Effects of melatonin, proline, and salicylic acid on seedling growth, photosynthetic activity, and leaf nutrients of sorghum under salt stress.

Soil salinization poses a significant challenge to the sustainability and productivity of agriculture worldwide. This issue continues to hinder plant growth, requiring innovative solutions to alleviate salt stress. Moreover, climate change accelerates soil salinization, which may soon spread to previously unaffected agricultural areas. Therefore, the present study evaluated the potential role of different seed priming agents (hydro (H), salicylic acid (SA), proline (P), and melatonin (MEL)) on seedlings and leaf macro and micronutrients of sorghum grown under four (.27, 2.5, 5.0, and 8.0 dS m-1) soil salinity conditions. Soil salinity drastically reduced all the growth parameters of sorghum seedlings, primarily the reduction in growth traits, which was remarkable after 2.5 dS m-1 soil salinity. In addition, plant height, shoot fresh weight, and stomata were reduced by 40.8%, 74.6%, and 36.5%, respectively, at 8.0 dS m-1 compared to .27 dS m-1. SA- and MEL-primed seeds mitigated the harmful effects of soil salinity by reducing Na+ accumulation in the leaves and increasing the K+/Na+ and Ca2+/Na+ ratios and photosynthetic activity under salt stress. However, the Zn2+, Mn2+, and Cu2+ contents of sorghum leaves increased with increasing soil salinity, and these nutrients also improved with seed priming by SA, MEL, and P. Considering all nutrients, MEL-primed sorghum seeds had better macro- and micro-nutrient uptake capacities than the H, SA, and P treatments under high soil salinity conditions. Finally, the present study showed that MEL-induced improvement in salt tolerance in sorghum seedlings was related to enhanced nutritional status, photosynthetic activity, and biomass production in salinized areas.

Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data.

This study investigates the effects of different water stress levels on spectral information, leaf area index (LAI), and the performance of three machine learning (ML) algorithms in estimating crop water content (CWC) of sorghum. The results show that the spectral reflectance of sorghum varies with growth stage and irrigation treatment, but consistent patterns are observed for each treatment. The LAI of sorghum gradually increased throughout the growth stages, with the most significant variation observed during the flowering stage. In this study, three machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements. Recursive feature elimination (RFE) method was used to select the optimal spectral reflectance wavelengths for the ML models, and principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data. The results indicated that the RF model achieved the highest R2 (0.90) and lowest of RMSE (56.05) value using selected wavelengths, while the XGBoost model demonstrated superior accuracy and reliability in estimating CWC using dimensionality-reduced hyperspectral data (r = 0.96, RMSE = 45.77). Also, the study highlights the importance of vegetation index (VI) in CWC estimate. Some VIs, such as NDVI and MSAVI, performed poorly, while others, such as CL_Rededge and EVI, performed better. The study provides valuable insights into the effects of water stress levels on spectral information, LAI, and the performance of ML algorithms in estimating the CWC of sorghum. The findings have significant implications for precision agriculture, as accurate and reliable estimates of CWC can help farmers optimize irrigation and fertilizer applications, leading to improved crop yields and resource efficiency.

Determining the gas-phase structures of α-helical peptides from shape, microsolvation, and intramolecular distance data.

Mass spectrometry is a powerful technique for the structural and functional characterization of biomolecules. However, it remains challenging to accurately gauge the gas-phase structure of biomolecular ions and assess to what extent native-like structures are maintained. Here we propose a synergistic approach which utilizes Förster resonance energy transfer and two types of ion mobility spectrometry (i.e., traveling wave and differential) to provide multiple constraints (i.e., shape and intramolecular distance) for structure-refinement of gas-phase ions. We add microsolvation calculations to assess the interaction sites and energies between the biomolecular ions and gaseous additives. This combined strategy is employed to distinguish conformers and understand the gas-phase structures of two isomeric α-helical peptides that might differ in helicity. Our work allows more stringent structural characterization of biologically relevant molecules (e.g., peptide drugs) and large biomolecular ions than using only a single structural methodology in the gas phase.

Improved simulation of cryogenic fluid mixing at supercritical pressures.

The combustion chamber pressure of rockets, gas turbines and diesel engines is known to be above the critical pressure of fuel and oxidizers. In the case of rocket engines the fuel and/or oxidizer is often injected at cryogenic temperatures. This elevated combustion chamber pressure and low temperature demands special treatment for numerical analysis of mixing. Thus a novel implementation of an improved equation of state has been proposed which provides better estimation of densities. Experimental and numerical data from literature has been used for validation of the analysis methodology.