dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by a  Scientific Installation Aid grant  ( n˚300 01003 ) to CM from Rennes Metropole (https://metropole. rennes. fr/lesaides-la-recherche?idParent=13&typeParent= taxonomy_term). This work, through the  involvement of Bioinformatics service of the GenoSol platform of the infrastructure ANAEE-Services , received a grant from the  French  state through the  National Agency for Research under the program  " Investments for the Future"  (reference  ANR-11-INBS-0001 ), as well as grants from the  Regional Council of Bourgogne Franche Comte. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The data presented in Figure  2  confirms the proper expression of each of the proteins. The same pattern of relative KAT1-BAG protein interaction (BAG4>BAG1>>BAG7) was observed in the functional complementation assay shown in Figure  2. The combinations between KAT1 and both versions of BAG4 show the highest growth in low potassium medium when KAT1 is limiting (black bars). Some growth is detected under these conditions upon co-expression of BAG1, but in the presence of BAG7 the level of growth is the same as the control. Thus, we provide evidence for some level of specificity between KAT1 and BAG family members, lending further support to the possible physiological relevance of the KAT1-BAG4 interaction. In order to confirm that the observed improvement in growth of the KAT1_BAG4 strain is due to improved potassium uptake, we analyzed potassium uptake using K + -specific electrodes (see Materials and Methods for a complete description).
Since both the yeast and oocyte experiments suggest that BAG4 does not affect overall KAT1 protein accumulation, we tested whether this was also the case in this plant model system. As described in materials and methods, for these transient expression experiments we constructed vectors containing multiple transcriptional units, including an internal control for infiltration efficiency (dsRED containing an HA tag) within the same plasmid. Plants were agroinfiltrated with strains containing KAT1-YFP:dsRED-HA alone or KAT1-YFP:dsRED-HA:BAG4-myc. We analyzed the amount of KAT1-YFP and the internal control (dsRED-HA) in the infiltrated areas using the same time course. As shown in Figure  7 , BAG4 coexpression does not increase the steady state amount of KAT1 protein. So, taken together, the data presented in Figures 7 clearly suggest that expression of BAG4 promotes KAT1-YFP arrival at the plasma membrane.
, 1995)  and were injected with 30 ng of KAT1 cRNA or co-injected with 30 ng of KAT1 cRNA and 22. 5 ng of BAG4 cRNA using a pneumatic injector. One day after oocyte injection, currents from whole oocytes bathed in K100 medium (100 mM KCl, 2 mM MgCl 2 , 1 mM CaCl 2 , 10 mM HEPES/Tris pH 6) were recorded using the two-electrode voltage clamp technique. Data acquisition and analyses were performed as previously described  (Corratgé-Faillie et al. , 2017). Voltage drops resulting from the series resistance of the bath were corrected by using two external electrodes connected to a bath probe (VG-2A x100 Virtual-ground bath clamp; Axon Instruments). KAT1 currents were obtained by subtraction of mean currents recorded in water-injected oocytes from the same oocyte batch. The percentage of KAT1 current increase with BAG4 = Mean current in the presence of BAG4 -Mean current in its absence / Mean current in its absence.
Kinetics of plasma membrane accumulation in N. benthamiana and A. thaliana For transient expression in N. benthamiana, leaves were infiltrated, treated and imaged as described for BiFC and Co-IP. However, in this case P19 silencing suppressor was not added to the infection mixture. The ImageJ program was used to quantify the percentage of KAT1-YFP present at the plasma membrane as follows: the free draw tool was used to trace the contour of individual cells, directly adjacent to the cell boarder (inside cell fluorescence). These forms were digitally enlarged by 7 pixel units For transient expression in Arabidopsis, the Col-0 control and the bag4 mutant lines (SALK_033845 and SAIL_144_A10) were agroinfiltrated following the AGROBEST protocol  (Wu et al. , 2014). The plasmid used was the alpha level plasmid containing KAT1-VENUS intron which included an intron in the Venus sequence (kindly provided by Stan Gelvin, Purdue University, USA).
