dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Lipodystrophy syndromes comprise a group of extremely rare and heterogeneous diseases characterized by a selective loss of adipose tissue in the absence of nutritional deprivation or catabolic state. Because of the rarity of each lipodystrophy subform, research in this area is difficult and international co-operation mandatory. Therefore, in 2016, the European Consortium of Lipodystrophies (ECLip) decided to create a registry for patients with lipodystrophy. Results: The registry was build using the information technology Open Source Registry System for Rare Diseases in the EU (OSSE), an open-source software and toolbox. Lipodystrophy specific data forms were developed based on current knowledge of typical signs and symptoms of lipodystrophy. The platform complies with the new General Data Protection Regulation (EU) 2016/679 by ensuring patient pseudonymization, informational separation of powers, secure data storage and security of communication, user authentication, person specific access to data, and recording of access granted to any data.
The phenotypic expressivity of each lipodystrophy subform and the associated co-morbidities and their prognosis is extremely variable, even within each category. With the exception of HIV-associated lipodystrophy, lipodystrophy syndromes are extremely infrequent  [1]. The prevalence figures that are being considered vary tremendously, largely due to the difficulty of gathering representative series of sufficient size. Thus, the prevalence for infrequent lipodystrophy subforms has been estimated as 1 in 10 million  [4]. Based on the study of a series of databases of electronic medical records and analysis of European literature since 2000, the global prevalence for all lipodystrophy subforms was recently estimated as 1. 3-4. 7 per million, being 0. 23 per million for generalized forms and 2.
Third, the registry provides a platform for nested investigations on specific clinical, molecular and functional topics. Patients with a particular lipodystrophy subform can be identified via the registry and invited for specific research projects through the physician who enrolled her or him in the registry. Due to the flexibility of the employed IT structure (see below), the results of such research projects can also be documented in the registry. This not only enables researchers of this specific project to use all previously documented information of the patients but also makes the raw data of this project available to future projects and researchers.
Quality aspects of the registry We attempt to reach a high level of compliance to international quality standards for rare disease registries. We adhere as closely as possible to the standard described for the two domains research quality and evidence quality as published earlier  [12]. The operational aspects of the registry are based on the standards defined by the European Platform for Rare Disease Registries 2011-2014 (EPIRARE) survey  [13]. In addition, the registry follows the FAIR principles  [14]  ensuring that (meta) data are findable, accessible, interoperable and reusable for example by using international accepted coding for diagnosis (ICD 10, OrphanCode and OMIM code) and treatment (Anatomical Therapeutic Chemical (ATC) Classification System). To avoid duplicate entries of a patient, the data set entered for each new patient is checked automatically by the pseudonymization tool for correspondence with existing data sets (record linkage).
They also elect 7-10 (currently 9) ECLip members to form the registry board for a period of 3 years. The registry board is the governing body of the registry and responsible for managing the registry. It controls the data storage and the Local Operating Group (see below), votes on research proposals from ECLip members, improves the registry, decides who can become registry member, and processes requests to inform individual physicians about specific research results important for their patients. In addition, for each research project a contact person from the registry board will be selected who will support this specific project and will be part of the research team of this project. Furthermore, in order to guarantee the quality of the data, on a 6 months rotating basis a member of the registry board acts as curator.
Pseudonymization The most important procedure for data protection is patient pseudonymization. The personal identification data of the patients (IDAT) are assigned a random code (pseudonym) by using a pseudonymization tool called Mainzelliste. Mainzelliste is an open-source software to create and manage patient-pseudonyms in research projects  [17]. It creates a unique identifier (PID) and a second-level pseudonym (PSNOSSE) for each patient. When a pseudonym is requested by the OSSE registry, Mainzelliste checks the data set for correspondence with existing data sets (record linkage). Depending on the degree of IDAT correspondence, a new data set is created or an existing one returned (Fig. 1 ). Communication between the identity management and the OSSE registry happens via a web browser. A temporary identifier ensures that the treating physician can see the identity (ID) management's entry mask (the patient's name) integrated into the user interface together with the corresponding medical data (MDAT) of his or her patient.
