dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was supported by the  Assistance Publique -Hôpitaux de Paris , by a grant from the  Université Paris Sud  ( EA 7361 ), by the  LabEx LERMIT  supported by a grant from the  French National Research Agency  ( ANR-10-LABX-33 ), and by a project of  ANR LabEx IBEID. This work was also funded in part by a grant from  Joint Program Initiative on Antimicrobial Resistance  ( ANR-14-JAMR-0002 ).
Antibody responses to Pfs48/45 (Pfs48/45-10C  36  ) and Pfs230 (230CMB  15  ) were determined by enzyme-linked immunosorbent assay (ELISA) using recombinant, correctly folded proteins. A protein microarray was constructed to assess responses to other gametocyte antigens. This array included a total of 315 proteins (Supplementary Data 1) that were broken up into 528 overlapping fragments <1000 amino acids in length (overlaps of 17 amino acids) for protein expression in an Escherichia coli based in vitro transcription-translation (IVTT) system  49, 50. Antigens on the array included proteins previously implicated as eliciting TRA after immunisation, proteins involved in early gametocyte development as possible markers of gametocyte exposure 1 , markers of asexual parasite exposure  51  and proteins expressed in gametocytes based on recent proteomic analysis  52.
TRA and mosquito infection rate in natural infections. Next, we determined whether the immune profile associated with TRA in the SMFA was also associated with mosquito infection rates observed in direct membrane-feeding experiments. Data on the infectivity of donor blood samples to mosquitoes at the time of plasma donation was available for 494 individuals for whom gametocyte status had been assessed by microscopy and TRA assessed in the SMFA. Of the 128 individuals without patent gametocytaemia, 18 (14. 1%) were infective to mosquitoes in the DMFA. Of the 366 gametocyte-positive individuals, 203 (55. 5%) infected at least one mosquito, with a median mosquito infection rate of 20% (IQR 7. 7-42. 5%). There was good agreement between transmission outcomes in the SMFA using cultured gametocytes and An.
For Dutch migrants who had returned from endemic areas ≥1 year before sampling, a seropositivity threshold was calculated as the mean+3 standard deviations of the averaged normalised OD values of eight naïve European control sera. Protein microarray. The selection of proteins to be cloned and printed on the arrays was made primarily based on observed expression by stage V P. falciparum gametocytes in a single recent proteomic analysis  52. To ensure that the array included most potential antibody targets, we preferentially included proteins that had transmembrane domains, signal peptides or GPI anchors. To ensure that the majority of proteins that were highly abundant or 'specific' to mature gametocytes were included on the array, we used mass spectrometry data from the analysis of the stage V gametocyte proteome  52  , alongside proteomic data from purified trophozoites  1  and schizonts (Supplementary Data 6, PRIDE accession number: PXD008250) generated in the same laboratory.
Gametocyte and asexual stagespecific expression data sets were generated in relative expression values to compare protein abundances (label-free quantitative (LFQ) values) between the stages, which are presented in Supplementary Data 1 as a fold-change value. We chose to also include proteins that were potential markers of gametogenesis identified by Silvestrini et al. 1  These proteins we identified as being expressed primarily by early gametocytes, or shared between stages (proteins for 32/59 were present in the stage V database), but some were more abundant in mature gametocytes or other parasite life stages (Supplementary Data 1). In addition to these markers, we included four putative markers of asexual parasite exposure: PF3D7_1036000 (Merozoite surface protein 11 (MSP11)), PF3D7_0711700 (erythrocyte membrane protein 1, PfEMP1 (VAR)), PF3D7_0731600 (acyl-CoA synthetase (ACS5)), and PF3D7_0423700 (early-transcribed membrane protein 4 (ETRAMP4)), based on work by Helb et al.
