dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole-Exome Sequencing Germline DNA extracted from peripheral blood was used for whole exome capture using Agilent SureSelect 38Mb paired-end sequencing and ran on Illumina HiSeq 2000s/2500s. FASTQ files were aligned to human reference genome (GRCh37) to generate BAM files using BWA v0. 7. 12. Picard tools was used for quality metric calculation and marking duplicate reads. GATK version 3. 5-0-g36282e4 was used for variant calling using the haplotype caller algorithm. Variant quality score recalibrated (VQSR) data was used for filtering variants. Variant level and interval level annotations used SNPEff, ANNOVAR, and CAVA programs. Downstream analysis consisted of filtering out low quality variant calls and common variants. Average coverage depth was 80X-100X. Variants with read depth (DP) of 10 or greater and a genotype quality (GQ) score of 20 or greater were included in analyses.
Variant, exon, and gene level data were obtained using information from the 1000 Genomes Project, NHBLI GO Exome Sequencing Project Exome Variant Server (EVS), Exome Aggregation Consortium (ExAC), and the combined annotation dependent depletion (CADD) server  (13). Deleterious variants were defined as loss-of-function (frameshift insertion or deletion, stop-gain, splice-site change) or missense variants with CADD score >15. We performed segregation analysis using either exomes from family members or targeted Sanger sequencing. Co-segregating qualifying variants in Family 1 (Figure  1A ) shared by exomes are listed in Supplementary Table  S2. Exome sequence data are accessioned as NCBI SRR5641111.
Digital Droplet PCR A Biorad assay specific for detection of the KDM1A frameshift mutation was designed (Forward Primer: 5′ CGTCATGGTCTTATCAACT3′; Reverse primer: 5′ AGGAGG-TCCTTACTTGGT3′; Wt probe: 5′CGGCATCTATAagAGGATAAAACC3′-; mutation probe:5′ CGGCATCTATAAGGATAAAACC3′. Cycling conditions were optimized for annealing/extension temperature and separation of positive from empty droplets on a QX200 ddPCR system (Biorad). Technical duplicates were run. PCR reactions contain primers/ probes, DNA and digital PCR Supermix for probes (no dUTP). Reactions were partitioned into median ~16,000 droplets/well using QX200 droplet generator. Emulsified PCRs were run using cycling conditions (95°C 10′;40 cycles of 94°C 30″ 55°C 1′, 98°C 10′, 4°C hold). Plates were read and analyzed with QuantaSoft software to assess the number of droplets positive for mutant DNA, wild-type (wt) DNA, both, or neither.
Mutation Burden Analysis Exome data from 879 sporadic Multiple Myeloma patients (dbGaP phs000748 and phs000348) and 2389 control subjects (phs000179, phs000276, phs000403, phs000687 and phs000806) were simultaneously processed and variants jointly called. Exomes from cases/ controls were required to have ≥75% of the 33. 27Mbps of CCDS with >10-fold coverage and ≤3% contamination using VerifyBamID  (14). Exomes from cryptically related individuals were removed using KING algorithm  (15). Principal component analysis (PCA) was performed using SMARTPCA  (16)  to identify European ancestry (EA). EA exomes meeting these quality control metrics were selected (733 sporadic Multiple Myeloma patients and 1,480 controls). We compared the sum of (a) predicted deleterious mutation or (b) neutral synonymous coding variant qualifying carriers for each gene in MM cases and controls using Fishers exact test (Statistical function scipy.
stats. fisher_exact in SciPy v0. 14. 0. ). Qualifying variant criteria includes (1) a quality score (QUAL) ≥50, (2) a genotype quality (GQ) score ≥20, (3) a quality by depth (QD) score ≥2, (4) a mapping quality (MQ) score ≥40, (5) a read position rank sum (RPRS) score ≥-3, (6) mapping quality rank sum (MQRS) score ≥-10, (7) coding variants with mean allele frequency (MAF) ≤ 0. 05% in all three databases of the 1000 Genomes Project, ESP, and ExAC. For rare synonymous variant burden testing, synonymous coding variants meeting criteria (1)-  (7)  were used. For mutation burden testing, loss-of-function (frameshift insertion or deletion, stop-gain, splice-site change) and missense variants with Phred CADD (Combined Annotation Dependent Depletion) score ≥15 meeting criteria (1)-  (7)  were used.
