dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank the health facility staff and volunteers who assisted us in identifying eligible participants, as well as the District and  Provincial Health Offices, Ministry of Health , and the local chiefs, who all provided their approval for the study. We also would like to express our gratitude to all of the participants of the study who provided their time and shared their experiences and perspectives. We would like to acknowledge the hard work of the data collectors who diligently worked in rural areas withstanding long distances and working days to obtain quality data. We also thank  Kathleen McGlasson  for her contributions and analytic support. Lastly, we thank  Meghan Guptill  who coded the IDIs and assisted in designing the codebook, as well as the many translators and transcribers.
Methods and findings This work is the first study of a recently completed dataset of monthly HFRS cases in Eastern China during the period 2005-2016. A methodological synthesis that involves a time-frequency technique, a composite space-time model, hotspot analysis, and machine learning is implemented in the study of (a) the association between HFRS incidence spread and climate dynamics and (b) the geographic factors impacting this association over Eastern China during the period 2005-2016. The results showed that by assimilating core and cityspecific knowledge bases the synthesis was able to depict quantitatively the space-time variation of periodic climate-HFRS associations at a large geographic scale and to assess numerically the strength of this association in the area and period of interest.
Data collection Ethics statement. The present study was approved by Institute of Disease Control and Prevention and Chinese Center for Disease Control and Prevention. All the HFRS data were anonymously analyzed for the consideration of confidentiality. The HFRS dataset. Yan et al. [32]  have reported that approximately 86. 4% of the total number of HFRS cases occurred in Eastern China and the Sichuan basin. Accordingly, in this study we used a recently completed dataset consisting of monthly HFRS cases at 127 cities in Eastern China during the period of January 2005-December 2016 collected by the China Information System for Disease Control and Prevention (CISDCP). These cities are distributed in 19 provinces, autonomous regions and metropolitan areas in Eastern China with a total area of approximately 2,820,000 Km 2 , including Beijing, Tianjin, Inner Mongolia, Heilongjiang, Jilin, Liaoning, Hebei, Henan, Shaanxi, Shanxi, Shandong, Hubei, Hunan, Anhui, Jiangsu, Zhejiang, Jiangxi, Fujian, and Guangdong, see  Multivariate El Niño southern oscillation index.
The basic set of BME equations of spatiotemporal coherency modeling and mapping are ð dχ ðg À g Þ e μÁg ¼ 0; ð2aÞ ð dχ ξ e μÁg ¼ a f C ; ð2bÞ where α is a normalization parameter, g represents the available G-KB, g denotes the expected value of g, μ are coefficients expressing the relative importance of g so that μ Á g = S i μ i g i , and ξ represents the S-KB (technical details can be found in the relevant literature; e. g. ,  [31] ; and references therein). By solving the above set of equations, the coherency values at unsampled points on a 10km × 10km × 1mo space-time grid covering the study area and period of interest were estimated by BME for mapping purposes. In particular, the SEKS-GUI software library  [39]  was selected to produce these space-time coherency maps. Using a 10-fold cross validation technique, the BME performance was subsequently evaluated in terms of three accuracy indicators: the mean absolute error (MAE), the root mean squared error (RMSE), and the R 2 of the simple linear regression model relating observed and predicted values.
Specifically, cropland, woodland, grassland, water, urban, barren (in a 50km buffer), elevation, distance to coastline, and spatial coordinates (using Krasovsky 1940 Albers projection in ArcGIS 10. 2) were selected as the explanatory variables for assessing the coherency maps obtained by BME. This process was computationally implemented by using the "gmb" package of the R statistics software. Similarly, the 10-fold cross validation technique was employed to test the performance of the GBM model in terms of MAE, RMSE and R 2.
