dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015b; Khosla et al. , 2020; Wang et al. , 2020; Yao et al. , 2021). Under this scenario, KAR 1 and KAR 2 might be activated at different rates. In our transcriptome dataset, six members of the WRKY transcription factor family were upregulated by karrikins. WRKY transcription factors can either repress or activate gene expression related to a vast array of plant developmental processes, including seed development and germination, senescence, abscisic acid (ABA) signalling and response to biotic and abiotic stress stimuli  (Rushton et al. , 2010). The closest Arabidopsis homologue of BdWRKY53 is AtWRKY46, which contributes to osmotic/ salt stress-dependent lateral root inhibition via regulation of ABA signalling and auxin homeostasis  (Ding et al. , 2015). Close homologues of BdWRKY31 are thought to mediate ABA sensitivity in Malus domestica  (Zhao et al.
Seed germination assays Arabidopsis seed germination assays were performed using triplicate batches of seed as described previously  (Sun et al. , 2020). For Brachypodium, we found that seed germination of intact seeds was low (<10%) and non-synchronous on soil, nutrient agar or saturated filter paper, and was not enhanced by pre-imbibition, addition of karrikins or by cold stratification. We found that physical removal of the awn from imbibed seeds was sufficient to induce synchronous germination of ~100% of seeds, irrespective of genotype, within 2 days of transfer to growth conditions described above (data not shown). Ó 2021 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd. , The Plant Journal, (2022), 109, 1559-1574.
Transcriptome analysis Seedlings were transferred to 250-ml Erlenmeyer flasks and treated with butenolides for 4 h as described above. RNA was extracted from shoot tissue and DNase I-treated. RNA integrity scores ≥ 7 were confirmed with an Agilent 2200 TapeStation. Paired-end libraries were prepared using the TruSeq RNA Library Prep Kit v2 (Illumina, San Diego, CA, USA). Sequencing was performed on an Illumina NextSeq 550 instrument with a NextSeq 500/550 High Output Kit v2. 5 (300 cycles) to generate 2 9 150 nt reads. Adapter trimming, read mapping onto the B. distachyon Bd21v. 3. 1 reference genome and read counts were carried out with CLC Genomics Workbench 11. 0. Differential expression analysis was performed using EdgeR  (Robinson et al. , 2010). Venn diagrams were generated using the Venn plug-in of the OriginPro (2018) software.
ACKNOWLEDGEMENTS The authors gratefully acknowledge funding support from an  Australian Research Council   Future Fellowship  ( FT150100162 ) and a fellowship support grant from the  University of Western Australia  to MTW. YM was a recipient of a joint PhD scholarship from the  China Scholarship Council. VS received funding from the  Hungarian National Research, Development and Innovation Office (NKFIH  Grants  K128644  and  GINOP-2. 3. 2-15-2016-00029 ). CG received funding from the  Emmy Noether Program  ( GU1423/1-1 ) of the  Deutsche Forschungsgemeinschaft (DFG) , and KV a doctoral student fellowship from the  German Academic Exchange Service (DAAD). The work conducted by the  US DOE Joint Genome Institute  is supported by the  Office of Science of the US Department of Energy  under Contract no.
DE-AC02-05CH11231. The authors thank  Adrian Scaffidi  and  Gavin Flematti  ( UWA ) for chemical synthesis, and  Gergely Mar oti  ( Biological Research Centre, Szeged, Hungary ) and  Seqomics Ltd  ( M orahalom, Hungary ) for Illumina sequencing and bioinformatics analysis. The TILLING work was supported by  INRAE  and the  LabEx Saclay Plant Sciences -SPS  ( ANR-10-LABX-40-SPS ). Open Access funding enabled and organized by Projekt DEAL.
Results: Our study used 241 poplar genotypes, phenotyped in two common gardens, with xylem and cambium RNA sequenced at one site, yielding large phenotypic, genomic (SNP), and transcriptomic datasets. Prediction models for each trait were built separately for SNPs and transcripts, and compared to a third model integrated by concatenation of both omics. The advantage of integration varied across traits and, to understand such differences, an eQTL analysis was performed to characterize the interplay between the genome and transcriptome and classify the predicting features into cis or trans relationships. A strong, significant negative correlation was found between the change in predictability and the change in predictor ranking for trans eQTLs for traits evaluated in the site of transcriptomic sampling.