RNAseq Analysis For mouse plasma cells, polyadenylated single-read RNAseq was performed using standard Illumina Truseq protocols and run at 3/lane on HiSeq 2500. Reads were trimmed of adapters with Flexbar then aligned to mm10 mouse genome  (20). Uniquely mapped reads were used for read counts. Raw read counts were processed in R with DESeq2  (21)  and accessioned as NCBI GEO GSE85596. Gene Set Enrichment Analysis (GSEA) scores were calculated as the negative log 10 of the False Discovery Rate (FDR) multiplied by the sign of the log 2 fold change (log FC). Scores were run against pre-ranked GSEA Hallmarks(  22 ) (weighted score type, 1000 permutations) or Boylan Multiple Myeloma gene sets  (23). Differentially expressed genes were graphed as a volcano plot in R. For analysis of MMRF CoMMpass RNAseq MM transcriptomes from KDM1A mutation carriers and wt MM patients, raw counts  [19]  were analyzed in R with DESeq2  [20].
Germline truncating KDM1A mutations in familial multiple myeloma Germline exome sequencing of 50 familial MM kindreds (Supplementary Table  S1 ) revealed an EA family carrying an N-terminal truncating KDM1A mutation (c. 805_806delAG [p. Arg269Aspfs*7]) (Figure  1A ) absent in ClinVar or Exome Aggregation Consortium (ExAc) databases. This was the only kindred with a qualifying truncating mutation (Supplementary Table  S2 ). Therefore we focused follow-up on this family. Sanger sequencing identified five relatives carrying this KDM1A mutation. Three were MM affected (II:4, III:6 and III:7), one (III:3) with both acute myelogenous leukemia (AML) and MGUS, and one unaffected at age 48 (III:1) (Figure  1A ). Three additional family members were diagnosed with pancreatic (II:2, III:2) and colon cancer (II:1).
1424T>C [p. Leu475Pro] and c. 2003G>C [p. Arg692Pro]) in MM patients with first degree relatives also affected with MM/ MGUS. Collectively, these data confirm the presence of rare truncating and predicted deleterious missense KDM1A mutations in familial and early onset MM patients. To understand the prevalence of KDM1A truncating mutations in controls, we examined the ExAC database. Including all ethnicities only 9 predicted truncating and splice-site loss-offunction (LoF) variants were identified, carried by 10 individuals (10/60,694) (LoF constraint metric pLI=0. 99). Seven of these are of European ancestry. All KDM1A truncating mutations had mean allele frequencies <1. 6 × 10 -5 and no homozygotes were identified. Joint analysis of LoF mutations in familial and early onset MM cases (2/450) and ExAC EA participants as controls (7/36,664), showed enrichment of KDM1A LoF mutations among familial and early onset myeloma patients (p=0.
23% LoF/missense mutations in MM patients and 0. 14% LoF/missense mutations in controls; Relative Risk 9. 08 95% confidence interval 1. 97 to 41. 95) (Table  1 ). The rate of predicted KDM1A LoF and missense mutations in EA controls is similar to the ExAc database (0. 22%; 81/36,664). However, none of the 7 tumors from MM patients carrying KDM1A missense mutations whose tumor somatic mutation data were available demonstrated LOH  (30). We analyzed published transcriptome databases containing gene expression from normal plasma cells (PC), MGUS and MM cells and found that KDM1A mRNA levels are significantly lower in MGUS or MM cells vs. PC (7. 45×10 -6 and 1. 7×10 -05 respectively, moderated t-test) (Figure  2E )  (31). Overall, these data support a broader role for KDM1A inactivation in sporadic MGUS and MM, in addition to its role in familial and early-onset multiple myeloma.
Acknowledgments We acknowledge funding from  R01 CA167824 ,  R01 CA13464 ,  R01 CA178765 ,  T15 LM007124 ,  R21 CA152336 ,  LLS 6067-09 ,  NCI P30 CA42014 ,  HHSN261201000026C , the  V Foundation  ( V2015-003 ), the  Weill-Cornell Program in Mendelian Genetics , the  Utah Genome Project ,  Huntsman Cancer Institute , Utah Population Database (UPDB), the  Utah Cancer Registry (UCR) ,  Icahn School of Medicine at Mount Sinai Office of Research Infrastructure of the National Institutes of Health  award number  S10OD018522  and a generous donation from  Matthew Bell.
