone biosynthesis in plants have been identified and characterized. Subsequently, they were engineered in various crop plants for the increased production of isoflavones. Furthermore, with the advent of high-throughput technologies, the regulation of isoflavone biosynthesis gains attention to increase or decrease the level of isoflavones in the crop plants. In the review, we begin with the role of isoflavones in plants, environment, and its benefits in human health. Besides, the main theme is to discuss the updated research progress in metabolic engineering of isoflavones in other plants species and regulation of production of isoflavones in soybeans.

The rapid advancement of genetically modified (GM) technology over the years has raised concerns about the safety of GM crops and foods for human health and the environment. Gene flow from GM crops may be a threat to the environment. Therefore, it is critical to develop reliable, rapid, and low-cost technologies for detecting and monitoring the presence of GM crops and crop products. Here, we used visible near-infrared (Vis-NIR) spectroscopy to distinguish between GM and non-GM Brassica napus, B. juncea, and F1 hybrids (B. juncea X GM B. napus). The Vis-NIR spectra were preprocessed with different preprocessing methods, namely normalization, standard normal variate, and Savitzky–Golay. Both raw and preprocessed spectra were used in combination with eight different chemometric methods for the effective discrimination of GM and non-GM plants. The standard normal variate and support vector machine combination was determined to be the most accurate model in the discrimination of GM, non-GM, and hybrid plants among the many combinations (99.4%). The use of deep learning in combination with Savitzky–Golay resulted in 99.1% classification accuracy. According to the findings, it is concluded that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used to distinguish between GM and non-GM B. napus, B. juncea, and F1 hybrids.

Bacterial communities in rhizosphere and root nodules have significant contributions to the growth and productivity of the soybean (Glycine max (L.) Merr.). In this report, we analyzed the physiological properties and dynamics of bacterial community structure in rhizosphere and root nodules at different growth stages using BioLog EcoPlate and high-throughput sequencing technology, respectively. The BioLog assay found that the metabolic capability of rhizosphere is in increasing trend in the growth of soybeans as compared to the bulk soil. As a result of the Illumina sequencing analysis, the microbial community structure of rhizosphere and root nodules was found to be influenced by the variety and growth stage of the soybean. At the phylum level, Actinobacteria were the most abundant in rhizosphere at all growth stages, followed by Alphaproteobacteria and Acidobacteria, and the phylum Bacteroidetes showed the greatest change. But, in the root nodules Alphaproteobacteria were dominant. The results of the OTU analysis exhibited the dominance of Bradyrhizobium during the entire stage of growth, but the ratio of non-rhizobial bacteria showed an increasing trend as the soybean growth progressed. These findings revealed that bacterial community in the rhizosphere and root nodules changed according to both the variety and growth stages of soybean in the field.

We have isolated and characterized a putative rice MAPK gene (designated OsMAPK44) encoding for a protein of 593 amino acids that has the MAPK family signature and phosphorylation activation motif, TDY. Alignment of the predicted amino acid sequences of OsMAPK44 showed high homology with other rice MAPKs. Under normal conditions, the OsMAPK44 gene is highly expressed in root tissues, but relatively less in leaf and stem tissues of the japonica type rice plant (O. sativa L. Donggin). mRNA expression of the gene is highly inducible by salt and drought treatment, but not by cold treatment. Moreover, the mRNA level of the OsMAPK44 is up-regulated by exogenously applied Abscisic acid (ABA) and H2O2. When we compared the OsMAPK44 gene expression level between a salt sensitive indica cultivar (IR64) and a salt resistant indica cultivar (Pokkali), they showed some difference in expression kinetics with the salt treatment. OsMAPK44 gene expression in Pokkali was slightly up-regulated within 30 min and then disappeared rapidly, while IR64 maintained its expression for 1 h following down-regulation. Under the salinity stress, OsMAPK44 overexpression transgenic rice plants showed less damage and greater ratio of potassium and sodium than OsMAPK44 suppressed transgenic lines did, suggesting that OsMAPK44 may have a role to prevent damages due to working for favorable ion balance in the presence of salinity.