Eventually, kernels can be further combined by the Hadamard product to add extra interaction terms between effects  [8, 11]. Integration can also be carried out across models, in what is known as model-based integration  [7]. Such integration can happen for a given omic type over different datasets or populations, each one summarized by its own model, with a final global model utilizing the top features contributed by each of the initial models. Another variant of the same model-based integration proceeds through a multistage approach, combining sequentially different omics for a given population  [12]. One of the simplest integration approaches, however, remains data concatenation  [13] , by which multiple omics are placed side by side into a single large input matrix. Unlike kernels, whose results are produced at the individual level, the concatenation allows for the effects of multiple features at each omic to be estimated, whether they are SNPs, transcripts or any other omic.
For Azodi et al. [13] , however, using concatenation of genomic and transcriptomic data for three maize traits yielded benefits for only one trait. Studies on other biological models also showed similar context dependent results. Based on the Drosophila melanogaster Reference Panel and different transcriptomic datasets, Li et al. [10]  and Morgante et al. [11]  found no advantage of integration following a multiple kernel approach in terms of predictive abilities, and over different sets of fitness-related traits. When the integration included a gene ontology (GO) category as an additional layer of information, accuracies were surprisingly improved  [8]. Using the same Drosophila panel, Ye et al. [12]  also found some benefit in following a model-based integration approach, with an initial modelling stage aiming to detect SNP associated with transcripts (eQTLs), and a subsequent prediction model focused on resulting eQTLs.
Multi-omic model displays performance advantages over the single-omic models for specific functional types of traits Twenty-one traits were phenotyped (Table  1 ) on 241 poplars grown in two common garden experiments located at contrasting sites (Orleans, France, and Savigliano, Italy). RNA sequencing analysis was also performed on young differentiating xylem and cambium tissues of the entire set of genotypes sampled at the Orleans site, resulting in large genomic (428,836 SNPs) and transcriptomic (34,229 transcripts) datasets. For each phenotypic trait, three ridge regression models were built: firstly, with genotypic data as predictors (denoted G), secondly, with transcriptomic data as predictors (T), and thirdly, with integration by concatenation of both omics data (G + T). The prediction accuracies (R 2 ) for the three models varied across trait types, with growth, pathogen tolerance and phenology traits having average performances above 0.
5 at both testing sites, while biochemical and architectural traits showed average performances below 0. 5 (Fig. 1 ). We compared the prediction accuracies of single-and multi-omic models for each trait, and tested for significant differences with a paired Wilcoxon signed-rank test. For all traits, the differences between the average accuracy of multi-omic model compared to the singleomic ranged from -0. 025 to 0. 054. Seven of the 21 traits showed a significant gain with the multi-omics model. These 7 traits included all the growth traits, the pathogen resistance trait, as well as 3 of the 14 biochemical traits (S. G_ORL, Extractives_ORL and Extractives_SAV). It is noteworthy that most of these traits (5/7) were measured in Orleans, the site where transcriptomics data were also collected.
It is important to note that some positions at the marker axis present highly populated vertical trails across the genome, corresponding to important regulatory hubs. We found a total of 18,248 eQTLs for 10,242 out of the 34,229 transcripts available in the transcriptomic dataset. Of these 18,248 eQTLs, 7,845 (43%) were located in cis, and 10,403 (57%) were located in trans (Fig. S1A ). Cis-eQTLs displayed on average a larger effect than trans-eQTLs (Fig. S1B ). The maximum distance between a cis-eQTL and its associated gene was 12 kb.
The ranking of predictor effects was more affected for traits displaying a predictive advantage with integration To gain insight into the factors explaining the gain or loss in predictive ability when using two omics rather than a single omic, we looked more closely at the variation in ranking of effects of the individual predictors over the two types of predictive models. For each of the three models, the rank was established based on squared effects from the ridge regression models (denoted rank hereafter). Fig. 1  Prediction accuracies. Violin plots of prediction accuracies (R 2 ) for 21 traits in the poplar dataset according to three models: genotypic data only (G model coloured in dark brown to the left in the panels), transcriptomic data only (T model coloured in dark blue), and concatenating both genotypic and transcriptomic data (G + T model coloured in light brown to the right).
