dataesr/scientific-paragraphs-categorization huggingface dataset:
The long term-light curve, derived with data from September 2008 1st to 2017 June 30, corresponding to MJD (54710-57934) is shown in  The strength of the signal during these flares allows to derive the lightcurve in one day time bins. The corresponding lightcurves in one day time bin around these flares are shown in the bottom part of Figure  4. These flares are investigated by deriving one day time bin lightcurves for a longer time period, centered around the highest flux value. The corresponding details are summarized in Table  4. 1. The strength of these flares allows to investigate the variability on shorttime scales and it is possible to go down to 4. 6 hours time bins. Fermi-LAT reported on the detection of a bright flare on 2014 Feb 14, seen simultaneously by VERITAS experiment at TeV energies.
Summary and Conclusions In this proceeding we have presented the long-term lightcurve of the BL Lac object 1ES 1215+303 in the energy range 100 MeV < E < 500 GeV with Fermi-LAT. Several flux increment have been detected from 9 years of Fermi-LAT data analysis. The major flares are reported on in this work. The three brightest flares ( I > 8. 0 × 10 -7 cm -2 s -1 ) occurred during 2008, 2014 and.
| Statistical analysis We used linear mixed effects models (LMMs) that were implemented within a Bayesian Monte Carlo Markov chain (MCMC) framework (MCMCglmm package;  Hadfield, 2010)  in the R software (R Development Core Team, 2012). We compared the different treatments using separate statistical models for hermaphrodites and males. We first tested for the effects of environment and generation (i. e. , generations 1, 4 and 22) and their interaction on fitness (i. e. , fertility multiplied by survival frequency) measured during the selection experiment. Replicate was used as a random effect to control for potential pseudo-replication. CG experimental data were used to test for the effects of treatment (i. e. , the environment in which the population had evolved), generation (i. e. , the generation at which the experiment was performed) and their interaction on hermaphrodite traits (i.
ACKNOWLEDGEMENTS We are very grateful to  H. Teotónio  for providing us with his base population. We especially thank  R. Beaudouin ,  A. Charmantier ,  L. De Meester ,  M. Gauduchon ,  R. Gilbin ,  E. Jumas-Bilak ,  P. Peres-Neto ,  G. Stora  and  J. Verreault  for discussion in the early stage of this project. We are also grateful to  C. Hall  for linguistic corrections and three anonymous reviewers and the associate editor for their valuable comments and suggestions on the manuscript. This work was part of the  Envirhom-Eco Research Program  supported by the  French Institute for Radioprotection and Nuclear Safety.
Our identification of a new large pedigree of autosomal dominant PD attributable to SNCA gene duplication with 12 presymptomatic PARK4 heterozygotes has provided a unique opportunity to explore blood biomarkers and permitted the definition of a molecular signature at the RNA level that predicts PARK4 PD. For validation, the results were assessed in individuals with a risk of developing PD because of manifestation of RBD as a highly specific prodromal sign. Our data on blood biomarkers as a diagnostic tool might contribute to the assessment of the risk of multifactorial PD in individuals without a positive family history. The most relevant biomarker is the SNARE component complexin 1, which acts as risk factor for PD by itself.
All data were deposited at the European Nucleotide Archive (ENA) public database (accession number PRJEB8960), and a table containing the ranked gene list of expression changes is provided in Table  S1. In order to identify pathway dysregulations, the data were assessed bioinformatically with Gene Set Enrichment Analysis (GSEA). Downregulated pathways were not apparent, but several strong upregulations (with P-values=0. 0 and q-values=0. 0) were documented and concerned cytokine signaling in the immune system (normalized enrichment score, NES 4. 8), adaptive immune system (NES 4. 5), hemostasis (NES 4. 4), lysosome (NES 4. 3), platelet activation signaling and aggregation (NES 4. 1), innate immune system (NES 4. 0), endocytosis (NES 3. 9), Toll-like receptor signaling (NES 3. 3), metabolism of lipids and lipoproteins (NES 3.
