dataesr/scientific-paragraphs-categorization huggingface dataset:
Until now, most of the rice root transcriptome datasets are based on the comparison at the whole root systems level between stressed/unstressed material, developmental stages, genetic backgrounds (knock out mutant or overexpressing lines versus wild type) or different root types or root tissues. For instance, transcriptomic analysis of root response to soil potassium deficiency revealed the importance of an ion transporter and several protein kinases encoding genes in the adaptation of the plant to this constraint  [10]. Comparison between CR, large and fine lateral roots revealed a significant enrichment in transcripts associated with secondary cell wall metabolism, as well as increased levels of transcripts encoding auxin-signaling related genes in CR  [11]. Recently, exogenous hormonal treatments and transcriptome analysis of rice stem base allowed the identification of a new gene involved in CR development  [12].
We previously used the conditional ectopic induction of the CROWN ROOTLESS 1 transcription factor in its respective mutant background to control the formation of CR in stem bases and generate a transcriptomic dataset capturing the kinetics of gene expression during very early stages of CR formation  [13, 14]. These approaches are powerful but lack spatial resolution. High spatial resolution transcriptomic analyses can be obtained using laser capture microdissection (LCM). For instance, Jiao et coll. produced a cell type transcriptome atlas that includes 40 cell types from rice shoot, root and germinating seed at several developmental stages  [15]. One of the most up-to-date root-based spatialized dataset focused on dividing the CR into eight developmental stages along the longitudinal axis, and three radial tissue types namely: (i) epidermis, exodermis and sclerenchyma; (ii) cortex; (iii) endodermis, pericycle and stele  [16].
However, while this dataset covers different tissues of emerged CR, it does not cover the early stages of CR development. In this paper, with the aim to complete our knowledge about genes expressed during CR formation, we used laser capture microdissection (LCM) to generate an atlas of genes differentially expressed during three early stages of CR primordia development before CR emergence. Using this data resource, transcriptome cross analysis identified key genes already known to be involved in CR initiation and development, and pinpointed new candidate genes involved in gene expression regulation, signaling pathways and cell differentiation and likely controlling CR development.
Stem base sampling, two-photon imaging, tissue preparation, laser capture microdissection and RNA extraction For two-photon imaging, stem bases were sampled and immersed in MS/2 medium at 4˚C overnight. They were included into 5% w:v agarose in sterile water (Duchefa Biochemie, The Netherlands). Agarose blocks were mounted into the Automate 880, a custom machine combining a LSM NLO 880 multiphoton microscope (Zeiss, Jena, Germany) equipped with a Chameleon Ultra II tunable pulsed laser (690-1080 nm range excitation, Coherent, Santa Clara, CA, USA) and a HM 650V vibratome (Microm Microtech, France) allowing automation of sample cutting and imaging. The images were obtained with a 20x/1. 0NA (2. 4 mm WD) objective. The instrument was controlled by the ZEN software and a custom ZEN NeCe software extension (Zeiss).
Array hybridization and analysis All array hybridization steps were performed at the Transcriptomics platform of Montpellier University Hospital (CHRU Montpellier). Array hybridization on Gene Rice (Jp) 1. 1 ST chips was done on the GeneAtlas system according to the manufacturer's instructions (Affymetrix, CA, USA). Probe intensities were normalized using Robust Multi-array Average  [19]  as implemented in Transcriptome Analysis Center software version 4. 0. 0. 25 (Applied Biosystems, Thermo Fisher Scientific). Our dataset was deposited in NCBI's Gene Expression Omnibus  [20]  and is accessible through GEO Series accession number GSE133593.
Cross-analysis with other rice root related transcriptome data sets pinpoint key genes involved in de novo root formation in rice In order to identify genes that may have a conserved function between the processes of CR and lateral root formation in rice, we crossed the list of DEG obtained in this study with a list of 204 genes (corresponding to the S5 Table from  [16] ) associated with lateral root initiation and development, identified from the RiceXpro RXP_4001 root gene expression dataset  [16, 63, 64]. Seventy three DEG of our dataset (43 upregulated, 30 downregulated) crossed with the list of genes suggested to be involved in lateral root initiation and development  [16]  (S5 Table ). Among these were genes encoding TF such as PLT family members or cell wall remodeling enzymes such as OsXTH10 or EXPANSINS (EXP).
