Hughesthyssen9245

When pathogens overcome PTI-usually through effectors in the absence of R proteins-effectors-triggered susceptibility (ETS) ensues. Qualitatively, many of the downstream defense responses overlap between PTI and ETI. In general, these multiple phases of Phytophthora-plant interactions follow the PTI-ETS-ETI paradigm, initially proposed in the zigzag model of plant immunity. However, based on several examples, in Phytophthora-plant interactions, boundaries between these phases are not distinct but are rather blended pointing to a PTI-ETI continuum.Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs. The objective of this study was to make this effect and its implications for practical breeding programs comprehensible for breeders and scientists with a limited background in quantitative genetics and genomic selection theory. We, therefore, compared genomic prediction accuracies obtained from different random cross validation approaches and within-family prediction in three different prediction scenarios. We used a highly structured population of 940 Brassica napus hybrids coming from 46 testcross families and two subpopulations. Our demonstrations show how genomic prediction accuracies obtained from among-family predictions in random cross validation and within-family predictions capture different measures of prediction accuracy. While among-family prediction accuracy measures prediction accuracy of both the parent average component and the Mendelian sampling term, within-family prediction only measures how accurately the Mendelian sampling term can be predicted. With this paper we aim to foster a critical approach to different measures of genomic prediction accuracy and a careful analysis of values observed in genomic selection experiments and reported in literature.Nuru is a deep learning object detection model for diagnosing plant diseases and pests developed as a public good by PlantVillage (Penn State University), FAO, IITA, CIMMYT, and others. It provides a simple, inexpensive and robust means of conducting in-field diagnosis without requiring an internet connection. Diagnostic tools that do not require the internet are critical for rural settings, especially in Africa where internet penetration is very low. An investigation was conducted in East Africa to evaluate the effectiveness of Nuru as a diagnostic tool by comparing the ability of Nuru, cassava experts (researchers trained on cassava pests and diseases), agricultural extension officers and farmers to correctly identify symptoms of cassava mosaic disease (CMD), cassava brown streak disease (CBSD) and the damage caused by cassava green mites (CGM). The diagnosis capability of Nuru and that of the assessed individuals was determined by inspecting cassava plants and by using the cassava symptom recognition assessment tool (CaSRAT) to score images of cassava leaves, based on the symptoms present. Nuru could diagnose symptoms of cassava diseases at a higher accuracy (65% in 2020) than the agricultural extension agents (40-58%) and farmers (18-31%). Nuru's accuracy in diagnosing cassava disease and pest symptoms, in the field, was enhanced significantly by increasing the number of leaves assessed to six leaves per plant (74-88%). Two weeks of Nuru practical use provided a slight increase in the diagnostic skill of extension workers, suggesting that a longer duration of field experience with Nuru might result in significant improvements. Overall, these findings suggest that Nuru can be an effective tool for in-field diagnosis of cassava diseases and has the potential to be a quick and cost-effective means of disseminating knowledge from researchers to agricultural extension agents and farmers, particularly on the identification of disease symptoms and their management practices.Increasing the understanding genetic basis of the variability in root system architecture (RSA) is essential to improve resource-use efficiency in agriculture systems and to develop climate-resilient crop cultivars. Roots being underground, their direct observation and detailed characterization are challenging. Here, were characterized twelve RSA-related traits in a panel of 137 early maturing soybean lines (Canadian soybean core collection) using rhizoboxes and two-dimensional imaging. Significant phenotypic variation (P less then 0.001) was observed among these lines for different RSA-related traits. This panel was genotyped with 2.18 million genome-wide single-nucleotide polymorphisms (SNPs) using a combination of genotyping-by-sequencing and whole-genome sequencing. A total of 10 quantitative trait locus (QTL) regions were detected for root total length and primary root diameter through a comprehensive genome-wide association study. These QTL regions explained from 15 to 25% of the phenotypic variation and contained two putative candidate genes with homology to genes previously reported to play a role in RSA in other species. These genes can serve to accelerate future efforts aimed to dissect genetic architecture of RSA and breed more resilient varieties.Recurrent polyploid formation and weak reproductive barriers between independent polyploid lineages generate intricate species complexes with high diversity and reticulate evolutionary history. Uncovering the evolutionary processes that formed their present-day cytotypic and genetic structure is a challenging task. We studied the species complex of Cardamine pratensis, composed of diploid endemics in the European Mediterranean and diploid-polyploid lineages more widely distributed across Europe, focusing on the poorly understood variation in Central Europe. Z-YVAD-FMK cell line To elucidate the evolution of Central European populations we analyzed ploidy level and genome size variation, genetic patterns inferred from microsatellite markers and target enrichment of low-copy nuclear