dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design This was a prospective population-based study. Data were entered into the BNDMR prospectively using the CEMARA software  [22]. A minimum data set (MDS) to be collected for each patient with a rare disease has been defined  [23]  (see Additional file 1). This MDS was defined following a long process involving health professionals of the reference centres for RD and a ministerial working group. The MDS aims to minimize the amount of data collected by a centre while guaranteeing the quality of information collected and its exploitation. It supports communication with the information systems of hospital care. It thus constitutes a base of information common to all the uncommon diseases and all the actors involved in their care  [22]. The national MDS for RD is constructed in the following way: assent/ consent (regulatory), national identification of patients, personal information, family information, vital status, course of care, activity of care, history of disease, diagnosis, confirmation of the diagnosis, treatment, ante and neonatal data, research protocol, structure of care.
National health network National network of centres of expertise for patients with head and neck rare diseases includes six Reference Centres (Paris (4), Lille (1) and Strasbourg (1)) and 34 Competence Centres in 18 French cities. In Paris Region, four reference centres and no competence centres exist (Fig. 1 ). We first analysed data from all the centres. Then, to assess the balance between demand for and supply of care, we analysed data in two of the Paris reference centres, MAFACE (specialized in orofacial diseases) and MALO (specialized in othorynolaryngology). These two centres were highlighted for this analysis because they offer the most comprehensive and therefore representative data on their activity.
Statistical method For this study analysis, nominative data captured were de-identified  [22, 25]. An anti-duplicate device was set up automatically. To guarantee an optimal control of the data quality, automated completeness and consistency checks were implemented. BNDMR data-managers routinely perform "queries" on themes of consistency. To verify the quality of data, the script is checked on a weekly basis by BNDMR computer scientists entirely devoted to this task. Duplicates are systematically sought and missing data in the minimal data set are verified. Each numeric variable has unauthorised values (e. g. , birth date or aberrant birth weight). There are also boundaries for each numerical variable (eg a patient's date of birth may not be later than the present day). Data analysed were, first, population descriptions for the French regions and Paris Region (age, sex, abnormalities diagnosed), time and age at first signs, time and age at diagnosis.
Next, supply of care was described (type of consultations, patient's reference). Two measures were performed: -The proportion of patients in French regions was evaluated as follows: people living in the region and registered in this region during the study period (patients seen in the reference/competence centres for one of the diseases and described as living in the region) are called "patients". The proportion was calculated as follows: P = (patient living in the region and registered in the database during the study time)/(population in the region). -Second measure: "included cases geography". In order to detect possible health service provision issues, we supposed that the Paris regional data can be used as a model to project onto other regions to estimate the number of rare Head, Neck and Teeth rare diseases patients.
Population description During the period from January 2008 to December 2015, 11,342 patients in France were diagnosed with RD and entered in the database. Seventy two thousand nine hundred four of these patients were coded in the Paris Region, and 4048 outside of Paris/other French regions (Table  1 ). Abnormalities were diagnosed antenatally for 34. 7% (n = 1142) of patients in other French regions and 29. 7% (n = 1480) of patients in Paris Region (Table  1 ).
Discussion The epidemiological study reported here provides the first information on rare diseases of the head, neck and teeth in terms of public health in France. This is the first time that data from a national database concerning these diseases have been analysed in France. This main results show a gap between demand for and supply of care, and reveal how patients travel from their place of residence to the reference centres in Paris with a view to recourse care and management of these very rare diseases. According to our data, nearly 45% of all patients resided in Paris Region. This disparity between patients From Paris Region and in other French regions is probably to be explained in part by difficulties experienced by the medical teams in completing the database. Patients were mostly diagnosed very early or at birth.
This is encouraging in terms of the network's ability to meet the patient's need for early management, which is the best guarantee of success in the treatment of these diseases  [26]. Early diagnosis does not depend on the centre (regions versus Paris). The clinical entities represented in the cohort are predominantly early-onset ones (oral clefts are becoming better diagnosed using ultrasound since this became a mandatory part of the checklist  [27] ). The five most frequent diseases accounted for nearly half of all patients. These most common diseases (oral clefts and Pierre Robin Syndrome) were those treated by the two Paris centres that completed the database the most. Our study confirmed that cleft lip and alveolus and cleft palate represented the most frequent disease type, as previously reported  [28].
