dataesr/scientific-paragraphs-categorization huggingface dataset:
It must be pointed out that in the Renfro et al. study,  [9]  BMI was analysed as a continuous variable, whereas in the Simkens et al. study,  [7]  BMI was analysed according to WHO categories and in our study by both methods. Analyses by category could have amplified the differences in prognostic values according to the category. In the ARCAD study,  [9]  risk of death decreases as BMI increases to approximately 28 kg/m 2 and then it plateaued, in contrast from our database, the spline curve is U-shaped as in the Kroenke et al. study  [6]. One explanation of the discrepancy of the result from our study and the ARCAD study  [9]  is the difference of patient's weight distribution. In the latter study, overweight and obesity rate were higher than in our study, and normal weight patients represented 42.
8% in the ARCAD study but 52. 8% in our study. Moreover, in our study, few patients had a severe or morbid obesity that impaired to draw conclusion for this subgroup of patients. A pooled analysis with other database for this subgroup of patients will be of interest. It must be pointed out that our data are from first-line trials. Results may change as patients with metastatic CRC progress along continuum because they may have a greater loss of muscle mass and an increase in sarcopenic obesity. Our study confirmed that low BMI is a strong negative prognostic factor. Low BMI could reflect cachexia, which is a surrogate marker of tumour aggressiveness  [20]. BMI is easy to calculate and a reliable tool to assess cachexia and malnutrition, but more precise data, independent of BMI, such as weight history just before diagnosis, albumin plasma level  [21, 22]  and CT scan sarcopenia, have a prognostic value  [23, 24].
The role of sex hormones is controversial  [3]. Unfortunately, we have no data of menopausal status or hormonal dosage in our database to explore the prognostic role of sex hormone according to BMI. It has been suggested that in women, the localisation of the fat is a more important risk factor than BMI  [34]. Abdominal obesity is better evaluated by waist circumference than BMI but was not assessed in our study. Thus, further study should investigate the differential effect of obesity according to sex and hormonal status. One previous study suggested that dose capping in obese patients is associated with worse PFS and OS  [35]. Our study did not confirm this result. Nevertheless, due to the small number of patients in our study, this point should be investigated further. In conclusion, patients with BMI 25 had a prolonged OS compared with normal weight patients with mCRC enrolled in first-line chemotherapy clinical trials.
Data priority scheme Data from the Gaia instruments are stored in the onboard mass memory unit in file structures that have associated priority levels for telemetry to the ground, and approximately inversely, for data deletion  (Ecale & Chassat 2010). Brighter objects have higher telemetry priorities, so that in the RVS, windows of Class 0 have the highest and Class 2 the lowest priority. The telemetry priorities of the astrometric, photometric, and spectroscopic data are arranged in an interleaved manner by magnitude. VOs and calibration faint stars have a different telemetry priority, higher than any of the RVS normal object windows, with the VO priority the higher of the two. This prioritisation takes effect when Gaia is accumulating more data than the mass memory unit can retain, generally when the satellite is scanning along the Galactic Plane.
The lack of two-dimensional information within and around the window requires the faint source background to be modelled. Given the low flux levels, and because the sources are differently spatially arranged with respect to the selected source for each epoch of observation, this approach can be successful. However, in densely crowded regions, sources with G RVS that would normally be assigned a window may not be, because the maximum number of assignable windows (72 per CCD) may be exceeded. These sources will be recorded as background. In this case, the background spectra may be moderately bright, and there may be no epochs at which a source spectrum is not overlapped by one source or another, so that a more specific treatment of the source data may be required.
Dead time The Gaia scan law (Gaia Collaboration,  Prusti et al. 2016 ) provides for more than the requisite average number of 40 transits across the RVS focal plane (Tab. 1). However, for a number of reasons, some transits may not be recorded. These include nominal orbital maintenance operations; inadequate resources for placement of the windows at the detection chain level in high-density regions; deletion in the onboard memory as a result of inadequate capacity, particularly when both telescopes are scanning the Galactic plane; and data transmission losses. All of these effects were modelled by  Fusero & Chassat (2011)  to ensure that the average number of transits and radial velocity accuracies were met despite this dead time. The derived dead-time fractions are shown in Tab. 4. The dead time increases for fainter stars because of the G RVS prioritisation assigned to windows and to the transmission of data.
