dataesr/scientific-paragraphs-categorization huggingface dataset:
Waves Data on wave characteristics have been derived from the ECMWF ERA-5 dataset  [54]. Data from the period 1979-2019 have been used in the analysis of wave characteristics for both the Lampedusa and MOBY areas. The median of the significant wave height at Lampedusa is about 0. 9 m, while it is about 1. 1 m in the MOBY area. The significant wave height shows a very limited spatial variability in the region around Lampedusa, much smaller than in the MOBY region. For Lampedusa, the significant wave height is expected to be > 1 m for about 40% of the time. Moreover, relatively high waves are mainly concentrated in winter. Waves from the North-North-West sector are the most frequent, with other smaller contributing sectors from East and South-East. Significant differences in the wave period exist between the Lampedusa and the MOBY areas.
Inherent Optical Properties The Inherent Optical Properties (IOPs) were investigated using different the CMEMS datasets, for the January 2017-September 2019 period in the Mediterranean, and for the July 2018-November 2019 for the MOBY area; in-situ measurements of chlorophyll, CDOM and backscatter made at the Lampedusa Oceanographic Observatory at 4. 5 m depth in the period June 2018-July 2019  [49]. Overall, a very good agreement between satellite and in-situ determinations of chlorophyll is found and confirms that the central Mediterranean is an oligotrophic region. The chlorophyll amount shows a clear annual cycle, with a maximum in winter and a minimum in summer. The annual average chlorophyll amount is <0. 08 mg m -3 in both the Lampedusa and MOBY regions. The day-to-day variability, as retrieved from satellite observations, is <10% at both sites.
Aerosols Aerosol transport patterns and optical properties over Lampedusa have been extensively studied using ground-based measurements and satellite data (e. g. , references [57,  [61] [62] [63] ). Measurements of the aerosol optical properties were started at Lampedusa in 1999 using an MFRSR instrument; a CIMEL sunphotometer contributing to AERONET was set up in 2000, although it worked with some discontinuity during the first years of operation. An extended dataset combining these two datasets has been implemented, and a correction scheme has been developed to take into account differences in the field of view of the two instruments  [63]. Several studies show that, except for the cases directly influenced by Saharan dust transport events, Lampedusa is characterized by low aerosol optical depth (AOD).
When such events are not considered in the statistics the annual mean aerosol optical depth at 870 nm is about 0. 09 (as derived from the data by  [61] ). The distribution of the AOD daily average values at 870 nm for the period 1999-2018 (> 4500 days with measurements) is shown in Figure  12  Aerosol transport patterns and optical properties over Lampedusa have been extensively studied using ground-based measurements and satellite data (e. g. , references [57,  [61] [62] [63] ). Measurements of the aerosol optical properties were started at Lampedusa in 1999 using an MFRSR instrument; a CIMEL sunphotometer contributing to AERONET was set up in 2000, although it worked with some discontinuity during the first years of operation. An extended dataset combining these two datasets has been implemented, and a correction scheme has been developed to take into account differences in the field of view of the two instruments  [63].
2015  [4]  for MOBY (reprint in Figure  14 , bottom-right, blue curve for MOBY). The perfect consistency between these two assessments, made under completely different datasets and methods, is noteworthy-the EURYBIA computation relies on the uncertainty budget of the optical system (MarONet quantification) over Lampedusa conditions observed by OLCI, while the Zibordi et al. 2015  [4]  estimate relies on the statistical analysis of real SVC gains computed at MOBY for the SeaWiFS sensor. These results demonstrate the relevance of EURYBIA, based on both the state-of-the-art optical system and favorable conditions, while other systems cannot meet such performance (Figure  14 , bottom-right, other systems than MOBY). This purely random assumption may, however, not be strictly true for various sources of uncertainty.
Acknowledgments: The SVC design greatly benefited from comments of the review panel composed by  M. Wang ,  B. C. Johnson ,  A. Reppucci ,  G. Zibordi ,  R. Gilmore ,  K. N. Babu , and  EUMETSAT  Team Members composed by  E. Kwiatkowska ,  F. Montagner ,  I. Cazzaniga ,  E. Obligis ,  J. Chimot , and  D. Dessailly. The MOBY team (B. C. Johnson ,  M. Feinholtz ,  A. Gleason ,  S. Flora ,  T. Houlihan ,  M. Yarbrough , and  K. Voss ) contributed to the design and construction of MOBY-Refresh. Contributions by  C. Bommarito  and  D. Sferlazzo  are gratefully acknowledged. V. Artale  and  S. Marullo  are thanked for making available temperature and salinity data from the  Lampedusa Oceanographic Observatory. Analyses of OMI NO 2 data used in this paper were produced with the Giovanni online data system (https://giovanni.