It may therefore show that the Paris Region reference centres have a secondary health care role. It is noticeable in the results that the more specialised the centre, the more it detects and quickly diagnoses the clinical entities. Moreover, as the Paris centres also appear as centres of second resort, delays may sometimes be longer before a confirmed diagnosis is made. In Paris, most of the medical and non-medical staff in reference centres use the database. This is why we decided to use the Paris data as a proxy to discuss the whole-of-France data (in view of the fact that the literature concerned has not shown ecological or environmental variables influencing the diseases studied)  [32] [33] [34]. This may explain why the prevalence differed between Paris Region and the rest of France. Prevalences found in the cohort for Paris Region were consistent with those of Orphanet  [3] , where the most frequent clinical entities were also cleft lip and alveolus, cleft palate, cleft lip and/or palate, vascular anomaly or angioma, isolated Pierre Robin Syndrome and hard palate cleft.
The ratio between proportion observed and proportion expected showed that patients living in the South of France were more apt to travel to be cared for than those living in Haute-Normandie for example. In some areas such as South of France or Alsace, therefore, low prevalence is likely to be related to nonfulfilment of the database criteria, and may not just occur because patients are leaving their own region to be treated elsewhere. This was also true for the other regions cited. Figure  1  confirms that these regions have competence and reference centres but do not yet have full compliance with the database requirements. In Paris, reference centres cover rare ENT malformations, Pierre Robin syndrome and congenital deglutitionsuction disorders, dental manifestations of RD, craniosynostosis and craniofacial malformations, and malformations of the face and the oral cavity.
Strengths The research project to exploit the data collected by the database aims, first of all, to obtain some epidemiological estimation of these diseases in France. Analysis of the demand for care and the supply of care, and of the balance between the two, was the primary methodological goal of the exploration of these data. In terms of the organisation of the territorial network of expert centres, the fact that many centres were not in a position to participate in the database meant that the data collected were not yet exhaustive. Indeed, the results obtained for the whole country differed significantly from the figures found in scientific literature, except for the previously discussed cleft palate results for Paris Region. It was for this reason that we made use of the results obtained in Paris Region, which were likely to be closer to epidemiological reality.
The Network improves access to diagnosis, treatment and the provision of highquality healthcare to patients who have conditions requiring a particular concentration of resources or expertise. These concentrations could also be focal points for medical training and research, information dissemination and evaluation, especially for rare diseases. In the United States, the National Organization for Rare Disorders (NORD) provides information for patients and their families, offers help to patients who cannot afford the required care, and links patients with many patient organisations that provide lists of medical experts. To find out if a particular disease has an organisation, the patient has to search in the NORD's organisational database because there is no national institutional network especially devoted to RD  [42] [43] [44].
Limitations A good network has been built in the past 8 years, but there are still some pitfalls in the analysis and also apparent issues in the patient health care circuit. In the database, observational data were not collected for research purposes. This is why we had to deal with limitations and biases such as knowledgeable data users time-varying clinical workflows, using idiosyncratic coding practices, and showing lack of motivation to collect data and misunderstanding of purposes… If centres are not participating in the database, this implies a selection bias that tends to under-estimate the number of patients. It is for this reason that we have taken Paris, whose prevalences are close to literature ones, as a proxy. Analysis for each French Region shows the major differences that appear for many regions, where the fit between supply and demand for care seems to be unsatisfactory (Fig.
1 ). The ratios between observed and expected numbers calculated for the different French regions compared to Paris Region clearly shows the tendency for patients to come to Paris for their treatment. Hospital coding for RD, using current coding and classifications systems and practices, tends to underrepresent RD cases. Hospital databases are made to evaluate hospital activity. They are not intended to produce epidemiological data. Indeed, the computer systems currently present in French hospitals do not specify the rarity of clinical entities, and demonstrate the need for specialised databases  [46]. To make data comprehensive and achieve exhaustiveness, it will be fruitful to cross-reference the data with additional databases such as the Medical Information Systems Program (MISP) and the National Health Insurance Fund.
