Citrus Huanglongbing detection and semi-quantification of the carbohydrate concentration based on micro-FTIR spectroscopy.

Citrus Huanglongbing (HLB) is nowadays one of the most fatal citrus diseases worldwide. Once the citrus tree is infected by the HLB disease, the biochemistry of the phloem region in midribs would change. In order to investigate the carbohydrate changes in phloem region of citrus midrib, the semi-quantification models were established to predict the carbohydrate concentration in it based on Fourier transform infrared microscopy (micro-FTIR) spectroscopy coupled with chemometrics. Healthy, asymptomatic-HLB, symptomatic-HLB, and nutrient-deficient citrus midribs were collected in this study. The results showed that the intensity of the characteristic peak varied with the carbohydrate (starch and soluble sugar) concentration in citrus midrib, especially at the fingerprint regions of 1175-900 cm-1, 1500-1175 cm-1, and 1800-1500 cm-1. Furthermore, semi-quantitative prediction models of starch and soluble sugar were established using the full micro-FTIR spectra and selected characteristic wavebands. The least squares support vector machine regression (LS-SVR) model combined with the random frog (RF) algorithm achieved the best prediction result with the determination coefficient of prediction ([Formula: see text]) of 0.85, the root mean square error of prediction (RMSEP) of 0.36%, residual predictive deviation (RPD) of 2.54, and [Formula: see text] of 0.87, RMSEP of 0.37%, RPD of 2.76, for starch and soluble sugar concentration prediction, respectively. In addition, multi-layer perceptron (MLP) classification models were established to identify HLB disease, achieving the overall classification accuracy of 94% and 87%, based on the full-range spectra and the optimal wavenumbers selected by the random frog (RF) algorithm, respectively. The results demonstrated that micro-FTIR spectroscopy can be a valuable tool for the prediction of carbohydrate concentration in citrus midribs and the detection of HLB disease, which would provide useful guidelines to detect citrus HLB disease.

Development of a Low-Cost Narrow Band Multispectral Imaging System Coupled with Chemometric Analysis for Rapid Detection of Rice False Smut in Rice Seed.

Spectral imaging is a promising technique for detecting the quality of rice seeds. However, the high cost of the system has limited it to more practical applications. The study was aimed to develop a low-cost narrow band multispectral imaging system for detecting rice false smut (RFS) in rice seeds. Two different cultivars of rice seeds were artificially inoculated with RFS. Results have demonstrated that spectral features at 460, 520, 660, 740, 850, and 940 nm were well linked to the RFS. It achieved an overall accuracy of 98.7% with a false negative rate of 3.2% for Zheliang, and 91.4% with 6.7% for Xiushui, respectively, using the least squares-support vector machine. Moreover, the robustness of the model was validated through transferring the model of Zheliang to Xiushui with the overall accuracy of 90.3% and false negative rate of 7.8%. These results demonstrate the feasibility of the developed system for RFS identification with a low detecting cost.

A Genome-Wide View of Transcriptional Responses during Aphis glycines Infestation in Soybean.

Soybean aphid (Aphis glycines Matsumura) is one of the major limiting factors in soybean production. The mechanism of aphid resistance in soybean remains enigmatic as little information is available about the different mechanisms of antibiosis and antixenosis. Here, we used genome-wide gene expression profiling of aphid susceptible, antibiotic, and antixenotic genotypes to investigate the underlying aphid-plant interaction mechanisms. The high expression correlation between infested and non-infested genotypes indicated that the response to aphid was controlled by a small subset of genes. Plant response to aphid infestation was faster in antibiotic genotype and the interaction in antixenotic genotype was moderation. The expression patterns of transcription factor genes in susceptible and antixenotic genotypes clustered together and were distant from those of antibiotic genotypes. Among them APETALA 2/ethylene response factors (AP2/ERF), v-myb avian myeloblastosis viral oncogene homolog (MYB), and the transcription factor contained conserved WRKYGQK domain (WRKY) were proposed to play dominant roles. The jasmonic acid-responsive pathway was dominant in aphid-soybean interaction, and salicylic acid pathway played an important role in antibiotic genotype. Callose deposition was more rapid and efficient in antibiotic genotype, while reactive oxygen species were not involved in the response to aphid attack in resistant genotypes. Our study helps to uncover important genes associated with aphid-attack response in soybean genotypes expressing antibiosis and antixenosis.

Optimization of Ultrasound Assisted Extraction (UAE) of Kinsenoside Compound from Anoectochilus roxburghii (Wall.) Lindl by Response Surface Methodology (RSM).

The purpose of this study was to establish an extraction method for the kinsenoside compound from the whole plant Anoectochilus roxburghii. Ultrasound assisted extraction (UAE) and Ultra-high performance liquid chromatography (UPLC) method were used to extract and determine the content of kinsenoside, while response surface method (RSM) was used to optimize the extraction process. The best possible range for methanol concentration (0-100%), the liquid-solid ratio (5:1-30:1 mL/g), ultrasonic power (240-540 W), duration of ultrasound (10-50 min), ultrasonic temperature (10-60 °C), and the number of extractions (1-4) were obtained according to the single factor experiments. Then, using the Box-Behnken design (BBD) of response surface analysis, the optimum extraction conditions were obtained with 16.33% methanol concentration, the liquid-solid ratio of 10.83:1 mL/g and 35.00 °C ultrasonic temperature. Under these conditions, kinsenoside extraction yield reached 32.24% dry weight. The best conditions were applied to determine the kinsenoside content in seven different cultivation ages in Anoectochilus roburghii.