The thermographic camera Bosch GTC 400 C was used to generate the infrared images of the plants 1. 5 hours after the lights turned on. The plants were sown in individual pots such that the rosette leaves were not touching the walls of the pot, the tray or other plants. For imaging, each plant was moved to a tray partially filled with water, to create a uniform environmental temperature around the pot. Leaf temperature was calculated using the GTC Transfer Software. A total of 6 plants were measured for each genotype. Ten points were measured for each plant (60 data points/genotype). Data from each genotype were pooled together and statistically analyzed (Student t-test) using the Graph Prism6 software. Voltage-dependence of KAT1-current activation in the presence of BAG4 (white circles) or with KAT1 expressed alone (black circles).
METHOD Participants Forty-nine participants were scanned in this study, but brain imaging data from three adults and nine adolescents had to be discarded before analysis: Two adults and five adolescents exhibited a within-run maximal amplitude of translational or rotational between-volumes displacement above 2 mm or 1. 5 degree, respectively; one adult and three adolescents had to be excluded due to technical scanner problems; finally, one adolescent was excluded because of a neurological abnormality. All remaining 37 participants (20 adults, 9 males, mean age = 21. 4, SE =. 4 ; 17 adolescents, 7 males, mean age = 13. 1, SE =. 3) were right handed native French speakers, scoring higher than + 40% to the Oldfield handedness scale. All participants or their parents/guardians gave written informed consent and received a 25 euro gift voucher.
Behavioral data analysis For each individual we calculated the mean accuracy for Conflict and No-Conflict problems. Two adults did not err on any of the conflict problems, and two adolescents never succeeded in solving conflict problems correctly. They were discarded from the corresponding contrast analyses. When available, individual mean Response Times (RTs) were extracted for a) Incorrect Conflict (C0), b) Correct Conflict (C1) and c) Correct No-Conflict (NC1) problems. An analysis of variance (ANOVA) with one within factor (type of problem: conflict or no-conflict) and one between factor (age group: adolescents or adults) was run on the accuracy scores. For response times, ANOVAs with one within factor (type of problem) and one between factor (age group) were run for each comparison of interest: 1) C0 versus NC1, and 2) C1 versus NC1.
fMRI data acquisition fMRI data were acquired at Cyceron (Caen, France) using a 3T scanner (Philips Achieva, Eindhoven, The Netherlands). Scanning was done as part of a larger one hour test session during which an unrelated task was also administered. For each participant, the MR data were collected using planes parallel to the anterior commissure -posterior commissure line. fMRI time series were collected using T2*-weighted.
fMRI data analysis Image pre-processing and statistical analyses were performed using Matlab 2017a (Mathworks Inc. , Natick, USA) and SPM12 (Wellcome Department of Imaging Neuroscience, London, UK). fMRI datasets were preprocessed as follows: individual fMRI time-series were (1) slice time corrected, (2) realigned to correct for head movement, (3) co-registered to the respective individual T2* non-EPI volume, which was already previously co-registered to the high-resolution structural image T1-weighted volume, (4) transformed into the MNI152 T1 template space using the parameters derived from the spatial normalization of the T1-weighted image in DARTEL, (5) resampled to 2-mm isotropic voxel size and (  6 ) smoothed with a 8-mm_FWHM Gaussian Kernel. We analyzed the data voxel-wise using the general linear model (GLM) approach.
TRANSPARENCY STATEMENT All behavioral and imaging data as well as the digital study materials can be retrieved from https://osf. io/nkgza/ or upon request to KM (mevel@cyceron. fr). No part of the study procedures and analysis was pre-registered prior to the research being conducted. We report how we determined our sample size, all data exclusions (if any), all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study Thomas Roger for generating scripts used for the statistical analyses. We also thank Cloélia Tissier, Anaëlle Camarda and Lisa Delalande for their help in training and testing the participants. Protocol. Each trial started with a white fixation cross in the middle of the screen, followed by the presentation of the item to be solved.
Therefore, this physical transport of OM and O 2 should stimulate heterotrophic aerobic respiration in the OMZ that was suggested to be the main pathway of OM remineralization in the upper OMZs by  Kalvelage et al. (2015). However, so far, no direct O 2 and DOM supply estimates exist for the Peruvian OMZ. Here, we combined physical and biogeochemical observational data that were collected during the R/V METEOR "M93" (M93) research cruise to the ETSP off Peru in February-March 2013. Specifically, we directly estimated the diapycnal O 2 and DOM supply into the upper oxycline off Peru. Additionally, we analysed diapycnal fluxes and the composition of dissolved combined carbohydrates (DCCHO) and dissolved hydrolysable amino acids (DHAA) to learn whether DOM and its labile and semi-labile constituents may be supplied to the upper OMZ and the potential contribution of DOM-based respiration to O 2 flux attenuation.