Our deposition  of PARK4 blood RNA-seq data at the ENA database aims to drive this effort, ensuring that PD risk, which is triggered by the genetically defined synucleinopathy of PARK4 in the Turkish pedigree and mirrored by specific pathways and molecular biomarkers, can now be compared with diverse ongoing data collections in individuals with manifest PD, usually without a family history and with multifactorial pathogenesis  (Scherzer et al. , 2007; Marek et al. , 2011; Santiago and Potashkin, 2015). It is unlikely that diagnostics of PD risk can be based on a single technique or a single molecular biomarker, so we evaluated the global transcriptome and the pathway dysregulations by GSEA bioinformatics. These approaches recently permitted us to identify three gene expression changes in dependence of SNCA function in brain tissue, concerning the chemotaxis factor Lect1 (Cnmd) in one digenic and two monogenic PD mouse models  (Gispert et al.
Molecular karyotyping Two PARK4 blood samples were assessed for copy number variation using a SurePrint G3 Human CGH (comparative genomic hybridization) microarray 2×400 K design 021850 (Agilent Technologies). Agilent's labeling kit was used, and all procedures were carried out according to the manufacturer's instructions. Scanning was performed on an Agilent microarray scanner and raw data were processed by Feature Extraction 9. 5. Deleted and amplified regions were determined on Agilent's Genomic Workbench Standard Edition 5. 0. 14. A minimum of four consecutive probes had to be affected to make a decision. The aberration detection threshold of the ADM-2 algorithm was set to 5. 9.
9, 20% glycerol, 4% SDS, 10% mercaptoethanol and 0. 005% Bromophenol Blue). Samples were incubated at 65°C for 16 h before being loaded on the 12% polyacrylamide gels and transferred to PVDF (polyvinylidene fluoride) membranes. The membrane was blocked in 5% milk powder and incubated with primary antibodies against α-synuclein (1:1000, BD 610786), β-synuclein (1:1000, Abcam ab6165 and Upstate 36-009), complexin 1 (1:500, Acris AP51050PU-N) and β-actin (1:10,000, Sigma, A5441), visualizing the signals by the ECL method. Densitometric analysis was carried out using ImageJ software.
Platelets were identified through CD61 positivity and their characteristic light scattering. Each analysis was performed on 5000 platelets. Quantification of surface expression of given antigens was obtained using CellquestPro software (BD Biosciences). The mean fluorescence intensity was used to define molecule expressions on the platelet surface. Results were compared with 20 healthy Central European volunteers (control group). To characterize the α-granule and lysosome release response upon platelet activation, 20 μl of 1:10 diluted citrated whole blood was stimulated with thrombin receptor agonist peptide (TRAP-6, final concentration 10 µmol/l, Roche) and DPBS (control) for 10 min at room temperature. Platelets were immunostained as described above.
RNA-seq Global blood RNA sequencing for the five PARK4 versus five age-and sexmatched healthy individuals was outsourced to Alacris Theranostics GmbH, Berlin, Germany. Strand-specific RNA sequencing libraries were prepared from 500 ng of total RNA according to the Illumina TruSeq stranded mRNA protocol. mRNA was selected using oligo(dT) Dynabeads. Sequencing was performed on the Illumina HiSeq2500 platform, using paired-end 2×50 bp sequencing mode. For each library, 40-50 million passed filter reads were obtained. Raw data were processed according to the manufacturer's instructions. Illumina's bcl2fastq software v1. 8. 2 was used for base calling and demultiplexing. The sequencing reads were mapped to the human genome reference GRCh37/hg19 using bwa (version 0. 5. 9, http://bio-bwa. sourceforge.
net/). Exonic reads in correct genomic orientation, defined by Ensembl Release 73 (http://www. ensembl. org/index. html), were counted using custom Python scripts. Expression values were calculated using RPKM (reads per kilobase per million mapped reads) normalization with custom R scripts  (Mortazavi et al. , 2008). Given that we observed huge amounts of hemoglobin in the samples, reads on hemoglobin genes were removed before the expression analysis. Differential expression between disease and control samples was calculated with the R bioconductor package edgeR  (Robinson et al. , 2010 ; http://www. bioconductor. org/packages/release/bioc/html/edgeR. html), applying an exact test for overdispersed data. As an additional criterion to judge variance and restrict the number of biomarker candidates, a custom R script was used to calculate Student's paired t-tests between disease and control expression values for each gene.