We aim to present a statistical analysis that includes all of the aforementioned variables and their possible interdependencies using a methodology that is typically used to assess the macroeconomic effects of fiscal and monetary policies. To provide a better understanding of the examined effects, our analysis distinguishes between flows of permanent migrants, as defined using international classifications (7), and flows of asylum seekers. The present study uses annual statistical data from 15 Western European countries from 1985 to 2015. The selected countries are Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Iceland, Italy, the Netherlands, Norway, Spain, Sweden, Portugal and the United Kingdom. These countries were selected on the basis of the availability of economic and migration data for the entire study period in two international databases, Eurostat and the OECD Economic Outlook  (8) ; this approach ensures the accessibility and comparability of the study data.
Notably, Greece, Luxembourg and Switzerland are not considered because fiscal data before 1990 are not available for these nations in the Economic Outlook database. The European countries that we consider receive most of the asylum applications in Europe (89% in 2015); moreover, in contrast to certain Eastern European countries, the selected nations are almost never the home countries of the asylum seekers. This study uses two variables related to international migration, both of which are constructed from Eurostat data. The first, the flow of asylum seekers, is measured as the number of first applications, pending at the end of the year, made by people who state that they are unable to return to their country of origin due to a well-founded fear of being persecuted. The lodging of an asylum application with a country entitles the applicant to reside legally in that country while the application is being processed but generally does not entitle the applicant to work and does not necessarily lead to being granted refugee status  (9).
We express flows of asylum seekers as rates per thousand inhabitants (based on average population). The second variable is the net flow of migrants, which is measured using the net migration (plus adjustment) rate per thousand inhabitants. The net flow of migrants therefore includes all immigrants and does not distinguish between nationals and foreigners. Net migration data are produced by Eurostat from population statistics that exclude persons temporarily staying in a country and, most notably, asylum seekers. In methodological terms, this point is important because it emphasizes the fact that our two migration variables count different set of people and thus are not mechanically correlated. When the asylum application procedure is completed, only applicants who obtain refugee status are allowed to settle long-term in their host countries; these individuals are then considered to be permanent migrants and included in population statistics.
As considered in  (10) (11) (12) (13) (14) (15) (16) , we use real GDP per capita and unemployment rate to assess macroeconomic performance. In addition, we use the same indicators for public finances that are used in studies that measure the multiplier effects of fiscal shocks  (11) (12) (13) (14) (15) (16). Two main variables are considered: public spending (that is, general government final consumption and investment expenditures) and net taxes (that is, general government revenues minus transfers). These variables, obtained from the OECD Economic Outlook database, are expressed in real terms and divided by average population. Moreover, we also calculate fiscal balance by subtracting public spending from net taxes and dividing the resulting value by GDP (see Supplementary Materials). To assess the effects of net flows of migrants and asylum seekers on Western European economies, we have constructed and estimated a panel vector autoregressive (VAR) model.
Materials and Methods The migration variables are from Eurostat database. Precisely, we use the asylum applications pending at the end of the year from the Asylum and managed migration (migr) database, series codes migr asyctz and migr asyappctza. The data on the average population (series code AVG), used to express the flows of asylum per thousand inhabitants, are from the Population change (demo gind) database. The net flow of migrants is measured by the CNMIGRATRT series from the Population change (demo gind) database. The economic and fiscal data are from the OECD Economic Outlook database  (8)  and are computed, as in  (12, 13, 15) , by using the series codes listed in Supplementary Materials. Our empirical analysis is based on a panel VAR model estimated by using the biascorrected fixed-effects technique (see Supplementary Materials for details).
Migration variables The first variable used to evaluate international migration is the flow of asylum seekers, which is measured by the first asylum applications pending at the end of the year. Asylum seekers are temporary migrants who are waiting for a decision on their asylum claims. While their application is being processed, asylum seekers have access to public services, such as education and health, and they receive conditional social transfers, but they are generally not allowed to work. Second, we use the net flow of migrants, which is measured by the crude rate of net migration plus a statistical adjustment. Given that the growth of a country's population is the sum of natural increases (births minus deaths) and net migration (the total number of arrivals minus the departures of foreigners and nationals), the net migration data are produced by Eurostat as the difference between the total population change and the natural change.
