dataesr/scientific-paragraphs-categorization huggingface dataset:
Local Environmental Conditions Are Highly Contrasted in the Pyrenees Mountains The 30 Pyrenean populations of A. thaliana as well as Lant, Sha and Col, were described using the altitude (m) and the following 5 climatic variables (Table  1 ): annual minimum, annual maximum and annual mean temperatures ( • C), total annual precipitation (mm) and total annual UV radiations (kWh/m 2 ). The variables describing temperature spectrum (minimum, mean and maximum) are highly positively correlated but negatively correlated with altitude, annual rain accumulation and solar radiation (Supplementary Figure  S4A ). To analyze the environmental datasets (climates and altitudes), PCA were used to calculate principal components (PCs) that explain the climatic variance (Supplementary Figure  S4 ). The PCs score approach is routinely used for climate quantification (Wolfe and Tonsor, 2014) and represent an index that well describes natural environmental conditions of each population (Table  1 ).
Cold Growth Conditions Induced Various Morphological and Physiological Responses The individuals, used for germination assays, were also phenotyped at later developmental stages. To better understand phenotypic variations in response to temperature variation, several traits of A. thaliana grown at 22 • C (optimal growth Gray circle included populations with lower TR/ER ratio. Data are available in Supplementary Figure  S3  and Supplementary Table  S3. condition for Col) were compared to those of plants grown at 15 • C (to reproduce low temperature of altitudinal growth conditions). Contrasted rosettes phenotypes were observed between the two temperatures (Figures  5A, B ) and quantified such as the number of leaves (Supplementary Figure  S6 ), the rosette weight (Supplementary Figure  S7 ) and their diameter (Supplementary Figure  S8 ), the time to reach the bolting stage (Supplementary Figure  S9 ), the rosette anthocyanin (Supplementary Figure  S10 ) and chlorophyll contents (Supplementary Figure  S11 ).
Multilevel Analyses Reveal Phenotypic Variations in Response to Growth Temperature All the phenotypes could have been analyzed independently to study the acclimation mechanisms of the Pyrenean populations of Arabidopsis to the different growth temperatures. In fact, the 10 traits measured (e. g. , number of leaves, anthocyanin content) varied not only between the growth temperatures but also between the populations/accessions; hence, PCA was performed to get more integrative results. Multilevel methods of PCA can be used if repeated measurements from different treatments are applied on the same individual. This multilevel approach was developed to highlight treatment effects within subject separately from the biological variation between subjects  (Liquet et al. , 2012). It was dedicated to take into account potential artifact in the data due to repeated measurements and allows investigation of the between subject variation which is completely separated from the within subject variation  (Westerhuis et al.
, 2010). This new approach, not widely used in the phenotypic studies, is very efficient to study the effect of different conditions within subjects separately from the variation between populations. Since our dataset fulfilled these conditions, a multilevel PCA of the phenotypic data was performed (Figures  6A, B ). According to this multilevel analysis, populations were clearly separated by the temperature effect in the first axis (55%). This separation was mainly due to the chlorophyll overall content which was more abundant in rosettes from plants cultivated at 22 • C as compared to 15 • C, and to the other phenotypic parameters generally higher at 15 • C than at 22 • C in most of the populations. Indeed, highland population such as Prad and Hosp appeared to contain more chlorophyll at 22 • C than at 15 • C unlike to the lowland populations Chau and Biel.
At 22 • C, the same tendencies are visible, although not significant despite the low variability of the data in this genetic cluster (Figure  7D ). Because populations of cluster 1 are specific of the Pyrenean mountain, one phenotypic characteristics of A. thaliana in the Pyrenees is to have bigger rosette at low temperature growth conditions. Finally, climate PC1 value obtained from normed PCA of six environmental datasets, allows classifying populations as a function of their environmental conditions considering altitude levels. The populations were distributed as plain (PC1 value > 1), medium (0 < PC1 value < 1) and high altitudes (PC1 value < 0) (Table  1  and Figure 7A ). Some correlations exist between phenotypic traits and the original environmental conditions characterized by the climate PC1 value.
