dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank  Raimund Bleischwitz ,  Matthieu Glachant ,  Frank Pothen ,  Sandy Skelton ,  Simone Cooper ,  Manfred Rosentock ,  Jun Rentschler , and all participants of the  Sino-European Circular Economy and Resource Efficiency (SINCERE)  project seminars in October 2015 in Shanghai and September 2016 in Paris for their valuable contributions and suggestions. The comments of two anonymous reviewers significantly improved the paper. All remaining errors are entirely ours. S€ oderberg acknowledges financial support from  ANR  [ ANR-14-ORAR-0001 ]. Agnolucci acknowledges financial support from  ESRC  [ ES/L015838/1 ].
We reconstructed a higher number of MAGs from Von Damm compared to Piccard, and the average diversity of the MAGs per sample was roughly equivalent between vent fields (average Shannon-Wiener Index for Von Damm samples = 1. 28, average for Piccard samples = 1. 88) (Table  1 ; Supplementary Fig. 2 ). We recovered MAGs from eight bacterial and four archaeal phyla in samples from Von Damm. The most commonly identified taxa among Von Damm MAGs included Aquificales, Thermoprotei, and Thiotrichales. We reconstructed MAGs from six bacterial phyla and one archaeal phylum at Piccard, many of which are Epsilonproteobacteria. The average coverage of each MAG varied greatly, ranging from ~10× to over 2000× (Fig. 1 ; Supplementary Data 2). The MAGs with the highest normalized coverage in Piccard were from the genus Sulfurovum.
46  using the illuminautils package 47 v1. 4. 4 using the program "iu-filter-quality-minoche. " For mapping, we merged and filtered reads also using the illumina-utils package  47  using the program "iu-merge-pairs" with the flag "-enforce-Q30-check", and removed sequences with more than two mismatches in the overlapping region with the program "iu-filter-merged-reads. " This resulted in ~170 bp long high-quality reads. Taxonomic distribution of metagenomic samples. We determined the relative abundance of different taxa in each metagenome by mapping the reads of each sample to the Silva SSU and LSU Parc databases (release 111)  48, 49  , followed by mapping of matching reads to the Greengenes 13_5 16S rRNA database  50. We used Bowtie2 51 v. 2. 2. 9 for mapping using default settings and local alignment.
Reads that mapped were classified using mothur  52  using classify. seqs with the Silva 16S rRNA database, using a cutoff of 50. Metagenome assembly, mapping, and binning. We assembled metagenomic reads using idba-ud 53 v1. 1. 2 with default settings. We only included contigs of at least 1000 bp in length to ensure robust contig clustering based on tetra-nucleotide frequency and coverage. We mapped the metagenomic reads of each sample to the assembled contigs using bwa aln v0. 5. 5 54 with default settings. We used anvi'o v2. 1. 0 55 to manually organize the metagenomic contigs of each sample into metagenomic bins based on tetra-nucleotide composition and relative coverage of each contig across all samples. To estimate the completion and redundancy of metagenomic bins, anvi'o used PRODIGAL 56 v2.
We visualized the trees using the Interactive Tree of Life website (ITOL) (itol. embl. edu)  62. To calculate ANI, we used the Python3 package pyani (https://github. com/ widdowquinn/pyani), which calculates ANI between two genomes following the methods described by Richter et al. 63  We used the "-anib" flag, which uses BLASTN to align 1020 nt fragments of the input FASTA files to calculate ANI among all Sulfurovum MAGs. Gene calling, annotation, and comparison of MAGs. For functional-based comparison, we used the JGI-IMG  64, 65  pipeline to call and annotate open reading frames (ORFs) in contigs from each MAG using the KEGG database. We included four reference Sulfurovum sequences that were also in the JGI-IMG database for comparison of key metabolic genes (Sulfurovum sp. AR, Sulfurovum sp.
NBC7-1, Sulfurovum G1, and Sulfurovum lithotrophicum ATCC BA797). For sequencebased comparison, we used the Integrated Toolkit for the Exploration of microbial Pangenomes (ITEP)  66  to conduct MCL clustering and comparison of ORFs that had been identified using the RAST pipeline  67. We clustered ORFs using an inflation value of 2 and a maxbit score of 0. 3. We used RPSBLAST to compare a representative sequence from each ORF cluster against NCBI's Conserved Domains Database to obtain the COG annotation for each ORF with a maximum e-value of 1e-05. We used ITEP to generate a presence-absence matrix of all gene clusters among all Sulfurovum MAGs. We converted this presence-absence matrix into a dissimilarity matrix and then created a cluster dendrogram using the "hclust" and "plot" functions using default parameters in R 3.
Anvi'o relies on a heuristic to identify SNVs that sets a minimum baseline that varies as a function of coverage depth in order to minimize the impact of sequencing or mapping error  55  (the URL http://merenlab. org/2015/07/20/ analyzing-variability/ gives access to a tutorial). We normalized coverage by dividing coverage depth by the total number of reads in the metagenome. We reported allele frequencies based on the frequency of the majority allele. We required all positions to have a minimum coverage of 10 in order to be included in SNV calculations, and only counted positions in which the minimum departure from the consensus (calculated as the total number of reads not matching the consensus divided by the total number of mapped reads) was 0. 05. Violin plots of allele frequency were created using the Seaborn Python visualization library based on matplotlib  70.
We used anvi'o to calculate the number of single-amino acid variants (SAAVs) per MAG using the -engine AA option in the program "anvi-gen-variabilityprofile," which reports amino-acid variants that were computed during the profiling of mapping results by the program anvi-profile with the inclusion of the flag --profile-AA-frequencies. For each codon position in open reading frames, the SAAV characterization framework of anvi'o employs only short reads that map to all three codons in a given context, which reduces the impact of noise due partial mappings and spurious inflation of SAAVs by maintaining the physical linkage of bases in a given short read  32. We used the "car" and "gdata" packages in R 3. 3. 3 to calculate statistical significance of the difference of means in SAAV/SNV distributions among MAGs in Piccard and Von Damm using the Welch twosample t-test.
Acknowledgements We would like to thank  Chip Breier ,  Leslie Murphy  and the captains and crew of  R/V Atlantis  and  R/V Falkor  as well as  ROVs Jason  and Nereus for assistance with sample collection and preparation. We would also like to thank  Ben Tully  and  Ramunas Stepanauskas  for providing invaluable comments on the manuscript, and  Andy Poppick  for assistance with statistical analyses. R. E. A. was supported by a  NASA   Postdoctoral Fellowship  with the  NASA Astrobiology Institute. This work was supported by a  NASA   Astrobiology Science and Technology for Exploring Planets (ASTEP)  grant  NNX-327 09AB75G  and a grant from  Deep Carbon Observatory's Deep Life Initiative  to J. A. H. and J. S. S. , and the  NSF Science and Technology Center for Dark Energy Biosphere Investigations (C-DEBI).
Data availability. Metagenomic reads are deposited under study accession code PRJEB15541 in the EMBL-EBI European Nucleotide Archive (ENA) database. The anvi'o merged profiles and contigs databases for the metagenomic data and metagenomic MAGs are available on Figshare (figshare. com/projects/Mid-Cay-man_Rise_Metagenome_Assembled_Genomes/20783) for re-analysis using anvi'o v2. 1. 0. The authors declare that all other data supporting the findings of the study are available in this article and its Supplementary Information files, or from the corresponding author upon request.
Culicoides were identified under a stereomicroscope (Nikon model SMZ) according to their pattern of wing pigmentation  [26]  and stored frozen (-20 °C) for further downstream processing. A map including Culicoides collection sites and endosymbionts spatial distribution was prepared with Quantum GIS software  [27]  using the coordinate reference System (CRS) EPSG:4326, WGS 84.
Phylogenetic analysis DNA sequences were edited using Bioedit sequence alignment editor software (version 5. 0. 9. for Windows  [31] ) and aligned with ClustalW Multiple alignment option without manual optimization. Phylogenetic and molecular evolutionary analyses were conducted using MEGA6 software  [32]. Phylogenetic analysis was inferred using Maximum Likelihood (ML) method incorporating best-fit models of sequence evolution determined using the Akaike information criterion with a resampling nodal support of 1000 bootstrap replicates. Best-fit models were Tamura 3-parameter (T92 + G) for Wolbachia dataset, and Kimura 2-parameter Gamma corrected (K2 + G) for Cardinium. Sequences published at the National Center for Biotechnology Information (NCBI) were used to better determine the evolutionary relationship of Wolbachia and Cardinium isolates (Additional file 1: Table  S1 ).