Informational separation of powers IDAT and the clinical information of the patients are housed on separate (and adequately protected) servers (Fig. 1 ). These servers are logically, physically and organizationally independent. The institution in charge of ID management (the Institute of Epidemiology and Medical Biometry of the Ulm University) operates its own legal responsibility and is not subject to the directives of the registry management. This ensures that individuals with access to clinical or Fig. 1  Data transfer within the ECLip Registry. Figure  1  shows data transfer within the ECLip Registry from the patient to two separate servers localized at Ulm University. The online tool Mainzelliste provides patients pseudonyms whereas the metadata repository is stored at the provider of the IT structure, OSSE.
Patient recruitment; inclusion and exclusion criteria All patients of all ages with any form of lipodystrophy presenting at a participating centre are asked to participate in the ECLip Registry. The only exclusion criterion is HIV-associated lipodystrophy. After written informed consent is reached, pseudonymised patient data can be entered into the registry. In most centres, ethical approval also covers inclusion of data of patients deceased or lost to follow-up in an anonymous way, because lipodystrophy syndromes are so rare that any information that can be gathered can be of vital importance for future patients.
Data collection, documentation and evaluation and statistical considerations Data collection and documentation Data entry is done at the individual locations via the web-based user interface; additionally, data can also be collected via data import interfaces. Also in this case, data pseudonymization takes place at the time-point of data entry. Based on the current knowledge about lipodystrophy syndromes including the recently published consensus paper  [1] , lipodystrophy specific data forms have been developed to cover all medical information relevant to patients with lipodystrophy (Table  2  and Additional file 1). These data collection forms exist also in paper form (see Additional file 1) and can be used directly in the physician's office at the time of presentation of the patient. They provide a valuable tool to ensure in-depth analysis of the patient's signs and symptoms.
Data evaluation If an ECLip Registry member or a third party wants to use data of a cohort of patients in the registry for scientific purposes, a formal request has to be submitted to the registry board (or to the ECLip Registry members in case of a third party). The request is analysed for scientific soundness, feasibility, ethical considerations, and adherence to the aims of the registry. If the request is granted, a data usage contract is drawn up between all researchers, the ECLip Registry board and all physicians providing patient information. Data and samples are available free of charge but a fee to refund the costs of sample handling. (including laboratory preperation of the sample, administration, and shipment) and in case of a third party organizational support (storage, maintenance) might apply.
Ethical considerations The ECLip Registry tries to take into account all important ethical considerations. Each registry site has to obtain approval from their local ethics committee before participation in the registry. Participation is entirely voluntary for the patients and written informed consent has to be obtained from every participating patient (with the exception to anonymous data collection from patients deceased or lost to follow-up as described above). Patients can withdraw their consent at any time and according to their wishes, all their data will be either anonymised or deleted (except for data already used in a published study or in a study in progress). As shown above, data protection is a fundamental aspect of the ECLip Registry. In addition, a further important aim is long-time preservation of the data to make it available not only for current but also future researchers.
Future plans The ECLip Registry is designed for a horizon of 50 years. Periodically, every 5 years, a descriptive analysis and publication of the data incorporated into the registry is planned, once agreements have been reached between the corresponding parties involved. In addition, the registry is open to specific studies and collaborations with third parties, both from the pharmaceutical industry, and from basic research groups to launch research projects that advance the knowledge of the molecular basis of lipodystrophy syndromes, response to different management strategies or start-up of clinical trials with new drugs. Many of the main expert centres in the field of lipodystrophy care and research in Europe are already involved in the ECLip Registry. Through personal contacts and by rising public awareness of this registry we aim to expand the registry further to include as many centres in Europe and neighbouring countries as possible.