To define and quantify the upper eyelid contour of normal Asian adults using a software program utilizing Bézier curves. Eighty eyes of 80 healthy Korean subjects were included in this study. The Bézier curve tool of Image J software was used to extract the upper eyelid contours. The x and y coordinate values of the four points of the Bézier curve were analyzed using graphical analysis software in Matlab. Various parameters were measured automatically using a custom-written software algorithm. The Bézier curves showed an excellent level of inter-rater reliability (intraclass correlation coefficient of 0.97 for absolute agreement, two-way random effects model, 95% CI 0.94 to 0.98). The Bézier curve for Asians was drawn from the medial palpebral commissure to the lateral canthus. The eye width was 24.8[Formula: see text]1.8 mm in women and 25.3[Formula: see text]2.0 mm in men. The contour peak was located at 1.7[Formula: see text]1.0 mm temporally from the pupil center in women and 1.5[Formula: see text]1.3 mm in men, and the height was 4.7[Formula: see text]0.7 mm in women and 3.8[Formula: see text]0.9 mm in men. The palpebral fissure obliquity (angle of the line connecting the medial canthal end to the lateral canthal end) was 9.98[Formula: see text]3.07° in women and 7.52[Formula: see text]2.89° in men. The upper eyelid contour was measured simply and efficiently using a Bézier curves function. The main features of Asian upper eyelid contour are that the inner part of the medial canthus is covered by the eyelids, and the lateral endpoint is located higher than the medial endpoint.

Background This study aimed to classify end-stage knee osteoarthritis (KOA) based on the pattern of joint space loss in standing extended view (SEV) and fixed flexion view (FFV) and to investigate clinical and radiological differences. Methods A total of 459 knees from 300 patients with Kellgren-Lawrence grade 4 KOA were retrospectively analyzed. The knees were divided into three groups based on the pattern of joint space loss in SEV and FFV: group 1 (all loss) with joint space loss in both SEV and FFV, group 2 (flexion loss) with joint space loss only in FFV, and group 3 (extension loss) with joint space loss only in SEV. The primary endpoints were clinical and radiological parameters, while the secondary endpoints included intraoperative measurements and the survival rate until total knee arthroplasty (TKA). Results A total of 459 knees from 300 patients were included. Among the participants, there were 77 men (25.7%) (average age of 72.21 ± 7.35 years), and 223 women (74.3%) (average age of 72.75 ± 6.56 years) ( p  = 0.546). Compared to group 2, group 1 showed a larger hip-knee-ankle angle (9.8 ± 7.0° and 6.3 ± 5.0°, p  < 0.001), higher VAS (6.3 ± 2.4 and 4.6 ± 2.5, p  < 0.001), shorter time to surgery (7.1 ± 7.7 months and 11.0 ± 8.7 months, p  < 0.001), smaller full flexion angle (114.3 ± 13.4° and 121.2 ± 11.9°, p  = 0.001), and a higher total knee arthroplasty rate (76% and 57.2%, p  < 0.001). Group 3 showed a larger flexion contracture angle compared to group 2 (10.00 ± 9.6° and 5.3 ± 5.4°, p  = 0.032). The posterior tibial slope (PTS) was largest in group 2 (11.3 ± 3.3°), followed by group 1 (8.1 ± 3.3°), and smallest in group 3 (5.4 ± 2.7°) (both p  < 0.001, respectively). There were no statistical differences in the intra-operative measurements. TKA was performed on 259 knees (64.3%), and the survival rates at 1 year were 48.1% for group 2, 29.2% for group 3, and 26.7% for group 1 (log-rank test, p  < 0.001). Conclusions This study demonstrates that radiological and clinical differences exist within end-stage KOA based on joint space loss patterns. Additionally, our findings suggest that a larger PTS may be associated with less symptom severity in advanced KOA, contrary to its currently recognized negative effects. These findings may be beneficial for developing patient-specific treatment plans. Level of evidence Retrospective cohort study, Level III