dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of the implementation We use laptops for development and production deployment. Our tool is designed for teachers who create their courses and for administrators of educational platforms based on the Open Source "Open edX" software platform. We designed our tool for any hardware platform which meets minimum requirements: 4GB RAM, 2. 5Ghz processor. For user interface we use React, Redux libraries, webpack, and Babel for modules bundling and converting JavaScript code to backward-compatible representation. Npm is a package manager we use for handling packages in development. Lodash is a modern JavaScript utility library delivering modularity, performance, and extras. (See Table  2  for licensing information. ) For analytical and ETL services we are using Java 8 with Spring 5  [15].
For better readability of Java code, we use the Lombok library  [16] , which allows you to reduce the boilerplate code (constructors, «Object»-methods and etc. ) by using annotations. Reflection allows us to look at existing tasks in the project, create a list of tasks and give it to the UI. For documentation we use swagger, it allows us to create documentation in a semi-automatic mode for our services. We use PostgreSQL [17] as a database. Our build system -Gradle  [18] [19]. Interaction between database and service is provided by JDBC [20] driver. Our system is RESTful. All microservices use the REST paradigm to interact with each other.
Innovative aspects of the implementation In the implementation of our system, we decided to record each log file (which is a JSON-file) as every row in our PostgreSQL database. So, we use it like a No-SQL database. SQL query for logs selection is presented in fig. 3. PostgreSQL has many standard functions such as working with strings or JSON-files, which allow working with logs more effectively. We are thinking about transferring our database to No-SQL  [21]  in the future if it is proved that it will be more productive.
System output As a result, we received a solution that provides course administrators the following functionalities: • download log files from educational platforms; • choose log files between different courses on a platform; • store log files in a database; • run analytic tasks based on downloaded logs, a few of them: 1) Calculate total user time on the course and user time distributed per day; 2) Show activity type for all users (or for a particular user) on course depending on the date; 3) Show the user way over the pages; 4) Show amount of video play events per day; 5) Show words from pdf search field; 6) Get video watching durations by elements of course. The system gives the calculations in the form of tables saved in XLSX or CSV format and generated graph from these tables. On the graph in figure  4  we can see the rule that the user visited on the course.
Challenge and issues The main challenge was to study the logs and their loading, conversion, and analysis. It was required to read and learn a lot of Open edX documentation. On the other hand, the initial log file that we took was 18Gb to analyze, which made it almost impossible to leant it manually, so we had to create SELECT log_line -> 'username' as user_name, log_line #>> '{context, user_id}' AS user_id FROM logs WHERE log_line -> 'username' != 'null' Barsukov N. D. , Sysoev I. M. , Pereskokova A. A. , Nikiforov I. V. , Posmetnijs D. Analysis of student activity on the e-learning course based on «OpenEdu» platform logs. Trudy ISP RAN/Proc. ISP  RAS, vol. 32, issue 3, 2020, pp. 91-100  98 simple pasting scripts to learn it fast, and only then we were able to retrieve a small log for testing purposes.
Our current database design has limitations that do not allow us to process the logs as fast as we wanted. Therefore, we have found a solution that is now improving. Logs can be larger than 18 GB and contain more than 10 000 000 events generated with semi-structured information inside, so the problem is to process the logs quickly enough. Open edX platform doesn't provide a good and quick API for logs downloading for offline analysis, so that feature request and feedback has been provided to the platform administrators, but unfortunately, until today they have not implemented the required functionality for us.
MATERIALs AND METHODs This was a retrospective study of the Centre for Maritime Health at the University Hospital (Centre Hospitalier Régional Universitaire  [CHRU] ) in Brest, between 1 st January 2002 and 31 th July 2019. The files of the 396 professional divers are recorded in our centre. The divers' files were selected from the French National Network for Occupational Disease Vigilance and Prevention (RNV3P). The data were collected anonymously on a CHRU computer, requiring no authorisation from the National Commission on Informatics and Freedom. Patients had given their prior consent for any anonymous retrospective studies of their medical data. To be included in the study, divers had to have been seen at least once in our centre, between 2002 and 2019, for a professional diving aptitude visit (initial visit before beginning a professional diving career or follow-up visit during the diving career, according to the regulations in force in France); divers also had to have had a particular restriction (depth, type of gas, temperature etc.
