dataesr/scientific-paragraphs-categorization huggingface dataset:
3. The third kind of spin-up technique is based on an inverse method of the poorly known basal conditions in such a way that simulated surface velocities match the observed surface velocities (e. g. Gillet-Chaulet et al. , 2012; Arthern and Gudmundsson, 2010; Morlighem et al. , 2010; Gudmundsson and Raymond, 2008). However this approach may lead to internal inconsistencies between the simulated internal conditions (temperature and velocities) and the actual ones. The inconsistencies within the different observational datasets (surface and bedrock topography, velocities) can also have an impact on the results. An alternative approach, which avoids the previously mentioned shortcomings, consists in considering the observed ice-sheet geometry as the final target by finding appropriate basal conditions that minimise the differences between observed and simulated ice thickness  (Pollard and DeConto, 2012).
(2012). We use the reconstruction from  Maule et al. (2005)  for the geothermal heat flux. Using these boundary conditions, GRISLI is run forward starting from the present-day observed ice thickness (Fig. 2a ), from which the ice volume is inferred  Bamber et al. (2013) , and from the bedrock elevation. Initial vertical temperature and velocity profiles as well as the initial map of the basal sliding coefficient (Fig. 2a ) are derived from previous GRISLI simulations carried out with boundary conditions close to those of the present study, and performed within the Ice2Sea project, which aimed at reducing the uncertainties on future sea-level rise projections  Edwards et al. (2014). In order to avoid large inconsistencies between  the different datasets used as boundary and initial conditions, GRISLI is first run for 5 years.
Ice volume trend This analysis demonstrates that satisfying the ice volume anomaly criterion is in direct competition with the RMSE minimi-  Even if our approach is based on minimising the volume trend and fitting ice volume and ice thickness to the observed ones, the reliability of the method also depends of its capability to simulate ice velocities in good agreement with observations. We therefore compare our results to the surface ice velocity dataset provided by  Joughin et al. (2010)  (Fig. 8 ). The simulated results are slightly different from the observations especially in the central plateau where the region of low ice velocities is less extended in the simulations than in the observations. However, the overall patterns are in a good agreement, especially in regions of fast ice flow, providing confidence in our method.
Code and data availability The developments of the GRISLI source code are hosted at https://forge. ipsl. jussieu. fr/mailman/listinfo. cgi/grisli. Access to those who conduct research in collaboration with the GRISLI users group can be granted upon request to C. Ritz (catherine. ritz@univgrenoble-alpes. fr). The model outputs from the simulations described in this paper are freely available from the authors upon request. Author contributions. The implementation of the iterative process in the GRSILI model was initially done by C.
For brevity, we focus on the thresholding-based volatility estimator, which is one of the most popular methods of obtaining jump robust test statistics. The second contribution of this paper is to prove that an appropriate version of the bootstrap jump test based on thresholding provides a second-order asymptotic refinement under the null of no jumps. To do so, we impose more restrictive assumptions on the data generating process that assume away the presence of drift and leverage effects. For this simplified model, we develop second-order asymptotic expansions of the first three cumulants of the BN-S test statistic and of its bootstrap version. Our results show that the first-order cumulant of the BN-S test depends on the bias of bipower variation under the null of no jumps. Even though this bias does not impact the validity of the test to first order because bipower variation is a consistent estimator of integrated volatility under the null, it has an impact on the first order cumulant of the statistic at the second order (i.
Empirical results This empirical application uses trade data on the SPDR S&P 500 ETF (SPY), which is an exchange traded fund (ETF) that tracks the S&P 500 index. Data on SPY have been used by Mykland, Shephard and Sheppard (2012) (see also  Bollerslev, Law and Tauchen (2008) ). Our primary sample comprises 10 years of trade data on SPY starting from June 15, 2004 through June 13, 2014 as available in the New York Stock Exchange Trade and Quote (TAQ) database. This tick-by-tick dataset has been cleaned according to the procedure outlined by Barndorff-Nielsen, Hansen, Lunde, and Shephard (2009). We   9 )), respectively. 'Boot2' takes into account the asymptotically negligible bias in T n which may be relevant at the secondorder, and under certain conditions provides the refinement for the bootstrap method.
referees and an associate editor for many valuable suggestions. Dovonon, Gonçalves and Meddahi acknowledge financial support from a  ANR-FQRSC  grant. In addition,  Ulrich Hounyo  acknowledges support from  CREATES -Center for Research in Econometric Analysis of Time Series  ( DNRF78 ), funded by the  Danish National Research Foundation , as well as support from the  Oxford-Man Institute of Quantitative Finance. Finally ,  Nour Meddahi  has also benefited from the financial support of the chair " Marché des risques et création de valeur" Fondation du risque/SCOR.