Lack of CLUH leads to mitochondrial clustering and respiratory deficiencies in vivo Liver proteomic data predicted that a lack of CLUH affects mitochondrial respiration. To this purpose, we measured the enzymatic activities of respiratory complexes I-IV in liver lysates from WT and KO mice at E18. 5. All respiratory complexes were affected, but complex I activity was particularly decreased (Fig. 3 A ). Because the TCA cycle represented one of the pathways most affected in the absence of CLUH, we assessed the activities of isocitrate dehydrogenase (IDH) and fumarate hydratase. These enzymatic activities were reduced by approximately half in the absence of CLUH, in agreement with the corresponding reduction of the protein level (Figs. 2 A and 3 B). Finally, we found that mt-DNA levels were decreased to a different extent in the liver, heart, and kidney of  Cluh -/-mice (Fig.
Antibodies for Western blots The following antibodies were used: polyclonal rabbit OPA1 (ab42364), monoclonal mouse PDHA1 (ab110330), monoclonal rabbit GOT2 (ab171739), monoclonal mouse ATP5A1 (ab14748), monoclonal rabbit CPT2 (ab181114), polyclonal rabbit HAD HA (ab54477), and monoclonal rabbit PCX (ab128952) from Abcam; polyclonal rabbit CLUH from Aviva (ARP70642_P050) or from Novus Biologicals (NB100-93305); polyclonal rabbit SPG7  (Ferreirinha et al. , 2004) ; monoclonal mouse GAP DH (MAB374), monoclonal mouse pan-actin (MAB1501R), and monoclonal mouse VDAC1 (529536) from EMD Millipore; polyclonal rabbit TOMM20 (sc-11415) and monoclonal mouse HMG CS2 (sc-376092) from Santa Cruz Biotechnology, Inc. ; polyclonal rabbit UQC RC2 (HPA019146) from Sigma-Aldrich; monoclonal mouse NDU FA9 (459100), monoclonal mouse SDHA (459200), and monoclonal mouse COX5B (459110) and COX4I1 (A21348) from Invitrogen; and polyclonal rabbit OXCT1 (12175-1-AP), polyclonal rabbit OGDH (15212-1-AP), polyclonal rabbit BDH1 (15417-1-AP), and polyclonal rabbit PCCA (21988-1-AP) from ProteinTech.
The pool was quantified by using the Peqlab KAPA Library Quantification kit (Roche) and the 7900HT Sequence Detection System (Applied Biosystems). The pool was sequenced by using a TruSeq PE Cluster kit (v3) and a TruSeq SBS kit (v3-HS) on a HiSeq 2000 sequencer with a paired-end (101 × 7 × 101 cycles) protocol (Illumina). Data were analyzed using QuickNGS, a highly scalable next-generation sequencing analysis pipeline developed in house  (Wagle et al. , 2015) ; basic read quality check was performed using FastQC (Babraham Bioinformatics), and read statistics were obtained with SAMtools. Reads were mapped to the human reference assembly, version GRCh38, using TopHat2  (Kim et al. , 2013) , and gene quantification was performed using a combination of Cufflinks  (Trapnell et al. , 2010)  and the DEseq2 package  (Anders and Huber, 2010)  with genomic annotation from the Ensembl database (version 80).
The results were uploaded into an inhouse MySQL database and merged with annotations obtained with BiomaRt from Ensembl (version 80). The gene lists were filtered according to FC and p-values in comparison to the library-size normalized read counts between samples and controls. The sample and control means as well as FC and p-values were calculated with the DESeq2 package from the Bioconductor project. Wald's test was used for significance testing. In contrast, gene expression for the individual samples was calculated by the Cufflinks package and returned as FPKM (fragments per kilobase of transcript per million mapped reads) values, which means that they have also been normalized by the molecule size. Enriched pathways were analyzed using the iPathwayGuide (Advaita Bio). Protein networks were assessed using STR ING network analysis  (STR ING Consortium 2016).
