dataesr/scientific-paragraphs-categorization huggingface dataset:
A database of MHC haplotype sequences The complete sequencing and assembly method was then applied to the remaining MHC homozygous cell lines. Sixty of the IHWG cell lines are homozygous through the entire target of ∼5 Mbp  (Norman et al. 2015). From these cells, we obtained a mean of 4. 86 Mbp (±71. 2 kbp) final sequence, consisting of ∼100 'super-scaffolds' per cell line (Fig. 3A ; Supplemental Table  S1 ). These data therefore represent unambiguously phased MHC region haplotypes with an estimated mean completion of 98. 3%. These final sequences contain short gaps (a mean of 192 ± 42 gaps per full haplotype) representing contigs that were identified to be adjacent but not completely joined. Many of these gaps could be closed using less stringent scaffold assembly or by simple manual intervention, but we chose to retain an approach that limits the potential for false scaffold joining.
High sequence and structural diversity of MHC region coding sequences The human MHC reference sequence contains 165 expressed genes (Supplemental Table  S3 ). We generated unambiguous full-length coding DNA sequence (CDS) for each of these genes. In analyzing these data, our emphasis was on the predicted polypeptide sequences. This was partly because phased CDS diversity has rarely been investigated in genome studies but mainly because HLA class I and II gene polymorphism involves nonsynonymous substitutions at sites that modulate protein function and are implicated in the mechanisms that underlie the associations of HLA class I and II with disease (de Bakker and Raychaudhuri 2012;  Illing et al. 2013; McLaren and Carrington 2015). For 23 of the MHC region genes, the allelic variation is curated by the Immuno Polymorphism Database (IPD)  (Robinson et al.
2015). In the IHWG cell lines, we observed 304 unique CDSs of these 23 genes (encoding 271 unique polypeptides), and they are given in Supplemental Table  S2. From the remaining 142 genes, we characterized 11,571 CDS. For these 95 homozygous cell lines, the CDSs represent 1. 82 Gbp of DNA, comprising 800 unique CDS. BLAST searches showed that 140/800 CDS matched the RefSeq allele sequence and that 131 of the remaining 660 CDS are present in sequence databases. Thus, 66% of the (non-IPD) CDSs we characterized have not been described previously (Supplemental Table  S3 ). Although a majority of variant sites are known and have dbSNP IDs, their contribution in the context of genomic CDS diversity has not been examined previously. The IHWG cells are monomorphic for 32 of the expressed polypeptides (Fig.
4A ; Supplemental Table  S3 ). Several genes, such as HLA-F, CCHCR1, and the heat-shock protein genes HSPA1L, HSPA1A, and HSPA1B, combine low polypeptide diversity with higher nucleo-tide diversity in the CDS. This observation raises the possibility that low frequency protein-coding variants of these genes might influence disease. Because the cell panel represents a variety of common HLA class I and II haplotypes, many of which are associated with diseases mediated by immune mechanisms that are poorly understood, we also compiled a database of the CDS alleles for use in future studies (Supplemental Table  S4 ).
Low-passage samples of the B cell lines were grown from archival material, available at Stanford University, or purchased from the International Histocompatibility Working Group (http://www. ihwg. org/). Included in our panel were the eight HLA class I and II homozygous cell lines studied by the MHC Sequencing Consortium (PGF, COX, DBB, QBL, MANN, SSTO, MCF, APD) and for which the MHC haplotypes were sequenced to varying extent  (Horton et al. 2004 ). Data from these 'Sanger 8 reference' cells have formed the reference, and alternative references for the MHC region of the human genome, since build NCBI36/hg18.
S2 ). The library preparation, enrichment process, and sequencing methodologies are all described in the Supplemental Information. Except where indicated otherwise, we used 800-bp DNA fragments and 2 × 100-bp paired-end reads. While optimizing methods using the COX cell line, we identified and confirmed 19 sequence differences-18 SNPs and one 4-kbp insertion-from the published COX haplotype (Supplemental Information; Supplemental Fig. S4 ). A modified reference haplotype (COX_IIb; 4,799,503 bp), containing these differences, was used to harvest and map reads having no nucleotide mismatches in order to generate a final set of coverage statistics for the data derived from COX. We optimized the methods using COX because this cell line is homozygous through the entire MHC region, while PGF is only homozygous through ∼4 Mbp  (Norman et al.
