Landrysinger7557

Salinity inhibits plant growth due to salt ion accumulation in plant cells and reduced absorption of other nutrients such as metal ions; however halophyte plants have evolved mechanisms to survive and thrive in high-salt conditions. selleck chemical The euhalophyte Suaeda salsa generates dimorphic seeds (black and brown), which show marked differences in germination and seedling growth under high-salt conditions. However, it is unclear whether their ionic status differs. Here, to provide insight on the role of ions in salt tolerance, we used inductively coupled plasma mass spectrometry to measure the ion contents in the dimorphic seeds from S. salsa plants treated with or without NaCl. link2 We measured the macroelements Na, K, Mg, and Ca, and the microelements Mn, Fe, Zn, Cu, and Mo. NaCl-treated S. salsa plants produced seeds with significantly reduced metallic element contents and significantly increased Na+ contents. The brown seeds of S. salsa plants treated with 0 and 200 mM NaCl had much higher contents of K+, Ca2+, and Fe2+ compared with the black seeds. 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. 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.Ectomycorrhizal fungi (EMF) grow as saprotrophs in soil and interact with plants, forming mutualistic associations with roots of many economically and ecologically important forest tree genera. EMF ensheath the root tips and produce an extensive extramatrical mycelium for nutrient uptake from the soil. In contrast to other mycorrhizal fungal symbioses, EMF do not invade plant cells but form an interface for nutrient exchange adjacent to the cortex cells. link3 The interaction of roots and EMF affects host stress resistance but uncovering the underlying molecular mechanisms is an emerging topic. Here, we focused on local and systemic effects of EMF modulating defenses against insects or pathogens in aboveground tissues in comparison with arbuscular mycorrhizal induced systemic resistance. Molecular studies indicate a role of chitin in defense activation by EMF in local tissues and an immune response that is induced by yet unknown signals in aboveground tissues. Volatile organic compounds may be involved in long-distance communication between below- and aboveground tissues, in addition to metabolite signals in the xylem or phloem. In leaves of EMF-colonized plants, jasmonate signaling is involved in transcriptional re-wiring, leading to metabolic shifts in the secondary and nitrogen-based defense metabolism but cross talk with salicylate-related signaling is likely. Ectomycorrhizal-induced plant immunity shares commonalities with systemic acquired resistance and induced systemic resistance. We highlight novel developments and provide a guide to future research directions in EMF-induced resistance.Germplasm should be conserved in such a way that the genetic integrity of a given accession is maintained. In most genebanks, landraces constitute a major portion of collections, wherein the extent of genetic diversity within and among landraces of crops vary depending on the extent of outcrossing and selection intensity infused by farmers. In this study, we assessed the level of diversity within and among 108 diverse landraces and wild accessions using both phenotypic and genotypic characterization. This included 36 accessions in each of sorghum, pearl millet, and pigeonpea, conserved at ICRISAT genebank. We genotyped about 15 to 25 individuals within each accession, totaling 1,980 individuals using the DArTSeq approach. This resulted in 45,249, 19,052, and 8,211 high-quality single nucleotide polymorphisms (SNPs) in pearl millet, sorghum, and pigeonpea, respectively. Sorghum had the lowest average phenotypic (0.090) and genotypic (0.135) within accession distances, while pearl millet had the highest average phenotypic (0.227) and genotypic (0.245) distances. Pigeonpea had an average of 0.203 phenotypic and 0.168 genotypic within accession distances. Analysis of molecular variance also confirms the lowest variability within accessions of sorghum (26.3%) and the highest of 80.2% in pearl millet, while an intermediate in pigeonpea (57.0%). The effective sample size required to capture maximum variability and to retain rare alleles while regeneration ranged from 47 to 101 for sorghum, 155 to 203 for pearl millet, and 77 to 89 for pigeonpea accessions. This study will support genebank curators, in understanding the dynamics of population within and among accessions, in devising appropriate germplasm conservation strategies, and aid in their utilization for crop improvement.Plants evolve innate immunity including resistance genes to defend against pest and pathogen attack. Our previous studies in cotton (Gossypium spp.) revealed that one telomeric segment on chromosome (Chr) 11 in G. hirsutum cv. Acala NemX (rkn1 locus) contributed to transgressive resistance to the plant parasitic nematode Meloidogyne incognita, but the highly homologous segment on homoeologous Chr 21 had no resistance contribution. To better understand the resistance mechanism, a bacterial chromosome (BAC) library of Acala N901 (Acala NemX resistance source) was used to select, sequence, and analyze BAC clones associated with SSR markers in the complex rkn1 resistance region. Sequence alignment with the susceptible G. hirsutum cv. TM-1 genome indicated that 23 BACs mapped to TM-1-Chr11 and 18 BACs mapped to TM-1-Chr 21. Genetic and physical mapping confirmed less BAC sequence (53-84%) mapped with the TM-1 genome in the rkn1 region on Chr 11 than to the homologous region (>89%) on Chr 21. A 3.1-cM genetic distance between the rkn1 flanking markers CIR316 and CIR069 was mapped in a Pima S-7 × Acala NemX RIL population with a physical distance ∼1 Mbp in TM-1.