Spatiotemporal mapping of the association between HFRS and global climate dynamics Given the selected character bands (30-42 mos), the mean coherency values for the entire 2005-2016 period were calculated at each city (thus providing the space-time coherency dataset needed for further processing). This dataset was then analyzed using the SEKSGUI software library  [39]. 0:88½1 À 3jhj 2 Â 10 5 þ 1 2 ð jhj 10 5 Þ 3 exp½À ð ffiffi ffi 3 p t 40 Þ 2 þ 0:12½1 À 3jhj 4:4 Â 10 6 þ 1 2 ð jhj 2:2 Â 10 6 Þ 3 exp½À ð ffiffi ffi 3 p t 72 Þ 2 ; ð5Þ where |h| and τ denote the spatial distance (in meters) and temporal separation (in months), respectively. The numerical 10-fold cross validation results confirmed that BME generates accurate coherency estimates (the corresponding accuracy indicators were R 2 = 0.
8 million Km 2 ) and during a relatively long time period  (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016). The study is characterized by the originality of the HFRS dataset and the large amount of local and regional information available about several features of the climate-HFRS association in the time-frequency and the space-time domains. A main outcome of the present study is the successful quantitative investigation of the association between global climate dynamics and human HFRS infections by wavelet analysis. Wavelet analysis uses the global climate index MEI (a proxy expressing global climate dynamics quantitatively) that has been found to be suitable for large-scale analysis of ecological processes  [45]. It has been postulated in the literature that climate affects HFRS infections by influencing rodent-borne physiology and interaction in a complex system  [46, 47].
For example, high positive MEI values infer the presence of El Niño phenomena, leading to higher precipitation levels in southeastern China during the months of December through May  [48]. Hence, sufficient precipitation will help the reproduction of rodents, directly impacting the probability of rodent-human contacts and human infections. In this work, it was found that the large-scale HFRS surveillance dataset collected at 127 cities in Eastern China during 2005-2016 and the global climate dynamics records available exhibit a strong synchronicity in multiannual cycles, including 1, 1. 5 and 3 yrs periods  (Fig 2, S4 and S5 Figs). This finding provides strong quantitative support to an earlier claim that HFRS infections are closely associated with global climate dynamics through complicated nonlinear dynamics, including multiannual and seasonal variational patterns of both climate dynamics and rodent population  [20, [49] [50] [51].
Acknowledgements We thank  Philippe Campagne-Ibarcq ,  Michel Devoret ,  Andrew Jordan ,  Raphaël Lescanne ,  Mazyar Mirrahimi ,  Klaus Mølmer ,  Pierre Rouchon ,  Alain Sarlette ,  Irfan Siddiqi  and  Pierre Six  for fruitful interactions. Nanofabrication has been made within the consortium  Salle Blanche Paris Centre. This work was supported by the  EMERGENCES  grant  QUMOTEL of Ville de Paris.
Data availability The experiment was carried out for 30 experimental configurations with Ω/2π ranging from 0 to (2 µs) -1 and Γ d ranging from (30 µs) -1 to (300 ns) -1. All the experimental results can be visualized in a small animated application available online at http://www. physinfo. fr/publications/Ficheux1710. html. The measurement can be chosen to take into account the measurement records of the dispersive measurement only, the fluorescence measurement only or both. The movies are also available to download at https://doi. org/10. 6084/m9. figshare. 6127958. v1. All raw data used in this study are available from the corresponding authors upon reasonable request. Author contributions Q. F. and S. J. contributed equally to this work. Q. F. , S. J. and B. H. designed research and performed research; S.
Following a request from the European Commission, EFSA periodically updates the database on the host plants of Xylella spp. While previous editions of the database (2015 and 2016) dealt with the species Xylella fastidiosa only, this database version addresses the whole genus Xylella, including therefore both species X. fastidiosa and Xylella taiwanensis. The database now includes information on host plants of Xylella spp. retrieved from scientific literature up to November 2017 and from EUROPHYT notifications up to May 2018. An extensive literature search was performed to screen the scientific and technical literature published between the previous database update conducted in December 2015 and December 2017. The literature screening was supported by the DistillerSR software platform. The applied protocol for the extensive literature review and extensive information search, together with examples of data extraction, are described in detail in this report.
