dataesr/scientific-paragraphs-categorization huggingface dataset:
117. 81. 224/DOOR3/index. php (Dam et al. , 2007; Mao et al. , 2009). The complete genome sequence of D. dadantii strain 3937 (Genbank accession NC_014500)  (Glasner et al. , 2011)  was used as a reference. Analyses of the biochemical pathways in which the selected genes might participate were done using KEGG  (Kanehisa and Goto, 2000). The results were visualized using iPath  (Letunic et al. , 2008). Similarly, proteins were evaluated for their predicted biological, functional, and metabolic roles in cellular networks using STRING (Search Tool for Retrieval of Interacting Genes/Proteins) v11 (https://string-db. org/) providing essential information regarding interactions of proteins of interest  (Szklarczyk et al. , 2019)  using the proteome of D. dadantii strain 3937 as a reference.
Statistical Analyses Bacterial counts were analyzed with ordinary linear regression using the statistical software package Past3  (Hammer et al. , 2001). To achieve normality data were transformed as log 10 (x + 1). Results were considered to be significant at p ≤ 0. 05 and pair-wise differences were obtained using the t-test. For experiments involving the in vitro plants, data were analyzed according to the experimental design in which two replicated experiments were done per each treatment of replicated plants. The adopted linear model was a complete block design with replicates as blocks, and the main effects analyzed for the contribution of time and treatment and the two-way interaction between time and treatment. A normal distribution of plant height and weight was assumed.
7%) did not express GUS activity in the absence of plants and these were retained for further analysis. Of these, 210 exhibited a GUS positive phenotype in the presence of one or another or both plant species (Figure  1 ) as well as in the presence of leaf, stem, root, or tuber tissue of either S. tuberosum or S. dulcamara tissues or both (Figure  2 ). Quantitative GUS assays of these 210 mutants revealed that the 13 Tn5 mutants exhibited the highest increase in relative GUS activity upon exposure to one of the plant environments. The genomes of these 13 mutants were sequenced (the draft genome sequences are available as Supplementary data) to identify the transposon insertion sites. A single insertion of the Tn5 transposon per genome was observed for each of the 13 mutants as confirmed by Southern blot analysis (data not shown) and genome sequencing.
Characterization of the Tn5 Insertion Sites in D. solani Mutants The DNA sequences flanking the inserted Tn5 transposons of the 13 mutants (ca. 1,000 to 5,000 bp) were analyzed using BlastP to acquire further insight of the genetic context of the genes disrupted by Tn5 insertion. These plant-inducible loci were found to encode membrane proteins, proteins involved in primary metabolism and/or chemotaxis, as well as some hypothetical proteins (Table  1 , Supplementary Table  1 , Supplementary Table  2 , and Supplementary Figure  1 ). From the 13 D. solani IPO2222 transcriptional units, interrogated for their transcriptional organization, 5 (insertions d32, d691, d741, d754, and d814) were predicted to be transcribed in a form of an individual gene, whereas 5 other (insertions d83, d363, d713, d748, and d1032) were predicted to be parts of the operons.
Interestingly, two such units (insertions d83 and d713) were predicted to be a part of the same operon. The transcriptional organization of three units (insertions: d481, d605, and d743) could not be assessed using the DOOR operon database (Table  1 ). In the qRT-PCR, the insertions d83 and d713 expressed the same gene induction pattern-both genes were induced exclusively in the presence of potato tuber tissue as well as were not induced in the presence of all other plant tissues tested. Likewise, the M83 and M713 mutants expressed similar phenotypes (Supplementary Table  2 ). Examination of the KEGG biochemical pathways corresponding to these 13 transcriptional units enabled assignment of some to the cellular pathways involved in: (i) organic acid and sugar metabolism (insertions ds32, ds83, ds691, and ds713), (ii) purine and pyrimidine metabolism (insertion ds363), (iii) chemotaxis (insertion ds745), (iv) phosphorylation (insertion ds481), and (v) transport across the outer membrane (insertion ds748 and ds814) (Supplementary Table  1  and Supplementary Figure  1 ).
