dataesr/scientific-paragraphs-categorization huggingface dataset:
The effects of g i , and e i were assumed to be independent and normally distributed with mean = 0 and variances σ g 2 and σ e 2. The statistical software SOLAR v4. 3. 1  [32]  was used to estimate simultaneously the mean and variances, as well as the covariate and genetic effects for each trait. We assessed the significance of such effects with a likelihood-ratio test. [33, 34]  Finally, we estimated the heritability for each trait as the proportion of the total phenotypic variability attributable to additive genetic effects. For this particular estimation, we considered only environmental covariates (i. e. we performed the analysis without BMI or age of menopause for post-menopausal women). To study the genetic relationships between categorical and quantitative phenotypes, we used a modified variance component method for mixed discrete/continuous traits  [35]  incorporated in SOLAR.
Acknowledgments The authors gratefully acknowledge the participation of all the families who participated in the  GAO  Project. Without them, this work could never have been accomplished. The Project was supported by the  National Fund of Sanitary Investigations  ( FIS PI 11/01175  and  PI 14/01036 ). Georgios Athanasiadis was supported by the  Subprogama Nacional de Contratación e Incorporación de Investigadores Juan de la Cierva (Secretaría de Estado, Ministerio de Economía y Competitividad ).
1). This gradient also correlates with sand erosion and deposition dynamics and reflects natural levels of habitat disturbance  (Forey et al. 2008; Maun 2009 , see below for a quantitative treatment). We investigated the sand dune plant community by combining three lines of evidence: (i) a detailed ecological survey of sand dune habitats to assess species diversity, (ii) a genetic analysis of seven representative plant species, and (iii) a detailed ecological survey to assess the drivers of SGDCs (with respect to natural and anthropogenic habitat disturbance). These three datasets were initially collected at distinct spatial resolutions and were all averaged over the same 5-km-resolution grid prior to statistical analysis.
(2011)  and  Frey et al. (2012a)  using two to three primer pairs (Table  S2 ) per species [note that the AFLP dataset of H. eriophorum was from  Frey et al. (2012a) ]. The complete protocol (from DNA extraction to genotyping) was replicated for 15% of the specimens to assess the reproducibility of the results  (Bonin et al. 2004 ). The AFLP bands were scored using RawGeno, an R CRAN library  (Arrigo et al. 2009) , following recommendations made by  Arrigo et al. (2012). Regional genetic diversity was assessed using a 'moving window' approach  (Arrigo et al. 2010 ; scripts available upon request) considering the 5-km-resolution grid defined earlier. In each cell, diversity levels were estimated with the Shannon information content, a non-model-based estimator frequently applied to dominant markers, and with the rarity index, an estimator based on the frequency of rare alleles within populations  (Sch€ onswetter and Tribsch 2005; Bonin et al.
Statistical analyses: SGDCs and association with habitat disturbance We merged the SD, GD, natural, and anthropogenic disturbance estimates into a single matrix by considering shared 5-km grid cells among datasets (resulting in 19 shared grid cells [Fig. 2] ). The association between SD and GD was assessed using pairwise Pearson's correlations. First, we corrected diversity indexes for spatial autocorrelation, to improve the statistical independence of our 19 data points and enhance the accuracy of our statistical tests, by (i) regressing each vari-able (S, E, Sh, R) against the latitudinal coordinates of each grid cell and (ii) extracting residuals for measuring pairwise correlations among diversity estimates. Significance levels were then evaluated using 50 000 randomized permutations. The procedure was performed using custom R scripts (available upon request).
The respective effects of natural and anthropogenic disturbance on diversity levels were assessed using a correlation decomposition procedure (as in  Lamy et al. 2013, Data S1). Briefly, this analysis quantifies how the predictors of interest contribute to SGDCs, by introducing environment-driven and covarying responses in the SD and GD datasets. Tests of statistical significance were performed with a partial constrained redundancy analysis (as implemented in the vegan R CRAN package). This multivariate approach allows visualization, quantification, and testing of how habitat disturbance (i. e. , H, St and their interaction St 9 H) accounts for local variations in species and genetic diversity (S, E, Sh and R) while removing the effects of spatial autocorrelation by considering the latitudinal coordinates of each grid cell as a The analysis was performed on standardized data, and significance levels were assessed using 50 000 random permutations.
