dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall quality was also defined as "good" (sum 9-12), "fair" (sum 5-8), or "poor" (sum 1-4). In addition to data quality, the data source for each question was collected. Respondents were able to choose one or multiple answers from a predefined list of potential data sources, including the option "other, " for which the respondent provided the name of the data source (Figure  1D ). Participants were also allowed to submit quantitative data associated with each question (not analyzed in this study) (Figure  1C ). Data relating to all cattle (i. e. , dairy and beef cattle together) were requested, but it was also possible to submit data separately if needed.
Data Collection and Management The SOUND control consortium included representatives by country: one management committee member and one or more management committee substitutes and/or workgroup members. Members who were participating in the workgroups related to data about CPs, one person per country, were responsible for providing the data for their respective country. Almost all participants had a doctoral degree in the field of veterinary sciences or epidemiology and most of them worked in the field of cattle health with a focus on infectious diseases or in surveillance and control of cattle infectious diseases. Depending on their knowledge, the participants could either collect all information to fill in the questionnaire themselves or ask others in their country to help them fill in the questionnaire.
Thirty-two SOUND control member representatives were invited by email in July 2020 to fill in the online data collection tool. Three reminders were sent at the end of August, September, and November 2020. The deadline to submit data in the data collection tool was closed in December 2020. Fully or partially completed questionnaires were extracted from LimeSurvey software to Microsoft Excel 365 and then imported into the R Statistical software, version 3. 6. 2 (26, 27) for analysis. Descriptive analysis was performed using the "dplyr" package in R  (28). Data analysis was structured by country and type of question to calculate and assess (i) the number and proportion of existing data, (ii) the overall quality and quality by criterion of existing data, and (iii) the variety of sources used to obtain data.
Variety of Sources Used to Obtain the Same Data The respondents used a variety of sources to assess and obtain the requested data. On average, two different sources of data were selected to answer one question. The most selected data source option in all sections was "other" followed by "national animal health databases" (Figure  5 ). The sources of data most frequently reported under the category "other" were agriculture statistics, central statistics databases, livestock registration databases, national animal traceability databases, and CPs databases.
The Availability of Data for Probability of Freedom From Disease Estimates A high proportion of the requested data about cattle demographics (75%) was reported as existing in the response countries. This section included general statistical information about the cattle sector, i. e. , average herd size, number of calves born, etc. As having a computerized database for tracing individual bovines is a requirement within the EU (EC 1760/200), it is expected that this data is routinely collected within the response countries  (31). Based on the responses, it appears that sufficient information about general demographics is available to describe the cattle industry within the responding countries and to use these data as parameters for modeling freedom from disease (e. g. , average herd size, number of animals).
Future Perspectives for the Online Data Collection Tool and Collected Data Our online data collection tool was initially converted from Excel spreadsheets to an online questionnaire using surveymaking software "Limesurvey. " However, it is difficult to assess how sustainable this form of data collection is. One option to consider would be to move toward an individual databaselike tool, to which countries would be able to submit data and receive the outcome. Another prospect could be to optimize the data-collection process and collect required data from primary sources directly to the database or model, similar to the results of the SIGMA project  (21). In addition, data that were reported as not existing in most of the countries, i. e. , more than 50%, could be excluded from the data collection tool as such data would not support a wide implementation of output-based standards in Europe.
To fill this gap, further work of SOUND control will focus on identifying the data that is necessary for output-based surveillance, and a joint research agenda for future research will be developed. Future work should also consider availability and validity of diagnostic tools as well as inherent differences in transmission dynamics among the diseases in question. The questions on diagnostic strategies in this study could potentially provide a basis for this, but a higher response rate would be required to evaluate them. Finally, in this data collection, respondents were able to voluntarily submit quantitative data that was not analyzed in this study (Figure  1C ). Quantitative data that were submitted by respondents will further assist in developing and testing the first models for probability of freedom from disease in an output-based framework.
Limitations of the Study The current study has limitations in the assessment of the existence and quality of data that could lead to possible bias. First, when a respondent reports "no existing data, " the information may exist although it was unknown to the respondent. Similarly, "unknown" data may actually reflect nonexisting data. However, if data exist and were easily accessible, the respondent would have most likely found it. As such, both "unknown" and "non-existing" data may represent not easily accessible or not well-described data. Nevertheless, this data collection was conducted within the framework of SOUND control, and our targeted respondent in each country are experts in the field of animal health surveillance, which increases the likelihood of the respondent being aware of existing data sources.