The focus was on the analysis of pathologies that motivated a restriction or incapacity (type of pathology, link with diving), as well as the impact on professional practice (i. e. permanent or temporary interruption). From these data, it was possible to determine the number of cases of decompression sickness (DCS) in our cohort, since all divers who had had DCS, with the exception of barotraumatic otitis, were seen again in our centre before resuming their professional activity. Data entry was done using Excel software, Microsoft Office 2017 ®. Averages and extremes were worked out on this software. The correlation analysis of pathologies was done using Biostatgv ® , by c 2 test or Fisher test, depending on the validity parameters. ic diagnosis was 37 years (extremes: 23-59 years). The average length of time in professional diving was 7 years (extremes: 0-19 years).
ACKNOWLEDGEMENTS This work was granted access to the HPC resources of CINES and  GENCI  (allocation  A1-A0012A10078 ). All simulations have been performed in the framework of the elsA three-party agreement between AIRBUS, SAFRAN, and  ONERA  which are co-owners of this software. The authors wish to thank gratefully  W. Riéra  who carried out the presented numerical test bench and URANS-SA/ZDES-SA simulations. The authors thank  G. Gerolymos  for useful discussions during this study.
Data were collected through 6 replicated sampling Agronomy 2020, 10, 1254 4 of 17 zones equally spread along an east-west transect on each crop treatment, which was identified as the only natural field gradient due to a slight slope (~3%) and water drainage direction. All samples were collected along this 90m east-west sampling transect and 20 m away from the perennial-annual treatment boundary. Six pairs (row + inter-row) of soil cores were taken at the end of the first spring (6 June 2018), the regrowth period (20 November 2018) and the second spring (13 June 2019). Four soil profiles were dug for each crop treatment at the end of the second spring (14 June 2019) along the sampling transect. Soil cores and soil profiles were obtained in June to avoid unfavourable sampling in dry conditions later in summer, and as root systems growth is greatly reduced after flowering, especially for annuals which are harvested in early July.
A typical uniaxial compressive test on a cylindrical sample of Au4G of radius 31 mm (similar to the loading piston radius) has been performed to check the value of Young's modulus. The macroscopic vertical strain ε YY -calculated from the relative variation of height of the sample-varies linearly with the vertical stress σ Y -calculated by division of the force F applied by the bearing surface of the piston S. The best linear regression fitted to the data points gives a slope of 74 GPa in accordance with Young's modulus provided by the manufacturer (see Figure  S1  in supporting information). We also assessed the P wave velocity in Duraluminium by measuring the first arrivals in an unstressed sample without scatterers and made of the same material (see Text S1). The P wave velocity directly measured in an unstressed and scatterers-free Au4G sample, that is, v P = 6,209 ± 47 m s -1 , is in good agreement with the one calculated from quasi-static values of elastic moduli and density.
Laboratory Setup Description Figure  2  also presents the mechanical loading of the sample. It is held between a vertical piston loaded by a servo-controlled 10 T press and a plate of Au4G attached to the force sensor below. The force applied by the piston is measured from a force sensor (REP-transducer of type TC4 with a maximum load capacity of 30 T) and is recorded using a NI-6133 data acquisition card. The maximal stress recorded in the experiments is 95 MPa. Two linear acoustic arrays of 32 piezoelectric transducers (Imasonic) are placed on the opposing faces of the sample as shown in Figure  2. All the piezoelectric sensors have a resonance frequency of 500 kHz. To ensure an efficient acoustic coupling and to reduce the acoustic impedance contrast between the sensors and the sample, we used a high-viscosity Shear Wave Couplant gel from Sofranel.
Numerical Scheme Description The numerical scheme is the same as proposed by  Azzola, Schmittbuhl, et al. (2018). It relies on the combination of a finite element deformation code (Code_ASTER) and a spectral element wave propagation code 10. 1029/2019JB018974 Journal of Geophysical Research: Solid Earth (SPECFEM2D). The mesh grid is iteratively deformed using the finite element software Code_ASTER, which is an open source general FEA software. Each step of the loading consists in the resolution of a static mechanical problem. The model assumes plane deformation. The bottom of the sample is fixed without any horizontal or vertical displacement. The right and left boundaries are stress free. We impose a given displacement, noted δ, along a 62 mm long "loading line" centered at the top of the sample which transcribes in 2-D, the contact area between the piston and the laboratory sample.
