dataesr/scientific-paragraphs-categorization huggingface dataset:
fastidiosa is inoculated in almonds in the spring in California, only some trees are found infected after winter  (Davis et al. , 1980). Climate data was obtained from Chelsa Climatology  (Karger et al. , 2017). Chelsa climate data are for the time period 1979-2013 and are based on the downscaled ERA-interim global circulation model with GPCC (Global Precipitation Climatology Centre) and GHCM (Global Historical Climatology Network) bias correction, and a resolution of 30 arc seconds (approximately 1 km)  (Karger et al. , 2017). Bioclimatic variables were derived from monthly temperature and precipitation values and are intended to approximate climate dimensions meaningful to biological species. We used climate data at 5 arc-min spatial (approximately 10 km grid cell). We used the 19 bioclimatic variables proposed by  Hijmans et al.
fastidiosa is known to be present; and (2) the geographic extent of the current known distribution of X. fastidiosa territory records were selected as background. This extent was estimated by the area delimited by the geographic coordinates of the presence data used in the analyses. The second extent was then used as background based on preliminary analyses that showed more consistent results across data sets comprising the global X. fastidiosa distribution. This extent was determined by selecting the geographic extent of the presence data used in the analysis.
Pathogen distribution data and pseudo-absence generation The presence records for X. fastidiosa were obtained from two sources:  (1)  the EFSA Update of the Xylella spp. host plant database (EFSA, 2018a);  (2)  Local data sets provided by Regional Authorities and National Plant Protection Organizations listed above including X. fastidiosa outbreaks in the EU, i. e. of Italy, France and Spain. The presence records used were filtered by: (1) selecting only records from infection observed under natural inoculum pressure either during surveys or research activities on natural habitat, omitting records from greenhouse, screenhouse or interceptions; (2) selecting records with precise geographic coordinates and (3) records with confirmed positives. In order to reduce spatial autocorrelation, i. e.
the tendency of closer locations being more similar than those further apart, the presence records were submitted to a spatial filtering approach. In this procedure, the presence records are randomly selected according to a minimum nearest neighbour distance greater than or equal to 10 km between each locality. This distance is equal to the spatial resolution used for the climatic data resulting in sets of 540 presences for X. fastidiosa as a whole, 43 for X. fastidiosa subsp. fastidiosa, 104 for subsp. multiplex and 63 for subsp. pauca, 26 for subsp. fastidiosa ST1, 11 for subsp. multiplex ST6 and 18 for subsp. pauca ST53. This procedure was performed using the spThin package in R  (Aiello-Lammens et al. , 2015). The procedure was repeated four times obtaining four different spatially filtered data sets.
In addition, we evaluated each variable importance by calculating the improvement of the model performance (AUC) over inclusion of each variable comparing to when the variable is excluded through a cross-validation procedure (Naimi and Ara ujo, 2016). We ran a total of 800 single models (i. e. 4 spatially filtered data sets, 4 pseudo-absence sampling replicates, 10 single modelling techniques and 5 cross-validation runs). For building the model ensemble, we chose the weighted average method to combine all single models with TSS values greater than or equal to 0. 7  (Guisan et al. , 2017). The output of this process is a continuous variable that ranges from 0 to 1, which may need to be classified into dichotomous predictions of species presence and absence for practical application and validation  (Jimenez-Valverde and Lobo, 2007).
Asymptomatic period 2. 1. 3. 1. Data collection and processing A literature review was undertaken using the EFSA Update of the Xylella spp. host plant database  (EFSA, 2018a). At the time of search, this database included 853 papers. All papers which described studies using artificial inoculation of plants (including insect transmission, grafting with infected material and mechanical inoculation of a bacterial suspension) were selected (n = 413). Information on the following was recorded: • The species of host plant and the source of X. fastidiosa. • The bacterial incubation conditions and the inoculation procedure. • Whether plant infection was confirmed using laboratory testing. • The time of first appearance of symptoms (in relation to inoculation time) and the number of symptomatic and asymptomatic plants at this time.
