dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of Forest Cover Study-specific definitions of forest cover should be kept in mind when interpreting and comparing the results from different quantitative approaches. REVEALS-based estimates of forest cover correspond to the overall regional tree abundance expressed in relative percentage cover. Age and size of trees are not assumed to be critical factors influencing the results, although they can have an influence on pollen productivity estimates and pollen dispersal and deposition  (Jackson, 1994; Matthias et al. , 2012; Baker et al. , 2016). Analog-based approaches draw their definition of forest from their respective land cover data sets. Hansen et al. (2013)  use a definition comparable to  DeFries et al. (2000) , describing forest-cover values per pixel as canopy closure percentages for all vegetation taller than 5 m.
Significantly,  DeFries et al. (2000)  point out that a satellite-based mapping approach might identify shrubs as herbaceous vegetation if they are low to the ground, and as woody vegetation if they are taller. In an attempt to address this potential issue, we combined global vegetation height data  (Simard et al. , 2011)  with the CORINE data set  (Büttner et al. , 2014)  in order to detect areas occupied by forests but with a dominant tree height lower than 5 m. Modern samples with a median tree height = 0 m within the area covered by 1σ of the distance-weight function (i. e. , the area surrounding the samples that contributes the most to tree cover quantification) were excluded from the analog pool. This filter was applied only to areas covered by the CORINE data set and only to lacustrine samples.
Quality Filtering The available EMPD metadata were used extensively in conjunction with additional land cover data sets in order to identify pairing inaccuracies between pollen samples and the related vegetation data (e. g. , due to location or chronology errors, see section "Sources of Uncertainty") through multiple lines of evidence. After duplicate removal, samples with estimated geolocation errors higher than 100 m were excluded from further elaborations. An additional reliability check was performed by comparing the elevation of each sample as recorded in the EMPD with the elevation for the same latitude and longitude extracted from a high-resolution Digital Elevation Model. Control elevation values were obtained from Mapzen's Elevation Service 2  , accessed through the R package "elevatr"  (Hollister and Shah, 2017).
assemblages. The vegetation parameters of a modern pollen sample can therefore be transferred to any fossil sample sharing a similar palynological composition. In order to define how different two samples are, the floristic assemblages of both the fossil and the training sample are reduced to a single coefficient of dissimilarity. Squared-chord distance was selected as the dissimilarity index of choice because of its ability to differentiate between vegetation types  (Overpeck et al. , 1985; Gavin et al. , 2003). Analog computations were performed using the R package 'rioja'  (Juggins, 2016). The optimal maximum number of analogs, k = 8, was determined via leave-one-out (LOO) cross-validation. Reconstructed forest-cover percentages were then recalculated as the weighted average of the forestcover values of the best analogs whose chord distance did not exceed a threshold T.
The value of T is not straightforward to define since it is data dependent  (Davis et al. , 2015). We opted for a value of T = 0. 3, as it has proved sufficient to discriminate between major vegetation assemblages in Europe  (Huntley, 1990). The fossil dataset was divided into 49 timeslices ranging from 12,000 to 0 calibrated years BP (years before AD 1950; hereafter BP). Each timeslice covers a 250-year window with the exception of the most recent one, which is asymmetric as it cannot project into the future, and therefore covers an interval of 185 years centered on 0 BP.
Mapping Procedure The reconstructed forest covers from each pollen record (i. e. , point estimates) within each timeslice have been interpolated onto a uniform spatial and temporal grid using a fourdimensional (longitude, latitude, elevation, and time) Thin Plate Spline algorithm (TPS) fitted to a Digital Elevation Model (five arc-minutes resolution) using the R package 'fields'  (Furrer et al. , 2013). A three-dimensional TPS (latitude, longitude, elevation) was applied to the 0 BP timeslice in order to limit skewed results due to its unbalanced sample distribution around the 0 BP mark. This follows the same procedure used in mapping Holocene climate and vegetation used by  Davis et al. (2003) ,  Collins et al. (2012), and Mauri et al. (2015). The maps of forest cover also include changes in European coastlines and ice sheets throughout the Holocene following  Mauri et al.