51  Details of the 315 proteins included on the array are given in Supplementary Data 1. Proteins with reference sequences longer than 1000 amino acids were split into multiple fragments (overlaps of at least 17 a. a) for cloning, in vitro protein expression and printing. Because the basic criteria for the selection of proteins for microarray construction was based on data generated from a single laboratory, we cross-referenced our protein list against a cross-study analysis of gametocyte specificity  53. In short, the specificity of any protein for the gametocyte stage was scored by determining how often it had been detected across 11 proteomic analyses (listed in Supplementary Data 1: 3 of gametocytes only, 3 of asexual stages only, and 5 of both asexuals and gametocytes). Proteins were binned from low to high abundance and weighted according to the retrieval rates of proteins in two curated lists of 'gold standard' gametocyte and asexual genes, consisting of genes that are known to be specific for either asexual stages (n = 45) or gametocytes (n = 41).
High expression of gametocyte gold standard proteins with concurrent absence of nongametocyte gold standard proteins resulted in a high gametocyte score, calculated from the fraction of retrieved gametocyte genes over retrieved non-gametocyte genes. All scores were log-transformed and summed over all data sets. Gametocyte scores were categorised using the scores' distribution of the gametocyte gold standard as follows: scores above the first quartile (9. 69) were considered gametocyte-specific. Proteins scoring at least as high as the lowest scoring gametocyte gold standard representative (-2. 46) were considered as enriched in gametocytes. Proteins with lower scores are not specific to a certain life stage and have some evidence for expression in gametocytes. All proteins scoring lower than the median of the asexual gold standard (-18.
98) were considered asexual specific. This analysis confirmed that 228 of the 315 proteins had consensus evidence for enrichment in gametocytes (scores >-2. 46), whereas 284/315 had conservative evidence for expression in gametocytes (peptides present in at least one gametocyte proteomic database, score >-10). 312/315 were present in at least one gametocyte proteomic database, giving rise to a score >-18. 93. Scores for all proteins on the array are included in Supplementary Data 1. To avoid further curation and potential bias, no proteins were excluded prior to analysis. Proteins were expressed using an in vitro transcription and translation (IVTT) system, the Escherichia coli cell-free Rapid Translation System (RTS) kit (5 Prime, Gaithersburg, MD, USA). A library of partial or complete CDSs cloned into a T7 expression vector pXI has been established at Antigen Discovery, Inc.
Microarray chip printing and protein expression were quality checked by probing random slides with anti-HIS and anti-HA monoclonal antibodies with fluorescent labelling. Each chip contained 28 IVTT negative control targets (for data normalisation), and 48 IgG positive control targets (for quality control). For analysis of antibody reactivity on the protein microarray, serum samples were diluted 1:200 in a 3 mg mL -1 E. coli lysate solution in protein arraying buffer (Maine Manufacturing, Sanford, ME, USA) and incubated at room temperature for 30 min. Chips were rehydrated in blocking buffer for 30 min. Blocking buffer was removed, and chips were probed with pre-incubated serum samples using sealed, fitted slide chambers to ensure no cross-contamination of sample between pads. Chips were incubated overnight at 4°C with agitation.
Acknowledgements This work was supported by a  Marie Curie Career Integration Grant  from the  European Community 's  Seventh Framework Programme  (SIGNAL,  PCIG12-GA-2012-333936 ), and by a  VIDI fellowship  from the  Netherlands Organization for Scientific Research (NWO ; Project Number  016. 158. 306 ). Data collection in Burkina Faso was supported by the  Bill and Melinda Gates Foundation  ( AFIRM OPP1034789 ). PfGEST mAb were produced by the  Hypothesis-driven Pre-erythrocytic Antigen Target Identification Consortium (HPATIC)  and provided by  PATH 's malaria vaccine initiative. We thank the participants of the various trials contributing samples and data to this study, and the staff involved in these trials. We thank  Jacqueline Kuhnen ,  Laura Pelser-Posthumus ,  Astrid Pouwelsen ,  Jolanda Klaassen ,  Maarten Eldering  and  Tonke Raaijmakers  for their role in sample preparation, mosquito breeding and conduct of the SMFA.