DNA demethylase Tet2 promotes the terminal maturation of natural killer cells.

The cytotoxicity feature to eliminate malignant cells makes natural killer (NK) cells a candidate for tumor immunotherapy. However, this scenario is currently hampered by inadequate understanding of the regulatory mechanisms of NK cell development. Ten-Eleven-Translocation 2 (Tet2) is a demethylase whose mutation was recently shown to cause phenotypic defects in NK cells. However, the role of Tet2 in the development and maturation of NK cells is not entirely clear. Here we studied the modulatory role of Tet2 in NK cell development and maturation by generating hematopoietic Tet2 knockout mice and mice with Tet2 conditional deletion in NKp46+ NK cells. The results showed that both hematopoietic and NK cell conditional deletion of Tet2 had no effect on the early steps of NK cell development, but impaired the terminal maturation of NK cells defined by CD11b, CD43, and KLRG1 expression. In the liver, Tet2 deletion not only prevented the terminal maturation of NK cells, but also increased the proportion of type 1 innate lymphoid cells (ILC1s) and reduced the proportion of conventional NK cells (cNK). Moreover, hematopoietic deletion of Tet2 lowered the protein levels of perforin in NK cells. Furthermore, hematopoietic deletion of Tet2 downregulated the protein levels of Eomesodermin (Eomes), but not T-bet, in NK cells. In conclusion, our results demonstrate that Tet2 plays an important role in the terminal maturation of NK cells, and the Eomes transcription factor may be involved.

Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View.

The last decades have witnessed a rapid development of noninvasive plant phenotyping, capable of detecting plant stress scale levels from the subcellular to the whole population scale. However, even with such a broad range, most phenotyping objects are often just concerned with leaves. This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view. First, plant sensing and responding to abiotic stress from the diverse vegetative organs (leaves, stems, and roots) and the interplays between these vital components are analyzed. Then, the corresponding noninvasive optical phenotyping techniques are also provided, which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ. Furthermore, we explore methods for analyzing compound stress situations, as field conditions frequently encompass multiple abiotic stressors. Thus, our work goes beyond the conventional approach of focusing solely on individual plant organs. The novel insights of the multi-organ, noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses, as well as the potential interactive effects among various stressors.

Applications of deep-learning approaches in horticultural research: a review.

Deep learning is known as a promising multifunctional tool for processing images and other big data. By assimilating large amounts of heterogeneous data, deep-learning technology provides reliable prediction results for complex and uncertain phenomena. Recently, it has been increasingly used by horticultural researchers to make sense of the large datasets produced during planting and postharvest processes. In this paper, we provided a brief introduction to deep-learning approaches and reviewed 71 recent research works in which deep-learning technologies were applied in the horticultural domain for variety recognition, yield estimation, quality detection, stress phenotyping detection, growth monitoring, and other tasks. We described in detail the application scenarios reported in the relevant literature, along with the applied models and frameworks, the used data, and the overall performance results. Finally, we discussed the current challenges and future trends of deep learning in horticultural research. The aim of this review is to assist researchers and provide guidance for them to fully understand the strengths and possible weaknesses when applying deep learning in horticultural sectors. We also hope that this review will encourage researchers to explore some significant examples of deep learning in horticultural science and will promote the advancement of intelligent horticulture.

Comprehensive Analysis of Photosynthetic Characteristics and Quality Improvement of Purple Cabbage under Different Combinations of Monochromatic Light.

Light is essential for plant growth. Light intensity, photoperiod, and light quality all affect plant morphology and physiology. Compared to light intensity, photoperiod, little is known about the effects of different monochromatic lights on crop species. To investigate how different lighting conditions influence crops with heterogeneous colors in leaves, we examined photosynthetic characteristics and quality (regarding edibility and nutrition) of purple cabbage under different combinations of lights. Eight different treatments were applied including monochromic red (R), monochromic blue (B), monochromic yellow (Y), monochromic green (G), and the combination of red and blue (3/1, RB), red/blue/yellow (3/1/1, RBY), red/blue/green (3/1/1,RBG), and white light as the control. Our results indicate that RBY (3/1/1) treatment promotes the PSII activity of purple cabbage, resulting in improved light energy utilization. By contrast, both G and Y lights alone have inhibitory effect on the PSII activity of purple cabbage. In addition, RBY (3/1/1) significantly boosts the anthocyanin and flavonoids content compared with other treatments. Although we detected highest soluble protein and vitamin C content under B treatment (increased by 30.0 and 14.3% compared with the control, respectively), RBY (3/1/1) appeared to be the second-best lighting condition (with soluble protein and vitamin C content increased by 8.6 and 4.1%, respectively compared with the control). Thus we prove that the addition of yellow light to the traditional combination of red/blue lighting conditions is beneficial to synthesizing photosynthetic pigments and enables superior outcome of purple cabbage growth. Our results indicate that the growth and nutritional quality of purple cabbage are greatly enhanced under RBY (3/1/1) light, and suggest that strategical management of lighting conditions holds promise in maximizing the economic efficiency of plant production and food quality of vegetables grown in controlled environments.