dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work used M4D facilities at the  Grenoble Instruct-ERIC Center (ISBG ;  UMS 3518 CNRS CEA-UGA-EMBL ) with support from the  French Infrastructure for Integrated Structural Biology (FRISBI ;  ANR-10-INSB-05-02 ) and GRAL, a project of the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ) within the  Grenoble Partnership for Structural Biology. Research reported in this manuscript was supported by the  French National Research Agency  (grant  ANR-16-CE11-0019 ) and GRAL (7C047GRAL). IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA. We are grateful to  Dr Ulrike Endesfelder  ( Max Planck Institute for Terrestrial Microbiology, Marburg, Germany ) to provide us the SWIFT tracking software.
Potential PD cases were identified through medical record linkage and further validated by experts in movement disorders through clinical records, according to the diagnostic criteria of the UK Brain Bank. Differential diagnosis with other movement disorders (multiple system atrophy, progressive supranuclear palsy, vascular parkinsonism, dementia with Lewy bodies, essential tremor, PD with essential tremor, unclassifiable parkinsonism) was conducted by neurologist experts. A total of 881 PD cases were ascertained. Cases who received a diagnosis after the date of recruitment were defined as incident cases (n = 734)  [20]. Reliability of diagnoses was determined by the quality of clinical data (rated as 'poor' , 'good' or 'excellent'), as well as the confidence degree of the expert neurologist's final judgement (rated as 'low' , 'medium' or 'high')  [20].
Diagnoses were defined as 'definite' only when the confidence degree of the neurologist was high and the data quality was excellent; 'very likely' when the confidence degree was high, but data quality was either good or poor; 'probable' when the confidence degree was medium and data quality was either excellent or good; and diagnoses were defined as 'possible' in all remaining cases. Here, we conducted a nested case-control study within the EPIC4PD cohort. A total of 352 incident PD cases for whom a plasma sample was available were included for current analyses. No subjects from Sweden and Greece were included as no blood samples were available in the central EPIC biobank. Among the included cases, 45 and 144 were categorized as 'definite' and 'very likely' cases, respectively. One control per case was selected by incidence density sampling matched by age at recruitment, sex and study center.
Acknowledgements The coordination of EPIC is financially supported by  International Agency for Research on Cancer (IARC)  and also by the  Department of Epidemiology   and Biostatistics, School of Public Health, Imperial College London , which has additional infrastructure support provided by the  NIHR Imperial Biomedical Research Centre (BRC). We are grateful to all the participants who have been part of the project. The national cohorts are supported by:  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy ,  Mutuelle Générale de l'Education Nationale ,  Institut National de la Santé et de la Recherche Médicale (INSERM) (France) ;  German Cancer Aid ,  German Cancer Research Center (DKFZ) ,  German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) ,  Federal Ministry of Education and Research (BMBF) (Germany) ;  Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy ,  Compagnia di SanPaolo and National Research Council (Italy) ;  Dutch Ministry of Public Health, Welfare and Sports (VWS) ,  National Institute for Public Health and the Environment (RIVM) ,  Netherlands Cancer Registry (NKR) ,  LK Research Funds ,  Dutch Prevention Funds ,  Dutch ZON (Zorg Onderzoek Nederland) ,  World Cancer Research Fund (WCRF) ,  Statistics Netherlands (The Netherlands) ;  Health Research Fund (FIS)-Instituto de Salud Carlos III (ISCIII) ,  Regional Governments of Andalucía, Asturias ,  Basque Country, Murcia  and  Navarra , the  Catalan Institute of Oncology -ICO ,  CERCA Program/Generalitat de Catalunya  for the institutional support to  IDIBELL (Spain) ;  Swedish Cancer Society ,  Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden) ;  Cancer Research UK  ( C864/A14136  to  EPIC-Norfolk  (https:// doi.
Funding This work was supported by  Stichting ParkinsonFonds. Yujia Zhao  was supported by the  China Scholarship Council (CSC)  during the PhD period in  Utrecht University-Institute   for Risk Assessment Sciences. Christina M Lill  was supported by the  Heisenberg programme  of the  German Research Foundation (DFG). Raul Zamora -Ros was supported by the " Miguel Servet II"  ( CPII20/00009 ) program from the  Institute of Health Carlos III  (Co-funded by  European Social Fund (ESF)  investing in your future).