The integration profile of GLOBE-AS3 in pre-transplant CD34 + cells was analyzed in each transduction and in the merged TD1+TD2 dataset. Sequencing of the LM-PCR libraries generated 2. 6-2. 8 million reads/sample, corresponding to about 5. 5 million reads in the merged TD1+TD2 dataset. Overall, a total of 41,979 ISs were retrieved from pre-transplant CD34 + cells, with 13,459 and 28,721 unique ISs retrieved from TD1 and TD2 CD34 + cells, respectively (Table  1 ). The GLOBE-AS3 vector showed the canonical LV genome-wide integration profile in CD34 + cells, with a prevalence of intragenic, mostly intronic ISs (81% in both TD1 and TD2; Figure  5B ). Overall, the 41,979 ISs targeted 7,989 genes, corresponding to 28. 5% of the annotated human genes. Most of the targeted genes were in common between TD1 and TD2, with the larger dataset (TD2) including the majority (77%) of the genes targeted in the smaller one (TD1) (Figure  5C ).
We then analyzed the genes targeted at a statistically significant (p < 0. 001) higher frequency with respect to an in silico-generated random set of integrations in the human genome, after Bonferroni correction for false discovery rate. By this analysis, 266 and 415 genes, respectively, were significantly over-targeted by GLOBE-AS3 in TD1 and TD2, respectively. Most of the genes over-targeted in TD1 (56%) were over-targeted also in TD2 (Figure  5C ), and among the top-100 over-targeted genes (by IS frequency), 55 were in common between the TD1 and TD2 datasets (Table  S1 ). The top-100 list contains most of the genes preferentially targeted by LV integration in HSPCs of patients treated by gene therapy for hematological and non-hematological disorders (in bold in Table  S1 ). Among the targeted genes (merged TD1+TD2), several functional categories were enriched by DAVID analysis (Bonferroni corrected, p % 0.
Integration Profile of GLOBE-AS3 in Human NSG-Repopulating Cells The integration profile of GLOBE-AS3 was then analyzed in the BM of each transplanted mouse in the TD1 and TD2 groups, as well as in the merged TD1+TD2 dataset, and the post-transplantation profiles compared with the corresponding pre-transplant CD34 + datasets. Sequencing of the LM-PCR libraries generated 1-2. 3 million reads/ sample, corresponding to about 12 million reads in the merged TD1+TD2 dataset (Table  1 ). We retrieved between 615 and 2,036 ISs from individual BMs, suggesting a highly polyclonal repopulation in all transplanted mice (Table  1 ). On average, TD2 BMs yielded a higher number of ISs, in line with the higher average VCN observed in these animals and possibly reflecting a better transduction of the TD2 CD34 + cells (Table  1 ).
The global post-transplant LV integration profile showed a prevalence of intragenic ISs (71% in TD1 and 78% in TD2), although their proportion was significantly decreased with respect to the corresponding pre-transplant CD34 + samples (c 2 test, p < 0. 0001; Figure  5B ), suggesting a negative in vivo selection of cells hosting intragenic integrations. The difference was significant also when considering only the integrations in exons (4. 88% in the TD1+TD2 pre-transplant CD34 + cells versus 4. 44% in the corresponding post-transplant BM; c 2 test, p < 0. 0001; values rounded to 5% and 4% in Figure  5B ). No selection for the orientation of the provirus with respect to the target gene was observed in the post-versus pre-transplant samples (Table  S3 ). Most of the genes hosting a GLOBE-AS3 IS in the post-transplant BMs were targeted also in the corresponding pre-transplant CD34 + dataset (68% in TD1 and 85% in TD2; Figure  5D ), and no gene was targeted at a statistically higher frequency in BM cells (merged TD1+TD2) with respect to the corresponding pre-transplant samples.
Vector integration analysis at the end of the study showed that BM repopulation was sustained by a largely polyclonal repertoire of HSCs, with no signs of vector-driven clonal dominance and no significant selection of cells carrying integration in specific genes, as evaluated by comparing pre-transplant and post-transplant dataset in individual animals. Unexpectedly, we found a few integrations in common among different mice transplanted with the same transduced cell batch, suggesting that at least some NSG-repopulating cells do expand in the pre-activation and transduction culture period without losing repopulation capacity. Alternatively, although less likely, these findings may indicate that LVs have a certain probability of integrating at exactly the same nucleotide in different repopulating cells.
