dataesr/scientific-paragraphs-categorization huggingface dataset:
For the analysis of ONLS development over time, missing values were replaced by the last observation carried forward method. Due to the retrospective nature of the data analysis and the rarity of the disease, no calculation of statistical sample size was performed. The full analysis set included all patients who received at least one dose of Octagam and met the inclusion/exclusion criteria. After enrolment, patients were grouped according to previous IVIG exposure. Patients who had never received an IVIG before starting Octagam were included in the IVIG-naı ¨ve group; patients who had stopped IVIG therapy C 12 weeks before starting Octagam were included in the IVIGwashout group; and patients who had replaced or stopped immunotherapy \ 12 weeks before starting Octagam were included in the IVIGswitch group.
Baseline Characteristics of the Total Patient Population Between June and October 2010, medical records of 75 patients with CIDP treated with Octagam were identified and collected from 11 participating centres. Following an initial analysis of these records, 28 patients who did not meet the eligibility criteria were excluded, leaving 47 patients who were eligible for data collection. Of the 47 patients included in the safety analysis set, 29 (61. 7%) were male. The mean age at diagnosis was 61. 3 ± 14. 8 (range 22--85) years. Fifteen patients were considered to be IVIG naı ¨ve; 14 patients were included in the IVIG-washout group; and 18 patients were included in the IVIG-switch group. Patients received a mean of 254. 5 ± 204. 3 days of Octagam treatment, corresponding to a mean of 5. 2 ± 2.
ACKNOWLEDGEMENTS Funding. This study was funded by  Octapharma, France SAS. The funder of the study was responsible for the design of the study, data extraction, data analysis and statistical analysis and contributed to data interpretation and writing of the manuscript. CB had final responsibility for the data provided. The article processing charges for this article were also funded by  Octapharma. Medical Writing, Editorial, and other  Assistance. The authors would like to thank  Jean-Charles Crave  of  Octapharma France SAS  for the management of the study, Frederick  El Kouri  for technical editing of the manuscript and  Pierre Clerson  of  Soladis Clinical Studies  for the statistical analysis of data. The authors would also like to thank  Simone Boniface  of  Springer Healthcare Communications  who provided English and technical editing and  Stefan Wietek , a consultant for  Octapharma , for critical review of the manuscript.
, 2018). The two types of functional evaluation were also performed in adult mice after unilateral vestibular neurectomy (UVN) to compare the functional consequences of transient versus permanent unilateral vestibular loss. The parallel analysis of the temporal evolution of vestibular histological lesions and the resulting functional alterations allows for the evaluation of the specific contribution of peripheral synaptic plasticity to the occurence of the vertigo syndrome. The data demonstrate that synaptic deafferentation is sufficient to produce vertigo symptoms and its associated deficits with a time course of development and recovery that resembles the clinical situation. In turn, the study highlights the primary vestibular synapses as areas that are highly exposed to excitotoxic damage and sets the stage for future pharmacological studies on the development of efficient therapeutic countermeasures.
5e -03 mm 2 , chosen in the central part of both the utricle (extra-striolar region) and the hemi-crista. Figure  3  illustrates the CtBP2 and SHANK-1 expression at a schematically represented vestibular bouton synapse (Fig. 3A ), the location of a selected observation field in the utricular macula (Fig. 3B ), the fluorescent spots detected using z-stack compilation 3D reconstruction with IMARIS software in a representative observation field (Fig. 3C ) and the characteristic expression of the CtBP2 and SHANK-1 fluorescence at a calyx synapse (Fig. 3D ), both distantly localised and colocalised (Fig. 3E, F ). The analysis of the distances between all CtBP2-and SHANK-1-labelled pre-and postsynaptic structures revealed a subpopulation of fluorescent spots separated by 1 μm or less (Fig. 3G ).