Acknowledgments The authors would like to thank  Alain Steyer  for sample preparation and his help with the construction of the experimental setup. For the use of SPECFEM2D, a free software is downloadable online (https://github. com/geodynamics/specfem2d. git); we thank the  Computational Infrastructure for Geodynamics  (http:// geodynamics. org)which is funded by the  National Science Foundation  under Awards  EAR-0949446  and  EAR-1550901. For the use of Code_Aster, an open-access software is downloadable online (https://www. code-aster. org/);we thank  Electricité de France  (EDF). The manuscript benefited from constructive comments by  Kasper van Wijk , an anonymous reviewer, Associate Editor  Nori Nakata , and  Editor Yehuda Ben-Zion.
Funding This work has been published under the framework of the LABEX  ANR-11-LABX-0050-G-EAU-THERMIE-PROFONDE  and benefits from a funding from the state managed by the  French National Research Agency (ANR)  as part of the  "Investments for the Future" program. It has also been funded by the  EU projects DESTRESS  ( EU H2020 research and innovation program , Grant Agreement 691728).
The data analyzed in this paper is part of the larger research project described in an unpublished diploma thesis. All the surveys and questionnaires that were used in the study were completed anonymously. Clinical data of patients were summarized using descriptive statistics. Obstetric data were summarized using the Robson classification  [18, 19]. The following observed variables were operationalized using the self-description method: anxiety as a trait, anxiety as a state, pain, negative affect, subjective pain resistance, and positive orientation. Anxiety, pain, and also positive and negative affect were measured twice at the same time: upon arrival at the recovery room (T1) and 5 h after arrival (about 6 h after anesthesia, T2). Patients between the T1 measurement (after completing the questionnaires) and the T2 measurement were given their children for kangaroo mother care (kangaroo mother care-early skin-to-skin contact between a mother and her newborn).
Data concerning subjective pain resistance were also collected using the authors' self-made questionnaire, in which patients were asked to mark the correct completion of their opinion about themselves: I believe that I am resistant to pain on a five-point scale ranging from 'Much less than an average patient' (1) to 'Much more than an average patient'  (5). The reliability indicator for the measures used in the study was examined. The reliability statistics (Cronbach's α) for the scales were as follows: anxiety measured STAI-trait α = 0. 85, affect measured I-PANAS: positive affect in T1 measurement α = 0. 76, in T2 measurement α = 0. 78, negative affect in T1 measurement α = 0. 72, in T2 measurement α = 0. 70, positive orientation α = 0. 65. IBM SPSS Statistics 25 software (IBM Corp. , Armonk, NY, USA) was used for the statistical analysis.
| Calculations and statistical analysis All statistical analysis was performed in R version 3. 5. 0. (R Core Team, 2018). The effects of plant species mixtures (1 vs. 2 or 4 species), species identity and functional trait dominance (CWM) and diversity (FD) on N pools, fluxes and biological drivers of N transformations were tested with linear mixed effects models, lme from package nlme  (Pinheiro et al. , 2016) , fitted with a type III SS ANOVA. Plant species mixtures, species identity and functional trait measures were considered as explanatory variables, and block was included as a random factor. When residual variances were heterogeneous, a variance structure (varIdent, package nlme) was included in the model. Models with and without the variance structure were compared based on Akaike information criterion (AIC) and the model with significantly lower AIC was retained.
The same models with monoculture data were used to test differences in functional traits at the plant species level. Since the abundances of N-cycling microbial communities were highly correlated (r = 0. 72-0. 99; p < 0. 001), we performed a principal component analysis (PCA) on gene abundances using the package vegan  (Oksanen et al. , 2013) , and PC1 was used in subsequent analysis. Spearman correlations were used to assess the relationships between biotic N drivers and N pools.