Mutation analysis To further explore the possible genetic cause of the phenotype, we performed whole-exome sequencing (WES) on DNA isolated from blood from the proband and his parents. Genomic DNA was extracted from peripheral blood lymphocytes using a Qiasymphony DNA Midi Kit (Qiagen). A SeqCap EZ MedExome Library kit (NimbleGen, Roche Sequencing) was used for genomic capture. A NextSeq 500 Sequencer was used for massive parallel. Raw data was analyzed with in-house annotation and analysis pipelines. For data analysis, raw reads were mapped to the human genome reference-build hg19 using the Burrows Wheeler Aligner (BWA MEM v0. 717) alignment algorithm (H. Li and Durbin 2010). The resulting binary alignment/map (BAM) files were further processed by Genome Analysis Tool Kit HaplotypeCaller (GATK HC v3.
8) (Van der  Auwera et al. 2013). The VCF files are then annotated on Snpeff version 4. 3T  (Cingolani et al. 2012 ). The variants are finally identified according to the house criteria. Only coding non synonymous and splicing variants were considered. Variant filtration was conducted according to the transmission mode (de novo, autosomal recessive and X-linked), frequency of the variant in the gnomAD database and to our in-house database of n=866 exomes. The SMS variant was then verified by Sanger sequencing. The variant has been submitted to the ClinVar database.
Discussion To date, 11 hemizygous variants in the SMS gene causing SRS have been reported in 20 males from 9 families, according to Human Genome Mutation Database missense mutations (Table  2 ). Until now, the variants in SMS known to cause SRS were all missense mutations, resulting, when tested, in partial LoF of spermine synthase activity. Here, we report a new SRS patient, which is the first patient with a complete LoF variant in SMS. The missense mutations in SMS result in reduced SMS activity and an increased spermidine/spermine ratio between 2-10 whereas normal controls have a ratio of close to 1. In fact, in these patients, the activity of the enzyme decreases due to either a lack of stability of the protein, which is degraded and therefore cannot dimerize properly, or to the dysfunction of the active domain caused by the conformational change induced by the substituted amino-acid.
(Landrum et al. 2014 LoF (pLi = 0. 98 in GnomAD) and functionally studied missense mutations lead to a decreased function of the protein. The variant has been deposited in ClinVar database (https://www. ncbi. nlm. nih. gov/clinvar/)(Landrumet al. 2014) and can be found using accession number SCV000899235. The variant was validated by conventional Sanger sequencing (Supporting Information Figure.
(Landrum et al. 2014 LoF (pLi = 0. 98 in GnomAD) and functionally studied missense mutations lead to a decreased function of the protein. The variant has been deposited in ClinVar database (https://www. ncbi. nlm. nih. gov/clinvar/)(Landrumet al. 2014) and can be found using accession number SCV000899235. The variant was validated by conventional Sanger sequencing (Supporting Information Figure trio analysis revealed a de novo hemizygous frameshift in SMS: (RefSeqGene)NG_009228. 1:g. 49463_49466del; (RefSeqTranscript)NM_004595. 4:c. 908_911del; NP_004586. 2:p. (Met303LysfsTer3) located in exon 9. According to the ACMG criteria (Richards et al. 2015), this variant is classified as pathogenic (class 5) because of: (i) its de novo inheritance (PS2), (ii) its absence in general population alleles in gnomAD ascertained by august 2018 (PM2), (iii) the fact that it is a likely null allele in a hemizygous proband with a predicted non-sense mediated decay (PVS1).
63 indicating mutational significance  6  , and were absent or very rare in public gene mutation databases (Supplementary Fig. 2b ). Two mutations (in families B and D) were located at the 5′ end of the kinase inhibitory region, two (in families A and E) were located in the SH2 domain, and one (in family C) was located at the 5′ end of the SOCS-box domain (Fig. 1c ). Three distinct missense variants (in families A, D, and E) affected phylogenetically conserved amino acids (Supplementary Fig. 2C ). It is noteworthy that residues P123 and Y154 were previously reported as a site of somatic mutations in B cell lymphomas  7, 8. An analysis of the SOCS1 three-dimensional (3D) protein structure showed that P123 and Y154 are located within the groove (Fig. 1d ) that binds phosphorylated JAK peptides with high affinity  5.
