Resultados de investigación

Figure S1: Scheme of strand-specific cDNA synthesis; Figure S2: Agarose gel electrophoresis of the amplified fragments of SCV, SMoV and StrV-1; Table S1: List of primers used in the study; Table S2: Overview of mappings of HTS reads on the viral references; Table S3: Detailed listing of detected nucleotide variation in SCV, SMoV and StrV-1; Table S4: Counts of variable positions and their effect on encoded proteins; Table S5: Overview of ∆Cq values for SCV, SMoV, StrV-1 and their variants in individual aphid adults. References [37,38] are cited in the Supplementary Materials., Peer reviewed

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions., Peer reviewed

S1 Fig. Cellular automaton model accurately predicts the experimentally observed evolution of early cln3. A) Scheme of the algorithm used in Model A (Fig 2A). B) Number of generations and times required to obtain <98% of cells with 220 rDNA repeats and an average generation increase (Delta) between 0.3 and 2 copies. A Delta between 1.2 and 1.4 fits the experimental results (between 160 and 180 generations). S2 Fig. Cellular automaton model by assuming that SIR2 repression linearly decreases. In this case, the cells with 125 copies divide 100% into a daughter with an amplified copy number by a given Delta factor and another cell with no amplification (125 copies). However, the cells with >125 copies have a linear increasing tendency (from 1% to 126 copies to 100% with 220 copies) to divide into two cells with no amplification. The table and the plot show the number of generations required for every possible integral Delta value. Note that only Delta >11 fits the number of experimentally observed generations. S3 Fig. Cellular automaton model by assuming that growth rate differences between cells have different rDNA copy numbers for models B. Scheme of the algorithm used for Models B (Fig 2D and 2E). The figure shows that the growth rate increases (generation time decrease, GTI -9 s). A model for a lowering growth rate would be similar, but with a GTI of +9 s. S1 Table. List of the yeast strains used in this work. S1 Appendix. Algorithm used in Model A1. This appendix describes the pseudocode of Model A1. Both the implementation details and source codes for all the models can be downloaded from https://www.uv.es/varnau/modelo/MODEL_A.c., Peer reviewed

A processing tool for the spaceborne GNSS-R complex waveform products available at IEEC's GOLD-RTR server. The project provides an example for accessing, searching and analyzing the compelx waveform product derived from the GNSS-R raw IF data collected by different spaceborne missions (e.g. TDS-1, CYGNSS, BuFeng-1, SPIRE)., Peer reviewed

Extended synoptic situation and flood events for each synoptic pattern., Peer reviewed

The supplementary material provide technical details about our numerical methods and convergence tests as requested by the referees. Appendix A: Covariant evolution equations Appendix B: Code validation, Peer reviewed

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions., Peer reviewed

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions., Peer reviewed

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions., Peer reviewed

Supplementary Table 1 Trunk diameters measured in 2017 before starting the experiment of the central 16 vines of each treatment. The analysis of variance was performed at P < 0.05. ns = not significant; * = significant differences. Supplementary Table 2 Summary of all the vine measurements carried out in the experiment, which vines were used, when they were taken and what were the objectives of each measurement. Supplementary Table 3 Parameters used to calculate respiration and the reference from which they were extracted and adapted. For fruit respiration Q10 = 2 was assumed. In Escalona et al., 2012, shoot respiration was presented in nmol O2/gDW. We assumed a respiratory quotient = 1. Supplementary Table 4 Environmental conditions in and out of the whole canopy gas exchange chamber. Note that on 29th May temperature and VPD in the chamber for CFlate were high values since a pipe obturation occurred for one hour. Supplementary Table 5 Maximum net carbon exchange per leaf area (maxNCEch) and net carbon exchange (NCEch), measured through whole canopy gas exchange chamber, and net carbon exchange modelled (NCEm). Supplementary Table 6 Inputs required to estimate net carbon exchange model in the experimental zone for the days in which open-top whole canopy gas exchange chambers were operating. Note that not all the measurements included a whole day data since data were adjusted for the time the vine was in the whole canopy gas exchange chamber. Supplementary Figure 1 Scheme of the experimental design on an NDVI map performed in year 2016 with an airborne (all the methodology was described in Bellvert et al., 2020). The different numbers are he replicates of each treatment. The distance between vines of the same replicate of different treatments is represented in red. Supplementary Figure 2 Open-top whole canopy gas exchange chamber used to validate the net carbon exchange model. Supplementary Figure 3 Linear regression between modelled net carbon exchange (NCEm) and measured using an open-top whole-canopy gas exchange chamber (NCEch) (R2 = 0.95, y=1.03x- 1.98, RMSE = 5.8; NSE = 0.95). Black circles indicate measurements conducted before applying the treatments and includes vines from all three treatments in 2019. Measurements after the forcing date correspond to Control (blue), CFearly (green) and CFlate (orange). The grey dotted line means 1:1 relation., Elevated temperatures during berry ripening have been shown to affect grape quality. The crop forcing technique (summer pruning that ‘force’ the vine to start a new cycle) has been shown to improve berry quality by delaying the harvest date. However, yield is typically reduced on forced vines, which is attributed to vine low carbon availability soon after forcing and likely incomplete inflorescence formation. The present study aims to estimate the carbon balance of forced vines and evaluate vine responses to changes in carbon patterns due to forcing. Three treatments were studied on Tempranillo cultivar: non-forced vines (Control), vines forced shortly after fruit set (CFearly) and vines forced one month later at the beginning of bunch closure (CFlate). Whole canopy net carbon exchange was modelled and validated using two whole canopy gas exchange chambers. In addition, non-structural carbohydrate reserves at budburst, forcing date and harvest, were analysed. Yield, yield components and vegetative growth were also evaluated. Harvest date was delayed by one and two months in the CFearly and CFlate, respectively, which increased must acidity. However, yield was lower in the forced treatments compared to the Control (49% lower for CFearly and 82% for CFlate). In the second year, at the time when CFearly and CFlate dormant buds were unlocked (forced budburst), forced vines had significantly lower non-structural carbohydrates than Control vines at budburst. Although the time elapsed from budburst to reach maximum net carbon exchange was longer for the Control treatment (80 days) than for the forced treatments (about 40 days), average daily net carbon exchange until harvest was comparable between Control (60.9 g CO2/vine/day) and CFearly (55.9 g CO2/vine/day), but not for CFlate (38.7 g CO2/vine/day). In addition, the time elapsed from budburst to harvest was shorter in forced treatments (about 124 days) than for the Control (172 days). As a result, the cumulative net carbon exchange until harvest was reduced by 35% (CFearly) and 55% (CFlate) in the forced treatments. However, no differences in carbon reserves at harvest were observed between treatments partly helped by the higher source:sink ratio observed in forced than Control vines., Peer reviewed