dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential transcript usage analysis. Using the input samples, we performed a differential transcript usage (DTU) analysis by following the workflow described in Love et al. 94. Isoform abundance estimates were computed with salmon v0. 13. 1  95  and scaled transcript-per-million estimates were imported with tximport v1. 10. 1  96. We performed a differential isoform usage analysis, using the same covariables as in the differential expression analysis, computed a per-gene adjusted p-value using DEXSeq v1. 28. 3  97  and used stageR v1. 5. 1  98  to compute overall false discovery rate per transcript. Isoforms were considered to be differentially used between two conditions if their overall FDR was smaller than 5% and their change in isoform fraction between the two conditions was larger than 0.
1. Motif presence. MEME-ChiP software  99  was used to search for motif in different lists of m 6 A peaks. For each tissue we used: List of all m 6 A peaks (e. g. Liver), list of non-differentially methylated peaks (e. g. Liver_NoMeth), list of differentially methylated peaks (e. g. Liver_Meth), list of differentially methylated peaks with corresponding gene differentially expressed (e. g. Liver_MethGene), list of differentially methylated peaks overlapping a gene which is not differentially expressed (e. g. Liver_MethNoGene). MEME-ChiP runs different motif search software from the MEME-Suite, such as DREME  100  and CentriMo 101 , and centralizes the results in one folder. Using in-house python scripts, all motifs fund by DREME were combined. Only motifs with DREME e-value <0. 00001 and found centrally enriched by CentriMo were kept.
Previously described motifs were added in the motif list: RRACH, NGGACN, and NBCAN  1, 2, 26. To avoid motif presence list size effect induced by small list of peaks, only lists of peaks with >50 m 6 A peaks were kept in the motif presence analysis. For motif presence we added the list of all peaks sorted by their overlapping region on a transcript (e. g. Liver_CDS, Liver_3UTR, Liver_5UTR). The cumulative occurrence of each motif was calculated for each list and divided by the number of m 6 A peaks per list. Each row of the final table obtained was then reduced and centered to perform clustering of the motif presence with in-house R scripts. qPCR from cecal content. gDNA was prepared from cecal content using the Power Soil DNA isolation kit (MoBio) following the manufacturer's instructions.
4. 0). Reads with a positive match against mouse genome (mm10) were removed. Filtered high-quality reads were merged into amplicons with Flash (v1. 2. 11). Resulting amplicons were clustered into operational taxonomic units (OTU) with VSEARCH (v2. 3. 4)  103. The process includes several steps for de-replication, singletons removal, and chimera detection. The clustering was performed at 97% sequence identity threshold, producing 1110 OTUs. The OTU taxonomic annotation was performed with the SILVA SSU (v132) database  104  using VSEARCH  103  and filtered according to their identity with the reference  105. Annotations were kept when the identity between the OTU sequence and reference sequence was ≥78. 5% for taxonomic Classes, ≥82% for Orders, ≥86. 5% for Families, ≥94. 5% for Genera and ≥98% for species.
In total, 56% of the OTUs were assigned a genus level annotation following these criteria. The input amplicons were then aligned against the OTU to get a contingency matrix giving the number of amplicons associated with each OTU using VSEARCH global alignment  103. On average 91. 5% of amplicons were aligned against the OTUs. The contingency matrix normalization was performed at OTU level using the weighted non-null normalization, described in detail by Volant et al. 106. Normalized counts were then summed within the genera. The generalized linear model (GLM) implemented in the DESeq2 R package  107  was then applied to detect differences in abundance of genera between each group. We defined a GLM that included the treatment (condition) and the time (variable) as main effects and an interaction between the treatment and the time.
Resulting P values were adjusted according to the Benjamini and Hochberg procedure  86. The results of the OTU picking process is available on shaman. pasteur. fr  106 with the key ff9551570bf15. The statistical analysis can be reproduced on SHAMAN by loading the datasets available on figshare: https://doi. org/10. 6084/m9. figshare. 8321165. v5. The bioinformatic workflow implemented in SHAMAN is available at github. com/ aghozlane/masque. Normalized OTU tables are provided as Supplementary Data 6. Quantification and statistical analysis. All data are expressed as mean and standard error of the mean. The number of animals or replicates (n) for each group is indicated in the figure legends. Either student's t-test or Ordinary one-way or two-way ANOVA for multiple comparisons were used for statistical analysis.