Results The ONF flora survey revealed the presence of 129 different plant species. Throughout the study area, diversity estimates varied from 2. 8 to 33. 7 species and 4. 7 to 13. 4 endemics per grid cell. The AFLP analysis produced a total of 555 polymorphic bands with an average of 80 bands per species and a median error rate of 4. 36% (Table  S2 ). Within all seven species, GD estimated by the Shannon information content and the rarity index varied markedly among grid cells (Fig. S2 ), with estimates of Sh ranging between 0. 06 and 0. 23 and estimates of R ranging between 0. 54 and 2. 93. The flora and AFLP surveys revealed a strong SGDC, with S, E, Sh, and R being significantly correlated with each other (Table  1 ). Accordingly, both datasets showed that the highest diversity levels were found in the southernmost and the northernmost parts of the study region.
Variable the regional scale. In contrast, a clear hotspot of anthropogenic disturbance (St) was detected in the upper half of the study area and corresponded to grid cells with decreased GD-SD levels. Accordingly, the correlation decomposition analysis revealed that habitat disturbance accounted for 33-64% of the quantified SGDCs (Table  1 ), with the best explanatory power occurring in SGDCs that involved the rarity diversity index. Also, anthropogenic disturbance appeared as the leading contributor to all SGDCs. Congruent results were obtained by the RDA analysis, where the overall habitat disturbance (i. e. , H, St and St 9 H) accounted for 34% of the variance shaping the joint GD-SD datasets (Table  2 ). Anthropogenic disturbance (St) accounted alone for 21% of the variance (Table  2 , highly significant) and was significantly associated with decreasing diversity levels (Fig.
Discussion In agreement with previous studies  (Vellend 2003 (Vellend , 2004;; Cleary et al. 2006; Papadopoulou et al. 2011; Struebig et al. 2011; Blum et al. 2012; Wei and Jiang 2012; Lamy et al. 2013) , our results show that a positive correlation between GD and SD can emerge; however, to our knowledge, this study is among the first to report a positive and significant correlation between SD and GD based on a multispecies approach at the regional scale. This correlation appears to be reasonably explained by varying levels of anthropogenic disturbance along the coastline, with more strongly disturbed areas characterized by reduced diversity and less disturbed sites characterized by greater diversity. However, only between 36% and 67% of the SGDCs (representing 21% of the variance present in the diversity dataset) could be traced back to a human impact.
Acknowledgements We thank  Y. Marbach  for his assistance during fieldwork and are grateful to the  French National Forestry Office (ONF, France)  for kindly sharing distributional data, to the  Conservatoire Botanique Nationale Sud-Atlantique (France) , to the  Natural History Museum Fribourg (Switzerland) , and to  B. Touzard  and  L. -F. Bersier  for their support. N. Arrigo  is funded by an  SNSF   Ambizione research grant  ( PZ00P3_148224 ).
MATERIALS AND METHODS CAGE libraries for human liver tissues. CAGE libraries for human liver tissues were prepared and sequenced as detailed in our previous study  (21). Raw data are available through the NCBI dbGaP database  (22)  under accession number phs000885. v1. p1 (controlled access). Briefly, liver tissues, including tumor and nontumor samples, were collected from patients resected for HCC (Table  1 ; see also Table  S1  [c18 http://gerg. gsc. riken. jp/JVI2016/SupplementaryMaterials. pdf]). CAGE libraries were prepared according to a published protocol  (12)  and sequenced with single-end reads of 50 bp on an Illumina HiSeq 2000 platform. The ethics evaluation committees of the INSERM (IRB00003888, FWA00005831) and RIKEN (H24-4) approved the use of human liver samples. All patients provided written informed consent.
5. 9  (23)  with default parameters on the MOIRAI pipeline  (24). The unmapped tags extracted from the mapping results were aligned to 16 representative HBV genomes downloaded from HBVdb  (25). Because the HBV genome is circular, each genome was tandemly repeated for mapping all genome sequences. After alignment of tag sequences with 16 representative HBV genomes, the HBV genotype in each sample was defined as the genome to which the highest tag counts were mapped. To unify the genomic positions of HBV, an HBV genome sequence (accession number GQ358158. 1 in GenBank) with genotype C and genome size: 3,215 bp was selected as a reference genome. The genomic positions for the other genomes were converted to those of the reference genome based on multiple alignments of HBV genomes. CAGE technology often adds an extra G base to the 5= end in the reverse transcription process  (26).