Integration success is driven by the fall in rank of predictors involved in covariation between genomics and transcriptomics Our main hypothesis was that reducing the sources of redundancy between SNPs and transcripts plays an important role in the success of integration. The eQTLs are an ideal source of redundancy between SNPs and transcripts, so we performed an eQTL analysis to identify these within our dataset. It is important to recall that the SNPs were derived from RNAseq, thus capturing SNPs from mainly the intragenic spaces. While, this might have affected our ability to detect some trans-eQTLs, our multi-locus analysis showed that trans-eQTLs remained the majority, with some hotspots or hub loci associated with a fairly large number of transcripts. Such overabundance of trans-eQTLs has previously been reported in other species such as yeast  [17] , maize  [30, 31] , or tomato  [16] , although cases with similar or fewer occurrences also exist  [14, 18].
Plant material, experimental design and phenotypic evaluation We studied 241 genotypes of Populus nigra originatingd from 11 major river catchments across 4 countries and representative of the species range in Western Europe. More details on the origin of these genotypes including their depositary are available in the GnpIS Information System  [32]  via the FAIDARE data portal (https:// urgi. versa illes. inra. fr/ faida re/), using the keys "Black poplar" and "POPULUS NIGRA RNASEQ PANEL" for the fields "Crops" and "Germplasm list", respectively. These poplars were evaluated in common garden experiments located on 2 contrasting sites (Orleans, denoted ORL and Savigliano, denoted SAV), as underlined by large differences in growth  [33, 34]. At each site, the experimental design consisted of a randomized complete block design with 6 blocks, and thus 6 repetitions per genotype.
Genotype and transcriptomic data RNA sequencing was carried out in 2015 on young differentiating xylem and cambium tissues collected from two replicates of the 241 genotypes located in two blocks of the Orleans design [. These two tissues corresponded to the location in the tree where wood production and differentiation occur. We obtained sequencing reads for 459 samples corresponding to 218 genotypes with two replicates and 23 genotypes with 1 replicate. These sequencing reads were used to provide both transcriptomic and genomic data. For transcriptomic data, the reads were mapped on the Populus trichocarpa v3. 0 primary transcripts using bow-tie2 v2. 4. 1  [39]  and read counts were retrieved for 41,335 transcripts with home made scripts. Only transcripts with at least 1 count in 10% of the individuals were kept, yielding 34,229 features.
1  in Rogier et al. [42]  for a schematic representation of the pipeline). Briefly, genotyping data was obtained, first by mapping the RNAseq reads on the P. trichocarpa reference genome (v3. 0)  [43]  using BWA-MEM v0. 7. 12  [44]. After mapping, the SNPs were called using 4 callers. In order to generate a high-confidence SNP set we selected only the SNPs identified by at least 3 of the 4 callers and with less than 50% of missing values. Remaining missing values were imputed using complementary genotyping data obtained with a 12 k Illumina Infinium Bead-Chip array  [45]. We then detected 874,923 SNPs. From these, 428,836 SNPs were retained for this study after filtering for a minimum allele frequency of 0. 05. eQTLs analysis eQTLs analysis was performed using the Multi-Loci Mixed-Model (MLMM) approach] and implemented in the R package MLMM v0.
1. 1. MLMM uses a step-by-step forward inclusion and backward elimination approach under a mixed-model framework which accounts for the confounding usually attributed to population structure with a random polygenic effect. For each of the 34,229 transcripts we ran MLMM for up to 10 steps and identified the optimal model according to the mBonf criterion (all selected SNPs are significant at a 5% Bonferroni corrected threshold). Based on the positional proximity of the genes, the significant SNPs detected with MLMM were classified as cis regulatory elements (DNA variation regulating the transcription of neighboring genes), and/ or as trans regulatory elements (regulating the transcription of distant genes), according to the following rules: -all SNPs associated with the expression of a gene located in a different chromosome are classified as trans, and the targeted gene is classified as a trans regulated gene; -all SNPs located on the same locus as the gene they target, according to the genome annotation, are classified as cis, and the targeted gene is also classified as cis regulated gene; -the remaining SNPs whose target gene is on the same chromosome but not on the same locus, were split into cis or trans according to their distance to the middle of the gene they target.
