dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). Mice were given a maximum of one booster injection of anaesthesia per session, and were never imaged on consecutive days. GCaMP was excited and imaged via a water immersion objective (20x, 0. 95 NA, Olympus; sterile saline was used as the fluid for the immersion objective) at 927 nm using a Ti:sapphire laser (Chameleon Ultra, Coherent) with 140 fs pulse width and 80 MHz repetition rate. Image acquisition, scanning, and stimulus delivery were controlled by custom-written software in LabVIEW (National Instruments). Eight odors were individually delivered via a custom-built olfactometer  (Kapoor et al. , 2016). The odour panel included: methyl propionate (Sigma, 81988), methyl butyrate (Sigma, 246093), ethyl valerate (Sigma, 290866), hexanal  (Sigma, 115606) , methyl tiglate (Penta, 13-73400), valeraldehyde (Sigma, 110132), propyl acetate (Tokyo Chemical Industry, A0044), and pentyl acetate (Sigma, 109549).
xls and. txt files respectively. Data were then analysed with custom scripts in Matlab (Mathworks). Mean response traces were calculated across three individual stimulus presentations of each odour for each cell from background-subtracted raw fluorescence values, before bleach correction was carried out by extrapolation and subtraction of a single exponential function fitted to the 7. 5 s pre-stimulus baseline. Mean bleach-corrected baseline fluorescence over a 3 s window immediately preceding odorant presentation (f) was then used to generate Df/f traces. In some analyses, comparing baseline fluorescence values across different animals and imaging sessions (but not when calculating Df/f values for all other measures), this baseline F value, averaged across all stimulus presentations, was normalised by the mean value for all small DA cells in a given field of view.
We then calculated the mean of the two smallest amplitude z-scores across the three stimulus repeats. Excitatory responses displayed a clear bimodal distribution of peak timepoints (see Figure  9D ), so in an pre-analysed subsample of our data (n = 408 cells from N = 4 mice) we used unbiased k-means clustering on this parameter to set a threshold timepoint at 6 s after stimulus onset -all significant excitatory responses which peaked at or before this timepoint were classed as 'early', and all significant excitatory responses which peaked after this timepoint were classed as 'late'. Tuning index (TI) values were calculated by summing the number of stimuli producing significant responses of a given type for each cell. These values were often zero, and many cells had zero TI values for all response types.
Statistical analysis Statistical analysis was carried out using Prism (Graphpad), Matlab (Mathworks) or SPSS (IBM). Sample distributions were assessed for normality with the D'Agostino and Pearson omnibus test, and parametric or non-parametric tests carried out accordingly. a values were set to 0. 05, and all comparisons were two-tailed. Principal component analysis (PCA) and k-means classification on electrophysiological data were performed (Matlab functions 'pca. m' and 'kmeans_lpo. m', respectively) on the five variables that differed significantly between monophasic and biphasic DAT-tdT neurons. All were normally distributed except onset rapidness, which was rendered normal by logarithmic transform. Results of the k-means analysis were validated with a 'leave-one-out' protocol, which revealed cell-type classification to be robust to the removal of any one cell from the dataset.
For multilevel analyses of in vivo GCaMP data, distributions of baseline noise and response amplitude measures were rendered normal by logarithmic transform, and outliers -defined as any value with an absolute z-score >3 -were removed  (Aarts et al. , 2014) ; a single outlier was removed from each dataset, representing <0. 5% of each sample). These parameters were then analysed using linear mixed models (SPSS) with mouse as the subject variable. Tuning index data could not be rendered normal by any standard transforms, so were analysed using generalised linear mixed models with a negative binomial target distribution (accounting for >90% of sample variance; SPSS) and mouse as the subject variable. Dummy variable analysis revealed significant intracluster correlations in all cases, stressing the importance of nesting cell-by-cell data on individual mouse subjects  (Aarts et al.