Acknowledgements We thank  Marion Bérard  and the Gnotobiology platform of the  Institut Pasteur  for support in conducting gnotobiology experiments; the  Pasteur Biomics platforms for Transcriptomics and Epigenetics and Metagenomics  for providing RNA sequencing and 16S rRNA sequencing services;  Cédric Fund ,  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ) and IBISA;  Carla Saleh  and  Olivier Dussurget  for helpful discussions;  Hugo Varet  ( Institut Pasteur Bionformatics and Statistics Hub ) for advice concerning statistical analysis;  Stevenn Volant  for help with analysis of 16S sequencing data. This work was supported by grants to P. C. :  European Research Council (ERC)   Advanced Grant BacCellEpi  ( 670823 ),  ANR   Investissement d'Avenir Programme  ( 10-LABX-62-IBEID ),  ERANET-Infect-ERA   PROANTILIS  ( ANR-13-IFEC-0004-02 ) and the  Fondation Le Roch-Les Mousquetaires.
Data availability MeRIP-Seq and RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-6560. 16S rRNA sequencing data have been deposited in the European Nucleotide Archive database at EMBL-EBI under accession number PRJEB25147. 16S data are also available in SHAMAN  106  (shaman. pasteur. fr; key ff9551570bf15) and Figshare (https://doi. org/10. 6084/m9. figshare. 8321165. v5). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  108  partner repository with the dataset identifier PXD016099. Proteomic data were searched against a uniprot database containing Mus musculus proteins (downloaded 03/2016; https://www. uniprot. org/ proteomes/); public databases used were: MeT-DB v2. 0 database (http://180.
Technical characterization Fourier transform infrared (FTIR) spectroscopies were obtained on a fresh geopolymer paste using a Thermo Ficher Scientific Nicolet 380 using the mode attenuated total reflectance (ATR). A few drops of the reactive mixture are simply deposited on the diamond before scanning. The scans are made every 10 min for 12 h and are performed between 500 and 4000 cm À1 , the number of scans is 72 and the resolution is 4 cm À1. The data were than treated using OMNIC program. The CO 2 in the air produce perturbations on the signal between 2400 and 2280, straight line was used on that part of the spectra in order to eliminate the noise. An automatic correction of the base line and normalization of the spectra were also done in order to compare different results. The evolution of Si-O-M (M ¼ Si, Al) peak position over time is then plotted and the shift value (initial peak position at 0 min (directly after mixing of the precursors) -final peak position at 720 min (after consolidation) is determined.
Data and Code Availability The NASA Life Sciences Data Archive (LSDA) is the repository for all human and animal research data, including that associated with this study. The LSDA has a public facing portal where data requests can be initiated (https://lsda. jsc. nasa. gov/Request/dataRequestFAQ). The LSDA team provides the appropriate processes, tools, and secure infrastructure for archival of experimental data and dissemination while complying with applicable rules, regulations, policies, and procedures governing the management and archival of sensitive data and information. The LSDA team enables data and information dissemination to the public or to authorized personnel either by providing public access to information or via an approved request process for information and data from the LSDA in accordance with NASA Human Research Program and JSC Institutional Review Board direction.
7 ng/ml and 79. 9 ng/ml plasma (mean = 27. 9 ng/ml, median = 23 ng/ml)(Table  1 ). We tested if there is a significant difference between HR, TW or MS as well as FR and AR samples using Wilcoxon rank test (R function wilcox) for pairwise comparisons and ANOVA (R function anova) for multi-group comparisons. We furthermore visualized the distributions of the groups HR, TW and MS as boxplots using the R package ggplot2.
Subsequently, all libraries were pooled again and sequenced on the NovaSeq 6000 using an S2 flow cell and 200cycle kits (2x100). We finally obtained 4. 9 and 4. 1 billion reads passing quality filters. cfDNA sequence analysis Samples were de-multiplexed using the standard Illumina tools. Low quality bases were trimmed and Illumina-specific sequences and low quality sequences were removed from the sequencing data using Trimmomatic-0. 32. Filtered, paired-end reads were aligned using BWAmem to the hg38 human reference genome with the bwa-postalt option to handle alternative alignments. The resulting BAM files were post-processed (e. g. sorted) using samtools. Duplicate sequences were removed and only reads aligning in concordant pairs were used for further analysis. The fragment length distribution was generated by plotting the distance between read 1 and 2 obtained from the BAM file of each sample.