1% FA in ACN. Raw data were processed using MaxQuant 1. 5. 3. 8  (Tyanova et al. , 2016a)  and the implemented Andromeda search engine  (Cox et al. , 2011). Tandem mass spectrometry spectra were correlated against the UniProt mouse database, including a list of common contaminants. We used 7-and 4. 5-ppm tandem mass spectrometry tolerances for the first and main search, respectively. The false discovery rate at the peptide spectrum match and the protein level was controlled by the implemented decoy algorithm using the reverse database approach. Match-between runs and the Requantify option were enabled. N-terminal acetylation and oxidation at methionine residues were defined as variable modifications, whereas carbamidomethylation at cysteine residues was defined as a fixed modification. Data were further analyzed using the Perseus computational platform  (Tyanova et al.
, 2016b). SIL AC ratios were log 2 transformed, and changes between KO and WT were determined as described previously  (Zanivan et al. , 2012). In brief, a one-sample two-tailed t test was used to calculate p-values (S0 was set to 0). The false discover rate was set to 0. 05 (number of permutations, 500). Mitochondrial proteins were annotated based on the mitocarta database  (Calvo et al. , 2016). The mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium via the PRI DE partner repository  (Vizcaíno et al. , 2013)  with the dataset identifier PXD005628. Significantly impacted pathways and diseases were analyzed using the iPathwayGuide (Advaita Bioinformatics).
Targeted metabolomics Targeted metabolomics analyses of liver tissue were performed using ultra pressure liquid chromatography coupled to a triple quadrupole mass spectrometer. A detailed metabolite extraction protocol and instrument parameters are given elsewhere  (Nikkanen et al. , 2016). In brief, ∼20 mg of frozen liver tissues was homogenized by Precellys 24bead homogenizer (1. 4-mm beads), with 20 µl of the labeled internal standard mix and 500 µl of 100% ACN and 1% FA extraction solvent in the first step and 500 µl of 90/10% ACN/H 2 O + 1% FA in the second step extraction process. To obtain a robust statistical analysis, metabolomics data were normalized using the probabilistic quotient normalization method  (Dieterle et al. , 2006). After this procedure, using the median value of the WT group as reference, each metabolite value was rescaled to a common virtual concentration level.
The data were further preprocessed with a log transformation. MetaboAnalyst 3. 0 software  (Xia et al. , 2009)  was used to conduct statistical analyses, and the unpaired two-sample t test was chosen to perform the comparisons. The Wilcoxon-Mann-Whitney test was preferred over the t test when a normal distribution did not show to be a good fit. Targeted metabolomics analyses of serum were performed as described previously  (Mackay et al. , 2015). In brief, 20 µl of serum was deproteinized by 1:20 dilution in a solution of 50% methanol, 30% ACN, and 20% water, vortexed for 5 min at 4°C, and then immediately centrifuged at 16,000 g for 15 min at 0°C. The supernatants were collected and analyzed by liquid chromatography-mass spectrometry. For the liquid chromatography separation, the Zic-pHilic column from EMD Millipore was used.
Mobile phase A consisted of 20 mM ammonium carbonate plus 0. 1% ammonia hydroxide in water. Mobile phase B consisted of ACN. The flow rate was kept at 100 ml/min, and the gradient was 0 min, 80% of B; 30 min, 20% of B; 31 min, 80% of B; and 45 min, 80% of B. The mass spectrometer (QExactive Orbitrap Plus; Thermo Fisher Scientific) was operated in a polarity switching mode. The data were analyzed using TraceFinder Software (Thermo Fisher Scientific). Statistical evaluation was performed using unpaired t tests of log-transformed data. Measured metabolites are listed in Table  S3.
(2009). Samples were analyzed using a spinning-disk confocal microscope (Ultra View VoX; PerkinElmer) with a Plan-Apochromat total internal reflection fluorescence 60×/1. 49 NA oil DIC objective. Z stack images at 200-µm intervals were taken using an electron-multiplying charge-coupled device camera (C9100-50; Cam-Link). For LD staining, cells were fixed in 4% PFA in PBS for 30 min, incubated with 5 µM Bodipy 493/503 (Invitrogen) and DAPI in PBS for 20 min, washed in PBS, and mounted with Fluorsave. For image acquisition, an epifluorescence microscope (Imager. M2m) mounting an Apotome module and a 63×/1. 4 NA Plan-Apochromat oil immersion objective lens (ZEI SS) at RT was used. Images were acquired using an MRm camera (Axiocam) and AxioVision software (version 4. 8 2. 0; ZEI SS). Images represent a single plane with a thickness of 1 µm.
