dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the challenges in genetic manipulation, one may exploit the Spiroplasma species differing in their helicity (Figure  2B ; e. g. , cells of S. platyhelix are flattened, helical filaments while S. ixodetis cells are tightly helical) to understand helicity determinants or explore the naturally occurring mutants (e. g. , S. citri ASP-I). The molecular basis for helicity can be studied by performing genome analysis and checking expression levels of cytoskeletal proteins. Indeed, natural species differ in their number of MreB paralogs, in cell diameter and helix pitch (Figure  2B ;  Regassa and Gasparich, 2006). These studies are feasible given the increasing number of Spiroplasma genomes available (NCBI database for genome assemblies contains 31 Spiroplasma genomes out of which 22 were deposited during the five last years).
FUNDING Work in the lab of LB is supported by grants for  Interdisciplinary Programs  from the  University of Bordeaux , and  CASDAR  grant  1617. Work in the lab of PG was supported by funds from  Department of Science and technology (DST)   INSPIRE Faculty Fellowship  ( IFA12/LSBM-52 ),  Innovative Young Biotechnologist Award  ( BT/07/IYBA/2013 ),  Department of Biotechnology   Membrane Structural Biology Programme Grant  ( BT/PR28833/BRB/10/1705/2018 ) and  IISER Pune. SH acknowledged IISER, Pune and  Raman-Charpak Fellowship  from  CEFIPRA  ( IFC//4109/RCF 2017/895 ).
High-performance thin-layer chromatography (HPTLC). Analyses were performed using 0. 20 mm silicagel 60 (20 × 10 cm) glass HPTLC analytical plates (Merck, Darmstadt, Germany). Material included a CAMAG HPTLC system (Muttenz, Switzerland) equipped with an Automatic TLC Sampler (ATS 4), an Automatic Developing Chamber (ADC2) with humidity control, a TLC Visualizer, VisionCATS software and for derivatization, a Chromatogram Immersion Device III and TLC Plate Heater III. Samples were obtained from the analytical extraction of 1 g of M. officinalis ground dry leaves or ipowder® in 100 mL of different solvents (water, ethanol/water: 50/50 v/v [50% ethanol] or methanol). After a 15 min sonication at room temperature, the samples were filtered. The RA standard solution was prepared from 10 mg of RA in 200 mL of 50% ethanol and sonicated for 5 min.
Acknowledgements The authors acknowledge writing and editorial assistance provided by  Claude Blondeau  and  Aurélie Berlin  ( PiLeJe Laboratoire ),  Lucie Etienne-Mesmin  ( Université Clermont Auvergne ), and  Vivien Paula Harry  (freelance medical writer). They also thank  Mustapha Aboussif ,  Augusto Hubaide-Nozella  and  Romain Barnouin  ( PiLeJe Industrie ) for HPTLC and HPLC analyses.
Multiple Long-Term Trajectories of River Control Factors For nearly 150 years, the social response to manage the uses of the river (navigation, flood control, and recreational purposes) or of its waters (drinking or agricultural water resources, dilution of the residual pollution from wastewater treatment plants) has taken place in successive stages  [61]  It has been possible to undertake such a study on the Seine River basin due to the fact that it is an exceptionally rich territory in terms of environmental archives: 1. We have been able to reconstruct the nitrogen circulation and the related water quality, going as far as back in the Middle Ages, based on quantitative economic archives from ecclesiastic records  [62]. 2. A cartographic database on the river course and its corridor showing its evolution since the end of the eighteenth century has been set up, thanks to the archives of Ponts et Chaussées engineers  [48].
For that, we investigated the rules governing the diversity of the distributions of leaf length and width along leaf ranks and translated our findings into model equations. Photoperiodic effects were not analysed here because the temperate genetic material is mostly photoperiod insensitive. A diversity panel of 127 lines with a large variability of flowering time was used to analyse, in five fields, the relation between leaf rank and final leaf length and width (Table  1 , Dataset B). A detailed study of two hybrids with contrasting leaf number (Dataset A) was analysed in one field for identification of the beginning and end of leaf elongation and widening at each leaf rank. Then, we used a panel of 14 hybrids with a restricted window of flowering time to derive model parametrization, in two experiments in the field and in a phenotyping platform  (Datasets C and D).