IVIM Assay The IVIM assay was performed as previously described. 18, 19, 21 We transduced 1 Â 10 5 cells and expanded them for 2 weeks, prior to limiting dilution replating with 100 cells per 96-well. VCN measurement was performed on day 4 after transduction. The MTT assay was used for quantification of cell proliferation in 96-well plates. 35 For determination of wells with proliferation, we used the highest absorbance value of the respective Mock plates, which were below a previously determine clonal proliferation level (5. 61-fold the absorbance value of microscopically confirmed negative wells). Based on meta-data from positive and negative controls of previous assays, we defined values between the lower limit of detection (LOD = 1. 05 Â 10 À4 ) and a Q1 quantification limit (RF = 34. 17 Â 10 À4 ) as background.
Amplicons ranging from 200 to 500 kb were manually extracted from the gel and purified by NucleoSpin Gel and PCR Clean-up kit. About 1 mg of the final libraries were subsequently processed with MiSeq Reagent Kit v3 (2 Â 300 bp pair-end sequencing) following the manufacturer's instructions, bar-coded with a 6-bp sample-specific tag, and sequenced to saturation on an Illumina MySeq machine at IGA Technology Services (Udine, Italy). Raw reads resulting from Illumina paired-end sequencing were bioinformatically trimmed to recover the human genome sequences adjacent to the 3 0 proviral LTR by the Skewer software (mismatch rate of 0. 12, minimal length of 20 bp after trimming, and a minimal match length equal to the pre-trimming sequence) and mapped on the reference genome (human GRCh37. 75/hg19) by the Bowtie2 software (>95% identity).
The genomic coordinates of the first nucleotide in the host genome adjacent to the viral LTR were indicated as ISs. ISs originated by different reads mapping in the same genomic position were collapsed recovering the number of corresponding reads (read count). Unique ISs were annotated on the RefSeq gene database as intergenic or intragenic (exonic or intronic). Genes (defined by the most upstream transcription start site to the most downstream 3 0 end) hosting at least one IS were identified as target genes. Genes with targeting frequency significantly higher than random, defined by 200 random sampling of virtual ISs corresponding to 5,330,124 and 5,812,911 Tru91 sites in the human and murine genome, respectively, were defined as over-targeted, after Bonferroni correction of the comparison p values.
g. ,  Silva and Kidwell 2000; De Almeida and Carareto 2006; Diao et al. 2006; Ludwig et al. 2008; Pace et al. 2008; Ray et al. 2008; de Setta et al. 2009; Dias and Carareto 2012; Thomas et al. 2010; Gilbert et al. 2010; de Setta et al. 2011; El Baidouri et al. 2014; Dias et al. 2015; Kofler et al. 2015; Suh et al. 2016; Peccoud et al. 2017; Gao et al. 2018 ). Among the genetic elements involved in HTs in eukaryotes, transposable elements (TEs) are the most frequently reported  (Modolo et al. 2014). TEs are repetitive sequences of DNA that have the ability to mobilize in the genome, and due to this mobility, they stand out in HTT studies. The number of identified HTT events increased markedly in the last decade, from 156 cases described prior to 2007 to 2,855 at the time of this query  (December 2, 2018) , as shown in the database HTT-DB (Horizontally Transferred Transposable Elements DataBase, http://lpa.
saogabriel. unipampa. edu. br:8080/HTTdatabase/; last accessed December 2016). This database, as well as the results of previous studies (reviewed in  Schaack et al. 2010; Carareto 2011; Peccoud et al. 2017) , showed that the frequency of HTT is not constant among the different types of elements. A greater number of HTTs involve DNA transposons (2,178) rather than long terminal repeat (LTR) retrotransposons (353) and non-LTR retrotransposons (324). The difference in the frequency of HTTs can be explained by both the structural characteristics and the mechanisms of transposition of the different types of TEs, along with the functionality of the enzymatic machinery necessary to perform the initial transposition events. The presence of a TE as a free DNA molecule in the cell facilitates the transfer out of the cell and the insertion into another host genome, particularly for DNA transposons.
PCR fragments were purified with an Illustra GFX PCR DNA and Gel Band Purification Kit (GE Healthcare) and cloned with a TOPO TA Cloning Kit (Invitrogen) according to the manufacturer's instructions. Three to ten clones were randomly selected and sequenced in an ABI 3730 xl DNA Analyzer (Applied Biosystems), at the Center for Biological Resources and Genomic Biology (CREBIO, UNESP, Jaboticabal, Brazil), using the M13 primers. The RT sequences generated have been deposited in GenBank under accession numbers MH047863 to MH047945.
