dataesr/scientific-paragraphs-categorization huggingface dataset:
1-7; Fig. 2 ). Members of the Abacarus genus are worldwide distributed and, so far, 65 species have been described. Among them, 37 species are specialized to monocotyledons, mainly grasses, and 29 to dicotyledonous plants (Amrine and Stasny 1994; J. Amrine unpublished eriophyoid database). Our analysis revealed that the genetic variation of grass-associated Abacarus members is much higher than anticipated, and suggests that the whole genus may need further integrative taxonomic research. Accurate identification of cryptic species within eriophyoid mites is important especially in those taxa that are under scrutiny because of their virus transmission capacity, invasive potential and host range that involves economically important plants, such us, for example, cereals. Their proper identification helps to understand their potential environmental impact  (Skoracka et al.
apart; distance between h1 and h2 (2). Type material-Holotype female and paratypes: 29 females, 11 males, 5 nymphs, collected from Bromus inermis (Leyss) (Poaceae) in Poznań, Poland (52. 46 N, 16. 93E). Leg. A. Skoracka, M. Hornyák. DNA barcode: 603-bp fragment of the cytochrome oxidase subunit I (COI) gene (Genbank Acc. No. MG267341), 509-bp fragment of the 28S rDNA D2 fragment (Genbank Acc. No. MG773120). Material is deposited in the collection of the Faculty of Biology, Adam Mickiewicz University in Poznań, Poland.
Acknowledgements The authors thank  Jędrzej Kuczyński  for consultation regarding the Latin name of the new species,  Jarosław Raubic  for help in the laboratory,  Evsel Denizhan  for collecting grass samples in Turkey,  James W. Amrine  for information about Abacarus species, and the three reviewers:  Radmila Petanović ,  Philipp Chetverikov  and  Enrico de Lillo  for their valuable remarks, which improved the manuscript. The study was financially supported by the  Polish Scientific Research Committee  (Grant No. 6 P04 C0 5418 ) and  National Science Centre  (Grant No. 2011/03/B/NZ8/00129 ).
1; burgundy-Abacarus sp. 2; red-Abacarus plumiger n. sp. ; green-Abacarus longilobus; pink-Abacarus sp. 3; lilac-Abacarus sp. 4; light brown-Abacarus sp. 5; dark yellow-Abacarus sp. 6; navy blue-Abacarus lolii; purple-Abacarus sp. 7. Genbank accession numbers are shown on the branches. (Color figure online).
To facilitate the calculations a single calculation period from 1946 to 2014 with a duration of 69 years was selected. The missing and failing data were reconstructed using the computer software package "Hydrologist"  (Volchek 1998)  with the involvement of river-analogues, according to requirements (RUE "Stroytekhnorm" 2010). Paired linear regression equations were used to solve the problem of the missing data. The value of the correlation coefficient ranged between r = 0. 65 … 0. 95, which is significantly larger than the critical value r cr = 0. 25. For the assessment of the impact of anthropogenic influences and natural factors on the runoff the original time series of runoff were analyzed for various av- Figure  1  shows the schematic map of the Viliya River basin with basic river network, location gaugingstations, studied reservoirs and the BelNPP area.
Forecast assessment of the runoff changes of the River Viliya on the territory of Belarus The time series of observations of air temperature and precipitation for the period from 1961 to 2010 according to the data provided by meteorological stations evenly spaced along the river basin were used for assessing the runoff changes. The trends in the meteorological parametersof the River Viliya basin were presented in  (Korneev et al. 2015). As a summary, the results of assessments of the trends in meteorological parameters, according to the information of the meteorological station at Vileyka Town are presented in Table  7. A detailed analysis of changes in runoff in the River Neman basin including the River Viliya at Mihalishki Village was made before  (Korneev et al. 2015; Volchak and Parfomuk 2014).
Experimental conditions and sample assembly Experiments are performed using the Griggs-type deformation apparatus at the University of Tromso, Norway. Experiments are run at confining pressures (Pc) of ∼ 1. 0 and 1. 5 GPa, temperatures (T) of 800 • C and (axial) displacement rates of ∼ 2•10 -8 to 2•10 -9 ms -1 resulting in bulk strain rates of ∼ 3•10 -5 to 3•10 -6 s -1. General shear type of flow is achieved by placing the rock powder (0. 11 g) between cylindrical alumina forcing blocks (diameter of 6. 33 mm) pre-cut at 45 • with respect to the load axis  (Appendix Figure A. 1). Descriptions of the experimental setup, data recording and data treatment can be found in the Appendices A1 -A3, experimental conditions are listed in Table  2.