(2016a). Apparent oxygen utilization (AOU) was then calculated as a difference of measured O 2 concentrations and its equilibrium saturation using the Gibbs-Sea Water Oceanographic Toolbox  (McDougall and Barker, 2011)  for MatLab (MathWorks, USA) for analyses of potential relationships between DOM reworking and the utilization of O 2. The original fluorometer calibration provided by the sensor manufacturer (WET Labs, USA) was used throughout the cruise, resulting in chl a concentrations in µg L -1. More details on the recalibration of the chl a fluorimeter can be found in  Loginova et al. (2016). Net primary production (NPP) was estimated for the study area off Peru (12-14 • S and 76-79 • W) and the corresponding time period (February 2013) after the model of  Behrenfeld and Falkowski (1997a)  with the Ocean Productivity toolbox (Oregon State University).
Diapycnal flux calculations To estimate the diapycnal fluxes of various solutes, CTD sensor (O 2 ) and bottle data (DOC, DON, DCCHO and DHAA) were combined with near-simultaneous measurements of turbulence in the water column. The turbulence measurements were performed with a microstructure profiling system (MSS) from the rear of the vessel. The loosely tethered profiler (MSS90-D, S/N 32, Sea & Sun Technology) was optimized to sink at a rate of 0. 55 m s -1 and was equipped with three shear sensors and a fast-response temperature recorder, as well as an acceleration sensor, two tilt sensors and CTD, sampling with lower response time. At each CTD station, three to six microstructure profiles were collected. Standard processing procedures were used to determine the rate of kinetic energy dissipation of turbulence in the water column (ε, m 2 s -3 ), as given in  Schafstall et al.
(3): -∇ S = - 1 ρ ∂ ∂z S , (3) where ρ is the in situ density of the seawater (kg m -3 ), z is depth (m) and S (mmol m -2 d -1 ) is the estimated mean diapycnal flux profile of a solute. The mean diapycnal solute supply was interpreted to balance the amount of a solute that is lost per unit of time over a specific depth interval of the water column due to the microbial utilization of the solute. This interpretation assumes that sources other than turbulent mixing or sinks other than microbial consumption are negligible. For DCCHO and DHAA the diapycnal flux estimates were based on 14 combined CTD/MSS stations, while for DOC and DON fluxes 22 stations were available (Fig. 1b ). The diapycnal O 2 flux was determined from 50 combined stations. All combined data sets include stations from the continental slope, as well as stations in deeper waters, where bottom depth was larger than 4000 m.
Acknowledgements. We thank the chief scientists of the M93 cruise,  Gaute Lavik  and  Torsten Kanzow , for station planning and support during sampling, as well as the crew and scientists onboard. We are also grateful to  Gerd Krahmann  for processing the CTD data, to  Caroline Mages  and  Ruth Flerus  for help with the water sampling, and to  Jon Roa  and  Ruth Flerus  for technician support. Special thanks to  Frederic Le Moigne  for help with the NPP calculation. We thank anonymous referee 1 and  Ronald Benner  for their valuable comments on improving the manuscript.
Data availability. The microstructure profiles are available at https://doi. org/10. 1594/PANGAEA. 868400 (Thomsen, 2016). The O 2 , temperature, salinity, chl a fluorescence are available at https://doi. org/10. 1594/PANGAEA. 848017 (Krahmann, 2015) and nutrient concentrations were published at https://doi. org/10. 1594/PANGAEA. 862055 (Lavik and  Krahmann, 2016). The DOC, TDN, DCCHO and DHAA concentrations are available at https://doi. org/10. 1594/PANGAEA. 900929 (Loginova and Engel, 2019).
Introduction The aim of this study was to develop groundmotion simulation code based on extended rupture modeling adapted for low-to-moderate seismicity regions, and to extend its range of applicability to the lowest source-to-site distances (i. e. typically distances smaller than the geometric extension of the fault, for which the variation of azimuth between the site and the different extremities of the rupture are non-negligible). This code is based on a kinematic source description of an extended fault according to the k -2 model  (Causse et al. 2009; Dujardin et al. 2016; Del Gaudio et al. 2018) , which is combined with the use of empirical Green's functions  (EGFs). It is freely available on github: https://github. com/ adujard/K2_FMD/. Following the EGF approach  (Hartzell 1978) , small magnitude events are used as EGFs to allow implicit consideration of the characteristics of the propagation medium (e.
