dataesr/scientific-paragraphs-categorization huggingface dataset:
Shehias labelled in black (bold) correspond to hotspots defined by both definition (b) and definition (a) (i. e. , category (c) hotspots, see Material and Methods, Persistent hotspots) where prevalence had not declined by at least 20% of its starting prevalence between baseline and Year 5 amongst the matched 9-12-year-old age group. (Error bars not included for visual clarity and because, as a mixed effects model was used, few efficient methodologies to incorporate uncertainty into random effects parameters are available). https://doi. org/10. 1371/journal. pntd. 0010419. g004 ). The maps were created in RStudio, using the tmap package: https://data. humdata. org/m/dataset/451bdd28-d06d-46ea-91c0-2e081f884395?force_ layout=light No changes were made. The License for these data can be found at: https://data.
humdata. org/m/dataset/451bdd28-d06d-46ea-91c0-2e081f884395? force_layout=light and are freely-available under the CC BY-IGO license. https://doi. org/10. 1371/journal. pntd. 0010419. g005 significant differences across a range of population genetic indices observed between islands, given that such control interventions were generally matched between islands prior to, and explicitly matched during, ZEST activities  [11]. Likewise, localised differences in snail density, and also the presence of a second recentlyreported intermediate host species, B. nasutus, along the eastern coast of Pemba,  [43]  could potentially help account for the observed within-island genetic variability and geographic clustering, and perhaps accentuate between-island variability, irrespective of intervention arm. However, it appears unlikely that snail differences alone could be sufficient to explain the observed between-island structuring, the major intermediate host species, Bulinus globosus, is present across all study regions and across both islands  [43].
Acknowledgments We are extremely grateful for the time and effort all participants involved, from the children and adults, their parents, teachers and community leaders. We are extremely grateful to all our miracidial hatching team, including  Haji Amer  and  Juma Kinole Mussa. We also thank  Khalfan A. Mohamed  for his initial involvement in neglected disease control activities across Zanzibar, and to  Julia Llewellyn-Hughes  and  Lisa Smith  in the  NHM Molecular laboratories  for assistance with microsatellite processing.
Palaeo-data confrontation: is the warming realistic? Our reconstructions depend mainly on our climatic assumptions. In particular, the chosen index governs the near-surface air temperature amplitude change over the GIS. Some reconstructions of the possible range of temperature change based on proxy estimates are however available. The estimated range of summer near-surface  temperature in Greenland based on various proxies is 4-5  Members, 2006). • C (CAPE Figure  9  shows the evolution of the July near-surface temperature at ice core locations. Note that, in our PDD formulation, melt is possible with surface temperatures greater than -5 • C. With this condition, the sites of Dye 3, Camp Century and even NEEM are affected by summer melting in all of our three experiments. If our estimates are correct, the NEEM ice core should show evidence of melt during the LIG.
Acknowledgements. We thank the  CISM  for providing the data sets of Shapiro and Ritzwoller (2004) and Joughin et al. (2010). We also thank  Janneke Ettema ,  Jan van Angelen  and  Michiel van den Broeke  ( IMAU, Utrecht University ) for providing RACMO2 climate fields,  Xavier Fettweiss  for providing the MAR climate fields and  Pascale Braconnot  for providing the IPSL 126 ka BP model outputs. Aurélien Quiquet is supported by the  ANR  project  NEEM-France  and European commission  FP7  project  PAST4FUTURE. NEEM is directed and organized by the  Center of Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (NRCan/GSC), China (CAS) ,  Denmark (FIST) ,  France (IPEV, CNRS/INSU, CEA and ANR), Germany (AWI), Iceland (RannIs), Japan (NIPR) ,  South Korea (KOPRI) , the  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) ,  UK (NERC)  and the  USA (US NSF, Office of Polar Programs).
This is Past4Future contribution no. 29. The research leading to these results has received funding from the  European Union 's  Seventh Framework programme  ( FP7/2007-2013)  under grant agreement no  243908 , " Past4Future. Climate change -Learning from the past climate". All  (or most of) the computations presented in this paper were performed using the CIMENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the  Rhône-Alpes region  ( GRANT CPER07 13  CIRA: http://www. ci-ra. org).