Clean phase maps contain segmented pyroxene and amphibole phases, and each pyroxene grain is in contact only with its own amphibole corona. The x-y coordinates of the clast (pyroxene) and the aggregate (pyroxene + corona) outlines are measured and exported using Fiji and a modified version of the Jazy XY export macro (by Rüdiger Kilian, available at https://earth. unibas. ch/micro/index. html). Using a MATLAB script (available from the author upon request), the x-y coordinates of clast and aggregate outlines are converted to polar coordinates (r-θ), and the corona thickness, thc(θ), is determined at each point along the pyroxene clast as the shortest distance between the clast to the aggregate outline (Figure  1c ) as a function of θ. The angle runs counter clock wise from the horizontal. This approach yields good results where coronas follow the clast shape, but tends to underestimate corona thickness where the corona becomes very elongated as e.
Dissolution-precipitation creep and grain boundary sliding From the mechanical data (including stress exponents), the determined grain sizes, from the nucleation of new grains, and from amphibole growth fabrics discussed above, it is concluded that in all samples, the dominant deformation mechanism cannot be frictional or crystal plastic (dislocation creep). Instead, the dominant deformation mechanisms for amphibole and plagioclase are inferred to be DPC (accompanied and/or accommodated by mineral reactions) and grain boundary sliding. Pyroxene is less involved in accommodating strain but plays an important part by being involved in mineral reactions and thereby aiding grain size reduction by nucleation of new grains. Mineral reactions change the initial phase assemblage of Px+Pl to mostly Pl+Px+Zo in the synthetic mixtures, and Pl+Px+Amp +Qz+Zo in Maryland Diabase samples.
-EBSD analysis For Electron backscatter diffraction (EBSD) measurements, thin sections were polished with colloidal silica suspension and subsequently coated with a thin layer of carbon. Samples were analysed in the Zeiss Merlin SEM at the University of Tromso, with a Nordlys nano camera in high vacuum at 15 keV acceleration voltage and probe currents of ∼ 15 nA. Data is acquired with the Oxford AZtec software and processed with Channel 5 and the MATLAB toolbox MTEX (available at https://mtextoolbox. github. io; Bachmann et al. , 2010). Pole figures are plotted using MTEX. For pole figure contouring, the de la Vallée Poussin kernel with a half-width of 9. 4 • and a bandwidth of 30 is used. Pole figure J-index (PFJ; e. g. ,  Bunge, 1982; Mainprice and Silver, 1993)  are given as a measure of texture strength.
-Image analysis Phase segmentation: Phase segmentation is performed on BSE SEM images, where individual minerals are identified by their Zcontrast. Using the software Fiji (link at: http://fiji. sc/Fiji) and the plugin Statistical Region Merger, automatic pre-segmentation was achieved. The automatic segmentation was then manually inspected and corrected where necessary. Grain maps: Grain maps were produced by manually tracing (supervised segmentation) grains from SEM or TEM images. Grains were analyzed for their 2D area using Fiji, and for each grain, the diameter of the area equivalent circle, d equ , is calculated. d equ = 2 • (A/π) (A4) where A is the area of the digitized shape. Grain size distributions are presented as histograms of the 2D number weighted distribution of equivalent diameters.
Microstructure analysis -Data acquisition SEM analyses are performed either with the Zeiss Merlin SEM at Tromsø University, or with a Philips XL30 ESEM at the 16 Solid Earth Discuss. , https://doi. org/10. 5194/se-2018-39 Manuscript under review for journal Solid Earth Discussion started: 7 May 2018 c Author(s) 2018. CC BY 4. 0 License. Nano Imaging Lab of the Swiss Nanoscience Institute (SNI) at Basel University. Chemical analyses are performed using standardless energy dispersive X-ray Spectrometry (EDS), at 15 keV acceleration voltage with a ZAF matrix correction for spectra quantification. (Scanning) transmission electron microscopy ((S)TEM) analyses are performed at Utrecht University using a FEI Talos 200FX equipped with a high-sensitivity Super-EDX system. TEM images are recorded in bright field (BF) mode, whereas STEM images in high angular annular dark field (HAADF) mode.
