dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Le Houérou, 2001)  and in the north-eastern Eurasian cold context (e. g. ,  Yurtsev, 1982). From 11,700 cal. BP onwards, the methods performed on the three databases show similar trends but with different amplitudes. Signals obtained with the EAPDB and TEMPSCAND databases are particularly close, especially for the MAT and BRT methods (Fig. 7a, d ). However, the absence of Mediterranean taxa in the pollen sequence (Fig. 6 ), as well as the poor R 2 and RMSE values (Table  3 ), allow the MEDTEMP calibration to be dismissed as a reliable calibration for MAAT quantification. Among the four methods, RF and WA-PLS appear as the least reliable (low R 2 and high RMSE, Table  3 ), so the MAATs reconstructed by these two methods will not be considered in the following discussion. For the MAT and BRT methods, the TEMPSCAND database calibration seems to produce a signal as reliable as the one produced with the EAPDB calibration set (close R 2 and RMSE values, Table  3 ).
The TEMPSCAND calibration produces a particularly close signal between the two methods and possess better R² and RMSE values, supporting the reliability of the reconstructions based on this calibration database, and will be considered in the following discussion (Fig. 9 ). MAT and BRT are two methods based on different mathematical and ecological concepts  (Chevalier et al. , 2020) , so their similar reconstructed trend supports the reliability of the methods and calibrations. However, the MAT shows a larger temperature range and in particular a slight thermal optimum in the Early Holocene, between 11,700-8,000 cal. BP (Fig. 7a ). A similar pattern, although marked by more pronounced positive anomalies, is shown by  Martin et al. (2020)  for Lake St Front where high percentages of Corylus (< 55 %) are also recorded.
Such a high proportion of Corylus does not exist in the modern pollen database assemblages. The highest proportion (25-35 %) is associated with sites located in Italy, Ireland, and Albania, explaining the bias in the signal towards higher temperatures for the EAPDB-and TEMPSCAND-based calibrations. In the CAN02 pollen sequence, the Early Holocene is also characterised by high Corylus percentages (up to 60 %, Fig. 6 ), which could explain the large temperature variation between the Lateglacial and Early Holocene proposed by the MAT, the only method based on the similarity of present-day/fossil assemblages. For the 10,700-6,600 cal. BP period, it can be suggested that the MAT method is less efficient than the BRT one, because Early-Holocene hazelnut woodland have no modern analogue. Nevertheless, both the MAT and BRT methods calibrated on the TEMPSCAND database can be retained in the following discussion because of the good reliability of their reconstructions.
Local climate from the CAN02 record based on two independent proxies The temperatures inferred from pollen data with the BRT method based on the TEMPSCAND modern database and brGDGT data with the Bayesian, Mrs and Index1 calibrations show very similar trends through the Holocene (Fig. 9 ). First, cold conditions are evidenced from 15,000 to 11,700 cal. yrs BP followed from 10,500 to 6,600 cal. BP by warmer conditions (plateau) with temperatures lower than those observed today. Contrary to the BRT and brGDGT signal, the MAT method based on the TEMPSCAND modern database shows a slight thermic optimum from 10,500 to 8,000. After the thermic optimum, a cooling trend until 6,600 cal. BP is evidenced (Fig. 9a ). Due to a possible bias during the shaded area, the 6,600 -5,000 cal. BP period will not be discussed.
Acknowledgements This study is a part of the  DEMETER  project supported by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No. 852573 ,  PI A. Evin ). The sampling of peat cores and surface samples was performed during field schools of the  University of Montpellier , as part of ORPAM teaching modules. The authors thank  Frédéric Néri  and the  CEN Languedoc-Roussillon  for fieldwork facilities,  ISEM  for financial contribution to radiocarbon dating,  Sandrine Canal  for the preparation and treatment of pollen samples, and  Jean-Frédéric Terral ,  Vincent Girard  and ORPAM students for help with fieldwork and with the preliminary study of the Canroute Peatland. This is ISEM contribution XXX-XXXX.