Quantitative assessment of pre-and postsynaptic proteins Quantitative analysis of CtBP2 (RIBEYE) and SHANK-1 spots was performed using IMARIS software (version 8. 2. 1) based on raw image stacks. We did not apply any deconvolution, filtering, gamma correction or resampling. We automatically detected the total number and relative location of CtBP2 and SHANK-1 puncta using the Spots function in the IMARIS 3D image visualisation. To evaluate the number of colocalisations/ juxtapositions between CtBP2 and SHANK-1 proteins, an isosurface of each signal was created in independent colour channels (488 and 568 nm). Puncta volumes were then computed using IMARIS software functions that provide 3D rendering and visualisation of isosurfaces enveloping all pixel clusters. Puncta volumes were computed along the x, y and z coordinates of their centres.
Automatically detected puncta were verified by eye in a rotary 3D reconstruction of the sample. Puncta that were not contained within or next to the hair cells were manually removed. The analysis software MatLab (The MathWorks, Natick, MA) was used to set the colocalisation distance threshold at 1 µm in vestibular end organ samples. We used a custom R software Script (R Foundation for Statistical Computing, Vienna, Austria) to calculate the distances between CtBP2 and SHANK-1 labels from the x, y and z coordinates. Considering there were fewer CtBP2 than SHANK-1 spots, we assessed the distances from CtBP2 to SHANK-1 and vice versa (see Figs  S3  and S4 ). Quantitative analyses were performed on observation fields of 4. 5e -03 mm 2 (observation field in Fig. 3 ), which include about 75-100 hair cells in both utricles and crista for each of the five to six animals used per time point.
Video-oculography and vestibulo-ocular procedures Video-oculography was performed to quantify vestibulo-ocular reflexes. A total of 27 C57Bl6/J mice of both sexes were subjected to either TTK, UVN or equivalent sham conditions. All surgical and pre-test procedures were similar to those previously described  (Péricat et al. , 2017). Briefly, mice were head-fixed in a custom-built Plexiglas tube secured on the superstructure of a vestibular stimulator. The aVOR were tested in complete dark (light intensity <0. 02 lux). Eye movements were recorded using an infrared video system (ETL-200, ISCAN, Burlington MA). Eye and head position signals were sampled at 1 kHz, digitally recorded (CED power 1401 MkII) with the Spike 2 software and later exported into the 10 Matlab programming environment for off-line analysis (Matlab, The MathWorks).
To maintain miosis throughout the experiment, 2% pilocarpine was applied 10 min before the start. Gaze stability was tested in the dark by recording spontaneous eye movements in the absence of any vestibular stimulation. Then, aVOR was tested during horizontal sinusoidal rotation of the turntable (0. 2-1 Hz; peak velocity 30°/s). To specifically test otolithic function (MOR), the turntable was tilted by 17°a nd off-vertical axis rotation (OVAR) was performed (50°/s constant velocity; in clockwise and counter-clockwise direction). Analysis was performed off-line. Horizontal and vertical eye and head movement data were digitally low-pass-filtered (cut-off frequency 40 Hz), and position data were differentiated to obtain velocity traces. Segments of data with saccades were excluded from aVOR slow-phase analysis.
Statistics Immunostaining counting and processing were analysed using one-way analysis of variance (ANOVA). When appropriate, the Dunnett multiple comparison test followed ANOVA. All data are reported as mean+s. e. m. Values of P<0. 05 were considered significant. For the aVOR and OVAR analyses, when considering the factor 'time', comparing values to the before-lesion values, significance was measured using a repeated measures ANOVA: two-way for aVOR values (time and frequency) and one-way for OVAR  (time). When comparing values between UVN and TTK populations, significance was measured using a three-way (time, frequency and lesion type) or two-way (time and lesion type) ANOVA. Post-hoc comparisons were performed using the Tukey HSD test. All data are reported as mean±s. e. m. Values of P<0.
05 were considered significant. We report the lack of statistically significant effects by the letters n. s. For behavioural exploration, performances recorded across the different paradigms were analysed using one-between, one-within factor ANOVA, which considered the factors 'time' (time points 1-8; i. e. BL, 4 h, 24 h, 48 h, 72 h, 1 week, 2 weeks and 3 weeks) and 'treatment' (vehicle vs TTK) or (vehicle vs UNV). The ANOVA was followed by a Bonferroni multiplecomparisons test when appropriate. For all ANOVAs reported, violations of the sphericity assumption (homogeneity of covariance) were corrected using the Greenhouse-Geisser procedure; the corrected P-value along with the epsilon correction factor (ε) are reported. As ε was lower than 0. 75 in all analyses, we used G-G corrected P-value for all data presented.