OsEXPA2 is detected through in situ hybridization during the development of the seminal root  [67]. Interestingly, most of the shared upregulated genes (33/43) are included in the co-expression cluster III of the RXP_4001 dataset which is suggested to play a role in regulating cell division during lateral root and CR initiation and development, particularly in the apical meristem  [16]. These shared upregulated genes included in the co-expression cluster III were mainly assigned to the "midnightblue" (20/33, including OsSHR1, previously discussed) and "skyblue" (8/33) coexpression clusters in our study, characterized by a strongest expression level in the early and intermediate primordia stages, respectively. Downregulated genes are included into two other co-expression clusters identified by  [16]  (16/30 for cluster I including OsXTH10, 12/ 30 for cluster II).
Also, we compared our dataset with the list of 1753 DEG during the 45 hours following the ectopic expression of CRL1 in the crl1 mutant background, which is used to synchronously restore CR formation  [14]. Three hundred and twenty-four DEG of our dataset (190 downregulated, 134 upregulated, representing about 8% of the total number of DEG) were induced or repressed for at least one time point during the 45 hours following CRL1 induction (S6 Table ). 229/324 of these genes showed expression values that were coherent with the time series, i. e. genes upregulated or downregulated in the time series were also upregulated or downregulated in the developing primordia from our experiment, respectively. This suggested that the expression of these genes is likely under the control of CRL1 during CR formation.
Conclusions In this study, we used LCM to generate tissue-level transcriptome gene expression atlas at 3 stages of rice CR primordia development between initiation and pre-emergence. Our results reveal a shift in transcriptional and post-transcriptional gene regulation occurring during CR development. Several genes already described for their involvement in CR development were retrieved in our dataset. New genes, such as members of the PLT gene family and OsPSK1, were also identified suggesting the involvement of peptide signal related pathway during CR formation similar to the pathways already described in Arabidopsis during lateral root formation. This suggest a conservation of some mechanisms during post-embryonic root formation in rice and Arabidopsis. Our analysis also pointed out a differential regulation of several cell wall remodeling enzyme belonging to XHT and EXP families during CR formation.
Functional analyses will be needed to confirm their implication in this developmental process. Our dataset constitutes an open valuable resource to identify new genes involved in CR development in rice, in particular via transcriptomic meta-analyses, similarly to what has been done in Arabidopsis for lateral root development using the VisuaLRTC tool  [7]  that revealed new key regulators of lateral root development that escaped mutant screening-based investigations  [8]. Similarly to what has been successfully proposed on other species such as cotton for fiber quality  [68] , this resource will be also useful to help the identification of candidate genes in QTL related to rice root development.
In addition to institutional funding, this work is supported by the  French Agence Nationale de la Recherche  ( Fondation Agropolis, Program Investissement d'Avenir Labex Agro  no. ANR-10-LABX-0001-01 ,  2016-2018 , by the  Partenariat de Recherche Collaborative Entreprise MASTEROOT  no. ANR-17-CE20-0028-01 ,  2018-2021 ) and by the  CGIAR Research Program (CRP)  on riceagrifood systems (RICE,  2017-2022 ). J. L. is supported by a  CIFRE fellowship  (No:  2015/0195 ) from the  Association Nationale de la Recherche Technologique, France , and a financial support from of the seed company  Limagrain. Limagrain  provided support in the form of salaries for authors J. L. , M. J. , J. R. and C. S. The specific contribution of these authors are mentioned in the 'author contributions' section. The imaging facility MRI, which is part of the  UMS BioCampus Montpellier  and a member of the national infrastructure  France-BioImaging , supported by the  French National Research Agency  ( ANR-10-INBS-04 ), provided access to biphoton microscopy and  Laser Capture Microdissection facility.
Acknowledgments: We would like to thank  F. Maloumian  for assistance with figure illustrations. Funding: Funding was provided by the  European Research Council  ( ERC-2013-ADG-339379  to A. P. ) and the  Agence Nationale de la Recherche  ( ANR-18-CE16-0013-01  to A. P. and A. A. D. and  ANR-17-CE11-0050  to A. J. ). Author contributions: A. A. D. , A. J. , and A. P. wrote the manuscript.