, 2020). JAK inhibitors aim to block interferon activation upon the JAK-STAT signaling pathway. An open-label study (NCT01724580 and NCT03047369) demonstrated that patients taking baricitinib had improvement in neurologic function and gained developmental milestones, and a phase 2 study (NCT03921554)  is underway. Establishing the pathogenicity of intronic variants, as demonstrated herein, would be critical in reaching the molecular diagnosis of AGS, and thus appropriately administering the available therapy  (Vanderver et al. , 2020). Since many US state newborn screening programs include C26:0-LPC as a screening marker for X-linked adrenoleukodystrophy, some patients with AGS including RNASEH2B-related AGS have been identified at birth  (Armangue et al. , 2017). The mechanism of C26:0-LPC elevation in patients with AGS has not been elucidated.
Background: Activated phosphoinositide-3-kinase d syndrome (APDS) is an inborn error of immunity (IEI) with infection susceptibility and immune dysregulation, clinically overlapping with other conditions. Management depends on disease evolution, but predictors of severe disease are lacking. Objectives: This study sought to report the extended spectrum of disease manifestations in APDS1 versus APDS2; compare these to CTLA4 deficiency, NFKB1 deficiency, and STAT3 gainof-function (GOF) disease; and identify predictors of severity in APDS. Methods: Data was collected from the ESID (European Society for Immunodeficiencies)-APDS registry and was compared with published cohorts of the other IEIs. Results: The analysis of 170 patients with APDS outlines high penetrance and early onset of APDS compared to the other IEIs.
The clinical course of APDS is highly variable. 7] [8]  This variability makes it difficult to advise patients about their individual prognosis and best treatment approach. 9] [20] [21] [22]  Yet, the standard of care and use of these therapies in the long-term management of patients with APDS remains to be defined. These interventions and their potential side effects must be balanced against the risks of the natural disease course. However, information on the natural history of APDS is still limited, and no clear risk factors for severe disease evolution have been identified. In this study, we used an updated dataset of the ESID (European Society for Immunodeficiencies)-APDS registry of 170 patients with APDS and published datasets  13, 15, 17  on other autoimmunelymphoproliferative IEIs to address the following questions: (1)  What are the clinical overlaps and characteristic differences among APDS, CTLA4 deficiency, NFKB1 deficiency, and STAT3 GOF disease? (2) Are there differences in the spectrum of disease manifestations between APDS1 and APDS2? (3) Can we identify early predictors of severe disease evolution in patients with APDS?.
The ESID-APDS registry ESID is a nonprofit association whose aim is to improve knowledge in the field of IEIs. The APDS subregistry is the first level 3 dataset within the international internet-based ESID registry (https://esid. org/Working-Parties/Registry-Working-Party/ ESID-Registry/The-3-levels-datasets-and-driving-questions). Documentation into the ESID registry is organized in 3 levels. Level  1 is open to capture all patients with IEIs and includes a minimal dataset on initial manifestations, age at diagnosis, immunoglobulin replacement, and HSCT with yearly follow-up on survival and changes in therapy. 23 Level 2 makes it possible to set up research projects that include some laboratory values and more details on treatments for a selected group of diseases. Level 3 cu allows the implementation of large datasets designed to address specific and extended clinical questions on a single IEI defined by a study protocol, including a statistical evaluation plan.
All level 2 and 3 projects include level 1 data. Requirements for patients' registration are positive vote from the local ethics committees, agreement between the treating center and ESID, and signed ESID patient consent. Patient registration in the APDS subregistry also requires approval of evidence supporting the functional relevance of the mutation by a principal investigator. Patient data can be entered by authorized users via a standard web browser through encrypted communication. 24 The first patient was registered in September 2015. To perform the comparison with other AD IEIs, the largest published cohort studies  13, 15, 17  were taken as reference, and the frequency of reported clinical and immunological features were compared among all 4 IEIs, because there are currently no level 3 ESID registry data on the other IEIs.
49  Finally, a recently reported higher incidence of enteropathy in patients with APDS1 and of elevated IgM in patients with APDS2  44  could not be confirmed. This registry analysis bears some relevant limitations: (1) The compared IEIs were not assessed using the same dataset, which may affect the reported frequency of some symptoms or diagnoses. (2) Some manifestations are per se difficult to categorize, for example, enteropathy can be difficult to distinguish from infectious enteritis. Internationally accepted standards of diagnosis and monitoring of these patients could help defining comparable datasets and, already, efforts have been taken in that direction. 50 (3) The registry and the retrospective cohort study structures are inevitably linked to the problem of missing data, leading to incomplete information and the eventual need of statistical corrections.
