dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, the p. Arg924Gln and p. Ala1810Glu were recognized as not harmful according to all prediction tools. Two variants (p. Ala1716Thr and p. Ser2469Pro) were evaluated as harmful only by 2 tools. The   variant p. Gly74Arg is due to the c. 220G. A at the last nucleotide of exon 3 and might affect the normal splicing process, as predicted by the splicing prediction tools (Table  3 ). Sixteen of the missense variants identified involve the loss of cysteine residues, and 9 of these (EU/IR Splicing variants. Table  3  reports the 29 splice site variants identified in this study (EU/IR 5 20/9). Seven of these (EU/IR 5 1/ 6) were not reported in the EAHAD, HGMD, or Ensembl databases. Eleven splicing variants have been previously evaluated at the messenger RNA (mRNA) levels,  [33] [34] [35] [36] [37] [38]  and the rest of them were tested by using 4 splicing prediction tools.
Most variants were predicted to alter the normal splicing process with at least 3 tools. However, in the cases of c. 7082-13G. C and c. 8155 1 3G. A, all 4 tools predicted no splicing alteration. Deletions, insertions, and duplications. Thirty-one different small deletions were identified (EU/IR 5 22/9); 12 of them (EU/IR 5 7/5) were novel in the EAHAD, HGMD, or Ensembl databases. All the small deletions cause frameshifts predicting premature termination codons, with the exception of 4. Three of these, c. 3101_3103del (p. Thr1034del), c. 5085_5087delCCT (p. Leu1696del) and c. 8244_8252del (p. His2748_Cys2750del), result in small in-frame deletions (1 up to 3 amino acids). The fourth deletion, located at the last portion of exon 16 and the first 5 nucleotides of intron 16 (c. 1931_1945 1 5del), obviously affects the normal splicing process (Table  3 ).
Nine distinct small duplications were identified (EU/IR 5 3/6); six of them (EU/IR 5 1/5) were not reported in the EAHAD, HGMD or Ensembl databases. All the small duplications cause frameshifts predicting premature termination codons, with the exception of c. 3222 1 2dupT located in intron 24, which might affect the normal splicing process, as suggested by 3 splicing prediction tools (Table  3 ). Nine different large deletions were identified (EU/IR 5 7/3), with delEx1_Ex5 32 found in both populations. All the large deletions were found in a single patient, with the exception of delEx1_Ex5 found in 2 patients, delEx1_Ex3 in 3 patients (all of Hungarian descent)  36, 39, 40   IR 5 1/2), and all of them were novel. Only 2 insertions were identified: a small one (c. 5981_5982insT) in a case of IR-VWD3 and a large one (insEx1_Ex28) in a case of EU-VWD3 (both were novel).
Discussion In this study, the genetic background of VWD3 was very heterogeneous, with 154 different unique VWF variants among 379 defective alleles, which is a very high rate considering the recessive nature of VWD3 and thus the inclusion of many homozygous patients. The main findings are that the VWF genotypes were completely different in EU-VWD3 and IR-VWD3. Indeed, among the 154 different variants identified, only 5 were shared by both populations, whereas all other variants were unique for each population. Based on these data, no specific genetic hotspots for mutation seem to play a major role in VWD3 pathogenesis, or at least not in Variants reported in bold are not listed in the EAHAD,  17  HGMD,  18  or Ensembl  19  databases. Variants reported in italics have been identified in both European and Iranian populations.
populations (8 in EU-VWD3 and 10 in the IR-VWD3) and accounts for 31% and 46. 7%, respectively, of the alleles identified in each VWD3 population. The distribution of the different type of variants in the 2 populations is shown in Figure 4. Variant data have been submitted to ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/; SUB9482148). Type of VWF variants in the 3WINTERS-IPS cohort Nonsense variants. Thirty-six different nonsense variants were identified, 23 in the EU-VWD3 and 16 in the IR-VWD3 population. Eight of the 11 "hotspot" arginine codons were found in EU-VWD3 patients and 3 of them were also found in IR-VWD3 patients. Of the 36 different nonsense variants, 9 (EU/IR 5 6/3) were not reported in the EAHAD, HGMD, or Ensembl databases. An additional nonsense variant, p. Gln1311 Ã , was found in 2 patients (EU/IR 5 1/ 1) in the homozygous state as a consequence of 2 different gene conversions (supplemental Tables.