Acknowledgements We acknowledge the PICSL imaging facility of the  CIML (ImagImm) , member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ) and, more precisely,  M. Fallet  for technical support. We are also grateful to  A. Gharbi  for technical assistance,  Y. Autier ,  E. Belometti ,  E. Mansour  and  I. Scotto  for their assistance in mouse production and animal care. The electron microscopy studies were performed at the  Scientific and Technological Centers of the University of Barcelona (CCiT-UB). We thank  J. M. Rebled ,  R. Rivera  and  A. Martıńez Genéfor  technical assistance.
Funding This work was supported by  Centre National de la Recherche Scientifique  (CNRS;  UMR7260 ),  Aix-Marseille Universite, MINECO/FEDER  ( Fondo Europeo de Desarrollo Regional ,  EU ;  BFU2015-66109-R ) and  AGAUR  ( 2017 SGR 621 ). R. C. and J. C. were recipients of a fellowship from  Amidex  ( project  A*MIDEX ,  ANR-11-IDEX-0001-02  from 2015 to 2017); P. B. was funded by a fellowship from  Sensorion.
Metabolomics profiling of patients after allogeneic-HSCT. Herein we studied two cohorts of patients, one single center cohort from Saint Louis hospital (cohort 1, n = 43) and one multicentric from 13 French transplantation centers (cohort 2, n = 56), who received an allogeneic-HSCT from an HLAidentical sibling donor. Plasma samples from sibling donors were obtained before stem cell collection and thus considered as a healthy group of reference. Recipients' samples were collected at the onset of acute GvHD before any treatment with corticosteroids or at day 90 after transplantation for those who did not develop GvHD (Fig. 1a ). Patient and transplant characteristics are described in Supplementary Data 1. Importantly, almost all patients had natural feeding at the time of sampling, excluding the possibility that metabolomics changes might be attributed to artificial parenteral or enteral nutrition.
05 but not significant after Bonferroni correction for multiple testing; red: frequency of non-detectable values significantly different between groups after Bonferroni correction for multiple testing (p < 7. 69 × 10 -5 ). Red area represents the threshold used for filtration of metabolites with more than 50% of non-detectable value. b A total number of 651 metabolites were analyzed for comparison of paired donor and recipients without GvHD. Among them, 37 metabolites were more frequently detected in one group of patients, including 5 metabolites that were still significant after Bonferroni correction (Supplementary Data 2). Eighty-three metabolites with more than 50% of nondetectable values were discarded for further analysis, and remaining non-detectable values were replaced after imputation with half of the minimal detected value for each corresponding metabolite.
Methods Patients. Patients and their related donors analyzed in this study underwent an allogeneic HSCT in Saint Louis hospital, Paris, France (cohort 1, n = 43) or in one of the 33 French national transplant centers involved in CRYOSTEM Consortium, funded under the French Government's National Investment Program (Investissement d'avenir) (cohort 2, n = 56). Inclusion criteria were adult patients (more than 18-year-old), with an HLA-identical sibling donor who underwent an allogeneic-HSCT. Patients with HIV or HTLV co-infection were excluded. Our objectives were to include at least 40 patients per cohort with an expected acute GvHD incidence of 40%. Clinical data were extracted from medical records and included gender, age, CMV status, underlying hematological diagnosis, HLA matching between donor and recipient, stem cell source, conditioning regimen, T cell depletion, GvHD prophylaxis, presence or absence of GVHD and nutrition type (oral vs.
GvHD grade was evaluated according to Glucksberg score and Consensus criteria  57, 58. All patients gave their written consent for clinical research. This noninterventional research study with no additional clinical procedure was carried out in accordance with the Declaration of Helsinki. Data analyses were carried out using a database with all patient identifiers removed. This study was declared to the CNIL (Commission National Informatique et Liberté, number KoT1175225K) and was approved by the local ethic committee and Institutional Review Board (CPP Ile de France IV, IRB number 00003835). Samples collection and preparation. Concerning CRYOSTEM plasma, samples annotated have been provided by the CRYOSTEM Consortium (https://doi. org/ 10. 25718/cryostem-collection/2018) and the SFGM-TC (Société francophone de greffe de moelle et de thérapie cellulaire).
