dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure. 1. Scheme of the choice device. Pods of each region and population were placed alternately, at an equal distance from the centre of the dish. In this example, the -Brit. 1‖ box represents a pod from population 1 of Brittany. At the beginning of an experiment, weevils were placed at the centre of the dish, near the water supply (bold black circle). Since these experiments were performed at different periods of the reproductive season, the weevil oviposition activity and gorse pod characteristics may have differed between experiments. Analyses were thus performed separately within each choice experiment without pooling the whole dataset.
Acknowledgements This work was supported by a  Centre National de la Recherche Scientifique   ATIP-grant. The authors thank  Stéphane Pélletier ,  Nicolas Chazot  and  Elissa Agudo Del Pozo  for technical assistance,  Alan Scaife  for English improvement, and  Joan van Baaren ,  Sébastien Dugravot ,  Philippe Louâpre , and  Véronique Martel  for their helpful comments on the manuscript.
PROCEDURE Schools in low socio-economic status (SES) areas from the Canning and Swan School districts in Perth, Western Australia, were randomly selected from the WA Department of Education and Training School Database. These schools were among the largest (top 50%) and poorest (bottom 30%) in the database. A control school was matched for each intervention school in terms of district, SES, class size, and school size. Informed consent was obtained from schools, parents and participants. Twenty-four schools were approached and 22 agreed to participate. Active and passive consent was sought from children and parents after the study was explained to them via consent and information forms, obtained after the first information session. To increase participation rates, additional consent and information forms were sent to non-respondents 1 week after seeking passive consent.
There exists great disparity in the number of clinical P. falciparum episodes among children of the same age and living in similar conditions. The epidemiological determinants of such disparity are unclear. We used a data-mining approach to explore a nineteen-year longitudinal malaria cohort study dataset from Senegal and identify variables associated with increased risk of malaria episodes. These were then verified using classical statistics and replicated in a second cohort. In addition to age, we identified a novel high-risk group of children in whom the history of P. falciparum clinical episodes greatly increased risk of further episodes. Age and a high number of previous falciparum clinical episodes not only play major roles in explaining the risk of P. falciparum episodes but also are risk factors for different groups of people.
falciparum episodes  [14] : the number of models to test increases exponentially with the number of variables with a risk of over-fitting for the more complex models. Different data mining approaches have been developed to overcome this problem of dimensionality. We have recently evaluated the utility of the HyperCubeH ''reverse engineering'' program for exploring large and complex epidemiological data sets in a hypothesis free manner  [15]. This method implements an exhaustive, non-Euclidean, non-parametric approach, and thereby enables detection of different combinations of explanatory variables (referred to as ''rules'') affecting occurrence of an outcome (dependent variable), in this case clinical malaria episodes. The aim of this approach is to characterize high risk groups within a study population based on local over-densities of clinical malaria episodes, and to identify risk factors.
Characteristics of the Cohort We studied the large dataset from the long-term epidemiological study of a family-based cohort followed in Dielmo village for 19  years (1990-2008). For each participant, the dependent variable was defined as a dichotomous trait ''experiencing no P. falciparum clinical episodes (PFA) during the trimester'' or ''having at least one PFA during the trimester''. Thirty-six explanatory variables for association with the occurrence of PFA were considered (Tables  1  & 2 ). These variables defined the status of an individual, such as sex, age, genetic traits, his history (such as number of previous P. falciparum clinical episodes), geographical and temporal information (such as distance to wells and drug treatment period). There were 4,357 person-trimesters with at least one PFA out of a total of 23,832 person-trimesters from the 726 participating individuals.
HyperCubeH Data Mining Algorithm The HyperCubeH technology is accessible as a web based software that requires no specific learning skills, though it requires a significant computing power provided through a SaaS architecture (Bussiness EffiScience, Paris, France). The program exhaustively searches for local over-densities of a class of the dependent variable in the n-dimensional space of the explanatory variables using a non-Euclidean and non-parametric approach. Each overdensity defined a hypercube, a subspace of the n-dimensional space, under two main constraints: the ''Lift'', the ratio of the prevalence of positive outcome events with at least one PFA/ trimester within a rule over the prevalence in the entire population, which is equivalent to the relative risk (RR), the ''Size'', the number of events included in the hypercube.