The registry is already registered at Clinical Trials. In the future, registering at further data banks such as RD-Connect and Orphanet are planned to further increase awareness of the registry. The ECLip registry is also involved in the European Registries for Rare Endocrine Conditions (EuRRECa), a new project that aims to develop a core registry and an e-reporting programme for patients with rare endocrine conditions in Europe  [18]. One of the ECLip Registry board members is member of a EuRRECa Expert Working Group  [19]  and in the future we plan to establish a functional connection between the EuRRECa eREC system and the ECLip OSSE Registry in order to systematically update the EuRRECa disease records on European patients affected by lipodystrophy using an agreed core of minimum data set.
Conclusion The ECLip Registry is today a fully operational reality  [21]  in which many expert centres in the field of lipodystrophy syndromes from Europe and its neighbouring countries are involved. Recruitment started on December 16th 2017; currently, 15 centres are so far participating all over Europe and neighbouring countries, with 9 centres already actively recruiting patients (Fig. 2 ). Six further centres are also dedicated to participate. The registry has been registered at ClinicalTrials. gov (Clini-calTrials. gov ID: NCT03553420) and is also part of EuR-RECa  [18]. We hope that the EClip Registry will allow in the near future to have a detailed portrait of lipodystrophy syndromes in Europe and beyond, which will result in an improvement in the welfare for and prognosis of patients affected with lipodystrophy.
Acknowledgments We thank  Stephanie Laviani  for her fundamental input in the creation of the registry structure. The ECLip Registry does not receive any funding from private companies. Some of the authors have however received fees or funding from private companies for work unrelated to the ECLip Registry: J. v. S. has worked as a consultant for  Rhythm Pharmaceuticals  and  Aegerion Pharmaceuticals  and received travel funds from  Ipsen ,  Novo Nordisk , and  Merck Serono. D. A-V. has worked as an advisor for  Bristol-Myers-Squibb, AstraZeneca , and  Aegerion Pharmaceuticals. M. W. received personal fees from the  Bristol-Myers Squibb ,  AstraZeneca  and the  Diabetes Alliance. C. Vi  has has received personal fees for consultancy from  AstraZeneca  and  Aegerion Pharmaceuticals , for lectures from  Ipsen, Lilly, Viiv Healthcare, Janssen-Cilag, Boehringer-Ingelheim and Merck Serono , for travel from  Aegerion Pharmaceuticals, Laboratoires Servier ,  Novo Nordisk, Orkyn, Sanofi, Vitalaire, Air Products, Boehringer-Ingelheim ,  Gilead ,  Novartis and Bristol-Myers Squibb , and has received institutional research fundings from  Aegerion Pharmaceuticals, Pierre Fabre Médicament, Laboratoires Servier and Roche Diagnostics.
Reverse transcription-polymerase chain reaction Total RNA was isolated from cells using QIAzol reagent (QIAGEN) and an miRNeasy Mini Kit (QIAGEN), according to the manufacturer's directions. The RNA quality was monitored using an RNA 6000 Nano Kit and an Agilent Bioanalyzer (Agilent). cDNA was generated by reverse transcription with a Maxima First Strand cDNA Synthesis Kit for RT-qPCR with dsDNase (Thermo Scientific). For qPCR assays, gene expression analysis was performed using 2 ng of cDNA and SYBR Green I MasterMix (Roche). mRNA levels were normalized to the mean expression levels of GAPDH, Arpp0, and TBP. Triplicate samples were run on a LightCycler 480 Instrument II (Roche). For the gene expression changes, we validated the RNA-seq data using the sequenced sample with qRT-PCR customized pairs of primers generated and validated by QIAGEN.
RNA-seq data analysis Raw data were retrieved from (GSM1112847, GSM1112845, GSM1112846, GSM111284, GSM1112835, GSM1112833, GSM1112834, GSM1112837)  (Gifford et al. 2013 ) and were aligned against the human reference genome Grch37 using the STAR aligner  [21]  (version 2. 5. 2a_modified) with the following Differential expression at the transcript level was assessed using Cuffdiff  [22]  and was run with the following options: «min-repsfor-js-tes 2library-type = 'fr-firststrand'dispersion-method = 'per-condition'multi-read-correct ». The list of splicing factors has been defined using Spliceosome Database.
