dataesr/scientific-paragraphs-categorization huggingface dataset:
It was advertised through various channels including national publication in French  (Streito et al. 2014) , Inrae institutional communication (website) or entomologist networks, such as "Le monde des insectes" (www. insecte. org). Our survey thus consisted of "passive monitoring" that is an approach where scientists did not actively collect data, but rather relied on citizen contribution. The data were stored in a database, examined and confirmed by entomologists. The survey started on 15 August 2012. Two additional sources of data were gathered alongside the Agiir system: a) extra-Agiir citizen reports received by phone or email and b) BMSB reports from "naturalist" networks. Naturalists refer to a special category of contributors having amateur or advanced knowledge in the field of entomology.
Their observations were directly reported to us by phone or emails or through different entomologist networks, for example, Le monde des insectes (www. insecte. org). JCS collected these reports from 15/08/2012 until the end of 2019 and checked specimen identification, dates and spatial location. It should be noted that Agiir reports, generated by naturalists, could not be distinguished from other reports. Without duplicated records, the dataset contained a total of 4002 reports, 3538 from Agiir application, 377 citizen direct reports and 87 extra-Agiir naturalist reports.
Data and methodologies This Statement is based on the guidance documents from the SCF and from different EFSA Panels and units, complemented with the assessment of approaches used by other organisations, for establishing HBGVs for nutrients and regulated products (focusing on food additives and pesticide residues). EFSA staff compiled information from previous assessments of nutrients as regulated products to map current practices and identify commonalities and differences in the approaches applied in the different areas, as a basis to formulate recommendations. A draft report was presented to the Scientific Committee and the revised version is included as Annex A. The EFSA international network ILMERAC was sent a dedicated communication in order to identify recent or ongoing activities in this area and opportunities for collaboration, but no similar activities were identified.
Assessment of regulated products The safety assessment of regulated products is based on data required according to the relevant sectoral legislation or guidance. 7 In practice, applications submitted to EFSA can relate to: the evaluation of products prior to their introduction on the EU market; the re-evaluation of products due to the expiry of their authorisation; a re-evaluation programme; a request for the extension of conditions of use or changes in technology; or development of new scientific knowledge. In the first case, a scientific dossier is submitted by an applicant, which contains the studies requested by the sectoral guidance with the aim of demonstrating the safety of the product. A set of toxicological studies is typically required, and this may vary depending on the sectors (see Annex A) and the nature and characteristics of the product.
Assessment of ULs for nutrients The assessment of ULs for nutrients is conducted upon request from risk managers through a generic mandate. EFSA is responsible for collecting relevant information pertaining to the adverse effects of a given nutrient. The process used is the same as that for non-nutrients. However, in general, the risk assessment for nutrients relies on the data published in the literature. There are few systematic toxicological data such as those generated for the risk assessment of additives  (SCF, 2000). In its framework on developing ULs for vitamins and minerals, the SCF noted that the process depends on using all relevant human experimental and observational studies, as well as animal studies  (SCF, 2000). It further noted that, given the limitations of available data, selecting an adverse event and assessing a tolerable intake involves 'scientific judgement' of the quality of evidence of adverse effects on humans, particularly the strength of causality and data on the underlying mechanisms.
These platforms and subsidiary platforms focussing on epigenetic effects and e. g. nutrient metabolism, enable the intelligent integration of data sources. The use of such databases would enable deeper exploration of the interconnectivity at the biological levels involved in the reactions to intakes of nutrients above their physiological requirements. Such an exercise could contribute to identify knowledge gaps and research needed for further risk assessments of nutrients. Furthermore, systematic analysis to identify and characterise critical endpoints would inform the use of genomics, proteomics, and metabolomics either as markers themselves or as means to validate other markers, e. g. markers in tissues, that are more practical and ethical for risk assessment. This may also enable integration of the homeostatic and adaptive metabolomic data with emerging approaches to assessing environmental exposure and the human exposome, as well as epidemiological approaches to high dietary intakes and health outcomes.