Contrasted Climatic Conditions for the Pyrenees Populations All the climatic variables are usually well correlated with altitude, although the field topology may play an important role. Precipitation and seasonality exert the largest influence on the regional climate variation  (Körner, 2007). Pyrenees mountains have a temperate climate where the gradient of precipitation is dependent on altitude. It is difficult to compare the characteristics of all populations at once but several conclusions can nevertheless be drawn from environmental data. Climate PC1 value obtained from normed PCA of six environmental datasets, allows classifying populations as a function of their environmental conditions considering altitude levels: population of plain, medium and high altitudes. The climate PC1 values are highly correlated with the population altitude but some differences could be noted.
ACKNOWLEDGMENTS We are thankful to the  Paul Sabatier-Toulouse 3 University  and to the  Centre National de la Recherche Scientifique (CNRS)  for granting their work. We are also grateful to the  National Botanical Conservatory of Pyrenees  and the  Midi-Pyrénées Region (CBNPMP)  and to the  National Park of the Pyrenees (PNP)  for providing us with the population coordinates and giving us sampling authorization within the park limitations. Thanks to  Etienne Florence  and  Alan Griffaut (PNP)  for their help during the sampling of populations, to  Nicolas Pouilly  ( LIPM, Toulouse ) for advice and assistance with the large scale extraction of Arabidopsis DNA, to  Catherine Mathé  ( LRSV, Toulouse ) for her help in choosing the appropriate peroxidase gene markers, to  Sarah Pignoly  ( LRSV, Toulouse ) for initial sequences management,  Fabrice Roux  and  Léa Frachon  ( LIPM, Toulouse ) for providing the Lant population and  Sophie Brando  and  Marie Morlans  ( LGDP, Perpignan ) for their technical assistance.
| Bioinformatic analysis Relevant DNA and protein sequences were obtained from the NCBI GenBank database  (Clark, Karsch-Mizrachi, Lipman, Ostell, & Sayers, 2016). Unannotated GenBank entries were manually interrogated for coding regions and the respective protein sequences using SnapGene ® software (from GSL Biotech; available at http:// www. snapgene. com). All protein homology analyses were performed using NCBI blastp and the nonredundant protein sequences (nr) database. T6SS-1 clusters were identified in a two-step process. First, the amino acid sequences of TssH (BCAL0347) and TagX proteins (BCAL0352) from B. cenocepacia J2315 were used as search queries to identify homologous proteins. Second, the loci encoding these proteins were interrogated for the presence of other T6SS-related genes.
ACK N OWLED G EM ENT This work was supported by a  BBSRC   Doctoral Training Grant  ( BB/ F016840/1 ) awarded to  H. L. S. Genome  sequencing was provided by  MicrobesNG  (http://www. microbesng. uk),which is supported by the  BBSRC  (grant number  BB/L024209/1 ). VBMI is supported by l 'Institut National de la Santé et de la Recherche Médicale INSERM and Université de Montpellier. L. Z. was a  Marie-Curie fellow in the Initial Training Network FishForPharma  ( PITN-GA-2011-289209 ). L. E. is funded by the  Swiss National Science Foundation (SNSF)  (Project  31003A_169307 ).
fungorum GAS106B n/a BLS41_RS32405-BLS41_RS32325 SAMN05443245_6595- SAMN05443245_6579 P. ginsengiterrae DCY85 n/a A6V36_RS34655-A6V36_RS34735 A6V36_13555-A6V36_13635 P. graminis C4D1M n/a BGRAMDRAFT_RS31300- BgramDRAFT_6363- BGRAMDRAFT_RS31220 BgramDRAFT_6347 P. insulsa LMG 28183 n/a BX589_RS19295-BX589_RS19215 BX589_111106-BX589_11190 P. kururiensis M130 n/a G118_RS0127045-G118_RS0127125 n/a P. megapolitana LMG 23650 n/a BM166_RS27120-BM166_RS27200 SAMN05192543_109158- SAMN05192543_109174 P. nodosa CNPSo 1341 n/a BFD71_RS23080-BFD71_RS22965 n/a P. oxyphila NBRC 105797 n/a BO1_RS31110-BO1_RS23945 n/a P. phenazinium GAS95 n/a BUS12_RS10920-BUS12_RS10830 SAMN05444165_2229- SAMN05444165_2211 P. phenoliruptrix BR3459a 2 BUPH_RS28500-BUPH_RS28580 BUPH_06127-BUPH_06111 P. phytofirmans PsJN 2 BPHYT_RS24375-BPHYT_RS24455 Bphyt_4909-Bphyt_4925 P.
