dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Validation The ability of the final B-MBG GLSM model to estimate the prevalence of O. volvulus microfilarial infection prior to the inception of the OCP control interventions in the area covered by the programme was assessed, for comparability with published work, using validation criteria which have been applied in other mapping exercises using Bayesian inference to generate predictive distributions. [e. g. 64, 65]. A K-folds cross-validation exercise  [66]  was performed as previously described. In each cross-validation (CV) run, a single subsample was retained to use as the validation dataset whilst the other K -1 datasets were used for training the model. For each CV, posterior predictive distributions of microfilarial prevalence were generated at unobserved validation locations for comparison against the known values.
volvulus is routinely reported. ) Raster global population counts are readily available from the Gridded Population of the World (GPW) v. 3 data (http://sedac. ciesin. columbia. edu/data/collection/gpw-v3) from the Center for International Earth Science Information Network (CIESIN) within the Earth Institute at Columbia University  [69]. These data were downloaded for 1990 (the earliest available data) and projected backwards to 1975, on a per-pixel basis, using the relevant national medium-variant, inter-censal country population growth rates from the United Nations (UN), World Population Prospects, 2012 Revision (WPP2012) database  [70]. As microfilarial prevalence is often expressed in terms of the population aged 5 years and older, the proportion of the population aged 0-4 years in 1975, according to the WPP2012 database, was removed (see above).
The resulting raster was then re-projected and cropped to match the study area output maps (Fig  7A ). Total population counts in the estimated 1975 map were very close to the census estimates from the WPP2012 database (Table  4 ). The area was delineated into rural and urban areas using the gridded rural-urban extents map (GRUMP v. 1)  [71]. Pixels falling in urban extents were considered not-at-risk and were excluded from calculations of at-risk and infected totals, as these areas are not suitable for simuliid breeding (Fig  7B ). We considered estimates of infection for the total study area (as shown by the extent of the maps), and within the OCP control area (delineated by the boundary marked in Fig  1A).
Model Output: Map of Baseline Microfilarial Prevalence prior to OCP Interventions The mean of the predictive posterior microfilarial prevalence in the population aged 5 years and older is shown in Fig 3A. The mean O. volvulus prevalence in pixels within the boundaries of the OCP area was 45. 2%, ranging between 2. 3% and 90. 0%. The mean prevalence in map pixels outside the OCP control programme was 18. 2%, ranging between 0. 5% and 70. 1%. It should be noted that we do not have ground-truth data points in areas outside the OCP boundary and, therefore, estimates made for such areas are more speculative and rely on data from within the OCP boundaries and their relationship with environmental covariates. A surface of mean prevalence discretized into the endemicity levels defined in the Methods (see 'Onchocerciasis Prevalence Data' section above) is shown in Fig 3B.
Map Accuracy and Predictive Probability Maps The predictive posterior distributions are an inherent realisation of the statistical uncertainty surrounding the mean estimates. We translated the posterior distributions into operationally useful maps, depicting areas where the prevalence is below or above a certain threshold. The probabilities that microfilarial prevalence is below 35% (where disease severity is low), or exceeds 60% (where disease severity is high) are given in Fig 4A  and 4B , respectively. In both cases, the greatest levels of uncertainty were associated with areas where the posterior predictive mean of prevalence fell in the mesoendemic endemicity class, and in areas where we do not have ground-truth data points to inform the model, such as areas outside the OCP boundaries.
There is a band of high-probability of exceedance of the hyper-endemic threshold across the core area of the OCP, from Guinea, through Côte d'Ivoire, Ghana and Southern Burkina Faso, to Togo and into Benin. This band also extends into Nigeria, despite there being no input data points there; a country which was later included in the mass treatment programmes under APOC (see  Fig 2 and Fig 3 of [37]  by way of validation of our results). The average probability of endemicity class membership across all map pixels was 66% (Fig 4D ), with a range of 34% to 100%. This means that on average, 66% of the posterior realisations for any given map pixel were within the same endemicity class. The most uncertain endemicity class membership for individual map pixels appears to be in the core areas of the OCP, predominantly in mesoand hyper-endemic classified pixels.