Hazard identification and characterisation of regulated products that are also nutrients Minerals, vitamins and some fatty acids have been the subject of a risk assessment by the EFSA NDA Panel or SCF with a view of establishing ULs, whenever possible, based on available data in peerreviewed published papers (see Appendix A). In some cases, the safety of nutrients has also been assessed by other EFSA Panels in the context of the evaluation of regulated products (e. g. phosphatecontaining additives (EFSA FAF Panel, 2019a); copper used as feed additive (EFSA FEEDAP Panel, 2019) and active substance in PPPs (EFSA, 2018a)). When available, EFSA's existing Scientific Opinions should be used as a starting point for a hazard identification and characterisation. As described above, data available for EFSA's assessment may differ depending on the framework under which the assessment is conducted (Section 3.
Human data provide the most relevant information for hazard identification. They are generally available for the risk assessment of nutrients, and, when they are of sufficient quality and extent, are given the greatest weight for the establishment of a HBGV. In such case, animal data (including data arising from the classical toxicity data set) may be used as supportive rather than as a primary source of evidence for the hazard identification and characterisation. Information from animal studies may also contribute to inform the biological-based model described above (Sections 3. 3. 2 and 3. 3. 3), through the identification of homeostatic and pathological critical events in the context of nutrient kinetics and dynamics. If animal studies are conducted or used for the purpose of demonstrating the safety of consumption of a regulated product which is also a nutrient, specific considerations are required, as follows.
Total dietary intake of nutrients can be estimated by combining data from food composition databases and food consumption surveys. An accurate nutrient intake assessment requires to capture intake from intentional use in foods (e. g. as food additives), their migration into foods (e. g. from food contact materials), their use in food supplements or as nutrient sources (i. e. added to food) or their presence as residues (e. g. pesticides or feed additives). Upon receipt of a new mandate/application for, or when conducting the re-evaluation of a regulated product that is also a nutrient, previous EFSA assessments of the total intake of the nutrient by consumers should be considered. When estimating total dietary intake of nutrients, potential sources of uncertainties regarding composition data include: • uncertainty regarding the extent to which the analytical values reported in the food composition databases include the contributions of nutrient-containing regulated products (e.
g. depending on the time of food sampling vs. the time of authorisation of the regulated uses, representativeness of the samples with regard to the authorisation conditions, and use patterns of the regulated products). This also includes uncertainties regarding nutrient contents in foods due to fortification. Also, composition data estimated from the ingredient list of foods require assumptions regarding the amounts of additives contained in foods (not reported on labels). • uncertainty related to speciation of the nutrient, i. e. distribution of its various chemical and physical forms, where applicable. Chemical analyses of whole foods do not allow to distinguish between the naturally occurring fraction of the nutrient and the fraction contributed by regulated products (either intentionally added or present as residues).
The relative contributions of regulated products to the overall nutrient intake have to be calculated from product-specific concentration data, which may be inferred from e. g. authorised or reported use levels. For instance, two sets of concentration data can be used to estimate the intake of a nutrient from its food additive uses: (1) maximum permitted levels as set down in the EU legislation, and (2) use levels reported by food operators. When available, biomarkers (e. g. blood levels, urinary excretion levels) may be useful to estimate overall nutrient intake. Biomarkers of exposure reflect the internal dose and exposure from all sources. However, reliable biomarkers of intake 9  are available only for a limited number of nutrients. When these biomarkers are used, back-calculation to dietary intake using kinetic modelling may be possible, but it triggers even more complex assessments to identify to which extent nutrients used as regulated products were included in the exposure assessment.