Northern blot analysis. Portions (20 g) of total RNA was denatured by formaldehyde, run on 1% agarose gels in 20 mM phosphate (pH 7. 0) and 1% formaldehyde, and blotted onto Hybond Nϩ in 20ϫ SSC (1ϫ SSC is 0. 15 M NaCl plus 0. 015 M sodium citrate). A DNA fragment covering the HBV genome was used as probe. DNA labeling and hybridization were performed using a DIG High Prime DNA labeling and detection starter kit II (Roche). RNA sizes were estimated according to a molecular weight ladder (RNA molecular weight marker I, DIG labeled; Sigma-Aldrich). Accession number(s). Supplementary materials are accessible at http: //gerg. gsc. riken. jp/JVI2016/SupplementaryMaterials. pdf. CAGE data for human HCC samples were released in the NCBI database of Genotypes and Phenotypes (dbGaP; http://www. ncbi.
nlm. nih. gov/gap/) under accession number phs000885. v1. p1. CAGE data for human blood samples were released in the NBDC human database (http://humandbs. biosciencedbc. jp/en/) under hum0050. CAGE data for HBV model systems were released in the Gene Expression Omnibus (GEO; http://www. ncbi. nlm. nih. gov/geo/) under accession number GSE84186.
The X promoter also shows a different pattern from the clinical condition. In addition to peaks 10 and 11, another broad peak was detected at around bp 1250, upstream of the 1st ATG, which can contribute to the full-length X protein (Fig. 6C ). This suggests that HBV might use different promoters for two sizes of X proteins in different conditions, although functional analysis such as reporter assays is essential for further understanding of the promoter usage. The preS/S promoter shows consistent pattern with the clinical peaks for genotype D with some variable signals at bp 7 (Fig. 6D ). Finally, we analyzed HBV RNA by Northern blotting in   two experimental models, including HepAD38 cells and mouse liver transduced with AAV-HBV to visualize major HBV transcripts. The data showing important differences in the relative levels of major transcripts (pgRNA and preS2/S RNA) between the two systems are in complete agreement with the CAGE data (Fig.
ACKNOWLEDGMENTS We thank  RIKEN GeNAS  for the sequencing of the CAGE libraries. This study was supported by the  Seventh Framework Programme  ( FP7)  under grant agreement  259743  (MODHEP consortium), as well as by a research grant from the  Japanese Ministry of Education, Culture, Sports, Science, and Technology (MEXT)  to the  RIKEN Center for Life Science Technologies. Y. T. , I. H. , M. S. , T. M. , A. T. , H. I. , S. N. , and H. S. were supported by  Research on the Innovative Development and the Practical Application of New Drugs for Hepatitis B  (principal investigator,  Soichi Kojima ;  H24 -B Drug Discovery-Hepatitis-General-003 ), provided by the  Ministry of Health, Labor, and Welfare of Japan  and the  Japan Agency for Medical Research and Development (AMED).
The location, timing, spatial extent, and frequency of wildfires are changing rapidly in many parts of the world, producing substantial impacts on ecosystems, people, and potentially climate. Paleofire records based on charcoal accumulation in sediments enable modern changes in biomass burning to be considered in their long-term context. Paleofire records also provide insights into the causes and impacts of past wildfires and emissions when analyzed in conjunction with other paleoenvironmental data and with fire models. Here we present new 1000-year and 22 000-year trends and gridded biomass burning reconstructions based on the Global Charcoal Database version 3 (GCDv3), which includes 736 charcoal records (57 more than in version 2). The new gridded reconstructions reveal the spatial patterns underlying the temporal trends in the data, allowing insights into likely controls on biomass burning at regional to global scales.
In the most recent few decades, biomass burning has sharply increased in both hemispheres but especially in the north, where charcoal fluxes are now higher than at any other time during the past 22 000 years. We also discuss methodological issues relevant to data-model comparisons and identify areas for future research. Spatially gridded versions of the global data set from GCDv3 are provided to facilitate comparison with and validation of global fire simulations.