Acknowledgements. We thank the team of the centre of nano imaging (SNI) at  Basel University and Tom Eilertsen at Tromso University  for help and assistance with the electron microscopy. Terry Tullis  is thanked for providing the Maryland Diabase material and the Cranberry Lake Diopside was kindly provided by  Jacques Précigout. Willy Tschudin  is thanked for excellent thin section preparation. We gratefully.
Code and data availability. The MATLAB code for analysing amphibole reaction corona thicknesses is available from the author upon request (sina. marti@ed. ac. uk). Mechanical data and chemical analyses are available from the author upon request. Solid Earth Discuss. , https://doi. org/10. 5194/se-2018-39 Manuscript under review for journal Solid Earth Discussion started: 7 May 2018 c Author(s) 2018. CC BY 4. 0 License. input for the SURFOR program (Panozzo Heilbronner, 1984; Heilbronner and Barrett, 2014), which determines the orientation distribution function (ODF) of the boundary segments. The ODFs are presented as rose diagrams. Solid Earth Discuss. , https://doi. org/10. 5194/se-2018-39 Manuscript under review for journal Solid Earth Discussion started: 7 May 2018 c Author(s) 2018. CC BY 4.
Cells were costained with propidium iodide (0. 5 μg/mL) to exclude dead cells. Fluorescence was measured by flow cytometry (FACS Calibur, BD Biosciences), and data was analyzed using FlowJo software (Tree Star, Inc. , Ashland, OR, USA). Ly-6G and 7/4 double positive cells were identified as neutrophils, Ly-6G negative and 7/4 positive cells were considered as inflammatory monocytes/macrophages  [34]. In another experiment, twenty C57BL/6 mice (female, 6-8 weeks of age) were randomly divided into 4 groups (n = 5/group) and injected intraperitoneally (i. p. ) with LPS (100 μg/peritoneal cavity), heme (300 nmol/peritoneal cavity), or LPS + heme in 200 μL apyrogen PBS. Control mice received PBS only. Total leukocytes in the peritoneal fluid was determined on Burker chambers after dilution in Turk solution as it was described previously  [21].
Objectives: Autoimmune and inflammatory diseases (AIDs) form a continuum of autoimmune and inflammatory diseases, yet AIDs' nosology is based on syndromic classification. The TRANSIMMUNOM trial (NCT02466217) was designed to re-evaluate AIDs nosology through clinic-biological and multi-omics investigations of patients with one of 19 selected AIDs. To allow cross-analyses of clinic-biological data together with omics data, we needed to integrate clinical data in a harmonized database. Materials and Methods: We assembled a clinical expert consortium (CEC) to select relevant clinic-biological features to be collected for all patients and a cohort management team comprising biologists, clinicians and computer scientists to design an electronic case report form (eCRF). The eCRF design and implementation has been done on OpenClinica, an open-source CFR-part 11 compliant electronic data capture system.
Results: The CEC selected 865 clinical and biological parameters. The CMT selected coded the items using CDISC standards into 5835 coded values organized in 28 structured eCRFs. Examples of such coding are check boxes for clinical investigation, numerical values with units, disease scores as a result of an automated calculations, and coding of possible treatment formulas, doses and dosage regimens per disease. Discussion: 21 CRFs were designed using OpenClinica v3. 14 capturing the 5835 coded values per patients. Technical adjustment have been implemented to allow data entry and extraction of this amount of data, rarely achieved in classical eCRFs designs. Conclusions: A multidisciplinary endeavour offers complete and harmonized CRFs for AID clinical investigations that are used in TRANSIMMUNOM and will benefit translational research team.