No threshold below 25 or Shown is an overview of the protocol for selecting reads as linkers, determining contig joins to make scaffolds, and ordering and making the final sequence. Yellow boxes denote data, red boxes denote existing computer programs that were used (these are named at the left), and blue boxes denote in-house scripts. Gray hexagons are used to show data that are not used further in the pipeline. above 80 was allowed (with the exception of COX and the other Sanger 8 cells that were sequenced to much higher depth; here, the threshold was set at 150). Potential joins were not allowed to become actual joins if more than one linker set was found that satisfied both the position and threshold criteria, or if a given contig was linked to more than one other contig in the same direction.
Genotyping complement component C4 To determine the number of C4A/C4B genes present in each cell, we randomly selected eight gene sequences from the MHC region data set (ABCF1, ATAT1, Complement C2, DHX16, KIFC1, TRIM27, VARS, and VPS52), trimmed them to the same length as the sequence of a C4BS gene (14,250 bp obtained from AL049547), and used them all as mapping targets. We then removed any reads that could map elsewhere in the MHC haplotype and calculated the copy number as the mean ratio of C4A/C4B reads to each of the eight targets, with guidance from previous results  (Wu et al. 2007)  to set the thresholds. We determined the ratio of C4A vs. C4B and L vs. S (described in the Supplemental Material) using virtual sequence probes and the method described for KIR  (Norman et al. 2016). Again, the threshold values were set according to genotypes previously reported for 40 of the cell lines  (Wu et al.
Acknowledgments This study was supported by  US National Institutes of Health (National Institute of Allergy and Infectious Diseases)  grant  U01 AI090905. We thank  Steven G. E. Marsh  for expert advice. Author contributions: P. J. N. , M. R. , K. L. G. , L. A. R. , and P. P. designed the project. S. J. N. , N. N. G. , W. C. , C. A. , and P. J. N. generated data. P. J. N. , T. R. , L. A. G. , T. D. , T. M. , and J. A. H. designed and implemented bioinformatics tools. P. J. N. , L. A. G. , T. R. , E. E. W. , S. J. N. , M. S. W. , and N. N. G. analyzed data. P. J. N. and P. P. wrote the paper, which was edited and approved by all authors.
Data access The raw sequence reads, contigs, and scaffolds from this study have been submitted to the EMBL-EBI European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena). All sequencing and assembled MHC haplotype data are linked to the NCBI BioProject (https://www. ncbi. nlm. nih. gov/bioproject/) accession number PRJEB6763. ENA accession numbers are listed in Supplemental Table  S1. All data are also available via ImmPort (https:// immport. niaid. nih. gov/) with the project accession SDY691 (EXP13735). The CDS alleles have been deposited at GenBank (https://www. ncbi. nlm. nih. gov/nucleotide/) under accession numbers KP774801-KP774802, KY497260-KY497428, KY500 931-KY500951, and KY500134-KY500924 and are listed in Supplemental Tables  S2  and S3. All scripts and filters are available at https://github.
The paper is organized as follows, Sect. 2 includes a brief presentation of the EMCORDEX simulations, the reference data sets and the statistical metrics applied to seasonal mean and extreme precipitation values. Section 3 presents the RCM evaluation in terms of mean and extreme precipitation values over the present period when the global scale is prescribed by ERA-Interim. Section 4 analyzes present climate simulations to understand the role of the GCMs in driving the RCMs. The impacts of climate change on precipitation are then examined in Sect. 5. Conclusions and perspectives are finally given in Sect. 6.
, 2016b). SAFRAN data set has been evaluated using the Météo-France and Spanish State Meteorological Agency (AEMET) gauging station network as well as independent data  (Quintana-Seguí et al. , 2008 , 2016a, b; Vidal et al. , 2010). SAFRAN product is used here as a reference data set to evaluate the simulated precipitation from EVAL and HIST ensembles. For that purpose, the 12 km RCM outputs and also SAFRAN Spain were regridded on the 8 km SAFRAN-France grid using the ESMF (Earth System Modeling Framework) bilinear regridding method. The impact of interpolation has been largely investigated in the literature, for instance in  Diaconescu et al. (2015) , and it should be quantified before interpretation of interpolated fields. In the present study, the impact of a bilinear scheme for the interpolation of precipitation fields from 12 km simulation grid to 8 km SAFRAN grid was assessed from an analytical test case based on the cosine function of longitude and latitude.
