Preliminary study on discrimination of transgenic cotton seeds using terahertz time-domain spectroscopy

High-precise determination of the drought and cold resistance of forage seeds using terahertz time-domain spectroscopy

Owing to the complicated geographical locations and climates, cultivation and selection of forage seeds are challenging. For the first time, we qualitatively distinguished the drought and cold resistance of forage seeds with the time domain and refractive index spectra using terahertz (THz) time-domain spectroscopy. A multilayer structure propagation (MSP) model was developed based on the effective medium and light transport theory to reveal the underlying biological mechanisms of drought and cold resistance of forage seeds. The proposed MSP model accurately explained the behavior of the THz waves transmitted through the forage seeds, with a high accuracy rate of 94.433%. The impact of THz wave transmission was influenced by the presence of various biological components in the alfalfa seeds, particularly protein and carbohydrate. More interestingly, the cold and drought resistance of forage seeds can be effectively differentiated with the ratio of the thickness-dependent argument parameter (Ψ) of protein and carbohydrate components. The obtained results offered important insights into the interaction mechanism between THz wave and forage seeds, and proposed a promising MSP model in the screening process for selecting high-quality forage seeds based on their stress resistance characteristics.

Mechanistic Study of L-Rhamnose Monohydrate Dehydration Using Terahertz Spectroscopy and Density Functional Theory

L-rhamnose has recently gained attention for its potential to enhance vaccine antigenicity. To optimize its use as a vaccine adjuvant, it is important to understand the dehydration behavior of L-rhamnose monohydrate, which plays a critical role in modifying its physicochemical properties. This study investigated the spectroscopic characteristics of L-rhamnose and its monohydrate using terahertz time-domain spectroscopy (THz-TDS), Raman spectroscopy, and powder X-ray diffraction (PXRD). The results indicate that THz-TDS can more effectively distinguish the spectral features of these two compounds and can be used to reflect the structural changes in L-rhamnose monohydrate before and after dehydration. THz spectral data show that dehydration of L-rhamnose occurs at 100 °C, and continuous heating at 100 °C can complete the dehydration process within 6 min. Density functional theory (DFT) calculations revealed that water molecule vibrations significantly affect the THz absorption peaks. These findings indicate that removing water during dehydration causes substantial changes in molecular structure and dynamics. Overall, this study highlights the value of combining THz-TDS with DFT calculations to investigate the structures of carbohydrates and their hydrates, providing an accurate method for understanding the dehydration process and molecular interactions in hydrated systems. This approach holds significant importance for the development of effective vaccine adjuvants.

Research on terahertz image analysis of thin-shell seeds based on semantic segmentation

Assessing crop seed phenotypic traits is essential for breeding innovations and germplasm enhancement. However, the tough outer layers of thin-shelled seeds present significant challenges for traditional methods aimed at the rapid assessment of their internal structures and quality attributes. This study explores the potential of combining terahertz (THz) time-domain spectroscopy and imaging with semantic segmentation models for the rapid and non-destructive examination of these traits. A total of 120 watermelon seed samples from three distinct varieties, were curated in this study, facilitating a comprehensive analysis of both their outer layers and inner kernels. Utilizing a transmission imaging modality, THz spectral images were acquired and subsequently reconstructed employing a correlation coefficient method. Deep learning-based SegNet and DeepLab V3+ models were employed for automatic tissue segmentation. Our research revealed that DeepLab V3+ significantly surpassed SegNet in both speed and accuracy. Specifically, DeepLab V3+ achieved a pixel accuracy of 96.69 % and an intersection over the union of 91.3 % for the outer layer, with the inner kernel results closely following. These results underscore the proficiency of DeepLab V3+ in distinguishing between the seed coat and kernel, thereby furnishing precise phenotypic trait analyses for seeds with thin shells. Moreover, this study accentuates the instrumental role of deep learning technologies in advancing agricultural research and practices.