Acknowledgements The authors gratefully acknowledge the staff of the  INRAE GBFOR experimental unit  for the establishment and management of the poplar experimental design in Orléans, the collection of wood samples in each site, and their contribution to phenotypic measurements on poplars in Orléans;  Alasia Franco Vivai  staff for management of the poplar experimental plantation in Savigliano, and  M. Sabatti  and  F. Fabbrini  for their contribution to phenotypic measurements on poplars in Savigliano. We acknowledge the staff of  BioForA  for their contribution to RNA collection in the field. We also acknowledge the platform POPS for their implication with RNA sequencing and sequence analyses and  Odile Roger  for her contribution to sequence analyses. Finally, we are grateful to  Amy Watson  for proofreading this manuscript.
Funding Establishment and management of the experimental sites were carried out with financial support from the NOVELTREE project (EU-FP7-211868). RNA collection, extraction, and sequencing were supported by the SYBIOPOP project (ANR-13-JSV6-0001) funded by the French National Research Agency (ANR). This study was funded by the European Union Horizon 2020 research and innovation programme under grant agreement No 773383 (B4EST project) and by INRAE SELGEN metaprogramme (EPINET project). European Research Council,EU-FP7-211868,Agence Nationale de la Recherche (FR),ANR-13-JSV6-0001,Vincent Segura,H2020 European Research Council,773383,INRAE metaprogramme SelGen,EPINET,Vincent Segura.
, 2015; Dudgeon, 2019). The contributions of land use features (as a proxy for habitat exploitation), geospatial variables, and invasion degree (i. e. , the abundance-based share of introduced species of the total community at each sampling site) to the freshwater fish diversity patterns were investigated using a fine-scale resolution fish data extended throughout the Italian peninsula. Non-native fish species have negative effect on fish diversity (e. g. ,  Clavero & García-Berthou, 2005) ; thus, we hypothesized that (H1) invasion degree is the strongest driver negatively influencing alpha diversity and LCBD in the overall fish community. We also investigated how invasion degree affects different beta diversity components (turnover and nestedness). Typically, alpha diversity and LCBD have a negative relationship, indicating that sites with unique species composition harbor low species richness (e.
To calculate LCBD and SCBD abundance-based, the site by species abundance matrix was Hellinger transformed  (Legendre & De Cáceres, 2013). The different components of beta diversity (total beta diversityβsor, species turnoverβsim, and nestednessβnes) were also investigated using Sorensen dissimilarity index  (Baselga, 2010). The turnover component identifies the degree of species replacement between sites, whereas the nestedness component identifies the variation in species richness. Alpha diversity, LCBD, and SCBD measures were calculated using "vegan"  (Oksanen et al. , 2017)  and "adespatial"  (Dray et al. , 2018)  R packages, respectively. The nestedness and turnover components of beta diversity were calculated using "betapart" R package  (Baselga et al. , 2017).
1 software, using the CORINE database (2012, https://www. eea. europa. eu/data-and-maps/data/coper nicus -land-monit oring -servi ce-corine). In the lowland areas, where the low slopes and the human flow modification do not permit an easy definition of watershed areas, the land cover of the whole river basin or of the administrative province was used. CORINE land cover classes were merged into 13 categories: artificial surface, nonirrigated arable land, irrigated arable land, rice field, permanent crop, pasture, heterogeneous agricultural area, forest, natural vegetated area, natural cover without vegetation, sand area, freshwater, and marine water. Land cover was expressed as the percent cover of each of these categories in the watershed of each site (Table  1 ).
e. , species occurrence) (Cribari-Neto & Zeileis, 2010). The Kruskal-Wallis test was used to investigate differences in SCDB values between native (i. e. species occurring originally in Italian waters) and exotic (i. e. species originating from outside Italy) species. All analyses were performed in R software version 3. 4. 3 (R Core Team, 2017). VIFs were checked using "car" R package  (Fox & Weisberg, 2020) , linear mixed models were fitted through the "lme4" R package  (Bates et al. , 2015) , the model selection was performed with "AICcmodavg" R package  (Mazerolle, 2019) , the marginal and conditional R 2 were calculated with "partR2" R package  (Stoffel et al. , 2021) , and the beta regression was performed with "betareg" R package  (Zeileis et al. , 2021).