The Eurasian Pollen Database (EAPDB, Fig. B), compiled by Peyron et al. (2013, 2017) and updated by Dugerdil et al. (2021a) was used as the first modern pollen database. In addition, two other modern pollen databases were used, originating from a sub-sampling of the EAPDB database, grouping sites from the Mediterranean and Temperate Europe (MEDTEMP, Fig. B) and sites originating from Temperate Europe and Scandinavia (TEMPSCAND, Fig. B). The temperatures reconstructed from those two.
This may explain why candidate HIV vaccines that did provide protection against SIV at the preclinical stage (which was attributed to ADNKA), were unsuccessful in human clinical trials  (107). Contributing to this limitation is the deficiency of species-specific or species-crossreactive reagents. With that said, murine reagents are widely available and the availability of NHP reagents is improving [NHP-reactive antibody clones are reported on databases such as NIH NHP Reagent Resource (www. nhpreagents. org/_)]. However, the sparser reagent pool and incomplete characterisation of animal model immune components, together limits our ability to yield results that are replicable in human studies. Furthermore, the scope for genetic manipulation at the NHP level is minimal, particularly in comparison to that of mice, where immunodeficient mice can help us understand the weight of the role of particular immune parameters in mediating protection.
Advances in human research to assist in the search for CoPs A combination of human and animal data often yields the greatest insights into a pathogen's CoP. Furthermore, the best model for human infection is no doubt the human itself. Hence, advances in biotechnology, immunology and human challenge trials must be applied to further improve CoP research. To delve into immune responses post-vaccination in detail, LN GC reactions are analysed using digestion or microscopic dissection of isolated animal tissue. However, until recently blood biomarkers such as CXCL13 and circulating Tfh cells were relied upon to detect GC reactions in humans  (98). The shortcomings of such techniques include the uncertainty of the antigen-specificity of the reactions, and the limited and short traceability of these markers  (98).
If sufficient concentration data were not available, concentration data were grouped at a less detailed level. For example, 'cow milk, <1% fat (skimmed milk)' was then recoded into to cow's milk (level 3), liquid milk (level 2) or milk and dairy products (level 1), wherever relevant. Data with empty cells in any important field of the SSD file, such as level of detection, level of quantification and analytical value, or invalid concentration units, were omitted. If a FoodEx code was missing, but the product name was available, the corresponding FoodEx code was added manually. Only data obtained from random sampling and convenient data were included. For each substance and composite food combination in the dataset it was decided to use the analytical data as such or to convert the food into its ingredients (see matching food consumption and concentration data).
Complexity of the food (e. g. the FoodEx1 code represents a broad range of composite foods rather than a single food, such as meat-based dishes), availability of recipe data, and number of measurements for the composite food and its ingredients were important criteria for this decision. Once decided to convert a composite food into its ingredients, the analytical data of the composite food were removed from the data set. Supplemental material A provides information on the decisions made for the foods, and Supplemental material B shows the FoodEx1 level used for each substance in the concentration dataset, together with the number of measurements, the percentage leftcensored data, i. e. measurements below the level of detection (LOD) or level of quantification (LOQ) and mean concentrations per food and substance.
Methyl mercury Data for methyl mercury were obtained for fish and sea food (60 foods) from 12 EU countries. Analytical results for both methyl mercury and total mercury were available. Fewer numbers of analytical values were available for methylmercury (n = 165) than for total mercury (n = 6,542). Therefore, we decided to include methyl mercury concentrations calculated out of total mercury concentrations using conversion factors established by EFSA (EFSA 2012a): • 1 for fish meat, fish products, fish offal and unspecified fish and seafood; • 0. 8 for crustaceans, molluscs and amphibians, reptiles, snails and insects; • 0 for all other food categories not containing fish or seafood. For the samples with measured methyl mercury concentrations, total mercury concentrations were also available, allowing comparisons of measured and calculated methyl mercury concentrations.
To do this, the mean of positive samples, i. e. samples with an analytical value of methyl mercury or total mercury above the LOQ value, was calculated. The mean calculated methyl mercury concentration was generally slightly higher than mean measured methyl mercury concentration (see Supplementary Material C). Given the smaller number of measured methyl mercury data and the slightly higher concentrations of calculated methyl mercury, it was decided to include only calculated methyl mercury data. After data cleaning, the dataset for methyl mercury contained 6,473 entries.