Bioinformatic systems biology RNA-seq data from five presymptomatic PARK4 heterozygotes and five matched controls were analyzed to compute average fold changes for each gene and rank them according the t-test statistics. For duplicate entries, the maximal value was used. The ranked list of gene symbols thus generated was subjected to nonspecific filtering and assessed by GSEA using the Javabased version GSEA-P  (Subramanian et al. , 2005 (Subramanian et al. , , 2007)). Permutations were performed on gene sets owing to the low number of biological replicates. We used the c2 (online pathway databases, Pubmed publications, expert of domain knowledge), c5 (Gene Ontology categories) and cc (Gene Ontology cellular component) gene sets from the MSigDB database (v. 4. 0, May 2013, http://www. broadinstitute.
Acknowledgements We thank  B. Meseck-Selchow ,  M. Babl ,  M. Bouzrou ,  K. Weckmann ,  C. Hoeft  and  E. Gerlach  for technical assistance, the staff of the ZFE at the  University Hospital Frankfurt  for support,  A. Ohle  at the  Animal Facility at the MPI for Experimental Medicine  for mouse husbandry, and  Tim Becker  at the  IMSDD of Bonn University, Manu Sharma at Tübingen University and Moritz Schütte at Alacris Theranostics in Berlin  for statistical advice. We are grateful to  Herbert Zimmermann  for critical reading, and to the Turkish  PARK4  family and the donors of PD and control DNA and brain autopsies for their cooperation.
Fires have influenced atmospheric composition and climate since the rise of vascular plants, and satellite data have shown the overall global extent of fires. Our knowledge of historic fire emissions has progressively improved over the past decades due mostly to the development of new proxies and the improvement of fire models. Currently, there is a suite of proxies including sedimentary charcoal records, measurements of fire-emitted trace gases and black carbon stored in ice and firn, and visibility observations. These proxies provide opportunities to extrapolate emission estimates back in time based on satellite data starting in 1997, but each proxy has strengths and weaknesses regarding, for example, the spatial and temporal extents over which they are representative. We developed a new historic biomass burning emissions dataset starting in 1750 that merges the satellite record with several existing proxies and uses the average of six models from the Fire Model Intercomparison Project (FireMIP) protocol to estimate emissions when the available proxies had limited coverage.
, 2010; Le Page et al. , 2010; Rabin et al. , 2015). Our knowledge about how these factors have influenced fire emissions over the past centuries or millennia has progressively improved over the past decades, leading to new biomass burning emission inventories  (Granier et al. , 2011). Dentener et al. (2006)  reconstructed fire emissions for the year 1750 by scaling Global Fire Emissions Database (GFED) fire emissions before the satellite era with population derived from the Hundred Year database for In-tegrated Environmental Assessments (HYDE; Klein  Goldewijk, 2001) , assuming that emissions related to deforestation fires were linearly related to population. For fires not associated with deforestation, only 60 % of the emissions were scaled by population; the remaining 40 % remained constant, assuming that these fires were natural.
For high northern latitudes, the fire emissions were doubled in 1750 to account for present-day fire suppression. Other approaches for global fire estimates were often based on the burned area dataset by  Mouillot and Field (2005) , which consists of gridded data from 1900 onwards, combining the Along-Track Scanning Radiometer (ATSR) observations with historic literature-based results (land use practices, qualitative reports, and country statistics), and tree ring records. This burned area inventory was used to estimate emissions in the Global Inventory for Chemistry-Climate studies (GICC) dataset  (Mieville et al. , 2010). GICC provides estimates of biomass burning emissions over the 20th century and emissions mimicked the patterns in burned area with a decrease over the beginning of the 20th century followed by relatively constant emissions until emissions increased rapidly from the 1980s to 2005.
The Reanalysis of the Tropospheric chemical composition (RETRO) inventory estimates global wildfire emissions from 1960 to 2000 with a regional approach by collecting and combining literature reviews with different satellite datasets, and a numerical model with a semi-physical approach to estimate fire spread and fire occurrence. From 1960 to 2000, RETRO-based fire emissions showed a global significant increase as a result of an increase in tropical forest and peat soil burning  (Schultz et al. , 2008). The biomass burning emissions dataset used in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment report (AR5) estimated biomass burning emissions from 1850 to 2000  (Lamarque et al. , 2010)  using a combination of  GICC for 1900 -1950 , the RETRO inventory for the 1960 -1997 period, and the satellite-based GFED version 2 for 1997 to 2000 (van der Werf et al.
