dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population We selected data of a control group and patients with symptoms compatible with one of four diseases (asthma, chronic bronchitis, cystic fibrosis, and interstitial lung diseases (ILD)) from pulmonary function tests (PFT) performed on daily basis between November 15 th 2012 and May 30 th 2016 in the PFT laboratory of the university hospital of Lille, France. PFTs providing all the following indices were selected: forced vital capacity (FVC), forced expiratory volume in 1 second (FEV 1 ), FEV 1 to FVC ratio (FEV 1 /FVC), total lung capacity (TLC), D LCO , V A and K CO. Only measurements performed before bronchodilation were included. Exclusion criteria were: patients under 18 years old; patients classified in more than one group; missing data or outliers for sex, weight or height; any PFT other than spirometry with D LCO measurement; D LCO <1mmol.
min -. kPa -  (minimal value for the D LCO predicted value calculation  [6, 11] ). For each test, patient's characteristics (sex, age, height, weight), PFTs indices values and patient's medical history were recorded in the laboratory database. For the tests' inclusion, data from the PFT performed between November 15 th 2012 and May 30 th 2016, and including the indices mentioned above, were extracted to a Microsoft Excel 1 spreadsheet by the laboratory engineer. Then tests meeting exclusion criteria were excluded using the statistical software SAS 1 (version 9. 4; Statistical Analysis System). This study was performed in accordance with the Declaration of Helsinki. This human study was approved by the Research Ethics Board of the University Hospital of Lille (Comite de Protection des Personnes Nord Ouest IV)-approval: HP20/02.
Groups creation Selected PFTs came from 4 groups of patients with symptoms compatible with one of the following diseases: asthma, chronic bronchitis, cystic fibrosis, and ILD. The chronic bronchitis group included patients with symptoms compatible with chronic bronchitis (with a FEV 1 / FVC ratio � 0. 7) or with chronic obstructive pulmonary disease (COPD) (with a FEV 1 /FVC ratio < 0. 7 according to the GOLD 2021 report  [16] ). ILD category covers more than 150 entities such as sarcoidosis, idiopathic pulmonary fibrosis or pneumoconiosis  [17]. The disease was initially filled out by the prescribing physician and recorded in the laboratory database at the time of the PFT validation. In order to ensure data quality, diagnoses were checked from patient files in random samples of each groups (see S1 Appendix and S1 Table in S1 Appendix).
Statistical analysis Data analysis was performed using the statistical software SAS 1 (version 9. 4; Statistical Analysis System) and R (version 3. 6. 1; R Foundation, www. r-project. org). Five types of ventilatory disorders were created from spirometric and TLC values: "obstruction" (FEV 1 /FVC below lower limit of normal (LLN) (z-score <-1. 645), TLC �LLN), "restriction" (TLC <LLN, FEV 1 / FVC �LLN), "mixed" (coexistence of obstruction and restriction)  [18] , Preserved Ratio Impaired Spirometry (PRISm) (FEV 1 /FVC and TLC �LLN, FEV 1 <LLN)  [19] , "normal" (FVC, FEV 1 , FEV 1 /FVC and TLC �LLN) and "other" (not elsewhere classified). D LCO parameters below LLN were considered altered. The ATS/ERS-2005 degrees of D LCO impairment were used: "Mild" (D LCO >60%predicted and <LLN), "Moderate" (D LCO >40% and �60%predicted), "Severe" (D LCO �40%predicted)  [18].
It is worth noticing that patients with uncertain or associated diagnosis (e. g. , a patient with asthma and chronic bronchitis) were excluded. Furthermore, all of the PFTs were performed on the same type of device, by a unique team, implying homogeneity in measurement method. Our study also shows some weaknesses. We have compared the GLI-2017 reference values only to the ECSC in adults. The ECSC equations are currently one of the most used in European laboratories. Also, focusing on only one equation set and only on adult patients allowed us to detail different types of results (%predicted, z-scores and the 2005-ATS/ERS degrees of D LCO impairment  [18] ) and study those results in controls and multiple categories of patients with several degrees of decrease in D LCO. This retrospective study uses highly suspected diagnoses, established by the referring physician (pulmonologist or internist) prescribing the PFT, and recorded in the laboratory database.