rhynchosiae WSM 3937 n/a C0Z16_RS18310-C0Z16_RS18230 C0Z16_18290-C0Z16_18210 P. sediminicola LMG 24238 n/a BLT79_RS22400-BLT79_RS22480 SAMN05192547_102957- SAMN05192547_102973 P. soli GP25-8 n/a C0Z19_RS08700-C0Z19_RS08620 C0Z19_08720-C0Z19_08640 Paraburkholderia sp. BL18I3N2 n/a B0G75_RS24645-B0G75_RS24565 B0G75_110100-B0G75_11084 Paraburkholderia sp. BL21I4N1 n/a B0G83_RS22810-B0G83_RS22905 B0G83_109119-B0G83_109138 Paraburkholderia sp. BL25I1N1 n/a B0G73_RS11290-B0G73_RS11370 B0G73_106134-B0G73_106151 Paraburkholderia sp. C35 n/a DK391_RS17735-DK391_RS17655 n/a Paraburkholderia sp. GV068 n/a C8K18_RS26440-C8K18_RS26520 C8K18_115102-C8K18_115118 Paraburkholderia sp. GV072 n/a C8K19_RS26210-C8K19_RS26130 C8K19_11551-C8K19_11535 P. symbiotica JPY 581 n/a C0Z20_RS18355-C0Z20_RS18280 C0Z20_18350-C0Z20_18275 P.
terricola LMG 20594 n/a BUE39_RS22490-BUE39_RS22570 SAMN05192548_102957- SAMN05192548_102973 P. tropica LMG 22274 n/a BMY06_RS23990-BMY06_RS23890 SAMN05216550_114137- SAMN05216550_114117.
Study population The Inclusion criteria were: documented paroxysmal, persistent or permanent AF on electrocardiography; age > 18 years; and provision of patient consent. Exclusion criteria were: valvular AF (mitral stenosis, defined as an area < 2 cm²; mitral, aortic or tricuspid regurgitation greater than moderate to severe; moderate or severe aortic stenosis; mechanical or biological prosthesis); contraindication to anticoagulant treatment; no affiliation to a social security regimen; severe psychiatric history; or unlikelihood to attend follow-up. A comprehensive physical examination and electrocardiogram were carried out and clinical data were collected at admission and every 6 months for 2 years. Biological samples were collected at admission and 1 year after admission. Comprehensive TTE and transoesophageal echocardiography were performed within 24 hours after admission (M0).
TTE 2D analysis Comprehensive TTE examinations were performed at M0 and M6. TTE was always performed by experienced cardiologists using an X5-1 transducer on the iE33 system or an X5-1 transducer on the EPIQ system (Philips Medical Systems, Andover, MA, USA). The data were transferred and analysed offline using the IntelliSpace CardioVascular (ISCV) workstation (Philips Medical Systems). All images and measurements were performed using standard views, according to American Society of Echocardiography and European Association of Cardiovascular Imaging guidelines  [12]. Three to five consecutive cardiac cycles were recorded and averaged, and all volumetric measures were indexed to body surface area.
3D analysis 3D full-volume and high-volume rate acquisitions were performed in the four-chamber view during end-expiratory apnoea, to evaluate 3D atrial volumes. The frame rate was set to between 20 and 35 frames/s. The 3D data sets were stored digitally, and quantitative analyses were performed offline (QLAB Advanced Cardiac 3D Quantification software, version 10. 7; Philips Medical Systems). The LA endocardium was defined on two orthogonal planes to obtain non-foreshortened views of the LA. The endocardial surfaces of the atria were drawn by a semiautomated contour tracing algorithm developed for the left ventricle. 3D TTE was used to determine: (1) body surface area-indexed minimal RA volume (Min 3D RA Voli) (defined as the volume immediately after tricuspid closure) and body surface area-indexed maximal RA volume (Max 3D RA Voli) (defined as the volume immediately before tricuspid valve opening); and (2) body surface area-indexed minimal 3D LA volume (Min 3D LA Voli) (defined as the volume immediately after mitral closure) and body surface area-indexed maximal 3D LA volume (Max 3D LA Voli) (defined as the volume immediately before mitral valve opening).