Annual cycle of precipitation bias The 30-year climatology for the monthly cumulative precipitation is spatially averaged over each region of interest and normalized by the SAFRAN monthly climatology. Figure  2a  displays the annual cycle of precipitation over the Aude box for SAFRAN data set. Figure  2b  displays the bias of the annual cycle over the Aude region for each RCM. Globally speaking, the RCM bias and the inter-model spread are smaller in autumn than in summer. This can be explained by the influence of the large-scale atmospheric circulation being weaker in summer, the control exerted by the LBCs on the RCM is reduced in this season and the RCM solution has more degrees of freedom to deviate from the large-scale forcing  (Déqué et al. , 2012; Lucas-Picher et al. , 2008). ALADIN52 and ALADIN53 clearly overestimate the cumulative monthly precipitation in late spring and summer.
The RCA4 overestimation of mean precipitation is in accordance with the results showed in  Prein et al. (2016). The underestima-tion of extreme precipitation diagnosed in our study is possibly even more severe than our reference, SAFRAN, found to underestimate observed extreme precipitation  (Quintana-Seguí et al. , 2008a). This last point was confirmed through a comparison between pluviometer data and SAFRAN over our catchments of interest. Parameterization and physical processes that are involved in the generation of low precipitation differ from those involved in stronger precipitation events. Thus the precipitation bias varies according to the precipitation intensity. Quantile classification is thereby relevant for the determination of past/future precipitation change. 4 Analysis of RCM historical simulations RCM intrinsic biases, determined from the EVAL ensemble, have been characterized and described in the previous section.
Discussion The present study assessed the intensification of extreme precipitation events under climate change on small Mediterranean catchments using high-resolution RCM simulations (∼ 12 km) from the EMCORDEX exercise. It was shown that over the past period , EVAL simulations (RCM driven by ERAI) and HIST simulations (RCM is driven by a GCM) underestimate extreme events with respect to SAFRAN data set. Despite SAFRAN being the best gridded observation data set available covering the region of interest as explained in Sect. 2. 3, the underestimation highlighted here is potentially more severe as SAFRAN is itself deemed to underestimate observed extreme precipitation events  (Quintana-Seguí et al. , 2008a). Indeed, complementary analysis (not shown here) on the Aude and Lez catchments highlighted that SAFRAN underestimates precipitation quantiles beyond the 95th quantile by up to 30 % with respect to pluviometer data from the Météo-France network.
It should be noted that comparison between the SAFRAN gridded product and the pluviometer sparse product is challenging and that these products are not independent. However, more representative computation of observed quantiles from pluviometer data set in place of SAFRAN ones is one potential gain from the present study. This paves the way towards a time-varying quantile classification with an intra-daily cycle, for instance by using 3 h pluviometer data. Such an intra-daily cycle could also be represented in the past/future change coefficients using 3 h EM-CORDEX precipitation outputs. Working on higher temporal resolution data (observation and simulation) would lead to a futurization method more consistent with flash flood timescales than the daily current method. Future changes in precipitation are assessed through a multi-model approach focusing on the mean and standard deviations of the EMCORDEX ensemble.
In the future, change coefficients could be used to provide a kind of futurized extreme precipitation event that occurred in the past. In further studies, the hydrological impact of this futurized precipitation will be assessed using rainfallrunoff models over the Lez, Aude and Muga catchments. This generic method could also be applied to other catchments. Data availability. The raw data corresponding to the precipitation simulated by the 8 RCMs from EMCORDEX can be accessed on the CORDEX or the ESGF websites. The postreated data are conserved by the CECI-CERFACS and WSP France. To consult it, please contact the authors.
Acknowledgements. This work is an initiative of  WSP-France. The financial support for this work has been provided by  WSP-France  under the CIFRE contract  2015/005. This paper was written under the framework of the  International HYMEX  project and the  Spanish HOPE  ( CGL2014-52571-R ) project. We acknowledge the  World Climate Research Programme's Working Group on Regional Climate , and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure an international effort led by the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison , the  European Network  for Earth System Modelling and other partners in the  Global Organisation for Earth System Science Portals (GO-ESSP ).
