dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2003; Hughes et al. , 2016 ). Kendall's tau was used to assess correlations between variables -both between the statistical moments of the study regions and between statistical moments and environmental variables. Kendall's tau is a non-parametric test that estimates the statistical interdependence of two datasets. If the two datasets are completely independent then the Kendall's tau value is zero, while positive and negative values correspond to correlated and anti-correlated datasets, respectively  (Kendall, 1955). A multivariate classification and regression tree (CART) analysis was also conducted in R software using the "rpart" package, in order to investigate the effect of environmental variables on the mean waterbody size, the number of waterbodies per km 2 , the areal fraction of water, and the statistical moments.
Analyzing the Temporal Development of Waterbody Size Distributions Change in size distributions and their moments over time were investigated using multi-decadal remote sensing data. Change detection analysis was carried out for two sites in the Kotzebue Sound Lowlands in the northern part of the Seward Peninsula, Alaska (66. 3 • N, 164. 8 • W), these being the Espenberg area (442. 2 km 2 in extent) and the Shishmaref area (379. 9 km 2 in extent) (Figure  3  and Supplementary Figure  1 ). Waterbody maps for the Espenberg area were available from  Jones et al. (2011)  for the years  1950/1951, 1978, and 2006/2007. Waterbody maps of the Shishmaref area were available from 1962 and 2009/2010  (Muster et al. , 2017). The minimum waterbody size was set to 0. 001 km 2 , based on the resolution and image quality of the historical data  (Jones et al.
ACKNOWLEDGMENTS All TerraSAR-X data were made available by the  German Aerospace Center (DLR)  through PI agreement  LAN1747 , within the framework of the  European Space Agency (ESA)  funded  DUE Permafrost project , the  EU   FP7   PAGE21  project, and the  Austrian-Russian  joint  FWF COLD Yamal project (I 1401 ). KOMPSAT data were provided by the  European Space Agency (ESA)  to the  ACCOnet Project (International Polar Year AO  Project  4133 ). GTOPO 30 data is available from the  U. S. Geological Survey.
FUNDING This work was supported by the  Helmholtz Association  through a grant ( VH-NG 203 ) awarded to SM, WR, CK, and CW were supported by the  US Department of Energy 's  BER program  under the  NGEE-Arctic  project (contract no. DE-AC02-05CH11231 ). GG was supported by the  European Research Council (ERC)  grant no. 338335. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the  US Government. BJ was supported by the  National Science Foundation  under grant  OPP-1806213.
Thirty-four of these include the full set of protein-coding, rRNA and tRNA genes (>15,000 bp), of which 17 were complete circular mitogenomes. The remaining 17 were not circularized due to difficulties with the assembly of the control region. Five additional mitogenomes were incomplete by the lack of one or two genes (sequence length between 12,221 and 14,453 bp). The mitogenomes were annotated using gene predictions with MITOS  (Bernt et al. 2013 ) and manually refined in Geneious. All mitogenomes were structured following the putatively ancestral gene order for the Coleoptera. Mitogenomes assembled from the shotgun mixture were linked to particular specimens using the cox1 barcode sequences obtained from the same specimens with PCR-Sanger sequencing. For those cases where Sanger sequencing failed (5/39), validation was performed by unambiguous match to the species level on BOLD Public Data Portal  (Ratnasingham and Hebert 2007, accessed on 20th March 2019).
The mitogenomes correspond to 39 different species from the families Carabidae (6), Chrysomelidae (1), Curculionidae (9), Dermestidae (1), Elateridae (1), Latridiidae (1), Scarabaeidae (3), Silvanidae (1), Staphylinidae (12) and Tenebrionidae (4), and include representatives from 37  Genbank Accession Numbers, voucher codes, taxonomic identification, life stage, and information on whether the provided mitogenomes are the first available for the genus (FG column) and for the species (FSP column). Ã All mitogenomes were obtained by bulk sequencing of a mix of specimens. Voucher code refers to the specimen with PCR-Sanger sequencing that matches (100% similarity) the obtained mitogenomes. Vouchers are not available for mitogenomes if PCR-Sanger sequencing failed for a particular specimen. ÃÃ FG: Marked with 'x' if the mitogenome is the first available for the genus.
