dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013). The evaluation in the Arctic Ocean clearly indicates an improved measurement of SLA using SAMOSA+ with the DTU21MSS. In conjunction with this physical retracker, the correction of the sea state bias (SSB) further improves the results. In all evaluations, the DTU21MSS delivers better results than the DTU15 MSS. With SAMOSA+, SSB, and DTU21MSS we obtain a mean SLA of -1. 5cm ±12cm instead of -5. 4cm±22cm over the 2013/10-2014/04 period. Coastal evaluation of the new DTU21MSS was performed in the Baltic Sea and the Aleutian trench zone in Alaska. The evaluation in the Baltic Sea confirms that DTU15MSS is frequently several cm too high is coastal and Archipelago regions due to the lack of 1 Hz data for the DTU15MSS. The comparison with Sentinel 3A tracks close to the coast of the Aleutian.
illustrated some oscillation problems with the CLS15MSS. For the DTU21MSS we found that the 5-year Sentinel-3A mean profiles (2016. 05-2020. 05) were too problematic to consolidate onto the 1993-2012 averaging period without degrading the MSS model, particularly in large current regions. Consequently we omitted these data in the DTU21MSS, but also found that we shorth need to extend the averaging period to 30 years soon to enable the use of the important new Sentinel-3A/B data in the next-generation MSS models.
Acknowledgments The authors are thankful to the space agencies for considering the Geodetic or Long-repeat missions as part of mission operations and for providing these high-quality data to the users. We would like to acknowledge  ESA-RSS  (Research  and Service Support ), and in particular  B. Abis  and  G. Sabatino , for their assistance in processing the data with G-POD (http://gpod. eo. esa. int/). We acknowledge the support of  ESA  to the  CryoTempo and Baltic Seal+  project through the contracts:  AO/1-10244/20/I-NS  &  4000126590/19/I-BG.
Acknowledgements We thank  Didier Paillard ,  Gaëlle Leloup , and  Dominique Raynaud  for their very insightful comments on this article. We thank  Michel Crucifix  for his help in the modelling work. We thank the two reviewers  Tijn Berends  and  Mikhail Verbitsky  for their insightful comments that significantly improve the final version of the manuscript. This study is an outcome of the Make Our Planet Great Again HOTCLIM project, it received the financial support from the  French National Research Agency  under the " Programme d'Investissements d'Avenir"  ( ANR-19-MPGA-0001 ). This publication was also generated in the frame of the  Beyond EPICA Oldest Ice Core (BE-OI ) project. The  BE-OI  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no.
All neutralization assay data are presented as the mean of at least two independent experiments. i, Protein microarray showing the distribution of autoantibody reactivity in plasma samples from patients carrying a p52 LOF /IκBδ GOF variant (n = 13). Data are represented as the fold change (FC) relative to 26 plasma samples from healthy control individuals. Data for HuProt are presented as the mean of at least two technical replicates. j, Representation of the global autoantigen profile of patients with APS-1 and patients with a p52 LOF /IκBδ GOF variant, with their overlap. Type I IFN autoantigens are highlighted in bold. C + p 5 2 L O F / Iκ B δ L O F p 5 2 G O F / Iκ B δ L O F p 5 2 L O F / Iκ B δ G O F H C A P S -1 P A D IF N α -1 / 1 3 IF N α -2 IF N α -4 IF N α -5 IF N α -6 IF N α -7 IF N α -8 IF N α -1 0 IF N α -1 4 IF N α -1 6 IF N α -1 7 IF N α - HC (n = 117) AR BAFFR -/-(n = 1) AR NIK -/-(n = 2) AR partial RELB -/-(n = 4) AR complete RELB -/-(n = 4) RELB -/WT (n = 8) XL CD40L -/Y (n = 3) p52 GOF /IκBδ LOF (n = 6) p52 LOF /IκBδ LOF (n = 1) APS-1 (n = 1) Healthy controls (n = 29) j i f g h IF N ω IF N β IF N ε IF N κ 33.
Participants and samples We enrolled 73 patients with rare variants of NFKB2 though an international collaborative study. Data were collected through an anonymized survey sent to specialists in immunology or paediatrics with reported or unreported patients with these IEIs (Supplementary Table  1 ). All index cases were genotyped after suspicion of an inborn error of immunity. An analysis of the familial segregation of each NFKB2 variant was performed in all relatives for whom genomic DNA was available. We included all individuals heterozygous for a rare (MAF < 0. 0001) NFKB2 non-synonymous variant (detected by Sanger sequencing: n = 19; IEI gene NGS panel: n = 26; whole-exome sequencing (WES): n = 23; or whole-genome sequencing (WGS): n = 5) for whom a plasma/serum sample was also available.