Indeed, several studies have highlighted that the use of GMPEs outside the magnitude and distance ranges covered by the datasets from which they are derived leads to missestimation of the ground motion  (Bommer et al. 2007; Chiou et al. 2010; Douglas and Jousset 2011). Moreover, close to the fault, the ground motion amplitude is affected by the saturation effect (e. g. ,  Yenier and Atkinson 2014). Although its influence is taken into account through most of the GMPE functional forms (see  Fukushima et al. 2003; Zhao et al. 2006) , it is only an average consideration for a set of different events and different azimuths. This approach is therefore not suitable for specific site prediction, and does not allow the large azimuth variability of the ground motion and the saturation effect to be taken into account (see  Dujardin et al.
2018). Furthermore, the radiation pattern correction allows some complex fault geometries to be taken into account, with variations of strike and dip angles. This is the case for the geometry of the Middle Durance Fault (MDF)  (Guyonnet-Benaize et al. 2015) , which constitutes our application case here (see  Sect. 4). Finally, this simulation code is developed in the framework of blind ground-motion prediction in lowto-moderate seismicity areas. This means that it is designed for simulation of a hypothetical future event for which the source characteristics (e. g. , stress drop, rupture velocity, rupture dimensions) are a priori unknown. Therefore, the input rupture parameters are defined by the probability density functions, the variability of which can be constrained to reproduce the ground-motion variability observed (for the target event characteristics) in strong-motion databases.
13 ). For comparison, we used several GMPEs. First, we used the  Berge-Thierry et al. 2003, (BT2003)  since this    2014 ),  Chiou and Youngs (2014)  and  Laurendeau et al. (2017). These GMPEs will be referred respectively as AM2017, AK2014, BI2014, BO2014, CA2015, CB2014, AB2014, CY2014 and LA2017. All these GMPEs use moment magnitude Mw and will be used here for strike-slip source mechanism. The AM2017, AK2014, BI2014 were derived using a Euro-Mediterranean database. BO2014, CA2015, CB2014, AB2014 and CY2014 were derived using a global database. LA2017 was derived using a subset of the Kiknet database, with a very different approach that will be discussed latter. AK2014, AM2017, BI2014 and BO2014 are computed using the Joyner-Boore distance (R JB ). AB2014, CA2015, CB2014, CY2014 and LA2017 are computed using the distance to rupture plan (R RUP ).
g. ,  Denolle et al. 2013 Denolle et al. , 2014)). Furthermore, the proposed EGF simulation code accounts for complex seismic rupture processes. First, it is possible to incorporate variable strike and dip angles to model complex fault systems as the MDF. Second, the code models complex rupture propagations and generates populations of rupture scenarios with variable rupture dimensions, positions of the rupture area on the fault, slip and rupture time distributions, and nucleation positions. Note that the variability of the simulations can be mastered by constraining the stress drop distribution, which is an input of the code. This makes it possible to generate a ground motion variability in agreement with that of the accelerometric databases, which is an advantage for seismic hazard assessment studies (e.
g. , for comparisons between site-specific simulations and GMPEs). Finally, our results are compared with various GMPEs. GMPEs moderately underestimate simulations at the CA04 sediment site (V S30 = 440 m/s) and strongly overestimate the CA02 hard rock site simulations (V S30 = 1800 m/s). As mentioned before, the recording stations of the Cadarache site benefit from direct in situ measurements that enable them to be assigned very well constrained V S30. This is a key information since most of the strong-motion records available in databases worldwide (on which the GMPEs are based and obtained) are not yet associated to a well characterized station, with respect to its specific geological and geophysical properties. These latter properties (e. g. , geological stratigraphy, density, P wave and S wave-velocities, depth of main impedance contrasts) are directly responsible for the local seismic site responses.