In cases such as this, it was unclear which data source to select and provide scores in the data quality evaluation tool. Thus, data evaluations (as good/fair/poor) should not be used as absolute values and be interpreted with care, and future studies further validating consistency of answers to the criteria would be useful. Finally, the results of the current study mainly represent country-level data on the cattle industry, risk factors for disease introduction, and CPs relevant to nonregulated cattle diseases. Therefore, respondents indicated that some data is collected on a regional level, but there is no centralized database from which to take the relevant data. In future studies, this data collection framework could be used to collect the data related to non-regulated cattle diseases in order to have a summarized overview of data originating from multiple sources.
ACKNOWLEDGMENTS We thank  Dinu Gavojdian  ( Research and Development Institute for Bovine Balotesti, Romania ),  Erja Tuunainen  ( Animal Health ETT, Finland ),  Ian Hutchinson  ( Epidemiology Research Unit, Scotland's Rural College, United Kingdom ),  Jožica Ježek ,  Matjaž Ocepek ,  Jože Starič ,  Ivan Toplak  ( Veterinary faculty, University of Ljubljana, Slovenia ),  Lawrence Gavey  ( Animal Health Ireland, Ireland), Linda van Wuyckhuise (Royal GD, the Netherlands ),  Luigi Ruocco  and  Marco Ianniello  ( Ministero della Salute, Italy ),  Marek Lipiec  and  Jerzy Rola  ( National Veterinary Research Institute, Poland ),  Marius Masiulis ,  Vaidas Jonušaitis  ( State Food  and  Veterinary Service , Lithuania), and  Rahel Struchen ,  Sara Schärrer  and  Heinzpeter Schwermer  ( Federal Food Safety and Veterinary Office, Switzerland ) for helping to gather the data for this study.
The muon trigger system covers the range || < 2. 4 with resistive-plate chambers in the barrel, and thin-gap chambers in the endcap regions. Interesting events are selected to be recorded by the first-level trigger system implemented in custom hardware, followed by selections made by algorithms implemented in software in the high-level trigger  [20]. The first-level trigger accepts events from the 40 MHz bunch crossings at a rate below 100 kHz, which the high-level trigger reduces in order to record events to disk at about 1 kHz. The data used in this analysis were collected in 2016, 2017 and 2018  [21]. After applying event-cleaning requirements which ensure that the data are of good quality for triggering and reconstructing jets containing -quarks, these datasets correspond to integrated luminosities of 24.
Adversarial Neural Network for Event Classification The sensitivity of the analysis is boosted by using an ANN to divide events into regions of varying signal to background composition. This type of multivariate classifier can be constructed such that the network is penalized for learning a feature of the dataset  [52, 53]. Because the Higgs boson signal is extracted from a fit to   , the ANN used in this analysis is penalized for learning the   distribution. Using this construction, the non-resonant background shape is independent of the classifier score and therefore the same for each region, which allows for extra constraints in the shape of the non-resonant background. Twelve variables are used as inputs to the ANN: •    : the invariant mass of the VBF jet pair. This is typically larger in the signal than in the background.
Experimental systematic uncertainties The most prominent experimental uncertainty is due to uncertainties in the -jet trigger scale factors which account for differences between data and MC events in the efficiency of the triggers. The per-jet online -tagging efficiency calculated with respect to the offline -tagging efficiency is measured in  t events in both data and MC events. A scale factor is then applied to the leading simulated -tagged jet as a function of the leading jet's  T. For the 2016 dataset, an additional scale factor binned in the leading jet's  was applied. The total uncertainty in the -jet trigger scale factor is typically 2-5%. These uncertainties are applied to signal events and the embedded  sample which uses simulated -jets. Additional jet trigger scale-factor uncertainties, which are determined by taking the difference between the leading jet trigger efficiencies as determined in data and MC events using the same procedure, are approximately 1%.
The second leading experimental uncertainty impacts the per-jet track multiplicity distribution used for quark-gluon separation  [57]  in the ANN. This quantity is affected by the modelling of the charged-particle multiplicity in jet fragmentation models as well as the track reconstruction efficiency in jets. The modelling uncertainties are determined through a measurement of the per-jet charged-particle multiplicity in data which is then compared with various models. The experimental uncertainties are evaluated through standard track-reconstruction efficiency techniques, and their estimation is fully described in Ref. [57]. Jet energy scale (JES) and jet energy resolution (JER) uncertainties comprise the next largest group of experimental uncertainties. The JES uncertainties are primarily determined using data-based -boson-jet, photon-jet and multi-jet  T -balancing techniques  [46].