The number of well-dated pollen diagrams in Europe has increased considerably over the last 30 years and many of them have been submitted to the European Pollen Database (EPD). This allows for the construction of increasingly precise maps of Holocene vegetation change across the continent. Chronological information in the EPD has been expressed in uncalibrated radiocarbon years, and most chronologies to date are based on this time scale. Here we present new chronologies for most of the datasets stored in the EPD based on calibrated radiocarbon years. Age information associated with pollen diagrams is often derived from the pollen stratigraphy itself or from other sedimentological information. We reviewed these chronological tie points and assigned uncertainties to them. The steps taken to generate the new chronologies are described and the rationale for a new classification system for age uncertainties is introduced.
Two thirds of these diagrams had some independent dating control with a total of more than 1,500 radiocarbon age determinations. However, compiling data from printed diagrams has limitations and it was realised that databases containing the pollen counts were needed for different applications including climate reconstructions  (Fyfe et al. 2009 ). As a consequence the European Pollen Database (EPD) was created in 1992 alongside with the North American Pollen Database (NAPD). The NAPD had a predecessor, the COHMAP pollen 'database' managed by T. Webb III, which was the basis for sub-continental climate reconstructions and data-model comparisons, and also yielded maps of past vegetation change (e. g. Prentice et al. 1991). This was followed by a comprehensive set of isopollen and derived biome maps for the past 21,000 years covering much of North America and mainly drawing on the data stored in the NAPD  (Williams et al.
This enables evaluation of climate simulations as well as the vegetation models, and eventually gives the opportunity to explore the relationship between shifting tree distributions and climate change over the last 20,000 years  (Giesecke et al. 2007 , Pearman et al. 2008). The construction of maps of past vegetation, as well as most other research questions utilizing pollen data, requires that the samples can be assigned an accurate age estimate. Studies that need accurate sample-age estimates include the reconstruction of continental-scale trends in climate change  (Davis et al. 2003)  or the reconstruction of landscape openness through time  (Gaillard et al. 2010 , Nielsen et al. 2012). The growing need for calendar time scale chronologies for the pollen data in the EPD is met with the work outlined here.
We also present a system indicating the uncertainty on the age estimate for individual samples and discuss constraints and considerations for estimating ages and uncertainties. We describe how the uncertainty information can be used for sample selection. Finally, the chronologies have been used to map the distribution of selected pollen types for the last 15,000 years based on the data in the EPD and taxonomic considerations for this type of analysis are discussed. All work presented here is based on the version of the EPD released on 2nd August 2012.
D. 1950 (0 B. P. ) and an uncertainty of 250 years. Uncertainty in time was used here also to indicate the probability of loss of material as in this case the topmost sample would be slightly older. As future ages are impossible for sediments that were deposited in the recent past we used a half Gaussian error distribution to allow uncertainty in the past, but not in the future. Annual laminations: Lakes with continuous annually laminated sediments provide the best possible age control and the EPD stores some pollen records from such sites. These datasets have ages assigned to all samples and each sample is therefore a control point. However, varve chronologies are rarely perfect. Slumps can interrupt the contiguous sedimentation and differences in varve-counting replication create uncertainties in sample ages (e.
Where authors had used more palynological control points in their data submission to the EPD we tried to assess how the age information was derived and assigned uncertainties or omitted control points that seemed vague. Knowledge of the timing of archaeological periods can also provide dating control, where the pollen diagram indicates individual settlement phases. The introduction of cultivated plants such as Juglans may serve as a date, but this only provides a ''younger than'' date as the pollen sample may post-date the introduction of the cultivar in the region. In some cases historical events, such as the 30-years war in central Europe , can be identified and provide very accurate and precise time control  (Giesecke 2001). The addition of each palynological control point reduces the degrees of freedom of the overall dataset and palynological control points were therefore only included where the chronologies could not be sufficiently constrained.