Finally, the model was tested for one of the 14 hybrids in 14 fields and for the 14 hybrids in one field  (Datasets E and F). Finally, we tested the model for a limited number of lines with later flowering time in a field of Mexico. This resulted in a flexible model that applies to a large range of genotypes with contrasted earliness. This model was developed as an independent executable component in the BioMA software framework (http://www. biomamodelling. org), inter-operable in different crop models and is freely available to the whole community  (Manceau et al. , 2019). This potentially allows one to compare the advantages of traits or associated alleles carried by real or virtual genotypes in a range of environmental conditions, and to define ideotypes for specific sets of environmental conditions  (Gouache et al.
Time course of leaf development and growth in two hybrids (Dataset A) A field experiment was carried out in Mauguio, France. (Table  1 , Dataset A) in a field (1000s of plants) sown with the two single cross hybrids Déa and Volga that differ in flowering time and final leaf number  (16 and 19, respectively). For each hybrid, we labelled 500 plants which emerged on the same day. Among those, 15 plants were chosen as references and scored every second day for the number of appeared and ligulated leaves. Leaves were numbered acropetally from the plant base, and the first true leaf was labelled as leaf 1. Leaves 5 and 10 were labelled once fully expanded to facilitate counting the leaves of higher ranks. Ten plants of the 500 were selected every second day, with phenological stages similar to the mean of the fifteen reference plants of the corresponding hybrid.
Final leaf length and width in a diversity panel of 127 maize lines differing in final leaf number in contrasting environmental conditions (Dataset B) We analysed 127 maize lines representative of the maize breeding pools (dent, flint, subtropical and tropical, supplementary tables S1 and S2) in six field experiments, four in France, one in Mexico and one in Kenya (Dataset B, Table  1 , supplementary Table  S1  and supplementary Table  S2 ). Initially, nearly all lines were present in all sites, but severe problems of maladaptation were obvious in many genotypes that could not be kept in the analysis. Eventually, data from 6 subtropical and tropical lines were kept in Mexico and Kenya, and data from three subtropical lines were kept in Europe in addition to temperate material (117 lines in total, supplementary Table  S2 ).
Parameters for leaf development and leaf expansion in 14 maize hybrids (Dataset C) An experiment was carried out in the phenotyping platform PhenoArch (Montpellier, France, https://www6. montpellier. inra. fr/lepse/M3P), presented in detail in  (Alvarez Prado et al. , 2018). We considered here 14 maize F1 crosses of 14 dent lines with a flint tester, with a restricted range of flowering time, selected for maximizing the genetic diversity among the 254 hybrids of the experiment. Plants were grown in well-watered or in water deficit conditions with two replicates per genotype and treatment (Table  1 , dataset C and details in supplementary Table  S3 ). For each plant, 13 RGB images (2056 × 2454 pixels) were taken every night (one from top and 12 side images with a 30° horizontal rotation) and processed as in  (Cabrera-Bosquet et al.
Final leaf number in the same 14 maize hybrids (Dataset D) The final leaf number ( ) was measured in a field experiment in Saint Martin de Hinx (France) for four plants per hybrid (Dataset D, Table  1 ). Plants were grown in well-watered conditions at a density of 10 plants m -². Leaf number was measured as above, its genotypic mean was calculated based on four plants. Details on environmental conditions are in supplementary Table  S3.
Parameters for leaf widening in the same 14 maize hybrids (Dataset C and D) Parameter values of leaf widening and sensitivity to intercepted radiation for the same 14 hybrids were calculated from datasets C and D (Table  1  and supplementary Table  S3  for environmental conditions). In both datasets, the final width of leaf 6 was measured as in  Lacube et al. (2017). The sensitivity to intercepted radiation (genotype-specific parameter r rad , Box 1) was calculated by fitting a linear regression on final width vs. intercepted radiation during the period of time during which the corresponding leaf was widening. We used this sensitivity to normalize the width of leaf 6 in all A c c e p t e d M a n u s c r i p t experiments. A genotype-specific width of leaf 6 was calculated for a reference intercepted radiation at a standard light of 1.