LLC. (St. Louis, MO) while succinic acid, pyruvic acid and NAD + were purchased from MP Biomedicals, LLC. (Santa Ana, CA). Limit of detection (LOD) for standards analyzed in a dilution series using reverse phase chromatography is available in Supplementary Data 15. One aliquot was analyzed using acidic positive ion conditions (LC pos), chromatographically optimized for more hydrophilic compounds. In this method, the extract was gradient eluted from a C18 column (Waters UPLC BEH C18-2. 1 × 100 mm, 1. 7 µm) using water and methanol, containing 0. 05% perfluoropentanoic acid (PFPA) and 0. 1% formic acid (FA) at pH = 2. 5. Elution was performed at 0. 35 mL min -1 in a linear gradient from 5% to 80% of methanol containing 0. 1% FA and 0. 05% PFPA over 3. 35 min. A second aliquot was also analyzed using acidic positive ion conditions; however, it was chromatographically optimized for more hydrophobic compounds.
Quality assurance and quality control (QA/QC). Several types of controls were analyzed in concert with the experimental samples: a pooled matrix sample generated by taking a small volume of each experimental sample (or alternatively, use of a pool of well-characterized human plasma, named MTRX for sample matrix) served as a technical replicate throughout the dataset; extracted water samples served as process blanks; and a cocktail of QC standards listed below, which were carefully chosen not to interfere with the measurement of endogenous compounds were spiked into every analyzed sample, allowed instrument performance monitoring, and aided chromatographic alignment. In LC neg conditions, internal standards were D7-glucose, d3-methionine, d3-leucine, d8-phenylalanine, d5tryptophan, bromophenylalanine, d15-octanoic acid, d19-decanoic acid, d27tetradecanoic acid, d35-octadecanoic acid, d2-eicosanoic acid.
A batch fails QC if any of the internal standards are more than 5 ppm away from the theoretical mass. Overall process variability was determined by calculating the median RSD for all endogenous metabolites (i. e. , non-instrument standards) present in 100% of the MTRX samples, which are technical replicates created from a large pool of extensively characterized human plasma. The median RSD for the MTRX samples was equal to 9-10%. Five MTRX samples and three process blank samples were processed per every batch of 30 samples. Experimental samples were randomized across the platform run with QC samples spaced evenly among the injections. Compounds identification and quantification. Raw data were extracted, peakidentified, and QC processed using Metabolon's hardware and software. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities  59, 60.
More than 3300 commercially available purified standard compounds have been acquired and registered for analysis on all platforms for determination of their analytical characteristics. The full list of identified metabolites in both cohorts is available in Supplementary Data 16. Microbiota-derived metabolites identification was based on the Human Metabolome Database (www. hmdb. ca). The QC and curation processes were designed to ensure accurate and consistent identification of true chemical entities, and to remove those representing system artifacts, mis-assignments, and background noise. Metabolon data analysts use proprietary visualization and interpretation software to confirm the consistency of peak identification among the various samples. Library matches for each compound were checked for each sample and corrected if necessary.
Peaks were quantified using area-underthe-curve. A data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound was corrected in run-day blocks by registering the medians to equal one (1. 00) and normalizing each data point proportionately Data analysis. Metabolite amounts were analyzed using a three-step procedure, both for the comparison between recipients according to the GVHD status [recipient with acute GVHD, Ra vs those without GVH, Rs], and for the comparison between donors and their recipients without GVHD [D vs Rs] or with GVHD [D vs Ra]. The first two steps dealt with each metabolite separately, whereas the third and fourth step analyzed the whole dataset at once. These steps are detailed below; briefly, the two first steps differed in terms of outcomes that were compared across groups, either the proportion of non-detectable metabolite quantities (first step) or the average metabolite quantity (second step); the third step specifically aimed to detect groups of metabolites that experience similar pattern of changes across groups.