Data analyses Since data on the time of development of symptoms for each individual host in each study were not available, it was not possible to estimate the duration of the asymptomatic period for each subspecies-host combination directly from the data. Instead, data on the number of symptomatic and asymptomatic individuals at two post-infection time points were available: the time of first recorded symptom expression and the time of the end of the study. As a result, the data are 'censored', meaning that the exact timing of symptom expression is unknown in the following three cases: • Hosts which exhibited symptoms before the time of first observation ('interval censored' data). • Hosts which developed symptoms between first symptom observation and end of the study ('interval censored' data).
This is based on the description of surveys in Decision (EU) 2017/2352 (Decision 2015/789 requests the same inspection effort in the whole BZ, but has been modified in December 2017). Furthermore, a very low surveillance effort was applied in the rest of the landscape to account for detection of disease foci beyond the demarcated zones. If the modelled visual inspection of a 100 m 9 100 m square locates symptomatic hosts, then the model selects one random symptomatic host for laboratory testing. If no symptomatic hosts are detected, the model assumes that a randomly selected asymptomatic host is tested. Test accuracy rates for infected symptomatic and asymptomatic host plants were estimated from the monitoring data from Apulia, suggesting a significant level of false negatives between around 5-10% but negligible false positive rates.
, for which at least some information/data on impact was found in the scientific and/or technical, and/or grey literature, the EKE process was applied  (EFSA, 2014). A short description of the EKE process is given in Appendix E. For the EKE on the impact on this hosts, the Panel adopted the general scenario assumptions applied by the EFSA Working Group on EU Priority Pests 1 and which assume that the entry, establishment and spread of the pest had already occurred. This corresponds to a scenario where the pest is already present throughout the area of potential distribution in the EU (i. e. it has spread to its maximum extent). In addition, the only vector that was considered in this scenario was Philaenus spumarius, the most common and widespread known vector of X. fastidiosa in Europe. The full list of scenario assumptions is given in Appendix E.
For forest species, the Panel overlapped the area of potential distribution of the bacterium based on the SDMs described in Section 3. 1 and Appendix B (which offers a finer level detailed compared to the K € oppen-Geiger approach) with the distribution area of host tree species to analyse the potential impact in terms of areas potentially affected. For nurseries, the Panel reviewed the potential impact with regard to the type of productions (outdoors vs protected cultivation) and plant species grown. 2. 1. 6. 1. Data  Statistical data of productions areas for olive, almond, grapevine and Citrus spp. were collected at NUTS 2 level from the websites of the national statistical institutes of each country. If data were not available in the websites, the statistical institutes were contacted, or the EUROSTAT database was consulted (Appendix H).
Uncertainties affecting the assessment of potential establishment A number of caveats should be considered for the analyses performed to estimate potential establishment: -SDMs correlate the presence data of a species with climatic conditions prevalent in those locations, and then predict suitable climatic areas outside the species native geographic range. However, other factors, particularly host distribution would have also an effect. Unfortunately, no global database is available mapping geographic distribution of potential hosts at the spatial resolution need for the analyses. -When the model classifies a certain location as suitable, it indicates that climatic conditions prevalent in this location could allow the establishment of X. fastidiosa, but it would establish only if a susceptible host to this particular strain of the bacterium is present, vectors are present and no control measures are applied.
However, to date, little information is available on the impact of X. fastidiosa on these tree species in the EU. Potential impact in the EU Should X. fastidiosa spread or be introduced to forest areas in the EU, impact could be observed on oaks, elms, maples, and other tree genera known to be affected by the disease in North America. However, there is a high level of uncertainty related to this impact since it is not known whether tree species in those genera that are native to the EU and absent in North America may serve as hosts for X. fastidiosa and their levels of susceptibility to the disease. An analysis of the EFSA Update of the Xylella spp. host plant database (EFSA, 2018a) combined with information on the distribution of tree species in the EU reveals that several confirmed hosts of X.