Post-launch developments On 2013 December 19, Gaia was launched on an accurate trajectory to its orbit insertion at L2. During this initial phase, the sunshield deployed and communications were established, and the payload underwent a controlled cool-down to its operational temperature (in the case of the RVS focal plane, 163K) during the transfer to L2. After spacecraft checkout, the payload was activated, and the first images were received on 2014 January 4. These trailed images from the non-spinning spacecraft indicated that the focus was approximately correct. The spacecraft spin rate was synchronised to the CCD readout rate as set by the onboard atomic clocks, and the focus of the two telescopes was optimised using the astrometric focal plane. The first RVS science data were received on 2014 January 17.
67 K5 star HIP 86564. Lines of Fe and Ti are visible, and most prominently, the Ca triplet is clearly evident, with the instrument spectral bandpass centred on the Ca triplet. The quality of the data in this single 4. 4s exposure is striking. Figure  16  also shows groundbased data with a high signal-to-noise ratio for comparison, and it is evident that even spectral features with low equivalent width in the two spectra are in common. Figure  17  shows sample RVS spectra 4  for spectral types B2 to M6. The dominant lines in hot stars are the Paschen series, with the Ca triplet dominating in spectral types F -K, and TiO molecular bands for M stars. The richness of astrophysical diagnostics in this short wavelength range is evident, vindicating the recommendations of the RVS Working Group (Sec.
2). Broadly speaking, the in-orbit RVS spectra showed the characteristics expected pre-launch  (Fusero & Chassat 2011; Chassat & Ecale 2014 ). Nevertheless, it had become clear during investigations of the impact on the RVS of the scattered light from the BAM laser almost immediately after receipt of the first RVS science data that unexpected variations were evident in the flux levels in the (nominally empty) VO windows.
1) of 300 m s -1 after only this limited period, and non-standard data processing. In addition, at this level, the mean is disturbed by some errors from the standards themselves. In respect of the radial velocity precision, the residuals in the upper plot of Fig. 20  are for single transits rather than the average 41 expected at the end of mission. The dispersion in the fit of 2. 48 km s -1 indicates that this should be easily met, but the distribution is dominated by stars brighter than that specified for 1 km s -1 in Tab. 1 (for example V > 13 in the case of a G2V star), so further analysis is required. See  Katz et al. (2018)  for a report in the Gaia DR2 dataset. The spectra from stars with ≥ 40 transits were combined and compared to fainter ground-based validation stars  (Frémat et al.
Acknowledgements. We wish to acknowledge the role of  Airbus Defence & Space  for their central role in the development of the Gaia RVS, and the  Gaia Project Team at ESA  for their support of the RVS instrument within the Gaia payload. This work has made use of results from the  ESA space mission Gaia , the data from which were processed by the Gaia Data Processing and Analysis Consortium (DPAC). Many of the authors are members of the  DPAC , and their work has been supported by the following funding agencies: the  United Kingdom Science and Technology Facilities Council  and the  United Kingdom Space Agency ; the  Belgian Federal Science Policy Office (BELSPO)  through various  Programme de Développement d'Expériences Scientifiques (PRODEX)  grants; the  French Centre National de la Recherche Scientifique (CNRS) , the  Centre National d'Etudes Spatiales (CNES) , the  L'Agence Nationale de la Recherche , the  Région Aquitaine , the  Université de Bordeaux , the  Utinam Institute of the Université de Franche-Comté , and the  Institut des Sciences de l'Univers (INSU) ; the  German Aerospace Agency (Deutsches Zentrum für Luftund Raumfahrt e.