Representativeness of the Data The OCP epidemiological dataset represents the most complete source of standardised parasitological survey data for onchocerciasis in West Africa. However, although extensive surveys were conducted (see Fig A in S1 File for a map of all locations surveyed during the OCP), only a small fraction of the total population was sampled (the subset of the OCP dataset which was selected as the most representative of pre-control prevalence consisted of approximately 145,000 persons skin snipped). One of the problems faced when using the OCP data for mapping purposes, was that the population and epidemiological surveys were not performed at random, introducing some bias in the data towards areas of higher endemicity. Original programme areas were delineated, for the purposes of enabling evaluation of the impact of vector control, using cartographic and entomological knowledge about the location of rivers, flow speeds and vector breeding sites.
In other mapping studies, pseudo-absence points have been introduced into the data in un-sampled areas which are suspected to be nonendemic, to help delineate risk-free areas. In this study we have avoided this, and instead allowed the limits to be defined by the environmental predictors and the spatial covariance function in the model. We found that the model estimated very low prevalence in the very dry arid areas north of the OCP boundaries, without recourse to including pseudo-absence points. There is a zone of southeastern Côte d'Ivoire in Figs  3  and 4  where the model predicts nonendemicity and hypoendemicity with patches of mesoendemicity, although it captures a hyperendemic hotspot around the lower Bandama river. The prediction for an area east of this location is consistent with a zone where there are very few S.
volvulus  [100]. A study of the distribution of onchocerciasis was conducted in 1987 in the Western Extension  [100] , partly conceived to overcome these limitations. After discarding population sizes greater than 800 inhabitants, the authors calculated a rural population of 4. 5 million persons and reported that, of these, 1. 5 million persons were infected. Using the Gridded Population of the World dataset, and satellite-derived GRUMP data as a clipping mask to remove urban extents, our model estimated that the rural population at risk within the Western Extension in 1975 would have been 4. 5 million, with 1. 9 million persons infected. The congruence between our study and that of  [100]  is excellent. Another study by De Sole et al. [101]  investigated the distribution and severity of onchocerciasis in Southern Benin, Togo and Ghana.
Directions for Future Work and Concluding Remarks The Disease Reference Group for Helminth Infections (DRG4) of the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR), recently identified that an essential pre-requisite for statistical and dynamical modelling to effectively aid and inform helminthiasis control programmes, is that of creating open access, high quality, epidemiology and control databases  [103]. It is the aim of other infectious disease mapping projects (e. g. Global Atlas of Helminth Infections, http://www. thiswormyworld. org; Malaria Atlas Project, http://www. map. ox. ac. uk; Global Atlas of Trachoma Prevalence, http:// www. trachomaatlas. org) to become an open-access information resource on the distribution of these infections, to which the community of public health workers can contribute their own data.
Version [52,53] Cover Type (UMD) usgs. gov/ 051 Yearly products/modis_ (MCD12Q1) products_table/ mcd12q1 UNESCO Vegetation type map 30 arc Africa Geographic WGS84 1968-1983 http://geonetwork. NA [54] (White's) seconds grid. unep. ch/ Vegetation Map geonetwork/srv/ of Africa en/metadata. show?uuid= cc2fbd96-ae4c- 4593-910f- c103b1b51299 Global 30 Arc- Altitude 2 30 arc Global Geographic WGS84 1996-Present http://webmap. NA [55,56] Second seconds ornl. gov/ Elevation wcsdown/dataset. (GTOPO30) jsp?ds_id=10003 Global Land Altitude 2 30 arc Global Geographic WGS84 NA http://www. ngdc. Version [57] One-km Base seconds noaa. gov/mgg/ 1. 0 Elevation topo/globe. html Project (GLOBE) (Continued).