Proteomic Analysis of the S. tuberosum and S. dulcamara Extracts Mass spectrometry analyses of the S. tuberosum and S. dulcamara plant extracts allowed identification of 138 and 200 unique plant proteins in the extracts, respectively. The mass spectrometry proteomics data were deposited to the Proteome Xchange Consortium via the PRIDE  (Perez-Riverol et al. , 2018)  partner repository with the dataset identifier: PXD017118. These unique proteins were assigned to five categories, viz. , energy metabolism, nucleotide processing, biotic and abiotic stress response, response to pathogen invasion, protein processing, and others (miscellaneous proteins for which one specific function could not be assessed). No statistically significant differences were found when comparing the percentage of proteins in each category present in the both plant extracts (Supplementary Figure  2 ).
ACKNOWLEDGMENTS The authors would like to thank  Dr. Jan M. van der Wolf  ( Plant Research International, WUR, Wageningen, The Netherlands ) for providing the D. solani strain IPO2222 and  Prof. Titti Mariani  ( Department of Molecular Plant Physiology, Radboud University, Nijmegen, The Netherlands ), for providing S. dulcamara in vitro plants and cultivation protocols. The authors would like to express their gratitude to  Prof. Steven E. Lindow  ( University of California, Berkeley, Berkeley, CA, United States ) for his comments on the manuscript and his editorial work and to  Dr. Anna Woziwodzka  ( Intercollegiate Faculty of Biotechnology UG and MUG, University of Gdansk, Gdansk, Poland ) for helpful discussion.
FUNDING The work was financially supported by the  National Science Centre, Poland  ( Narodowe Centrum Nauki, Polska ), via a research grant  SONATA 8 (2014/15/D/NZ9/00605 ) to RC, by  Polish Ministry of Science and Higher Education (Ministerstwo Nauki i Szkolnictwa Wyzṡzego, Polska)  funds  DS 531-M034-D786-19  to SJ and by  Polish Ministry of Higher Education (Ministers two Nauki i Szkolnictwa Wyzṡzego, Polska)  funds  DS 531-N104-D800-20  to RC.
Acknowledgments: The authors acknowledge the " Toulouse Réseau Imagerie " core IPBS facility support ( Genotoul, Toulouse, France ), and  CMEAB (TRI-Genotoul platform)  is gratefully acknowledged for TEM experiments. We also would like to thank  Géraldine Alberola  and  Muriel Golzio  for scientific discussions. Research was conducted in the scope of the  EBAM European Associated Laboratory (LEA ) and resulted from the networking efforts of the  COST Action   TD1104.
Consequently, assessors in each panel must be effectively trained for correct classification of samples and for correct recognition of the intensities of perceived attributes. In this context, the OLEUM project "Advanced Solutions for Assuring Authenticity and Quality of Olive Oil at Global Scale" funded by the European Commission within the Horizon 2020 Programme (2014-2020, grant agreement No. 635690), is engaged in reinforcing the methodology for sensory evaluation through design of a global procedure named the "quantitative panel test". This approach aims to improve the activity of sensory panels, whose work remains central to ensuring the quality of the product by: (i) reducing the number of samples to be assessed by the sensory panel by establishing chemometric models (calibrated on a large dataset of reliable sensory classified VOOs) that are able to predict assignment of samples to a specific quality grade using rapid instrumental screening methods, which could allow pre-classification with a certain level of probability that can allow the panels to focus more on sensory analysis of uncertain samples; (ii) increasing the panel's performance by introducing new artificial reference materials validated by a number of sensory panels (six in the case of the OLEUM project) and formulated ad hoc to resemble specific sensory attributes (e.
Many studies in the literature have discussed the relationship between the official sensory method applied by a trained panel and consumer perception  [16] [17] [18] [19] [20] [21] , but to our knowledge there are few studies comparing the results of different trained panels and none aimed to reinforce the application of the official method and increase harmonization among different panels. Specifically, the key elements of this work are: (i) the very large dataset obtained by collecting 334 oils from two olive harvest seasons, representative of the most common olive cultivars, different geographical origins, different sensory profiles, and, especially, the main sensory defects perceived; (ii) the processing of data provided by several panels to obtain a reliable classification by the application of a new decision tree useful for possible correlations with instrumental data and/or for building discriminating models by different instrumental approaches.