The population statistics do not include persons who are temporarily staying in the country. Short-term movements for tourism or business are excluded. More importantly, foreigners who are applying for a first residence permit are not included. Thus, asylum seekers are not considered to be a part of the "usual resident population" and they are not included in the population statistics that are used by Eurostat to produce net migration data. The Eurostat annual data on asylum applications cover persons whose claim has not been definitively evaluated at the end of the year. Therefore, for a given year, an asylum applicant that is computed in our flow of asylum seekers variable is not counted by Eurostat as a permanent migrant, and he or she is not included in our net flow of migrants variable for this given year.
Economic and fiscal variables The economic and fiscal data are computed by using the following series codes from the OECD Economic Outlook database  (8). We use annual data similar to  (12, 13, 15) , who emphasized their advantages in fiscal studies compared with quarterly data used in  (11, 14, 16). The first reason for this use is that fiscal changes are generally decided in the Budget Act before the new fiscal year (while sometimes these changes are decided for mid-year budget revisions, but they are almost never determined quarterly); therefore, the estimated shocks from annual data can be considered good approximations of actual shocks  (15). Second, potential issues that relate to the anticipation of fiscal policy changes can be neglected with annual data. Third, for many countries, the data on fiscal variables (and also international migration) are not generally available quarterly; for these countries, quarterly series are often interpolated from annual data.
A value of AGB and its uncertainty has been assigned to each pixel. This approach allows estimating AGB until 85 Mg•ha -1 approximately, while dense forests and non-vegetated areas are masked out using the ESA CCI Land Cover dataset. The resulting map is visually compared with existing AGB maps and is validated using a cross-validation approach and a comparison with AGB estimates obtained from LiDAR datasets, leading to an RMSD of 8 to 17 Mg•ha -1. Finally, carbon stocks for savannahs in Africa and in 50 countries are estimated and compared with estimates by FAO and from AGB maps available over Africa.
, 2014) , and which are not uniformly distributed over the different forested biomes  (Gibbs and Brown, 2007; Houghton et al. , 2009). For instance, forest plantations are fairly well represented through commercial inventories but savannahs and woodlands, with no commercial value, have received much less attention. Also, they are not always considered as forests regarding the United Nations Framework Convention on Climate Change national forest definitions, and may therefore be disregarded in some monitoring activities. Besides, most carbon estimates are based on a handful of biome-average datasets where a single representative value of forest carbon per unit area is applied to broad forest categories or biomes  (Achard et al. , 2002 (Achard et al. , , 2004;; DeFries et al. , 2002; Fearnside, 2000; Houghton et al.
The approach consists in spatially extrapolating above ground woody carbon stocks obtained from in situ inventory plots and from ICESat GLAS transects, using MODIS only  (Baccini et al. , 2012)  or using MODIS and Quick Scatterometer (QuikSCAT) data  (Saatchi et al. , 2011). While these two pan-tropical biomass maps, being the first of their kind with such a large coverage, have been widely used, they however suffer from a number of limitations: they have low spatial resolutions (0. 5 to 1 km), no temporal repetition, and show large uncertainties  (Mitchard et al. , 2013a). In order to reduce the biases and improve the accuracy, independent reference plot datasets have been used to combine these two maps into an integrated map  (Avitabile et al. , 2016) , but the limitations related to spatial and temporal resolutions remain.
An update of the Baccini map has been recently produced at 30 m resolution, using Landsat data, and is available in the Global Forest Watch project  (Baccini et al. , 2015) , but with reported large uncertainty at pixel level. For savannahs and woodlands in Africa, the uncertainty is expected to be very large, because of the above mentioned limitations of MODIS, Landsat, and ICEsat, worsened by the sparser GLAS coverage of such ecosystems used in the production of these datasets. It has long been known that SAR data have high potential for AGB mapping and AGB stocks estimation, due to the relationships between the SAR backscatter and biomass  (Le Toan et al. , 1992). These relationships are affected by other parameters such as soil moisture  (Harrell et al. , 1997; Kasischke et al. , 2011) , topography  (Castel et al.