Implementation All age-depth relationships were constructed using the R-code CLAM version 1. 0. 2 (Blaauw 2010; R Core Team 2012), which offers the advantage that radiocarbon dates are internally calibrated. This means that the dates can be stored as uncalibrated dates together with control points expressed as calendar-year ages. After evaluating all control points, they were run as a batch yielding two age-depth relationships per site, based on linear interpolation and smoothing-spline fitting with a default smoothing factor of 0. 3. The two alternative models were visually evaluated, selecting the more appropriate one, with a general preference for the smoothing spline. However, smoothing splines presented problems when the density of dates varied markedly within a sequence, and could not be produced for sites with a low number (\4) of control points.
General-purpose chronologies and sample selection The chronologies that were produced following the outlined rationale and procedure will rarely be the best chronology that may be achievable for any given site. Where pollen data were used to derive control points, the chronologies are dependent on the assumed timing of vegetation change. Nevertheless these chronologies provide more independent age control for the mapping of vegetation change than was possible in the dataset compiled by  Huntley and Birks (1983). The chronologies should be adequate for most continental-scale questions and database queries. We consider them as general-purpose chronologies and stress that users should consider whether they are adequate to pursue their own research questions, particularly if these are site-specific.
The new calendar time-scale chronologies are an important step towards the mapping of European vegetation change and related questions. In addition the computation of sample age uncertainties on a database-wide scale provides new opportunities for sample selection and database queries. Individual sites are often well dated for some sections, while only vague age estimates can be provided for other samples. The twofold uncertainty classification provided here allows more flexibility for the inclusion/ exclusion of samples for a particular analysis. Where high confidence in age control is necessary, for example in comparisons of vegetation patterns and with archaeological periods or to investigate vegetation responses to climate perturbations such as the 8. 2 ka event, the investigator can restrict the analysis to samples with three stars and uncertainties within 250 years.
The effect of the onset of the Holocene as a control point is visible in the histograms depicting the number of samples or core segments assigned to 3 and 4 stars (Fig. 2 ). Taxonomy: what are we mapping? Since von Post's lecture in 1916 pollen identification has advanced substantially, leading to the creation of ever more detailed pollen determination keys, pollen atlases (e. g. Faegri et al. 1989; Moore et al. 1991; Reille 1992 Reille , 1995 Reille , 1998;; Punt et al. 1976 -2009 , Beug 2004) , and pollen reference collections. Thus the detail of taxonomic identification has considerably improved through time. Unfortunately, nomenclature and definition of pollen types is not consistent. Depending on the determination key in use, different laboratories or schools use different nomenclatures, which make it difficult for database users to synthesise all relevant information.
The EPD aims to harmonize nomenclatural inconsistencies by implementation of a standard catalogue of pollen-type names and the introduction of a hierarchy. The EPD nomenclature is geographically confined to Europe and plant names follow the Flora Europaea  (Tutin et al. 1964 (Tutin et al. -1993)). It finally enables database users to access the full data set of particular taxa regardless of whether different pollen-type names were originally assigned. However, the EPD stores the original nomenclature as supplied by the author. A table (P_VARS) within the database links the author's pollen-type names to a common EPD nomenclature, while retaining all details carried by the original naming. The name of an EPD pollen type consists of either (1) the name of a plant taxon (e. g. Calluna vulgaris, Fagus, Brassicaceae), (2) two plant-taxon names separated by a slash (e.
g. Humulus/Cannabis), or (3) a plant-taxon name with the suffix -type (e. g. Rumex acetosa-type). In most cases the EPD nomenclature follows the naming of recognised determination keys or pollen atlases (e. g. Faegri et al. 1989; Moore et al. 1991; Reille 1992 Reille , 1995 Reille , 1998;; Punt et al. 1976 Punt et al. -2009;; Beug 2004 ) with the reference of the type name given. When a new EPD name is created, a full description or explanation is provided. The P_VARS database table is also meant to link up each pollen type to a higher-level collective type. For example, the higher-level collective type for Secale, Zea mays, Hordeumtype, Triticum-type and Avena-type is Cerealia-type, which also comprises all ambiguous cereal pollen determinations such as cf. Triticum, Avena/Triticum, Cerealia undiff.