Methods Study participants. All study participants and their parents provided written informed consent. The study was performed in accordance with the 1975 Declaration of Helsinki and subsequent revisions, and was approved by the local institutional review board (CPP Ile de France II (Paris, France), the French Advisory Committee on Data Processing in Medical Research (Paris, France) and the Ethics committee, University of Freiburg, Germany (No. 409/16). The probands (patients A1, A2, B1, B2, C1, D1, and E1) were included in clinical studies of early onset autoimmune diseases by three independent institutions (Imagine Institute (Paris, France), Lyon Hospital (Lyon, France), and Freiburg Hospital (Freiburg, Germany)). Genetic analysis. Samples of DNA were prepared from the patients' whole peripheral blood, using standard extraction methods.
Sequences were aligned with the hg19 reference human genome, using the Burrows-Wheeler Aligner. Downstream processing was carried out with the Genome Analysis Toolkit (GATK), SAMtools, and Picard, in line with documented best practices (http://www. broadinstitute. org/gatk/ guide/topic?name=best-practices). Variants were called with the GATK Unified Genotyper. All calls with a read coverage of ≤2X or a Phredscaled SNP quality score of ≤20 were removed from the analysis. All variants were annotated using a software system developed by the Paris Descartes University Bioinformatics platform. All the annotation procedures were based on the latest release of the Ensembl database. Whole-genome sequencing was performed by Edinburgh Genomics. Genomic DNA was prepared from whole blood samples from probands and parents, using the Qiagen Mini Kit according to the manufacturer's instructions.
The raw sequencing data were quality-checked using FastQC and MultiQC, and subsequently aligned with the GRCh37 hs37d5 reference using the Edico Dragen V2 single-sample workflow. Indels and SNPs were called using the same workflow on an FPGA AWS instance. The resulting variant call format files were then uploaded to the Congenica platform via the latter's Upload Client (v1. 7. 4) for annotation and causal variant prioritization. Variants were annotated using the Ensembl Variant Effect Predictor and scored for disease causality using Exomiser. For each variant in the proband, we determined the mode of inheritance in the family trio (de novo, autosomal recessive homozygous, autosomal recessive compound heterozygous, or X-linked recessive). DoNovoGear was used for the joint analysis of samples within a trio, in order to detect de novo mutations.
Variants with qualifying genotypes under each mode of inheritance were ranked using the Exomiser combined score and evaluated for rarity (MAF in ExAC and UK10K), pathogenicity (Polyphen2 and SIFT scores), genic intolerance to functional variation (RVIS and pLI scores) and biological relevance in the context of the proband's phenotype (using a Phenotype Consensus Analysis and a literature search). A rule-based scheme was used to identify candidate genes. The pathogenicity of the identified variants was defined according to the minor allele frequency in the general population according to the GnomAD database (https://gnomad. broadinstitute. org/); and the Combined Annotation-Dependent Depletion score  37. Sanger sequencing was performed to confirm the next-generation sequencing results and to analyze each mutation's familial segregation.
The primers used for PCR are reported in Supplementary Table  1. Purified PCR products were directly sequenced using BigDye Terminators (version 1. 1) and a 3500xL Genetic Analyzer (Applied Biosystems). Three-dimensional structure modeling. The model of the human SOCS1 3D structure was built on the basis of the experimental 3D structure of chicken SOCS1 (71 % sequence identity, pdb 6C7Y), according to the alignment given in Supplementary Fig. 2. The possible path of a phosphotyrosine peptide, represented in purple in Fig. 1d  (bottom view), with the side chain of the phosphotyrosine shown, was taken from the experimental 3D structure of the SOCS3:gp130 complex (Protein Data Bank 2HMH). The model of the whole JAK1/SOCS1/elongin B/ elongin C complex shown in Fig. 1d  (top) is based on the chicken SOCS1/JAK1 (Protein Data Bank 6C7Y) and X.