Clinical and immunological data were collected with a standardized questionnaire using Microsoft Excel, together with at least one plasma sample. The plasma samples from patient P1 were obtained through the NCT03394053 and NCT03610802 protocols with the approval of the National Institutes of Health institutional review board. We also enrolled 14 patients with other inborn errors of the alternative NF-κB pathway including patients with autosomal-recessive complete NIK deficiency (n = 2 from 2 kindreds 57 and unpublished results); autosomal-recessive partial (n = 4 from 2 kindreds) or complete (n = 4 from 2 kindreds) RELB deficiency  [57] [58] [59] [60] [61]  (and unpublished results), or the related TNF receptors (TNFR) (autosomal-recessive complete BAFFR deficiency (n = 1) 58 ); or X-linked recessive CD40L deficiency (n = 3 from 3 kindreds (unpublished)).
The arrays were scanned later the same day with an Innoscan 1100AL Fluorescence scanner (Innopsys) using Mapix v. 9. 1. 0 and the resulting images were analysed with the Jan 18-22 Huprot v4. 0 Genepix Array List file and either of GenePix Pro v. 5. 1. 0. 19 or GenePix Pro 7. Normalization was used to compensate for variation in the signal intensity between experiments. Data from additional healthy donors from separate protein array experiments was included. Signal intensities were extracted from the scanned image with GenePix Pro v. 5. 1. 0. 19 and GenePix Pro 7, with the subtraction of the local background. IgG-reactive proteins were identified as proteins with a fluorescence intensity log 2 [fold change] ≥ 1. 5. Autoantigens identified in patients with APS-1 were extracted from previous studies  46, 47, 65.
CyTOF The whole-blood mass cytometry panels used were custom produced, and their contents are shown in Supplementary Table  8. Labelled cells were frozen at -80 °C after overnight dead-cell staining, and acquisition was performed on the Helios machine (Fluidigm). All of the samples were processed within 48 h of sampling. The six patients with autosomal-recessive APS-1 included in the analysis were on treatment with JAK inhibitors at the time of blood collection. Data analysis was performed using OMIQ software. The gating strategy for CyTOF immunophenotyping is shown in Supplementary Fig. 15.
RNA extraction, sequencing and analysis Total RNA was isolated from whole blood as previously described  67. RNA sequencing was performed using the Illumina NovaSeq S2 instruments (2 × 100 bp), at a read depth of 70 million. Single samples were sequenced across two lanes, and the resulting FASTQ files were merged by sample. All FASTQ files passed quality control and the sequences were aligned with the GRCh38 reference genome using STAR (v. 2. 6. 1d). BAM files were converted to a raw count expression matrix using featurecount. Raw count data were normalized using DEseq2 (v. 1. 40. 2). The ensemble IDs targeting multiple genes were collapsed (average), and a final data matrix gene was generated for single-gene set enrichment analysis with the BloodGen3Module gene set  71. Statistical analysis was performed on a predefined gene set.
Specifically, we used a fixed repertoire of 382 blood transcriptional modules that were thoroughly annotated and characterized functionally as described previously  71. In brief, this repertoire of transcriptional modules (BloodGen3) was identified on the basis of co-expression, as measured in a collection of reference blood transcriptome datasets encompassing 16 pathological or physiological states and 985 individual transcriptome profiles. Sets of co-expressed transcripts were derived from a large weighted coclustering network in which edges represented the number of times a pair of genes coclustered in the 16 reference datasets (with a weight of 1 to 16). We calculated an IFN module enrichment score for individual samples by performing single-sample gene set enrichment analysis (ssGSEA) (GSVA package v.