This report also includes published information on resistance or tolerance of plant varieties to Xylella spp. The current database includes 563 plant species reported to be infected by X. fastidiosa, of which for 312 plant species the infection has been determined with at least two different detection methods. These species cover hundreds of host plant genera in 82 botanical families (61 botanical families when considering only records with at least two different detection methods). The update of this database of host plants of Xylella spp. reported world-wide provides a key tool for risk management, risk assessment and research on this polyphagous bacterial plant pathogen.
In 2015, EFSA published an electronic version of the database of the host plants of X. fastidiosa, together with a categorisation of plants for planting on the risk of introduction of X. fastidiosa  (EFSA, 2015). EFSA updated its X. fastidiosa host plant database at the end of 2015  (EFSA, 2016). Some preliminary results from the current EFSA Xylella spp. host plant database were presented at the European Conference on Xylella fastidiosa 1 held in Palma de Mallorca in November 2017 (Figure  1 ). The increase in the total number of hosts of Xylella spp. reported in the database from 2013 to 2018 is particularly related to the new plant species reported being infected by X. fastidiosa in Europe since 2013. However, it also reflects the most extensive coverage of scientific literature in all languages and the inclusion of reports on the other species Xylella taiwanensis.
Background The extensive literature search (ELS) protocol was used in the context of the EFSA mandate M-2013-0321 on urgent technical assistance on the regulated harmful organism Xylella spp. periodical updates of a database of host plants of Xylella spp. (EFSA-Q-2017-00215). This update integrates the list of X. fastidiosa host plants, published on 20 November 2016. 1 https://www. efsa. europa. eu/en/events/event/171113 A negative reply to one of the two questions was enough to exclude the reference. Whenever the information provided in the title and abstract was insufficiently clear, the reference was accepted and passed to the following step for further consideration. All publications that passed the title/abstract screening were subjected to the full-text screening (second step), except for 10 publications for which the full text was not retrieved (despite best efforts to carry this out).
Data warehouse The Xylella spp. host plant database has been considered a good candidate for the prototyping of a pest in the plant data repository within the EFSA Scientific Data Warehouse (S-DWH). A harmonised data model has been established, also taking into account the feedback of a previous pilot focusing on the creation of a database about pests in apple. The aim was to establish a In this section, the identification methods applied to detect a Xylella spp. infection are listed. Eight detection methods were considered and for each of them the outcome of the analysis (positive or negative), together with the number of infected plants and the total number of analysed plants, were reported. Moreover, additional information could be added in the comment column beside each detection method.
Data management Data have been collected through DistillerSR and then submitted to the EFSA Data Collection Framework (DCF). DCF is the upfront system in the EFSA pipeline of data collection tools and allows a first step of harmonisation against the EFSA controlled reference terminology (as known as EFSA catalogues). Data have been then included in the S-DWH by means of a standardised Extract Transform Load (ETL) procedure and they have been further analysed and managed to generate needed statistics. Raw data and related metadata are published in Zenodo in the EFSA Knowledge Junction community (https://doi. org/10. 5281/zenodo. 1339344). Data will be available soon as interactive reports at the following link (expected to be active by end September 2018): https://www. efsa. europa. eu/en/microstrategy/xylella.
The 853 references in which Xylella or Xylella-associated diseases were studied in association with a host plant (i. e. in vivo) were selected for the data extraction phase. To catch the latest available information on the topic, the EUROPHYT outbreak database was consulted on 8 May 2018 and additional information was provided by scientific experts and national authorities. The full list of the selected references used for data extraction step is reported in Appendix E. In total, 8,391 data extraction forms were filled in with informative data and subsequently analysed to retrieve the list of Xylella host plants.
Trend of publications The oldest publications retrieved through the literature search and included in the data extraction step were published in the USA in 1930 on the phony disease of peach  (Hutchins, 1930). The publication of  Saponari et al. (2017)  that describes the isolation and pathogenicity of X. fastidiosaassociated with the olive quick decline syndrome in southern Italy was published in December 2017 and it is the most recent publication retrieved through the literature search. The temporal trend of published publications about Xylella spp. and related diseases is shown in Figure  3. The number of publications used to extract data for this database stayed more or less constant between 1930 and the 1960s, increased to 10-20 per year between the 1970s and the 1990s, increased again in the period 2000-2010, with the highest number of publications in 2007 (47 publications).