Model test (Datasets E and F) A test of the model was performed based on two field datasets. In dataset E (Table  1 ), the final leaf lengths and widths of all leaves of the reference hybrid B73 × UH007 were measured in both water deficit and well-watered conditions in 14 experiments presented in  (Millet et al. , 2019) , in 8 field sites from 2011 to 2013 spread along a climatic transect in Europe (Table  S3 ). In dataset F (Table  1 ), the 14 parametrised hybrids were analysed in Mauguio in 2016, with the same measurements (hybrid names in SI. Table  S4 ). In both cases, light, air temperature, relative humidity (RH), rainfall and wind speed were measured every hour in each experiment. Light was measured with PPFD sensors or pyranometers depending on local practices; air temperature and RH were measured in ventilated shelters.
The genetic variability of the distributions of leaf length and width along leaf ranks primarily depended on flowering time The analysis of 119 maize lines (dataset B) presenting final leaf numbers from 12 to 25 was performed in two temperate field sites. This revealed that the effect of the genetic variability of time to flowering was adequately represented by that of final leaf number (Fig. 1 , R 2 = 0. 79), whereas the time elapsed between the appearance of successive leaves (phyllochron) was independent of flowering time in the same diversity panel (Supplementary Fig. S2 ). Our ability to predict flowering time from leaf number was further tested by a 5-fold cross validation. We split randomly the dataset (Supplementary Fig. S3 ). Hence, the genetic variability of flowering time was expressed as that of leaf number in further analyses.
The time courses of leaf tip and ligule number were used for modelling leaf development Modelling the expansion of individual leaves requires identification of the dates at which leaf elongation and widening begin and finish, for each leaf rank on the stem of plants presenting different final leaf numbers. Direct observation of these dates involves daily dissection and measurement of all leaves that grow inside the plant whorl, an unfeasible task for hundreds of genotypes. Conversely, the dates of leaf tip and ligule appearance can be monitored for thousands of plants in a phenotyping platform and are consistent with those measured in the field  (Millet et al. , 2019). Our model therefore considered the latter information to infer the timing of elongation and widening. We linked the dates of beginning of leaf elongation to those of leaf tip appearance, and the date of end of leaf elongation with those of ligule appearance, based on the dataset A (Table  1 ) involving two hybrids presenting contrasting flowering times and final leaf numbers.
The distribution of maximum leaf elongation rate along the stem was modelled with genotypic and environmental effects Leaf elongation rate at a given time depends on a maximum rate, itself depending on the considered leaf rank and genotype, and of environmental effects  (Lacube et al. , 2017; Welcker et al. , 2011). -The genotypic effect was taken into account via one parameter per genotype, the maximum leaf elongation rate of leaf 6 that can be measured on a phenotyping platform for many  (Welcker et al. , 2011). This rate ranged from 4. 07 to 6. 17 mm °Cd -1 in the diversity panel of dataset B (Supplementary Table  S4 ), and showed a clear genetic correlation with final leaf length  (Dignat et al. , 2013). -The effect of leaf rank was analysed (Fig. 4 E  the distribution of normalized rates along leaf ranks showed a bell-shaped curve (Fig.
The distribution of leaf width along leaf ranks was modelled with genotypic and environmental effects In the same way as for leaf length, we considered a reference distribution of leaf width ( ) along leaf ranks, which depends on genotype and leaf rank, and an environmental effect that depends on intercepted light ( ) (Box 1, Eq. 13). -The genotypic effect was taken into account via the width of leaf 6 (W 6 ), which can be measured on a phenotyping platform  (Lacube et al. , 2017)  and ranged from 66 to 83 mm in a panel of hybrids (dataset C). A c c e p t e d M a n u s c r i p t which leaf length along leaf ranks and final leaf number were measured in a field in Mexico with parameters a, b and c collected in the platform Phenodyn  (Welcker et al. , 2010). The distributions of leaf length and width were similar for observed and simulated data.
As in dataset B, the length of the longest leaf increased with final leaf number and was located at a higher rank for latest genotypes. The same tendency was observed, for final leaf number from 19 to 23, in the experiment in Mexico (supplementary Fig. S6 ). It was not observed for leaf width, consistent with the shapes presented in Fig. 2. Simulated leaf length and width averaged for leaves 9-11 accounted for observed length and width at the same ranks in dataset F (r 2 = 0. 58 and 0. 87 for length and width, respectively). The same was observed for the experiment in dataset G for leaves 15-17. Notably, the regression applied to hybrids with different final leaf numbers. This was due to genotypic differences in sensitivity measured in the greenhouse, which accounted for genotypic differences in the field.