However, in order to ensure data quality, diagnoses were checked from patient files in random samples of each groups. Some PFTs from the chronic bronchitis group are classified as normal (16. 5% in women and 15% in men). There are 2 main reasons. First, we included tests performed at every stage of the diseases, including patients with mild symptoms of chronic bronchitis without obstruction. Secondly, we used zscore to define obstruction according to the 2005-ATS/ERS recommendations for lung function test interpretation  [18]  (z-score of FEV 1 /FVC ratio < -1. 645 and TLC z-score � -1. 645). Using the GOLD 2021 definition for COPD (FEV 1 /FVC < 0. 7)  [16]  could have led us to classify fewer PFTs as normal in the chronic bronchitis group in our sample due to the fact that a FEV 1 /FVC ratio < 0.
Calculations and statistical analyses Energy-corrected milk (ECM) was calculated as: ECM kg=d ð Þ¼12:55 Â fat yield kg=d ð Þþ7:39 Â protein yield kg=d ð Þ þ 5:34 Â lactose yield kg=d ð Þ Feed efficiency was calculated as daily milk production of ECM (kg/ d) divided by daily DMI (kg/d). Efficiency of protein utilization (EPU) was calculated as CP in milk/CP intake and expressed as g/kg following  Bach et al. (2005). The paired-end FASTQ files obtained from sequencing of DNA were joined using a proprietary analysis pipeline by MR DNA (Shallowater, TX, USA) and processed as described by  Maynou et al. (2019). For taxonomic assignment, a representative sequence from each OTU was selected and compared with those in the SILVA reference database version 111  (Quast et al. , 2013)  using the default taxonomy classifier in QIIME.
Alpha diversity  (Chao, 1984; Magurran, 1988 ) at the genus level was calculated as Chao1, Shannon, Simpson, and inverse Simpson indices with the phyloseq (version 1. 24. 2; McMurdie and Holmes, 2014) function estimate_richness(). Beta diversity across all samples was calculated using the Bray-Curtis dissimilarity index  (Bray and Curtis, 1957) , and a gradient analysis for each treatment (CS or SDL) was performed using non-metric multidimensional scaling also at the genus level. All performance data were averaged by week and then checked for normality before being submitted to a mixed-effects model which accounted for the fixed effects of treatment (CS or SDL), week of study, plus their two-way interaction, and the random effect of cow and block (parity). Week entered the model as a repeated measure using the autoregressive order 1 covariate-variance matrix, which yielded the lowest Bayesian criterion value.
Data pertaining to microbial analysis were assessed using a multivariate ANOVA with the Limma package (version 3. 36. 5; Ritchie et al. , 2015)  for R (version 3. 5. 1; R Core Team, 2018) correcting for false discovery rate following  Benjamini and Hochberg (1995). The false discovery rate-adjusted P-values are shown throughout the manuscript.
Numbers of genes pre and post-filtering were 17557 and 21797, respectively. We retrieved the parameters of the mixture distribution using function normalmixEM from mixtools package and determined that the 0. 95 quantile for the noise distribution was 1. 6. We subsequently removed all genes whose expression was below that threshold in more than 95% of samples. We performed a differential analysis (COVID versus mock) in each cell line using the limma R package and eBayes function (with mock group corresponding to healthy & no treatment patients). Disease signatures were then extracted by considering differentially expressed genes (DEG) as those with adjusted p-value below 0. 05 with an absolute fold change superior to 1. 3 (commonly used as a threshold for biological significance). Canonical pathway enrichment analyses were subsequently performed by using the Ingenuity Pathway Analysis (IPA) software.
Our computational repurposing approach (Fig 1A ) takes advantage of the proximity between disease-related proteins and drug targets through an established network of proteinprotein interactions (PPIs, referred to as an interactome). Drug-target links were gathered from the Therapeutic Target Database (TTD, version 7. 1. 01) and Drugbank  [43, 44]. The PPIs network was derived from previous work by Cheng et al  [45]. It was built from 15 different databases such as BioGRID and HPRD by compiling binary PPIs tested by high-throughput yeast-two-hybrid (Y2H) systems, kinase-substrate interactions from literature-derived lowthroughput and high-throughput experiments, high-quality PPIs from three-dimensional (3D) protein structures, and signaling networks from literature-derived low-throughput experiments.
Cmap-based drug repurposing We complemented the network-based approach by using Cmap as a supportive method  (Fig 1B). Cmap identifies drugs inducing a reverse gene expression profile compared to the disease state using a method of similarity  [47]. The Cmap database comprises human cancer cell lines either treated or not with chemical drugs, referred to as perturbagens. We used the R package ccdata which encompasses expression profiles for 1309 perturbagens over 13832 genes. Disease state was obtained from gene expression profiles induced in NHBE and Calu-3 cells following infection by SARS-CoV-2. We compare expression profiles induced by disease state with those induced by perturbagens, using mainly the Pearson correlation between transcriptome values of the query signature and the perturbagen signature.
Supportive Cmap-based for drug repurposing Given the rather limited set of transcriptomics data available and the small Cmap coverage for repurposable drugs (i. e. only 17% of molecules in our drug database, with none of the biologics), results were taken as supportive in the present study. Among the top network-based drugs proposed for repurposing, only 2 corticosteroids (betamethasone and hydrocortisone) were confirmed to elicit a reversed gene expression profile (Cmap score < -0. 3) when compared to the disease gene expression state.
Anti-IL6 Olokizumab [R-Pharm] Antibody Yes Completed phase 3 in acute respiratory distress syndrome [NCT04380519]. Results not yet available. Anti-IL6 Siltuximab [EUSA Pharma] Antibody Yes Ongoing phase 3 in acute respiratory Distress Syndrome [NCT04616586]. Anti-IL6 Sirukumab [Johnson & Antibody No Ongoing phase 2 in severe COVID-19 infection Johnson] [NCT04380961]. Anti-IL6R Sarilumab [Sanofi] Antibody Yes Completed phase 3 in severe or critical COVID-19 infection [NCT04327388], which did not meet its primary endpoint. Some improvement in survival when treating critically ill COVID-19 patients in association with dexamethasone. Anti-IL6R Tocilizumab [Roche] Antibody Yes Several trials completed in severe COVID-19 showing only limited efficacy [NCT04381936]. Some improvement in survival when treating critically ill COVID-19 patients in association with dexamethasone.
Anti-TNFα Infliximab [Johnson & Antibody Yes Ongoing phase 3 in COVID-19 [NCT04593940]. Johnson] Anti-TNFα Adalimumab, [AbbVie] Antibody Yes Ongoing phase 3 in mild to moderate COVID-19 [NCT04705844]. TNF-α inhibitor XPro-1595 Peptide No Ongoing phase 2 in pulmonary complications of [INmune Bio] COVID-19 [NCT04370236].
Anti IL17 [Ixekizumab, Eli Lilly], anti IL17R [Brodalumab, Astra Zeneca/ Amgen], anti IL23 [Ustekinumab, Johnson & Johnson; Tildrakizumab, Merck] antibodies are commercialized as treatments for inflammatory diseases. C5, C5aR Anti C5 Eculizumab [Alexion] Antibody Yes Proof-of-concept evidence suggesting that [53, eculizumab provides some benefit in severe 64-66] COVID-19. Ongoing phase 2 in moderate, severe or critical COVID-19 pneumonia [NCT04346797]. Anti C5aR Avdoralimab [Innate Antibody No Ongoing phase 2 in severe COVID-19 pneumonia Pharma] [NCT04371367]. Alarmins and their IL1R1 antagonist Anakinra [Sobi] Peptide Yes Completed phase 2 in severe COVID-19 [71, 75, receptors: IL1α, TSLP, [NCT04366232]. Results not yet available. 77] IL33 Anti-IL33R [ST2] Antibody No Ongoing phase 2 in severe COVID-19 Pneumonia AMG282-Astegolimab [Genentech] [NCT04386616].
TSLP inhibitor HY-209-NuSepin Small molecule No Ongoing phase 2 in COVID-19 pneumonia [Shaperon] agonist for G protein- [NCT04565379]. coupled TGR5 receptor Anti IL25, -IL33, -TSLP Antibodies No No evaluation yet in COVID-19. Anti IL25 [ABM- 125, Abeome], Anti-IL33 [REGN3500, Regeneron] and anti TSLP [Teepelumab, Amgen] are in clinical evaluation as treatments for asthma or atopic dermatitis. Anti S100A4, -S100A7,-S100P Antibodies No No evaluation yet in COVID-19. Antibodies in preclinical development in cancer or autoimmune diseases by Cancer Res Technol and Lykera Biomed. Thrombopoietin receptor Receptor agonist Romiplostim Peptibody Yes Case study documenting platelet recovery following [54] [Amgen] [peptide agonist treatment by Romiplostim of a pediatric patient fused to Fc IgG1] with thrombocytopenia due to COVID-19.
Larger are the non-Gaussianities, more we can expect MCAlens to improve over linear methods such as the Wiener one. The prior signal auto-correlation of the Gaussian component is included within the signal covariance term of the Gaussian component. We encode no explicit prior auto-correlation for the non-Gaussian signal and no explicit prior cross-correlation between the Gaussian and non-Gaussian component. Clearly, such correlations exist but including their contribution in the prior in this framework would be extremely difficult theoretically and in practice. However, such correlations will still appear in the final reconstruction, driven by the correlation information in the data.