genes (Hyb-Seq), and environmental niche differentiation. We observed almost continuous variation in chromosome numbers and genome size in C. pratensis s.str., which is caused by the co-occurrence of euploid and dysploid cytotypes, along with aneuploidsof diverse processes that have driven the evolution of the species studied, including allopatric and ecological divergence, hybridization, multiple polyploid origins, and genetic reshuffling caused by Pleistocene climate-induced range dynamics.Global climate change and the expected increase in temperature are altering the relationship between geography and grapevine (V. vinifera) varietal performance, and the implications of which are yet to be fully understood. We investigated berry phenology and biochemistry of 30 cultivars, 20 red and 10 white, across three seasons (2017-2019) in response to a consistent average temperature difference of 1.5°C during the growing season between two experimental sites. The experiments were conducted at Ramat Negev (RN) and Ramon (MR) vineyards, located in the Negev desert, Israel. A significant interaction between vineyard location, season, and variety affected phenology and berry indices. The warmer RN site was generally associated with an advanced phenological course for the white cultivars, which reached harvest up to 2 weeks earlier than at the MR site. The white cultivars also showed stronger correlation between non-consecutive phenological stages than did the red ones. In contrast, harvest time of red cultivwhile this tendency was very slight among white cultivars. White cultivars seem to harbor a considerable degree of resilience due to a combination of earlier and shorter ripening phase, which avoids most of the summer heat. Taken together, our study demonstrates that the extensive genetic capacity of V. vinifera bears significant potential and plasticity to withstand the temperature increase associated with climate change.Elevated concentrations of CO2 (CO2) in plants with C3 photosynthesis metabolism, such as wheat, stimulate photosynthetic rates. However, photosynthesis tends to decrease as a function of exposure to high (CO2) due to down-regulation of the photosynthetic machinery, and this phenomenon is defined as photosynthetic acclimation. Considerable efforts are currently done to determine the effect of photosynthetic tissues, such us spike, in grain filling. There is good evidence that the contribution of ears to grain filling may be important not only under good agronomic conditions but also under high (CO2). The main objective of this study was to compare photoassimilate production and energy metabolism between flag leaves and glumes as part of ears of wheat (Triticum turgidum L. subsp. durum cv. Amilcar) plants exposed to ambient [a(CO2)] and elevated [e(CO2)] (CO2) (400 and 700 μmol mol-1, respectively). Elevated CO2 had a differential effect on the responses of flag leaves and ears. The ears showed higher gross photosynthesis and respiration rates compared to the flag leaves. The higher ear carbohydrate content and respiration rates contribute to increase the grain dry mass. Our results support the concept that acclimation of photosynthesis to e(CO2) is driven by sugar accumulation, reduction in N concentrations and repression of genes related to photosynthesis, glycolysis and the tricarboxylic acid cycle, and that these were more marked in glumes than leaves. Further, important differences are described on responsiveness of flag leaves and ears to e(CO2) on genes linked with carbon and nitrogen metabolism. These findings provide information about the impact of e(CO2) on ear development during the grain filling stage and are significant for understanding the effects of increasing (CO2) on crop yield.Selenium (Se) is considered a beneficial element in higher plants when provided at low concentrations. Recently, studies have unveiled the interactions between Se and ethylene metabolism throughout plant growth and development. However, despite the evidence that Se may provide longer shelf life in ethylene-sensitive flowers, its primary action on ethylene biosynthesis and cause-effect responses are still understated. In the present review, we discuss the likely action of Se on ethylene biosynthesis and its consequence on postharvest physiology of cut flowers. By combining Se chemical properties with a dissection of ethylene metabolism, we further highlighted both the potential use of Se solutions and their downstream responses. We believe that this report will provide the foundation for the hypothesis that Se plays a key role in the postharvest longevity of ethylene-sensitive flowers.Floral scent, a key mediator in plant-pollinator interactions, varies not only among plant species, but also within species. In deceptive plants, it is assumed that variation in floral scents and other traits involved in pollinator attraction is maintained by negative frequency-dependent selection, i.e., rare phenotypes are more attractive to pollinators and hence, have a higher fitness than common phenotypes. So far, it is unknown whether the rarity of multivariate and/or continuous floral scent traits influences the pollination success of flowers. Here, we tested in the deceptive orchid Cypripedium calceolus, whether flowers with rarer scent bouquets within a population have a higher chance to getting pollinated than flowers with more common scents. We collected the scent of more than 100 flowers in two populations by dynamic headspace and analyzed the samples by gas chromatography coupled to mass spectrometry (GC/MS). From the same flowers we also recorded whether they set a fruit or not. We introduced rarity measures of uni- and multivariate floral scent traits for single flowers, which allowed us to finally test for frequency-dependent pollination, a prerequisite for negative frequency-dependent selection.