Fire-history data from sediment records highlight the importance of fire as a force of longterm global environmental change. Syntheses of data in the Global Charcoal Database (GCD), for example, reveal important variations in biomass burning during the last glacial period  (Daniau et al. , 2010) , the last 21 000 years  (Power et al. , 2008; Daniau et al. , 2012) , and the last 2000 years  (Marlon et al. , 2008). With the increasing number of sites in the GCD, regional syntheses became possible, including longterm analyses of climate and human influences on burning in Australasia  (Mooney et al. , 2011; Williams et al. , 2015) , the Mediterranean  (Colombaroli et al. , 2009; Vanniere et al. , 2011) , the western USA  (Marlon et al. , 2012) , and the Americas more broadly  (Whitlock et al. , 2007; Power et al.
, 2012). Here we briefly review the history of biomass burning reconstructions based on charcoal data, and we introduce version 3 of the GCD (GCDv3, n = 736), which improves on GCDv1  (Power et al. , 2008)  and GCDv2  (Daniau et al. , 2012)  by adding 57 records. We also present the GCDv3 in a new globally gridded format along with several broad-scale syntheses created using the open-source paleofire R package  (Blarquez et al. , 2014). The new gridded maps illustrate the spatial and temporal variability in fire activity over the past 22 000 years, highlighting recent departures from the longterm trends. The maps should be useful for modelers as well as others in the Earth sciences, particularly given the wide-ranging impacts of fire. Finally, we review several important limitations to charcoal-based records and identify promising future directions for the field.
2 Reconstructing fire history with sediment-charcoal data Fire-history research based on sediment-charcoal data has advanced rapidly in recent decades. Early analyses of sedimentary charcoal were typically conducted to support studies focused primarily on reconstructing past vegetation changes  (Heusser, 1995; Fuller et al. , 1998; Haberle, 1998; Behling, 2001). A few early studies focused more directly on fire  (Swain, 1973; Burney, 1987; Delcourt et al. , 1998). In many cases, microscopic (< 100 µm) charcoal particles were tallied alongside pollen grains. Pollen and charcoal particles were converted to concentrations using the abundance of exotic markers of a known quantity added to each sample, and charcoal data were presented as ratios of the relative abundance of charcoal to pollen. Records were usually sampled at low temporal resolution due to the intensive labor and time required to analyze pollen.
The Global Charcoal Database (version 3) The structure and contents of earlier versions of the GCD are outlined in  Power et al. (2010). Here we review the database design and focus primarily on detailing new entries in GCDv3. Version 3 extends the total number of sites in the GCD to 736. It includes 679 sites from version 2  (Daniau et al. , 2012)  as well as new sites from recent regional syntheses from Australasia  (Mooney et al. , 2011) , the Americas  (Marlon et al. , 2009 (Marlon et al. , , 2012;; Power et al. , 2012) , and Europe  (Vanniere et al. , 2011).
Type of records, data entry, and database structure The majority of sites in the database are associated with a single record in which charcoal was quantified using a single method. However, 96 sites in GCDv3 have more than one charcoal record, typically because charcoal was quantified using multiple metrics or laboratory techniques. The charcoal data and metadata from the GCDv3 are stored in several formats. The primary complete data set is stored in a Microsoft Access relational database with four main and 23 supporting tables. The four main tables hold (1) site metadata such as site name and type, geographical coordinates, elevation, catchment size, data source, and dating type; (2) sample data, including depths, volume, and estimated ages; (3) charcoal data, including quantity, units, and quantification method; and (4) date information, including depth and type of dates, laboratory identification numbers, material dated, and associated errors.
Additional tables include information such as the contact (i. e. , the corresponding data contributor) and publications associated with each record, index tables (e. g. , linking sites to publications and contacts), and full descriptions of codes used in the main tables. The original database was not designed to be a longterm archival repository but rather a research database, and it is therefore currently being replaced with a new structure. A significant percentage of the site metadata, such as geographic characteristics and methodological details, remains undocumented, however, and requires completion if scientific questions that draw on such data are to be addressed. In addition to the database format, the GCDv3 data set is now available as part of the paleofire R package  (Blarquez et al.
, 2014)  for use with the R computer programming environment (R Development Core Team, 2013). The R package currently lacks some of the metadata that are contained in the full database, but the site metadata, charcoal data, and modeled ages are available. The complete GCD in the form of a relational database can be downloaded from paleofire. org and from the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI;  Power et al. , 2008)  website (http://www. ncdc. noaa. gov/paleo/impd/gcd. html). Records in the GCD come from diverse environments (Tables 1, 2). Most of the sites in the database (n = 390) are lacustrine, which are primarily natural lakes that are often of glacial origin, but may also be of tectonic, volcanic, or thermokarst origin.