Spherical Data We have created a full shear map from the full sky MICE simulated map and its covariance matrix, which has around 3 galaxies per arcmin 2. We ran the spherical MCAlens method with 200 it-A&A proofs: manuscript no. main  erations, λ = 5 (i. e. detection at 5σ), and we used the power spectrum of the simulation as the theoretical power spectrum. The top row of Fig. 6  shows the simulated noise-free map at 1 and 3 degrees, while the bottom row shows the MCAlens non-Gaussian and Gaussian components.
6) Since the data noise P κ will still be noisy, a final denoising step or a function fitting can be done. As an illustration, we fitted the function f (k, a, u, e, c) = exp(| a * (uk) -e |) + c to the estimated noisy P κ. Figure B. 2 shows an example of an estimated power spectrum following this approach. Experiment: Impact of an unknown theoretical power spectrum In this experiment, we used the same public MICE simulations, and we extracted 18 shear different shear maps with different noise realizations. For each of them, we applied the forwardbackward Wiener algorithm using the true theoretical power spectrum, and we applied the inpainted agnostic Wiener method on the same data, re-estimating for each of the 18 shear data sets the theoretical power spectrum. We can easily see that the inpainting has no impact on the reconstruction error, which is expected since only the area where the mask is equal to one is used in the error calculation, and also that the agnostic approach also has very little impact on the final solution.
Observations and data reduction From November 2019 to June 2021, we have observed a total of twenty-one (21) BL Lacs, conducted with the Robert Stobie Spectrograph (RSS,  [8] ) -SALT's main workhorse instrument that has a wide range of capabilities. We use the RSS in long slit mode with a slit width of 2" and the PG0900 grating in first-order. This configuration results in a spectral range of roughly 4500 to 7500 Å with resolution /Δ ∼ 1000 and a throughput of more than 20 % and  [9]. We use PySALT  [10]  to perform data reduction -correcting for cross-talk, bias, gain and flat fielding the frames -and the standard IRAF  [11]  routines for wavelength calibration. Cosmic-ray cleaning and flux calibration are performed using algorithms in the Interactive Data Language software. Absolute flux calibration is difficult to achieve with SALT due to the telescope's moving pupil  [12]  and for this reason, some of our targets get observed photometrically around the same period that spectroscopic observations are conducted, including the two sources 1RXS J015658.
Discussion and conclusions As a 10m-class telescope, SALT allows reasonably short exposures to get good S/N, which is a great advantage as longer exposures result in more cosmic ray hits, which makes the data reduction process more challenging. Over 90% of the spectra for all the 21 sources taken with SALT/RSS reached our target S/N range of 50-150, making SALT one of the best tools we have in our quest for successful measurements of redshifts for sources in our sample. The S/N of the spectra for both 1RXS J015658. 6-530208 and 1RXS J020922. 2-522920 were well within our target S/N interval for the former and the latter exceeded the maximum value of the interval, an aspect that is crucial to the work of the CTA Redshift Determination Group (herein CRDG). While the above is true, we are also cognisant of the fact that not all spectra with S/N falling within our required interval result in successful redshift measurements as the necessary spectral features are undetectable in some of such spectra.
Cows with an SCC >200,000 cells/mL  (Gygax et al. , 2008; Cerqueira et al. , 2017)  or with teat lesions  (Rousing et al. , 2004)  were also found to kick more during milking. To limit the occurrence of these behaviors, they should first be detected objectively and easily. Behavioral parameters are often monitored by means of video recordings or direct visual observations. However, these methods are time consuming and labor intensive, Detection of cow hind-leg activity during milking by using a 3-dimensional accelerometer attached to the milking cluster C. M. C. Raoult, 1 * † A. A. Margerit, 2 S. Fricker, 3,4 F. E. Blümel, 4 and P. E. Savary 1 require trained observers  (Winckler and Willen, 2001; Rushen et al. , 2012) , and cover only a short period of time. Automated data recording using, for example, a wireless system can thus be seen as a solution  (Müller and Schrader, 2003).