Isoform expression measurements were performed and differential gene expression was obtained using the Fasteris Tuxedo pipeline  [22]. Ninety per cent of the sequence reads (100 million reads per sample and 2 biological replicates) were mapped to the reference genome Grch37 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE119700). These sequences were then used to analyse the expression profile of each sample, and.
Alternative splicing analysis To investigate the regulation of AS events, we used the computational method VAST-TOOLS (version 2. 0. 0) [23] , which enabled us to obtain broad coverage of AS events with a 95% probability that |ΔPSI| is greater than 10. To extend our analysis, we also determined mRNA isoforms identified as AS events using VAST-TOOLS, which has additional feature types and a different event database, VastDB (doi. org/10. 1101/ gr. 220962. 117), underlying the method. VAST-TOOLS profiles and classifies AS events into several types, calculates Ψ values for events, and assesses the difference in these values in two conditions. VAST-TOOLS recognizes skipped exons, microexons, and alternative 3ʹ/5ʹ SS, and is more sensitive to retained intron detection than other tools, in part due to the much larger annotation database.
Compared to other tools, VAST-TOOLS can identify more retained intron (RI) events, as there are >10fold more of these events in its splicing database. VAST-TOOLS is freely available at https://github. com/vastgroup/ vast-tools. git. Subsequently, MATT is applied on Vast-tools splicing analysis to analyse additional features of pre-mRNA sequences. The sashimi plots have been generated using a standard splicing visualization tool called MISO and are showing alternative splicing changes with a 95% probability that |ΔPSI| is greater than 10 and an absolute ΔPSI greater than 15. The drawing of reads cropped automatically by the software due to the basal threshold applied by the developer. The examples of AS events controlled by U2AF1 expression level presented in Fig. 4  and supplementary information, are sashimi plots automatically generated using Miso, based on the splicing analysis performed using VAST-Tools and reads quantifications localized at the corresponding coordinates showing a modulation of a functional domain.
Clustering and heat maps illustrating the differential expression of cell-type-specific AS events driven by U2AF1 have been generated using XLSTAT software. The expression levels of each isoform in a specific cell type were extracted from the VastDB EXPRESSION Table, which provides gene expression values across tissues, cell types and developmental stages measured in cRPKM. These Expression values were reported to the control condition represented by the average value of the expression of each AS event in several hiPSCs clones. A colour code is assigned to the expression levels, a gradient from red to blue is used. When the expression of a specific event is higher than the hiPSCs control condition the associated colour is red while a lower expression is associated to blue and when the expression is unchanged or close to the control condition the white is assigned by the software.
MetaCoreTM analysis To identify the meanings of the outcome of our RNAsequencing computational analysis, in addition to literature exploration, we used MetaCoreTM. MetaCoreTM is an integrated software suite (version 6. 36) for functional analysis of Next Generation Sequencing and other multi-OMICs data. MetaCoreTM is based on a high-quality, manually curated database of (A) transcription factors, receptors, ligands, kinases, drugs, and endogenous metabolites, as well as other molecular classes; (B) species-specific directional interactions between protein-protein, protein-DNA and protein-RNA interactions, drug targeting, and bioactive molecules and their effects; (C) signalling and metabolic pathways represented on maps and networks; (D) rich ontologies for diseases and processes with hierarchical or graphic output.
Expression of CSFs and U2AF1 in hESCs and differentiated cells To investigate whether CSFs are involved in the control of cell-fate determination, we analysed the expression levels of all splicing factors, referenced in the Spliceosome Database, in the male hESC line, HUES64, and in three embryonic cell types ectoderm, endoderm and mesoderm, which were invitro differentiated. To achieve this, we retrieved highthroughput RNA-seq open access data for these four cell lines from (GSM1112847, GSM1112845, GSM1112846, GSM111284, GSM1112835, GSM1112833, GSM1112834, GSM1112837)  [20] , and we aligned the raw data to the human reference genome, Grch37, using the STAR aligner  [21]. Using Cuffdiff  [22] , we identified 40 transcripts, genes (Fig. 1A  and S1 ) and known isoforms (Figure  S2A ), encoding CSFs, SR proteins and hnRNPs, that are differentially expressed in hESCs and the three differentiated cell types.