ÃÃÃ FSP: Marked with 'x' if the mitogenome is the first available for the species. genera. For 30 of these genera, we provide the first available mitogenome and only two species (Oryzaephilus surinamensis and Hypera postica) have an available mitogenome. The new mitogenomes include endogean representatives of the genera Torneuma, Coiffaitiella, Otiorhynchus, Oligotyphlopsis, and Typhlocharis. For further details on specimens and mitogenomes see Figure  1 , Tables  1  and 2 , and GenBank Accession Numbers. For the 39 newly generated mitogenomes, the 13 proteincoding genes (PCGs) were extracted using Geneious and individually aligned using the FFT-NS-i-x2 algorithm of MAFFT  (Katoh et al. 2002). Individual gene alignments were trimmed and concatenated to get a final dataset of 39 taxa and 12,940 bp.
This alignment was used for phylogenetic inference using Maximum-likelihood in IQ-TREE  (Nguyen et al. 2015) , performed through the IQ-TREE web server  (Trifinopoulos et al. 2016)  without data partitioning, allowing the software to determine the best-fit substitution model and estimating an ultrafast bootstrap approximation with 10,000 replicates. The obtained tree showed the expected relationships among the families within Coleoptera, including the monophyly of the suborders Adephaga and Polyphaga and the monophyly of all families (with more than one mitogenome) (Figure  1 ).
Randomisation and masking After screening, eligible patients were randomized at a 2:1 ratio to receive either AMG 714 or matching placebo for a total of seven doses of intravenous (IV) infusion over 10 weeks. The biostatistician generated the randomization code using permuted blocks. No stratification was applied due to the small sample size and lack of clearly established confounding factors. Each dose of study drug (AMG 714 or placebo) was prepared by an unblinded study pharmacist at each site. The prepared solutions were identical in appearance between the active drug and placebo. The study patients and study personnel involved in patient enrolment, study drug administration, patient assessments, data collection, and analysis remained blinded to treatment assignment until the study ended.
Statistics Because RCD-II is a rare disease, the sample size was not based on any statistical power calculation. All patients who received at least one dose of the study drug were included in the intent-to-treat (ITT) population. The per-protocol (PP) population included patients who received study treatment and provided evaluable data (ie, biopsies before and after treatment) for efficacy analysis. The PP population was used for analyses of IEL-related and histological endpoints, while the ITT population was used for clinical and safety endpoints. The primary endpoint, the change from baseline to Week 12 in the percentage of aberrant IELs with respect to total IELs by flow cytometry, was compared between the AMG 714 and placebo groups using analysis of covariance where the baseline value was included as a covariate and treatment group as a fixed effect.
The same analysis was used for the secondary immunological and histological endpoints. The proportion of patients with diarrhoea, defined as BSFS score of 6 or 7, over time was plotted, and the difference in the area under the curve was compared between the treatment groups using one-way analysis of variance. The change from baseline in GSRS weekly total score was analysed using a linear mixed effects model repeat measurement. Safety endpoints were summarized using descriptive statistics. All statistical analyses were performed using SAS® for Windows (SAS Institute Inc. , Cary, NC, USA), version 9. 4.
The patients with atypical RCD-II were allowed to participate in the study but not included in the analysis involving aberrant IELs, since these endpoints were based on the enumeration of surface CD3-cells. Therefore, these patients were not included in primary endpoint analysis. However, these patients' data were included in other endpoint analyses. In addition, ad hoc analyses were conducted on a subset of patients referred to as "pure" RCD-II patients, which was comprised of the majority of the patients (N=19) after excluding patients with major protocol deviations (N=1, insufficient washout period post cladribine), early terminations (N=1, adverse event), atypical RCD-II (N=4), and patients with only polyclonal/irregular polyclonal T cell Receptor gamma chain clonality (N=4; one patient had both atypical RCD-II and polyclonal IELs).
Safety oversight and informed consent The study protocol was approved by local ethics committees before initiation. All patients gave written informed consent before undergoing any study-related procedures. Randomization and initial dosing of the first 10 patients were staggered to ensure that the study drug was given to no more than one patient per day and no more than two patients per week. An independent Data Safety Monitoring Board reviewed the safety data from these patients and determined whether subsequent patients could proceed with study treatment. This study is registered with ClinicalTrials. gov (number: NCT02633020) and EudraCT (number: 2015-004063-36).
Data sharing statement There is a plan to share data. This may include de-identified individual patient data for variables necessary to address the specific research question in an approved data-sharing request; also related data dictionaries, study protocol, statistical analysis plan, informed consent form, and/or clinical study report. Data sharing requests relating to data in this manuscript will be considered after the publication date and 1) this product and indication (or other new use) have been granted marketing authorization in both the US and Europe, or 2) clinical development discontinues and the data will not be submitted to regulatory authorities. There is no end date for eligibility to submit a data sharing request for these data. Qualified researchers may submit a request containing the research objectives, the Amgen product(s) and Amgen study/studies in scope, endpoints/outcomes of interest, statistical analysis plan, data requirements, publication plan, and qualifications of the researcher(s).
In general, Amgen does not grant external requests for individual patient data for the purpose of re-evaluating safety and efficacy issues already addressed in the product labelling. A committee of internal advisors reviews requests. If not approved, a Data Sharing Independent Review Panel will arbitrate and make the final decision. Upon approval, information necessary to address the research question will be provided under the terms of a data sharing agreement. This may include anonymized individual patient data and/or available supporting documents, containing fragments of analysis code where provided in analysis specifications. Further details are available at the following: http://www. amgen. com/datasharing Contributors CC, FL, EB, OH and CM designed the study. GB, TVG, SK, GM, LC, PC, PG, SEC, and CM conducted the study procedures and collected and analysed data.
Some journals had to be eliminated because bibliometric data were missing. The inclusion criterion was the availability of full instructions for authors on the journal's webpage. We selected only Englishand French-language journals. Ultimately, 75 journals were assessed from the original list of 82 journals. The data for "journal impact factor 2013" were compiled from the impact factor listing for dentistry, oral surgery and medicine journals in the 2013 JCR Science Edition (https://jcr. incites. thomsonreuters. com). The JIF is calculated by the number of citations (C) that a journal receives in a given year divided by the number of all "citable" articles it published during the last two years (A). The formula is JIF= C/A.
Statistical methods We used a weighted kappa coefficient to measure the inter-observer reproducibility of IAQR tool. The weighted kappa coefficient goes from 1maximum (excellent reproducibility) to 0-minimum (bad reproducibility). This scale could be further divided into five intervals: 1) excellent (0. 81 to 1), 2) good (0. 61 to 0. 8), 3) mean (0. 41 to 0. 6), 4) low (0. 21 to 0. 4), and 5) bad reproducibility (0. 00 to 0. 2). To know if there was a significant difference between the observers for all or any of the qualitative questions we measured the difference of repartition of the data with Chisquared test. For paired values we used the score of Cochran-Mantel-Haenszel (CMH). We used a Pearson correlation coefficient to analyze the correlation between IAQR (quantitative variable) and JIF.
Analytic statistics Inter-observer reproducibility testing for the IAQR was performed on nontransformed variable ratings. The weighted kappa coefficient for all the data was κ= 0. 81, and IAQR presented with an excellent interobserver reproducibility. The kappa values were measured for each question (Table  4 ) and for each journal (Table  5 ). With CMH test we found a significant difference for all the data (p<0. 0001). When looking at each question we found a significant difference between observers for question 1 (p<0. 0001), for question 8 (p<0. 0001), for question 13 (p=0. 0008), for question 14 (p<0. 0001), and for question 16 (p<0. 0001). These findings could seem contradictory when looking at the previous analysis of inter-observer reproducibility of IAQR. However, for the CMH test the score of the difference is the same between 1 point-strongly disagree (observer 1) and 2 points-disagree (observer 2) as between 1 point (observer 1) and 4 points-strongly agree (observer 2).