Data analysis First criterion = reduction in mite counts The difference in the number of mites between treated and control animals was calculated. A percentage reduction in mite counts > 95% was considered to be the first criterion for treatment success. Efficacy was calculated for the treated dogs at each assessment Day according to the formulas given below: Percentage reduction ¼ 100 Â ðMc À MtÞ=Mc; where: Mc = Mean number of mites on all dogs in the control group (group 1) at a specific time point. Mt = Mean number of mites on all dogs in the NexGard Ò group (group 2).
Second, evaluation of causative relationships between environmental cues and physiological variables are required to assess drivers of behavior. Third, methods are required to accurately determine hibernation duration. We sought to overcome these three challenges by developing novel applications of both behavioral change point analysis, BCPA  [10] , to accurately estimate den entry and exit dates, and convergent cross-mapping (CCM)  [11] , to assess causation between given biotic and abiotic time-series variables. CCM works on the premise that the Pearson correlation (ρ) coefficient increases significantly with increasing length (L) of the period of association. We applied these methods to a unique longitudinal data set collected from 14 free-ranging Scandinavian brown bears (Ursus arctos) over a period of three years, to examine the interplay between ecological, behavioral, and physiological time-keeping mechanisms involved in the hibernation processes of free-ranging brown bears.
Data collection Fourteen bears (8 males, 6 females, 2-8 years old, 30-233 kg) were captured by darting from a helicopter from April to June 2010, 2011, and 2012  [14, 15]. The bears were fitted with collars, which included a global positioning system (GPS), dual-axis motion sensors to monitor activity, described in detail previously  [16] , very high frequency (VHF) transmitters, and a Global System for Mobile modem (Vectronic-aerospace, Berlin, Germany). GPS positions were recorded every 30 min. The offspring of marked females were followed from birth; otherwise, age was determined by counting the annuli of a cross-section of the premolar roots  [17]. We excluded six females that became pregnant during the study (also excluded from above total; these have been reported elsewhere  [16] ). All implants were sterilized with ethylene oxide gas (Anaprolene AN74i 60 L, Andersen Europe, Kortrijk, Belgium).
We programed temperature loggers (DST Centi, Star Oddi, Gardabaer, Iceland, 46 x15 mm; 19 g) to record body temperature (T b ) at intervals ranging from 1 to 30 min, depending on other ongoing studies. With a memory capacity of 175,000 temperatures, the data loggers could record T b every 3 min for up to 1 year  [18]. Each temperature logger was individually calibrated by the manufacturer for 41 set points over the range 5 °C to 45 °C with a guaranteed accuracy of ±0. 1 °C for the full temperature range one year post calibration. The equipment used for the calibration of the loggers, as stated on the calibration certificate from the manufacturer, is a Hart 7012 temperature bath and the reference measurements are conducted with a Hart 1504 thermometer and a Hart 5610-9 thermistor probe with combined absolute accuracy better than ±0.
The device determined heart rate variability (HRV) by calculating 5-min medians of ventricular intervals in milliseconds during sinus rhythm and computing the standard deviation of those medians over each 24-h period (SDANN: standard deviation of all the five-minutes NN interval means; the term "NN" was used in place of RR, to emphasize that the processed beats were "normal" beats, which means that extra systolic beats were not included). We obtained ambient temperature (T A ) and snow depth data for all of Sweden (620 weather stations) from the Swedish Meteorological and Hydrological Institute (SE-601 76 Norrköping, Sweden). This data were interpolated to a 1-km scale, which resulted in a daily map of T A and snow depth for the entire country. From these maps, we extracted the local temperature at each bear location.
Den entry and exit dates We estimated den entry and exit dates using BCPA on the GPS data. This method sweeps through changes in the magnitudes of animal movement speeds and changes in direction, to detect points of speed and directionality change  [10]. Because we were interested in the dates on which bears entered and exited the dens, we calculated mean daily location, which we used to estimate the velocities and changes in direction of the study animals at the daily scale. The method we used first computed the velocity (V) and changes in direction (Ψ) from the data and then decomposed these results into orthogonal components of persistence velocity V p (t) and directional change V t (t) defined as: V p T i ð Þ¼ V T i ð ÞCos Ψ T i ð Þ ð Þ V t T i ð Þ¼ V T i ð ÞSin Ψ T i ð Þ ð Þ where V p is the tendency and magnitude of a movement to persist in a given direction, and V t is the tendency of movement to head in a perpendicular direction in a given time interval.