This is typically informed by human data about levels of nutrient intake significantly above those obtained from the diet (e. g. from food supplements), which have not been associated with adverse effects. Prevalence of adverse effects can be assumed to be low in populations with intakes below this value. However, characterisation of the risk is uncertain in populations with intakes above this value, because the relationship of such value to the toxicity for the nutrient is not known. In addition, the relative contribution of the regulated use under assessment to the overall intake should be discussed as part of the risk characterisation. Whenever relevant, this information will be used by the relevant Panel or unit for proposing regulatory limits (e. g. maximum residue levels (MRLs) for pesticides in food) or for making specific recommendations for risk managers to consider, in accordance with EFSA's remit in the different sectors.
3) When an existing HBGV for the nutrient established by EFSA is available, it should be used as the HBGV for the risk characterisation of regulated products that are also nutrients. The Panel/unit that has received the mandate should assess, in consultation with the NDA Panel and other relevant EFSA Panels/units, whether the data available for the assessment of the regulated product are consistent with the existing value or whether an update of the HBGV is needed. 4) When an existing HBGV (e. g. UL) requires an update, or there is no existing HBGV, the hazard identification and hazard characterisation steps should take into account the specificities pertaining to nutrient risk assessment, e. g. consideration of their biological role, homeostatic mechanisms and their regulation, and requirement, as described in Sections 3.
3. 2 and 3. 3. 4 of this Statement. The assessment should be conducted in consultation with the NDA Panel and other relevant EFSA Panels/units throughout the process. 5) When an update of the existing HBGV (e. g. UL) for a nutrient is required, the scientific output should clearly state that the new value replaces the previous one. The Scientific Committee recommends that EFSA maintains a centralised database of HBGVs for nutrients. 6) To establish a HBGV for regulated products that are nutrients, EFSA should ensure an integrated and harmonised hazard characterisation across EFSA's sectors. The Scientific Committee should be engaged whenever, during the assessment of a regulated product, a Panel or unit finds evidence that a pre-existing HBGV for a nutrient (e. g. UL) needs to be updated (see point 2), or a new HBGV for the nutrient is needed (see point 3), because of the multidisciplinary nature of such assessment and the need to evaluate cross-sectoral implications.
The Panel recommends that an integrated testing strategy, which may include alternative approaches, should be used to further support the risk assessment. The Panel has sought to provide an overall concept with clear information on a tiered approach for risk assessment. Using this tiered approach, a minimal dataset applicable to all compounds has been developed under Tier 1. Compounds which are systemically absorbed or for which toxic or genotoxic effects are found in Tier 1 will require Tier 2 testing to generate more extensive data. Tier 3 defines detailed testing for specific endpoints, for which Tier 2 testing results raised concerns, and is performed on a case-by-case basis'. The guidance uses the term Margin of Safety (MoS) to represent the margin between the NOAEL or BMDL and the estimated exposure.
When establishing such values an appropriate safety margin of at least 100 shall be ensured taking into account the type and severity of effects and the vulnerability of specific groups of the population. When the critical effect is judged of particular significance, such as developmental neurotoxic or immunotoxic effects, an increased margin of safety shall be considered, and applied if necessary' (Regulation (EC) No 1107/2009). Following the evaluation of the submitted dossier by the rapporteur Member State (RMS) and the peer review by EFSA, the proposed values are discussed by risk managers, included in the review report prepared by the European Commission, and following the risk managers agreement, become mandatory and included in the EU pesticides database. 22  specific guidance, defined as a working document of the European Commission services, for setting ARfDs has been prepared for pesticides in the EU context, 23  whereas no specific guidance for setting an ADI for pesticides at the European level is available, with the exception of the legal requirements for a UF of at least 100, and that data collected on humans shall not be used to lower the UF resulting from tests or studies on animals.
Based on these legal principles, the derivation of ADIs through the peer-review process in the EFSA Conclusions on Pesticides (EFSA Scientific Committee, 2012) is based on the standard default approach and a safety margin of 100 is applied to the selected RP. The RP is usually the NOAEL for the critical effect observed in the animal studies. Although the use of the BMD approach for selecting the RP has been discussed and proposed as a scientifically justified improvement (Chemicals Regulation Directorate and Safety Executive, 2013; EFSA, 2014), it has not been used by EFSA in the regulatory assessments of pesticides yet, and the NOAEL, and alternatively the LOAEL with an additional safety factor, are still the RP currently used for pesticides. The UF may be increased in case of incomplete datasets, uncertainties, or concerns related to the severity of the observed effects.