For each hypercube, three steps were performed: First, the program defined the minimal number of variables necessary to explain an overdensity satisfying constraints defined by the user; Second, its ''Size'' is maximized under the constraint of the ''Lift'' by using genetic algorithms; Third, statistical significance of each hypercube is tested by random permutation of the dependent variable. Each hypercube defines a rule, which is a combination of explanatory variables each of which is associated with either a range (for a continuous variable) or a modality (for a discrete variable). The program stops either when all the events of the learning dataset of the dependent variable have been explained (i. e. all the events are present in at least one rule) or when all the possible hypercubes have been studied or after a manual stop order.
Acknowledgments We are grateful to the villagers of Dielmo and Ndiop for active participation and continuing collaboration in the project. We particularly thank  Abdoulaye Badiane ,  Fatou Bintou Badji ,  Gaoussou Diakhaby , and  Marie Louise Senghor  as well as the field workers for their sustained contribution to the project and in generating and maintaining the malaria databases. We thank  Genevieve Milon  for helpful discussion,  Nicolas Levillain  and all other members of  Institute of Health & Science for management of HyperCubeH technology. We thank the two anonymous reviewers for their fruitful comments, which have greatly improved our manuscript.
Traces of OTA were observed in the three highest contaminated samples (F1, F2, F20) after extraction with ACN/water and purification on IAC. On the contrary, the samples where OTA was detected in large amount after IAC purification correspond to samples containing simultaneously significant amount of CIT (F10, F18, S2, S14). Three samples apparently positive for OTA after extraction with ACN/water and purification on IAC at pH 7. 4 (F19, S9, S16) contained in fact only CIT. This data suggests possible interferences between OTA and CIT for OTA antibodies.
Acknowledgments This study was supported by  National Research Agency of France. (ANR "mycodiag" 2010-2013 A. P. -L. ,  ANR  " Combitox "  2011-2014 ), Program CTP (2011-2012 A. P. -L. /J. -L. M. ),  Program county "Midi-Pyrénées" (Food Safety ,  2008-2012 A. P. -L ).
We also propose applications for broad-scale forest characterization and mapping in West-Central Africa over an area of 141 000 km 2. Eleven years of the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data were averaged over the wet and dry seasons to provide a data set of optimal radiometric quality at a spatial resolution of 250 m. Sample areas covered at a very-high (GeoEye) and high (SPOT-5) spatial resolution were used to identify forest types and to quantify the proportion of leaved trees in the canopy. The dry-season EVI was positively correlated with the proportion of leaved trees in the canopy. This relationship allowed the conversion of EVI into canopy deciduousness at the regional level. On this basis, ecologically important for-est types could be mapped, including young secondary, open Marantaceae, Gilbertiodendron dewevrei and swamp forests.
(1) We maintained an optimal spatial resolution of 250 m for the temporal data by aggregating eleven years of MODIS terra EVI data over the two main seasons. (2) We used high-(10 m resolution) and very-high-resolution (0. 5 m resolution) data to bridge the scale gap between the stand and MODIS pixel levels and thus reached a quantitative interpretation of the EVI variations in terms of the proportion of leaved trees during the dry season. (3) We distinguished and mapped several dense-forest types based on this quantitative phenological proxy.
We downloaded all available dates for this tile between January 2000 and December 2011. In spite of atmospheric corrections, the presence of almost permanent clouds and haze largely corrupted the data. To generate a good quality composite at a 250 m resolution, we aggregated eleven years of valid data (after filtering for pixel reliability and quality using layers provided with the product) for the EVI index and middle-infrared reflectance (MIR) for both the principal dry season (15 November-15 February, months with less than 100 mm rainfall, consistent with Aragao et al. (  2007 )) and wet season (15 February-15 November). This approach is an important simplification that neglects both inter-annual variations in dry-season length or severity and the existence of a shorter and less intense dry season centered on July.