Acknowledgements This work was supported by a  Sir Henry Wellcome fellowship  ( 103044 ) to EG, a  Wellcome Trust Career Development Fellowship  ( 088301 ),  BBSRC  grant ( BB/N014650/1 ) and  ERC   Consolidator Grant  ( 725729 ; FUNCOPLAN) to MSG, a Medical Research Council 4 year PhD studentship to DJB, and an  NIH  grant ( DC013329 ) to VNM. We wish to thank  Casper Hoogenraad  and  Phillip Gordon-Weeks  for antibodies,  Maxim Volgushev  for Matlab code,  Mackenzie Mathis  for DAT-GCaMP6s animals,  Guilherme Neves  and  Lynette Lym  for technical assistance with viral injections, and  Vikrant Kapoor  and  Joseph Zak  for guidance with in vivo imaging. Gordon Shepherd ,  Pierre-Marie Lledo ,  Oscar Marin ,  Juan Burrone , and all members of the Grubb and Murthy laboratories provided helpful discussions, while  Alex Fleischmann  and  Ian Thompson  made invaluable comments on the manuscript.
Data availability The data is available at Dryad Digital Repository. The following dataset was generated: Author(s) Year Dataset title Dataset URL Database, license, and accessibility information Galliano E, Franzoni E, Breton M, Chand A, Byrne D, Murthy V, Grubb M 2018 Data from: Embryonic and postnatal neurogenesis produce functionally distinct subclasses of dopaminergic neuron http://dx. doi. org/10. 5061/dryad. b5hg8d6 Available at Dryad Digital Repository under a CC0 Public Domain Dedication.
The pathogen is regulated under Council Directive 2000/29/EC and through emergency measures under Decision (EU) 2015/789 (as amended Decision (EU) 2017/2352). The pest could enter the EU via host plants for planting and via infectious insect vectors. The host range includes hundreds of host species listed in the EFSA host plant database. In the EU, host plants are widely distributed and climatic conditions are favourable for its establishment. X. fastidiosa can spread by movement of host plants for planting and infectious insect vectors. X. fastidiosa is known to cause severe direct damage to major crops including almonds, citrus, grapevines, olives, stone fruits and also forest trees, landscape and ornamental trees, with high impacts. The criteria assessed by the Panel for consideration as a potential Union quarantine pest are met (the pathogen is present in the EU, but it has a restricted distribution and is under official control).
Literature search A literature search on X. fastidiosa was conducted both in 2017 and 2018. The final search was made in the Web of Science bibliographic database on 16. 03. 2018 in 'All databases' includes: all subscribed databases like: BIOSIS Citation Index, CABI: CAB Abstracts, Chinese Science Citation Database, Current Contents Connect, Data Citation Index, FSTA (the food science resource), KCI-Korean Journal Database, MEDLINE, Russian Science Citation Index, SciELO Citation Index, Zoological records etc. The key word used in this search was simply 'Xylella' in order to retrieve as many updated references as possible published between 2015 and 2018. There were no language limits in this search. We obtained around 480 records and after removal of the duplicates, we ended up with 460 records.
Some of the references were eliminated, because Xylella was not the main topic of the study and Xylella taiwanensis studies were not taken into account in the present pest categorisation. A separate search was conducted for the update of the EFSA Xylella host plant database and it will be published in a separate scientific report in the following month. Xylella host plant database was first time initiated in 2013  (EFSA, 2013)  and the first list of host plant species of X. fastidiosa was based on the University of Berkeley online list. In 2015, EFSA published the full pest risk assessment (EFSA PLH Panel, 2015a) with a long appendix, showing the X. fastidiosa host plant database, which was updated in 2016 (EFSA, 2016) and since that time EFSA is requested to maintain a regularly updated Xylella (including all species, also X.
Database search Pest information on host(s) and its distribution was obtained from the European and Mediterranean Plan Protection Organization (EPPO) Global Database (EPPO, 2018) and compared with the Xylella host plant database (EFSA, 2018) and relevant publications. Data on the importation of commodity types that could potentially provide a pathway for the pest to enter the EU and on hosts grown in the EU were obtained from Eurostat (Statistical Office of the European Union) and from the ISEFOR database (2017 update). The Europhyt database was consulted for pest-specific notifications on interceptions and outbreaks. Europhyt is a web-based network 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.
