However, the S. salsa seeds (both black and brown) from NaCl-treated plants were significantly larger, and had higher germination rate and higher seedling salt tolerance compared with seeds from plants not treated with NaCl.  https://www.selleckchem.com/ALK.html  Interestingly, we measured significantly higher Zn2+ contents in the brown seeds from plants treated with NaCl compared with the black seeds. This suggests that the high contents of Zn2+ and other cations affected seed development and salt tolerance during germination under high-salt conditions. These observations provide insight into the mechanisms of salt tolerance in this halophyte and inform efforts to increase salt tolerance in salt-sensitive species.Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.The stimulation of plant innate immunity by elicitors is an emerging technique in agriculture that contributes more and more to residue-free crop protection. Here, we used RNA-sequencing to study gene transcription in tomato leaves treated three times with the chitooligosaccharides-oligogalacturonides (COS-OGA) elicitor FytoSave® that induces plants to fend off against biotrophic pathogens. Results showed a clear upregulation of sequences that code for chloroplast proteins of the electron transport chain, especially Photosystem I (PSI) and ferredoxin. Concomitantly, stomatal conductance decreased by half, reduced nicotinamide adenine dinucleotide phosphate [NAD(P)H] content and reactive oxygen species production doubled, but fresh and dry weights were unaffected. Chlorophyll, β-carotene, violaxanthin, and neoxanthin contents decreased consistently upon repeated elicitations. Fluorescence measurements indicated a transient decrease of the effective PSII quantum yield and a non-photochemical quenching increase but only after the first spraying. Taken together, this suggests that plant defense induction by COS-OGA induces a long-term acclimation mechanism and increases the role of the electron transport chain of the chloroplast to supply electrons needed to mount defenses targeted to the apoplast without compromising biomass accumulation.SKIP, a component of the spliceosome, is involved in numerous signaling pathways. However, there is no direct genetic evidence supporting the function of SKIP in defense responses. In this paper, two SKIPs, namely, SlSKIP1a and SlSKIP1b, were analyzed in tomato. qRT-PCR analysis showed that the SlSKIP1b expression was triggered via Pseudomonas syringae pv. tomato (Pst) DC3000 and Botrytis cinerea (B. cinerea), together with the defense-associated signals. In addition, the functions of SlSKIP1a and SlSKIP1b in disease resistance were analyzed in tomato through the virus-induced gene silencing (VIGS) technique. VIGS-mediated SlSKIP1b silencing led to increased accumulation of reactive oxygen species (ROS), along with the decreased expression of defense-related genes (DRGs) after pathogen infection, suggesting that it reduced B. cinerea and Pst DC3000 resistance. There was no significant difference in B. cinerea and Pst DC3000 resistance in TRV-SlSKIP1a-infiltrated plants compared with the TRV-GUS-silencing counterparts. As suggested by the above findings, SlSKIP1b plays a vital role in disease resistance against pathogens possibly by regulating the accumulation of ROS as well as the expression of DRGs.Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https//github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.