Photosystem II Repair Cycle in Faba Bean May Play a Role in Its Resistance to Botrytis fabae Infection

Chocolate spot, which is caused by the necrotrophic fungus Botrytis fabae, is a major foliar disease occurring worldwide and dramatically reducing crop yields in faba bean (Vicia faba). Although chemical control of this disease is an option, it has serious economic and environmental drawbacks that make resistant cultivars a more sensible choice. The molecular mechanisms behind the defense against B. fabae are poorly understood. In this work, we studied the leave proteome in two faba bean genotypes that respond differently to B. fabae in order to expand the available knowledge on such mechanisms. For this purpose, we used two-dimensional gel electrophoresis (2DE) in combination with Matrix-Assisted Laser Desorption/Ionization (MALDI-TOF/TOF). Univariate statistical analysis of the gels revealed 194 differential protein spots, 102 of which were identified by mass spectrometry. Most of the spots belonged to proteins in the energy and primary metabolism, degradation, redox or response to stress functional groups. The MS results were validated with assays of protease activity in gels. Overall, they suggest that the two genotypes may respond to B. fabae with a different PSII protein repair cycle mechanism in the chloroplast. The differences in resistance to B. fabae may be the result of a metabolic imbalance in the susceptible genotype and of a more efficient chloroplast detoxification system in the resistant genotype at the early stages of infection

The Orbit of the New Milky Way Globular Cluster FSR1716 =VVV-GC05

Indexación: Scopus.We use deep, multi-epoch near-IR images of the VISTA Variables in the Via Lictea (VVV) Survey to measure proper motions (PMs) of stars in the Milky Way globular cluster (GC) FSR1716 = VVV-GC05. The colormagnitude diagram of this object, made by using PM-selected members, shows an extended horizontal branch, nine confirmed RR Lyrae (RRL) members in the instability strip, and possibly several hotter stars extending to the blue. Based on the fundamental-mode (ab-type) RRL stars that move coherently with the cluster, we confirmed that FSR1716 is an Oosterhoff I GC with a mean period aPabn = 0.574 days. Intriguingly, we detect tidal extensions to both sides of this cluster in the spatial distribution of PM-selected member stars. Also, one of the confirmed RRabs is located -11 arcmin in projection from the cluster center, suggesting that FSR1716 may be losing stars due to the gravitational interaction with the Galaxy. We also measure radial velocities (RVs) for five cluster red giants selected using the PMs. The combination of RVs and PMs allow us to compute for the first time the orbit of this GC, using an updated Galactic potential. The orbit results to be confined within|Zmax| < 2.0 kpc, and has eccentricity 0.4 < e < 0.6, with perigalactic distance 1.5 < Rperi (kpc) < 2.3, and apogalactic distance 5.3 < Rapo (kpc) < 6.4. We conclude that, in agreement with its relatively low metallicity ([Fe/H] =-1.4 dex), this is an inner-halo GC plunging into the disk of the Galaxy. As such, this is a unique object with which to test the dynamical processes that contribute to the disruption of Galactic GCs. © 2018. The American Astronomical Society. All rights reserved.https://iopscience.iop.org/article/10.3847/1538-4357/aacd0

Predicting non-native seaweeds global distributions: The importance of tuning individual algorithms in ensembles to obtain biologically meaningful results

ABSTRACT: Modelling non-native marine species distributions is still a challenging activity. This study aims to predict the global distribution of five widespread introduced seaweed species by focusing on two mains aspects of the ensemble modeling process: (1) Does the enforcement of less complex models (in terms of number of predictors) help in obtaining better predictions? (2) What are the implications of tuning the configuration of individual algorithms in terms of ecological realism? Regarding the first aspect, two datasets with different number of predictors were created. Regarding the second aspect, four algorithms and three configurations were tested. Models were evaluated using common evaluation metrics (AUC, TSS, Boyce index and TSS-derived sensitivity) and ecological realism. Finally, a stepwise procedure for model selection was applied to build the ensembles. Models trained with the large predictor dataset generally performed better than models trained with the reduced dataset, but with some exceptions. Regarding algorithms and configurations, Random Forest (RF) and Generalized Boosting Models (GBM) scored the highest metric values in average, even though, RF response curves were the most unrealistic and non-smooth and GBM showed overfitting for some species. Generalized Linear Models (GLM) and MAXENT, despite their lower scores, fitted smoother curves (especially at intermediate complexity levels). Reliable and biologically meaningful predictions were achieved. Inspecting the number of predictors to include in final ensembles and the selection of algorithms and its complexity have been demonstrated to be crucial for this purpose. Additionally, we highlight the importance of combining quantitative (based on multiple evaluation metrics) and qualitative (based on ecological realism) methods for selecting optimal configurations.This work was funded by the National Plan for Research in Science and Technological Innovation from the Spanish Government 2017-2020 [grant number C3N-pro project PID2019-105503RB-I00] and co-funded by the European Regional Development’s funds. SS-V acknowledges financial support under a predoctoral grant from the Spanish Ministry of Education andVocational Training [grantnumber:FPU18/03573]. CH acknowledges the financial support from the Government of Cantabria through the Fénix Programme and under a postdoctoral grant from the University of Cantabria [grant number: POS-UC- 2020-07]. This work is part of the PhD project of SS-V

Vector-based word representations for sentiment analysis: a comparative study

New applications of text categorization methods like opinion mining and sentiment analysis, author profiling and plagiarism detection requires more elaborated and effective document representation models than classical Information Retrieval approaches like the Bag of Words representation. In this context, word representation models in general and vector-based word representations in particular have gained increasing interest to overcome or alleviate some of the limitations that Bag of Words-based representations exhibit. In this article, we analyze the use of several vector-based word representations in a sentiment analysis task with movie reviews. Experimental results show the effectiveness of some vector-based word representations in comparison to standard Bag of Words representations. In particular, the Second Order Attributes representation seems to be very robust and effective because independently the classifier used with, the results are good.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI