Ecto- and arbuscular mycorrhizal symbiosis can induce tolerance to toxic pulses of phosphorus in jarrah (Eucalyptus marginata) seedlings

In common with many plants native to low P soils, jarrah (Eucalyptus marginata) develops toxicity symptoms upon exposure to elevated phosphorus (P). Jarrah plants can establish arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) associations, along with a non-colonizing symbiosis described recently. AM colonization is known to influence the pattern of expression of genes required for P uptake of host plants and our aim was to investigate this phenomenon in relation to P sensitivity. Therefore, we examined the effect on hosts of the presence of AM and ECM fungi in combination with toxic pulses of P and assessed possible correlations between the induced tolerance and the shoot P concentration. The P transport dynamics of AM (Rhizophagus irregularis and Scutellospora calospora), ECM (Scleroderma sp.), non-colonizing symbiosis (Austroboletus occidentalis), dual mycorrhizal (R. irregularis and Scleroderma sp.), and non-mycorrhizal (NM) seedlings were monitored following two pulses of P. The ECM and A. occidentalis associations significantly enhanced the shoot P content of jarrah plants growing under P-deficient conditions. In addition, S. calospora, A. occidentalis, and Scleroderma sp. all stimulated plant growth significantly. All inoculated plants had significantly lower phytotoxicity symptoms compared to NM controls 7 days after addition of an elevated P dose (30 mg P kg−1 soil). Following exposure to toxicity-inducing levels of P, the shoot P concentration was significantly lower in R. irregularis-inoculated and dually inoculated plants compared to NM controls. Although all inoculated plants had reduced toxicity symptoms and there was a positive linear relationship between rank and shoot P concentration, the protective effect was not necessarily explained by the type of fungal association or the extent of mycorrhizal colonization

Effects of soil pH and arbuscular mycorrhiza (AM) inoculation on growth and chemical composition of chia (Salvia hispanica L.) leaves

In this study, chemical composition and growth responses of chia plants (Salvia hispanica L.) to inoculation with an arbuscular mycorrhiza (AM, Glomus mosseae, Nicol. & Gerd.) fungal inoculum (namely MC10) under the influence of soil pH were investigated. The experiment project included six treatments, i.e., control-non-arbuscular mycorrhiza fungi (NAMF, pH 7.1), control-arbuscular mycorrhiza fungi (AMF, pH 7.1), acid-NAMF (pH 5.1), acid-AMF (pH 5.1), alkaline-NAMF (pH 8.2), and alkaline-AMF (pH 8.2). Stunted growth and leaf chlorosis were noticed mainly in plants grown in soil with acidic pH. An increase in fresh biomass was attained in plants amended with AM fungi in alkaline soil pH. Alkaline sandy soil with low levels of available P stimulated AMF colonization of chia roots, which subsequently enhanced P uptake and translocation in plant tissues. Total proteins, carbohydrates, and total fat content in leaves increased in AMF-inoculated plants in neutral and alkaline soil pH, while only fat content enhanced under acidic soil pH. MC10 inoculum resulted in reduced levels of total phenolics under alkaline conditions, whereas under acidic soil resulted in increased levels compared to the non-inoculated plants. The predominant fatty acids of chia leaves were palmitic (18.3 %), a-linolenic (17.1 %), pentadecenoic (11.0 %), linoleic (7.5 %), oleic (7.5 %), and stearic (6.3 %). Higher concentration of stearic, oleic, linoleic, and a-linolenic acids was observed in the leaves of chia plants grown on control (neutral pH) and alkaline soil in the presence of the MC10 inoculum. Alkaline soil combined with AM inoculation enhanced the nutritional value of chia leaves. © 2015, Botanical Society of Sao Paulo

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset