dataesr/scientific-paragraphs-categorization huggingface dataset:
These data were compared to those in healthy controls and siblings. We first predicted that higher motor noise in patients with schizophrenia should be reflected by increased variability of force and of EMG activity during steady hold. Secondly, we expected that (higher) variability in force and EMG activity would relate to reduced SICI in the group of patients with schizophrenia, reflecting GABAergic dysfunction and decreased signal-to-noise ratio. Finally, we analyzed whether EMG variability would relate to clinical symptomatology.
Three participants in each group were left-handed (Edinburg handedness inventory  [37] ), hence, 9 of the 67 subjects performed the visuomotor task with their nondominant hand. All participants were assessed with the Diagnostic Interview for Genetic Studies v3. 0 to ascertain the diagnosis in patients and to preclude axis 1 and 2 diagnosis in healthy control participants and siblings  [38]. The study received ethical approval from the regional ethics committee (Ile de France VIII; Clinical Trials: NCT02826629) and all participants provided written informed consent.
Clinical assessments Clinical symptomatology of patients was assessed using the Positive and Negative Syndrome Scale (PANSS  [39] ). PANSS subscores were calculated as follows: positive PANSS (sum of items P1 to P7), negative PANSS (sum of items N1 to N7), and disorganization (sum of items P2, N5, G10, G11). For complete data on clinical and neuropsychological assessments see Ref. [1].
Analysis of grip force control Visuomotor force-tracking performance was analyzed using MatlabV9. 1 (The MathWorks, Inc. , Natick, MA, USA). Grip force was down-sampled to 100Hz and smoothed (20 ms sliding window). The following performance measures were extracted trial-by-trial for each participant: tracking precision (quantified as the root mean square tracking error (RMSe) between the target and the tracking-force during the ramp and hold periods); onset of force production and of force decrease (relative to the respective target); release duration (time taken to abruptly reduce the force from 75% to 25% of the target force). For investigating task-related motor noise, the coefficient of variation (CV; standard deviation/mean) of tracking force (CV-GFT) was computed over a steady-state interval of 1s (i) during the pause between trials (minimal postural force while holding the manipulandum in the hand), and (ii) during the middle of the hold period at 10%MVC target force.
Acknowledgement We thank the  'Delegation a la Recherche Clinique et l'Innovation' (DRCI)  and the  Clinical Research Center (CRC) of Sainte-Anne Hospital , and the  URC/CIC Paris Descartes of Necker -Cochin Hospital  for their help in conducting this study. Thanks also to  Narjes Bendjemaa  for her helpful contribution to this work. This study was supported by the " Fondation pour la Recherche M edicale " ( FRM -DPP20151033970 ). LC received a PhD-fellowship from  Sorbonne Universit  e. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Generally, proteins are enriched, mainly by precipitation, and further denatured, reduced, alkylated, and digested chemically or enzymatically into peptides. After separating the peptides by liquid chromatography (LC) or capillary electrophoresis (CE), the ionization process converts the peptides into gas-phase ions, which enables the mass spectrometer to separate the ionized peptides based on their m/z ratios. Targeted proteomics (SRM/MRM/PRM) allows absolute quantification of selected proteins if stable isotope standards are included in the analysis. In classical label? free data-dependent acquisition (DDA) proteomics or 'shotgun' proteomics, peptide ions are specifically selected for fragmentation based on their detection intensity in precursor ion scans collecting MS/MS spectra for as many peptides as possible, which are identified against a proteomics database.
Data-dependent analysis (DDA) and data-independent analysis (DIA) Historically most 'bottom-up' MS approaches use commercially available mass spectrometers (ion traps, Q-TOFs, IMS-TOF MS, Orbitraps, FT-MS, LC-MALDI MS/MS) for Data-Dependent Acquisition (DDA) compilation of proteomics data, particularly for discovery-based strategies. In the DDA strategy, as peptides pass through an LC column and enter the mass spectrometer, the instrument scans all ions but then chooses a subset of those, typically the most abundant ones (MS1 spectra), as precursor ions for further fragmentation usually by Collision-Induced Dissociation (CID). The fragmented ions obtained are then acquired to generate MS/MS data (MS2 spectra), which is used to identify peptides by reconstructing the peptide's amino acid sequence by database searching.
These reference spectral libraries should contain all prior known information regarding peptide components of proteins in a study (e. g. retention time, precursor m/z or MS2 spectra) in order to be extracted from the DIA data. The peptide MS2 assay libraries can be generated locally through DDA analysis, obtained from community data repositories or even a combination of both. Thus, using an in-house generated library or combining it with a publicly accessible one, the overall information should ideally have high degree of protein coverage. The major repository of the peptide library assay  [112] , covering ~51% of human proteome (>10,000 proteins) is accessible to the scientific community for SWATH-MS analysis in human samples. Other public libraries have been made available by SWATH atlas (www.
swathatlas. org). However, at this point, it seems conceptual that a locally generated inhouse library is favored as it can be used for several SWATH-MS measurements, with the same LC conditions used to elute the unknown samples. This allows the retention times of eluted peptides to be within a similar range of those in the library. The quality and coverage of the library are of crucial importance for SWATH methodology performance. There are several detailed protocols  [113, 114]  that have been successfully used to build a high-quality library for targeted analysis of SWATH-MS data, supported by DIA analysis software tools. An in-house library spectrum can be expanded at any time just with the addition of more information from DDA acquisition. To build more robust libraries, several sample processing strategies can be applied to reduce proteome complexity before LC-MS analysis, such as protein or peptide fractionation, PTMs or protein-specific enrichment, and depletion of most abundant proteins  [114].
Despite all of the advances achieved in the last years, DIA still has limitations in sensitivity as a tool for routine clinical diagnosis  [107]. The definition of the best operating procedures, the standardization of protocols for sample preparation and acquisition between laboratories, as well as the need for appropriate and inexpensive internal standards will possibly globalize the method but the application of DIA strategies in clinic or large-scale studies is not reached yet at this stage. Currently, efforts have been made in order to achieve these goals and this will contribute to the increase of both databases repositories (spectral libraries and digital maps) (Table  2 ).
Figure2. Representation of common MS-based proteomics approaches, i. e. targeted proteomics or data-dependent proteomics. In both bottom-up strategies, peptide detection is applied for protein identification or quantification. Generally, proteins are enriched, mainly by precipitation, and further denatured, reduced, alkylated, and digested chemically or enzymatically into peptides. After separating the peptides by liquid chromatography (LC) or capillary electrophoresis (CE), the ionization process converts the peptides into gas-phase ions, which enables the mass spectrometer to separate the ionized peptides based on their m/z ratios. Targeted proteomics (SRM/MRM/PRM) allows absolute quantification of selected proteins if stable isotope standards are included in the analysis. In classical label? free data-dependent acquisition (DDA) proteomics or 'shotgun' proteomics, peptide ions are specifically selected for fragmentation based on their detection intensity in precursor ion scans collecting MS/MS spectra for as many peptides as possible, which are identified against a proteomics database.
Oliveira et al. [11]  studied a given train station for evidence of concentrated boarding around the platform entrance and discussed their impact on dwell time. The impact of overcrowding and the difference among different vehicles was demonstrated in a study by Fritz  [12]. Along the same lines, Lin and Wilson  [13]  proposed linear and non-linear dwell time models to consider the number of boarding, alighting and on-board passengers for one-and two-car vehicles. Cornet et al. [14]  used a dataset acquired from automatic passenger counting (APC) and automatic vehicle locating (AVL) sensors on commuter rail to investigate the concept of minimum dwell time and estimate the conditional distribution of dwell time with respect to the passenger flows. Currie et al. [15]  conducted a survey to establish a positive correlation between dwell time and on-board congestion.
Data Collection The data used in this study were collected from the Opthor system, used by the operator. Opthor is an APC system measuring the number of passengers boarding and alighting at each station, on one hand, and the dwell time, as well as a number of other performance-related measures, on the other. Passengers are detected by an infrared system installed at each door of the vehicle. Dwell time is measured as the difference between the opening of the first door and the closing of the last door of the trainset. Since door operation is not forced by the driver, the value corresponds roughly to the duration of the passenger exchange. The operator has installed the system on a limited number of vehicles (of Alstom TFS and Bombardier Incentro class only) and runs frequent counts with it for various purposes.
The data employed here were collected during a count conducted between 5 September 2013 and 10 April 2014. As part of the count, the operator collected 134,500 valid entries from 124 weekdays and a total of 4900 runs. This sample corresponds to roughly 10% of the number of daily runs. Each data entry reports the total number of boarding and alighting passengers (as detected by the APC sensors), the number of passengers on board, and the corresponding dwell time at a station and for a given run. Further filtering of the data was conducted to identify and limit the impact of extreme values. Specifically, only dwell time values between 1 and 120 s and passenger exchange volume values between 5 and 150 are considered. The reasoning behind this filtering is that the excluded values either lacked significance (less than 5 s of dwelling) for the scope of the research conducted, or were beyond the normal operational values.
In addition, the influence of the vehicle type on the dwell time is examined (''VEH'', categorical). Ordinary least squares (OLS) regression is used to specify the models through the LIMDEP statistical software package, and the estimation results are shown in Table  2. The statistics of the model fitting are shown in part (a) of Table  2. As can be seen, the models achieve Rsquared values of between 0. 26 and 0. 32, which initially suggests that they may not be very good fits of the data. However, it should be considered here that the models are based on a very large number of observations, which increases the overall variability in the dataset, and hence makes it more difficult to achieve an accurate prediction. Also, it should be noted that the dependent variable takes generally small values, thus making the goodness of fit less significant, in the sense that prediction errors of a few seconds may appear as large from a statistical perspective, but are rather minor from a practical perspective.
Statistical Modelling of Dwell Time Reliability A multiple linear regression model is used to model dwell time reliability and field observational data for all three lines together. The descriptive statistics of the model and the variables included in the regression are shown in Table  3 ; as can be seen, the number of observations is now significantly lower due to the aggregation of the data to establish the reliability values. Dwell time reliability is modelled against the following variables: the location of the station along the line (''RANK'', ordered); the time of day (''HOUR'', categorical); the location of the station near a POI, a transport hub or a P?R facility (''POI'', binary); the average number of on-board passengers upon arrival at the station (''AVONBO'', continuous); the average number of passengers boarding and alighting (''AVBNA'', continuous); and six variables denoting the line serving the station and the direction (''LINE1A'', ''LINE 1B'', ''LINE2A'', ''LINE2B'', ''LINE3A'' and ''LINE3B'').
Acknowledgments: The authors would like to thank sincerely the  French Embassy in Bangkok (Thailand)  and  Campus France  in supporting  Burin Yodwong 's Ph. D. thesis within the framework of the  Franco-Thai scholarship program. Besides, the authors are very thankful to the  GREEN laboratory of the Université de Lorraine and Thai-French Innovation Institute of King Mongkut's University of Technology North Bangkok  for their constant support in developing research cooperation between France and Thailand.
Model description The simulations were carried out in AdvantEdge 2D, which has a coupled thermo-elasto-plastic Lagrangian code with continuous remeshing and adaptive meshing  [21]. The mesh was created with a minimum element size of 2 µm to ensure accurate results. The residual stress analysis was activated to achieve a fine mesh in the machined surface. The material model previously explained was implemented in the software by user-defined subroutine developed in Fortran language. To reduce the time to reach the steady state, the heat capacity of the tool was set to a value much lower than the physical one, as it was stated in  [22]. Finally, to represent the tool-chip friction law, a stickingsliding model was chosen and, due to the low cutting speeds, the friction coefficient was set to 1 according to  [21].