Taxonomic level in phytosanitary measures The plant pathogenic bacterium X. fastidiosa is currently listed as EU quarantine plant pest in the Directive 2000/29/EC. This means that all subspecies and strains of X. fastidiosa are quarantine plant pests, whereas the other described species of the genus Xylella, Xylella taiwanensis, is not. Although this species in only reported currently in Taiwan, a further specific pest categorisation and pest risk assessment are recommended for X. taiwanensis. With regard to the intraspecific diversity of X. fastidiosa, information from literature and official reports on host range at subspecies and ST levels are available in the EFSA Update of the Xylella spp. host plant database (EFSA, 2018a) and in the updated X. fastidiosa pest categorisation  (EFSA PLH Panel, 2018b).
E. 1. 4. Evidence on the pest in support to the EKE According to the EFSA methodology on EKE, invited experts received in due time a factsheet with general information regarding the biology of the pest, list of hosts, geographical distribution of hosts and pest, area of potential establishment. Since this information is provided in the main body of this Scientific Opinion and in the most recent EFSA outputs regarding X. fastidiosa (e. g. EFSA pest categorisation, EFSA Update of the Xylella spp. host plant database), it is not repeated here in the Appendix. In addition, for each estimated parameter, a specific table possibly including quantitative evidences on the observed impact in a given host was prepared and used during the EKE procedure. These tables are included in Appendix E. 4.
X. fastidiosa pest free countries (+++) or countries with pest free areas (++) or pest free production sites (+) according to articles 16 or 17 of Commission Implementing Decision (EU) 2015/789(a)Trade partners with literature reports of X. fastidiosa from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x)Trade partners with literature reports of other Xylella species from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x) X. fastidiosa pest free countries (+++) or countries with pest free areas (++) or pest free production sites (+) according to articles 16 or 17 of Commission Implementing Decision (EU) 2015/789(a)Trade partners with literature reports of X. fastidiosa from EFSA Update of the Xylella spp.
host plant database, excluded dubious reports and eradicated outbreaks (x)Trade partners with literature reports of other Xylella species from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x) X. fastidiosa pest free countries (+++) or countries with pest free areas (++) or pest free production sites (+) according to articles 16 or 17 of Commission Implementing Decision (EU) 2015/789(a)Trade partners with literature reports of X. fastidiosa from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x)Trade partners with literature reports of other Xylella species from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x) X. fastidiosa pest free countries (+++) or countries with pest free areas (++) or pest free production sites (+) according to articles 16 or 17 of Commission Implementing Decision (EU) 2015/789(a)Trade partners with literature reports of X.
fastidiosa from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x)Trade partners with literature reports of other Xylella species from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x) X. fastidiosa pest free countries (+++) or countries with pest free areas (++) or pest free production sites (+) according to articles 16 or 17 of Commission Implementing Decision (EU) 2015/789(a)Trade partners with literature reports of X. fastidiosa from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x)Trade partners with literature reports of other Xylella species from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x) X.
fastidiosa pest free countries (+++) or countries with pest free areas (++) or pest free production sites (+) according to articles 16 or 17 of Commission Implementing Decision (EU) 2015/789(a)Trade partners with literature reports of X. fastidiosa from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x)Trade partners with literature reports of other Xylella species from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x) X. fastidiosa pest free countries (+++) or countries with pest free areas (++) or pest free production sites (+) according to articles 16 or 17 of Commission Implementing Decision (EU) 2015/789(a)Trade partners with literature reports of X. fastidiosa from EFSA Update of the Xylella spp.
host plant database, excluded dubious reports and eradicated outbreaks (x)Trade partners with literature reports of other Xylella species from EFSA Update of the Xylella spp. host plant database, excluded dubious reports and eradicated outbreaks (x) Mean export Mean export Mean export Mean export Mean export Mean export Mean export Trade partner Trade partner Trade partner Trade partner Trade partner Trade partner Trade partner 2013-2018 (quantity in 2013-2018 (quantity in 2013-2018 (quantity in 2013-2018 (quantity in 2013-2018 (quantity in 2013-2018 (quantity in 2013-2018 (quantity in 100 kg) 100 kg) 100 kg) 100 kg) 100 kg) 100 kg) 100 kg) NORTH MACEDONIA THAILAND FAROE ISLANDS CAPE VERDE MYANMAR (BURMA) NAMIBIA FIJI 68,032. 00 8,081. 50 2,123. 17 292. 83 55. 67 7. 33 0. 83 (+++) (+++) CHINA (PEOPLE'S INDIA PAKISTAN SENEGAL MONGOLIA NIGER SAO TOME AND PRINCIPE 63,048.