We then considered the leaf length and width of plants of the reference hybrid B73 x UH007, measured in 14 field experiments under well-watered conditions but contrasting evaporative demand and light, (Dataset E Table  1 ). The environmental conditions sensed by plants during leaf growth resulted in appreciable differences in measured leaf lengths along the stem, but less so for leaf width. (Supplementary Fig. S7 ). The mean length of ranks 9 to 11 across sites was adequately simulated (r 2 = 0. 66). The same applied to leaf width with a looser correlation (r 2 = 0. 44). (Parent et al. , 2018). This requires a dialogue between model formalisms and phenomic methods  (Wang et al. , 2019)  for measuring genotype-dependent model parameters of hundreds of genotypes in phenotyping facilities  (Tardieu et al.
, 2017). This paper participates to this approach. The rules for simulating leaf growth in a wide genetic diversity of flowering time and environmental conditions were established based on experimental data, rather than on theoretical laws that are not available at this scale of plant organization. As presented previously, we considered as 'metamechanisms' those response curves to environmental conditions or developmental relations which are valid across a large range of conditions, involving both in-door and field conditions  (Tardieu and Parent, 2017; Tardieu et al. , 2018). This required the use of four datasets, namely five field experiments with 127 maize lines showing contrasting leaf numbers, one field experiment with lines with higher final leaf numbers, one experiment in a phenotyping platform, and one lowthroughput field experiment with two genotypes for detailed time courses of leaf length and width.
We have recently shown that changes in distribution of leaf area along leaf ranks allowed a genetic progress of the amount of light intercepted by the canopy layer hosting maize ears  (Perez et al. , 2019). Finally, the ten genotype-dependent parameters of the model may be predicted via genomic prediction based on set of platform experiments such as those in dataset C, thereby allowing one to predict the leaf area of hundreds of genotypes from their genotypic markers in hundreds of fields as recently carried out for flowering time  (Li et al. , 2018)  or for the responses of yield to environmental conditions  (Millet et al. , 2019).
Materials and methods: The data were systematically collected from the scientific electronic data bases including SciFinder, Scopus, Elvevier, PubMed and Google Scholar. Results: This literature overview reported several traditional uses of different species of Polygala, mainly against wounds, inflammation, cardiovascular and central nervous system disorders. P. altomontana, P caudata, P. flavescens, P. glomerata, P. japonica, P. molluginifolia, P. sibirica,, P. tenuifolia are the main species which have been studied in the last few years. Phytochemical studies showed that they contain triterpene saponins, triterpenes, terpenoids, xanthones, flavonoids, coumarins, oligosaccharide esters, styryl benzoic compounds, benzophenones, and polysaccharides. Pharmacological in vitro and in vivo studies and proposal of the mechanisms of action indicated that pure constituents and extracts of Polygala ssp exhibited significant anti-inflammatory, neuroprotective, antiischemic, antidepressant, sedative, analgesic, antiatherosclerosis, antitumor and enzyme inhibitory properties.
The structure elucidation is achieved by extensive 1D and 2D NMR and MS spectroscopic analyses, and sometimes by X-ray diffraction. More recently, a rapid, sensitive and robust method for the characterization of multiple components in P. tenuifolia roots by ultra-high performance liquid chromatography coupled to quadrupole time of flight mass spectrometry (UPLC/Q-TOF MS) was developed. By verifying elemental compositions and MS/MS fragmentations ions, along with their chromatographic retention times as well as retrieving reference literature, 35 compounds (mainly xanthones, oligosaccharide esters, saponins) from Radix Polygalae were tentatively characterized by comparing the in-house formula database  (Liu et al. , 2017).