Acknowledgments The authors thank the Members and current Chair of the Sub-Committee on VWF of the Scientific Standardization Committee of the  International Society on Thrombosis and Haemostasis  who promoted the endorsement of the  3WINTERS-IPS  project among the scientific activities of this Sub-Committee. The authors also thank  Javier Battle ,  Erik Berntorp ,  Cosimo Ettorre ,  Charles R. M. Hay ,  Massimo Morfini ,  Maria Gabriella Mazzucconi ,  Johannes Oldenburg ,  Rafael Parra L opez , and  Omidreza Zekavat  for patients' enrollment and collection of clinical data. The 3WIN- TERS - IPS  project received unconditional research grants from several pharmaceutical companies, and the authors therefore acknowledge the representatives of  Baxter-Shire-Takeda, Grifols ,  CSL Behring , LFB, and  Octapharma.
The authors thank  Paolo De Simoni ,  Luca Maravigna , and  Elisabetta Musazzi  of the  CRO Sintesi Research  for the study coordination and  L. F. Ghilardini  for the illustration work. This work was partially supported by the  Hungarian National Research Development and Innovation Office (NFKI)  grant  OTKA-K19_131945  (I. B. ). This work was also partially supported by the  Italian Ministry of Health-Bando Ricerca Corrente  (F. P. ). Conflict-of-interest disclosure: O. B. reports advisory board participation for  Sobi, CSL Behring , and  Pfizer. F. W. G. L. received unrestricted research grants from  CSL Behring, Shire/Takeda , and uniQure; is a consultant for  CSL Behring, Shire/Takeda ,  BioMarin , and uniQure, the fees of which go to the university; has received travel support from  Sobi ; and is a data and safety monitoring board member of a study sponsored by  Roche.
71; 0. 06 VUS CS1310562 59 c. 5455 1 2T. C Variants reported in bold are not listed in the EAHAD, 17 HGMD, 18 or Ensembl 19 databases. Splice prediction tools: varSEAK, Alternative Splice Site Predictor (ASSP), 25 Berkeley drosophila genome project (BDGP), 26 NatGen2.
Turbulent heat flux A detailed explanation of the procedure for obtaining the turbulent heat fluxes from raw eddy covariance data is provided by  Lackner et al. (2021). In short, turbulence data were processed using EddyPro ® (version 7. 0. 3; LI-COR Biosciences, USA), a software package that computes fluxes from raw 10 Hz data, while accounting for several corrections, including the application of a double rotation on the raw data to align the coordinate system with the current snow surface. EddyPro also includes a thorough QA/QC procedure. A program called PyFluxPro  (Isaac et al. , 2017)  was also used to remove spikes and erroneous data that persisted despite the EddyPro ® processing. Data gaps were filled using ERA5   (Hersbach et al. , 2020)  reanalysis data and an artificial neural network procedure  (Hsu et al.
However, some patients may present with very severe erythroderma (personal communication from E. B. ). In some patients, mutations in these genes lead to SICI. In most cases of SICI, mutations in ALOX12B were found, followed by mutations in ALOXE3  [1, 15]. To date, 88 pathogenic mutations in ALOX12B and 27 pathogenic mutations in ALOXE3 have been reported in ARCI (Human Gene Mutation Database Professional 2020. 3). In the present study, we present a large cohort of 224 patients affected with ARCI carrying mutations in ALOX12B or ALOXE3. We add 74 novel mutations in ALOX12B and 25 novel mutations in ALOXE3. Furthermore, we investigate the quality and spectrum of mutations, the potential hotspot regions, and the possible correlation of specific mutations with the phenotype.
Materials and Methods In 224 patients with ARCI, mutations in ALOX12B or ALOXE3 were detected using different sequencing methods, including Sanger sequencing and next-generation sequencing (NGS). Here, we present the results of the mutation testing for only one affected individual from each family. Our data show there were 224 patients of the cohort belonging to 223 families, as one patient was homozygous for both ALOX12B and ALOXE3 mutations. This study was conducted according to the Declaration of Helsinki principles. In all patients, genomic DNA was isolated from peripheral blood lymphocytes, and we performed PCR amplification using Sanger sequencing or NGS methods. All coding exons and flanking intronic sequences of ALOX12B (reference NM_001139. 2, GRCh37. p13) and ALOXE3 reference (NM_021628.