Histograms and boxplots of the fragment length distribution for the autosomes (Figure  1 ) and for the mitochondrial genome (Figure  2 ) for all samples were generated using the R package ggplot2. Analysis of cell free mitochondrial DNA cfDNA read counts by chromosome (including the mitochondrial genome labeled ChrMT) were extracted using a 'edtools coverage -a feature_file -b sample. bam -counts', where feature_file contains the definition (name, start, end) of all chromosomes. Read counts per feature were length-normalized using the well-established reads-per-kilobase per million formula frequently applied to normalize RNA-seq data  (Mortazavi et al. , 2008). We used the R package ggplot 2 to visualize differences in the normalized cfDNA fraction (RPKM) originating from the mitochondrial genome between HR, TW and MS and over time during the mission (longitudinal analysis).
2 Ti rotor, Beckman Coulter Optima XE or XPE ultracentrifuge. The final exosome pellet was resuspended in PBS, and protein concentration was measured by BCA (Pierce, Thermo Fisher Scientific). Mass spectrometry analyses of exosomes were performed at the Rockefeller University Proteomics Resource Center using 10 μg of exosomal protein as described previously  (Hoshino et al. , 2015; Zhang et al. , 2018). Heatmap and complete Euclidean clustering was performed with Morpheus, (https://software. broadinstitute. org/morpheus). Pathway analysis was performed with Metascape  (Zhou et al. , 2019).
ACKNOWLEDGMENTS We would like to thank  Jeremy Geddes  for providing the artwork for the graphical abstract figure. Furthermore we would like to thank the  Epigenomics Core Facility  and the  Scientific Computing Unit (SCU)  at  Weill Cornell Medicine , as well as the  Starr Cancer Consortium  ( I9-A9-071 ) and funding from the  Irma T. Hirschl  and  Monique Weill-Caulier Charitable Trusts ,  Bert L. & N. Kuggie Vallee Foundation , the  WorldQuant Foundation , the  Pershing Square Sohn Cancer Research Alliance ,  NASA  ( NNX14AH51G  (all Twins Study principal investigators);  NNX14AB01G  (S. M. B. ); and  NNX17AB26G  (C. E. M. ),  NNX14AH52G ), the  National Institutes of Health  ( R25EB020393 ,  R01NS076465 ,  R01AI125416 ,  R01ES021006 ,  R01AI151059 ,  1R21AI129851 ,  1R01MH117406 ), TRISH (NNX16AO69A: 0107 ,  NNX16AO69A:0061 ,  NIH/NCATS KL2-TR-002385 ), the  Bill and Melinda Gates Foundation  ( OPP1151054 ), and the  Leukemia and Lymphoma Society (LLS)  grants ( LLS 9238-16 ,  LLS-MCL-982 ).
Signal searches The signal-search strategies and methods employed are similar to those used in Ref. [15]. The dimuon mass spectra are scanned in around 6000 steps of about σ[m(µ + µ -)]/2 searching for X → µ + µ -contributions. For m(X) < 20 GeV, the data are binned in p T (X) and each p T bin is searched independently for each m(X) hypothesis; whereas at higher masses, p T bins are not necessary since both the resolution and efficiency are nearly independent of p T (X). All searches use the profile likelihood method to determine the local p-values and the confidence intervals on the signal yields. The trial factors are obtained using pseudoexperiments in each search. The confidence intervals are defined using the bounded likelihood approach  [41] , which involves taking ∆ log L relative to zero signal, rather than the best-fit value, if the best-fit signal value is negative.