3. 4 Operating conditions of the selected GTP Operating data for gas turbine units in the energy utility sector of Afam power station was collected from  Abam et al. (2011). The data was sourced from the power station daily turbine control log sheet. The power station is one of the three existing main stations supplying over 60 % of electrical energy to the national grid system. The daily average operating thermodynamic variables were calculated using MS-Excel worksheets and MATLAB, and it is reported in Table  1. The conditions and properties for crucial points in the diagram are also given in the same table. Taking the surrounding temperature (Ts) to be 298K, based on 1 mole of methane burned with 30 % excess air, the equation of the reaction occurring in the gas chamber can be represented as follows: CH4 + 2O2 → CO2 + 2H2O, which was in line with literature for methane combustion reaction process.
Clinical Assessment. Given all the issues raised above, we deployed a crowd-sourced, multilingual, online study with a global reach (as of May 1, 2020 deployed in 27 languages); this survey has the potential to provide reproducible data from a large number of participants around the world. In this pre-registered report, we present data from 4039 participants who reported a COVID-19 diagnosis either via Lab Test or Clinical Assessment and who A c c e p t e d M a n u s c r i p t completed the questionnaire during the first 11 days the study was available online. Here we address two main research questions. First, we asked what chemosensory changes are observed in participants with COVID-19, compared to before illness (i. e. , within participants). Next, we asked whether the two diagnostic groups differ in chemosensory changes (i.
Preregistration We preregistered our hypotheses and analyses on April 19, 2020, at 12:20 AM Eastern Daylight Time (EDT), before the data became available (data reflected questionnaires submitted between April 7, 2020 6:00AM EDT and April 18, 2020 at 8:34 AM EDT)  (Veldhuizen et al. , 2020). In line with the pre-registration, and according to the Sequential Bayes Factor Design (section 2. 3), one of the authors (AJB) not involved in the development of the pre-registration queried the database to check whether the A c c e p t e d M a n u s c r i p t 10 minimum number of participants per group was reached. The data reported in this manuscript, along with analysis scripts, will be available at OSF (https://osf. io/a3vkw/) upon the acceptance of the manuscript. The project is structured according to the research compendium created with the rrtools package  (Marwick, 2019).
This case, we argue, would fall within Bartoshuk's guidelines for when valid across group comparisons can be made with a VAS  (Bartoshuk et al. , 2003). For a list of the questions analyzed in the present work, please see Figure  S2. Participants were also asked to report demographic information (i. e. , year of birth, gender, and country of residence) as well as information related to their COVID-19 diagnosis and their respiratory illness-related symptoms, including smell and taste, in check-all-that-apply (CATA) format. We summarized the questions used in the present study in Supplementary Figure  S1. Please refer to the full questionnaire, included in the A c c e p t e d M a n u s c r i p t 12 Supplementary materials, for question order and the labels on the anchors of each question. The questionnaire was implemented in 10 languages as of April 18th, 2020 (the date on which the database was last queried for this report): English, French, German, Italian, Japanese, Kannada, Norwegian, Spanish, Swedish, and Turkish.
Our translation protocol was modeled after the process developed by the Psychological Science Accelerator  (Moshontz et al. , 2018). Briefly, translations of the original English questionnaire involved three steps: i) the original (English) questionnaire was translated to the target language by independent translators, resulting in Translation Version A; ii) Version A was translated back from the target language to English by a separate group of independent translators, resulting in Version B; iii) Versions A and B were discussed among all translators, with the goal of resolving potential discrepancies between the two versions, resulting in the final Version C. All questionnaires in all languages were then implemented in Compusense Cloud, Academic Consortium (Guelph, Ontario), a secure cloud-based data collection platform with multilingual support.
Statistical analysis All analyses were performed in R (Team R Core Development, 2013) via RStudio. The scripts along with information on the computational environment and dependencies A c c e p t e d M a n u s c r i p t will be found, upon acceptance of the manuscript, at https://osf. io/a3vkw/. Information on the computational environment and dependencies used is also shared for future reproducibility. The code will be also available on GitHub at https://github. com/GCCR/GCCR001, and will include a Jupyter notebook replicating the core analyses in Python. We hypothesized there would be no difference between groups. Therefore, we elected for a Bayesian approach, which allows us to estimate the strength of evidence supporting the null hypothesis, over a frequentist approach that would only allow us to present evidence against the null hypothesis.