laevis SOCS1/Elongin B/Elongin C (Protein Data Bank 6C5X) 3D structures. Modeling was made using Modeller v9. 22  38  and 3D vizualisation using Chimera 39. Cell culture. Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll separation (Lonza) and then cultured in Panserin 401 medium (Dutcher) with 5% human male AB serum (BioWest), 2 mM L-glutamine, 100 U/ml penicillin, and 100 μg/ml streptomycin (Thermo Fisher Scientific) at 37 °C in a humidified 5% CO 2 incubator. Expansion of T cell blasts was obtained by incubating PBMCs for 72 h with CD3/CD28 beads (LifeTechnologies) in complete Panserin 401. After 3 days, dead cells were removed by Ficoll-Plaque density gradient, and blasts were expanded in complete Panserin supplemented with IL-2 (100 IU/ml). Epstein-Barr-virus (EBV)-transformed B lymphoblastoid cell lines (EBV-B cells) were generated from PBMCs obtained from patients (A1, B1, and D1) and HCs using standard methods and then cultured in RPMI 1640 (Life-Technologies) supplemented with 10% fetal bovine serum (FBS) and penicillin/ streptomycin.
Human embryonic kidney (HEK) 293T cells were cultured in DMEM (LifeTechnologies) supplemented with 10% FBS and penicillin/ streptomycin. Flow cytometry analysis. For surface staining, PBMCs (either freshly isolated or thawed after storage in liquid nitrogen) were washed with cold PBS and then incubated for 30 min at 4 °C (in the dark) with appropriate fluorochrome-labeled monoclonal antibodies. For intracellular staining, PBMCs were fixed and permeabilized after surface staining using the FoxP3 staining kit (eBioscience) according to the manufacturer's instructions, and then incubated with anti-FOXP3 and anti-CTLA4 for 30 min on ice. The antibodies used for flow cytometry are listed in Supplementary Table  2. All samples were measured using flow cytometry (Becton Dickinson LSRFortessa X-20) and the data were analyzed using the FlowJo software (TreeStar).
6 h after transfection, the cells were transferred back into medium containing 10% FBS and cultured for 24 h. Transfected cells were then stimulated (or not) with IFN-γ (10 3 IU/ml) for 24 h and subjected to luciferase assays with the Dual-Glo luciferase assay system (Promega). Experiments were performed in triplicate, and firefly luciferase activity was normalized against the level of Renilla luciferase activity. The data are expressed as fold-induction, relative to unstimulated cells. Proliferation assay. Expansion of T cell blasts was obtained by incubating PBMCs for 72 h with anti CD3/CD28 beads in complete Panserin 401 medium. After 3 days, dead cells were removed by Ficoll-Plaque density gradient, and T-cell blasts were expanded in complete Panserin supplemented with IL-2 (100 IU/ml).
After cell sorting, Teff was washed and stained with CellTrace CFSE. Next, the cells were washed, incubated with DCs (Teff:DC ratio 1:0. 4), Tregs (Teff:Treg ratio 1:0. 5) and stimulated by Staphylococcus enterotoxin E (0. 2 ng/ml). The cells were cultured for 5 days in complete Panserin medium (Dutscher) in 96-well plates and the proportion of proliferating cells was measured with a MACSquant system (Miltenyi). The data were analyzed with FlowJo software. The percentage of suppression was calculated with the following formula: [log2(y) of (Teff cells alone) -log2(y) of (Teff + Treg cells])/log2(y) of (Teff cells alone) × 100. The y value corresponds to the mean fluorescence intensity of the CFSE of the whole Teff cell population, divided by the mean fluorescence intensity of the CFSE of undivided Teff cells  40.
Acknowledgements The authors are grateful for the contribution of  Anne Rensing-Ehl ,  Maria Elena Maccari ,  Sarah Grün ,  Myriam Lorenz  and  Lawrence J Petherbridge. The study was supported by the  Institut National de la Santé et de la Recherche Médicale  (INSERM) and by government grants managed by the  Agence National de la Recherche  as part of the " Investment for the Future" program  ( ANR-10-IAHU-01  and  ANR-18-RHUS-0010 ), the  Ligue Contre le Cancer-Comité de Paris, Fondation ARC pour la recherche sur le CANCER , the  Centre de Référence Déficits Immunitaires Héréditaires (CEREDIH) , the  Agence National de la Recherche  ( ANR-14-CE14-0026-01  "Lumugene" to F. R. L. ;  ANR-18-CE17-0001  "Action"to BN), the  German Reseach Foundation (DFG)  under German's Excellence strategy ( CIBBS-EXC-2189-Project ID 390939984 ), the  German Ministry for Education and Research (BMBF  grant  GAIN FSE2018-041 ) and a reseach grant from  UCB Pharma  (to SE).