, 2006). For the 1850-1900 time period, biomass burning emissions were held constant because no additional data were available  (Mouillot and Field, 2005). The reconstructed global signal indicated that fire emissions were relatively stable until the 1920s. They then decreased until the 1950s, after which they increased until the end of the dataset in 2000  (Lamarque et al. , 2010). Besides these estimates based on historic datasets and satellite data, individual fire models are also used to estimate biomass burning emissions on a global scale (Fig. 1 ). Over the past decades, these models have been embedded in dynamic global vegetation models (DGVMs), Earth system models (ESMs), and terrestrial ecosystem models (TEMs), enabling studying the feedbacks between fire, vegetation, and climate  (Hantson et al.
, 2016). Fire models have been growing in complexity and a large variety now exist. To better analyse and evaluate these models, the Fire Model Intercomparison Project (FireMIP) was initiated, where models use FIRE MODELS Figure  1. Spatial and temporal resolution of various data streams available to estimate fire emissions (adapted from  Kehrwald et al. , 2016). the same forcing (meteorology, lightning, land use, population density, atmospheric CO 2 ) datasets  (Rabin et al. , 2017). While fire models in general have a global focus, they often do not include anthropogenic fires (for example, those used in the deforestation process). However, another data source is available to estimate these fires and their emissions: the country-level estimates of deforestation and afforestation provided by the United Nations' Food and Agricultural Organization's (FAO) Forest Resource Assessment (FRA) (Food and Agriculture Organization of the United Nations, 2010).
These area estimates can be subsequently used in a bookkeeping model to calculate carbon emissions  (Houghton, 2003). All of these emission inventories rely on different datasets and different assumptions. The most consistent estimates of fire patterns are based on satellite-observed burned area or active fires. These usually have a high temporal resolution and are available globally (Fig. 1 ). These satellite observations are used in combination with a biogeochemical model to estimate fuel loads and calculate emissions in GFED  (van der Werf et al. , 2010)  or using fire-emitted energy scaled to GFED in the Global Fire Assimilation System (GFAS;  Kaiser et al. , 2012). Unfortunately, these datasets only cover about 2 decades, i. e. since 1997 for GFED and shorter for other datasets, including those based on atmospheric observations of fire-emitted species that can be used to infer emissions when combined with an atmospheric transport model  (Edwards et al.
, 2006; Huijnen et al. , 2016; Krol et al. , 2013). Proxy records cover longer timescales, of which the charcoal record is probably the most extensively explored  (Daniau et al. , 2013; Marlon et al. , 2013; Power et al. , 2008). Charcoal records can be used for reconstructing fire patterns and emissions on a local to regional scale covering time periods of decades to millennia and beyond. Regional-and global-scale analyses have been done compositing multiple records within a region or globally. The Global Charcoal Database (GCDv3) consists of 736 charcoal records glob-ally, with most samples taken in North America, Europe, Patagonia, and southeast Australia  (Marlon et al. , 2016). Ice cores are another widely used proxy for retrieving information about fire history on decadal to longer timescales and are representative of regional to continental scales.
Later on, biomass burning emissions increased and peaked in the late 19th century according to these datasets. This peak was also seen in an Antarctic ice core record of CO concentrations and its isotopic ratio  (Wang et al. , 2010). Observations of CH 4 concentrations and its isotopic ratio also indicated an increase; however, this increase continued until the present without a peak in the 19th century  (Ferretti et al. , 2005). This pattern is also observed in firn air samples in both the Northern Hemisphere  (Wang et al. , 2012)  and Southern Hemisphere  (Assonov et al. , 2007). Although biomass burning reconstructions based on isotopic ratios of CO and those of CH 4 , as well as those derived from charcoal records, show similar features, there are key differences. These differences are most pronounced for the past 50-100 years and could be the result of different lifetimes of CO (2 months, thus providing more regional information) and CH 4 (about a decade, thus providing information on a global scale), but also because of the distribution of the charcoal datasets, which is denser in temperate regions than in the tropics.