However obtaining a coherent data set required finding a compromise between the spatial and the temporal resolution, and we chose here to sacrifice the latter (e. g. as opposed to  (Gond et al. , 2013; Bradley et al. , 2011). We thus obtained aggregated EVI and MIR images for the wet season and for the main dry season. We obtained 25 archive SPOT-5 ©CNES images at pretreatment level 1b (high resolution, HR) for the main dry season from the CNES-ISIS program. Two particularly cloudand haze-free dry-season images (lat 5. 14,long 12. 97 and lat 3. 51,long 16. 02) were chosen within this data set to calibrate quantitative relationships between leafiness and EVI variations. Each image covered an area close to 4000 km 2. These images had a pixel size of 10 m and were composed of four spectral bands: Green (0.
5-0. 59 µm), Red (0. 61-0. 68 µm), NIR band (0. 78-0. 89 µm) and MIR band (1. 58-1. 75 µm). Four very-high-resolution (VHR) main-dry-season images (GeoEye) were acquired within areas covered by the SPOT-5 image data set. Each image covers an area of the study area close to 100 km 2. The sensor spatial resolution of the multispectral bands (2 m) was improved using a Brovey Pansharpening algorithm  (Vrabel, 1996) , resulting in a final pixel size of 0. 5 m. Each image is composed of four spectral bands: Blue (0. 45-0. 51 µm), Green (0. 51-0. 58 µm), Red (0. 655-0. 69 µm) and NIR (0. 78-0. 92 µm). All images were orthorectified and referenced against the Landsat GeoCover mosaic  (Tucker et al. , 2004)  and SRTM topography  (Farr et al. , 2007)  products. Both HR and THR images were visually analyzed as color (R-G-B) composites of the Red-NIR-Green bands.
However, radiometric corrections were crucial to ensure the consistency of spectral responses within and between images. Large patches of haze, particularly noticeable in the Red band, were removed via a high-pass Fourier filter  (Niblack, 1986). Radiometric histograms were then aligned between the images by standardization around the mean, under the assumption that forest images had globally similar radiometric amplitude. To separate leaved vs. leafless pixels, threshold values in the Red and NIR bands were carefully selected for each SPOT-5 image, using the VHR GeoEye data as a visual reference. Binary masks were thus created and leaved SPOT-5 pixels were counted within areas corresponding to the MODIS 250 m pixels. We used model II linear regression to compare the proportion of leaved pixels on SPOT-5 images taken in the dry season against aggregated (2011 and 2000-2011 periods) dryseason EVI values.
It is possible however, that both water and light reach their least limiting levels during the main wet season. In any case, the multi-resolution approach presented here could be easily applied to different contexts, thereby enabling comparisons. The share of EVI variation that remained unexplained by leafiness values may have come from a number of different sources, including the imperfect temporal match between the data sources. HR and VHR images were opportunistic snapshots through the clouds, whereas a consistent coverage of MODIS data required aggregating data over long periods of time. Additionally, the radiometric (e. g. , atmospheric, Bidirectional Reflectance Distribution Function -BRDF) corrections performed over the different data sets were imperfect. For instance, the sun height is not accounted for in MODIS data filtering algorithms  (Vermote et al.
Acknowledgements. SPOT images were acquired in the framework of the  SPOT/Programme ISIS, Copyright CNES. This research is part of the pilot regional program (PPR) "Global changes biodiversity and health in the tropical wet forests of  Central Africa" of the IRD  and has benefited from the support of various partner institutions and researchers. We are especially grateful to  Bonaventure Sonké ,  Vincent Droissart , and  Mr. Pierre Ploton  for sharing their field experience and helping us to interpret our results. Edited by:  P. Stoy.