Phylogenetic analyses of endosymbionts Wolbachia Wolbachia DNA partial sequences of the wsp gene were obtained for 23 Wolbachia-infected Culicoides out of 67 (GenBank: MF179641-MF179663). The DNA dataset was representative of all Culicoides taxonomic groups that were found infected with Wolbachia in each geographical area (Fig. 1 , Table  2 ). Nine haplotypes were detected among the 23 wsp gene sequences, with haplotype diversity (Hd) of 0. 787 and nucleotide diversity (π) of 0. 0916. Phylogenetic analyses placed wsp sequences derived from Spanish Culicoides within the Wolbachia supergroups A (n = 4) and B (n = 19). Spanish isolates were grouped into four clades, two into supergroup A and two into supergroup B (Fig. 2 ). Wolbachia supergroup A sequences were grouped in two clades, III-WA and IV-WA, and were from Culicoides collected in Andalucía (Table  3 ).
pulicaris (s. l. ) population was infected with Wolbachia from clades IV-WA and I-WB. Moreover, C. pulicaris (s. l. ) from Quintos de Mora (Castilla la Mancha) were infected with Wolbachia belonging to clades I-WB and II-WB. Wolbachia sequences previously detected in Australian Culicoides spp. [20]  were segregated into two separated clades within supergroup B and a third clade into supergroup A (Fig. 2 ). The first clade contained three sequences of the species C. narrabeensis (GenBank: KR026947-KR026949) and was the phylogenetically related to clade I-WB (evolutionary distance, ED = 0. 161). A sequence from C. narrabeensis (GenBank: KR026950) and one from C. brevitarsis (GenBank: KR026943) were included into clade II-WB. The last group of Wolbachia wsp gene sequences detected in Australian Culicoides were placed in a third clade into supergroup A (C.
brevitarsis, GenBank: KR026944, KR026945; C. antennalis, KR026942; C. bundyensis, KR026946; C. parvimaculatus, KR026951). This clade was relatively distant from clades III-WA (ED = 0. 182) and IV-WA (ED = 0. 206). Within each of the four clades, nucleotide variation was minor or absent regardless Culicoides species or geographical location (Fig. 2 ). Within clades, evolutionary distances were 0. 001 (I-WB), 0. 004 (II-WB) and 0 (IV-WA).
Cardinium A fragment (416 bp) of the 16S ribosomal DNA gene was obtained from 8 Cardinium-infected Culicoides midges out of 12 (GenBank: MF188893-MF188900). The DNA dataset was representative of all Culicoides taxonomic groups that were infected with Cardinium in each geographical area (Fig. 1 , Table  2 ). Nucleotide diversity among the eight Cardinium 16S RNA gene sequences was low (π = 0. 0008), with two haplotypes differing by a singleton (Hd = 0. 333). When analysed   S1 ), the new sequence data exhibited low genetic variation (π = 0. 0073, Hd = 0. 504). However, the cladogram showed that all Cardinium sequences derived from Culicoides biting midges (n = 32) were grouped in a single clade (Fig. 3 ). This clade formed the recently described Cardinium Cgroup, attributed exclusively to Culicoides infections  [17].
The phylogenetic analysis including sequences for the few known Wolbachia infecting Culicoides revealed up to six well-defined clades. Some of the sequences were highly divergent as shown by the separation of the clades into different Wolbachia supergroups (A or B). Phylogenetic analyses based on 16S rDNA showed that Cardinium strains detected in Fig. 3  Cladogram for Cardinium phylogenetic relationships inferred from 16S sequences. Cardinium 16S rDNA sequences obtained in the present study are indicated with black diamonds. Accession numbers followed by host species are shown at terminal nodes. A. asiaticus has been used as outgroup to root the cladogram. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Maximum likelihood (ML) bootstrap support values over 50% are displayed on the nodes Culicoides were almost identical, as was reported in previous studies  [17] [18] [19].