Credits: Anders Krogh, Bjorn Larsson, Gunnar von Heijne, and Erik L. L. Sonnhammer), SP: Signal peptides (SignalP: PlasmoDB. PMID15223320. Credits Bendtsen JD, Nielsen H, von Heijne G, Brunak S), Parasite stage: Gametocyte score category based on an analysis of previous gametocyte and asexual proteomic databases, as indicated in methods53. Details in Supplementary Data 1 and 5, Predicted/known cellular component (homology): Gene ontological terms (curated or computed) from PlasmoDB version 28, or annotation for previously characterised proteins. For proteins without annotation or predicted function/location, domain prediction based on protein homology is presented in square brackets where available (HHPred protein prdomain prediction, The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis78 ).
Abstract. The extent to which climate conditions influenced the spatial distribution of hominin populations in the past is highly debated. General circulation models (GCMs) and archaeological data have been used to address this issue. Most GCMs are not currently capable of simulating past surface climate conditions with sufficiently detailed spatial resolution to distinguish areas of potential hominin habitat, however. In this paper, we propose a statistical downscaling method (SDM) for increasing the resolution of climate model outputs in a computationally efficient way. Our method uses a generalised additive model (GAM), calibrated over present-day climatology data, to statistically downscale temperature and precipitation time series from the outputs of a GCM simulating the climate of the Last Glacial Maximum (19 000-23 000 BP) over western Europe.
The SDM based on kriging interpolation, providing the best accuracy, was then validated on present-day data outside of the calibration period. Our results show that the downscaled temperature and precipitation values are in good agreement with paleoclimate reconstructions at local sites, and that our method for producing fine-grained paleoclimate simulations is therefore suitable for conducting paleo-anthropological research. It is nonetheless important to calibrate the GAM on a range of data encompassing the data to be downscaled. Otherwise, the SDM is likely to overcorrect the coarse-grain data. In addition, the bilinear and bicubic interpolation techniques were shown to distort either the temporal variability or the values of the response variables, while the kriging method offered the best compromise.
The SDM using kriging interpolation is demonstrated to be a good compromise between computational complexity and accuracy. Although a longer time frame could be used to validate the method, potentially reducing the influence of interdecadal internal variability, we validated on an 11-year present-day time series distinct from the calibration period due to computational constraints. Section 2 is organised as follows. First, we present the GCM to be downscaled, detailing its characteristics and biases. Next, we detail how the GAMs are constructed and how they are used to perform the downscaling. The calibration data used to parameterise the GAMs are presented, distinguishing between the dependent variables, the large-scale predictors drawn from the GCM and the fine-scale geographical predictors.
Outputs from this simulation are used in the calibration process (below). The simulation of LGM climate conditions follows the PMIP3 protocol  (Braconnot et al. , 2011 (Braconnot et al. , , 2012)). The concentrations of atmospheric greenhouse gases were lowered to their LGM values derived from ice-core data (185 ppm for CO 2 , 350 ppb for CH 4 and 200 ppb for N 2 O), and the ice sheets are prescribed according to the product developed for PMIP3  (Abe-Ouchi et al. , 2015). The model is run for several hundred years until the response to the LGM forcing in terms of surface climate variables is stabilised  (Kageyama et al. , 2013a, b). For this research, we extracted 50 years of monthly mean data (temperature 2 m above the surface, precipitation, sea level pressure and relative humidity) from the stabilised part of the simulation.
GAMs were calibrated using the mgcv package  (Wood, 2011)  in R, and the number of knots was determined automatically using generalised cross validation. Using a combination of geographical and physical predictor variables has been shown to improve spatial downscaling results  (Vrac et al. , 2007). The GAM uses these predictor variables, in addition to the original variables to be downscaled, to correct the biases of the GCM described above. This implies that a given GAM is only valid for the GCM for which it was calibrated, as it corrects its specific biases. Using GAMs on climate variables requires the predictor and dependent variables to have the same spatial scale. The presentday dependent variables (precipitation and temperature) are at a fine spatial scale. The elevation variable is also at a fine spatial scale, whereas the predictor climate variables generated by the GCM are at a coarser scale.