0 for both inclusive and VBF production. The two analyses are orthogonal, as the measurement described in this paper vetoes events with photons. They are combined in a joint likelihood fit, sharing the Higgs boson signal strength ( VBF, → b or   → b) as a common parameter of interest. All experimental systematic uncertainties are correlated with the exception of the jet energy resolution and offline flavour-tagging uncertainties, as the two analyses use different jet definitions. The jet energy scale is partially correlated because both jet definitions use similar datasets to derive the uncertainties. Theoretical uncertainties are correlated except for the parton shower uncertainties, which are determined using a variation of the H HardScale parameter in the photon analysis, and a comparison between P and H in this analysis.
In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada, CRC and IVADO, Canada; Beĳing Municipal Science & Technology Commission, China; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaĳan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRT, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MES.
The average CV for mtCN Cts was 1. 02%. Data were manually curated and in cases in which samples yielded a standard deviation >0. 3, the divergent sample was removed. Mitochondrial Health Index: We calculated MHI as was previously reported in blood  (Picard et al. , 2018) , using the following equation: 2. 4. 5 | Fiber typing (immunohistochemistry) and expression (RT-PCR) Exercise is known to affect fiber type composition, area, and expression  (Campos et al. , 2002; Taaffe & Marcus, 1997). Shifts in fiber type composition might not be achieved so easily in human interventions; however, changes in myosin heavy chain expression patterns might occur even after short interventions  (Eigendorf et al. , 2018). Thus, in our study, we have measured both parameters to see whether trainability is observed in either measurement.
Fiber type distribution was quantified using Fiji software and values are presented in percentage distribution. We have attempted where possible to keep our fiber counts above 150 as previously suggested  (Nederveen et al. , 2020) , and a minimum of 100 fibers was considered the lowest threshold for analyses. RT-PCR for MHCs was performed on muscle tissue that was flash frozen in liquid nitrogen. RNA was extracted using the AllPrep DNA/RNA FFPE Kit (#80234 Qiagen). RNA (10 ng) was then diluted into 50 µl and reverse transcription was conducted using the iScript ™ Reverse Transcription Supermix for RT-qPCR (Bio-Rad) with a thermomixer. Primers for myosin heavy chain I, IIa, and IIx used for this experiment have been described elsewhere  (Eigendorf et al. , 2018). RT-PCR was conducted using the QuantumStudio-7 (Bio-Rad).
MRNA expression levels were quantified by realtime PCT using SYBR green fluorescence. Cycle threshold (Ct) values were normalized to a housekeeping gene, Cycl1. Samples were analyzed in triplicates and data were manually curated. In cases where samples yielded a standard deviation >0. 4, the divergent sample was removed. We utilized a linear mixed model (using the lmer package in R  [Kuznetsova et al. , 2017] ) of the form: where outcome was either the physiological measure of fitness (W peak or LT or VO 2peak ) or a mitochondrial marker (CS activity, mtCN, SDH, COX) or a fiber type (Type I, Type IIa, or Type IIx), timepoint was a numeric variable (0, 4, 8 or 12 weeks). The fixed effect for "timepoint" estimates whether there were changes in outcome at the group level over time (i. e. , mean slope).
The random intercept accounts for baseline differences in outcome between individuals, and the random slope assesses whether there are significant differences in how individuals change in outcome with time (subject-by-training interaction, or trainability). The individual segmental changes between the different timepoints estimates within-subject variability. Next, we used bivariate latent growth curve models (using the package Lavaan in R  [Rosseel, 2012] ) to test whether changes in physiological measurements were correlated with changes in molecular markers (i. e. , correlation between slopes). A complete explanation of this method can be found in the Supplementary Files. Due to limited muscle there were missing data points therefore multiple imputations using the mice package was used to impute these missing values (van Buuren & Groothuis-Oudshoorn, 2011), and results were pooled from all imputed iterations for both mixed models as well as parallel growth models with miceadds package  (Robitzsch et al.
Study Design and Patients The EAP (ClinicalTrials. gov identifier, NCT02477891) enrolled patients from the UK (15 sites), Spain (15 sites), Italy (15 sites), and Russia (6 sites). The protocol and amendments for this study were reviewed by an independent ethics committee and institutional review board in each country (Electronic Supplementary Material). This study was conducted in accordance with the Declaration of Helsinki, Good Clinical Practice guidelines, and applicable regulatory requirements. All patients provided written informed consent. Eligible patients were C 18 years of age with documented MM and evidence of disease progression on or after the most recent prior treatment regimen, as defined by International Myeloma Working Group (IMWG) criteria  [25, 26] , had an Eastern Cooperative Oncology Group (ECOG) performance status score of 0 to 2, and had received C 3 prior lines of therapy that included a PI and an IMiD or were double refractory to a PI and an IMiD.