The geomagnetic daily variation at mid-to-low latitudes, referred to as the geomagnetic Sq field, is generated by electrical currents within the conducting layers of the ionosphere on the dayside of the Earth. It is enhanced in a narrow equatorial band, due to the equatorial electrojet. The upcoming ESA Swarm satellite mission, to be launched end of 2013, will consist of three satellites in low-Earth orbit, providing a dense spatial and temporal coverage of the ionospheric Sq field. A Satellite Constellation Application and Research Facility (SCARF) has been set up by a consortium of research institutions, aiming at producing various level-2 data products during the Swarm mission. The Dedicated Ionospheric Field Inversion (DIFI) chain is a SCARF algorithm calculating global, spherical harmonic models of the Sq field at quiet times.
doi:10. 5047/eps. 2013. 08. 006  2002, 2004 ). However, satellite data are collected above the ionosphere. They cannot separate the primary ionospheric field from the secondary, induced field generated by electrical currents within Earth's mantle. Such a separation is possible, however, if a pre-determined mantle conductivity model is available. The upcoming ESA Swarm satellite mission, to be launched in 2013, will provide measurements of the Earth's magnetic field with unprecedented precision from three identical satellites orbiting at different altitudes and local times  (Friis-Christensen et al. , 2006). Several research institutions have teamed up with ESA to form the Swarm Satellite Constellation Application and Research Facility (SCARF), a distributed processing facility that will produce geomagnetic field models during the Swarm mission  (Olsen et al.
Several failure cases have been investigated as part of the preparation phase within SCARF, for example the complete failure of one satellite or the lack of vector data from one of the satellites. If such an unhappy event were to occur, the DIFI algorithm would still be able to ingest reduced level 1b dataset. In what follows, we describe the nominal scenario where level 1b vector magnetic data are available from both Swarm A and C satellites. The first block of the DIFI algorithm consists in reading all level 1b Swarm magnetic field data available during the considered time interval, selecting data at magnetically quiet times, and correcting these data for non-ionospheric fields. This block is refered to as "Data pre-processing" in Fig. 1. Level 1b magnetic data are selected using standard geomagnetic indices: K p , D st and the interplanetary magnetic field (IMF) B y and B z components.
The minimum and maximum acceptable values for these parameters will be adjusted for each real dataset, in order to maximize spatial, local time and seasonal data coverage, while minimizing the overall magnitude of disturbance fields. Also, in order to minimize the size of the dataset to be inverted, level 1b data are decimated to one sample every 15 s prior to the inversion. The next step of the pre-processing block is data correction. It aims at removing non-ionospheric contributions from the total vector field recorded by each satellite. Three main sources are considered: the core, the lithosphere and the electrical currents in the magnetosphere. By default, SCARF dedicated  (Hamilton, 2013; Rother et al. , 2013; Thébault et al. , 2013)  and/or comprehensive  (Sabaka et al. , 2013)  models are used to remove contributions from these sources.
Test Results During the preparation phase, the DIFI algorithm was tested using synthetic data. The way the synthetic orbits and magnetic data were generated is described in  Olsen et al. (2013). We used only one year of synthetic data, from January 1, 2000 to December 31, 2000, and relied on real K p , D st , IMF and F 10. 7 values for the selected period. The synthetic data were selected using the following criteria: The data were further decimated so that the time difference between two successive data never fell below 15 s, which corresponds to a minimum distance of about 105 km (since Swarm satellites orbit at about 7 km/s). Removal of non-ionospheric contributions were implemented in different ways, depending on the tests carried out. During the so-called "AR1 test", data were corrected using the same reference field models as the ones used for generating the synthetic data (see  Olsen et al.
, 2013 , for details about these models). As the random noise added to the synthetic data had a very small standard deviation (between 0. 1 and 0. 7 nT, depending on the component) compared to the typical amplitude of ionospheric fields, the data after correction were very close to the synthetic ionospheric field data themselves. Thus, the AR1 test essentially amounted to a closed-loop simulation of both the pre-processing and inversion blocks. During the second, so-called "AR2 test", data were corrected using core, lithospheric and magnetospheric field models derived as part of the test by SCARF partners, thus imperfectly reproducing the contribution of each source to the synthetic data, as would be the case with real data. K p ≤ 2o, -20 nT ≤ D st ≤ 20 nT, -8 nT ≤ For both tests, we used the same Q matrix as the one used for generating the synthetic dataset (see  Olsen et al.