Introduction The primary functions of peer reviewers are poorly defined. Thus far no body of literature has systematically identified the roles and tasks of peer reviewers of biomedical journals. A clear establishment of these can lead to improvements in the peer review process. The purpose of this scoping review is to determine what is known on the roles and tasks of peer reviewers. Methods We will use the methodological framework first proposed by Arksey and O'Malley and subsequently adapted by Levac et al and the Joanna Briggs Institute. The scoping review will include all study designs, as well as editorials, commentaries and grey literature. The following eight electronic databases will be searched (from inception to May 2017): Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Educational Resources Information Center, EMBASE, MEDLINE, PsycINFO, Scopus and Web of Science.
Two reviewers will use inclusion and exclusion criteria based on the 'Population-Concept-Context' framework to independently screen titles and abstracts of articles considered for inclusion. Full-text screening of relevant eligible articles will also be carried out by two reviewers. The search strategy for grey literature will include searching in websites of existing networks, biomedical journal publishers and organisations that offer resources for peer reviewers. In addition we will review journal guidelines to peer reviewers on how to perform the manuscript review. Journals will be selected using the 2016 journal impact factor. We will identify and assess the top five, middle five and lowest-ranking five journals across all medical specialties. Ethics and dissemination This scoping review will undertake a secondary analysis of data already collected and does not require ethical approval.
search strategy The electronic literature search strategy will follow the three-step process recommended by the Joanna Briggs Institute. 27 The first step consisted of an initial preliminary search of at least two online databases relevant to the topic. This was undertaken for MEDLINE (via Ovid) using the 'peer review, research' medical subject headings and 'peer review' keyword in the Cochrane Library, resulting in 2,017 studies in the Cochrane Library and 13,717 in MEDLINE. In the second step, we will closely review potentially relevant text words in the titles and abstracts of the most pertinent papers in order to compile a list of terms that can be used to inform our search strategy. Index terms used to describe the articles will also be included. This list will be combined with search strategies from existing scoping and systematic reviews on peer review  9 21 29  to develop database-specific search strategies.
The Peer Review of Electronic Search Strategies 2015 Guideline statement will be used to guide the electronic literature search strategies. 30 These will be further refined in collaboration with a health sciences librarian. Subsequently, the following databases will be searched: Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Educational Resources Information Center, EMBASE (via Ovid), PsycINFO (via Ovid), MEDLINE (via Ovid), Scopus and Web of Science. The search strategy for MEDLINE can be found in online supplementary appendix 1. There will be no time or language restrictions. The authors involved in this protocol are in command of the following languages: Catalan, Croatian, English, French, German, Italian, Russian and Spanish. Relevant articles identified in any other language will be translated.
Data will also be extracted independently by KG and DC. We expect that some of the grey literature might subsequently be published elsewhere in the indexed literature. This will be accounted for by cross-checking authors' names across grey literature and index literature results in order to identify potential duplicates. An adapted version of the PRISMA flow diagram will be used to report final numbers in the resulting study publication once the review is completed. Reasons for exclusion will be recorded at the full-text review stage. stAgE 4: chArtIng thE dAtA A draft charting form (see table  1 ) has been developed at the protocol stage to aid the collection and sorting of key pieces of information from the selected articles. It will be pilot-tested and refined during the full-text screening to capture detailed information on each study.
The information from research-based and non-research-based publications will be collected in separate extraction forms. Additional categories that may emerge during data extraction will be added accordingly. Another form will be developed for the extraction of information from the journal guidelines to peer reviewers. In addition to the general and specific descriptions of expectations and competencies of peer reviewers, variations according to journals and their peer review models (such as single-blind peer review, doubleblind peer review, open peer review, postpublication peer review) and whether peer reviewers have to provide specific recommendations (ie, no revision, minor revision, major revision, reject) will be noted. In order to create a useful summary of the data, we will combine all expectations and competency-related statements retrieved from all sources.
Acknowledgements The authors would like to thank  Dr Dario Sambunjak  and  David Blanco de Tena-Dávila  for providing advice during the writing of this protocol. contributors All authors have made substantive intellectual contributions to the development of this protocol. KG, DH, EC, IB and DM jointly conceived the idea for the project. DC, EC, IB, DM and DH contributed to the study design and development of research questions. KG conceptualised the review approach and led the writing of the manuscript. DH led the supervision of the manuscript preparation. All authors provided detailed comments on earlier drafts and approved this manuscript.