Resonance assignments and CSP mapping Because of the complex being in slow exchange and the significant perturbations observed following addition of GW7647, it was necessary to undertake separate 2D and 3D NMR experiments to obtain the sequential assignments of apo and holo FABP1. Full backbone assignments were obtained for the 1 H, 15 N, and 13 C resonances of WT FABP1 (Met-1-Ile-127) in the absence and presence of GW7647. In the case of the triplemutant FABP1, the protein was observed to have lower solubility, which necessitated acquiring the data at lower protein concentration, and only 98 and 97% of the backbone resonances could be assigned for the apo and holo protein, respectively, due to the lower signal-to-noise arising from the data. The assignments for apo and holo WT and triple-mutant FABP1 in the absence and presence of GW7647 have been deposited into the BMRB database under accession numbers 30477, 30478, 27509, and 27510, respectively.
Structural basis for the role of FABP1 in PPAR agonism summary of the structural statistics for the 10 conformers of apo FABP1 (residues 1-127) is presented in Table  1. The structure of apo FABP1 determined at 35 °C is consistent with the previous NMR structure solved at 25 °C (PDB code 2L67) and the crystal structures (PDB code 3STN and 2F73, chain A) with C ␣ RMSD of 1. 36, 1. 12, and 1. 22 Å, respectively (Tables  S1  and S2 ). All four structures adopt the canonical FABP fold.
Solution structure of holo FABP1 A similar strategy was used to determine the structure of holo FABP1, where the data were recorded in the presence of a sat-urating concentration of GW7647. The structure of the protein was first calculated from the experimental data in the absence of GW7647 (Fig. 5C ). The experimental constraints used to generate the structure and the structural statistics for the ensemble of 10 conformers chosen to represent the structure of holo FABP1 (residues: Met-1-Ile-127) are shown in Table  1. The structures are of high precision and superimpose with RMSD of 0. 46 Ϯ 0. 06 Å for the backbone atoms and RMSD of 0. 81 Ϯ 0. 06 Å for all heavy atoms in FABP1 (Fig. 5D ). The lower precision of the structure in complex with GW7647 relative to apo FABP1 structure is most likely due to the lower number of long-range NOE constraints from the holo FABP1 sample,.
In contrast, we observed no FRET signal between the triple-mutant FABP1 and PPAR␣ in either the presence or the absence of GW7647 (data not shown). Mutation of Lys-57, Glu-77, and Lys-96 to Ala also abolished nuclear accumulation of FABP1 and PPAR␣ activation following stimulation with OA (Fig. 1 ), suggesting that this change in the surface electrostatics may be a common mechanism for PPAR␣ agonists that bind FABP1. To test this hypothesis, we made a comparison between the structure of FABP1 bound to OA (PDB code 2L68) and the corresponding apo FABP1 structure (PDB code 2L67). These two structures were calculated in solution from NMR data acquired under similar solution conditions. There are several regions where displacement of the backbone atoms is observed between these two structures (Fig.
ITC data ITC experiments were undertaken and analyzed as reported previously  (13). Data were recorded at 25 °C with stirring at 1000 rpm using an iTC 200 microcalorimeter (MicroCal, Malvern, UK). Titrations were performed in ITC buffer (20 mM HEPES, pH 8, 50 mM NaCl, 0. 5 mM EDTA). A solution of GW7647 (7 M) in ITC buffer was prepared by diluting from a concentrated stock in DMSO resulting in a final DMSO concentration of 0. 5% v/v in the sample cell. The triple-mutant FABP1 (70 M) was prepared in an identical buffer and placed into the syringe. 16 serial injections at intervals of 220 s were made with continuous stirring of the solution in the sample cell. The iTC 200 control software was used to operate and acquire raw data as power (microcalories/s) versus time (minutes). The data were processed using the Origin7.