Inorganic arsenic From the EFSA data warehouse, samples containing 'arsenic' between 2014 and 2018 were retrieved. Since we focused on the exposure to inorganic arsenic (iAs), 'organic arsenic' samples were omitted from the data and samples coded as 'arsenic and derivatives' and 'arsenic' were recoded to 'total arsenic' samples, following the approach taken by EFSA (2021b). Of samples for which both 'total arsenic' as well as 'inorganic arsenic' values were reported, the 'total arsenic' samples were omitted from the database. In addition, after closer examination of the original data, samphire ("zeekraal") samples analysed for 'arsenic' from the Netherlands were originally coded as 'leafy vegetables' and were consequently recoded as 'sea weeds'. The fraction of iAs was translated from the remaining 'total arsenic' samples according to the median ratios described in EFSA's Scientific Opinion (2021b).
Similar to EFSA, total arsenic was not converted into iAs for fish. Supplemental material F lists the factors used for the conversion of total arsenic into iAs. Like EFSA, we used an additional LOQ-cut off of 100 μg/kg for iAS in cereal-based food for infants and young children. The original dataset also contained 3104 entries for drinking water (tap and bottled). High concentrations of iAs in tap water (typically up to 7920 μg/litre), especially originating from one country, were present in the data set. In addition, the dataset contained non-detects with high LOQs (up to 900 μg/kg). A maximum level of 10 μg/L has been established for water intended for human consumption, without distinguishing among different arsenic forms (EU, 2020). In addition, a maximum level of 10 μg/L was established for total arsenic in natural mineral water  (EC, 2003).
In the most recent EFSA opinion on iAs, EFSA used concentration data over the years 2013-2018 and excluded values obtained from analytical methods with LOQs higher than 10 μg/L for that reason (EFSA, 2021b). To perform calculations using representative European iAs in drinking water, we did not use the received data from the Data Warehouse but used the mean values for the lower and upper bound as reported by EFSA in 2021. After data cleaning, 3,021 entries for iAs were obtained from 13 EU countries and for 117 different Foo-dEx1 codes.
Fluoride Only fluoride concentrations in drinking water were available. Data were obtained from the EFSA data warehouse for bottled water, carbonated mineral water, still mineral water, well water and drinking water. Because of food conversions containing water, such as soft drinks and liquid infant formulae which are converted to water and other ingredients (see matching food consumption data and concentration data), all types of water were recoded into drinking water (A. 15). Within the EFSA data warehouse information from limited countries was available. Therefore, additional fluoride concentration data obtained from the Dutch monitoring program for drinking water between 2014 and 2018 were included. It should be noted that those data were provided as mean values per pumping station. Mean values were calculated using a middle-bound scenario, in which samples below the limit of detection were substituted with a value equal to half the value of the level of detection.
NDL-PCBs Concentration data were obtained for 6 NDL-PCBs, which are regarded as indicator congeners for the exposure to NDL-PCBs via food  (EFSA 2005; JECFA 2016). Concentration data (n = 3,363 samples) for each of the 6 NDL PSBs were obtained from 9 countries. For many samples, the sampling type was not specified. To enlarge the number of observations, those samples were included. NDL-PCB concentrations in food were expressed on a whole weight-or on a percentage fat weight-basis. If for a sample data were available for both whole weight and percentage fat weight, the data expressed on whole weight were selected. If data were expressed based on percentage fat weight, the percentage fat in the original sample was provided in the SSD format. However, the original percentage fat in the sample was not always provided or higher than expected (up to 100%).
To calculate the NDL-PCB concentration in those samples, the percentage fat weight according to the Dutch food composition database (NEVO; accessed November 2021) 2  was used. If NEVO provided two or more values for the percentage fat, the average fat weight was used for the calculations. After an initial run, high exposure estimates were obtained for NDL-PCBs in vegetable oil. This was mainly due to extreme NDL-PCBs concentrations analysed in one country. Average concentrations were approximately 250 times higher than those described for vegetable oil in the EFSA opinion  (EFSA, 2012b). We therefore omitted the extreme NDL-PCBs concentrations analysed in one country. The mean concentration of the sum of 6 congeners now fell within the range published by EFSA  (EFSA, 2012b). As the sum of 6 indicator congeners comprises 50% of the total exposure to NDL-PCBs (EFSA 2012b), the 6 NDL-PCBs were summed per sample assuming equipotency (see paragraph scaling factors) and multiplied by 2 as a proxy for the total concentration of NDL-PCBs in food.