Combining these different data sources may provide a more complete history of fire on Earth than focusing on one single line of evidence  (Kaiser and Keywood, 2015; Kehrwald et al. , 2016). We have reconstructed global fire emissions from 1750 onwards using observation-based data streams (fire emissions based on satellite data for the 1997 period onwards, charcoal datasets in temperate and boreal regions, and visibility records from weather stations in deforestation zones of South America and Indonesia) and multi-model mean emission estimates from FireMIP when no observations were available, and anchored them to satellite-based fire emissions. The new biomass burning (BB) dataset will be most useful for largescale assessments such as the Coupled Model Intercomparison Project Phase 6 (CMIP6) and will be distributed as the BB4CMIP6 database.
Datasets and methods To leverage the specific strengths of the various proxies, we divided the globe into the 14 regions used within GFED, which feature relatively homogeneous fire seasons and characteristics, but further subdivided some of these regions to allow input from additional datasets for a total of 17 regions (Fig. 2 ). For these 17 regions, we combined the satellite-based emissions from GFED  (version 4s; van der Werf et al. , 2017)  for 1997 to 2015 with either proxies (when available) or fire models to calculate the fire history starting in 1750 (Fig. 3 ). We used visibility observations from the World Meteorological Organization (WMO) stations in the arc of deforestation (ARCD) and equatorial Asia (EQAS). Dimensionless charcoal records were scaled to the range of the fire models and were used for Europe (EURO) and North America, where boreal and temperate North America was split into eastern (BONA-E, TENA-E) and western (BONA-W, TENA-W) parts.
For all other regions, no proxy observations were available, and we used the median of fire model outputs anchored to GFED4s to extrapolate back to 1750. Both proxies and models were only used to reconstruct annual regional totals; these were distributed over the 0. 25 • × 0. 25 • grid and months based on the GFED4s climatological patterns  (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015). In the next sections, we describe the datasets and methods in more detail.
Global Fire Emissions Database (GFED) We used the Global Fire Emissions Database version 4 with small fires (GFED4s) for 1997-2015 and as an anchor point for all proxies and model results. In GFED, satellite-derived burned area is used as a key input dataset in a revised version of the Carnegie-Ames-Stanford approach (CASA) biogeochemical model  (Potter et al. , 1993). The burned area estimates from 2000 onwards are from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 500 m burned area maps aggregated to 0. 25 • × 0. 25 • spatial resolution and a monthly time step  (Giglio et al. , 2013). These estimates are "boosted" using a revised version of the small fire estimates of  Randerson et al. (2012) , which are based on overlying mapped burned area and active fires. Finally, the burned area estimates are used in combination with active fire detections from the Visible and Infrared Scanner (VIRS) and the ATSR sensors to extend this time series back to 1997  (van der Werf et al.
, 2017). In CASA, the burned area estimates are then converted to carbon emissions using modelled fuel consumption. Fuel consumption depends on the amount of flammable biomass and combustion completeness, and is calculated in the model as a function of satellite-derived plant productivity, fractional tree cover estimates, and meteorological datasets including solar insolation and moisture levels  (van der Werf et al. , 2010 (van der Werf et al. , , 2017)). The fuel consumption parameterization in the model was tuned to match observations compiled by van  Leeuwen et al. (2014). As a final step, these carbon emission estimates are converted to trace gas and aerosol emissions using emission factors based mostly on the compilation of  Akagi et al. (2011).
, 2016). Models are developed with different complexity and some models combine both empirical and process-based approaches. We used carbon emissions of all five models available at the time (14 May 2016) within FireMIP, which covers the 1750-2013 time period, as well as one model that did not participate in FireMIP, the SIMFIRE-GDP model. These six models are described in more detail below. FireMIP's main goal is to evaluate fire models with benchmark datasets to understand differences between models and improve the representation of fires in DGVMs. The models within FireMIP used identical forcing datasets with prescribed meteorological forcing , global atmospheric CO 2 concentrations  (1750-2013), lightning (1871-2010) , land use change (1700-2013), and population density (1700-2013)  (Rabin et al.