Acknowledgments We thank  Houria Djoudi  and  Bruno Locatelli  for input to discussions on rankings of the Lake Faguibine and  Central Java  and Kalimantan case studies and  Houria Djoudi  for kindly supplying Figure 1b. This paper is a contribution from the  Transformative Adaptation Research Alliance  (TARA, https://research. csiro. au/tara/);an international network of researchers and practitioners dedicated to the development and implementation of novel approaches to transformative adaptation to global change.
Funding This research was funded in part by  CSIRO Land  and  Water. SL  acknowledges support from the  CSIRO   Visiting Scientist Program  and the  French Agence Nationale pour la Recherche for MntnPaths  ( ANR-16-CE93-0008-01 ) and Investissements d' Avenir CDP Trajectories  ( ANR-15-IDEX-02 ). UP and IP acknowledge support from the  Spanish Ministry of Economy and Competitiveness , under BC3 'Unit of excellence' (MIMECO,  MDM-2017-0714 ). IP was also supported by grant  IJCI-2016-28475.
Statistical Analysis The results are presented as the mean value ± standard deviation. Analysis of variance (ANOVA) and Tukey's multiple-comparison test were carried out to determine differences in parameters among the samples. Differences were considered as significant at p ≤ 0. 05. Multivariate analysis of variance (MANOVA) was used to evaluate the influence of factors such as plant part, wall material, and core-to-wall ratio on the dependent variables (total phenolic, total flavonoid, total flavan-3-ol, and total phenolic-acid contents, as well as antioxidant activity and individual phenol profiles). Estimated marginal means for each factor were presented. A linear correlation analysis was carried out using SPSS version 17 (SPSS Inc. , Chicago, IL, USA).
Experimental sites Data were collected from field experiments carried out in seven Mediterranean locations where durum wheat (Triticum turgidum L. subsp. durum) is widely grown (Fig. 1 ). The experimental sites were located in Florence (43. 76   Metzger et al. , 2005) , the sites belong to northern (Florence) and southern (the other sites) Mediterranean environmental zone, with hot and dry summers and mild winters. Monthly maximum temperatures are in August (from 30 • C in Florence and Morocco to • C in Spain), while January is the coldest month with minimum monthly temperature ranging from ~3 • C in Florence to 6 • C in Morocco. Rainfall are mainly concentrated in late autumn (Florence and Spain) and winter (Foggia and Morocco), with a secondary peak during spring in Florence and Foggia. Annual cumulated rainfall shows a clear latitudinal trend ranging from 750 mm in Florence to 350 mm in Morocco (Fig.
Experimental field data In Florence, data were collected from two rainfed experiments carried out at the University of Florence in the 2002-2003 and 2004growing seasons  (Ferrise et al. , 2010)  with the Italian durum wheat cultivar Creso, a medium-late maturation cultivars characterized by high yield quantity and good quality. Sowing (150 seeds m -2 ) was performed on the 11 December 2002 and 05 November 2004 (normal sowing dates), and on 27 January 2003 and 18 January 2005 (late sowing dates). Four nitrogen treatments were applied with a total amount of 0, 6, 12 and 18 g N m -2 ; one-third of which was applied as ammonium sulphate at Zadoks' growth stage  (Zadoks et al. , 1974)  (ZS) 15/22 (leaf 5 emerged at 50 %, 2 tillers visible) and the remaining two third as ammonium nitrate at ZS 31 (first stem node detected).
In Foggia, data were obtained from a long-term field experiment carried out at the CRA-SCA experimental farm in the periods of  1997-2000 and 2007-2013 (11 growing seasons). Data used in this study were collected on the cultivars Simeto, a medium-early maturation Italian cultivars characterized by an excellent grain quality. Crops were sown between mid-November and early January, depending on the weather and soil conditions, at a density of 400 seeds m -2. Two treatments were applied each year. In the first treatment (hereafter treatment A), crops received 10 g N m -2 of ammonium nitrate at ZS 31, and in the second treatment (hereafter treatment B), crop residues were incorporated with 30 mm of water and crops received 15 g N m -2 as urea at sowing and 10 g N m -2 as ammonium nitrate at the ZS 31.