Large-scale predictor variables We used the data from a CMIP5 historical simulation run with the IPSL-CM5A-LR model to produce the predictor climate variables used for the calibration of the GAMs. We calculated monthly climatological averages from the simulation outputs for the period from 1961 to 1990, i. e. the same years as the CRU data (see above). The predictor variables we used are temperature (T ), precipitation (P ), atmospheric pressure at sea level (SLP) and relative humidity (RH). The variables were spatially interpolated to match the spatial resolution of CRU data, which is 10 arcmin; each grid point in the CRU data therefore matches a value for each of the predictor variables. Three interpolation methods were tested: bilinear, bicubic and kriging (Figs. S2-S4 ). For the krig-ing method, we used the "krig" function from the vacumm Python package (http://www.
ifremer. fr/vacumm/index. html; https://github. com/VACUMM/vacumm, last access: 25 June 2018) using an exponential fit of the variogram, with the fit computed independently for every month and every variable. Different interpolation methods generate differences in the fine-scale predictor data. For example, the bicubic interpolation generates values outside of the initial range of values, contrary to the other two techniques. The bilinear interpolation generates abrupt changes in the slope of the values at the initial data points, whereas the other two techniques generate smooth surfaces. It is therefore important to assess the potential impact of the interpolation method on the output of the downscaling process.
Fine-scale predictor variables We extracted present-day elevation data from the CRU dataset gridded at the same fine-scale spatial resolution as the dependent variables. We computed the advective (Aco) and diffusive (Dco) continentalities, following  Vrac et al. (2007). Dco is bounded between 0 and 1, and corresponds to the shortest distance to the ocean. A low value means that distance to the ocean is small, and vice versa. Aco is also bounded between 0 and 1, and takes the direction and intensity of prevailing winds into account along with the distance to the ocean. The change of Aco during a time dt is governed by Eq. (  2 ): dAco = [-Aco (1 -i co ) + (1 -Aco) i co ] U 0 U l 0 ln 2dx, ( 2 ) where i co is 0 over sea and 1 over land, dx is the distance travelled by the air mass during dt, U is the mean wind norm, obtained from the GCM, and l 0 /U 0 is the distance / wind ratio corresponding to a change of Aco of 1/2.
Evaluation data (palynological data and vertebrate remains) To evaluate the performance of the SDM for the LGM, we compared our temperature and precipitation outputs to local climate variables estimated on the basis of pollen and vertebrate fossils from 29 test locations (Table  S1 , Fig. 1 ). For each of our 29 test sites, we estimated the mean, minimum and maximum temperature and precipitation rate on a monthly basis over the course of the 50 downscaled years, and compared the ranges of downscaled values to the ranges of temperature and precipitation values reconstructed using the palynological data and vertebrate remains. Reconstruction of local temperature and precipitation values (annual mean temperature, mean temperature of the coldest month, mean temperature of the warmest month, mean annual precipitation, precipitation in January, precipitation in July) were obtained from pollen data reported in an in- dependent study using inverse vegetation modelling for 14 sites located in Europe  (Wu et al.
Ranges of temperature and precipitation values (minimum, mean and maximum) for each zonobiome were estimated by mapping the modern distribution of zonobiomes in the Northern Hemisphere and compiling present-day temperature and precipitation data from the CRU data (see  Burke et al. , 2014). The zonobiomes (calculated using BCI) were then used to predict the climate ranges for each test location. Note that the range of values for each zonobiome corresponds to the minimum and maximum values for the zonobiome over western Europe (see  Burke et al. , 2014). It is therefore not specific to a given location and encompasses a large range of values. The intervals generated by these two different climate reconstruction methods, therefore, are not equivalent. Nevertheless, they provide useful references for evaluating the values generated by the SDM.
, 2013) , comparing GCM projections interpolated at fine scale with the present-day CRU data shows that the GCM overestimates precipitation over the south of Europe in winter and underestimates them over the north in summer (Fig. S13 ), which results in the non-linear spline for precipitation (Fig. 4 ). Coarse-grain GCMs are known to perform poorly when simulating the small-scale physical processes that drive local surface variables such as precipitation  (Wood et al. , 2004). This explains the discrepancies between the present-day simulations and the CRU data and, by extension, explains the adjustments performed by the SDM. The precipitation ranges for the 19 sites for which the BCIs were computed are in accordance with the precipitation ranges reconstructed for the LGM. Simulated precipitation ranges for all sites fall within the reconstructed ranges corresponding to the BCIs, as shown by the overlap of the horizontal error bars (in red) with the diagonal (Fig.