This is in line with the international provisions in the area of pesticides  (WHO, 2015). At the international level, there are no legal limitations for using human data for reducing the UF. JMPR also uses the standard justification for the default UF of 100 and the IPCS (WHO/IPCS, 2005) recommendation for subdividing the two 10-fold factors into toxicokinetic and toxicodynamic subfactors (WHO/IPCS, 2015). There are also numerical recommendations for the 'extra' UF to be used to account for the use of a LOAEL instead of a NOAEL, use the NOAEL from short-term toxicity studies in order to account for the short duration of the study and for covering deficiencies in the database. According to a review conducted in 2013, in the EFSA assessments of pesticides the default value of 100 has been applied in 187 out of 213 (88%) cases; additional factors, ranging from 2 to 20, have been added, mostly related to the use of a LOAEL instead of a NOAEL or to the severity of the observed effects (Chemicals Regulation Directorate and Safety Executive, 2013).
The review also indicated that for 128 compounds (57%) the original proposal by the RMS was in agreement with the final ADI value; and that for the other cases the changes were justified by the use of different RP or a different UF, in addition to the consideration of new data submitted during the EFSA procedure (Chemicals Regulation Directorate and Safety Executive, 2013). A case of particular interest is copper, also used as pesticidal active substance, for which the ADI is based on human data for infants (adults: 0. 2 mg Cu/kg bw per day and infants: 0. 15 mg Cu/kg bw per day). The ADI is supported by animal data (90-day rat study) with a NOAEL of 16 mg Cu/kg bw per day (EFSA, 2018a). It should be noted that the EFSA Conclusion on copper used the term ADI as this is the term included in the sectorial legislation and in the EU pesticides database.
The resulting images were processed with ImageJ software  (Schneider et al. , 2012)  in order to quantify the fluorescence in the infiltrated areas (Suppl. Methods). For this, the infiltrated areas were manually delimited using the oval The leaf was scanned using the Typhoon FLA 9000 laser scanner. A 635-nm laser was used for excitation and the light emitted by the leaf was quantified after passing through a 665-nm long-pass red filter. (c) Infiltrated areas of the leaf were delimited using the oval selection tool of ImageJ and the ROI (region of interest) manager tool. Parameters were set to quantify the mean grey value of each ROI after threshold adjustment. (d) A threshold was applied to the image to exclude dark areas caused by leaf veins and leaf shape. Within each ROI, the mean grey value is calculated using exclusively the pixels that appear in red.
Equivalent parameters were used for all leaf images within each replicate of an experiment   (Slootweg et al. , 2017; Tait & Hik, 2003). The total number of replicates for each condition is 45. The experiment was carried out three times and data were plotted and analysed as in panel (a). (e,f) Quantification of immune responses induced by various concentrations of agrobacteria carrying RGA4:HA (e) or AVR-PikD:HA (with constant levels of Pikp-1:Flag/Pikp-2:HA) (f) by ion leakage measurements. Samples were harvested from infiltrated areas 48 hr after infiltration, and the conductivity was measured after 20 hr incubation in deionized water. The experiment was carried out twice and the total number of replicates for each condition in each experiment is 24. Data were plotted and analysed as in panel (a) red fluorescence (Figure  3a ,b; Figure  S3 ).
The database As plague is a notifiable disease, according to the National Plague Control Program (NPCP), all data relating to the reporting of plague cases in Madagascar are centralized in a database at the Central Plague Laboratory (CPL), which is managed by the Institut Pasteur de Madagascar/Ministry of Public Health. The database contains plague cases declared in health facilities between 1 January 2006 and 31 December 2015 for the two districts in the central highlands. The database contains various information including information on individuals such as address, age, sex, reference health facility where the case was reported, onset date of the disease (day/month/year), clinical form of plague (bubonic, pneumonic, septicaemic, etc. ), patient status (alive or deceased), travel made before the case was reported (yes/ no), presence of dead rats observed in the vicinity of the reported case (yes/no), case definition according to the type of tests performed and other criteria (suspect, probable, confirmed).