The analysis revealed early disease onset as a prognostic factor, with the clinical implication that early-onset cases should be followed closely and evaluated early for treatments such as HSCT. It will be interesting to see in the future how targeted therapy with PI3Kd inhibitors will impact on the long-term evolution of disease manifestations in APDS. Recent results of a phase 3 trial show promising efficacy, especially regarding the lymphoproliferative disease, with a very good safety profile. 22 he poorer prognosis for patients with early disease onset identified in this study highlights the importance of clinical trials involving younger patients (such as the recently started Pediatric Patients Aged 4 to 11 Years With APDS study; NCT05438407).
Results and discussion Evidence of endogenized dinoRNAVs in coral holobiont metagenomes. Putative dinoRNAV EVEs were detected in metagenomes generated from 42 cnidarian holobionts out of 269 sampled across the South Pacific Ocean (Supplementary Data 1). The majority of endogenized dinoRNAVs were identified in hydrocoral metagenomes (Millepora spp. ; 70. 5%, n = 105) which predominantly harbored Symbiodinium dinoflagellates but EVE-like sequences were also observed in scleractinian coral metagenomes (Pocillopora spp. ; 29. 5%, n = 15. ) which predominantly harbored Cladocopium and Durusdinium dinoflagellates (Fig. 1a, c ). No dinoRNAV-like sequences were detected among Porites spp. metagenomes (Figs. 1, 2 ). Hydrocoral metagenomes were sequenced at equivalent depths as scleractinian corals and had comparable levels of annotation (Supplementary Fig.
We examined the Symbiodiniaceae ITS2 profiles associated with each metagenome and found that putative dinoRNAV EVEs were primarily associated with Symbiodinium, Cladocopium, and Durusdinium, which exhibited variation on both host and regional scales (Fig. 1c , Supplementary Data 4). DinoRNAV EVEs were more common in Symbiodinium-dominated cnidarians (F 2,1044 = 25. 8, p < 0. 0001, nested ANOVA; Supplementary Fig. 2 , Supplementary Data 5) relative to cnidarians hosting other Symbiodiniaceae genera, regardless of host. This suggested that dinoRNAV integration may be particularly recurrent or conserved within the genus Symbiodinium (Fig. 1 ). To determine if these putative viral integrations were specific to cnidarian holobiont metagenomes and ensure that they were not artifacts of shared sample processing and sequencing procedures of the Tara Pacific pipeline, we also analyzed seawater metagenomes and publicly available metagenomes from the stony coral-dinoflagellate holobiont, Acropora spp.
Methods Identification and computational validation of dinoRNAV EVEs leveraging meta'omics. The Tara Pacific Expedition (2016-2018) sampled coral reefs to investigate reef health and ecology using multiple methods, including amplicon sequencing and metagenomics (see Pesant et al. 2020  95  and https://doi. org/10. 5281/zenodo. 4068293 for coral reef sampling and processing methods). In this study, we explored metagenomes generated from hydrocorals (n = 60 Millepora), stony corals (n = 108 Porites, n = 101 Pocillopora) sampled from 11 islands (three replicate sites per island) across the South Pacific Ocean during the Tara Pacific Expedition for dinoRNAV EVEs (Fig. 1 , Supplementary Data 1A, 1B  95  ). Amplicon libraries of the dinoflagellate Internal Transcribed Spacer 2 (ITS2) gene fragment were sequenced in tandem with the metagenomes, to characterize the dominant Symbiodiniaceae harbored by hydrozoan and stony coral colonies  95.
To confirm that these dinoRNAV EVE sequences were affiliated with coral holobionts and reduce the possibility that they are technical artifacts, publicly available metagenome libraries were analyzed (Supplementary Data 1B). These additional libraries included 120 assembled pelagic water samples presumed to include pelagic dinoflagellate sequences from the Tara Oceans dataset (2009-2013  70  ) and 30 MiSeq metagenomes from unfractionated samples of the stony coral genus Acropora, which were processed and sequenced via a different pipeline (Supplementary Data 1B, Supplementary Fig. 7 ). Publicly accessible transcriptomes from nine Symbiodiniaceae assemblies (Supplementary Data 1B) were also queried to determine if dinoRNAV-like sequences were present in poly(A)-selected dinoflagellate transcriptomes and resembled EVEs in terms of proximal gene composition and presence of a characteristic pre-mRNA spliced leader (dinoSL) sequence (as in Levin et al, 2017  57  ).
Details regarding the collection of samples, generation of metagenomes and associated Symbiodiniaceae amplicon libraries, and associated bioinformatic analyses are provided in Supplementary Fig. 7 ). Metagenomic and transcriptomic scaffolds were annotated against a curated database of dinoRNAV-like sequences (Supplementary Data 14) via BLASTx (evalue < 1 × 10 -5 ; see Supplementary Fig. 7  for workflow  96  ). Alignments to the custom database with a bit score <50 and percent shared amino acid identity <30% were excluded from further analysis. A length penalty was not imposed during this step due to the limited length of assembled scaffolds (average N50 = 3341 ± 127 nt across all queried libraries). Open reading frames (ORFs) from selected scaffolds were called via Prodigal (v. 2. 6. 3  97  ) and annotated against the NCBI-nr database (evalue < 0.