Strain analysis RA and LA reservoir strain were analysed using semiautomated software developed for the left ventricle (QLAB Automated Cardiac Motion Quantification software, version 10. 7; Philips Medical Systems). Three to five consecutive cardiac cycles were recorded and averaged. The frame rate was set between 60 and 80 frames/s. The initiation of strain calculation was chosen as the onset of the QRS complex (R-R gating), as proposed in most studies  [13]. The endocardial surfaces of the atria were drawn by a semiautomated contour tracing algorithm: first, the operator manually defined the septal, lateral and apex of the atrium, and then the software automatically generated a region of interest in a four-chamber view for the RA, and in four-chamber and two-chamber views for the LA; these were manually adjusted, as needed, to allow for adequate speckle tracking.
Statistical analysis The baseline characteristics of the patients, stratified by cardiac rhythm at M0 and M6 follow-up, are presented as means ± standard deviations or medians (25 th and 75 th percentiles) for continuous data, and as absolute numbers and percentages for categorical data. The Kruskal-Wallis rank-test was used to compare continuous variables, and Fisher's exact test was used to compare categorical variables. Comparison between inclusion and follow-up echocardiographic data measurements was done using the paired t-test after assessment of the normal distribution of the data using the Shapiro-Wilk test. The results are presented as means ± standard errors. Intraobserver and interobserver variability of the echocardiographic measurements were evaluated using the concordance correlation coefficient and Bland-Altman plots.
Strain and 3D analysis in AF Most studies of atrial strain or 3D analysis in AF have only included patients with paroxysmal AF, and echocardiographic variables were analysed during SR. AF is a difficult condition in which to use 3D and strain analysis. In our study, AF was present in 38 patients during the first TTE, and in 13 patients during the second TTE (Fig. 2 ). All patients had 3D and strain acquisition, but not all data were interpretable, because of artefacts or echogenicity. 3D analysis was possible in 68. 8-72. 9% of cases, and strain analysis in 83. 3-100%. Currently development of 3D allows single beat acquisition, thus extending the use of 3D to patients with AF. Furthermore, the intraobserver and interobserver variability showed good results for strain and 3D analysis (Table  A.
Study limitations First, the rate of AF recurrence is likely to have been underestimated, given the absence of Holter monitoring. However, with evaluation of rhythm during TTE at 6 months, we had a global assessment of the evolution of rhythm. Second, with a 6-month follow-up, our results need to be confirmed with longer clinical and echocardiography follow-up. Third, we evaluated biatrial remodelling only with TTE imaging; it would be interesting to have complementary data with MRI. However, most patients in our study were in AF during the first TTE, which can make interpretation of the MRI more difficult. Finally, concerning the software used for the strain analysis, we used LV-dedicated software. Our results will improve with the current development of strain and 3D software specific for RA/LA analysis.
The commonly followed approach, i. e. , mapping first then combining soil property and aggregating soil layers, is one of the possible trajectories (shown in green on Figure  1 ), but many others exist. Mapping could be the last executed process after having combined the soil properties and the soil layers over the soil input dataset ("mapping last" in blue on Figure  1 ). Combining first the soil properties at each single soil layer, then mapping and lastly aggregating SFA outputs is an intermediate inference trajectory (shown in red on Figure  1 ) that can be envisaged too. Apart from these three examples, one can imagine a large number of other inference trajectories since both partial combinations of soil properties and partial aggregations of soil layers can be envisaged. Soil Syst. 2019, 3, x FOR PEER REVIEW 2 of 17 multiple functionalities of soils and are applicable to ecosystem service supply assessments.
The inference trajectories may differ in the order with which "combining primary soil properties", "aggregating soil layers across depths", and "mapping" are executed to provide the common targeted SFA output. The commonly followed approach, i. e. , mapping first then combining soil property and aggregating soil layers, is one of the possible trajectories (shown in green on Figure  1 ), but many others exist. Mapping could be the last executed process after having combined the soil properties and the soil layers over the soil input dataset ("mapping last" in blue on Figure  1 ). Combining first the soil properties at each single soil layer, then mapping and lastly aggregating SFA outputs is an intermediate inference trajectory (shown in red on Figure  1 ) that can be envisaged too. Apart from these three examples, one can imagine a large number of other inference trajectories since both partial combinations of soil properties and partial aggregations of soil layers can be envisaged.