The frequency of non-detectable values in both groups was compared for each distribution profile using a Fisher exact test for a 2 × 2 contingency table and colored as follows: green: frequency of non-detectable values non-significantly different between groups; orange: frequency of non-detectable values significantly different between groups with p < 0. 05 but not significant after Bonferroni correction for multiple testing. Red area represents the threshold used for filtration of metabolites with more than 50% of non-detectable value. b A total number of 524 metabolites were analyzed for comparison of recipients with or without GvHD. Among them, 14 metabolites were more frequently detected in one group of patients, but none was significant after Bonferroni correction (Supplementary Data 9).
confirmatory analysis used the same filters and procedures than the exploratory analysis to assess whether the results obtained in the first cohort of patients were consistent in an independent cohort. To ensure the comparability of the results, both analyses used only the 653 metabolites that were shared by the two datasets (excluding 150 metabolites detected only in the Saint Louis cohort and 274 metabolites detected only in the Cryostem cohort). Unless specified, all analyses were done using a 5% type I error rate (p < 0. 05, after multiplicity correction when required). To handle obvious confounders, all analyses of steps two and three (detailed below) were secondly adjusted. More specifically, unpaired analyses were adjusted for age, sex, and BMI. Analyses on paired cases (either D vs Rs, or D vs Ra) were adjusted for differences of age and of BMI, as well as for sex match between the two subjects of the pair.
For paired analysis of Ra vs paired Rs, given matching was performed on age and sex, estimates were only adjusted for difference in BMI between the two subjects of a pair. All analyses were performed using R and appropriate additional packages, as described below. The first step of analysis consisted in selection of metabolites and imputation of undetectable amounts. Since some metabolites could not be detected in several patients, indicating low amounts of metabolites in these patients, we first wondered whether these non-detectable amounts occurred randomly or preferentially in one of the two groups. This was done, for each metabolite, by replacing the values by 1 if an amount could be detected (that is, a numerical value was present in the dataset) and 0 otherwise (that is, the value was missing in the dataset).
5). For unpaired data, the comparison was done using the exact Fisher's test for 2 × 2 contingency tables. A metabolite was declared to be significantly more frequently undetectable in one group (hence, present at significantly lower amounts in patients of that group) when the test was significant after Bonferroni correction for multiple testing. For the D vs Rs analysis, all the 653 metabolites were analyzed, leading      to significant differences for p < 7. 66 × 10 -5. For the Rs vs Ra analysis, 524 metabolites were analyzed, leading to significant results when p < 9. 54 × 10 -5. After this first step, chemicals that could not be detected in at least one half of the patients either overall or in either group of the Saint Louis hospital cohort were excluded for the following two steps. As stated above, the resulting list of excluded chemicals also applied to the Cryostem cohort dataset.
Based on the experimental protocol, missing data can be considered mainly to rely on the limit of quantification (LOQ) of the analytical method: any value below this limit could not be measured and reported (BLQ data). To consider the information of such BLQ data-known as left-censoring-the most usual method is to impute half the LoQ to missing data. Since LoQ is unknown for each chemical, imputations used half the minimal observed value. It allows unbiased estimates unless a large amount of data is missing (but such cases were removed by filtering at the end of step 1 of the analysis). These findings were notably reported in Keizer et al. 61  , who showed that except when the percentage of missing data is high, imputing values by LOQ/2 achieves less bias than simply discarding the data, and in fact is similar to having complete data or using more complex methods than the ones used in this paper.
At the end of this step, a principal component analysis (PCA) and a sparse partial least square discriminant analysis (sPLS-DA) were performed to check that the different groups were indeed separated, allowing identifying metabolites whose level differ between the groups (R package FactoMineR  63  , function PCA and R package mixOmics  64  , function plsda and splsda). Due to the small sample size and the very low (sample number)/(predictor number) ratio, PLS-DA is prone to overfitting and cross-validation cannot be used here. Hence, PLS-DA results should be taken as descriptive. Metabolites that were identified in sPLS-DA were then used to build an ORA. Enrichment (E) was calculated by considering the number of metabolites identified with sPLS-DA in each pathways (k), the total number of metabolites identified in sPLSD-DA (n), the number of metabolites in each pathway (m), and the total number of metabolites used for analysis (M) as follows: E = (k/m)/((n-k)/(N-m)).