001; DIAMOND v. 2. 0. 6  98  ) to confirm homology to dinoRNAVs and to identify adjacent dinoflagellate sequences (e-value < 1 × 10 -5 , bit≥50). In the absence of complete ORFs (potentially due to the limited size of scaffolds, partial integrations, etc. ), homology was confirmed through comparison of the initial alignments to the curated database and 300nt of upstream/downstream flanking sequences (bedtools v. 2. 30. 0 99 ) against the NCBI-nr database (e-value < 0. 001; DIAMOND v. 2. 0. 6  98  ). This served as further curation and verification, as EVEs can exist in fragmented or degraded states. Non-normalized quality-controlled reads were mapped via bbmap (v. 38. 84 100 ), and putative EVEs were assessed for uniform read coverage across scaffolds, reducing the probability of chimeric assembly.
RNA secondary structure was predicted via mfold (v. 3. 5 101 ). dinoRNAV EVEs in dinoflagellate and aposymbiotic cnidarian genomes. Publicly available dinoflagellate and aposymbiotic (dinoflagellate-free) cnidarian genome assemblies were queried to resolve the putative host(s) of dinoRNAVs, to assess homology among detected dinoRNAVs within coral holobionts, and to compare genes proximal to dinoRNAV EVEs in different host species/strains. A chromosome-scale dinoflagellate genome assembly generated from a Symbiodinium microadriaticum culture (Accession: GSE152150)  82  , and scaffold-scale genome assemblies were examined for dinoRNAV EVEs (Supplementary Data 1B, Supplementary Fig. 7 ). Scaffold-scale genome assemblies were from the closely related families Symbiodiniaceae and Suessiaceae, and included representatives from the genera Symbiodinium (n = 9), Breviolum (n = 1), Cladocopium (n = 3), Durusdinium (n = 1), Fugacium (n = 2), and Polarella (n = 2), as well as 25 aposymbiotic cnidarian genome assemblies, including the stony coral genera Acropora (n = 13), Astreopora (n = 1), Galaxea (n = 1), Montastraea (n = 1), Montipora (n = 3), Orbicella (n = 1), Pocillopora (n = 2), Porites (n = 1), and Stylophora (n = 1), and the jellyfish Clytia (n = 1; Fig.
2 , Supplementary Data 1B). All publicly available genome assemblies had undergone a form of microbial decontamination, trimming, and quality control prior to assembly, minimizing risk of microbial contamination. Genome completeness and quality further were assessed via BUSCO (v3) 102 with the Eukaryota dataset and QUAST (v5. 0. 2 103 ), respectively. Scaffolds/chromosomes containing putative dinoRNAV EVEs were identified by aligning sequences to the protein version of the Reference Viral DataBase (RVDB v. 19 104 ) using DIAMOND BLASTx (v0. 9. 30)  98. The same exclusion criteria were maintained for alignments of metagenomic scaffolds, also omitting alignments <100 amino acids. Regions of dinoflagellate genomes exhibiting similarity to the MCP or RdRp of reef-associated dinoRNAV reference genomes  57  or other closely related +ssRNA viruses (Supplementary Data 14) were extracted and re-aligned to the NCBI-nr database to further confirm viral homology.
We tested the relationship between the number of identified dinoRNAV EVEcontaining scaffolds, dinoflagellate genera, and genome quality metrics using a linear model. Model selection was performed with an F-test (package car, v. 3. 0-12) and assumptions were visually checked. Pairwise comparisons between genera were conducted using the package emmeans (v. 1. 7. 2). Putative whole dinoRNAV-like genomes within scaffolds were identified based on the presence of MCP and RdRplike sequences on the same scaffold no further than 1. 5 Kbp apart (Table  1 ; Supplementary Fig. 3 ). IRESPred 105 was utilized to identify internal ribosomal entry sites (IRES) with default parameters on putative dinoRNAV EVE with whole sequence integrations. ORFs were predicted and annotated from dinoRNAV EVE-containing scaffolds and all dinoflagellate chromosomes using Prodigal  97  and MAKER2 annotation pipeline 106 with the AUGUSTUS gene prediction software 107.