Statistical Analyses The analysis population included all patients who received C 1 dose of daratumumab. SAS software version 9. 4 was used for data analysis (SAS Institute, Cary, NC, USA). No sample size calculations were performed. Globally, the plan was to provide daratumumab to up to 2000 patients, and the final number was determined by medical need and local health authority approvals. Unless otherwise specified, continuous endpoints were summarized using descriptive statistics. All categorical endpoints were summarized using frequencies and percentages. The Kaplan-Meier method was used to analyze progression-free survival (PFS), defined as the time between the first dose of study treatment and disease progression or death, whichever occurred first. The mean and median changes from baseline for all PRO assessment scores were determined for each patient who completed the assessments at baseline and each respective time point.
CONCLUSIONS In conclusion, the safety profile of daratumumab monotherapy was confirmed by the large cohort of European and Russian patients enrolled in the MMY3010 EAP; the safety profile was consistent with that reported in earlier clinical trials of daratumumab in patients with heavily pre-treated RRMM. No new safety concerns were identified, and there was a low incidence of treatment discontinuations due to TEAEs. PRO results indicate that HRQoL was maintained during daratumumab treatment. Collectively, this EAP provides additional evidence of the favorable safety profile of daratumumab in patients with heavily pre-treated RRMM and the associated maintenance of patient-reported HRQoL. Funding. This work was supported by Janssen Research & Development, LLC. The journal's Rapid Service fee was funded by Janssen Global Services, LLC.
Alessandro Corso, Larisa P. Mendeleeva, Vadim V. Ptushkin, and Maria Teresa Petrucci participated in the acquisition/collection of data. Edmond Chan, Wafae Iraqi, Huiling Pei, and Maren Gaudig participated in the analysis or interpretation of data. Jon Ukropec participated in the conception and design of the work being described in the publication and analysis or interpretation of data. Assem Al-Akabawi participated in the acquisition/collection of data and analysis or interpretation of data. Disclosures. Gordon Cook reports grants and personal fees from Janssen, Celgene, and Takeda; and personal fees from Amgen, Sanofi, and Karyopharm. Matthew Streetly reports personal fees from Janssen-Cilag, Sanofi S. A. , Celgene, and Takeda. Edmond Chan, Jon Ukropec, Wafae Iraqi, Assem Al-Akabawi, Huiling Pei, and Maren Gaudig are employees of Janssen.
This study was conducted in accordance with the Declaration of Helsinki, Good Clinical Practice guidelines, and applicable regulatory requirements. All patients provided written informed consent. Data Availability. The data sharing policy of Janssen Pharmaceutical Companies of Johnson & Johnson is available at https://www. janssen. com/clinical-trials/transparency. As noted on this site, requests for access to the study data can be submitted through the Yale Open Data Access (YODA) Project site at http://yoda. yale. edu. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Magnetic setup The setup is mounted on an Axio A1 inverted microscope (Carl Zeiss, Germany) with an oil-immersion 100× objective [1. 4 numerical aperture (NA)] mounted on a piezo-controlled translator (Physik Instrumente, Karlsruhe, Germany). The magnetic field is generated by two coaxial coils (SBEA, Vitry, France) with mu metal core (length, 40 mm; diameter, 26 to 88 mm; 750 spires). The coils are powered by a bipolar operational power supply amplifier 6A/36V (Kepco, Flushing, NY) controlled by a data acquisition module (National Instruments, Austin, TX). The maximum field generated is 100 mT with a gradient less than 0. 1 mT•mm -1 over the sample. The chains of beads are formed with a constant field of 5 mT. Time-lapse images are recorded with an Orca Flash4 complementary metal-oxide semiconductor camera (Hamamatsu Photonics, Hamamatsu, Japan).
Cell fluorescent imaging Spinning disc images were acquired on a Leica DMi8 microscope (Leica, Wetzlar, Germany) with an Orca Flash4 camera and a 100× objective. The setup was heated at 37°C using the box and the cube from Life Imaging Systems. Acquisition was done using the Meta-Morph software. Confocal images were acquired using a laser scanning microscope (Leica TCS SP8) with a 40× oil immersion objective (1. 4 NA), a HyD detector (Leica), and the LAS-AF software.