Performance of the Transfer Function and Interpolation Using Modern Data The ability of MAT to reconstruct forest cover for the present time was first assessed using a two-fold cross-validation exercise. The modern pollen dataset was randomly split into two separate sets of equal size, subsequently using one as a training dataset to reconstruct the forest cover of the other and vice-versa. The whole process was repeated 999 times. The resulting r 2 , RMSEP, plus a simple reading of the residuals, were used to assess the basic performance of the method. The RMSEP has the same unit as the environmental variable, and is therefore expressed as percentages of forest cover. An h-block cross-validation test  (Telford and Birks, 2009 ) was not performed. With the h-block test, all samples within a distance h of a test sample are omitted from analog selection.
Different approaches toward forest-cover quantification might play a notable role too, i. e. , sum of all forest-forming taxa, regardless of their habitus (REVEALS) vs. tree-height detections limits of the underlying forest cover map (MAT). Undetectable trees lower than 5 m might then account for at least part of the difference between the MAT and REVEALS curves. Consistently, the only two no-analog situations (gaps in the MAT curves of Krageholmssjön and Raigstavere) are recorded at around 8,000 BP, testifying further to limits in the training data set concerning the reconstruction of early Mid-Holocene forests. The occurrence of no-analog situations in our reconstructions is addressed in section "Quantification of No-analog Occurrences. " The agreement between MAT and REVEALS curves improves in the Late Holocene, when forest-cover decline brings the REVEALS values below 80%.
The first, second, and third quartile were extracted within each window. Robust empirical quantiles for modeled vs. observed forest cover were estimated via Quantile Mapping (R package 'qmap,  ' Gudmundsson, 2016) , based on every second percentile between 1 and 100. A smooth spline regression curve was fitted to the resulting quantile-quantile plot (Figure  7 ) and used as a calibration curve for the modeled forest-cover values. The resampling strategy and regression parameters were tested via trial-and-error, opting for a solution that minimizes the bias for high forest-cover classes. The application of the bias correction curve to the two-fold crossvalidation iterations is visible in Figure  8 , where it shows a largely corrected median bias across all forest-cover classes. The correction of MAT values in the MAT-REVEALS comparison (Figure  4 ) shows how the calibration curve succeeds in closing the gaps between the two models during the Mid-Holocene (Supplementary Table  S4 ), while at the same time preserving the relative trend between timeslices.
g. , bad preservation, low volume). Applying a minimum pollen count (terrestrial taxa only) of 600 grains would lead to a >60% sample loss in both the training and the fossil databases, affecting the spatial coverage and the general variability of both data sets. In our reconstruction we used a smaller sub-set of taxa selected from the total terrestrial count, and therefore we chose a smaller threshold of 100 terrestrial pollen grains as the minimum count.
, 2015). This specific issue is at least partly mitigated by the PFT approach: B. nana pollen grains are assigned to the arctic-alpine PFT, while undifferentiated Betula pollen may be categorized as either boreal summer-green or arctic-alpine depending on the remaining floristic assemblage of a given sample  (Peyron et al. , 1998  and Supplementary Tables  S1-S3 ). Woody vegetation growth habit and size might be variously affected by environmental properties too, such as water availability and soil composition. These variables are not accounted for in the analog selection process, and can lead to associate fossil pollen spectra to incorrect modern vegetation parameters. An additional source of uncertainty stems from the definition of forest in the  Hansen et al. (2013)  forest-cover dataset, which specifies that "forest" has a minimum height of 5 m.
However, the LANDSAT sensors used to create the dataset do not detect tree height. Therefore, "forest" cover mapped in this dataset is subject to uncertainty and areas mapped as forest could in reality be something else, which would lead to irreconcilable differences between pollen spectra and tree cover. For example, forest could be mapped in the dataset when the vegetation cover is in fact rather a dense, tall shrubland, e. g. , in the arctic. While indistinguishable to a satellite-based sensor, an arctic shrubland might have a very different floristic composition than, e. g. , boreal forest, and this difference would be obvious in a pollen spectrum. This mismatch between observations from satellites and modern pollen samples would result in calibration uncertainty, where areas with similar forest cover have different pollen spectra, and vice versa.