2, GRCh37. p13) were analyzed. In general, Sanger sequencing methods for individual genes were used until 2010-2015, depending on the laboratory; NGS methods through multigene panel testing, either in a targeted way of through whole-exome sequencing, were applied and mutations validated by Sanger sequencing. In a large part of the cohort, DNA sequences were enriched by a HaloPlex Custom Kit or SureSelect Custom Kit (Agilent Technologies, Inc. Santa Clara, CA, USA). Resulting data were analyzed using an in-house bioinformatics pipeline and the commercial software SeqNext (JSI medical systems, Ettenheim, Germany). For in silico analysis we used the following bioinformatics tools: Mutation Taster ( http://www. mutationtaster. org/) [16] , PolyPhen-2 (http://genetics. bwh. harvard. edu/p ph2/)  [17] , fathmm v2.
3 (http://fathmm. biocompute. org. uk/) [18] , SIFT (http://sift. jcv i. org/)  [19] , Provean v1. 1. 3 (http://provean. jcvi. org/index. php) [20] , NetGene2 v2. 4 ( http://www. cbs. dtu. dk/services/NetGene2/) [21] , NNSplice version 0. 9 (http://www. fr uitfly. org/) [22] , and SSP v2. 1 (https://varseak. bio/, developed by JSI medical systems GmbH, Ettenheim, Germany). In addition, the following databases were used: the Genome Aggregation Database version v2. 1. 1 (gnomAD; http://gnomad. broadinstitute. org/), HGMD ® Professional version 2020. 3 (http://www. biobase-international. com/product/hgmd), Database of Single-Nucleotide Polymorphisms version build 151 (dbSNP; http://www. ncbi. nlm. nih. gov/p rojects/SNP/), PubMed (http://www. ncbi. nlm. nih. gov/pubmed/), and ClinVar version December 2020 (https://www.
Results A genetic analysis of patients with ichthyosis over a period of 26 years revealed 170 families with mutations in ALOX12B or ALOXE3 in our laboratories; a further 54 families were contributed by other coauthors within the ERN-Skin network. All patients are listed in Supplementary Tables  S1  and S2. A total of 31 of these pedigrees have already been published  [1, 3, 23, 24]. We found an additional 74 novel mutations in ALOX12B and 25 novel mutations in ALOXE3. All novel mutations are summarized in Table  1. To our knowledge, these mutations have not been reported in the literature before. Known and novel mutations were verified by HGMD ® Professional 2020. 3. The results of prediction tools and databases for all novel mutations in ALOX12B and ALOXE3 were listed in Supplementary Tables  S3  and S4.
Phenotype Due to the high number of cases, this study did not aim to evaluate every single genotype-phenotype correlation. We obtained clinical data from almost two-thirds of our cohort. In many of these patients, we have only little information about the phenotype. We can confirm, however, that a collodion phenotype at birth, including the self-healing form, frequently occurs in patients with mutations in ALOX12B or ALOXE3, since a large proportion of our cohort shows these phenotypes. In our patients with available clinical data, about 76% of the patients with ALOX12B mutations were born with a collodion membrane (Supplementary Table  S1 ). In ALOXE3, the proportion was about 36% (Supplementary Table  S2 ). We did not find any association between specific mutations and specific clinical findings.
Spectrum of Mutations in ALOXE3 In ALOXE3, most frequent mutations are nonsense and frameshift mutations  (12 and 8, respectively) , which lead to a predicted premature stop codon and potentially to nonsense mediated decay (combined, these are 20 out of 52 from our cohort and the literature, corresponding to 38%), followed by missense mutations (18 out of 52, 35%) and splice site mutations (10 out of 52, 19%). Three large deletions and one in frame deletion/insertion mutation were found (4 out of 52, 8%) (Figure  2F ). Compared to ALOX12B, there is no apparent uneven distribution of the mutations within the gene (Supplementary Table  S5 ). Only 18 missense mutations were found, most of them are located in the lipoxygenase domain. It is possible that a larger dataset would reveal a similar pattern as in ALOX12B.
This meta-analysis allows us to better understand the distribution of the mutations in ALOX12B and ALOXE3. In ALOX12B, 67% of all mutations were detected in the second part of the protein from exon 9 (from amino acid 358). At the beginning of the lipoxygenase domain from exon 3 to 6 (corresponding to amino acids 118 to 251), very few mutations were found. The cause of the uneven distribution of mutations within the gene is not yet clear. Compared to gnomAD, missense variants in this database are equally distributed within the gene (Supplementary Table  S5 ). We speculate that some regions are much better conserved because they have important functional meaning. Less well-conserved regions may allow for changes that do not cause disease. Given the similar structure of ALOX12B and ALOXE3, it is surprising that we did not observe an uneven distribution in ALOXE3 as we did in ALOX12B.