U2AF1 down-regulation modulates gene expression and impairs the stoichiometry of pluripotency genes Next, to investigate the functional role of U2AF1 in stem cell maintenance and differentiation we used a doxycyclineinducible shRNA approach to decrease U2AF1 levels (60%) to that previously reported in hiPSC-derived differentiated cell types (Fig. s 1B (down), 1 C (right), 1D and S2 C). Using MetaCoreTM software, analysis of U2AF1 transcriptional regulators revealed that U2AF1 expression is under the control of NANOG and KLF4 via CREB1, which may explain the difference of U2AF1 expression levels between stem cells and differentiated precursors in vivo (Fig  S2D ). Therefore, we investigated changes in pluripotency gene expression following U2AF1 down-regulation by qRT-PCR and found that the genes POU5 F1, NANOG, ZFP42 (or REX1) and KLF4 showed altered expression and stoichiometry in hiPSCs upon U2AF1 down-regulation, compared to their expression levels in the shScramble control cells and in the differentiated hiPSCs (Fig.
1E ). U2AF1 could act directly at the transcriptional level as a regulator/co-regulator or at a posttranscriptional level via the regulation of miRNA such as let-7 c, Mir-34b/c that target NANOG and KLF4. To survey the landscape of gene expression upon U2AF1 down-regulation, we performed high-throughput RNA-seq analysis on shU2AF1 and shScramble hiPSCs samples and evaluated the differential expression of genes, as well as of protein isoforms between the two samples. After 48 h of U2AF1 down-regulation, 609 genes showed significant differential expression between the two samples (p-value < 0. 05) (Fig. 2A ). Among these genes, 193 genes were upregulated and 93 genes were downregulated with a fold change equal to or greater than 1. 5. Using MetaCoreTM, we performed a Gene Ontology analysis on the differentially expressed genes, and found genes involved in organ and tissue development, morphogenesis and differentiation were enriched in this dataset (Fig  S2E ), particularly those dependent on developmental Hedgehog, BMP/GDF and NOTCH signalling (Fig  S2E ).
Function of differentially expressed genes in hiPSCs cells following U2AF1 down-regulation Following U2AF1 down-regulation, we also analysed the enrichment by protein function of the differentially expressed genes, with a fold change equal to or greater than 1. 5. Using MetaCoreTM software, our analysis showed that transcription factors (TFs) represent >10% of the differentially expressed genes (Fig. 2D ). This proportion reaches 20% if transcription coactivators and chromatin modifiers are included in the list of TFs with specific regulatory sequences. Indeed, if we consider that TFs represent only 3. 15% of the human genes in the MetaCoreTM database, the modulation of TFs upon U2AF1 down-regulation represents a threefold enrichment. Among these differentially expressed TFs, we detected a large group involved in cell differentiation (Figs 2E and S3A), confirming the impact of U2AF1 on cell-fate-related TFs and their stoichiometry (Fig.
Finally, U2AF1 down-regulation decreased the expression of TFs, such as RFX4, ERG and MAF (Fig. 2E ), which are involved, respectively, in the conversion of fibroblasts to self-renewing totipotent neural precursors, in haematopoietic stem cell self-renewal and in promoting the self-renewal of human mesenchymal stem cells  [35] [36] [37]. Thus, these results show that the down-regulation of U2AF1 leads to altered TF expression, which could ultimately lead to the loss of self-renewal and to impaired hiPSC differentiation potential. To further dissect the transcriptional regulation mediated by U2AF1, using MetaCoreTM software, we analysed the transcriptional regulatory networks that control the differentially expressed genes upon U2AF1 down-regulation (genes with a 1. 5-fold change in expression).