Effect of phosphorus supply on root traits of two Brassica oleracea L. genotypes

BACKGROUND: Phosphorus (P) deficiency limits crop production worldwide. Crops differ in their ability to acquire and utilise the P available. The aim of this study was to determine root traits (root exudates, root system architecture (RSA), tissue-specific allocation of P, and gene expression in roots) that (a) play a role in P-use efficiency and (b) contribute to large shoot zinc (Zn) concentration in Brassica oleracea. RESULTS: Two B. oleracea accessions (var. sabellica C6, a kale, and var. italica F103, a broccoli) were grown in a hydroponic system or in a high-throughput-root phenotyping (HTRP) system where they received Low P (0.025 mM) or High P (0.25 mM) supply for 2weeks. In hydroponics, root and shoot P and Zn concentrations were measured, root exudates were profiled using both Fourier-Transform-Infrared spectroscopy and gas-chromatography-mass spectrometry and previously published RNAseq data from roots was re-examined. In HTRP experiments, RSA (main and lateral root number and lateral root length) was assessed and the tissue-specific distribution of P was determined using micro-particle-induced-X-ray emission. The C6 accession had greater root and shoot biomass than the F103 accession, but the latter had a larger shoot P concentration than the C6 accession, regardless of the P supply in the hydroponic system. The F103 accession had a larger shoot Zn concentration than the C6 accession in the High P treatment. Although the F103 accession had a larger number of lateral roots, which were also longer than in the C6 accession, the C6 accession released a larger quantity and number of polar compounds than the F103 accession. A larger number of P-responsive genes were found in the Low P treatment in roots of the F103 accession than in roots of the C6 accession. Expression of genes linked with "phosphate starvation" was up-regulated, while those linked with iron homeostasis were down-regulated in the Low P treatment. CONCLUSIONS: The results illustrate large within-species variability in root acclimatory responses to P supply in the composition of root exudates, RSA and gene expression, but not in P distribution in root cross sections, enabling P sufficiency in the two B. oleracea accessions studied

Plasma-wall interaction studies within the EUROfusion consortium: Progress on plasma-facing components development and qualification

This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission.The provision of a particle and power exhaust solution which is compatible with first-wall components and edge-plasma conditions is a key area of present-day fusion research and mandatory for a successful operation of ITER and DEMO. The work package plasma-facing components (WP PFC) within the European fusion programme complements with laboratory experiments, i.e. in linear plasma devices, electron and ion beam loading facilities, the studies performed in toroidally confined magnetic devices, such as JET, ASDEX Upgrade, WEST etc. The connection of both groups is done via common physics and engineering studies, including the qualification and specification of plasma-facing components, and by modelling codes that simulate edge-plasma conditions and the plasma-material interaction as well as the study of fundamental processes. WP PFC addresses these critical points in order to ensure reliable and efficient use of conventional, solid PFCs in ITER (Be and W) and DEMO (W and steel) with respect to heat-load capabilities (transient and steady-state heat and particle loads), lifetime estimates (erosion, material mixing and surface morphology), and safety aspects (fuel retention, fuel removal, material migration and dust formation) particularly for quasi-steady-state conditions. Alternative scenarios and concepts (liquid Sn or Li as PFCs) for DEMO are developed and tested in the event that the conventional solution turns out to not be functional. Here, we present an overview of the activities with an emphasis on a few key results: (i) the observed synergistic effects in particle and heat loading of ITER-grade W with the available set of exposition devices on material properties such as roughness, ductility and microstructure; (ii) the progress in understanding of fuel retention, diffusion and outgassing in different W-based materials, including the impact of damage and impurities like N; and (iii), the preferential sputtering of Fe in EUROFER steel providing an in situ W surface and a potential first-wall solution for DEMO.European Commission; Consortium for Ocean Leadership 633053; Institute of Solid State Physics, University of Latvia as the Center of Excellence has received funding from the European Union’s Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART

Investigating cross-lingual training for offensive language detection.

Platforms that feature user-generated content (social media, online forums, newspaper comment sections etc.) have to detect and filter offensive speech within large, fast-changing datasets. While many automatic methods have been proposed and achieve good accuracies, most of these focus on the English language, and are hard to apply directly to languages in which few labeled datasets exist. Recent work has therefore investigated the use of cross-lingual transfer learning to solve this problem, training a model in a well-resourced language and transferring to a less-resourced target language; but performance has so far been significantly less impressive. In this paper, we investigate the reasons for this performance drop, via a systematic comparison of pre-trained models and intermediate training regimes on five different languages. We show that using a better pre-trained language model results in a large gain in overall performance and in zero-shot transfer, and that intermediate training on other languages is effective when little target-language data is available. We then use multiple analyses of classifier confidence and language model vocabulary to shed light on exactly where these gains come from and gain insight into the sources of the most typical mistakes

Automating News Comment Moderation with Limited Resources: Benchmarking in Croatian and Estonian

This article describes initial work into the automatic classification of user-generated content in news media to support human moderators. We work with real-world data — comments posted by readers under online news articles — in two less-resourced European languages, Croatian and Estonian. We describe our dataset, and experiments into automatic classification using a range of models. Performance obtained is reasonable but not as good as might be expected given similar work in offensive language classification in other languages; we then investigate possible reasons in terms of the variability and reliability of the data and its annotation.https://jlcl.org/content/2-allissues/1-heft1-2020/jlcl_2020-1_3.pd

Dynamics of online hate and misinformation

Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of “pure haters”, meant as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents’ community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of the number of comments and time. Our results show that, coherently with Godwin’s law, online debates tend to degenerate towards increasingly toxic exchanges of views