Unconfirmed records A subset of unconfirmed studies (or of dubious relevance) or single records, included in the data extraction, was excluded from the data analysis. The records excluded from the data analysis are as follows: • Berisha et al. (1996, 1998) were considered unconfirmed/dubious. The EPPO Global Database states: 'Absent, invalid record. EPPO Reporting Service (98/006): in an abstract  (Berisha et al. , 1996) , it was claimed that X. fastidiosa had been isolated from diseased grapevine grown in Kosovo. The authors of the abstract have not, when requested, provided any details to substantiate this claim, which can only be regarded as dubious. EPPO Reporting Service (98/ 157): a fuller report  (Berisha et al. , 1998)  stated that the material came from Cermjan, Kosovo (near Albanian border).
fastidiosa. • The publications of Sadovskii (1985), Sadovskii and Shevchenko (1991) and Gvozdyak et al. (1990) were considered unconfirmed/dubious as the presence of X. fastidiosa has never been confirmed in Russia and Ukraine and the given publications describe only symptoms of the disease on plums. • The record by Jindal and Sharma (1987) in India was considered unconfirmed/dubious. The EPPO Global Database states: 'Unreliable record. The identification requires confirmation by modern techniques'. • Temsah et al. (2015) was considered an unconfirmed/dubious record, as a later publication  (Habib et al. , 2016)  stated that X. fastidiosa does not occur in Lebanon. The EPPO Global Database states: 'Absent, invalid record. EPPO Reporting Service (2016/037): in 2015, a publication suggesting the presence of Xylella fastidiosa (EPPO A1 List) in Lebanon was published  (Temsah et al.
Such is the case for example of the paper by Denanc e et al. (  2017 ) which reports from four samples from Corsica (France) a complete MLST profile while stating that the samples were not detected positive based on the EPPO protocol for MLSA used by the French National Laboratory of Reference. Such cases are included in the database and the stated divergences are reported in the column 'Comment_PCR' of the 'observation' spreadsheet of the Excel in Zenodo.
in 1987  with the name of Xylella fastidiosa. In 2016, X. taiwanensisa new species within the Xylella genuswas proposed  (Su et al. , 2016). The X. fastidiosa species is divided into at least six genetically different subspecies but only the subspecies fastidiosa and multiplex are officially considered viable by the International Society of Plant Pathology Committee on the Taxonomy of Plant Pathogenic Bacteria (ISPP-CTPPB)  (Bull et al. , 2012). The other remaining subspecies are: morus, pauca, sandyi and tashke. All the subspecies are listed in the Xylella spp. host plant database and a list of hosts for each subspecies is shown in Appendices A and B. The EFSA Xylella spp. host plant database contains data from different types of studies. Some of the studies reported natural infections (e.
g. surveys in the fields) and some were performed under controlled artificial conditions (laboratory or controlled conditions screenhouses). Following the classification of the species into A, B, C, D, E detection categories (see Section 2. 4. 2), two lists of host plant species were created: • Appendix A and Table  9  -Xylella fastidiosa subspecies in experimentally infected plants. • Appendix B and Table  10  -Xylella fastidiosa subspecies in naturally infected plants. Both appendices show the results for the subspecies fastidiosa, multiplex and pauca, as they are the most studied in the database. Few records are available for the subspecies sandyi, morus and tashke, which are presented in the tables below (Tables  7  and 8 ). The total number of positive host plant species of X. fastidiosa was counted, regardless of the detection method (Category E: 563 species, 264 genera, 82 families).