Acknowledgements We thank the patients and their families for participating in the study; the members of the  Laboratory of Human Genetics of Infectious Diseases  for discussions;  Y. Nemirovskaya ,  D. Liu ,  M. Woollett ,  L. Lorenzo-Diaz ,  C. Rivalain ,  M. Charabieh  and  T. Kochetkov  for administrative and technical assistance;  D. Chaussabel  for assistance with RNA-seq analysis; the  National Facility for Autoimmunity and Serology Profiling at SciLifeLab  for technical support. The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St Giles Foundation , the  National Institutes of Health (NIH)  ( R01AI088364 ,  R01AI127564-06  and  R01AI163029 ), the  National Center for Advancing Translational Sciences (NCATS) , the  NIH Clinical and Translational Science Award (CTSA)   programme  ( UL1 TR001866 ), the  Fisher Center for Alzheimer's Research Foundation , the  Meyer Foundation , the  JBP Foundation , the  French National Research Agency (ANR)  under the ' Investments for the Future' programme  ( ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research (FRM)  ( EQU201903007798 ), the  ANRS-COV05 ,  ANR   GENVIR  ( ANR-20-CE93-003 ),  ANR   AABIFNCOV  ( ANR-20-CO11-0001 ),  ANR   AAILC  ( ANR-21-LIBA-0002 ) and  ANR   AI2D  ( ANR-22-CE15-0046 ) projects, the  ANR-RHU programme  ( ANR-21-RHUS-08-COVIFERON ), the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no.
824110  ( EASI-genomics ), the  HORIZON-HLTH-2021-DISEASE-04 programme  under grant agreement  01057100  (UNDINE), the  Square Foundation ,  Grandir-Fonds de solidarité pour l'enfance , the  Fondation du Souffle , the  SCOR Corporate Foundation for Science , the  Battersea & Bowery Advisory Group , the  French Ministry of Higher Education, Research, and Innovation  ( MESRI-COVID-19 ),  Institut National de la Santé et de la Recherche Médicale (INSERM)  and of  Paris Cité University. J. R. was supported by the  INSERM PhD programme  (poste  d'accueil  INSERM). J. R. , T. L. V. , P. B. and Q. R. are supported by the  Bettencourt-Schueller Foundation  and the  MD-PhD programme  of  Imagine Institute. P. B. was supported by the  French Foundation for Medical Research  (FRM,  EA20170638020 ) and a ' Poste CCA-INSERM-Bettencourt ' (with the support of the  Fondation Bettencourt-Schueller).
Q. P. was supported by the  Assistance Publique Hôpitaux de Paris (AP-HP, Année Recherche)  and the  MD- PhD programme  of  INSERM (Ecole de l'INSERM Liliane Bettencourt ). M. O. was supported by the  David Rockefeller Graduate Program , the  Funai Foundation for Information Technology (FFIT) , the  Honjo International Scholarship Foundation (HISF) , the  New York Hideyo Noguchi Memorial Society (HNMS). Nils Landegren is funded by The  Swedish Research Council  and the  Göran Gustafsson Foundation. B. Keller is funded by  Bundesministerium für Bildung und Forschung  ( GAIN 01GM1910A ). Q. P. -H. is supported by the  Swedish Research Council , and the  Knut and Alice Wallenberg Foundation (KAW). F. Hauck  was funded by the  Else Kröner-Fresenius Stiftung  ( EKFS ,  2017_A110 ) and the  German Federal Ministry of Education and Research (BMBF ,  01GM1910C ).
L. D. N. and H. C. S. are supported by the  Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health. C. S. M. and S. G. T. are supported by Investigator Grants awarded by the  National Health and Medical Research Council of Australia. CIRCA Investigators (A. P-C, A. S, L. D, D. S, M. W, A. K, C. S. M, C. C. G, P. E. G, S. G. T) are funded by grants awarded by the Jeffrey Modell Foundation and the John Brown Cook Foundation. M. Momenilandi was supported by ANRS, Maladies infectieuses émergentes (ECTZ173053). F. R. L. 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' programme (Institut Hospitalo-Universitaire Imagine, grant ANR-10-IAHU-01, Recherche Hospitalo-Universitaire, grant ANR-18-RHUS-0010), the Centre de Référence Déficits Immunitaires Héréditaires (CEREDIH), the Agence National de la Recherche (ANR-14-CE14-0026-01 'Lumugene'; ANR-18-CE17-0001 'Action'; ANR-22-CE15-0047-02 'BREAK-ITP'), the Fondation pour la recherche Médicale (FRM : EQU202103012670).
Data availability All the data supporting the findings of this study are available within the Article and its Supplementary Information. The gel source data are shown in Supplementary Fig. 1. The RNA-seq data generated in this study have been deposited in the NCBI database under NCBI-SRA project PRJNA989123. All the other data and material supporting the findings of this study are available under a data transfer agreement from the corresponding authors on reasonable request. Source data are provided with this paper.