Identification of Elements in silico Sequences of the Helena and BS retrotransposons were also obtained both from public databases and from 26 genomes of Drosophila  (Sessegolo et al. 2016 ) and three genomes of Zaprionus made available by A. Haudry (supplementary tables S2, S3 and S4, Supplementary Material online). Raw reads were automatically filtered for quality using UrQt  (Modolo and Lerat 2015). TEs were identified by de novo assembly and annotation in dnaPipeTE  (Goubert et al. 2015)  from random samples corresponding to 0. 25Â coverage. To identify the TEs in the publicly available genomes, we searched for sequence similarity with the reference sequences of the Helena element of D. simulans (4,912 bp;  Rebollo et al. 2008)  and the BS element of D. melanogaster (5,124 bp;  Udomkit et al.
1995)  using BLASTn  (Altschul et al. 1990 ) with a cut-off value 1e À10. The three best hits were extended by 3 kb in the 5 0 and 3 0 flanking regions to allow identification of the ends of the elements. The most conserved and most complete sequence obtained by BLASTn was considered as the reference sequence for each element in each genome. With the reference sequence of each species, the BLASTn search was performed a second time, using as parameters length !300 bp and identity !80%. Finally, each putative TE sequence was analyzed for the presence and integrity of the PRE_C2H2 (ORF1), ENDO_EXO and RTASE (ORF2) of the Helena and BS coding sequences using a BLAST CD-search against the Conserved Domain Database (CDD) (www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi; last accessed July 2016).
Phylogenetic Inferences From all Helena and BS sequences obtained, the RT sequences were extracted and aligned with the RT-amplified sequences of Zaprionus species using MAFFT software  (Katoh et al. 2017). The phylogenies were reconstructed using the maximum likelihood (ML) method with 1,000 bootstrap replicates to validate the robustness of the phylogeny  (Felsenstein 1985). Additionally, Bayesian inferences of phylogeny (BI) were performed with the program BEAST v16. 1  (Drummond et al. 2012 ) with an a posteriori phylogenetic support test, which involved the sampling of 100,000 trees with 10% burn-in. The evolutionary model of substitution that best fit the data was determined by the Find Best DNA Model (MEGA7,  Kumar et al. 2016). The sequences of elements belonging to the Jockey clade to which Helena and BS belong (Metcalfe and Casane 2014), Jockey1 (zind_LINE_comp657, zafr_LINE_comp2868, divir_LINE_ comp1826) and Jockey2 (dan_LINE_comp1636, dbip_LINE_ comp1936) obtained from the dnaPipeTE data set, were used as an outgroup.
Sequences of the elements Doc (X17551. 1) and TART (U02279. 1), which also belong to the Jockey clade, were obtained from GenBank and used as an outgroup. The nucleotide divergence (p-distance) was estimated using MEGA 7. The evolutionary relationships between the sequences of Helena and BS were also inferred using the median-joining algorithm implemented by the Network 5. 0. 0. 1 program  (Bandelt et al. 1999) , with the default parameters. Amino acid sequences were used due to the high nucleotide variation between sequences of distantly related species. The networks show a feature not found in phylogenetic analyses: the proposition of median vectors that represent sequences inferred either because they were not sampled or because they were lost during evolution, a likely pattern in the evolutionary cycle of TEs.
These vectors can also represent the ancestral state of a sequence, a plausible scenario, because once the element transposes, it begins to evolve independently in the genome  (Cordaux et al. 2004). The divergence times of the BS and the Helena sequences from the most recent ancestral sequence shared by the species were estimated using the Bayesian approach and BEAST v16. 1  (Drummond et al. 2012) , with a neutral nucleotide substitution rate of r ¼ 0. 016/site/My  (Sharp and Li 1989)  for the calibration of the phylogenetic tree. The phylogeny of the 26 drosophilid species involved in this study was reconstructed based on the nuclear gene Amyrel, which was obtained from GenBank for most of the species (supplementary table S5, Supplementary Material online). For D. mojavensis and D. ananassae, the sequences were obtained using Genome Browser tools (https://genome.