Sensory Analysis The panel test method was carried out using six OLEUM panels. Positive and negative descriptors were evaluated according to the official procedure (  [8]  and later modifications). The intensity of each attribute was graded by assessors using a continuous unstructured line scale of 10 cm. Each 15 mL sample was tasted at 28 ± 2 • C in a tasting booth, regulated in terms of shape and equipment  [4]. Each panel leader collected the profile sheets completed by each taster  (8) (9) (10) (11) (12)  from his/her panel, reviewed the intensities assigned to the different attributes, and inserted the sensory data in the IOC Excel program for statistical elaboration based on the calculation of the median. The robust coefficients of variation (CVr%) were calculated and validated (  [8]  and later modifications).
Statistical Analysis For processing sensory data from the assessors of each panel (by each respective panel leader), the IOC excel spreadsheet was applied according to official methodology (  [6, 8]  and later modifications). Sensory data from each panel were processed (by the UNIBO panel leader), and after application of the proposed decision tree, the coefficient of variation (CV), was calculated  [26]  (dataset). A limit for the CV based on its frequency distribution was also proposed to check the level of variability. The CV frequency distribution was also expressed as cumulative probability by the t-test (Student's test distribution). For control of the performance of the panel, estimation of both precision and trueness of panels was performed according to IOC guidelines  [25]. The estimation of the precision of panels was made during the procedure of replicate analysis by the calculation of both normalized error (En) and repeatability number (rN), whereas control of panel trueness was obtained by z-score estimation.
7). A slightly modified version of this method (OLEUM z-score) was also adopted; the only difference from the previous one was, in case of V and L categories, the use of the median (Me) of the defect identified as predominant by consensus of the panels (even if it was not the predominant defect for each panel). Therefore, the intensity, and also the type of the mpd, was considered in the OLEUM version of the z-score to obtain a reliable dataset for comparison with instrumental data (e. g. , in OLEUM for developing screening methods based on the analysis of volatile compounds). The detailed formulas of both the methods used to calculate the z-score are shown in Figure  3. olive oils, but not for common commercial ones, and therefore it should be applied only for Protected Designation of Origin (PDO) and other peculiar EVs.
7). A slightly modified version of this method (OLEUM z-score) was also adopted; the only difference from the previous one was, in case of V and L categories, the use of the median (Me) of the defect identified as predominant by consensus of the panels (even if it was not the predominant defect for each panel). Therefore, the intensity, and also the type of the mpd, was considered in the OLEUM version of the z-score to obtain a reliable dataset for comparison with instrumental data (e. g. , in OLEUM for developing screening methods based on the analysis of volatile compounds). The detailed formulas of both the methods used to calculate the z-score are shown in Figure  3. Results of the z-score estimation were illustrated by quality control charts, as part of internal quality control. Some examples of panel performance evaluation are reported in Figures  4  and 5 ; the vertical axis represents the z-score and the horizontal one identifies the sample codes.
Only 45 of 334 oils were reassessed (formative reassessment) and 41 of 45 samples were definitively classified, confirming the importance of alignment between panels, which can be achieved by sharing the same sensory reference materials. In fact, sensory information on both quality grades of samples and main perceived defect/s is fundamental for testing possible correlations with physical-chemical data and/or for building classification models; in this way, instrumental screening approaches can allow for a reduction in the number of samples that have to be assessed by panels, excluding, for example, oils defintely classified by chemometric models as extra virgin or lampante, focusing the sensory analysis on samples that are not classified or classified with a low probability. This thus reduces the number of samples to be assessed by the sensory panel.
The data provided by the panels were also used to verify performance in terms of discriminating capacity, agreement between panels, and accuracy of results by applying some of the procedures reported in the IOC guide for internal quality control of sensory laboratories. In general, the panels showed very good (sometimes excellent) performance even if, in some cases, problems were noted that were related to the use of the scales, lack of recognition of some sensory defects, or intensity values that were too distant between panels for the same sample, especially in the case of oils in which more than one defect was perceived. The large set of samples evaluated over 2 years allowed estimation of the performance of the panel test: the utility and peculiarity of this official method is undisputed, also considering that it has definitively improved the quality of VOOs over the last 28 years, opening the possibility to have a wide range of excellent oils with a deserved added value on the market.