These results indicated a strong relationship between the flow magnitude of blobs (i. e. their dynamics) and their local density. Notably, the blob dynamics appear enhanced in regions of high blob density, and conversely isolated blobs in chromatin-void regions appear less mobile. This and other studies resulted in complex high-dimensional datasets, which reflect stochastic and heterogeneous quantities characterizing chromatin in space and time. In order to allow inferences of relationships in such data, correlations between variables are commonly investigated (Figure  1(b) ). While this is a valid approach, one has to be aware of its limitations  [26]. In particular, a correlation between variables does not directly imply a causal relationship and in the case that a direct causal relationship indeed exists, it is not clear in which direction.
While both correlation as well as causality analyses suffer from the presence of unobserved variables, identification of causal relationships even among the limited subset of observable variables in a system can, however, indicate which variable is a cause and which one is an effect (possibly via a yet to be discovered pathway). Here, we adapt the abstract concept of Granger-causality to the analysis of Granger-causal relationships between chromatin dynamics and structure at the nanometer scale using the unique data set of our recent chromatin live-cell superresolution imaging based on deep learning (Deep-PALM)  [24]. We use the chromatin blob flow magnitude as a measure of local chromatin dynamics and the blob nearest neighbor distance (NND) as well as the blob area as structural parameters.
PALM imaging The Deep-PALM imaging conditions are described in our recent publication  [24]. Briefly, a fully automated Nikon TI-E/B PALM (Nikon Instruments) microscope equipped with incubator was used for live cell imaging. NIS-Elements software was used for acquiring the images at 30 ms per frame. PATagRFP was illuminated using a laser line of 561 nm (~50-60 W/cm 2 at the sample) along with the 405 nm laser line for photo-activation (~2-2. 5 W/cm 2 at the sample). Excitation wavelengths were merged into a TIRF oil immersion objective (1. 49 NA, 100x; Nikon). The same objective was used for collecting the fluorescence emission signal and spectrally filtered by a Quad-Band beam splitter (ZT405/488/561/647rpc-UF2, Chroma Technology) with Quad-Band emission filter (ZET405/488/561/647 m-TRF, Chroma).
Additionally, a Diebold-Mariano test is used to consider only significant differences in the prediction accuracy of the two models (Materials and Methods). The full and base models are evaluated at all pixels within the nucleus. Pixels at which the adjusted R 2 value of the full model is higher than of the base model indicate pixels at which a Granger-causal relationship can be detected (Figure  2 (c)). While this framework is not able to address headon the true causality for a system as complex as a human cell (the current state-of-the-art experimental data do not allow to do so), it is able to infer relative causalities (Granger-causalities) among the set of accessible parameters. The detection of Granger-causality thus indicates that some path exists from one variable to another such that the latter appears to arise as a consequence of the former.
These protocols are syntheses of published good practices about a rare disease, or a group of rare diseases, followed by recommendations. Their objective is to guide healthcare professionals (HCP) for an optimal diagnostic and therapeutic management. Two open access sources of information were used: the list of National Diagnosis and Care Protocols (NDCP) published by the HAS (Source #3)  (13) ; and the list of NDCPs written or under writing by the 23 FSMR according to their websites (Source #4)  (14). Finally, the identi ed pathologies were matched with Orphanet nomenclature database (Source #5 (15). The detail of the information sources used in this work is available in the Supplementary Information section.
A con dence index was introduced to characterize the degree of certainty on the correspondence (High / Medium / Low): 248 matches with a "High" correspondence (74%), 39 matches with a "Medium" correspondence (12%) and 28 matches with a "Low" correspondence (14%) were found. The list was nally reviewed by an expert on rare diseases, with proposals for modi cation, grouping or removal of pathologies. An output table including 273 RD entries was nally produced. Information on each RD (ORPHA number, ICD 10 code, synonyms, inheritance, age of onset and prevalence) was then collected from the Orphanet database for the purpose of producing statistics. The inheritance codes were simpli ed in three categories: "Genetic origin" encompasses all diseases with a genetic origin whatever the mode of inheritance.
Without surprise, the largest groups are inborn errors of metabolism and multi-systemic diseases, followed by developmental disorders, hematological disorders and neurological disorders. Developmental disorders are represented because of the large number of clinical guidelines available, despite a small number of drug therapies (Table  2 ). On the contrary, inborn errors of metabolism rank high because of the large number of marketed drugs, despite a small number of clinical guidelines. In all categories, the number of RD with both a marketed drug and clinical guidelines is very small (15%). The proportion of RD with a genetic origin is 68. 9% in this list, comparable to the 75% for the whole set of RD in the Orphanet database (Table  3 ). Most of the 251 conditions are pediatric disorders (Figure  3 ), which is similar to what is generally described in RD.