(Nunney et al. , 2012a). X. fastidiosa subsp. sandyi is responsible for oleander leaf scorch, but also associated with Jacaranda spp. , Daylily and Magnolia  (Schuenzel et al. , 2005; Hernandez-Martinez et al. , 2007). Additionally, there are two other proposed subspecies, X. fastidiosa subsp. tashke  (Randall et al. , 2009) , in association with leaf scorch of Chitalpa tashkentensis, and X. fastidiosa subsp. morus  (Nunney et al. , 2014) , causing leaf scorch of red mulberry (Morus rubra). These last two subspecies were only rarely reported  (EFSA PLH Panel, 2015a; EFSA, 2018). Based on the Xylella host plant database (EFSA, 2018), the distribution of each subspecies has been mapped (Figure  1  and Distribution maps in Distribution maps of Xylella fastidiosa subspecies), and updated from the previous EFSA PLH Panel (2015a).
Multilocus sequence types Multilocus sequence typing  (Maiden et al. , 1998)  is largely accepted as a very useful genetic typing methodology. This method is based on the sequence of a set of seven housekeeping genes (for Xylella, the sirohaem synthase gene (cysG), glutamate symport protein gene (gltT), DNA polymerase III holoenzyme chi subunit gene (holC), 2-isopropylmalate synthase gene (leuA), ABS transporter sugar permease gene (malF), NADH ubiquinone oxidoreductase NQO12 subunit gene (nuoL) and ubiquinol cytochrome C oxidoreductase gene (petC)see X. fastidiosa MLST databases. Each sequence of a given housekeeping gene is assigned to a distinct allele. For a given Xylella isolate, the alleles at each of the seven genes define the sequence type (ST). MLST has been widely used to characterise X.
A database of validated tests for the identification of X. fastidiosa and its subspecies, as referred to in article 3(  2  For the identification of the subspecies, the method proposed is the MLST analysis  (Yuan et al. , 2010). PCR  (Hernandez-Martinez et al. , 2006; Pooler and Hartung, 1995)  is still also accepted, despite their more limited use. The method proposed by Hernandez-Martinez et al. (  2006 ) is targeting the three subspecies fastidiosa, multiplex and sandyi while the one proposed by  Pooler and Hartung (1995)  targets only the subspecies pauca. The data provided by comparative genomics as well as the current discussions on the subspecies status stress the usefulness of using a multi-gene strategy for the identification at infraspecies level.
Pest distribution outside the EU The distribution of X. fastidiosa has already been documented in the EFSA PLH Panel (2015a): the pest has been reported in North America, Central America and Caribbean, South America, Asia and Europe. A new detailed global distribution map has been compiled based on the EFSA host plant database (EFSA, 2018) and is shown in Figure  4. Additional information on the different STs (sequence types) found in the EU is also provided (Figure  2 ).
Pest distribution in the EU In the European territory, the pest status is considered as 'transient, under eradication' process, except for the South of Apulia (Italy), Corsica (France) and Balearic Islands (Spain), where the current status is 'present with a restricted distribution'. Additionally, X. fastidiosa subsp. fastidiosa ST1 was also found in 2015 in Saxony, Germany, at one location in isolated potted plants of Nerium oleander, Rosmarinus, Streptocarpus hybrid and Erysimum hybrid kept in greenhouse. According to EU emergency measures, a demarcated area was established in Saxony and Thuringia and placed under surveillance, but it was then lifted since March 2018 because of the eradication of the local infestation (Europhyt Outbreak (C) N°-946). The current distribution of X. fastidiosa in the EU based on the Xylella host plant database (EFSA, 2018) is shown in Figure  4.
Current situation in France Several cases of X. fastidiosa infections in imported plants or plant material, involving mainly coffee plants have been reported in France from 2012 to 2015  (Jacques et al. , 2016; Denance et al. , 2017 ; EPPO Global database). The first detection of X. fastidiosa in natural settings in France was reported in July 2015 from Corsica  (Chauvel et al. , 2015 ; EPPO Global database). Then, foci were detected in the Provence-Alpes-C ôte-d'Azur (PACA) region, continental France. A case of contamination of an apple tree (Malus domestica) and a peach tree (Prunus cerasifera) in continental France, and a holm oak (Quercus ilex) in Corsica were detected but not further confirmed  (Denance et al. , 2017).