The horizontal dashed line represents the mutation significance (MSC) cutoff threshold of 33 for NFKB2. (b) Genomic constrained coding regions across NFKB2, as estimated by the missense tolerance ratio (MTR) score evaluating region-specific intolerance to missense variants. A score <1. 0 indicates a lower-than-expected ratio of missense to synonymous variants in the gnomAD v2. 0 dataset for the 21 bp window surrounding an amino-acid residue. The horizontal-coloured dashed lines represent the percentiles for the most missense-depleted regions of NFKB2. The NIK-responsive sequence (NRS) is within the 5th percentile for the most missense-depleted regions for NFKB2. The lower graph shows the distribution of the heterozygous NFKB2 variants reported in gnomAD 2. 1. 1 and from the patients reported in this study, by location within the protein and CADD score.
All data modeling was in Woollam's CompleteEASE software package (ver. 6. 61 a). The ex situ SE modeling of thick ZnO reference sample was performed using a three-layer model that accounted for the silicon substrate, the silicon native oxide layer, which, again, was measured prior to deposition and fixed to the value obtained in this measurement, and a (Sellmeier) layer that accounted for the ZnO film. In Situ Multi-Wavelength Ellipsometry. In situ SE measurements during ALD of ZnO were performed with a four-wavelength ellipsometer from Film Sense (Lincoln, NE, USA), and the data were modeled using Film Sense's FS-1 software (Ver 2. 66W). 8, 25, 26  Two approaches were used to model the data. In the first, the optical constants for ZnO were obtained from an ex situ SE analysis of the ZnO film, which yielded values for n at 464, 523, 597, and 638 nm (the wavelengths of the Film Sense ellipsometer) to be 2.
1 eV step size, and a 50 ms dwell time. Charge neutralization of the samples during analysis was accomplished using a low energy electron source that is in-lens in the analyzer column plus an auxiliary combination low energy electron/low energy argon ion source in the analysis chamber. The base pressure in the analysis chamber is typically 2. 0 x 10 -8 mbar or better prior to XPS data acquisition. During the XPS data acquisition, the pressure in the analysis chamber is typically 2. 0 x 10 -7 mbar because of the low energy argon ions being leaked into the auxiliary change neutralization source. High sensitivity-low energy ion scattering (HS-LEIS). Ion scattering experiments were performed with a Qtac100 (IonTof GmbH, Germany) instrument. Small (ca. 100 𝜇𝜇m) diameter beams of He + (3. 0 keV) were scanned over selected 2 × 2 mm 2 areas.
35, 36  The modified Auger parameter was then obtained from Equation  2 : Modified Auger parameter (eV) = BE of Zn 2p3/2 + KE of Zn LMM (2)   where BE stands for 'binding energy'. The modified Auger parameter we obtained for the zinc in our film is 2010. 2 eV, which is the same as that reported for ZnO, and very different for that of Zn metal/Zn(0) (2013. 8 eV). 37 The contrast of this image was enhanced using the ImageJ software package. 38. We emphasize that these peak fits were only performed to find the positions of the maxima of the Zn 2p3/2 and Zn LMM signals from which we obtained the modified Auger parameter. We do not claim that these fits have any chemical significance. To assess the inhibitory effects of TDMAMS in ALD, ALD of ZnO was followed by in situ ellipsometry on Si/SiO2/TDMAMS and bare Si/SiO2.
, 1995). Consequently, the  Foster et al. (1985)  equations can sometimes yield d50 values that are too high for edge-of-field runoff situations. Deizman et al. (1987)  performed a surface runoff experiment with simulated rainfall on a silt loam soil with two different tillage treatments (conventional tillage CT and no-till NT). From this dataset  Deizman et al. (1987)  derived multiple linear regression equations for particle size fractions in eroded sediment, separately for CT and NT, and aggregates (undispersed sediment) and primary particles (dispersed sediment), using the following predictor variables: i) particle size distribution of the undispersed matrix soil (% gravel, sand, silt and clay), ii) rainfall volume (mm), iii) slope (%), and iv) antecedent soil moisture (%). Unfortunately, the equations in  Deizman et al.
(1987)  contain errata and with the inputs and units specified in Table  S4  do not produce plausible results (i. e. predicted particle size fractions are often negative or >100 %). Furthermore, the  Deizman et al. (1987)  equations have additional limitations: i) although the equations use soil particle size fractions, they were developed using only one soil (silt loam) and variation of soil texture within tillage treatments was not reported; ii) in practice, the particle size fractions for aggregated (undispersed) matrix soil are only rarely available because particle size fractions of the matrix are usually determined after dispersion; iii) antecedent soil moisture is often not reported in publications on field experiments (also missing on  Deizman et al. , 1987). In conclusion, while the dataset published by  Deizman et al.