Sample misplacement is another source for incorrect matches between pollen and satellite data. The high grid resolution of the remote sensing data and the small σ-value of the pixel-weighting equation result in a high sensitivity of the search window to shifts in its position. The EMPD provides reliability estimates for sample geolocation, which -combined with randomized location checks -have been used in the present work to exclude samples with known large uncertainties. This information is not available for the whole dataset, especially for older samples, due to gaps in the documentation or specific limitations (e. g. , lack of GPS logging devices during fieldwork operations). Surface samples with unknown location reliability amount to >50% of the total. Despite this lack of metadata, they were included in the elaboration in order not to depopulate the analog pool.
An additional issue revolves around the availability of remotesensing data. While satellite-based forest-cover data are available only for selected time periods, i. e. , the year 2000 for  Hansen et al. (2013)  and years 1992-1993 in the case of  DeFries et al. (2000) , during the past decades European forest cover experienced a general increase, with localized areas of forest loss  (Lambrechts et al. , 2009; Potapov et al. , 2015). For comparison, the EMPD samples with known age were collected mostly between the years 1978 and 2010. As a consequence, pollen spectra might be paired with non-contemporary vegetation patterns. The relevance of this issue can be proportional to the age difference between the two data sets, as a wider gap would simply leave more time for landscape transformations to take place.
Nonetheless, chronology-based filters were explicitly avoided, since wide chronological differences do not necessarily imply significant vegetation changes. All of these examples are meant to highlight how surfacepollen datasets would benefit from both enhanced sample density and metadata quality, ideally not only covering a wider range of vegetation assemblages, but also containing redundancy that would allow for stricter and task-oriented selection of samples. Furthermore, improvements in remotely sensed land-cover data would be desirable, e. g. , using new sensors that directly integrate the spectral and physical characteristics of the vegetation into a single dataset. It is important to remember that good transfer function performance may equally be dependent on having a reliable calibration dataset as on having good pollen data.
g. ,  Perego et al. , 2011) , and possibly also contributed to early extinction events  (Wick and Möhl, 2006). A combination of these and other factors led to the rise and decline of plant communities that potentially have no close parallels in contemporary vegetation. Changes in pollen productivity within the same species represent an issue that is difficult to address and common to all pollen-based models. On the other hand, the sensitivity of the MAT to migration lags, extinction events, and gaps in the training data set can be lessened by avoiding a taxa-based approach. The use of PFT scores, as used in the present study, groups pollen taxa in broader classes defined by physical, phenological and climatic factors. The absence of specific taxa from an area does not hinder the performance of a PFT-based reconstruction as long as their potential bioclimatic space is occupied by species with comparable traits.
, 2005) , and to the dieback identified by  Heiri et al. (2006). Major dieback events are described by  Heiri et al. (2006)  also between ∼9,500-9,300 BP and ∼6,500-6,000 BP, both matching mild -but not necessarily significant -elevation drops of the 50% threshold in our reconstruction. The prominent dieback described by  Heiri et al. (2006)  at 8,000 BP does not appear to be resolved in Figure  11 , probably because of the temporal resolution of samples in the underlying pollen dataset. An overall regional change is anyway visible in Figure  10 , where alpine forestcover values drop noticeably after ∼8,250 BP. Between 9,750 and ∼7,000 BP the 50% threshold fluctuates mostly between 2,400 and 2,600 m. The lower value is in good agreement with the timberline elevation proposed by  Tinner and Theurillat (2003)  for the Swiss Central Alps, based on macrofossil analysis (Figure  11 ).