In the absence of valuable biomarkers, this approach should be reserved for highly selected patients, including those with contraindications to chemotherapy, patient's with a strong preference against chemotherapy or those with a long disease-free interval, minimal disease burden, in particular in terms of visceral involvement, and/or strong ER/PgR expression. Trials directly comparing chemotherapy plus anti-HER2 therapy versus endocrine therapy plus anti-HER2 therapy are currently ongoing (Detect V/CHEVENDO (NCT02344472), SYSUCC-002 (NCT01950182) and PERNETTA trials) and their results will allow for better recommendations. In addition, in several countries anti-HER2 therapy, namely trastuzumab, can only be used once in the metastatic setting since its use beyond progression is either not approved or not reimbursed; in those cases, preference should be given to a combination of chemotherapy plus anti-HER-2 therapy.
Our study is guided by the following main question: how well can state-of-the-art dust (regional/global) forecast models (SKIRON, MACC/CAMS, NMMB/BSC-Dust; see Sect. 3) reproduce our shipborne lidar observations of light extinction and mass concentration profiles as a function of transport length from about 1500 to more than 5000 km from the dust source regions? A special focus is on dust removal and the extent to which the simulated fine and coarse dust fractions are in consistency with the lidar observations. In this context, we discuss the hypothesis of  Kok (2011a, b)  and  Nabat et al. (2012)  that dust models overestimate the fine dust fraction and underestimate the coarse dust fraction. The cruise with a state-of-the-art continuously running dust profiling lidar across the Atlantic and the comparisons with the dust forecasts give us, in addition, the favorable opportunity to inform the broader atmospheric science community about the recent progress in dust lidar observations and data analysis methods.
, 2016)  which consists of permanent and temporary continuously running automated multiwavelength lidar stations. PollyNET is part of the European Aerosol Research Lidar Network  (Pappalardo et al. , 2014). One of the goals of PollyNET is building up a small network of autonomously running dust-monitoring lidar stations. Polly stations close to deserts are available in Cabo Verde, Portugal, Greece, Cyprus, Israel, and Tajikistan. The aim is to support dust forecast modeling and to pave the way towards lidar data assimilation into dust forecast models. The Polly data analysis software was extended during recent years by introducing the POLIPHON retrieval technique which is applicable to polarization lidar observations  (Mamouri and Ansmann, 2014, 2017). Polarization lidar is a very powerful remote sensing tool for aerosol and cloud research.
(2017). The POLIPHON retrieval consists of four steps. These steps are shown in Fig. 2c-e. We applied the method to case 4 in Fig. 1. On 5-6 May 2013, the R/V Meteor was 600 km west of Barbados. An aged Saharan dust plume was detected between 800 and 2000 m height after 9 days of travel across the tropical Atlantic. As mentioned, the height profiles of the particle backscatter coefficient (Fig. 2a ) and particle linear depolarization ratio at 532 nm (Fig. 2b ) as presented and discussed in  Rittmeister et al. (2017)  are the input of the POLIPHON data analysis. In the first step, the vertical profiles of the particle depolarization ratio and backscatter coefficient are used to separate the dust and non-dust contributions to the measured (total) particle backscatter coefficient (Fig. 2c ). Non-dust aerosol components are biomass burning smoke, anthropogenic haze (from industry, traffic, heating), rural (background) aerosol, and marine particles.
The SKIRON model covers the entire area of northern Africa, the Mediterranean Sea, Europe, the North Atlantic Ocean, part of the eastern coast of the United States, and the Caribbean. This particular setup runs operationally at the servers of the National and Kapodistrian University of Athens and has a horizontal resolution of 0. 2 • × 0. 2 •. Only dust (no other aerosol component) is considered in the model. In the vertical, it uses 38 levels from the surface to the top of the atmosphere. The main goal of this setup was to examine the transatlantic transport of Saharan dust and associated weather patterns; therefore, such a substantial domain is needed. The outputs from this operational model are the ones used in this paper. No weather data assimilation is performed (apart from the analysis fields used every day for initial conditions) due to the size of the area covered.