Methods study design PERFORM was a 2-year multicenter, prospective, noninterventional cohort study in outpatients with MDD enrolled by either a general practitioner (GP) or a psychiatrist in five European countries (France, Germany, Spain, Sweden, and the UK; NCT01427439). In each country, the proportion of recruited GPs and psychiatrists was balanced against the national proportions of these clinicians treating patients with depression. Eligible patients were aged 18-65 years, had a current diagnosis of MDD according to the Diagnostic and Sta tistical Manual of Mental Disorders (4th Edition, Text Revision criteria; confirmed by the Mini International Psychiatric Interview questionnaire [depression module]), and were either initiating antidepressant monotherapy or undergoing their first switch of antidepressant.
study assessments and data collection Study assessments and data collection occurred during routine visits within the normal course of care at baseline and approximately 2, 6, 12, 18, and 24 months. Variables documented included demographic information, physical characteristics, history of MDD, characteristics of the current episode of depression, MDD management and health care resource use, and the presence of any other mental disorders or functional syndromes. Functional impairment was assessed at all visits using the Sheehan Disability Scale (SDS)  24  and the depression-specific version of the Work Productivity and Activity Impairment (WPAI) questionnaire. 25 The SDS is a brief self-report tool that assesses functional impairment over the previous 7 days in three domains: work/school, social life/leisure activities, and family life/home duties.
statistical analysis The population for analysis comprised all patients who met study inclusion criteria and no exclusion criteria, and who completed a baseline and at least one post-baseline assessment or patient-reported outcome questionnaire. All assessment data were summarized at each time point using descriptive techniques. Continuous variables are presented as mean ± standard deviation (SD); categorical and binary variables are presented as counts and percentages. To describe the course of functional impairment, depressive symptoms, and perceived cognitive symptoms, SDS total score, WPAI absenteeism and WPAI presenteeism scores, PHQ-9 total score, and PDQ-5 total score were plotted over time from baseline to month 24. To investigate which patient-related factors were associated with functional impairment, univariate analyses of variance were performed to identify relevant factors that were then included in multivariate analyses of covariance (ANCOVA).
Associations with functional impairment (measured by SDS total score and WPAI scores) were investigated at four time points: baseline, month 2, months 6 and 12 combined, and months 18 and 24 combined. For functional impairment at baseline, associations with other baseline factors were assessed. For analyses of functional impairment at month 2, month 6/month 12, and month 18/month 24, associations with additional factors at month 2 were assessed as well as associations with baseline factors that were not measured at later time points. Therefore, baseline and month 2 analyses are cross-sectional by nature, whereas month 6/month 12 and month 18/month 24 analyses had a predictive aspect. As data from two visits were included in the month 6/month 12 and month 18/month 24 analyses, a mixed model for repeated measurements including a random effect for patients was used for these analyses.
The factors included in the univariate analyses were selected based on a literature review and clinical experience (Supplementary materials). Factors with P,0. 20 in the univariate analyses were then selected for the multivariate ANCOVA model to which backward stepwise variable selection was applied (ie, sequential removal of the least significant variable from the model and recomputation of the model until all remaining variables had P,0. 05). Four factors were forced into the model because they were identified as potential confounders (country, age, and sex at baseline for analyses at all time points; PHQ-9 total score at baseline for the baseline analyses and at month 2 for analyses at the other time points). Missing data were not replaced in any of the analyses, ie, all data included in the analyses were observed cases.
study population A total of 1,895 patients were screened for inclusion in this study, 1,402 of whom were enrolled. The first patient was screened on February 25, 2011, and the last patient completed the study on February 19, 2015. In all, 1,159 patients (82. 7%) completed the baseline visit and at least one follow-up visit without any violation of inclusion or exclusion criteria and were therefore included in the analyzable population, 862 of whom completed the full 2 years of follow-up. Reasons for exclusion were violation of at least one of the inclusion and/or exclusion criteria at baseline (n=167) and/or lack of post-baseline data (n=101, including 76 who met the inclusion criteria at baseline). Analyzed patients were from the UK (29. 4%), France (29. 2%), Spain (23. 3%), Germany (14. 2%), and Sweden (3.