Thus, the speed and turning velocities were comparable across individuals. We applied this method to each individual bear to identify the den entry and exit dates. We then overlaid the GPS position of the bear on this specifically identified date with the GPS location of the den to confirm if the bear was at the site. Some bears entered or exited the dens several times before the final entry or exit, as detected by the BCPA. After estimating the den entry and exit dates, we aggregated (using means) the activity, T b , and HR data sets at the daily scales, to compare to movement data and to infer simultaneous daily changes in these variables and their values on the days of den entry and exit. We constructed plots for each bear showing T b , HR, activity, displacement (change in GPS positions), and T A.
Generalized additive mixed models (GAMMs) We used GAMMs to identify the days of increases and decreases in T b and other variables. We modeled these changes for all recorded variables (T A , T b , HR, photoperiod, and activity) using GAMMs in the mgcv package in R  [21]. For the variables measured at the individual animal level for multiple years (T b , HR, HRV, and activity), we fitted the GAMMs with a spline of 'Day of the year/Julian day' as a fixed effect and 'animal-year ID' as a random effect. For variables measured at the daily level, but for multiple years (T A , snow depth, and photoperiod), we fitted the GAMMs with the spline of 'Julian day' as a fixed effect and 'year' as a random effect. We used GAMMs to fit trends to the variables, because all of the variables showed nonlinearity (increasing and decreasing at different times of the year).
We also explored the autocorrelation function (ACF) and partial autocorrelation function (PACF) for the variables to account for temporal autocorrelation and decided to use autocorrelation moving average correlation structures (cor-ARMA)  [21] , because these autoregressive (AR) models fit the data better (tested using ANOVAs). After fitting the models, we determined the periods in which the variables were significantly increasing or decreasing. We determined these periods by computing the first derivatives of the fitted trends (GAMMs above). We used a method of finite differences, where we calculated the values of the fitted trend at a grid of point over the entire data set. We then swept through the grid by one point and recalculated the values of the trend at the new locations. The differences between the two sets of values provided a slope of the trend at that particular point, and the trend was calculated at 365 points.
Relationship between physiology and the external environment To identify the predictors of changes in T b , HR, and activity, we again used GAMMs. We aggregated all of the variables measured at the individual animal level (T b , HR, and activity) to a mean daily value for that variable across individuals. We then merged all the variables (T A , T b , HR, snow depth, photoperiod, and activity) into a data set at a daily scale for multiple years. We then created three sets of GAMMs using snow depth, T A , and photoperiod as explanatory variables to predict changes in T b , HR, and activity and used year as a random effect. We again used the corARMA structure to account for temporal autocorrelation for both sets of models. To test for which ecological variable drove the changes in physiological variables during entry and exit, we split the year into two halves and ran the same GAMMs, as described above.
Discussion During the den entry period, T b and activity level appeared to be influenced by environmental factors, such as T A and snow depth. T A declined before activity levels and both parameters preceded the decline in T b and the first snow event (HR and the SDANN both declined later than these variables). The den emergence process began with increases in T A , T b , HR, SDANN, and activity level, in that order, apparently independently of snow depth. The observation that SDANN increased only 13 days after the increase in HR (i. e. a gradual shift in autonomous nervous system balance) suggested that the (See figure on previous page. ) Fig. 2  The relationship between physiological parameters of brown bears in Sweden and the dates of den entry and exit indicated by time zero. The data have been aligned to the date of den entry and exit (blue vertical line) to determine the sequence of physiological a: body temperature; b: Activity; c: heart rate; d: SDANN, and environmental e: Ambient temperature; f: Snow levels events.