It is also necessary to point out some important aspects about the use of GMPEs (as detailed in Berge-Thierry et al. 2017a, b, concerning the BT2003 with respect to its use within the framework of the French nuclear regulation). Using generic GMPEs to predict ground motion for a specific site where V S30 is far from the class interval boundaries necessarily provides inappropriate results. From this point of view, the results can be anticipated for comparisons between the physics-based ruptures and site-specific predictions of the present study and the GMPEs study. Indeed, with V S30 of 440 m/s, CA04 is clearly in the subset of records that are well represented in the motion databases used to derive GMPEs and their associated soil-site coefficients. On the contrary, with its particularly high shear-wave velocities, CA02 is out of the range of the data that are considered to be representative of ''hard-rock'' responses.
2017 ). These effects can induce large amplifications of ground motion at high frequency (e. g. [ 10 Hz), even on rock and hard-rock sites; • impedance contrast or soil properties; • finally, geometric configurations and fillings for specific substratum. The GMPEs are indeed based on the use of real databases where characterization of the so-called rock sites is still poorly, if at all, constrained with very few measurements of the V S30 proxy and/or the frequency resonance of the site effects. The average prediction is therefore naturally higher than what is expected for a true hard-rock site. Secondly, this result raises the question of taking into account the site effects. Although GMPEs are not suitable for site-specific predictions, the improvement of the functional forms, however, allows at least the onedimensional site effects to be taken into account.
RNA-seq analysis Adaptor clipping and quality trimming of the raw reads were performed using Trimmomatic-0. 35. The sequenced raw reads were processed to obtain high-quality clean reads. Ambiguous reads (reads with unknown nucleotides 'N' >5%) and low-quality sequences (>10% quality threshold QV <20 phred score) were removed. A minimum length of 100 nt after trimming was applied. Finally, high-quality (QV >20), paired-end reads were used for reference-based read mapping. The genome of Triticum aestivum L. was taken as reference for analysis. The Gene Feature Format files were downloaded from Ensembl Plants (TGACv1. 37). The reads were mapped to the reference genome using TopHat v2. 1. 1 with default parameters. The Cufflinks v2. 2. 1 program assembled the transcriptome data from RNA-seq data and quantified their expression.
Mapped reads were subjected to Cufflinks, followed by Cuffmerge and Cuffdiff. Log2 fold change (FC) values >1 were considered up-regulated whereas those with an FC <1 were considered as down-regulated. These genes were further categorized on the basis of statistical significance (P<0. 05) and the false discovery rate (FDR 0. 05) after Benjamin-Hochberg corrections for multiple testing for their significant expression. Comprehensive gene annotation of wheat sequences was done using the KOBAS 3. 0  (Xie et al. , 2011)  annotate module by alignment with rice sequences (BLASTP, cut-off 1e-5). MapMan was used to visualize the pathways involving wheat differentially expressed genes (DEGs). Pathway enrichment analysis was performed using the KOBAS standalone tool. MeV was employed to construct heatmaps for selected DEGs using the normalized expression values of genes.
Homoeologue-specific expression analysis for genome bias To identify wheat homoeologous triplets; ensembl biomart TGACv1 was used to extract all possible homoeologous relationships. This led to 86 830 pairwise homoeologous relationships. An in-house script was used to select only the triplets where the contribution from each genome was 1:1:1 (A:B:D), thus generating 16 850 triplets. Further, triplets resulting from potential translocation events were not considered; that is, only homoeologue triplets from the same chromosome (e. g. the 1A, 1B, 1D triplet is accepted, whereas, 2A, 3B, 3D is rejected) were taken for analysis. Finally, 15 604 triplets (15 604×3=46 812 genes) were used for studying genome induction bias in response to Fe stress. Paired-end reads were aligned to the reference (selected scaffolds from the genome that harbour 15 604 triplets) using TopHat v2.
1. 1 with a specific argument (--b2-very-sensitive)  (Powell et al. , 2017) , which leads to more stringent alignments as required for homoeologues. The Cufflinks pipeline was used to obtain FPKM (fragments per kilobase of transcript per million mapped read) values and DEGs. Further, relative abundance levels and expression bias for homoeologues were studied by considering the homoeologous gene triplets with expression of FPKM ≥1 each in both the control and Fe-starved conditions. For this, the normalized relative expression for each homoeologue within a triad was calculated. For example, the relative expression from A will be represented as: Relative expression A = FPKM A FPKM A +FPKM B +FPKM D Thus, the relative expression levels of A, B, and D homoeologues within each triad were calculated similarly.