Diego Abalos https://orcid. org/0000-0002-4189-5563 Laurent Philippot https://orcid. org/0000-0003-3461-4492 Abalos, D. , De Deyn, G. B. , Kuyper, T. W. , & van Groenigen, J. W. (2014). Plant species identity surpasses species richness as a key driver of N 2 O emissions from grassland. Global Change Biology, 20, 265-275. https://doi. org/10. 1111/gcb. 12350 Abalos, D. , De Deyn, G. B. , Philippot, L. , Oram, N. J. , Oudová, B. , Pantelis, I. , Clark, C. , Fiorini, A. , Bru, D. , Mariscal-Sancho, I. , & van Groenigen, J. W. (2020). Data from: Manipulating plant community composition to steer efficient N-cycling in intensively managed grasslands. Dryad Digital Repository, https://doi. org/10. 5061/ dryad. 9p8cz 8wf0.
| Participants Baseline characteristics are shown in Table  1. Eleven young male participants completed the study. All participants were healthy and none took medication at the time of the study. Body composition was determined by dual energy X-ray absorptiometry (DXA) scanning (Lunar iDXA; GE Healthcare, Brøndby, Denmark) (Table  1 ). Consent to participate was obtained after the participants had read a description of the experimental protocol, which was approved by the Scientific Ethics Committee of the Capital region of Copenhagen (H-17030187). One participant was excluded from the study due to a pathological oral glucose tolerance test. This study was not registered on clinicaltrials. gov, because it was considered as a kinetic observational study with oral ingestion of normal dietary components (salt and sugar) and pure observational measurements.
| Statistical analysis The primary endpoint in this study was the postprandial GLP-1 plasma concentration response. When we used a two-tailed α = 0. 05 and an 80% power threshold, a sample size of n = 9 was calculated to be needed to detect an appreciable effect of a NaCl load in addition to a glucose load on postprandial circulatory levels of GLP-1. Data were analyzed, using GraphPad Prism 8 (GraphPad Software Inc. La Jolla, CA, USA). Area under the curve (AUC) was calculated using the trapezoidal rule, and the t-test (2-tailed) for paired data was used for comparing incremental AUC values between the two study days. Values of p <. 05 were considered statistically significant. We confirmed that data on hormone and electrolyte levels in this study passed the Shapiro-Wilk test (GraphPad Software Inc.
ACKNOWLEDGEMENTS The excellent technical support by technicians  Bente Matthiessen  ( Bispebjerg Hospital, University Hospital of Copenhagen ),  Susanne Hansen  and  Gitte Kitlen  ( Department of Cardiovascular and Renal Research, University of Southern Denmark ),  Lene Kristoffer Racz  ( Department of Biomedical Sciences, University of Copenhagen ),  MA Marquès  ( I2MC Toulouse ), and  Alice von der Lieth  ( Department of Clinical Biochemistry, Rigshospitalet, University Hospital of Copenhagen ) is gratefully acknowledged.
Methods Six sputa, randomly selected from routine samples in Mondor Hospital (Creteil, France) and treated anonymously, were tested after bacterial and fungal routine culture. Two of which were spiked with Aspergillus Fumigati and Aspergillus Nigri (10 5 conidia/mL). After mechanical lysis, DNA was extracted using automated QIAsymphony® extraction (AQE) or manual PowerSoil® MoBio extraction (MPE). DNA yield and purity were compared. DNA extracted from spiked sputa was subjected to (i) real-time PCR for Aspergillus DNA detection and (ii) combined metagenomic analyses targeting barcoded primers for fungal ITS1 and ITS2, and bacterial V1-V2 and V3-V4 16S regions. Amplicon libraries were prepared using MiSeq Reagent V3 kit on Illumina platform. Data were analysed using PyroMIC© and SHAMAN software, and compared with culture results.
Another issue concerns the choice of the DNA amplification target. Most mycobiota studies have targeted the internal transcribed spacer (ITS) region, as recommended by the Fungal Barcoding Consortium  [18] , and most authors chose ITS1  [14, 15, [19] [20] [21]  rather than ITS2 region  [2, 3]. Deciding which is better remains uncertain, though ITS1 database is broader than that of ITS2  [14, 22]. In the present study, we compared the impact of two recent extraction protocols: the commonly used manual PowerSoil1 MoBio extraction (MPE), and the automated QIAsymphony extraction (AQE) using DSP DNA Midi kit, on the fungal and bacterial diversity where both communities were concomitantly assessed via 16S/ITS targeted amplicon sequencing in respiratory samples. We also compared the diversity of (i) fungal community using amplification with ITS1 or ITS2 regions and of (ii) bacterial community using V1-V2 or V3-V4 16S regions.