Acknowledgments: The information expressed in this article reflects the authors' views; the  European Commission  is not liable for the information contained herein. We are grateful to all producers who provide us with virgin olive oils for this study as well as all panel members who performed sensory analysis of virgin olive oils from each institution involved:  Eurofins Analytik GmbH, Hamburg, Germany; Institute of Agriculture and Tourism, Poreč, Croatia; Institut des Corps Gras, Pessac, France; Alma Mater Studiorum-Università di Bologna; Science and Research Centre Koper, Slovenia and Ulusal Zeytin ve Zeytinya gı Konseyi, Izmir, Turkey.
Literature search A literature search on non-EU phytoplasmas infecting S. tuberosum was conducted at the beginning of the categorisation in the Web of Science (WoS) database, using the scientific name of the pests as search term. Table  1  lists the search strings used. Relevant papers were reviewed and further references and data were obtained from citations within the references. Due to the little information found on 'Ca. P. americanum' following the WoS search, a search in Google Scholar was performed with the following search string: "Candidatus phytoplasma americanum".
Database search Pest information, on host(s) and distribution, was retrieved from the EPPO Global Database (EPPO GD) (EPPO, 2020) and relevant publications. Data kindly provided by National Plant Protection Organisations of the EU MS were also considered. Information on pest vectors was retrieved from the Hemiptera-Phytoplasma-Plant biological interaction database  (Trivellone, 2019). Data on the EU distribution of pest vectors were retrieved from the EPPO GD (EPPO, 2020), the Fauna Europaea database  (de Jong et al. , 2014)  and the Catalogue of Life 2020 checklist  (Roskov et al. , 2019). When an insect species was not listed in the EU on any of the above-mentioned databases, a further WoS search was performed using the species name as a search string. All results were individually checked.
Data about the area of S. tuberosum grown in the EU were obtained from EUROSTAT (Statistical Office of the European Communities). The Europhyt and TRACES databases were consulted for pest-specific notifications on interceptions and outbreaks. Europhyt and TRACES are web-based networks run by the Directorate General for Health and Food Safety (DG SANT E) of the European Commission, and is a subproject of PHYSAN (Phyto-Sanitary Controls) specifically concerned with plant health information. The Europhyt and, since 2020, TRACES databases manage notifications of interceptions of plants or plant products that do not comply with EU legislation, as well as notifications of plant pests detected in the territory of the EU MS and the phytosanitary measures taken to eradicate or avoid their spread.
(2009) , and representative isolate sequence was EU010008. According to the US National Center for Biotechnology Information (NCBI), the EU010008 sequence was found in grapevine in Italy  (Pacifico et al. , 2009) , and consequently included in the 'Ca. P. solani' species description (16SrXII-A  Quaglino et al. , 2013). The 11 Chinese potato isolates show less than 97. 5% homology with EU010008 on the 16SrDNA gene sequence, and therefore, they are assigned to 16SrXII-I  (Cheng et al. , 2015)  and categorised here as 'Ca. P. fragariae'-related strains. Indeed the reference accession numbers of Chinese potato isolates are filed as 'Ca. P. fragariae' (SOURCE; ORGANISM) in NCBI 'Ca. P. pruni'related strains Several strains related to the 'Ca. P. pruni' species (16SrIII-A,  Davis et al. , 2013)  are known to infect S.
Pest distribution 3. 2. 1. Pest distribution outside the EU The distribution outside the EU of the phytoplasmas categorised here is reported in Table  8 , based on data from the EPPO GD (EPPO, 2020) and/or the CABI Crop Protection Compendium (CPC) (CABI, 2020), and, when not available in these sources, from extensive literature searches. The available distribution maps from the EPPO GD (EPPO, 2020) for 'Ca. P. americanum' (PHYPAE), and for two related strains of 'Ca. P. aurantifolia' (Sweet potato little leaf (PHYP39), and Tomato big bud (PHYP01) phytoplasmas) are provided in Appendix B.