These results highlight some of the differences between the bear and small hibernators, reinforcing the importance of not generalizing results from small hibernators to bears. This work is an example of how different types of datasets can be combined to provide coherent ecophysiological timeseries with potential applications for other ecophysiological and adaptation studies beyond hibernation. Other time-series datasets that could be analyzed in a similar way include phenological and reproduction data on different organisms that are commercially important (crops) or used as indicator species for habitat/ ecosystem quality measures (e. g. birds, butterflies). The results from such analyses would provide management strategies and production optimization, while minimizing ecosystem-level impacts.
Acknowledgements The Scandinavian Brown Bear Research Project is funded primarily by the  Norwegian Environmental Agency , the  Swedish Environmental Protection Agency , and the  Research Council of Norway. The  French Space Agency and the Idex H2E Projex of the University of Strasbourg France  supported this experiment. The research leading to these results has received funding from the  Polish-Norwegian Research Program  operated by the  National Center for Research and Development  under the Norwegian Financial Mechanism 2009-2014 in the frame of Project Contract No  POL-NOR/198352/85/2013. We also acknowledge the support of the  Center for Advanced Study in Oslo, Norway , that funded and hosted our research project " Climate effects on harvested large mammal populations " during the academic year of 2015-2016.
truncatum from the Counties of Somerset, Dorset, Gwent and Powys; M. bilis from Bedfordshire, Cambridgeshire, Essex, Hertfordshire and Suffolk). For continental samples DNA sequencing was performed on all samples. A general linear model (GLM), with an associated binary error distribution, was used to compare the parasite prevalence (the infection status of an individual regardless of the number of parasites present; infected = 1, uninfected = 0) between European regions where the sample size was equal to or larger than 10. To investigate intensity (the number of parasites infecting each individual, excluding those without infection) differences across host populations in Europe, a GLM (with a negative binomial error distribution) was fitted to the intensity data for regions where sample size of infected hosts was greater than or equal to 4.
DNA analysis The internal transcribed spacer II (ITS2 P. truncatum: GenBank Accession number: JF710315; M. albidus (synonymous with M. bilis): GenBank Accession number: JF710316) of the ribosomal DNA region was used for species discrimination because it has been shown to be relatively conserved in the Digenea and has been used previously for interspecific analyses  [12, 14, [19] [20]  while fragments of the mtDNA COX1 and COX3 genes were used to examine genetic variation of P. truncatum and M. bilis populations across Europe. DNA was extracted from whole individuals. The tissues were stored in 90% ethanol, which was evaporated by gentle heating (55 C) prior to digestion of the sample. Whole individual trematodes were digested for 3 h at 55 °C in 15 μl TE buffer containing 0. 45% Tween 20 and 2 μg Proteinase K, followed by 10 min at 95 °C to denature the proteinase K [adapted from 21].
2  [24]. One million Markov Chain Monte Carlo (mcmc) generations were carried out with the initial 25% discarded as burn in. Convergence was assessed through effective sample size values and correlation plots. MrModeltest version 2  [25]  was used to estimate the adequate model of sequence evolution of these datasets. For both loci in each species the inferred model of evolution was Hasegawa, Kishino and Yano. We used a gamma-shaped rate variation with a proportion of invariable sites (Invgamma). The human liver fluke Clonorchis sinensis (Trematoda: Opisthorchiidae) was used as an outgroup for both datasets. To complement the phylogenies, and to further explore mtDNA structure, we also constructed haplotype.
Unique COX1 and COX3 haplotypes were identified as follows, and have been assigned GenBank Accession numbers KP869069-KP869078, KP869080-KP869096 (Supplementary information 2): seven P. truncatum COX1 (unique haplotypes from the Czech Republic, France, Germany, Poland, Sweden, 1 unique haplotype from England and Wales, and 1 haplotype common to England and Wales, Denmark and Sweden); eight P. truncatum COX3 (unique haplotypes from the Czech Republic, Denmark, England and Wales, Poland, Sweden, two unique sequences from Germany, and one haplotype common to Denmark, England and Wales, Germany and Sweden); nine M. bilis COX1 (unique haplotypes from the Czech Republic, Denmark, England and Wales, and Germany, three unique haplotypes from France, one haplotype common to Denmark and England and Wales, and one haplotype common to Denmark and Sweden); three M.