Barcoded PCR products were purified using Agencourt AMPure XP, analysed on Agilent 2100 Bioanalyzer and quantified with Quant-iT PicoGreen dsDNA Assay Kit. Samples were normalized to 4nM and pooled (5μl of each normalized sample) into a library. A sequencing PhiX control (Illumina, Evry, France) was prepared following manufacturer's instructions. The libraries were sequenced using MiSeq Reagent Kit V3 (2 x 300 bp with the 600-cycle kit, Illumina, Evry, France) on Illumina MiSeq platform (Illumina, Evry, France), and resulted in 115,286; 392,274; 68044 and 196230 sequence reads for V1-V2, V3-V4 16S, ITS1, and ITS2, respectively. Raw data are available on Genbank NCBI NIH (SRA accession number: PRJNA548447; https://www. ncbi. nlm. nih. gov/ sra/PRJNA548447).
Taxonomic assignment, diversity and statistical analyses The generated data were analysed with PyroMIC© (protected software, IDDN FR. 001. 400018. 000. S. P. 2014. 000. 31230) which contains a cleaned fungal and bacterial database created from NCBI sequences. Ultimately, only sequences of >100bp for ITS reads and >150bp for 16S reads, with �98. 7% and �94. 5% homology, respectively, and 0. 0 e-value were considered for identification at species and genus level  [31]. Accordingly, we only retained 93,451; 276,475; 65,112 and 191,349 sequence reads for V1-V2, V3-V4 16S, ITS1, and ITS2, respectively (Table  1 ). Rarefaction curves were computed to evaluate the quality of the taxonomic diversity assessment. The fungal and bacterial diversity patterns of samples were compared, at genus level, with the diversity patterns of negative controls for each amplification target.
The generalized linear model (GLM) implemented in DESeq2 R package was used to detect differences in abundance of taxa between the extraction protocols (MPE vs. AQE), the 16S (V1-V2 vs. V3-V4), and the ITS (ITS1 vs. ITS2) targets. GLM was computed to include the patient, the extraction protocol, and the amplification target as main effects, in addition to interaction between the extraction and the amplification target. This interaction was useful to model the pairing between successive measurements coming from the same individual. Resulting Pvalues were adjusted according to Benjamini and Hochberg procedure. Wilcoxon rank-sum test (α = 0. 05) was used to compare DNA yield by the extraction protocol type.
Discussion In the present study, we compared, simultaneously and under the same conditions, the impact of two DNA extraction protocols (manual PowerSoil MoBio1  [12]  and automated QIAsymphony extraction) on the diversity of respiratory bacterial and fungal microbiota assessed by 16S/ITS targeted amplicon sequencing using 2 bacterial (V1-V2 and V3-V4 16S) and 2 fungal (ITS1 and ITS2) amplification targets. Data of bacterial and fungal cultures were available and used to compare with the results of 16S/ITS targeted amplicon sequencing and Aspergillus qPCR. In our work, the use of automated extraction enhanced the DNA yield and quality with better reliability and shorter technical time than the manual protocol. The bacterial and fungal taxonomic diversity detected in respiratory samples was not highly influenced by the extraction protocol (low and moderate effect, respectively), whereas the amplification targets had a significant influence.
This limit is set at 10 times the minimum illuminance on WS. I. e. , if the law establishes 150 lx on WS, this limit will be equal to 1500 lx. Let us determine the dependence of DA in office rooms on DF value at RP for Ternopil city, Ukraine. The problem was solved using the software Relux (v. 2020. 1. 3. 0) [41]. The reliability of the results of this software package was tested in  [42] [43] [44]. Since the standard CIE overcast sky conditions were used in the DA calculation in the Relux program, and the calculation was made only for the diffused light, we assume that the TSBE in this study oriented to the north. In this study minimum daylight autonomy values were used. For calculations, we adapted 9 hours working day (from 9:00 am to 6:00 pm). In accordance with  [32, 45] , minimum illumination prescribed by regulations for office rooms is 300 lx (DA300).