Acknowledgments This study was funded by  Lundbeck SAS. The authors thank  Professor Martin Knapp  ( Personal Social Services Research Unit, London School of Economics and Political Science, UK)  for his advice regarding the study design and interpretation of the study findings. The authors also thank  Mapi  for logistical management of the study. The authors would also like to express their gratitude to all the patients and physicians who participated in the study. Writing and editorial assistance was provided by  Jennifer Coward of Anthemis Consulting Ltd , funded by  Lundbeck SAS.
13 14 TA could suppress the secretion of some neurotransmitters, thus directly decreasing peripheral sensitisation and indirectly decreasing central sensitisation. 15 BTA could also have an inhibitory role on the release of mediators involved in nociception, such as substance P, calcitonin gene-related peptide and glutamate. Trials assessing the clinical effect of intra-articular BTA in knee OA suggest a mid-term positive effect on pain  16  and no marked toxicity,  17  but the overall level of evidence is low. Only one study assessing the effects of intra-articular BTA in eight participants with TMC OA was registered but was stopped early for lack of funding and slow accrual ( ClinicalTrials. gov Identifier: NCT01045694). We hypothesised that a single ultrasound-guided intra-articular injection of BTA associated with splinting in people with painful TMC OA may reduce pain at 3 months.
We did not plan any scoring of X-rays neither at baseline nor during follow-up because we do not expect our treatment to have a structural effect in the short term. For participants who discontinue or deviate from intervention protocols, the same outcome data will be collected if possible. Safety outcome will be recorded by the investigator by asking an open-ended question ('Have you had any adverse events since last contact?') during a face-to-face Open access visit at 3 months and by mail, email or phone at 6 months after the intra-articular injection. If the participant missed the appointment, a biomedical research technician will request information on adverse events by mail, email or phone and record them. All adverse events will be recorded in the 'adverse events' section of the electronic case report form by the investigator.
All analyses will be performed on an intention-to-treat basis, in that all patients will be considered in the analysis and will be analysed in the group to which they had been assigned. There will be no interim analyses. Participants lost to follow-up will not be replaced. In case of missing data on the primary endpoint criterion, baseline carried forward imputation will be carried out. For descriptive analyses, categorical variables will be reported with absolute numbers and percentages and quantitative variables with mean (SD) or median (IQR). For comparative analyses, categorical data will be analysed by Fisher's exact test or X 2 test and quantitative data by Student's t-test or Wilcoxon signed-rank test with an alpha risk of 5% and statistical significance level p=0. 05. The difference in the change between baseline and postbaseline values will be assessed by analysis of covariance or constrained longitudinal data analysis.
Open access contraceptive method during the whole study. Indeed, clinical guidelines for the use of BTA advise against injection during pregnancy because there is no clear data to evaluate a clinical malformative risk or fetotoxicity. This was also a request from our institutional review board (Comité de Protection des Personnes de Tours Ouest-1) (table  1 ). On the same day as the baseline visit and the randomisation Participants will receive a thermoformable plastic splint custom made as described previously and will receive a single ultrasound-guided injection of 50 Allergan Units of BTA resuspended in 1 mL saline (experimental group) or 1 mL saline (control group) in the TMC joint with one of the two senior radiologists in accordance with the allocated treatment and the procedure described previously.
Data management All data will be securely stored at the Unité de Recherche Clinique. They will be anonymised by a predefined code, given to every patient included, according to a defined protocol. Data will be recorded in a secured electronic case report form completed by the investigators and biomedical research technicians. The forms will be protected by a personal access code. A clinical research associate will be responsible for the safety and protection of the participants, the accuracy of the information and the compliance with the predefined protocol. The clinical research associate can conduct an audit trail at any stage of the project. The clinical research associate will have access to source data through the investigator, at any time of the study, as legally required (articles L.