ANN pattern construction: flux and transport model of the theoretical aquifer ANNs are trained by using a set of patterns created by means of the flux and transport contaminant modelling software TRACES (Transport of Radio ACtive Elements in the Subsurface  [Hoteit et al. (2004) ]). As documented by Hoteit et al. (  2004 ), TRACES performs the simulation of flow and reactive transport in saturated porous media. It is based on mixed and discontinuous finite element methods for solving hydrodynamic state and mass transfer problems. The patterns describe, for a theoretical hydrogeological basin, both spatial location and duration of activity of the contamination source and the set of contaminant concentrations measurements in the monitoring wells. The theoretical hydrogeological basin and its principal features have been defined as reported in Table  1.
This study investigates the spatial distribution and temporal variability of chlorophyll-a (Chl-a) biomass in the Algerian Basin (AB) along its meridional and cross-shore dimensions, focusing on coastal enrichments. After correcting most atmospheric disturbances in the daily MODIS Level-2 data series between 2003 and 2018, a fortnightly climatology of 1-km resolution Chl-a has been generated to account for specific coastal features previously poorly evidenced from 4-km Level-3 data. The AB is characterised by two extreme seasons of high and low biomass, separated by sharp transitions, that characterise the offshore domain. The coastal area (<10 km) reveals an intense and distinct dynamic associated with highly productive local hotspots rather than seasonal variability. A biomass index is proposed as the horizontally integrated Chl-a concentration from the coastline to the most offshore extension of the 0.
Turbid waters in the Med are rare compared to other seas  (Morel et Prieur, 1977). According to  Antoine et al. (1995) , the coastal case-2 waters (where other constituents as mineral particles are also present) in the whole Med are estimated to be 5%. Currently, ocean colour analysis coupled with in-situ data could be used to characterise and monitor phytoplankton blooms  (Barale et al. , 2008; Cerino et al. , 2019; Groom et al. , 2019; Lavigne et al. , 2015; Mayot et al. , 2016; Palmiéri et al. , 2018). In previous studies, a significant limitation was related to the poor representation of coastal patterns due to Level-3 data at 4-km resolution (as in  Lavigne et al. , (2015) ,  Marañón et al. , (2020) ,  Mayot et al. , (2016) ,  Volpe, (2012) ,  Volpe et al. , (2018) ). To this end, our study proposes a practical approach to improve the quality of the standard MODIS-Aqua Level-2 1-km resolution (swatch) Chl-a product, specifically in coastal environments, where the higher data resolution provides more detailed information.
A comparison between simultaneous in-situ and satellite Chl-a was performed to assess the accuracy of MODIS Chl-a data in AB. In addition, a cumulative Chl-a biomass index (IB) was developed to synthesise the spatial patterns and variability of Chl-a. This paper describes the seasonal climatology of IB in AB from 16 years of data  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) , focusing on offshore and coastal areas separately. Finally, a discussion on the influence of different sources of coastal enrichment on the Chl-a biomass in the AB is presented.
Comparison between in-situ and satellite Chl-a data We compared the resulting 1-km satellite Chl-a data with an initial set of 70 high-performance liquid chromatography (HPLC) measurements of surface Chl-a concentration obtained during the SOMBA (Système d'Observation à la Mer dans le Bassin Algérien) cruise in the AB between August 13 and September 10, 2014  (Mortier et al. , 2014). Further details about the cruise are available at 8  https://doi. org/10. 17600/14007500. Calibration precision was estimated to be 0. 4% from the HPLC 1200 instrument used for the Chl-a measurements. A total of 34 measurements were retained according to their correspondence with satellite passes within ± 6 hours. The nearest pixel at 1-km resolution was considered. A representativity error (RE) was also considered, based on the proximity of a high Chl-a gradient, which we defined at a value of 0.
Altimetry data Geostrophic ocean currents and total kinetic energy (TKE) were extracted from the CMEMS (Copernicus Marine Environmental Service) database of the SEA-LEVEL GLO PHY L4 REP OBSERVATIONS 008 047 altimetry data product (http://marine. copernicus. eu, last accessed February 27, 2019), for the same period, and remapped in the AB at a spatial resolution of 0. 25°( Fig. 1  for geostrophic currents and Fig. S2b  for TKE).