1) where ␦ HN and ␦ N denote the change in chemical shift between apo FABP1 and holo FABP1 for H N and N resonances, respectively. All spectra were processed using either NMRPipe  (25)  or Topspin 3. 0 (Bruker Biospin) and analyzed with NMRView (26), SPARKY,  7  or CARA  (40)  software. The RMSD values between structures were calculated using MOLMOL  (27). CSPs were mapped in PyMOL version 2. 0 (Schro ¨dinger, LLC).
NMR assignments Sequential backbone assignments ( 1 H, 15 N, and 13 C nuclei) for the proteins were generated from two-dimensional (2D) HSQC and three-dimensional (3D) experiments, including 3D HNCA  (28) , 3D CBCA(CO)NH  (29) , and 3D HNCACB  (30). 3D experiments for the triple-mutant samples, including HNCA, CBCA(CO)NH, and HNCACB were recorded with nonuniform sampling in the 15 N and 13 C dimensions. The datasets were processed with the multidimensional decomposition algorithm, MddNMR  (31). Side-chain assignments were obtained from 3D HBHA-(CO)NH, 3D 15 N-edited ( 1 H, 1 H)-NOESY, 3D 13 C ali -edited ( 1 H, 1 H)-NOESY, and 3D 13 C aro -edited ( 1 H, 1 H)-NOESY experiments (all recorded with a mixing time of 60 ms) using standard Bruker pulse sequences. For the sample of FABP1 in complex with GW7647, the same three NOESY spectra were collected at a protein concentration of 0.
Structure calculations Prior to NMR structure determination, backbone torsional angles from H N , N, C ␣ , C ␤ , and H ␣ chemical shifts were predicted using the default mode on the TALOSϩ server  (34). The output from TALOSϩ for each residue was manually examined against a Ramachandran map for the 10 best database matches. Residues defining consistently "good" predictions based on clustering in the Ramachandran map (8 or 9 out of the 10 best matches) with random coil index predicted order parameter S 2 Ͼ0. 5 were retained and converted to dihedral restraints in CYANA format. Where the TALOS-derived dihedral angles were tightly constrained, the tolerance relaxed to Ϯ15°, otherwise the TALOS values were used. For the assignment list, dihedral restraints from TALOSϩ and NOESY data were used as input for UNIO-ATNOS/CANDID (35, 36) using CYANA3.
Structural basis for the role of FABP1 in PPAR agonism published previously  (13). Cells were co-transfected with 0. 3 g of the luciferase reporter (PPRE 3 -TK-LUC), 0. 15 g of ␤-gal (as a transfection efficiency control), 0. 05 g of either empty vector (pSG5) or PPAR␣, and 0. 3 g of either empty vector (pSG5), FABP1 WT, FABP1 K20A, K31A, or FABP1 K57A, E77A, K96A. 24 h following transfection, cells were treated with vehicle (0. 1% v/v DMSO), oleic acid (10 M), or GW7647 (600 nM) for 24 h before the cells were lysed and assayed for luciferase activity using the luciferase assay system (Promega). In parallel, lysates were assayed for ␤-gal activity using the ␤-gal enzyme assay system (Promega) to correct for differences in transfection efficiency. The data are expressed relative to vehicletreated control, from three independent experiments conducted in duplicate.
The center of each well of a 96-well plate was imaged with an Olympus LUCPlanFLN ϫ20 (NA 0. 45) objective, and the nuclear redistribution of FABPs was determined by measuring the fluorescence intensity in the nucleus and cytosol of every cell within the field of view using automated protocols within the Harmony High Content Imaging and Analysis software (version 3. 5. 2). For each experimental repeat, ϳ50 cells were analyzed. Experiments were performed on four separate occasions.
Accession numbers The chemical shift assignments of apo and holo wild type FABP1 have been deposited in BMRB, accession number 30477 and 30478, respectively, and the chemical shifts of triple mutant K57A/E77A/K96A FABP1 in the absence and presence of GW7647 have been deposited in BMRB with accession numbers 27509 and 27510, respectively. Coordinates for the structure of wild type FABP1 calculated from data obtained in the absence and presence of GW7647 have been deposited in the PDB with accession numbers 6DO6 and 6DO7, respectively. Coordinates for the structure of holo FABP1 in complex with GW7647 have been deposited in the PDB with accession number 6DRG.