Temporal trend of plague incidence during the study period in both districts A time series analysis was performed based on the deviation from the decadal mean of the incidence (DDMI) for a given month. DDMI is the difference between monthly incidences compared to monthly decadal mean incidences of plague cases during the study period. Based on demographic data from the RGPH 2018 (the last official census in Madagascar dates back to 1993), we estimated the number of people in both districts between 2006 and 2015 (for an annual population growth rate of 2. 9% in Madagascar). We calculated the monthly mean incidence (number of cases per 100,000 inhabitants) and the mean of human cases (P + C) of plague between the studied periods for each district. We then estimated the incidence per month for each year of the study period and divided the study period into 120 months or 10 years.
Data processing and analysis Processing, database management and statistical analyses were carried out using Microsoft Excel® and Stata 13® software. Qualitative variables are represented as frequencies and/or proportions. For the comparisons between the two districts, we used the chi-squared test of comparison or Fisher's exact test, if appropriate, for the qualitative variables. For the comparison of the quantitative variables between the two districts, we used Student's t-test for independent samples. The significance threshold was set at p < 0. 05.
Characteristics of overall reported plague cases in both districts Out of the 840 cases reported in the two districts for the period under review, approximately 33% (277/840) were suspected cases (S), 28. 4% (239/840) were probable cases (P) and 38. 6% (324/840) were confirmed cases (C) (Fig. 2 ). In terms of demographic characteristics, approximately 41% (341/840) of the cases were females. The age of the individuals was recorded in the database for a 98. 7% (829/840) of the cases. The median age of reported cases was 12 years old. Out of the 563 probable and confirmed cases (P + C), 4. 4% (25/563) were cases of pneumonic plague (PP), and approximately 91% (512/563) were cases of bubonic plague (BP). Cases with an unreported clinical form represented 4. 6% (26/563) of the reported P + C cases.
Age and sex of P + C cases by district For the Ambositra district, ages were reported for all 103 P + C cases, and just over 47% (49/103) of the individuals were female. Individuals under 9 years old accounted for approximately 33% (34/103) of the total P + C cases in this district. Individuals less than 19 years old accounted for almost 61% (63/103) of the total P + C cases reported during the study period (Fig. 3A ). Approximately 1% (1/103) of the P + C cases were at least 60 years old. Concerning the district of Tsiroanomandidy, out of the 458 P + C cases reported between 2006 and 2015, 455 cases had age information in the database. Almost 39% (178/455) of the individuals were female. Individuals under 19 years old accounted for approximately 71% (324/455) of the total P + C cases during the study  period.
Analysis of virus infection in the presence of elicitors To test the effect of elicitors on TMV-GFP infection in N. benthamiana, the inoculum with infectious viral RNA was mixed with specific elicitor to contain a final concentration of 0. 5 µg/μl (equals ~1 µM) poly(I:C) or 1uM flg22. Infection sites on the inoculated leaves were imaged at 7 days post inoculation (dpi) using a hand-held camera and UV lamp (BLAK RAY B-100AP; UVP Inc. , Upland, California) in the presence of a ruler for size normalization. The areas of infection sites in each leaf were measured with Image J software upon selection of infection site as regions of interest using fluorescence thresholding and the wand tracing tool, and by setting the scale according to the ruler. To test the effect of elicitors on ORMV infection in Arabidopsis, 4 µl of elicitor solution (10 mg/ ml poly(I:C) or 10 µM flg22) or 4 µl PBS were placed on rosette leaves of 3 weeks old Arabidopsis wildtype or mutant Col-0 plants.