However, JIF can be modified by many questionable editorial strategies including: 1) Increasing the number of authors per article to increase further self-citations and, secondarily the JIF; 2) Selecting the type of articles to increase the number of systematic reviews, which are more frequently cited than are primary studies, or case studies  [6] , and avoiding the publication of case studies; 3) selecting shorter articles that are more quickly accessed, read and cited; 4) selecting specific words and phrases in titles, and abstracts to improve keyword searches in databases and increase the number of citations for a given article; 5) selecting an arbitrary 2-year time window at the beginning of the JIF definition  [7]  to encourage authors to publish research within 2 years, which requires an editorial effort to reduce the review and publication times, although some editors use unethical practices such as encouraging authors after submission to cite articles in their reference list from the 2 last years of that journal  [8] ; 6) selecting English or providing translations of abstracts and articles in English instead of the national language because higher JIFs are associated with English language journals (JIF was created as a tool for US librarians)  [7] ; and 7) using the asymmetry of the JIF equation, in which the denominator indicates "citable" articles, which are original articles, reviews, and notes, and the nominator is created from citations from all sources, including editorials, and letters.
However, the conclusion is that the ITER CTS diagnostic can provide measurements of the projected 1D fast-ion velocity density function of the confined alphas and fast ions, as well as the alpha fast ion density (under isotropy assumptions) with a temporal resolution of 100 ms and within the required statistical uncertainty of 20%. A series of fitting tests with synthetic data has also shown that the present CTS system setup and analysis codes can reproduce the true alpha density used in the tests with only small discrepancies. CTS has also been assigned a supplementary role for measuring the bulk ion temperature profile as well as the nH/nD and the fuel ion ratio nT/nD in the plasma core. As presented in  [9, 10]  the two latter will require a rather perpendicular scattering geometry, i. e.
φ=∠(k δ ,B)~near 90 degrees. As the CTS mirror geometry is fixed, and deliberately off-perpendicular as described above, the ideal setup for fuel ion and isotope ratio measurements cannot be achieved. Thus these measurements are not design driving for the ITER CTS diagnostic. However, for a given plasma scenario and scattering spectrum, it may be possible to infer some information relating to the fuel ion ratio, as there will be spectral content containing information on this even with the off-perpendicular geometry. However, integrated data analysis accounting for other measurements may be required. In the analysis of the CTS spectrum with the aim of getting the information on the fast ion dynamics, the analysis will additionally provide some information on the bulk ion temperature and the ion rotation velocity, and may thus contribute to the determination of these via integrated data analysis.
The performance analysis results presented briefly above are based on the (conservative) assumption of a background ECE level of 100 eV. Since the CTS frequency range is far below the (cold) fundamental resonance the estimation of ECE background levels are not straightforward. Some modelling results predict virtually 0 eV, but high temperatures, multiple reflections of the beams on the vessel walls and Doppler shift of the resonance will give rise to a non-zero ECE level. While still searching for a good method/code for prediction of the level, we have assumed 100 eV. Presently, no official full data sets of ITER plasma scenarios, plasma parameters and profiles, fast ion densities etc. exist. We have assembled the required information from sources at ITER IO and F4E. We follow closely the development of the official and traceable sets of ITER plasma scenarios and parameters a See last paragraph of Section 4 concerning lack of traceability of plasma scenario data performed in the IMAS project at ITER IO, and will update our analysis and predictions based on this data.
Protein Detection by Immunofluorescence After the termination of treatment, the cultures were fixed with 4% formaldehyde (12 min, 4 • C), washed with PBS, and incubated overnight with antibodies against IBA-1 (1:500 in PBS with 0. 02% Triton X-100) to detect microglial cells. As secondary antibody, we used a goat anti-rabbit Alexa Fluor -555 (Invitrogen). When needed, nuclei of labelled cells were counterstained with Hoechst 33342 (10 µM). Phase contrast and fluorescent images were acquired using a Nikon TE 2000 inverted microscope (Nikon, Tokyo, Japan) equipped with an ORCA-ER digital camera and HCImage imaging software (Hamamatsu Corp. , Bridgewater, NJ, USA).