In 2012 and 2013, crops were irrigated (both treatments) with 30 mm, during the emergence phase, and 80 mm, before anthesis, respectively. Otherwise, the crops were rainfed. In Spain, experimental data were provided by the Andalusian Network of Agricultural Trials (RAEA in Spanish). For this study, data from wheat trials conducted in Carmona and Santaella in the period  2011-2015, and 2011-2016, respectively, were considered. Crop data used in this study were from the cultivar Amilcar, a short-cycle cultivar selected for high potential production and disease resistance. At both sites, crops were sown within late November and mid-December at a density of 350 and 360 seeds m -2 in Carmona and Santaella, respectively. In Carmona, crops received 8-13 g N ha -1 , of which 30-50% was applied at sowing as diammonium sulphate and the remaining in one to two splits between ZS 23 (3 tillers visible) and 37 (flag leaf just visible) as urea and ammonium nitrate.
In Santaella, crops received 11. 5-18. 5 g  the second half of November in Sidi El Aydi and in the second half of December in Kemis Zemamra. In Marchouch, sowing was done on 12 December. Cultivar Karim, a medium semi-dwarf high yielding cultivar, was used in all the experiments in Morocco. In all the experiments, the sowing density was 400 seeds m -2 , and the crops received 18 g N ha -1 as diammonium phosphate at sowing and 46 g N ha -1 as urea at ZS 39 (flag leaf ligule just visible). In Kemis Zemamra, crops were rainfed, in Marchouch, they were fully irrigated, and in Sidi El Aydi, a rain fed and fully irrigated treatments were applied. Fully irrigated crops received 40 mm every 1-7 days depending on the crops' need. At each location, maximum and minimum air temperature, rainfall and global solar radiation data were collected from automatic weather stations located close to the respective experimental fields.
Soil properties were available for all sites except that of Morocco, for which they were extracted from the ISRIC's SoilGrids database, from the Africa Soil Information Service (www. soilgrids. org, http://africasoils. net/). Phenological stages, such as emergence, anthesis, physiological maturity, were recorded in all the experiments following the BBCH scale  (Meier, 2001). In Spain, heading dates were recorded instead of anthesis dates. Grain yield was measured in Italy and Spain but not in Morocco, while total above ground biomass data were available in Italy and in Morocco. Total above ground N, grain N content and grain number data were available in Florence.
, 2006). Dry mass is transferred to the grain at a constant rate in thermal time, thus any stress affecting (reducing) the potential duration of the grain filling, including water and N stresses, reduces the quantity of biomass translocated. All the processes are driven by non-linear responses to temperature  (Wang et al. , 2017)  and considers the impact of heat, water and nitrogen stress on canopy senescence  (Maiorano et al. , 2017). The version 2. 0. 2 of SiriusQuality (https://github. com/SiriusQuality/Release) was used.
Model calibration and validation Because the availability of experimental data varied between locations, ad-hoc criteria were developed to calibrate/evaluate the model. For central Italy (Florence), data collected without N fertilization treatment were used for the calibration, whereas data with N fertilization treatments (6, 12 and 18 g N m -1 ) were used for model validation. Although the parameters, except those related to stress responses (none was included in the calibration) are usually estimated with treatments close to potential production conditions, in Florence, the treatment with no fertilizer application was used, so that the rate of soil N mineralization could be estimated. For southern Italy (Foggia), treatments A and B for the growing seasons from 2006 to 2011 were used for model calibration, and treatments A and B for the growing seasons from 1996 to 1999 and 2012-2013 were used for model validation.
For southern Spain, data from Carmona were used for model calibration, whilst data from Santaella were used for model validation. In Morocco, the model was calibrated using the irrigated treatments at Khemis Zemamra and Sidi El Aydi and the rainfed treatments at the three sites were used for model validation. In Morocco, the weather conditions around sowing were very dry and the observed emergence occurred on the average of 20 days after sowing and coincided with the first rainfall event. Once the sowing date is set, SiriusQuality simulates the emergence date, considering only the thermal time from sowing to emergence, without taking into consideration the soil water content. Thus, to correctly simulate the observed emergence, and consequently the other observed phenological stages, the sowing dates used for the calibration process were postponed until the soil moisture was favorable for germination and plant growth.