Consequently database users can map either single, well-determined pollen types or the sum of all cereal pollen. An example from southern Europe is, experience and preservation permitting, the separation of Quercus into Quercus robur-type, Q. cerris-type and Q. ilex-type. The latter types predominantly include evergreen Quercus species. If circumstances do not allow the recognition of Q. cerris or Q. ilex-type, such pollen would be identified only to the genus level Quercus. A map of evergreen Quercus pollen can thus only be a minimum map: the true distribution and abundance of these pollen types is likely to be greater than the mapped area and/or abundance. All distinguished Quercus-types and Quercus together constitute the higherlevel collective taxon Quercus. When mapping the higherlevel collective taxon Quercus, database users receive a map of all Quercus pollen records including the more precise determinations.
Applying the chronological information to the making of maps The work outlined here had the overarching goal to produce chronologies that would allow maps to be produced that depict the data currently held in the EPD as a basis for further spatial analysis. The resulting maps will be presented elsewhere, and an example with a particular motivation is shown here to illustrate further considerations in the making of maps based on these chronologies. There is considerable interest in assessing the intensity of human land-use in Europe during particular archaeological periods, such as the Medieval forest clearance, Migration period, Iron Age, Bronze Age and the Neolithic period. Pollen from Plantago lanceolata is a good indicator for human land-use and its presence and abundance may give some indication of the intensity of land-use  (Behre 1981).
Concluding remarks This paper presents chronologies for the majority of sites currently stored in the EPD, which are based on a calendar time scale and include associated assessments of uncertainties for the inferred sample ages. A variety of control points were evaluated for 1,036 sites and resulting age-depth relationships were constructed based on either linear interpolation or (preferentially) smoothing splines. These line-fitting techniques were used to propagate the error from the uncertainties of the control points to the samples using the R-code CLAM. To complement these sample-age confidences, a classification capturing the dating density and the complexity of the agedepth relationship is provided. It thus becomes possible to select samples based on different requirements on the accuracy of the age control.
ACKNOWLEDGEMENTS Authors are grateful to  Ysaline Billier ,  Laetitia Cormier ,  Claudine Gaudon ,  Nicolas Cougot ,  Franc¸oise Wyers ,  Gwenael Badis ,  Alain Jacquier  and  Sylvie Friant  for providing plasmids, strains and/or antibodies, and to all laboratory members for discussions. They thank the IGBMC mass-spectrometry, peptide synthesis and imaging platforms for their help and  IGBMC (Institut de Ge´ne´tique et de Biologie Mole´culaire et Cellulaire)  for assistance. They are indebted to  Dr Noureddine Lazar  for his help with the MALLS experiments.
Malaria Diagnosis by PCR In the case of diagnosis discrepancies between RDT and microscopy, a PCR method was used to determine the species present in the sample. DNA was extracted from 200 ml of venous blood collected from EDTA tubes using the QIAmpH DNA mini kit (Qiagen). All the manufacturer's instructions were followed with the exception of the elution step, which was carried out with 100 ml of elution buffer instead of 200 ml. A nested-polymerase chain reaction (PCR) was performed for samples with nonconcordant results. Species-specific nested PCR tests were carried out for each of the four main malaria species, as previously described  [20]. The sensitivity of this method in our laboratory was evaluated at 2. 5 and 1 parasites/ml of blood to detect P. falciparum and P. vivax, respectively (data not shown).
Specificity is the ability of the test to correctly classify negative samples among negative microscopy results. The specificity value was the number of samples lacking P. falciparum or P. vivax (i. e. those containing other malaria species + negative samples) divided by the total number of negative P. falciparum or P. vivax microscopy results, respectively. Data were analyzed using the Epi-info 3. 5. 1 software (CDC, Atlanta, GA). Results were accompanied by 95% confidence intervals (95% CI) calculated using the Clopper Pearson exact method  [21].