LATEBLIGHT was modified by several authors, for example to account for effects of fungicides and potato cultivars  [18]. In an approach similar to that of Berger and Jones  [26]. Andrade-Piedra et al. [27]  modified the temperature effects on lesion expansion and sporulation, and added temperature effects on the latent period. The modified model was programmed using the Statistical Analysis System (SAS Institute Inc. , Cary, NC, USA) and was referred to as LB2004. This model was evaluated for a wide range of ecoclimates with the ultimate goal to predict global late blight risk under climate change conditions  [27]. Very few other models have been used to predict potential effects of climate change on late blight  [9, 23] , and these were empirical models based on BLITECAST or SimCast, a modification of BLITECAST using average temperatures during the hours per day with high RH  [3, 5].
Response curves for parameter estimates used in the model BLIGHTSIM. All data except those in B were obtained from growth chamber experiments performed on potato cv Red LaSoda using an isolate of Phytophthora infestans clone US-23  [40]. The data for A, C and D were fitted with a four-parameter thermodynamic model (Table  S1 ). The data for B were fitted using a logistic regression model is SAS (version 9. 4). A, effect of temperature on relative sporulation*infection efficiency; B, effect of relative humidity on sporulation of P. infestans race 4,10,11 on potato cv Bintje  [48] ; C, effect of temperature on relative radial lesion growth rate; and D, effect of temperature on relative latency progression rate.
The rate of change from the infectious to the removed state is determined by the removal rate, 1/24 h-1, which is the inverse of the infectious period. Relative reproduction rate (h -1 ) 0 BLIGHTSIM uses hourly temperature and RH data as input to simulate the relative disease severity (as proportion of all sites), which is calculated as the sum of the proportions of infectious and removed sites. Temperature and RH are the driving variables of the system which affect the latency progression and relative "lesion growth" rates, as well as RRR. At the end of the simulation run, the area under the disease progress curve (AUDPC) is calculated by adding the hourly relative severity values  [55]. The program was implemented using the R programming environment (version 3. 2. 2, Auckland University, Auckland, New Zealand) with hourly time steps and using Runge-Kutta methods for integration.
Effect of Relative Humidity on Sporulation and Derivation of Function f2 Function f2 (Equation (  2 )) (Figure  2B ) was derived from the sporulation data measured 10 days after inoculation on detached potato leaflets at 0. 3 mm. s -1 air speed, at 15 • C and 80%, 85%, 90%, 95% or 100% RH  [48]. To derive the parameters for function f2, the sporulation data were logit-transformed and fitted to a linear regression model is SAS version 9. 4. The relative lesion growth rates for 3 to 5 consecutive days (depending on temperature) were derived using the data from growth chamber experiments for the US-23 isolate as described under "Estimation of relative lesion growth rate" (Section 4. 5). In the model, these rates were adjusted for the effect of temperature on "lesion growth" using function f3.
Model Equations The change in each state variable of the system is governed by the following equations (the code is given in Table  S3 ). dY = dI + dR ( (12) ) 13 where dH is changes in healthy sites, dL0 to dL6 are changes in latent sites and dI, dR and dY are changes in infectious, removed and sum of infectious and removed sites, respectively. f1 = a reducing function that describes the effect of temperature on sporulation and infection (Table  S1 ); f2 = a reducing function that describes the effect of RH on sporulation; f3 = a reducing function that describes the effect of temperature in radial "lesion growth"; f4 = a reducing function that describes the effect of temperature on the latency progression rate. For detailed and completed definition of parameters refer to Table  3.
Each day started at 7:00 am and ended at 7:00 pm  [40]. For scenario testing, the temperature data entered into the model were obtained from the same sinewave as used for the growth chamber experiment. A modified version of this sine-wave equation was used to generate RH data: RHH = A* Sin (Radians (15*H + 30)) + Mrh ( 15 ) where RHH = relative humidity at hour H, A = amplitude (RHmax-RHmin)/2, H = time in 24 h, Mrh = mean relative humidity, RHmax = maximum RH in 24 h, RHmin = minimum RH in 24 h.
The values of the other parameters such as RLGR, LPR were the same as those used for simulation of the growth chamber data. The weather data of the next year (1998) were used to validate the model simulations based on the estimated initial inoculum and RRR obtained in the calibration process. Goodness of fit (R 2 ) was evaluated by regressing the observed field data on the simulated data. Unlike the model fitting with growth chamber data when a delay was used until the end of the latent period, no delay time was used for calibration and testing the model with the field data. Instead, the latent and infectious periods were accounted for by taking the inverse of these periods as latency and infectiousness development rates  [45].