, 2001) , or the forest structure  (Imhoff, 1995) , which requires the adoption of specific strategies to mitigate the impacts of these environmental effects on the retrieval of AGB. Despite these limitations, SAR systems remain a promising tool for AGB estimation. Generally speaking, the backscatter in images acquired at short wavelengths like X-band or C-band show little dependence on biomass, although it has been shown that this weak sensitivity can be counterbalanced with the use of hyper-temporal observations, in particular in boreal forests  (Santoro et al. , 2011 (Santoro et al. , , 2013)). Images acquired at long wavelengths like L-band or P-band are more strongly correlated with AGB. In the last decades, L-band spaceborne SAR data have become available, with the JERS-1 satellite active from 1992 to 1999 and ALOS PALSAR from 2006 to 2011, followed by PALSAR-2 onboard ALOS-2 launched in May 2014.
SAOCOM (SAtélite Argentino de Observación COn Microondas) and NISAR (NASA/ISRO SAR Mission) L-band SAR systems are planned for launch in 2018 and 2020 respectively. Many theoretical and experimental studies showed that L-band SAR data are sensitive to forest AGB until a saturation level is reached and sensitivity is lost; however, a negative correlation between L-band backscatter and high AGB may occur when the forest is dense  (Mermoz et al. , 2015). The saturation is generally reported to occur between 70 and 150 Mg•ha -1  (Mermoz et al. , 2014; Mitchard et al. , 2009; Yu and Saatchi, 2016) , depending on the experimental dataset and on the model used to relate the backscatter to AGB. L-band SAR data are therefore well suited to the estimation of savannah AGB, typically below 100 Mg•ha -1.
For the denser woodlands, typically between 100 and 200 Mg•ha -1 , and higher biomass values, P-band SAR systems have a better potential, but are not available so far in space because of frequency allocation limitations that were lifted only recently. The BIOMASS mission  (Le Toan et al. , 2011) , planned for launch by the European Space Agency in 2021, will fill this gap and make it possible to exploit the synergy between L-band and Pband. Yearly PALSAR  (2007) (2008) (2009) (2010)  and PALSAR-2 (2015-onwards) mosaics at 25 m have been built by the Japan Aerospace Exploration Agency (JAXA), and are freely available (http://www. eorc. jaxa. jp/ ALOS/en/palsar_fnf/fnf_index. htm), which facilitates the use of large datasets for forest monitoring  (Shimada et al. , 2014). In the recent years, ALOS PALSAR data have therefore been used for AGB estimation in savannahs and woodlands for limited regions or countries in Africa and Australia  (Carreiras et al.
, 2012 (Carreiras et al. , , 2013;; Lucas et al. , 2010; Mermoz et al. , 2014; Mitchard et al. , 2009 Mitchard et al. , , 2011;; Mitchard et al. , 2013b; Naidoo et al. , 2015; Ribeiro et al. , 2008). However, these local studies have not been extended so far to continental scales, therefore the amount and distribution of the AGB in the whole African savannahs and woodlands remains to be better quantified  (Bastin et al. , 2017). In this paper, we aim at filling this gap, by estimating woody AGB at a spatial resolution of 25 m over the entire savannah biome of Africa using ALOS PALSAR 2010 mosaics. The method comprises two steps. The first step consists in defining a direct model that relates the PALSAR backscatter to AGB, with the help of a selected set of field measurements. In the second step, a Bayesian inversion of this model is performed to produce the AGB gridded dataset.
The paper is organised as follows: Section 2 gives general information on the savannahs of the African continent. Section 3 provides information on the data used in this study, both in situ plot data and SAR data. Section 4 presents the data analysis results and describes the development of the direct model as well as the Bayesian inversion scheme. Section 5 discusses the resulting estimates of savannah woody biomass in Africa and their associated uncertainties, and provides a validation assessment and a comparison with other large-scale AGB datasets available over Africa and national statistics.
Field data selection In order to train a model that relates PALSAR intensities to AGB, we use AGB estimates derived from field measurements, from several campaigns carried out in different countries in Africa between 2000 and 2013, both from published literature and from original campaigns. To increase the reliability of the model, it is necessary to reduce as much as possible the perturbations that affect the relationship between the SAR backscatter and the AGB estimates. The error sources include firstly mismatches between the size of the field plots and the pixel size of the SAR images  (Réjou-Méchain et al. , 2014) , tree canopy layover and border effects, errors in data geolocation, and topographic effects. In order to minimize these errors, we decided to consider only in situ AGB plots with a size larger than 0.