Detection of the pfhrp2 and pfhrp3 Genes We amplified either exon of the pfhrp2 gene and the exon 2 of the pfhrp3 gene from 2 ml of DNA using the primers and conditions described in Table  1. The A262/A263 primer pair amplified the exon1/intron 1 part of the pfhpr2 gene from 10 bp before the start codon to 305 bp after; the A118/A119 primer pair from 225 bp after the start codon to 274 bp after the stop codon of the pfhrp2 coding sequence and A266/A261 amplified the exon 2 part of the pfhrp3 gene from 226 bp after the start codon to 20 bp after the stop codon. PCR products were analyzed by electrophoresis in 1. 5% agarose gel. Sensitivities of these PCRs were evaluated at 25 and 50 parasites/ml of whole blood, respectively (data not shown). The pfhrp2 gene was sequenced for a panel of 30 P.
falciparum isolates identified during the prospective study. These were randomly selected with the exception of M618, the isolate of particular interest. The PCR products obtained after the amplifications, with the primer pairs A118/A264 and A262/ A263 were purified using spin columns (QIAGEN) then sequenced using standard dye-terminator sequencing reactions (Applera). The sequences reported in this article have been deposited in the GenBank database (accession number KC558574 to KC558602). Nucleotide sequences were translated to amino acids and each repeat was identified using a previously-described numeric code system  [9]. The sequences were aligned using the MegaAlignH software (DNAstarH Lasergene) and categorized into groups and sub groups according to their similarities.
The SD Malaria Ag P. f/PanH Kit Demonstrates High Performances in French Guiana The prospective study to evaluate the performances of the SD Malaria Ag P. f/PanH kit was conducted from July 2010 to July 2011. During this period, 960 EDTA blood samples were collected at Cayenne Hospital from patients with suspected malaria. The male/female ratio was 1. 2:1 with a median age of 25. 8 years [range 1-92 years]. The place of contamination was unknown for most of the patients. According to the data analysis plan, three P. malariae cases were excluded and the 7 P. falciparum/P. vivax mixedinfections detected by microscopy were analyzed as P. falciparum infection. Microscopic examination revealed the majority of the samples (n = 738, 77. 9%) to be negative for malaria, whilst 93 (9. 7%) and 129 (13.
f/PanH and Core MalariaH) kits failed to diagnose it, yet it did not lack the whole pfhrp2 gene, neither the exon 2 part of the pfhrp3 gene. The possibility of a prozone effect due to an excess of either antigen or antibodies was excluded after testing a 1/10 and 1/ 100 dilution of the sample (data not shown)  [28]. Its pfhrp2 sequence did not exhibit a frame shift, or particular mutations. Its haplotype was part of the 1 d group composed of two isolates. The second isolate from this group, with a parasitaemia of 0. 66%, was detectable by the SD Malaria Ag P. f/PanH. With a parasitaemia of 0. 4%, this failure of detection of the M618 could not therefore be explained by inadequate kit sensitivity. Then, we calculated the product of the number of type 2 6 type 7 repeats as previously described to classify the isolates as sensitive or not to detection by a PfHRP2 RDT  [9].
Far-ultraviolet (FUV) and far-infrared (FIR) luminosity functions (LFs) of galaxies show a strong evolution from z = 0 to z = 1, but the FIR LF evolves much stronger than the FUV one. The FUV is dominantly radiated from newly formed short-lived OB stars, while the FIR is emitted by dust grains heated by the FUV radiation field. It is known that dust is always associated with star formation activity. Thus, both FUV and FIR are tightly related to the star formation in galaxies, but in a very complicated manner. In order to disentangle the relation between FUV and FIR emissions, we estimate the UV-IR bivariate LF (BLF) of galaxies with GALEX and AKARI All-Sky Survey datasets. Recently we invented a new mathematical method to construct the BLF with given marginals and prescribed correlation coefficient.