CONCLUSION In this study we applied the MAT to generate a continuous, continental-scale reconstruction of European forest-cover spanning the last 12,000 years. Our reconstruction follows the general methodology published by  Tarasov et al. (2007)  and  Williams et al. (2011). When compared with these studies, our work presents a series of improvements that extend the reliability and spatial/temporal coverage of the reconstruction. An increased number of samples in the modern database allows for stricter quality control and provides a better representation of different vegetation types. A larger fossil database allows for higher spatial density and better dating control. The satellite-based forest-cover data set used in previous studies  (DeFries et al. , 2000)  was replaced with a higher resolution map  (Hansen et al.
forestcover reconstructions based on the MAT (this study) and on the LRA were compared for selected sites  (Marquer et al. , 2014)  in the circum-Baltic area. Both methodologies present widely comparable trends. Notable divergences are visible especially during the Mid-Holocene, with REVEALS consistently producing higher values. The limits of the MAT concerning the reconstruction of high forest covers can be ascribed to limitations within both the available training data set and the method itself. A visible role is played by the scarce representation of densely forested areas in the EMPD, an issue furtherly exacerbated by the averaging nature of the MAT. Furthermore, the MAT does not have any intrinsic capability to correct for differential pollen productivity, or identify samples largely affected by long-distance pollen transport.
A region-by-region comparison is presented in Figure 10, together with the full extent of the fossil-database reconstructions. The synthesis in Figure 10 highlights how a combination of MAT and interpolation succeeds in producing forest-cover values that are within the range derived from Hansen et al. (2013), while AP values regularly exceed it. Non-overlapping values are produced only for the British Isles, presumably due to the relatively poor spatial distribution of modern samples available for this specific region (Supplementary FigureS2).
Flow cytometry Retinal cell suspension was fixed in 4% PFA and subjected to indirect immunolabeling using anti-GS (mouse, 1:500, MAB302, EMD Millipore, Billerica, MA, USA) or anti-Rhodopsin (mouse, 1:1000, MAB5336, EMD Millipore, Billerica, MA, USA) and fluorochrome labeled secondary antibodies (AlexaFluor 488 conjugated secondary antibodies raised in donkey, 1:500, Life Technologies, Carlsbad, CA, USA) in 10% NDS/PBS containing 0. 1% Triton X-100 at 4˚C. Flow cytometry of immunolabeled cell suspension was performed using a BD™ LSRII Fortessa flow cytometer and FACS Diva Software (BD Biosciences, San Jose, CA). Retinal cell suspension from Prss56 Cre/+ ; R26 TdTomato/+ mice incubated with AlexaFluor 488-conjugated secondary antibody were used as negative controls to establish gating parameters.
Microscopy Bright-field images were captured using AxioVision software and an AxioImager M1 microscope equipped with an AxioCam ICc3 digital camera (Carl ZeissMicroscopy, LLC, Germany). Fluorescent images were acquired using AxioImager M1 microscope equipped with an MRm digital camera and AxioVision software, with an LSM700 confocal microscope and a Zen software (Carl Zeiss Microscopy, LLC, Germany). Amira software was used for 3D visualization and analysis.
Human ocular biometry We obtained illustrative biometric data from a nanophthalmic patient affected with a homozygous missense variant (p. G320R) in PRSS56 previously identified by one of the authors (ACO)  [38] , and also a representative normal volunteer for comparison. Approval for this study was obtained from the Research Ethics Board of the Nova Scotia Health Authority, Halifax, Nova Scotia, Canada. In brief, ocular dimensions measured via ultrasound biomicroscopy A-and Bscan techniques revealed a very small globe bilaterally featuring crystalline lenses that were normally positioned, but large in size relative to that of the eye. The choroid was also observed to be diffusely thickened.
Statistical analysis Statistical comparisons between control and mutant samples were performed by a two-tailed unpaired Student's t-test using Prism version 6. 0f software. p values of <0. 05 were considered significant. Power for a two-tailed two-sample t-tests was calculated using a range of means and standard deviation values of axial length that could be reasonably expected based on published data and our initial assessment  [26]. Although the difference between the two group means and within-group standard deviation are statistically independent parameters, in many biological data sets they show various levels of collinearity. Therefore, for small, medium and large μ1-μ 2 values, we based our power calculation on correspondingly increasing expectations of SD. The detection of differences with 80% power is possible in these scenarios with sample sizes 7.