Funding: This research was funded by  program Investissements d'Avenir (Agence Nationale de la Recherche -10-IAIHU-06 ) and the  Translational Research Infrastructure for Biotherapies in Neurosciences  ( ANR-11-INBS-0011-NeurATRIS ). This work benefited from equipment and services from the CELIS and ICMQuant core facilities, at  Institut du Cerveau et de la Moelle épinière. L. A. and N. S. C. received a fellowship from the  Bernardo Houssay Program, MINCyT-CONICET-CAMPUS FRANCE. M. S. P. received a fellowship from  Fundação de Amparo à Pesquisa do Estado de São Paulo.
(b) Detection of CRISPR-Cas systems CRISPR-Cas systems were detected with CasFinder v. 2. 0  [33]. CasFinder exploits MacSyFinder (v. 1. 0)  [34] , a program that uses protein profiles and a set of rules concerning quorum and organization of components to identify molecular systems in genomes. Briefly, Cas proteins were detected using HMM profiles and systems were then discriminated at the subtype level based on the appropriate models. Three proteins are required to form a Cas system in class I systems, one for class II. The subtype assignment is achieved through signature proteins (Cas9 and Csn2 for subtype II-A, for example). All results are reported in electronic supplementary material, table  S1.
We compared our results with that of Cromie  [35] , which had several orders of magnitude fewer genomes, and checked that both works identified AddAB in all genuses analysed in both works (with the exception of Wolbachia, where the hits to the profiles developed in this work were not statistically significant). We sometimes identified AddAB pseudogenes (e. g. in some Staphylococcus, Burkholderia or Bordetella), in which case the system was indicated as absent. For AdnA, AdnB, SbcB and SbcE, we used curated proteins from Uniprot as a starting point and used Blast (Blast-p -. NCBI, May 2016) to fetch homologues from the non-redundant protein sequences database of NCBI. All hits belonging to different clades among the 250 best hits with more than 40% identity were selected and aligned as described above.
(e) Persistent genomes of Firmicutes and Proteobacteria We inferred the families of orthologous proteins for a set of 1189 genomes of Firmicutes and a set of 2897 genomes of Proteobacteria (larger than 1 Mb) available in the GenBank RefSeq dataset, as indicated above. A list of orthologues was identified as reciprocal best hits using end-gap free global alignment, between the proteome of a pivot and each of the other strain's proteomes (as in  [41] ). We used as a pivot Escherichia coli K12 MG1655 for Proteobacteria and Bacillus subtilis str. 168 for Firmicutes. Hits with less than 37% similarity in amino acid sequence and more than 20% difference in protein length were discarded. The persistent genome of each clade-the list of families of orthologous proteins present in more than 90% of the genomes-was defined as the intersection of pairwise lists of orthologues that were present in at least 90% of the genomes representing 411 families for Firmicutes and 341 for Proteobacteria.
(h) Statistical analysis of the associations We built 2 Â 2 contingency tables for all possible associations between DSB-RS and CRISPR-Cas subtypes that were present in more than 1% and less than 99% of the studied bacterial genomes (for example, RecA was not analysed as it is present in more than 99% of Proteobacteria and Firmicutes). This information was the basis for two analyses. First, we assessed if the presence of components DSB-RS was more associated with the presence of Cas systems than random proteins with similar frequency in the genomes of the same phyla. For each DSB-RS component, we randomly selected 10 proteins with a similar frequency in our dataset of the respective clade (allowing for a margin of +1% in frequency) which we call the control genes. We computed 2 Â 2 contingency tables for the co-occurrence of DSB-RS and Cas subtypes.
This resulted in a dataset of 10 different DSB-RS components in Firmicutes and 13 in Proteobacteria, as well as nine CRISPR-Cas subtypes in Firmicutes and 11 in Proteobacteria (see Material and methods for the complete lists). We then investigated whether CRISPR-Cas are more frequently associated with the presence or absence of DSB-RS than with other cellular functions. For the analysis of each DSB-RS component, we selected 10 random genes with other cellular functions that displayed a similar frequency in the genomes of the same phyla (Proteobacteria or Firmicutes). We then computed the coefficient of association of the 11 (10 controls and the DSB-RS) 2 Â 2 contingency tables (F). Note that the F values between the DSB-RS and the controls were positively correlated because most DSB-RS genes are present in most genomes and thus their control genes are also very frequent in the same genomes.