Validation of present-day data The comparison of average downscaled (based on kriging interpolation) and observed (CRU) monthly temperature and daily precipitation for the 1950-1960 period shows that they are in good agreement (Figs. S16, S17). For temperature, the main difference occurs in the far north of the study area (southern point of the Scandinavian peninsula) and on the Italian side of the Alps. The downscaled temperature is similar on both sides of the Alps, whereas there is some differ-ence in the CRU data (Fig. S16 ), suggesting the inclusion of orographic wind as a predictor may improve the SDM for specific areas. For precipitation, the main difference occurs in areas of high precipitation, especially the western coast of Great Britain (Fig. S17 ). Nonetheless, these areas were the areas with higher levels of precipitation for both datasets.
The overall patterns of variability were overall similar between the downscaled and CRU datasets (Figs. S18, S19 ), with nonetheless some local differences. Temperature variability was higher in the north-east region of the study area and lower in the south-west region, especially in winter (Fig. S18 ), and the range of values for the standard deviation was very similar for the different seasons. Downscaling tended to slightly overestimate temperature variability in the north of the study area and underestimate it in the north-east compared to the CRU data. Some small-scale differences are nonetheless difficult to interpret, since the CRU data showed some spatial artefact, for example, in the centre of the Iberian Peninsula. The amplitude of the variability values was more Reconstructed precipitation Simulated precipitation -bilinear q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q (a) Reconstructed precipitation Simulated precipitation -bicubic q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q (b) Reconstructed precipitation Simulated precipitation -kriging q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q (c) different for precipitation, with the variability observed in the CRU data being higher (Fig.
, 2014 (Burke et al. , , 2017)). Downscaled time series for a present-day period  (1950) (1951) (1952) (1953) (1954) (1955) (1956) (1957) (1958) (1959) (1960)  falling outside of the calibration period  were in good agreement with an independent time series for both averaged values and measures of variability. Our study, therefore, demonstrates that the SDM, originally designed to downscale climatology data (averaged over several decades), can be applied to a time series, thus allowing us to compute spatiotemporal patterns at a fine scale appropriate for studying the spatial dynamics of prehistoric human populations. SDMs must be carefully parameterised, however, including selecting the appropriate size of the area used for calibration. Overall, the downscaled temperature and precipitation values produced by the SDM are in agreement with the climate reconstructions obtained from vertebrate remains and palynological data, with few exceptions  (Figs.
6, 8). The SDM results for the LGM differ from the interpolated data in the north-east of the study area, reflecting the adjustments made in the GAM to counter biases inherent in the IPSL-CM5A-LR GCM used in this study. These discrepancies have little consequence in the present study, since this region was covered by ice sheets during the LGM. It was included for calibration because it represented present-day climate conditions that were close to those of the southern part of the study area during the LGM. This region was also included in the downscaling to illustrate the fact that, since it was colder during the LGM than any present-day region of the study area, results for this region should be interpreted with caution. The choice of interpolation technique used in the SDM also proved critical as it has a strong impact on the distribution of climate variability.
The IPSL-CM5A-LR GCM is known to predict lower temperatures than the values observed at high latitudes in winter  (Dufresne et al. , 2013). This bias was indeed observed when comparing the interpolated temperature with the CRU present-day data. As a result, the spline for temperature has a shallow slope at low temperatures (Fig. 2 ). The resulting correction applied by the GAM was emphasised for the LGM data generated by the GCM in winter in the north of Europe (Fig. S9 ), which lie outside of the range of present-day temperature and therefore relied on a linear interpolation of the spline. The large differences in temperature are therefore likely to be a combination of an underestimation of temperature by the GCM and an overcorrection of the very low temperature by the SDM. The spatial domain used to calibrate the GAM is larger than the domain of interest, namely west-ern Europe south of the ice sheets (the region occupied by human populations during the LGM) for reasons discussed above.