Dissemination plan We aim to publish the findings of RHIBOT in a peer-reviewed scientific journal and diffuse them to physical Open access and rehabilitation medicine physicians at national and international conferences. The authors will be responsible for all articles or press releases regarding the results, with no help from professional writers. No results will be communicated before the end of the study. Assistance Publique-Hôpitaux de Paris will be the owner of the data. The original protocol and the full dataset will be available by contacting the coordinating investigator, CN ( christelle. nguyen2@ aphp. fr). RHIBOT is the first randomised controlled trial to assess the efficacy of BTA in patients with TMC OA. If the results are positive, intra-articular BTA could be an efficient and safe complementary therapeutic option for patients with TMC OA.
1,5-Diaminonaphthalene (1,5-DAN; handle with care, check material safety data sheet), α-cyano-4-hydroxycinnamic acid (HCCA), horse myoglobin, and acetonitrile (MS grade) were purchased from Sigma-Aldrich (St. Louis, MO, USA). A stock solution of myoglobin at 2 mg/mL was prepared in water. MALDI-Spotting. For ISD MS analysis, each sample (i. e. , myoglobin, intact and digested mAbs) was diluted 1-to 10-fold with MQ water and spotted onto a MALDI ground steel target plate (Bruker Daltonics, Bremen, Germany) as follows. First, 1 μL of diluted sample was put on the plate, followed by addition of 1 μL of 1,5-DAN (saturated solution in acetonitrile/water/ formic acid, 50%:49. 95%:0. 05%) using a 10-μL pipet tip (Rainin tips from Mettler-Toledo, Tiel, Netherlands). Gentle mixing of the 2-μL droplet was performed until small crystals were visible by eye; then the mixture was allowed to dry at RT.
In addition, 1 μL of diluted IdeS digest was spotted with 1 μL of α-cyano matrix (saturated solution in acetonitrile/water/ formic acid, 50%:49. 95%:0. 05%) in a similar way. A 200 μg/ mL solution of myoglobin (1 μL) was spotted with 1 μL of 1,5-DAN (saturated solution in acetonitrile/water/formic acid, 50%:49. 95%:0. 05%) and used for external calibration of the FT-ICR MS system. MALDI Mass Spectrometry. All MALDI-and MALDI-ISD FT-ICR MS experiments were performed as previously reported, with some modification of the acquisition methods. 26 riefly, all MS experiments were performed on a Bruker 15 T solariX XR FT-ICR mass spectrometer equipped with a CombiSource and a ParaCell (Bruker Daltonics, Bremen, Germany). The FT-ICR MS system was controlled by ftmsControl software and equipped with a Smartbeam-II Laser System (Bruker Daltonics) that operated at a frequency of 500 Hz, and each single spectrum was generated from 200 laser shots.
Absorption Mode FT Spectra Representation. To display an FTMS spectrum in the aFT, it is necessary to correct for systematic phase shifts across the frequency range that result from the methods used to induce coherent ion motion in the respective instruments. Various methods have been presented for solving this phase correction problem on the Orbitrap 20,30 and on FT-ICRs. 28, 29, 31, 35 In this study, the FT-ICR MS data files were recorded to include the transient data (fid) file. The aFT mass spectra were generated directly from the transient data files using AutoVectis software suite (Spectroswiss, Lausanne, Switzerland) that incorporates an improved version of the AutoPhaser method 31 using a genetic algorithm  35  to optimize the phase correction function,  36  applies an asymmetric apodization function to minimize baseline deviations in the resulting absorption mode spectrum,  37  and generates synthetic isotopic distributions with aFT peak profiles to aid peak identification.
To this end, we used AutoVectis software to perform a phase correction of ICR time-domain data with reduced baseline deviations. The results of such a correction, in terms of improved resolving power and increased identification confidence, are exemplified in Figure  2. Here, magnitude-mode visualization did not allow the resolution of the isotopic distributions of the fragments c 49 of the Lc and of the fragment c 51 and z + 1 48 of the Hc with pyroglutamate at the N-terminus and a lysine loss at the C-terminus (Figure  2A ). The postacquisition phase correction increased the resolving power and thus resolved the three isotopic distributions (Figure  2B ). Note that the observed and theoretical isotopic distribution of the z + 1 49 of the Lc did not match, thus this fragment was not identified and not included in the sequence coverage (Figure  3 ).