Data availability SOCS1 variants sequences are deposited in the ClinVar database [https://www. ncbi. nlm. nih. gov/clinvar?term=603597[MIM]] with the following accession codes SCV001430913, SCV001430914, SCV001430915, SCV001430916, SCV001430917. Gnomad database (https://gnomad. broadinstitute. org/) was used to calculate the minor allele frequency of all the mutations. PopViz webserver (http://shiva. rockefeller. edu/PopViz/) was used to calculate the CADD score and the mutation significance (MSC) cutoff of all the mutations. The datasets generated and/or analyzed during the current study are available from the corresponding authors on reasonable request. Source data are provided with this paper.
2015; Zhang et al. 2019). In this work we expand the sample of SZ-selected clusters available for such studies using a new survey conducted using the SPTpol receiver  (Austermann et al. 2012)  on the South Pole Telescope (SPT). This wide and shallow survey complements the deeper surveys conducted with the SPT  (Henning et al. 2018; Benson et al. 2014 ) and will provide additional overlap for the comparison of cluster properties with the ACTPol  (De Bernardis et al. 2016 ) and Planck surveys. Here we present 266 cluster candidates detected at a signal-tonoise ξ > 5, 244 of which are confirmed as clusters using optical and near-infrared data as well as via a search of the literature. We also report an additional 204 confirmed systems at 4 < ξ < 5. Combining this dataset with the previously published SPT-SZ cluster sample  (Bleem et al.
2015b, hereafter B15) , we use this expanded cluster sample to explore the SZ properties of massive optically selected clusters identified using the red-sequence Matched-Filter Probabilistic Percolation (redMaPPer) algorithm  (Rykoff et al. 2014)  in the Dark Energy Survey Year 3 dataset. We organize this work as follows. In Section 2 we describe the survey observations and data reduction process. In Section 3 we describe the identification of cluster candidates including checks on the radio contamination of the sample and in Section 4 the cluster confirmation process including details on the external datasets used for this process. In Section 5 we present the full sample and several internal consistency checks with the SPT-SZ cluster sample. Detailed comparisons to the Dark Energy Survey redMaPPer sample including determination of the SZ-optical center offsets and SZ-mass-to-optical richness relation are presented in Section 6.
2015 , 2016c). The first step follows a similar method as the absolute temperature calibration conducted in previous SPT power spectrum analyses (e. g,  Hou et al. 2018; Henning et al. 2018). We derive normalization factors to rescale each coadded map by first convolving the Planck maps with the SPT beams and transfer functions (the latter resulting from the TOD filtering process described above) and the SPT maps with the Planck beam and window function. Then, masking bright point sources in the field, we set the normalization as the ratio from 900 ≤ ≤ 1600 of the cross spectrum of the Planck half mission maps to the cross spectrum of the Planck full mission map with the SPT maps. The 95 GHz data required an additional calibration step as we found-especially in the fields centered at δ = -25 • -that the responsivity of the detectors decreased with increasing airmass.
Masking To prevent spurious decrements from the filtering process we mask regions around bright emissive sources before applying the matched filters to the maps. These sources are detected in the 150 GHz data using a matched filter designed to optimize the signal-to-noise of point sources. Masks of 4 radius are placed over sources detected at > 5σ and candidates detected within 8 of these sources are excluded from the final cluster lists. Additionally, as referenced above in Section 2. 2, there are three extended sources in these fields-NGC 55, 253, and 7293  (Dreyer 1888 ) and one exceptionally bright quasar-QSO  B0521-365 (e. g. , Planck Collaboration et al. 2018 )-that require additional masking. Masks of radius 0. 33 • are used for the NGC sources and radius 0. 25 • for the quasar. Regions around these sources are also inspected following the cluster filtering process and a small number of spurious candidates are rejected.
1998 ). 5 NVSS covers the full sky north of declination -40 • and thus has nearly 100% overlap with the survey fields in this work. The data for the NVSS were taken between 1993 and 1997, so source variability will limit the fidelity of the estimate of contamination to any individual cluster, but we can make some statements about the average or median contamination across the catalog and the fraction of clusters expected to be strongly affected by radio source contamination. For each of our survey fields, we download all NVSS postage-stamp maps (each 4 × 4 degrees) that have any overlap with that field and reproject them onto the same pixel grid as used in our cluster analysis. We then make beam-and transfer-function-matched NVSS maps for each of the SPTpol observing frequencies by convolving the NVSS maps with a kernel defined by the Fourierspace ratio of the SPTpol beam and transfer function at that frequency and the effective NVSS beam (a 45-arcsec FWHM Gaussian).