PBDEs Concentration data were obtained from 4 countries and for 24 foods of animal origin (meat and meat products, fish and other seafood, eggs and milk). Like the NDL PCBs, PBDE concentrations in food were expressed based on whole weights or on percentage fat weight. Again, concentrations expressed on a whole weight basis were preferred over those expressed on percentage fat weight. In addition, the fat weight of the original sample was not always available, and the fat weights provided in the Dutch NEVO database was used to calculate PBDE concentrations expressed on whole weight. After data cleaning, the data set contained 1557 measurements for each of the four PBDEs. Because some exposome studies or aggregated external exposure studies express the sum of PBDEs, concentration data for the four PBDEs -47, -99, -153 and -209 were summed per sample as lead-equivalents thus considering their SFs compared with lead (see paragraph scaling factors).
Mixture risk assessment Mixture risk assessment was performed using the MCRA tool version 9. 1 (https://mcra. rivm. nl) for each country and subpopulation listed in Table  2 , assuming dose additivity. Chronic (long-term) exposure was calculated using the Observed Individual Means (OIM) model. For each substance s in the assessment group and for each individual i in the food consumption data base, the consumed amount of a certain food f averaged over the total number of consumption days q if was multiplied with the average concentration present in that food c ifs. This was done for all consumed foods per individual. The subsequent obtained exposures per food were summed for each chemical s per individual over the F numbers of food consumed and divided by the bodyweight of the individual bw i , which yielded the chronic exposure E is to chemical s of the individual i.
In addition to the calculation of percentiles, the contribution of substances to the personalised mRPI of the total population was assessed. For a particular substance s, the sum of the exposure E to that substance (expressed as lead-equivalents) of all individuals (n) in the food consumption database relative to the sum of the cumulative exposures of all individuals was calculated: % contribution s = ∑ n i=1 E s ∑ n i=1 Cum E i *100 (equation 5) Calculating the contributions for combinations of foods and substances is done in a similar way.
Uncertainty The bootstrapping approach was used to quantify sampling uncertainty in food consumption and concentration data caused by a limited sampling size  (Efron 1979; Efron and Tibshirani 1993). This approach C. Sprong et al. International Journal of Hygiene and Environmental Health 251 (2023) 114167  re-samples (with replacement) the original food consumption and concentration dataset to obtain a bootstrap of n observations. In the present calculation, we performed an uncertainty analysis using 100 re-sampling cycles with 10,000 iterations. This yielded 100 alternative exposure distributions, which might have been obtained during sampling from the population of interest and during sampling of foods. The mean and P95 were estimated for each of those 100 alternative exposure distributions, yielding 100 alternative exposure statistics.
The use of these HBGVs would have biased our assessment. There is currently no HBGV for fluoride. We therefore estimated the corresponding ESRV for IQ loss for iAs, NDL-PCBs and fluoride based on epidemiological data. While there is evidence of associations of PBDE exposures with IQ loss  (Eskenazi et al. , 2013) , there is no information on PBDE congener-specific associations which would have made it difficult to utilize the PBDE congener-specific food occurrence data. We therefore adopted the congener-specific hazard data derived by EFSA (2011a) for developmental neurotoxicity in rodents. Thus, assessments based on reference doses for specific endpoints, as we used in our approach and which shaped the personalised mRPI approach in  Vejdovszky et al. (2019) , the POD index (EFSA, 2019a), the chemical risk calculator  (Boberg et al.
, 2021)  and the normalized total margin of exposure approach  (Sprong et al. , 2020) , provide a more realistic risk assessment. However, the de novo derivation of reference values for specific endpoints can require extensive literature reviews and may be rather resource-intensive, while HBGVs or HBM-GVs are usually more readily available, e. g. in databases such as EFSA's OpenFoodTox database  (Kovarich et al. 2016). Approaches based on such values also have merits in that they can provide lower tier MRAs which can be refined if the assessment indicates exceedance of combined acceptable levels (EFSA, 2019a).