AR2 test For the AR2 test, data corrections relied on the dedicated core  (Rother et al. , 2013)  and lithospheric  (Thébault et al. , 2013)  field models, and the magnetospheric part of the comprehensive  (Sabaka et al. , 2013)  field model, all of them derived from the AR2 dataset. Data residuals statistics (see Table  2 ) show that using imperfect field models for data correction leads to significant residuals after the inversion. This is confirmed by the analysis of residuals maps; see for example Fig. 7 , which was selected for the same season and UT as Fig. 3  in order to facilitate comparison. Like for the AR1 test, we plotted maps of the field calculated from the DIFI-derived model and of the differences between that field and the reference field. Examples of such maps are shown in Figs.
Concluding Remarks In the present paper, we described the algorithm of the DIFI chain to be used during the upcoming Swarm mission to calculate global, spherical harmonic models of the quiet-time ionospheric field at mid-to-low latitudes. This algorithm relies on quasi-dipole coordinates to minimize the number of parameters, while describing the smallest spatial features of the average field such as the equatorial electrojet. It can take advantage of a 3D conductivity model of the mantle and oceans when separating the primary and induced fields. Closed-loop tests using synthetic data showed that the DIFI algorithm is able to reproduce the original primary and induced ionospheric fields to a very high accuracy, at ground and satellite altitudes. Tests relying on other SCARF models produced from the same dataset for data correction of the core, lithospheric and magnetospheric fields revealed that the pre-processing block of the algorithm is very sensitive to the quality of the correcting models.
Acknowledgments. The research reported here was financially supported by the  Centre National d'Etudes Spatiales (CNES)  through the " Travaux préparatoires et exploitation de la mission Swarm " project, and by the  European Space Agency (ESA)  through  ESTEC  contract  4000102140/10/NL/JA  " Development of the Swarm Level 2 Algorithms and Associated Level 2 Processing Facility ". The authors thank the reviewers for their very helpful comments. This is IPGP contribution number  3423.
Free list interviewer training was held at the end of July 2011, with data collection taking place the first week of August in three CPT villages (Lushebere, Bishange, and Luvungi). The goal was to interview 30 CPT participants (10 in each site). For the interviews, respondents were asked two questions: 1. What are all the changes you and your family have experienced in the last 6 months? 2. What are all the changes you and your family have experienced in the last 6 months because of the CPT program? These two questions were selected to elicit information on the unintended impact of the CPT. The interviewers were trained to probe for general changes as well as positive and negative changes. Each interview was attended by the respondent, the interviewer and a note-taker. The free list analysis was conducted by the interviewers and facilitated by JHU.
Quantitative Follow-up: Quantitative follow-up data were collected at two time points, within one month post-CPT completion and approximately six months later. The followup interview was conducted using the same instrument as was used at baseline, with the addition of the questions generated from the post-intervention qualitative assessment (described above). At each follow-up data collection, interviewers hired and trained by the IRC, working in pairs, spent one week in each village interviewing study women. The interviewers were blind to whether the village was a CPT or IS village.
Mental Health and Functionality Outcomes Intervention impact was calculated by comparing average scores for women in CPT and IS villages at each follow up. Analyses included all participants accounting for loss-to-follow up through a weighting process. Missing values for any specific symptom or function item were imputed based on mean values for other items in the scale. Random effects models (xtmixed and xtmelogit) were used for all impact analyses  (Laird & Ware, 1982). As the data were clustered within therapy groups, villages and randomization block, multiple random effects were evaluated. Models with and without the randomization block, compared using the Hausman test  (Hausman, 1978) , were not significantly different (p=. 99) so the three-level model was used which incorporated the individual study participant measured over 3 time points, the therapy group she participated in if in the CPT condition, and the village in which the participant lived.