Acknowledgments We are grateful to  Jie Gao  for fruitful discussions,  Anja Gruszczyk  for assistance with C-sections,  Hue-Tran Hornig-Do  for support to measure TCA enzyme activities,  Jatin Nandania  for technical help in metabolomics sample analyses,  Peter Frommolt  for bioinformatics analysis of RNA sequencing data, and  Christian Freese  and  Markus Krüger  from the  Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases  ( CEC AD)/Center for Molecular Medicine Cologne proteomics facility  for help with proteomics analysis. Furthermore, we thank  Christian Jüngst  of the  CEC AD imaging facility , the  CEC AD proteomic facility , and  Janine Altmüller  and  Christian Becker  of the  Cologne Center for Genomics. This work was supported by  Deutsche Forschungsgemeinschaft  grants  RU 1653/2-1  and  SFB-1218 ,  A05  to  E.
MLP-and CMP-Derived cDC1s Are Transcriptionally Identical Although cDC1s are thought to be homogeneous, the finding that CD1a + HLA-DR + CD141 + DNGR-1 + cells could be generated from MLPs (efficiently) or CMPs (less efficiently) prompted the question of whether they are the same cells. We therefore carried out a transcriptomic analysis of MLP-or CMP-derived cDC1s and compared both profiles with a published dataset of DC subsets and monocytederived DC (MoDCs) generated in vitro from total CD34 + HSCs or purified from peripheral blood  (Balan et al. , 2014). We found that both MLP-and CMP-derived cDC1s expressed the classical cDC1 gene signature, which includes, among others, IRF8, TLR3, CLEC9A, and XCR1 transcripts (Figure  4A ; Figure  S4 ). We could also confirm that MLP-and CMP-derived cDC1 did not express any of the signature genes of MoDCs or pDCs (Figure  4A ; Figure  S4 ).
Flow Cytometry Analysis Cells were stained in ice-cold PBS containing fetal calf serum (FCS, 2%) and EDTA (2 mM) using appropriate antibody-fluorophore conjugates. Prior to staining for DNGR-1, cells were pre-incubated on ice with mouse serum (Jackson ImmunoResearch Laboratories) and purified immunoglobulin G2a (IgG2a, BioLegend) to block Fc receptors. See the Supplemental Experimental Procedures for antibodies used. Multiparameter acquisition was performed on a Fortessa analyzer (BD Biosciences), and data were analyzed with FlowJo software (Tree Star). Prior to acquisition, cells were resuspended in PBS/FCS and 2%/EDTA (2 mM) solution with 1 mg/ml of DAPI to exclude dead cells.
ACKNOWLEDGMENTS We thank the  Francis Crick Institute FACS laboratory  for technical help. We are grateful to all members of the  Immunobiology Laboratory  and the  Haematopoietic Stem Cell Laboratory  for helpful discussions and comments. We thank  Irene Sanjuan-Nandin  from the  Lymphocyte Biology Laboratory  for advice and Cell Reports 20, 529-537, July 18, 2017 535  Pierre Guermonprez  for critical reading of the manuscript. This work was supported by the  Francis Crick Institute , which receives core funding from  Cancer Research UK  ( FC001136  and  FC001045 ), the  UK Medical Research Council  ( FC001136  and  FC001045 ), and the  Wellcome Trust  ( FC001136  and  FC001045 ) and by  ERC   Advanced Investigator Grant AdG 268670  (to C. R. S. ).
Recombination is a complex biological process that results from a cascade of multiple events during meiosis. Understanding the genetic determinism of recombination can help to understand if and how these events are interacting. To tackle this question, we studied the patterns of recombination in sheep, using multiple approaches and datasets. We constructed male recombination maps in a dairy breed from the south of France (the Lacaune breed) at a fine scale by combining meiotic recombination rates from a large pedigree genotyped with a 50K SNP array and historical recombination rates from a sample of unrelated individuals genotyped with a 600K SNP array. This analysis revealed recombination patterns in sheep similar to other mammals but also genome regions that have likely been affected by directional and diversifying selection.