Host plants of X. fastidiosa were detected using different methods and some of the results were contradictory. Those contradicting host plant species were compared with all other host plant species in the database and if there was the same host plant species in the other studies without contradiction positive, we counted it as positive. If the host plant species occurred in the extraction table only once and it was contradictory, the species was not counted in the total number of species but is listed in Table  11. In most cases, contradictions of results occurred for positive ELISA and negative PCR-based methods, but in a few cases three detection methods were used. The geographical distribution of plant species infected by Xylella spp. is shown in Figure  5. Confirmed findings of X. fastidiosa have been reported in several countries of North, Central and South America, in Asia (only in Iran) and more recently in Europe (Italy, France and Spain).
Host plants and Xylella fastidiosa sequence type association Information about the genetic characterisation, such as the sequence type (ST), has also been recorded from all the selected publications. The full list of records of plant species infected by the different STs in artificial and natural conditions is shown in Appendix C. The country has been reported for plant species that have been found naturally infected. In total, 889 records have been reported in the database, describing 176 plant species in which the X. fastidiosa ST has been characterised. Actually, 81 different STs have been described world-wide. The highest number of records refers to plant species in which the detected X. fastidiosa STs belong to the subspecies pauca. Those plants have been identified in Central and South America (Argentina, Brazil, Costa Rica and Ecuador) and in European countries (Italy, France and Spain).
Tolerant or resistant host plants Eighty-seven out of 853 publications contained information about the tolerance and resistance status of 64 plant species. As expected, the most studied genera belonged to economically important crops: Vitis and Citrus followed by Prunus. The fourth more studied genus, and with the most recent publications, was Olea. The full list of plant species together with the number of records of tolerant/ resistant response for each plant species is listed in Table  12. For each record, the host status as reported in the publication has been inserted in the database. Moreover, categories have been created to group and analyse the outcome of the tolerant/resistance response. Those categories reflect the response (one or more than one) for which the authors of the studies considered the plant species as tolerant or resistant.
Conclusions Following the request of the European Commission, EFSA was asked to update and regularly maintain a Xylella spp. host plant database. In July 2018, the 'Updated Xylella fastidiosa pest categorisation' was published, which was an update of part of the EFSA PLH Panel, 2015 scientific opinion. Some of the information from the Xylella spp. host plant database was used in this categorisation. The Xylella host plant database was enriched with recent world-wide scientific literature, grey literature, EUROPHYT notifications, internet sources and communications from different scientific groups. An ELS was performed in 2017 starting from 3,614 publications with no time and language limits. In total, 853 publications were selected for data extraction and information on botanical identification of the plant, kind of infection, geographical data, detection methods, host status (resistance/tolerance) were retrieved from the publications.
A new detailed distribution map has been drawn and findings of unconfirmed records distinguished. The nomenclature of the host plants was linked to the automatic EPPO codes, to facilitate further updates or changes. All data have been stored in Data Warehouse, which allows the storage and harmonisation of data. The data of natural and artificial studies were distinguished and all numeric data recalled. A total of 122 species with evidence of artificial infection and 234 from natural infections were recorded. In this scientific report, a comprehensive list of host plant species of X. fastidiosa and X. taiwanensis was created taking into account different detection methods and new genetic characterisations (multilocus sequence typing). The total number of plants reported infected by X. fastidiosa regardless of the detection method was 563 species, 264 genera and 82 families.
When considering only records determined with at least two detection methods (excluding only symptoms, microscopy and unspecified detection method) or by sequencing or culturing, 312 host plant species remained from 152 genera and 61 botanical families. Host status has been considered in the current database. Special categories with extensive comments have been added. In total, 87 publications were identified having information on resistance, tolerance response of the plants. This update of the database of host plants of Xylella spp. reported world-wide provides a key tool for risk management, risk assessment and research on this polyphagous bacterial plant pathogen. Prunus persica 9 P. webbii  Celtis occidentalis Cytisus scoparius (A-E) A B C D E A B C D E A B C D E Nicotiana tabacum 0 0 1 0 1 0 1 2 0 0 3 3 4 1 1 1 1 1 Olea europaea 0 0 1 1 1 0 0 4 4 4 1 0 0 1 1 1 0 0 1 1 1 Prunus salicina 0 0 1 1 1 Prunus s p.