Other records (n = 197) are from terrestrial environments, such as bogs, marshes, mires, and fens. A smaller number of records were obtained from soils (n = 52) and from coastal/fluvial (n = 35) or marine environments (n = 12). Depending on the objective of a particular study, some site types will be more suitable than others. Marine records, for example, are among the longest in the database, making them suitable for analyses of biomass burning during the last glacial cycle  (Daniau et al. , 2010). However, marine sites have large catchment areas, making them suitable for regional but unsuitable for fine-scale analyses of fire activity.
Charcoal quantification methods Important differences exist in the types of quantification methods within the database. Taken together, the 736 sites in GCDv3 have 134 269 charcoal samples with estimated ages. For most of the sites, charcoal is quantified as concentration (n = 402) or influx (n = 212); 105 are expressed in terms of charcoal to pollen ratios or similar measures of relative abundance; and the remaining 17 sites have uncommon units, such as cumulative probabilities or presence/absence of charcoal. Influx is the preferred unit of measurement for most biomass burning reconstructions because it accounts for variations in sedimentation rates over time, which can vary widely. If concentrations, depths, and ages exist, then influx can be calculated prior to analyses. Charcoal-to-pollen ratios, which were common in early analyses, are now relatively rare due to the ambiguities inherent in their interpretation  (Conedera et al.
, 2009). Different laboratory methods are used to quantify charcoal (Table  3 ). The majority of charcoal records included in the database (436 sites) are quantified using the pollen-slide method (POLS); 271 sites by sieving method (SIEV); 14 sites using image analysis (IMAG); and 15 sites were quantified using other methods such as hand picking charcoal from soil samples, gravimetric chemical assay  (Winkler, 1985) , and charcoal separation by heavy liquid preparation. Several records included were based on the cumulative probability of charcoal in alluvial fan deposits  (Pierce et al. , 2004) , and several records employed other chemical, thermal, or optical treatments or some combination of these methods to quantify black or elemental carbon  (Verardo et al. , 1990).
The most recent upturn in fire activity globally, but particularly in the Northern Hemisphere reconstruction, is supported by a larger data set than GCDv1. Marlon et al. (2008)  used GCDv1 to document the large decrease in biomass burning in the 20th century, but the reconstruction had large uncertainties in the trend over the last few decades. The addition of new records to versions 2 and 3 of the GCD, along with a finer-scale temporal focus now reveals the most recent increases in fire activity observed not only in the charcoal data but also in several lines of independent evidence, including satellite and observational data  (Giglio et al. , 2013; Dennison et al. , 2014). • ••• ••• •• • •• •• •• • • • •• • • • • • •• • • •• • •• • • • • • • • • • • • • • •• • • •• • • • •• • ••• • • • • • • • • • • • • • • • • ••• • • • ••• • •• • • ••• • • • • • • • • • • • • •• • • • • •• • • • • • • • • • • • • • • • • • •• •• • ••••• • • • ••• •• •• • ••• • •• •• ••• ••• •• ••••••••• • • •• • • • • ••••••• •• • • •• •• • •• • • • ••• • •• ••• ••• ••• • • • ••• •• •• •••• • •• • • • • • ••• • • • ••••• • •• • • •••• •••••• • • • • • • 6500 -5500 yr BP 500 yr BP -2014 CE (c) (b) (a Global biomass burning since the LGM, 21 000 years ago, shows a long-term increase (Fig.
Conclusions The GCDv3 incorporates 736 charcoal records and can now be gridded globally for the modeling community to ease future data-model comparisons. Fire-history reconstructions from the GCDv3 demonstrate that increases in biomass burning since the last glacial period were widespread, as are unusually high levels of burning over the past several decades. Present-day burning inferred from the charcoal data is particularly high in western North America and southeastern Australasia. Detailed reconstructions of temporal variations in biomass burning during the past 1000 years reveal that a global biomass burning decline from 1000 to the LIA was more pronounced in the Northern than Southern Hemisphere. In addition, variations in fire activity during the past 200 years show very different spatial patterns.