(19; 20). For this project, it was decided to be as close as possible from the ICD 11 classi cation system, as it is the most recent attempt to establish an international consensus (21). However, many choices are disputable. For example, Neuro bromatosis type 1 is classi ed as a dermatological disease when it could be also in the developmental anomaly group. Turner and Klinefelter syndrome are considered here as endocrine disorders, when they could also be considered as developmental anomalies. Glucose-6-phosphate dehydrogenase de ciency is in the group of hematological conditions when it could be in the inborn errors of metabolism group. Alpha-1-antitrypsin de ciency is here as hepatological disease and could be a pneumological disease for instance. In addition, the dataset is a snapshot of the situation as of January 2018, based on information sources from July 2017 to January 2018.
Conclusion This study led to the establishment of a tentative list of 251 RD for which a delayed diagnosis would be particularly detrimental. It remains being established whether the diagnosis of these RD can be considered as especially delayed, or not, before setting up targeted initiatives to reverse the situation. The next step could be, for instance, to validate this list using the data from the French national RD database (BNDMR)  (25). The time to diagnosis for these diseases will show whether this delay is acceptable, or not, according to the opinion of RD experts. If not, this will clearly indicate that eHealth solutions should be considered in priority for those RD. Clarifying choices when taking initiatives to develop solution in a eld with so many unmet needs is an element of an ethical approach.
Agence nationale de sécurité du médicament et des produits de santé, the French Agency for the Safety of Health Products ATU: Authorization for Temporary Use BNDMR: Banque Nationale de Données Maladies Rares, the French national data bank for rare diseases DGOS: Direction Générale de l'Offre de Soins, the French general directorate for care provision ERN: European Reference Network EU: European Union.
Acknowledgements We would like to extend our gratitude to all those who contributed to the initiative and supplemented our work, especially:  Rémy Choquet  ( Orange Healthcare ),  Véronique Schneider  ( Sano Genzyme France ),  Claude Messaien ,  Elisa Salamanca ,  Arnaud Sandrin , and  Anne-Sophie Jannot  ( AP-HP BNDMR team ).
ANSM: Agence nationale de sécurité du médicament et des produits de santé, the French Agency for the Safety of Health Products ATU: Authorization for Temporary Use BNDMR: Banque Nationale de Données Maladies Rares, the French national data bank for rare diseases DGOS: Direction Générale de l'Offre de Soins, the French general directorate for care provision ERN: European Reference Network EU: European Union.
Study design This prospective study was performed among patients diagnosed by the doctors, acting as the expert panel of this study -with OA of the lower limb. The recruitment was performed between June 2017 and November 2017, in six hospitals situated in six different geographical regions of Hungary. The participating hospitals were selected in a way to represent different geographical and cultural areas of the country; the patients were selected randomly by the doctors of the given sites after evaluating the inclusion criteria, which were as follows: age over 18 years, clinically/doctor diagnosed knee and/or hip OA, native Hungarian language, the ability of self-filling and completely filled patient data sheet. Exclusion criteria were age under 18 years, other type of OA, psychiatric disorders and those who had surgeries within 1 month, or who were incapable of filling the forms.
Within each dimension there is a three-level response option: level 1: no problem; level 2: moderate problem; level 3: severe problem; 3  5  = 243 health status could be stated by calculating the EQ-5D index. Based on the given responses, an official online calculator considers the EQ-5D index number with regard to visual analogue scale (VAS) and time trade of method (TTO). Several countries in Eastern and Central Europe have no individual national value set for calculation, neither has Hungary. In this case for the Hungarian calculation the British value set is recommended to be used according to the practical proposals in the concerning literature. [43] [44] [45]  Patient data sheet. Each participating patient received the patient data sheet. The patient data sheet contains questions with regard to patients' demographic characteristics: gender, year of birth, height and weight -to calculate the body mass index (BMI), residence type (urban or rural), education level, marital status, OA duration, income level, type of OA (knee, hip, both).