Entry As of September 2020, there were no records of interception of non-EU phytoplasmas of S. tuberosum plants in the Europhyt and TRACE databases. The entry pathways in the EU for the categorised phytoplasmas are potato plants for planting (i. e. seed potatoes), ware potatoes (tubers intended for consumption or processing), plants for planting of other natural hosts and viruliferous vectors. Table  13  reports a summary of the major potential entry pathways of the categorised phytoplasmas. All categorised phytoplasmas are transmitted by vegetative propagation material, hence seed potatoes and more generally, plants for planting, are considered the most important pathway for entry. Tubers of S. tuberosum and seed potatoes as well as plants for planting or tuber-forming species of Solanum L.
flm-3 or FLM-Can were associated with significantly lower LMA when compared to their controls respectively in the Col-0 and rHIF099 genetic backgrounds, while A mass was significantly higher only in the rHIF099 genetic background, although a similar trend was observed in the flm-3/Col-0 comparison (Fig. 4a ). We also observed that FLM functional variation is associated with a negative correlation between these two traits, as observed across species and within A. thaliana natural accessions (Fig. 4b )  12, 14. Regression slopes were not significantly different (SMA test p > 0. 05) when comparing our FLM dataset (rHIF099-Can/Col and flm-3/Col-0 together) with the variation observed across 340 natural A. thaliana (SMA slope in FLM = -1. 80, 95% confidence interval = [-2. 11;-1. 54], r 2 = 0. 17; SMA slope in natural Arabidopsis accessions = -2.
04, 95% confidence interval = [-2. 16;-1. 92], r 2 = 0. 54). By contrast, regression slopes were significantly different (SMA test p < 0. 05) between our FLM dataset and interspecific measurements (SMA slopes in all species from the GLOPNET database = -1. 32, 95% confidence interval = [-1. 39;-1. 25], r 2 = 0. 50; SMA slope in b PCR products of the FLM coding region of Col-0 and Can-0 (from the start to the stop codons) were used for subcloning and the colonies obtained were screened by PCR to estimate the size of many subcloned fragments. Plasmids of one or two colonies carrying a PCR product with a distinct band size were sent for Sanger sequencing. This provided the sequences illustrated here with the horizontal blue lines. An estimate of the frequency of each isoform sequenced is given according to the size of the PCR product obtained during the colony screening.
The vertical dashed lines specify a discrepancy between the annotation and the isoform sequenced. New donor (GT) and acceptor (AG) splice site are depicted when present. Red stars represent premature stop codon. In Col-0, FLM-β as well as variants previously identified  38, 39  are mentioned (FLM-δ, NSF13, and NSF14). The candidate polymorphism responsible for the splicing shift observed in most of the FLM-Can transcripts is depicted in red ("SNP28958"). Source data underlying Fig. 2a  are provided as a Source Data file. herbaceous species from the GLOPNET database = -1. 24, 95% confidence interval = [-1. 47;-1. 04], r 2 = 0. 21), suggesting that although the LES represents an axis of physiological constraints between fast and slow growth strategies, this axis might vary slightly between species.
To evaluate the impact of SNP28958 on FLM expression in natural IP strains, two groups of nine IP strains carrying either the Can or the Col allele were selected for qRT-PCR experiments. A clear association between the allelic form and the accumulation level of exon2-containing transcripts was observed (Fig. 5a ). Moreover, a shift in exon3-containing transcript size was only detected in the Can-like IP accessions indicating that this SNP most likely retain the same hypoactive molecular function as the Can allele (Supplementary Fig. 6 ). We then wanted to know if the functional impact of SNP28958 on multiple traits could play a substantial role in IP strains adaptation to their habitat. We used a set of 186 IP strains for which we were able to gather geographic and phenotypic data from previous publications, including the 46 strains carrying SNP28958  29, 42  (Supplementary Data 3).
We used this dataset to fit a general linear model corrected by a kinship matrix to avoid bias due to population structure (Supplementary Table  1 ). Consistent with our previous observations, we found significant associations between the FLM causal polymorphism and leaf production rate with or without vernalization (Fig. 5b ). Can-like IP strains do not cluster geographically and are scattered across Spain and Portugal (Supplementary Fig. 7 ). We used climatic variables (http://www. worldclim. org/) collected over 30 years to know whether the location of the Can-like IP strains was associated with particular environmental parameters. We found a significant association between the causal polymorphism and isothermality (p = 0. 0017), which corresponds to the mean of the monthly temperature range during the day, divided by the maximal variation of temperature over the year (Fig.