Statistically significant deviation is interpreted as indicative of HTT. Thirty orthologous genes from 20 species were used. The gene sequences of D. melanogaster, D. sechellia, D. simulans, D. yakuba, D. erecta, D. ananassae, D. biarmipes, D. bipectianta, D. elegans, D. ficusphila, D. takahashii, D. kikkawai, D. grimshawi, D. persimilis, D. pseudoobscura, D. mojavensis, and D. virilis were obtained from the HTT-DB (http://lpa. saogabriel. unipampa. edu. br:8080/httdatabase; last accessed December 2016) whereas those of the species Z. africanus, Z. gabonicus and Z. indianus were obtained through searching directly the genomes. The orthology of these genes was verified using OrthoDB-The Hierarchical Catalog of Orthologs v9. 1 (http://www. orthodb. org/; last accessed February 2017). All genes were single-copy, except for the CG4386 gene of D.
Combined with the HTT events published before 2018, 302 HTT cases are reported at http://lpa. saogabriel. unipampa. edu. br:8080/httdatabase/resultado/resultado. jsp?organism; last accessed December 2016, but in most cases, the TEs involved are identified only at the superfamily level. Among them, 18 HTT cases deserve special mention because they exemplify the ability of specific LINE families to travel across genomes from species belonging to the same genus to higher taxa. Of these cases, 16 events involved the transfers of five LINE families in Drosophila: two events involved Jockey  (Mizrokhi and Mazo 1990; S anchez-Gracia et al. 2005) , one event involved the F element, one event involved Doc (S anchez-  Gracia et al. 2005) , one event involved the I element  (Kidwell 1983; Br egliano and Kidwell 1983; Bucheton et al.
Acknowledgments This research was supported by a grant from the  Brazilian agency FAPESP-Fundac¸ão de Amparo a Pesquisa do Estado de São Paulo  (Grant number  2016/19271-2  awarded to CMAC), by  APEGE CNRS DynET and BQR Universit e Lyon1 EC Grants  awarded to AH, and by  CNPq-Conselho Nacional de Desenvolvimento Cient ıfico e Tecnol ogico  (Grant number  303455/2017-9 ) and  CAPES (Coordenac¸ão de Aperfeic¸oamento de Pessoal de N ıvel Superior ) fellowships awarded to CMAC and MCS, respectively. We thank  J. R. David  for kindly providing the Zaprionus lines,  G. L. Wallau  for supporting the VHICA analyses, and  I. L. Tambones  for helping with the design of the figures. We also thank two anonymous reviewers for their valuable contributions.
Abstract. Soil moisture is a key variable of land surface hydrology, and its correct representation in land surface models is crucial for local to global climate predictions. The errors may come from the model itself (structure and parameterization) but also from the meteorological forcing used. In order to separate the two source of errors, four atmospheric forcing datasets, GSWP3 (Global Soil Wetness Project Phase 3), PGF (Princeton Global meteorological Forcing), CRU-NCEP (Climatic Research Unit-National Center for Environmental Prediction), and WFDEI (WATCH Forcing Data methodology applied to ERA-Interim reanalysis data), were used to drive simulations in China by the land surface model ORCHIDEE-MICT(ORganizing Carbon and Hydrology in Dynamic EcosystEms: aMeliorated Interactions between Carbon and Temperature).
Simulated soil moisture was compared with in situ and satellite datasets at different spatial and temporal scales in order to (1) estimate the ability of ORCHIDEE-MICT to represent soil moisture dynamics in China; (2) demonstrate the most suitable forcing dataset for further hydrological studies in Yangtze and Yellow River basins; and (3) understand the discrepancies of simulated soil moisture among simulations. Results showed that ORCHIDEE-MICT can simulate reasonable soil moisture dynamics in China, but the quality varies with forcing data. Simulated soil moisture driven by GSWP3 and WFDEI shows the best performance according to the root mean square error (RMSE) and correlation coefficient, respectively, suggesting that both GSWP3 and WFDEI are good choices for further hydrological studies in the two catchments.
, 2011; Teuling et al. , 2010; Wanders et al. , 2014). In the investigation of spatial and temporal SM variations, in situ measurements  (Dorigo et al. , 2011; Liu et al. , 2001; Piao et al. , 2009; Robock et al. , 2000)  are too sparse and not always representative of larger scales. Although they can provide firsthand records of SM fluctuations, the density of in situ networks cannot meet the requirement for continentalscale studies. , and the different measurement techniques make it difficult to combine different datasets. Satellite-based SM products  (Dorigo et al. , 2015; Njoku et al. , 2003; Su et al. , 2003; Wagner et al. , 2012)  provide excellent spatial coverage and temporal sampling, but their accuracy varies between instruments and retrieval algorithms used  (Liu et al. , 2012).