EU emergency measures Decision (EU) 2015/789 as amended by Decision (EU) 2017/2352 A. Regulated plant species Two different categories of plant species are regulated (https://ec. europa. eu/food/plant/plant_hea lth_biosecurity/legislation/emergency_measures/xylella-fastidiosa/susceptible_en): À the host plants, i. e. plants for planting, other than seeds, belonging to the genera and species listed in the Commission database of host plants susceptible to X. fastidiosa (and its subspecies) in EU territory. À the specified host plants, which means host plants and all plants for planting, other than seeds, belonging to those listed in Annex I of Decision (EU) 2015/789 and found infected worldwide. B. Control measures to prevent spread within the Union: • Establishment of demarcated areas, as soon as the presence of X.
Host range EFSA periodically updates the Xylella host plant database. The extraction table presented in Annex A summarises the 'host range' of X. fastidiosa based on peer-reviewed literature from the EFSA Xylella host plant database (EFSA, 2018). The list is based on hosts reported in the current literature in association with X. fastidiosa EFSA host plant database (EFSA, 2018). A major question raised in the previous EFSA risk assessment  (2015)  was the way X. fastidiosa would be able to infect indigenous European host plants. In an attempt to provide an overview of the current situation in the EU, a table of the current reported host plants and of the associated STs is given (Annex A). This clearly shows that indigenous European plant species are hosts for X. fastidiosa. Further, the different subspecies and sequence types sometimes share common hosts, like Asparagus, Cystus, Ficus carica, Fraxinus angustifolia, O.
europaea, Prunus sp. , P. myrtifolia, Rosmarinus officinalis, Westringia fruticosa, but sometimes infect some specific host plant species (Annex A). Also to be considered are the EU official list of the 'host plants', i. e. plants for planting, other than seeds, belonging to the genera and species listed in the Commission database of host plants susceptible to X. fastidiosa (and its subspecies) in the Union territory, and the list of 'specified plants', i. e. host plants and all plants for planting, other than seeds, belonging to the listed annex I of Decision (EU) 2015/789 (as amended) and found infected worldwide (see Section 3. 3. 2). 'Host plants of economic importance (i. e. crops and certain ornamentals) known to be susceptible to disease caused by this bacterium are thus listed. Additionally, it is important to stress that Koch's postulates have not necessarily been fully fulfilled for each of the host-X.
Entry In the previous EFSA PLH Panel (2015a), seven pathways were analysed including plants for planting infected with X. fastidiosa, plants or plant material imported for research or breeding purposes, seeds, fruits, cut flowers and ornamental foliage infected with X. fastidiosa, detached wood and infectious insect vectors. Both the plants for planting infected with X. fastidiosa (including the plants or plant material imported for research or breeding purposes) and the infectious insect vectors are considered to be major pathways, while the others were considered as unlikely or very unlikely with high uncertainties (EFSA PLH Panel, 2015a). Since 2014 and until April 2018, there were 51 records of interception of X. fastidiosa in plants for planting in the Europhyt database. There are four records of Cicadellidae interceptions since 2004, whether these were potential vectors of Xylella (subfamily Cicadellinae) is not known.
Biology of European vectors and potential vectors A list of potential vectors of X. fastidiosa in Europe, gathering all the sharpshooters and spittlebugs was drawn from the Fauna Europaea database (de  Jong, 2013)  and is reported in Annex C of the EFSA PLH Panel (2015a). The large majority of these species shares two biological features, they are polyphagous and univoltine. Among sharpshooters, only Cicadella lasiocarpae and Anoterostemma ivanoffi have a restricted host range on Carex and Juncus, respectively, while the nymphs of G. fennahi are associated with Rhododendron but the adults may visit several other woody hosts. Among spittlebugs, only A. salicina and A. pectoralis (mainly associated with Salix spp. ) and Philaenus italosignus, P. maghresignus and P. signatus (whose nymphs develop on Asphodelus spp.
3. 6. Availability and limits of mitigation measures • Host plants: i. e. plants for planting, other than seeds, belonging to the genera and species listed in the Commission database of host plants susceptible to X. fastidiosa in the Union territory, as having been found to be susceptible in the EU to X. fastidiosa and its subspecies. • Specified plants: i. e. host plants and all plants for planting, other than seeds, belonging to the genera or species listed in Annex I of Decision (EU) 2015/789 (as amended by Decision (EU) 2017/2352) and found infected worldwide. Besides official control measures, survey activities have been reinforced. Guidelines for the survey of X. fastidiosa in the Union territory have been published (European Commission guidelines for the survey Are there measures available to prevent the entry into, establishment within or spread of the pest within the EU such that the risk becomes mitigated? Yes, there are measures available to prevent entry into, establishment within or spread of the pest within the EU territory, like the emergency measures adopted by the EU.