The Problem SAWC is a well-known concept that has been used for a long time to express the capacity of soils to store water for plants  [13] ; SAWC is computed using a classic expression: SAWC = n i=1 dh i * bd i * 100 -st i 100 * (θr i -θw i ) ( 1 ) where SAWC is the soil available water capacity (cm), dh i = thickness of the ith horizon (cm), bd i = bulk density of the ith horizon, st i = coarse fragment content of the ith horizon (% volumetric), and θr i and θw i are soil water contents at field capacity (FC) and at permanent wilting point (PWP) of the ith horizon (cm 3. cm -  ), respectively. When the mapping of SAWC is targeted, some modifications to Equation (1) are necessary. First, bulk density, soil water contents at FC and at PWP are expensive to measure soil properties that are rarely available in current soil databases, which prevent their mapping.
The alternative solution is to estimate these data from more easily mappable primary soil properties (e. g. , particle size fractions and organic carbon) by using pedotransfer functions (PTFs)  [14]. Many PTFs have been developed for estimating the soil properties required for computing SAWC  [15, 16]. In particular, PTFs can be used to calculate volumetric water contents at FC and PWP (e. g. , Wösten et al. [15] ), which embeds the bulk density information and avoids using a specific PTF to estimate bulk density. Volumetric water contents are estimated as follows θr i = n j=1 α j PP j + ε ( 2 ) θw i = n j=1 α j PP j + ε (3) where θr i = volumetric soil water content at FC (cm 3. cm -  ), θw i = volumetric soil water content at PWP (cm 3. cm -  ), α 1. α n = the coefficient of the model, PP 1.
Soil Data Input In this study, we used a legacy dataset of 640 measured soil profiles selected from the 2024 used in Vaysse and Lagacherie  [11, 12]. This selection was made to ensure that the same input data was used by all of the inference trajectories. This motivated to select only the profiles that were fully documented for each soil property and for each layer. Indeed, this restrictive condition was necessary for providing non-null inputs for applying the "combine first" inference trajectory (i. e. , combining all soil properties and for all soil layers before mapping). Finally, the density of this dataset of the study area was one soil profile for each 41 km².
Soil Data Input In this study, we used a legacy dataset of 640 measured soil profiles selected from the 2024 used in Vaysse and Lagacherie  [11, 12]. This selection was made to ensure that the same input data was used by all of the inference trajectories. This motivated to select only the profiles that were fully documented for each soil property and for each layer. Indeed, this restrictive condition was necessary for providing non-null inputs for applying the "combine first" inference trajectory (i. e. , combining all soil properties and for all soil layers before mapping). Finally, the density of this dataset of the study area was one soil profile for each 41 km 2.
Documenting Layers with the Other Required Soil Properties As the described pedological horizons available in the soil database were defined by variable soil depths, a prior interpolation was required to document the soil layers. Spline functions work as an interpolator, respecting the average values of the target soil property, and assuming a continuous variation with depth  [13]. As an outcome, spline functions deliver a set of interpolated values at specific depths which are, in our study, the depth intervals provided by GlobalSoilMap. net specifications (0-5, 5-15, 15-30, 30-60, 60-100, and 100-200 cm). Then, the mass-preserving spline functions were applied to clay, silt, sand, and coarse fragment contents. The values of the soil properties for the tested aggregated layers (see Figure  2 ) were derived from their values at the six depth intervals using a weighted mean by soil layer thickness.
Local Continuous PTFs As presented in Section 2, the common way to obtain soil water contents, that partially drives AWC (i. e. , soil water contents at FC and PWP), is to use pedotransfer functions. The continuous PTFs used in this study are obtained from an investigation based on the analysis of a dataset of 294 pedological horizons belonging to 115 soil profiles located in the Hérault River Valley in the former Languedoc Roussillon region  [21]. The dataset contained soil water volumetric contents at FC (10 kPa) and at PWP (1500 kPa), particle size fractions (clay, silt, and sand), bulk density, and organic carbon content. Before starting the investigation, bulk density and organic carbon were discarded from the dataset because not all horizons of the STIPA database reported their values, making PTFs inapplicable for local prediction.
PTFs were fitted using a point estimation approach based on multiple linear regression carried out with the stats R package  [22]. Model variables were selected using the Akaike's information criterion (AIC)  [23]  applied to a backward/forward stepwise procedure. Linearity was visually analyzed on linear model graphs, then tested with the RESET test for nonlinearity  [24]. Linear models that fitted to the data set are reported in Table  1. Since particle size fractions are compositional data, stepwise selection will always remove at least one of them from the model. In this case, clay was selected for both models, sand better explained water content at field capacity predictions, and silt, water content at permanent wilting point predictions. Performance assessments showed great performances for both PTFs, with a certain advantage for PWP's PTF.