Recent studies have successfully identified molecular signatures associated with specific autoimmune diseases  4, [13] [14] [15] [16]  as well as in physiological and pathological contexts  17- 19. Those results led us to setup an observational clinical trial, TRANSIMMUNOM, (NCT02466217) the main goal of which was to revisit the nosology of AIDs through a systems immunology approach. TRANSIMMUNOM participants include patients diagnosed with one out of 19 selected AIDs or one out of 5 control diseases (Figure  1 ), and healthy donors with no history of autoimmune disorders. The systems immunology approach used a multi-scale deep immunophenotyping on peripheral blood (including transcriptome, TCR repertoire, cytokine expression) and microbiome NGS studies. Importantly, classic routine biology assays as well as clinical investigations are fully part of the data collection strategy.
Cohort Management Team Set up to interact with a Clinical Expert Consortium (CEC), a Cohort Management Team (CMT) of biological experts, routine laboratory personnel, clinical trial methodologists and clinical investigation centre harmonized the clinical and laboratory outcomes/results. The CMT ensured that all required data are collected in an appropriate format for analyses and that the questions are unambiguous. The computer scientist defined the data and metadata structure required to minimize non-controlled data entry and to specify the expected values. The overall design was supervised by an immunologist involved in the scientific part of the clinical trial, who liaised between the clinicians and the computer scientist.
OpenClinica implementation Given the amount of data to be collected across 19 AIDs and 5 control diseases, OpenClinica v3. 14, an open-source CFR-part 11 compliant electronic data capture platform has been selected for the design and capture of selected clinico-biological data. A test and production instances have been installed on dedicated and secured CentOS virtual machines with 16Go RAM, 8 cores and 15 Go disk space each. OpenClinica's application server (Tomcat v9. 0. 6) and database server (PostgreSQL v. 9. 5) parameters have been upgraded to fit multiple simultaneous data entry and data extraction, in particular JAVA_OPTS for heap memory have been upgraded to 8 Go instead of 1Go Mo by default.
OpenClinica as the compromise in designing a multi-disease eCRF The TRANSIMMUNOM observational trial targeted recruitment of 1,000 patients suffering from one out of 24 diseases and healthy controls. During a single visit, patient medical history and clinical investigations are performed together with the collection of samples (blood, serum and feces) for further multi-omics analyses. The goal of the trial is to revisit the nosology of AIDs by defining groups/clusters of patients based on clinical and molecular signatures that cut across disease classification. To deal with the expected amount of heterogeneous (such as disease severity scores, imaging data, biological measures) from routine clinical investigations, and to allow the cross-evaluation of clinical and omics data, we needed to develop an eCRF with a system that allows further omics data integration.
We selected OpenClinica (OC) as an electronic data capture (EDC) tool to support our eCRF design. OC is an open-source CRF-part 11 compliant EDC able to design complex eCRFs for large studies  20, 21. One of the major features of OC is to rely on Clinical Data Acquisition Standards Harmonization (CDASH) from the Clinical Data Interchange Standards Consortium (CDISC)  22  , which allows the harmonization of clinical and biological data coding. Finally, OC includes the mandatory validation of all recorded data to ensure data quality  20. In addition, the main strategy of TRANSIMMUNOM is to cross-analyze data from multiple AIDs, each of which is usually characterized by particular clinical investigation records and biological data measures. We anticipated the final cross-analysis, which would require the same information for all the diseases.
A multidisciplinary workflow ensuring the design of a robust multi-disease eCRF Expertise in different but converging fields was pooled in the CMTs, each of which participated in a 3-step workflow to (1) define the protocol, (2) design and (3) validate the eCRF (Figure  2 ). The first step of protocol definition involved a Clinical Expert Consortium (CEC) to define the list of items for all the patients with the aim of collecting exactly the same information regardless of the disease. All the clinical specialists together selected a sample of items per specialty so that the CRF was reasonably comprehensive and synthetic. Biology lab experts were also questioned to ensure the feasibility of sample drawing and of the required biology assays. Upon collection and validation of the actual items to be recorded, the specification of the database started with the design of an e-template where the computer scientist structured the information for each item by imposing the format of the data and metadata.