Our results show that mutations in ALOX12B can be found significantly more often than in ALOXE3. It can be argued that the stronger conservation of amino acids in ALOX12B can explain the higher incidence of mutations in ALOX12B. We obtained clinical data from almost two-thirds of our cohort, with a partial containing little information about the phenotype. We did not, however, find any indication for the correlation of specific mutations with phenotypes. Patients in our cohort were often born with a collodion membrane. In patients with obtained clinical data, a collodion phenotype occurred more often in patients with ALOX12B mutations (76%) compared to ALOXE3 mutations (36%). This is in accordance with the findings of Simpson et al. [27] , who found a collodion phenotype at birth in 71% of the ALOX12B cases and in 45% of the ALOXE3 cases.
Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues.
Here we address the possibility of generating and making publicly available a dataset combining both approaches in a fully non-targeted, standardized, and reproducible way. As part of the large-scale characterization of mouse models in which each gene is inactivated  3, 22  , we focused on two knock-out mouse models for the Lat (linker for activation of T cells; MGI:1342293) and Mx2 genes (MX dynamin-like GTPase 2; MGI:97244), respectively, as well as the control line (WT), generated by the PHENOMIN infrastructure, member of the International Mouse Phenotyping Consortium (IMPC). On the one hand, Lat, besides its role in T-cell receptor (TCR) signalling  23  , has been shown to be involved in neurodevelopmental diseases  24. On the other hand, Mx2 is one of the coding genes in the genome region modelling Down syndrome in mice  25.
The characterization of mouse lines is currently based on a battery of animal phenotypic tests (anatomy, behaviour, histology, haematology, physiology), the results of which feed the IMPC database (https://www. mousephenotype. org/). To further characterize these models, global molecular approaches are required  26. The originality of our study is to provide, in addition to the preclinical data, a comprehensive molecular characterization by proteomic and metabolomic analyses of liver and plasma samples from the Lat, Mx2 and WT mouse models. Our dataset consists of 9 datasets (1 preclinical, 2 proteomics and 6 metabolomics), generated by the four French infrastructures for mouse phenogenomics, proteomics, metabolomics and bioinformatics, and are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability following the FAIR principles  27.
12002 m/z). Up to 10 most intense multi-charged precursors per full MS scan were isolated using a 2 m/z window and fragmented using higher energy collisional dissociation (HCD, normalised collision energy of 27 eV). MS/MS spectra were acquired at a resolution of 17,000 at m/z 200 with an AGC value set to 1 x 10 5 , a maximum injection time set to 100 ms, and the peptide match selection option was turned on. Dynamic exclusion of already fragmented precursors was set to 60 seconds. The system was fully controlled by the Xcalibur software (v3. 1. 66. 10; Thermo Fisher Scientific). MS data were saved in. raw file format (Thermo Fisher Scientific) using XCalibur. For analysis of plasma samples, peptides were separated using a nano-UPLC system (Ultimate NCS-3500RS System; Thermo Fisher Scientific) coupled to a quadrupole-Orbitrap hybrid mass spectrometer (Q-Exactive HFX, Thermo Scientific, San Jose, CA).
Dynamic exclusion was used within 30 s to prevent repetitive selection of the same peptide. All sample analyses, for either the liver or plasma series, were randomly ordered (considering the genotype, sex and weight of the mice). Pooled QCs were injected every 5 samples. To minimize carry-over, one solvent blank injection was included between all samples. Data processing. Spectrum identification: Raw files were converted to. mgf peaklists using MsConvert and were submitted to Mascot database searches (version 2. 5. 1, MatrixScience, London, UK) against a murine protein sequences database downloaded from the SwissProt website (2018_08_20), to which common contaminants, IRT and decoy sequences were added. The concatenated database contains 2 x 17 111 protein entries. Spectra were searched with a mass tolerance of 5 ppm in MS mode and 0.