As such, sowing dates were estimated using SiriusQuality with a fixed sowing window (15 November to 30 December). Sowing occurred the first day within the sowing window when the soil water content of the 0-30 cm soil layer was > 75 % of the plant available water content and the cumulative precipitations over 3 consecutive days was > 10 mm. If the conditions were not met on the last day of the sowing window, then, the sowing occurred the following day. Since grain yield data were not available in Morocco, the calibration and validation of the model was performed on phenological development and above ground biomass accumulation. Seven genotypic parameters were estimated for each cultivar (Table  1 ) using a covariance matrix adaptation-evolutionary strategy  (Hansen and Ostermeier, 2001 ) that minimized the root mean squared error (RMSE) between all simulated and observed available data.
Model application To analyze G × E×SW interactions, the four wheat cultivars used in model calibration and validation at Florence, Foggia, Santella, and Sidi El Aydi in the period of 1980-2010 were simulated, testing different 30day sowing windows ranging from October to January. In each site, the soil type used for calibration (typical of each location) and the standard local management practices (as communicated from local experts) were adopted as inputs for the model (Table  S1 ). The four cultivars, Creso, Simeto, Amilcar and Karim were tested in all the locations. To be consistent with international studies, such as AgMIP and MACSUR, and provide longer time series for risk assessment due to interannual climate variability and climatic extremes, 100 years of daily weather were generated for all sites using the LARS-WG weather generator  (Semenov and Barrow, 1997; Semenov and Stratonovitch, 2015)  and the ELPIS dataset  (Semenov et al.
, 2010)  of site parameters for Europe, derived from the interpolated daily 25 km gridded meteorological dataset, Crop Growth Monitoring System (CGMS) of the Joint Research Centre for the period of 1980-2010. This latter dataset has been created for agricultural modelling applications, since the interpolation procedure adopted (van der Goot and Orlandi, 2003) accounts for the agricultural areas within a cell and makes these data representative of an agricultural area typical to this grid. CGMS was used to compute the ELPIS dataset of site parameters for the LARS-WG weather generator to enable the generation of local-scale daily climate scenarios across Europe. LARS-WG in Europe and the ELPIS 1980-2010 dataset was validated against independent meteorological dataset ECAD  (Semenov et al. , 2013)  and demonstrated a good performance particularly for agricultural application.
Statistical analysis All the statistical analyses and the model simulations were carried out using R programming environment (R core team, 2013). The accuracy of SiriusQuality in reproducing observed phenology, above ground biomass, grain yield and N grain yield was evaluated by considering the Pearson's correlation coefficient (r), the mean absolute error (MAE), the normalized root mean square error (nRMSE), and the index of agreement (d). The Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations: r = ∑ n i=1 ( O i -O ) (S i -S) ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ∑ n i=1 (O i -O) 2 √ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ∑ n i=1 (S i -S) 2 √ (2) Where, O i is the observed data, O is the mean of the observed data, S i is the simulated data, S is the mean of simulated data, and n is the number of data points.
A principal component analysis (PCA) considering grain filling duration (GF), leaf area index (LAI), cumulated rain during the grain filling (RAIN), photosynthetic active radiation (PAR), yield, single grain dry matter (GDM), and grain number m -2 (GN) was carried out on all sowing windows using R factoextra package  (Kassambara and Mundt, 2019) , in order to estimate the relative importance of each trait in capturing data variation and the importance of sowing windows and wheat cultivar as possible factors structuring the data.
Genotype × environment × sowing window effects G × E × SW interactions were highly significant (P < 0. 001) for anthesis date, leaf area index (LAI) at anthesis and grain number, while yield showed highly significant G £ E and E £ SW interactions (Table  3 ). The PCA results suggested that different environments affected cultivars performances. For instance, whilst in Florence and in Foggia, the cultivars behaviors tended to diverge, in Santaella and Sidi El Aydi, they were more overlapped. Overall, the PC1 and the PC2 explained a high share of the total variation available in the dataset subjected to analysis (Fig. 4 ). In all the locations, GF, RAIN and GDM contributed largely to the first principal component (Table  S2 ) as well as yield and LAI in Florence, Foggia, and Santaella, yield and PAR in Sidy El Aydi.