Once the e-template was defined, we proceeded to the eCRF design: the CEC, in close collaboration with the computer scientist, designed the clinical coding of clinical investigation data following an unambiguous format for each item with maximized use of a predefined list of responses in order to avoid erroneous data entry. Biology lab experts defined for each parameter measured the value type (string, integer, decimal, Boolean), as well as the unit and range, when applicable. All the information was summarized in a spreadsheet and converted by the computer scientist into a PostgreSQL relational database following the OpenClinica structure. Finally, clinical research technicians evaluated the user-friendliness of the eCRF, the clinical research assistant evaluated the 207 item relationship constraints, and finally the CMT validated the eCRF with a patient "Zero" 208 simulation before release for production.
These included biological assessment of organ function (liver, kidney, bone marrow) and of inflammation state and safety, organized in 6 CRF and covering 91 parameters. Finally, the last set of CRFs recorded "Disease-specific monitoring" data and was subdivided into 3 CRFs (Figure  3D  & Supplementary material) capturing 616 items, including disease activity scores as described in Table  2. This is thought to be as wide as possible in identifying clinical parameters not usually collected in a particular disease including imaging and histology features to allow the identification of disease profile, disease severity and features possibly shared by diseases. Each clinician of the CEC identified a collection of features observed in his/her specialty as classic or rare parameters. The CMT gathered all the parameters from the different specialties and listed them in the clinical status and clinical evaluation CRFs.
We then coded all the possible/expected values that each item could take and identified 1 to 8 possible variables per item coded as one of the value type. This work was especially critical for the description of patient treatments. The list of all possible treatments regimen within each specialty was fully generated with clinicians and is available in the database as a menu list of 637 variables. Altogether, we built a database with 3815 uniquely coded variables. However, since clinical status and evaluation of several diseases share identical CRFs, we    First, the main goal of our trial is cross-analysis of multi-omics data obtained with clinical and lab biology data from 1,000 patients selected for one out of 24 AIDs or control diseases. Therefore, we needed an efficient and homogenized set of clinical and routine biology lab for all the patients, which led to the selection of 865 parameters and to the coding of more than 5000 values.
This vast amount of data would have been unmanageable using classic paper CRFs and spreadsheets. Second, the amount of information to be collected requires a thorough validation with automated rules and limited free text data entry to avoid mistyping and errors. Again, this cannot be handled using classic methods. Third, for crossanalysis, we need to be able to extract clinical and routine biology data efficiently so that we can filter for parameters as variables of interest (such as gender, BMI, disease activity, autoantibody level). Again, considering the number of patients to be recruited in the TRANSIMMUNOM trial, this would have been impossible. And finally, as regards the disease heterogeneity, it would have been too expensive and complicated to ask an eCRF provider to design such integrated CRFs.
Clinical data management is of utmost importance for any clinical study. This includes clinical information collection, validation and storage, usually completed through the use of CRFs. While generally clinical research organizations (CROs) propose and use eCRFs, most academic sponsored clinical studies still take advantage of the cost-benefit of paper versions of CRFs, sometimes in combination with Excel based databases 20. However, such tools, although convenient, lack validation and data traceability. In addition, they do not usually use harmonized vocabulary and allow free text data entry. These drawbacks were particularly counterproductive in our multi-disease clinical trial from several points of view.
14clinicians, 1 computer scientist, 7 scientists, 3 clinical research technician and assistant as well as 2 medical biologists for more than 100 hours of meetings and discussion over a year and a half. Clinical data coding has the enormous advantages that it (i) pools reported terms in medically meaningful groups, (ii) facilitates identification of common data sets for evaluation of CRFs covers a wide spectrum of clinical and routine biology data of interest for most AIDs, offering the community a pre-designed set of CRFs that can be used together or individually. Although clinical safety was not added to our set of CRFs, because of the noninterventional nature of the TRANSIMMUNOM trial, this could easily be done if needed. This complex set of data has been harmonized and the database designed to store and query efficiently the massive amount of data stored.
5 DISCUSSION 5 Clinical data management is of utmost importance for any clinical study. This includes clinical information collection, validation and storage, usually completed through the use of CRFs. While generally clinical research organizations (CROs) propose and use eCRFs, most academic sponsored clinical studies still take advantage of the cost-benefit of paper versions of CRFs, sometimes in combination with Excel based databases 20. However, such tools, although convenient, lack validation and data traceability. In addition, they do not usually use harmonized vocabulary and allow free text data entry. These drawbacks were particularly counterproductive in our multi-disease clinical trial from several points of view.