We programmed and synchronized all the loggers identically and transferred the MSR data using the software MSR 5. 24. 02 (MSR Electronics GmbH). We recorded the acceleration on the x-(left/right lateral acceleration, perpendicular to the cow body axis), y-(forward/backward acceleration, parallel to the cow body axis), and z-(vertical acceleration) axes with an accuracy of ± 0. 15 g, a resolution of ± 0. 03 g, and a range of ±15 g (where 1 g = 9. 81 m•s -2 ) and set the sampling rate at 50 Hz. The aim of this work was to test whether hind-leg activity during milking could be detected indirectly by using a 3D accelerometer attached to the claw's hook of the milking cluster. Data processing and statistical analyses were performed in R version 3. 6. 1. (R Core Team, 2019). We imported the MSR data and direct visual observations into R, so that the milking place, cow number, time of day (morning or evening milking), start and stop of the milking, and observed activities coincided.
We observed from 10 reference graphs (described later) that the activity of the hindlegs creates a left-right rocking motion of the milking cluster (i. e. , on the x acceleration axis) with enough intensity to be detected by the accelerometer (see Figure  1 ). Data from the y acceleration axis provided a similar response curve to that of the x acceleration axis and did not improve the detection accuracy, whereas data from the z-acceleration axis led to detection of more activities than were visually observed. Therefore, to detect hind-leg activities, we used only the x acceleration axis values. Additionally, to remove small variations but preserve large acceleration variations, we applied a standard deviation filter over a sliding window of 1 s, meaning that we calculated the absolute value of the difference between the value measured and the rolling mean of the values measured during the previous second (at a sampling rate of 50 Hz, 1 s corresponds to 50 values).
Furthermore, we wanted to avoid detection of cluster movements unrelated to hind-leg activities as measured with the yand z-axes. The activity detection threshold was defined at 0. 13 g (i. e. , 1. 27 m•s -2 ), based on the visual evaluations of 10 graphs from 1 morning and 1 evening milking of 5 randomly selected cows, for which we also had observed activities (referred to as "reference" graphs; for an example, see Figure  1 ). These cows were excluded from the remaining analyses, as were data from the morning milking of 1 additional cow because its direct visual observations were incomplete. Therefore, statistical analyses were performed on 79 milkings from 40 cows. The aim of this study was to determine whether observed hind-leg activities could be estimated by using a 3D accelerometer directly attached to the milking cluster rather than to differentiate kicks from steps.
Study Design After the screening visit, adolescents in the training group were randomly allocated to either the MICT or the HIIT group for 16 weeks (permuted-block randomization conducted using a computer-generated random allocation) (Stata software, version 13, StataCorp, College Station, TX, USA). The intervention consisted of a 16-week residential multidisciplinary weight management program combining physical activity (4 sessions per week) and nutritional education (2 sessions per month). During the intervention, the adolescents were prescribed a normo-caloric diet (without energy restriction) based on their age and sex. Adolescents of the CTR group did not perform any physical training during the 16 weeks and served as CTRs to distinguish the influence of growth on bone parameters. Adolescents from the CTR group received the same nutritional counseling (normo-caloric diet) throughout the 16-week period.
Body Composition and Bone Measurements by DXA All subjects underwent DXA (Discovery A; Hologic Inc. , Bedford, MA, USA) in a fasted state. Standardized procedures were followed by a trained blinded technician for the assessment of body composition (FM and LM), bone densitometry assessment, trabecular bone score (TBS) and hip structural analysis (HSA) (QDR-4500A DXA; Hologic, Bedford, MA, USA). Body composition data were analyzed using the Hologic QDR Software for Windows version 12. 6 to assess total LM and FM for the whole body  [28]. According to the International Society for Clinical Densitometry recommendations for adolescents  [29] , BMD (in g/cm 2 ) and BMC (in g) were determined at the total body less head (TBLH), lumbar spine (L2-L4), and nondominant hip. BMD measurements were converted to Z-scores.
The TBS, which estimates fracture risk based on a determination of bone texture (an index correlated to bone microarchitecture)  [30] , was calculated using TBS iNsight software (Medimaps SA, France). The HSA was performed at the narrow neck (NN; narrowest part of the femoral neck), femoral shaft (FS; across the shaft 1. 5 cm from the NN to the intersection of the neck and shaft axes), and the intertrochanteric region (IT; along the bisector of the angle of the axes of the NN and FS). The following parameters were obtained: cross-sectional area (CSA, in cm 2 ; index of resistance to axial forces), BMD (g/cm 2 ), endocortical diameter (in cm), average cortical thickness (ACT, in cm), width (in cm), cross-sectional moment of inertia (in cm 4 ; estimate of resistance to bending forces in a cross-section), section modulus (Z, cm 3 ; index of bending strength), and the buckling ratio (BR; index of susceptibility to cortical buckling under compressive loads)  [28].