Translated ORFs were then aligned to a hybrid database containing the UniProt/ Swiss-Prot database and protein version of RVDB (v. 19; DIAMOND-BLASTp). ORFs on putative dinoRNAV EVE-containing scaffolds and chromosomes were further annotated using InterProScan (v5. 48-83. 0, Pfam analysis with default parameters) to identify sequences proximal to putative dinoRNAV integrations. The presence of dinoflagellate spliced leaders ("dinoSLs") were examined within 500nt of dinoRNAV EVEs using BLASTn with default parameters (except word size=9, excluding two ambiguous positions as specified in Gonzalez-Pech et al. 2021  83  ). Phylogenetic analysis of dinoRNAV EVEs. Amino acid-based phylogenetic trees were generated with dinoRNAV EVE ORFs (MCP and RdRp) from scaffold-scale genomic assemblies, metagenomes, transcriptomes, and sequences from exogenous and closely related +ssRNA reference viruses (Supplementary Data 1 A, B, Supplementary Data 14).
Acknowledgements Special thanks to the  Tara Ocean Foundation , the R/V Tara crew and the  Tara Pacific Expedition Participants  (https://doi. org/10. 5281/zenodo. 3777760 ). Thank you also to  Carsten G. B. Grupstra  and  Clark Hamor  for their input regarding analyses. We are keen to thank the commitment of the following institutions for their financial and scientific support that made this unique Tara Pacific Expedition possible:  CNRS ,  PSL ,  CSM ,  EPHE ,  Genoscope ,  CEA ,  Inserm ,  Université Côte d'Azur , ANR, agnès b. ,  UNESCO-IOC , the  Veolia Foundation , the  Prince Albert II de Monaco   Foundation ,  Région Bretagne ,  Billerudkorsnas ,  AmerisourceBergen Company ,  Lorient Agglomération ,  Oceans  by  Disney ,  L'Oréal ,  Biotherm ,  France Collectivités ,  Fonds Français pour l'Environnement Mondial (FFEM) ,  Etienne Bourgois ,  FRANCE GENOMIQUE  (# ANR-10-INBS-09  to P.
Data availability Metadata are accessible in zenodo: https://zenodo. org/record/6299409#. Y-ClwuzMKml. Metagenomes are available via https://doi. org/10. 5281/zenodo. 7839794. Seawater metagenomes are available through the European bioinformatics institute (Tara Oceans; ERP001736) and NCBI (PRJEB1787). NCBI accession numbers for individual holobiont species metagenomes, genome assemblies and reference sequences can be found in Supplementary Data 1B, 3 and 14, respectively.
116 , Liu et al. 89 , Shoguchi et al. 117 , Lin et al. 118 , Stephens et al. 78. Further genome citations (including accession numbers) and BUSCO Dinoflagellate Species (strain) Total # Scaffolds Symbiodiniaceae Symbiodinium linucheae 37,772 (CCMP2456) 83 Symbiodinium microadriaticum 57,558 (04-503SCI. 03) 83 Symbiodinium microadriaticum 67,937 (CassKB8) 83 Symbiodinium microadriaticum 9688 (CCMP2467) 113 Symbiodinium natans 2855 (CCMP2548) 83 ** Symbiodinium necroappetens 104,583 (CCMP2469) 83 * Symbiodinium pilosum 48,302 (CCMP2461) 83 ** Symbiodinium sp. A5 (formerly S. 6245 tridacnidorum) 83 Symbiodinium tridacnidorum (sh18 16,176 A3 Y106) 114 Brevolium minutum (Mf1. 05b) 115 21,899 Cladocopium C15 116 34,589 Cladocopium sp. C1acro (formerly 41,289 C. goreaui) 89 Cladocopium sp C92 (Y103) 114 6686 Durusdinium trenchii 117 19,593 Fugacium kawagutti (CS156 16,959 CCMP2468) 89 Fugacium kawagutti 30,040 (CCMP2468) 118 n = 314 scaffolds with DinoRNAV EVE-like sequences Suessiaceae Polarella glacialis (CCMP1383) 78 ** 33,494 Polarella glacialis (CCMP2088) 78 ** 37,768 n = 38 total scaffolds with DinoRNAV EVE-like sequences , Shoguchi et al.
S3K -N, Supplemental Dataset 3). Analyses using the Gene Ontology Biological Processes (GO-BP) resource  (27, 28)  revealed that there were 105 dysregulated GO-BP pathways in MUT-Hoxa1 CMPs compared to MXIE, with 33 of these also significantly dysregulated in WT-Hoxa1 CMPs. The pathways that were dysregulated were involved in leukocyte activation, inflammation and immune responses, with higher dysregulation observed in MUT-Hoxa1 compared to WT-Hoxa1.