6% for children ages 2-3 years and 6-12 years and adults, respectively  (Lehmann et al. , 2015). Other dietary sources (e. g. tea) and routes of exposure are also relevant for fluoride, such as dental hygiene products, but the information accessible to us was too limited to draw conclusions on their contribution to total fluoride exposures (EFSA 2013). To obtain a more complete picture of the combined exposure to chemicals relevant for IQ loss, other routes of exposure can be included in external exposure assessment. Methodologies to aggregate the exposure from several routes are available (e. g. Husøy et al. , 2020 : aggregated exposure of di (2-ethylhexyl) phthalate from diet and personal care products) and have been implemented in MCRA (  Van der Voet et al. , 2020). However, chemical concentration data in consumer products and indoor air was not yet available for the substances included in our case study, and neither were levels in soil and outdoor air (IPCHEM database accessed 22 March 2022).
ESRVs Benchmark dose modelling is the preferred approach to establish ESRVs, since it makes a more extended use of dose-response data and it allows for quantification of the uncertainties in the dose-response data, in contrast to more simple approaches such as the NOAEL (EFSA 2017). To take the uncertainty of the benchmark dose into account, the lower bound of the confidence interval BMDL around the bench mark dose is used to derive the POD. In our case study, ESRVs based on BMDLs were obtained for lead, methyl mercury, fluoride, NDL-PCBs (Table  1  main text; all based on epidemiological data) and PBDEs (animal data). For iAs, only a LOAEL was available, which indicates that the ESRV of iAs is less robust. In our study, we predominantly used ESRVs that were already published. As the aim of our case study was a proof of principle rather than a comprehensive risk assessment, we did not update established ESRVs as this was beyond the scope of our case study.
Kinetic model The fractional retention of fluoride is only constant (i. e. 36% for adults) at a daily dietary intake of 2 mg/kg or higher  (Villa et al. , 2010). Below a total daily intake of 0. 8 mg/day, fluoride excretion exceeds the intake, resulting in a negative fluoride balance. This means that at a daily urinary excretion of 0. 3 mg, the consequent intake would be smaller than 0. 3 mg/day instead of 0. 6 mg/kg. Taken together, a urinary excretion of 0. 3 mg/day leading to 1 IQ point loss is highly uncertain. - based on other grains, the personalised mRPI was overestimated. Exclusion of data for which no occurrence data were available (e. g. methylmercury in foods other than fish and seafood, NDL-PCBS in vegetable foods) resulted in an underestimation of the personalised mRPI. The issue of limited concentration data used for certain substances can be addressed by including the entire EFSA data set which comprises 27 countries, rather than the 4-13 countries included in our study.
However, this would not resolve the issue for NDL-PCBs and PBDEs congeners. For NDL-PCBs, concentration data for congeners other than the 6 indicator congeners were very limited. As those 6 indicator NDL-PCBs comprise approximately 50% of the total PCBs  (EFSA 2005 (EFSA , 2012b ; JECFA 2016), we calculated the exposure by multiplying the sum of the 6 congeners of NDL-PCB with a factor two. Particular if congeners would differ in potency, as described under uncertainties in ESRVs, the sum of 6 indicator congeners multiplied by two could have led to an under-or overestimation of the exposure. Once better information on potencies of the different congeners is available, the NDL-PCB congeners to be analysed in food can be reconsidered. Regarding PBDEs, concentration data (life stock meat, cow milk and eggs) for the four other congeners deemed relevant for dietary exposure by EFSA (2011a) were available in the concentration database.
Non-quantified uncertainties in exposure data. Not all food consumptions surveys were available in FoodEx1, the food coding used to match food consumption data to occurrence data. FoodEx2 is more refined, but currently the Dutch recipes database is not available in FoodEx2 Food coding not always discriminate different food products (e. g. crackers which could be rice-based or wheat based). Exclusion of foods for which no data was obtained and for which above mentioned assumptions could not be used _ Concentration data expressed per fat weight Inaccurate description of the percentage fat weight in a sample. Assuming mean fat content of Dutch food composition database (NEVO).