The authors wish to especially acknowledge  Samuel Somot  ( CNRM ) for his fruitful discussions about regional modeling and the MedCORDEX database. Thanks are given to  Elisabeth Harader  for some methodological ideas examined in their PhD thesis. Special thanks are also given to  Jean-Marc Lacave  and  Gwenaëlle Hello  from  Météo-France  for providing the daily and 3 h pluviometer data, and to  Céline Vargel  for her useful work on the comparison between SAFRAN and pluviometer data. Edited by:  Shraddhanand Shukla Reviewed  by: two anonymous referees.
The European Association Research Ltd (EARL) accreditation program  [15]  is an SUV harmonization strategy aiming at minimizing the variability in SUV measurements by harmonizing patient preparation and scan acquisition and processing  [16]. While many sources of error in SUV measurements are overcome by complying with the EANM guidelines for PET tumour imaging  [17] [18] [19] , reconstruction-dependent variations require either the use of an additional filtering step  [20]  or the generation of two sets of images: one to provide optimal diagnostic quality and another to meet quantitative harmonization standards  [21]. Previous research from the collaborators in this study have shown that SUV max is more sensitive to reconstruction inconsistency than SUV peak  [20]  and that reconstruction inconsistencies may affect PERCIST classification  [22].
Consequently, one could expect a more significant impact of these inconsistencies on EORTC classification, which is based on SUV max variation, than on PERCIST, which is based on SUV peak. The aim of this study was to evaluate the impact of SUV reconstruction dependency on PERCIST and EORTC classification and the ability of the EARL program to minimize variability in response assessment. To assess this, we reconstructed the same PET raw data with an OSEM algorithm known to meet EANM requirements and also with PSF with or without TOF reconstruction (PSF ± TOF). Post-reconstruction filtering was then applied to the PSF ± TOF reconstruction with EQ. PET (Siemens Medical Solutions), a proprietary software solution allowing visualization of optimized images while simultaneously obtaining harmonized SUV values  [20, 23].
Patient preparation, PET acquisition and reconstruction parameters All patients were requested to fast for 6 h prior to the 18 F-FDG injection. Patient height, weight and blood glucose levels were recorded. Patients were injected intravenously with 18 F-FDG, followed by a 60 min rest in a warm room. A daily calibration of each PET system was performed with a 68 Ge source according to the manufacturer's protocol. A quarterly cross-calibration of each PET system was performed according to the EANM guidelines, as described elsewhere  [17, 18] , and clocks from workstations were synchronized weekly. Patients were scanned from the skull vertex or base to the mid-thighs. All raw PET data were reconstructed with the local PSF ± TOF settings for optimal lesion detection and an OSEM-3D reconstruction algorithm fulfilling the EANM guidelines regarding recovery coefficients (Table  1 ).
EQ. PET methodology For each PET system, the EQ. PET filter was calculated on the phantom data of each PSF ± TOF reconstruction as described in details elsewhere  [21]. Briefly, the recovery coefficients (RCs; defined as the ratio between the measured and true activity concentration for each sphere) of a National Electrical Manufacturers Association NU2 phantom scanned as per EANM guidelines were aligned to the EANM reference RCs by applying a Gaussian filter.
PERCIST and EORTC evaluation All PET exams were analyzed on Syngo. via software equipped with EQ. PET (Siemens Medical Solutions). For interpretation purposes, both the reconstruction for optimal Table 1 PET/CT acquisition and reconstruction parameters for the three participating centres lesion detection (PSF ± TOF) and the OSEM reconstruction were displayed on the screen together with the EQ. PET-filtered harmonized SUV results for the tumour region(s) of interest. The EQ. PET-filtered images were not displayed on the screen. For PERCIST criteria  [8] , the measurable target lesion is the single most intense tumour site on pre-and post-treatment scans, which means that the target lesion is not necessarily the same pre-and post-treatment. As per EORTC PET response criteria, the volumes of interest (VOI) should involve the same tumour lesion on pre-and post-treatment scan.
Statistical analysis Quantitative data from clinical PET/CT examinations are presented as mean (standard deviation ± SD). The relationship between PSF ± TOF, PSF ± TOF. EQ and OSEM quantitative values were assessed with Bland-Altman plots. Levels of agreement between the different types of reconstruction were evaluated using the kappa statistic. The use of OSEM reconstruction for both pre-and post-therapeutic PET examinations (OSEM-PET1 /OSEM PET2 ) was used as the "current standard" to classify the therapeutic response of each lesion and compared to other scenarios. Kappa values were reported using the benchmarks of Landis and Koch  [24]. Graphs and analyses were carried out using Prism GraphPad and the Vassar University website for statistical computation (http://vassarstats. net).