(1987)  is valuable, their equations are not usable. Another common method to estimate d50 is based on USDA soil texture classes (Table  S5 ) and used in the sediment erosion model KINEROS  (Woolhiser et al. , 1990). However, ranges are very wide and often strongly overlap between texture classes. In the EU pesticide risk exposure analysis context, the default value of d50 used in the higher-tier SWAN tool (ECPA, 2018), which comprises VFSMOD, is fixed to 20 μm, chosen by  Brown et al. (2012)  as a very low percentile (1st or 2nd) of an experimental d50 dataset comprising data from three studies  (Elliot et al. , 1989, n = 178; Chisci and Martinez, 1993, n = 1; Slattery and Burt, 1997, n = 6). The vast majority of the data came from the study of  Elliot et al. (1989)  where surface runoff was measured from 33 agricultural sites across the USA.
Step 1: data compilation and curation A d50 dataset was compiled from 13 surface runoff studies with multiple runoff events from Europe and the US (Table  1 ). The studies were conducted with natural or simulated rainfall over a source area that generated measured runoff flow and sediment. Some of the studies also incorporated an adjacent experimental VFS area to filter runoff from the source area. For the different studies, d50 was obtained with 3 methods: i) directly measured from observed sediment particle size fractions (3 studies), ii) optimized from measured event hydrographs and sedigraphs with a Global Multilevel Coordinate Search/Nelder-Mead Simplex (GMCS-NMS) algorithm built within VFSMOD  (Ritter et al. , 2007)  (2 studies), and iii) optimized from total flow and sediment measured values at the end of the event with DREAM_ZS  (Vrugt, 2016)  (8 studies).
The GMCS-NMS algorithm consists of the sequential combination  (Lambot et al. , 2002; Ritter et al. , 2003) , of the global optimization algorithm developed by  Huyer and Neumaier (1999)  and the classical Nelder-Mead Simplex  (Nelder and Mead, 1965 ) that allows to perform automatic inverse optimization of the hydraulic and sediment transport parameters of VFSMOD when experimental hydrograph and sedigraph data are available. DREAM_ZS is a variant of the DiffeRential Evolution AdaptiveMetropolis (DREAM) algorithm  (Vrugt, 2016)  implemented in MATLAB. DREAM_ZS is a multi-chain Markov Chain Monte Carlo (MCMC) simulation algorithm, which can be used for maximum-likelihood-based calibration and uncertainty analysis. It yields posterior distributions of the calibrated parameters according to Bayesian inference  (Vrugt, 2016).
In the first phase of the DREAM_ZS calibration, all parameters (14 or 17 for with and without shallow water table conditions, respectively) were calibrated against observed ΔQ and ΔE. In the second phase, individual sediment parameters were calibrated against ΔE, for the best hydrological parameter set. The following data quality screening criteria were defined: i) the observed ΔE for the event should be <100 %, i. e. the event should have measurable sediment runoff from the VFS; ii) the absolute deviation of observed and simulated ΔE (%) should not be larger than 25 %; iii) the ratio of Fig. 1. Workflow of methods followed in this study. simulated to observed ΔE should not be larger than 1. 5. After applying the quality criteria to all events from the studies, the final d50 dataset comprises n = 93 records (Table  1 ).
For one of the studies  (Pieri et al. , 2009)  the independent variable peak runoff rate was not available, and the 21 missing values were imputed based on the values in the full dataset using the R implementation of the missForest algorithm  (Stekhoven, 2022). This is a nonparametric machine learning (Random Forest) imputation method suitable for mixed data, does not require calibration, and typically outperforms other imputation approaches like K-Nearest Neighbors  (Stekhoven and Bühlmann, 2012). The relative out-of-bag error of the imputation (defined as a root mean square error normalized to the variance of the observed data) was <5 %.
Step 2: dimension reduction -identification of important variables To identify the most important explanatory variables, the variables that can be neglected from the original dataset and potential important interactions between variables we employed a novel dimension-reduction approach  (Carmona-Cabrero et al. , 2022)  based on a combination of Global Sensitivity Analysis  (Sobol' method)  and machine learning techniques (Random Forest, Gradient boosting). Sensitivity analysis is the study of how the uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model inputs  (Saltelli, 2002). Global Sensitivity Analysis (GSA) studies output variation when all input factors (parameters, initial and boundary conditions, structure selection) vary globally, within their validity domain defined by probability distribution functions (pdfs), as opposed to locally, i.