Integrating spectral and image information for prediction of cottonseed vitality

Cotton plays a significant role in people's lives, and cottonseeds serve as a vital assurance for successful cotton cultivation and production. Premium-quality cottonseeds can significantly enhance the germination rate of cottonseeds, resulting in increased cotton yields. The vitality of cottonseeds is a crucial metric that reflects the quality of the seeds. However, currently, the industry lacks a non-destructive method to directly assess cottonseed vitality without compromising the integrity of the seeds. To address this challenge, this study employed a hyperspectral imaging acquisition system to gather hyperspectral data on cottonseeds. This system enables the simultaneous collection of hyperspectral data from 25 cottonseeds. This study extracted spectral and image information from the hyperspectral data of cottonseeds to predict their vitality. SG, SNV, and MSC methods were utilized to preprocess the spectral data of cottonseeds. Following this preprocessing step, feature wavelength points of the cottonseeds were extracted using SPA and CARS algorithms. Subsequently, GLCM was employed to extract texture features from images corresponding to these feature wavelength points, including attributes such as Contrast, Correlation, Energy, and Entropy. Finally, the vitality of cottonseeds was predicted using PLSR, SVR, and a self-built 1D-CNN model. For spectral data analysis, the 1D-CNN model constructed after MSC+CARS preprocessing demonstrated the highest performance, achieving a test set correlation coefficient of 0.9214 and an RMSE of 0.7017. For image data analysis, the 1D-CNN model constructed after SG+CARS preprocessing outperformed the others, yielding a test set correlation coefficient of 0.8032 and an RMSE of 0.9683. In the case of fused spectral and image data, the 1D-CNN model built after SG+SPA preprocessing displayed the best performance, attaining a test set correlation coefficient of 0.9427 and an RMSE of 0.6872. These findings highlight the effectiveness of the 1D-CNN model and the fusion of spectral and image features for cottonseed vitality prediction. This research contributes significantly to the development of automated detection devices for assessing cottonseed vitality.

Inter-tissue and inter-organ signaling in drought stress response and phenotyping of drought tolerance

Plant response to drought stress includes systems for intracellular regulation of gene expression and signaling, as well as inter-tissue and inter-organ signaling, which helps entire plants acquire stress resistance. Plants sense water-deficit conditions both via the stomata of leaves and roots, and transfer water-deficit signals from roots to shoots via inter-organ signaling. Abscisic acid is an important phytohormone involved in the drought stress response and adaptation, and is synthesized mainly in vascular tissues and guard cells of leaves. In leaves, stress-induced abscisic acid is distributed to various tissues by transporters, which activates stomatal closure and expression of stress-related genes to acquire drought stress resistance. Moreover, the stepwise stress response at the whole-plant level is important for proper understanding of the physiological response to drought conditions. Drought stress is sensed by multiple types of sensors as molecular patterns of abiotic stress signals, which are transmitted via separate parallel signaling networks to induce downstream responses, including stomatal closure and synthesis of stress-related proteins and metabolites. Peptide molecules play important roles in the inter-organ signaling of dehydration from roots to shoots, as well as signaling of osmotic changes and reactive oxygen species/Ca²⁺ . In this review, we have summarized recent advances in research on complex plant drought stress responses, focusing on inter-tissue signaling in leaves and inter-organ signaling from roots to shoots. We have discussed the mechanisms via which drought stress adaptations and resistance are acquired at the whole-plant level, and have proposed the importance of quantitative phenotyping for measuring plant growth under drought conditions.

A bHLH transcription factor from Chenopodium glaucum confers drought tolerance to transgenic maize by positive regulation of morphological and physiological performances and stress-responsive genes' expressions

The basic helix-loop-helix (bHLH) transcription factor has been shown to play an important role in various physiological processes. However, its functions and mechanisms in drought tolerance still remain poorly understood. Here, we reported a bHLH transcription factor - CgbHLH001 - from Chenopodium glaucum, which was able to confer drought tolerance in maize. CgbHLH001-overexpressed maize lines exhibited drought-tolerant phenotype and improved ear traits by accumulating the contents of soluble sugar and proline and elevating the activities of antioxidant enzymes (SOD, POD, and CAT) under drought stress, accompanying with the upregulation of some stress-related genes, which may balance the redox and osmotic homeostasis compared with the non-transgenic and CgbHLH001-RNAi plants. These findings suggest that CgbHLH001 can confer drought tolerance and has the potential for utilization in improving drought resistance in maize breeding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01267-4.