Impact of reconstruction-dependent variation on SUL changes between baseline and post-treatment scans The same target lesion for baseline and post-treatment scans was used for EORTC classification except for two patients. The first patient displayed a large tumoural and nodal complex for which the EQ. PET software was unable to differentiate nodes from a tumour on post-treatment scan. The second patient had a complete disappearance of the initial target lesion in a patient with multiple tumour lesions, requiring to use the hottest remaining lesion on post-treatment scan. The variations in SUL max and SUL peak between the pre-and post-treatment scans are shown in Fig. 2. For the OSEM PET1 /OSEM PET2 scenario, which was taken as the reference standard, the change in SUL max was -57. 5% ± 23.
9% NaCl) was achieved to reach an overall volume of 1 mL. Hence, such studies required significant concentrations in fluorophores, which may induce some stacking and affect their optical properties, possibly resulting in the 20 nm bathochromic shift noticed in the fluorescence emission spectra. Measurement parameters were: bioluminescence mode, gate time of 10 s, 20 nm emission slit, 3 nm of data interval, Stavinsky smoothing (factor 5). Radioactivity was measured with a dose calibrator (MEDI 405, Medisystem, France). Baseline curves were found to be pretty dependent upon the volume of saline solution added and the nature of the solvent in the fluorophore solution. That is why, to be comparable, curves were adjusted to a same value at 700 nm by substraction with an adapted constant value  20.
Wavelength resolved spectral imaging was carried out using a set of narrow bands emission filters: 520, 570, 620, 670, 710, 790 and 845 nm. Depending on the experiment the filter of choice were selected, either all to get a complete view (Fig. 6 ) or narrowed to a few filters (Fig. 7 ). For the phantom and mice studies the acquisitions were performed over wavelength (emission) ranging from 520 to 845 nm and acquisition parameters were as follows: binning factor 8; field of view 12. 5 cm, F number 1, exposure time from 10 to 60 seconds. Images were acquired and analyzed using Living Image 4. 5. 2 software (Perkin Elmer, USA). Name of the institutions or licensing bodies that approved the experimental protocols. Nuclear safety. (a) Safety rules and protocols were set at and approved by the Centre George-François Leclerc (CGFL) and applied at CGFL Preclinical Imaging Platform; (b) The protocols strictly comply with the regulations governing radiopharmaceuticals  34  and standards of the French Nuclear Safety Agency (ASN) that approves CGFL protocols.
Acknowledgements Burgundy General Council CRB (FABER program: RD; PhD fellowship: YB), and  CNRS  ( Chaire d'Excellence Program: RD ) are acknowledged. Oncodesign for providing EMT6-tumor bearing BALB-c mice,  A. Romiev  for Cy5,  V. Lioret/C. Drovet  for repeating a few experiements. This work was supported by the  3MIM  agreement (CNRS, uB, CRB), and labelled by the  PharmImage consortium. This work was also supported by a  French Government  grant managed by the  French National Research Agency (ANR)  under the  program 'Investissements d'Avenir'  (with reference  ANR-10-EQPX-05-01/IMAPPI Equipex ) and by the  Fondation de Coopération Scientifique Bourgogne Franche-Comté.
5Å (PDB: 1LUJ,  [22] ) and 2. 1Å resolution (PDB: 1M1E,  [23] ) have shown that the two proteins interact in an anti-parallel orientation (Fig  1A ). Two main regions in ICAT have been identified: i) the N-terminal domain consists of a bundle of three α helices, with helix 1 (H1) directly interacting with Arm repeats 10-12 of β-catenin; ii) the C-terminal domain is intrinsically unstructured but adopts a βstrand like structure when bound to β-catenin Arm repeats 5-9. The latter domain contains a β-catenin peptide-binding motif DXθθXFX 2-7 E, where θ is an aliphatic hydrophobic amino acid, F an aromatic residue (mainly Phenylalanine) and X 2-7, two to seven variable residues  (Fig 1B). This consensus motif is present in several β-catenin ligands including E-cadherin, APC, Axin and members of the TCF/LEF family, to mediate their interaction with the groove of β-catenin  [24] [25] [26].