Exploratory non-preregistered analyses To quantify the association between the reports of (a) parosmia and phantosmia, (b) smell, (c) taste, (d) chemesthesis, and (e) a change in perceived nasal obstruction, we computed a correlation matrix that is visualized with ggstatsplot  (Patil and Powell, 2018). To assess whether the proportion of parosmia and phantosmia reports differs between groups, we used a two-sample test for equality of proportions with a continuity correction. To characterize the relationship between perceived nasal blockage and chemosensory change, we used a principal component analysis (PCA) using prcomp from the R default stats package and we plotted the results with functions from the FactoMineR package  (Lê et al. , 2008). Additionally, to test whether different chemosensory function profiles exist in our sample, we performed a cluster analysis.
The best clustering scheme was with 3 clusters as determined with NbCluster  (Charrad et al. , 2014) , which tests 30 methods that vary the combinations of number of clusters and distance measures for the k-means clustering. Cluster stability was estimated through a bootstrapping approach (100 iterations) with the bootcluster package  (Yu, 2017). A c c e p t e d M a n u s c r i p t.
The observation that some participants reported loss of only a subset of taste qualities may reflect their difficulty in correctly identifying and naming individual taste qualities  (Pilkova et al. , 1991; Welge-Luessen et al. , 2011)  rather than quality-selective hypogeusia/ageusia (e. g. ,  Gudziol and Hummel, 2007; Henkin et al. , 1970; Lugaz et al. , 2002; Huque et al. , 2009). However, these possibilities cannot be clarified with the present database. Compared to smell, the literature has described fewer examples of post-viral taste loss  (Adour, 1994; Rubin and Daube, 1999). As the number of people responding to this questionnaire continues to grow on a rolling basis, the differences among different types of respiratory illnesses and their relationship to the degree of taste loss will be a major focus of forthcoming analyses.
Acknowledgements This work was supported financially with discretionary funds from the  Pennsylvania State University  ( Penn State ), including a gift from  James  and  Helen Zallie  given in support of  Sensory Science at Penn State. The authors also wish to thank  Paule V. Joseph  for her continuous support and for thoughtful discussions as well as  Jacqueline Dysart  and  Karen Phipps at Compusense and Olivia Christman at Penn State  for all their help in rapidly deploying the GCCR survey in multiple languages. We are grateful to  Marek Vondrak  for their help with programming the automatization of the authorship list,  Jae-Hee Hong  for their editing contribution, and  Tristam Wyatt  for his role of facilitator of communication among the authors. Additionally, we would like to thank in their role as translators:  Aditi Prasad ,  Alexandros Delides ,  Ali Khorram-Toosi ,  Aline Pichon ,  Amin Homayouni ,  Amol P Bhondekar ,  Angela Bassoli ,  Anshika Singh ,  Antti Knaapila ,  Arijit Majumdar ,.
Funding: This research was funded by the  Poitou-Charentes region  for the funding of the PhD grant of  Maïté Audemar. ANR agency  for the funding of  FurCab  Project  ANR-15-CE07-0016. Région Nouvelle Aquitaine  for the funding of this project through the  FR CNRS INCREASE 3707 , the chaire TECHNOGREEN and  FEDER , the  University of Poitiers  and the  CNRS  for their financial support. China Scholarship Council (CSC).
DATA ANALYSIS 2. 1. Structure of the Data The characteristics of the test bench data used in the analysis are as follows: • 14 variables are chosen in an expert-manner by domain experts of the performance team of Safran. They are not generally interested in other variables and thus we limit ourselves to this subset of variables. However, in future works we will consider a larger set of variables. • 591 engines are observed. • 4 stabilized points are considered. A stabilized point, is a fixed level of performance for which all engines are tested and measurements acquired. They are ordered from the lowest to the highest level of performance. In the database, six are available but we do not consider the two first because they are taken at low engine speeds where there is a lot of variance in measures which makes them difficult to analyze.