This work was made possible in part, by  NIH-NEI   EY002162 -Core Grant for Vision Research , by the  Research to Prevent Blindness   Unrestricted Grant  ( UCSF, Ophthalmology ) and  William and Mary Greve   Special Scholar award (KSN), Knight Templar Eye Foundation Career Starter Award  (SP),  National Eye Institute  grants  EY022891  (KSN), EY011721 (SWMJ),  Barbara and Joseph Cohen Foundation (SWMJ). SWMJ is an investigator of  Howard Hughes Medical Institute. Funding support from  That Man May See Inc (KSN) ,  Research Evaluation and Allocation Committee (REAC)-Tidemann fund (KSN) ,  Marin Community Foundation-Kathlyn McPherson Masneri and Arno P. Masneri Fund (KSN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Material and methods In April 2016, veterinary practitioners in 26 French departments were contacted by electronic mail (https://fr. surveymonkey. com/). The geographic area of the survey covered a large part of southwestern France. The number of questions was limited in order to encourage participation. The veterinary practitioners were asked whether they knew the disease and, if so, whether they diagnosed cases of ocular thelaziosis in a dog during the last 12 months (from April 2015 and to April 2016) and how many in case of positive answer. Information about the zip code of the clinic was also collected for geo-referencing and map construction. Maps indicating the distribution of reported cases were generated using the software MapInfo version 10. 0 (Pitney Bowes). A grid surface map displaying data as continuous color gradations across the map was created.
Interactions were considered statistically significant if p < 0. 0015 (0. 05/ 33 tests). Example of interpretation: the HR of 1 SD difference in fruit fibre on incident T2D is 1. 03 in those who have the highest genetic predisposition for T2D and 1. 01 for those with lower genetic predisposition for T2D. There was no statistically significant difference between those with different genetic predispositions for T2D. Black circles, high GRS; white circles, low GRS. MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid European Prospective Investigation into Cancer and Nutrition (EPIC) study, described previously  [2]   1 ). After confirming no obvious between-imputation variation across 20 multiple imputation datasets, a single imputation was used for analyses because of computational efficiency (ESM Fig.
Acknowledgements We thank all participants and staff who contributed to the study within their respective EPIC centres. We thank  N. Kerrison  of the  Medical Research Council (MRC) Epidemiology Unit, Cambridge, UK  for managing the data for the InterAct project. We thank staff from the  Technical ,  Field Epidemiology  and Data Functional Group Teams of the  MRC Epidemiology Unit in Cambridge  for carrying out sample preparation, DNA provision and quality control, genotyping and data-handling work. S. X. Li  thanks the  Commonwealth Scholarship Commission  and the  Cambridge Trust  for supporting her PhD studies. Data availability Researchers seeking the analysis dataset for this work can submit a data request to the  EPIC-InterAct  study central contact point by emailing interact@mrc-epid.
Logistic regression data are presented as odds ratios (ORs) and respective 95% confidence intervals (CIs). All analyses were performed using SAS 9. 2 software (SAS Institute, Cary, NC, USA). The two-tailed significance level was set at P < 0. 05. Left ventricular ejection fraction was similar between the two groups (51% ± 10% vs. 52% ± 7%; P = 0. 45) (Table  1 ). Among the 137 patients with BNP, PIIINP and Gal-3 predict diastolic dysfunction in MI DD at baseline, 36 (26%) normalized diastolic function at 1 year post-MI and 101 (74%) remained with DD. Compared with patients that remained with DD at 1 year, those who improved their diastolic function were younger (53 ± 11 vs. 62 ± 12 years; P = 0. 0002) and had higher eGFR (88 ± 21 vs. 78 ± 19 mL/min/ 1. 73 m 2 ; P = 0. 011) (Supporting Information, Table  S1 ).
Funding This study was supported by the  CHRU de Lille   PHRC 2005R/1901 ; the  Fondation de France ,  Paris ; the  ANR  ( ANR-15-CE17-0016-01 ) (main study) and the Lorraine region (collagen peptides, sST-2, and Gal-3 measurements),  E. U. FP7 HOMAGE (305507 ), and  E. U. FP7 FIBRO-TARGETS  (grant agreement number  FP7 # 602904 ) projects. J. F. , P. R. , N. G. , R. F. , Z. L. , and F. Z. are supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  second 'Investissements d'Avenir' programme  (reference:  ANR-15-RHU-0004 ).