Conclusions X. fastidiosa meets the criteria assessed by EFSA for consideration as a Union quarantine pest. The identity of the pest is clearly established. X. fastidiosa is present in EU territory, either under eradication or containment in demarcated areas, but with limited distribution. There is no doubt about the economic consequences of its presence in EU territory. Spread within EU territory following establishment is likely, via plants for planting and insect vectors (both on their own and as hitchhikers) (Table  3 ). 3  X. fastidiosa infected symptomatic plants recorded in the European outbreaks. Figure  4  Global distribution map based on the EFSA Xylella host plant database 2018. Figure  5  General overview of the insect-proof net greenhouse. Figure  6  Species richness of spittlebugs, sharpshooters and cicadas in different regions.
104. 3 Regulation (EC) No 178/2002 of the European Parliament and of the Council of 28 January 2002 laying down the general principles and requirements of food law, establishing the European Food Safety Authority and laying down procedures in matters of food safety. OJ L 31/1, 1. 2. 2002, p. 1-24. Annexes of Directive 2000/29/EC. With mandate ARES (2017) 6346828 -22/12/2017, the European Commission has requested that the pest categorisation of Xylella fastidiosa should be jointly delivered by June 2018 together with the update of the Xylella host plants database, in order to provide a common and comprehensive base for the follow-up development of an update of the pest risk assessment for X. fastidiosa by March 2019. List of harmful organisms for which pest categorisation is requested. The list below follows the annexes of Directive 2000/29/EC.
1a , b and Supplementary Fig. 1a, b ). We identified a total of 176 SE clusters on 17q of which six were present in at least 20 NB cell lines (Supplementary Fig. 1c ). These six SE clusters are located in the vicinity of 86 candidate genes of interest, including 11 transcription factors  24  , of which 5 are actively transcribed in NB cells, i. e. TBX2, RARA, SP2, NFE2L1, and VEZF1. Next, we assessed the expression levels of these transcription factors in relation to patient survival in two independent NB tumor cohorts (GSE85047 n = 276, GSE62564 n = 498) and observed the strongest association with overall and progressionfree survival for TBX2  25  (Fig. 1c  and Supplementary Fig. 1d , Kaplan-Meier analysis). Moreover, TBX2 is marked by a SE in all investigated NB cell lines, but not in the human neural crest line (hNCC) and the MCF-7 breast cancer cell line (Fig.
1b  and Supplementary Fig. 1b ). Of further interest, the highest expression levels for TBX2 were observed in NB cell lines and primary tumors compared to other tumor entities, based on the online pan-cancer analysis in the CCLE database (cancer cell lines) and R2 platform (primary tumors and normal tissues) (Fig. 1d ). TBX2 expression levels were also high in normal embryonic tissues in keeping with the established role of TBX2 in early development. Taken together, our data suggest a possible important role for TBX2 as hitherto unrecognized transcriptional regulator in NB tumor development. 4C-seq defines TBX2 promotor-super-enhancer interactions. To provide further evidence for a functional role of the assigned SE for TBX2 gene regulation, three different viewpoints residing in the SE region (20 kb and 260 kb up TSS) or the promoter site of TBX2 (4.
Indeed, we found a strong correlation between the expression levels of the tapRNA TBX2-AS1 and TBX2 in a large cohort of NB tumors (n = 79, Supplementary Fig. 2c ). In addition to the potential regulatory connection of TBX2-AS1 and TBX2, we also found strong correlation with expression levels of the PPM1D gene which maps within a 1. 5 Mb distance from the TBX2 locus (Supplementary Fig. 2d ). In summary, the above findings support a physical interaction between the TBX2 locus and its nearby SE and suggest that the proposed chromatin looping drives TBX2 expression. TBX2 is a copy-number affected dosage-sensitive gene. Next, we investigated in more detail the genomic aberrations that account for the high TBX2 expression in NB (Fig. 1d ). We first analyzed the CCLE database and found that NB was the tumor entity exhibiting the most frequent gains for the TBX2 locus, the highest expression and lowest methylation levels (Figs.