07 Da in MS/MS mode. Two trypsin missed cleavages were tolerated. Carbamidomethylation of cysteine residues was set as a fixed modification. Oxidation of methionine residues and acetylation of proteins n-termini were set as variable modifications. Identification results were imported into the Proline software (http://proline. profiproteomics. fr/)  30 for validation. Peptide Spectrum Matches (PSM) with pretty rank equal to one were retained. False Discovery Rate was then optimized to be below 1% at PSM level using Mascot Adjusted E-value and below 1% at Protein Level using Mascot Mudpit score. Label Free Quantification: Peptides Abundances were extracted thanks to the Proline software version 1. 6 (http://proline. profiproteomics. fr/) 30 using an m/z tolerance of 5 ppm. Alignment of the LC-MS runs was performed using Loess smoothing.
Metabolomics Three complementary untargeted metabolomics methods based on Ultra High-Performance Liquid Chromatography coupled to High-Resolution Mass Spectrometry (which we will abbreviate as LC-MS in the following for simplicity) were used to ensure a good metabolome coverage. Acquisition of plasma metabolomics data was performed on the 3 analytical platforms, while metabolomics data from mouse liver was obtained only on 2 platforms (HILIC and Hypersil C18 as named below) essentially due to limited sample amounts. Sample preparation: for further analysis under HILIC and Hypersil C18 conditions: mouse plasma metabolite extraction was performed twice from 50 µl of plasma following methanolassisted protein precipitation as previously described  37. Briefly, a volume of 200 μl of methanol containing internal standards at 3.
The software interface was Xcalibur (version 2. 1; Thermo Fisher Scientific, Courtaboeuf, France). The mass spectrometer was calibrated before each analysis in both ESI polarities using the manufacturer's predefined methods and recommended calibration mixture provided by the manufacturer (external calibration). The Exactive mass spectrometer was operated with capillary voltage at -3 kV in the negative ionization mode and 5 kV in the positive ionization mode and a capillary temperature set at 280 °C. The sheath gas pressure and the auxiliary gas pressure were set, respectively, at 60 and 10 arbitrary units with nitrogen gas. The mass resolution power of the analyzer was 50,000 (full width at half maximum) at m/z 200, for singly charged ions. The detection was achieved from m/z 85 to 1000 for RP conditions in the positive ionization mode and from m/z 75 to 1000 for HILIC conditions in the negative ionization mode.
Mobile phases A and B were water and acetonitrile with 0. 1% formic acid, respectively. The elution gradient was 0% B (2 min), 0-100% B (13 min), 100% B (7 min), 100-0% B (0. 1 min) and 0% B (3. 9 min for re-equilibration). The mass resolution of the mass spectrometer was 50,000 and mass accuracy ranged from 0. 8-2 ppm. Samples were analysed in the positive and negative ionisation modes (Pos, Neg). Capillary and end plate offset voltages were set at 2,500 V and 500 V for the ESI source. The drying gas temperature was 200°C and nebulisation gas flow was 10 l/min. Mass spectrum data was acquired in fullscan mode over mass range 50-1000 mass-to-charge ratio (m/z). The orders of injection of the samples into the LC-HRMS instruments were randomised to ensure that there was no distribution differences between genotypes and between sexes.
Data processing. Mass spectra were processed using the xcms R package 39 deployed in the Workflow4Metabolomics Galaxy platform (https://workflow4metabolomics. org)  40. In particular, the peak detection and quantification was performed with the centWave algorithm 41 by using a value of 10 ppm and peak width values of 10,40 (Hypersil C18), 15,90 (HILIC) and 5,20 (Acquity HSS T3 C18). Annotation of the chemical redundancy (adducts, isotopes, fragments) was performed with the CAMERA software  42. Statistical post-processing. Variables whose mean intensity in the biological samples was less than 3 times that of the blanks were discarded. The inclusion of diluted quality controls (QC; pools of the biological samples) in the Orbitrap datasets enabled further filtering of variables whose linear (Pearson) correlation between the intensity and the inverse of the dilution factor was lower than 0.
7. The signal drift observed in the plasma datasets was estimated for each variable by locally weighted (loess) regression and corrected  43, 44. The reference samples for the regression were the pooled QC samples for the Hypersil C18/HILIC datasets, and the biological samples for the Acquity HSS T3 datasets: for the latter datasets, the normalization based on the pooled QCs was shown to provide only limited correction of the signal drift (as assessed both visually and by the Drift PCA metric described in the Technical Validation section), and the biological samples were therefore used as the reference instead  45, 46. Variables with a coefficient of variation in the QCs above 30% or greater than the coefficient of variation in the samples were then filtered out. Chemically redundant features (i.