dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. ABCFS thank  Maggie Angelakos ,  Judi Maskiell ,  Gillian Dite. ESTHER  thanks  Hartwig Ziegler ,  Sonja Wolf ,  Volker Hermann ,  Christa Stegmaier ,  Katja Butterbach. GENICA  thanks  Christian Baisch ,  Hans-Peter Fischer ,  Anne Lotz , and  Beate Pesch. KARMA and SASBAC thank the  Swedish Medical Research Counsel. MARIE thanks  Petra Seibold ,  Sabine Behrens ,  Ursula Eilber  and  Muhabbet Celik. MTLGEBCS  would like to thank  Martine Tranchant  ( CHU de Québec -Université Laval Research Center ),  Marie-France Valois ,  Annie Turgeon  and  Lea Heguy  ( McGill University Health Center, Royal Victoria Hospital; McGill University ) for DNA extraction, sample management and skilful technical assistance.
Genotyping of the OncoArray was funded by the  NIH  Grant  U19 CA148065 , and  Cancer Research UK  Grant  C1287/A16563  and the  PERSPEC-TIVE  project supported by the  Government of Canada  through  Genome Canada  and the  Canadian Institutes of Health Research  (grant  GPH-129344 ) and, the  Ministère de l'Économie, Science et Innovation du Québec through Genome Québec  and the  PSRSIIRI-701  grant, and the  Quebec Breast Cancer Foundation. Funding for iCOGS came from: the  European Community 's  Seventh Framework Programme  under grant agreement n°  223175  ( HEALTH-F2-2009-223175 ) (COGS),  Cancer Research UK  ( C1287/A10118 ,  C1287/A10710 ,  C12292/A11174 ,  C1281/A12014 ,  C5047/A8384 ,  C5047/A15007 ,  C5047/A10692 ,  C8197/A16565 ), the  National Institutes of Health  ( CA128978 ) and  Post-Cancer GWAS initiative  ( 1U19 CA148537 ,  1U19 CA148065  and  1U19 CA148112- the  GAME-ON initiative ), the  Department of Defence  ( W81XWH-10-1-0341 ), the  Canadian Institutes of Health Research (CIHR)  for the  CIHR Team in Familial Risks of Breast Cancer , and  Komen Foundation for the Cure , the  Breast Cancer Research Foundation , and the  Ovarian Cancer Research Fund.
Melissa C. Southey  is a  NHMRC   Senior Research Fellow. The CECILE study was supported by  Fondation de France ,  Institut National du Cancer (INCa) ,  Ligue Nationale contre le Cancer ,  Agence Nationale de Sécurité Sanitaire, de l'Alimentation, de l'Environnement et du Travail (ANSES) ,  Agence Nationale de la Recherche (ANR). The ESTHER study was supported by a grant from the  Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the  German Cancer  Aid (Deutsche Krebshilfe). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0 and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, as well as the Department of Internal Medicine, Johanniter GmbH Bonn, Johanniter Krankenhaus, Bonn, Germany.
The Ontario Familial Breast Cancer Registry (OFBCR) was supported by grant U01CA164920 from the USA National Cancer Institute of the National Institutes of Health. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The SASBAC study was supported by funding from the Agency for Science, Technology and Research of Singapore (A*STAR), the US National Institute of Health (NIH) and the Susan G. Komen Breast Cancer Foundation.
The SBCS was supported by Sheffield Experimental Cancer Medicine Centre and Breast Cancer Now Tissue Bank. SEARCH is funded by Cancer Research UK [C490/A10124, C490/A16561] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The University of Cambridge has received salary support for Paul D. P. Pharoah from the NHS in the East of England through the Clinical Academic Reserve. The SZBCS was supported by Grant PBZ_KBN_122/P05/2004 and the program of the Minister of Science and Higher Education under the name "Regional Initiative of Excellence" in 2019-2022 project number 002/RID/2018/19 amount of financing 12 000 000 PLN.
Data availability The datasets analyzed in the current study are available via the BCAC Data Access Coordinating Committee (bcac@medschl. cam. ac. uk) upon reasonable request. Summary-level genotype data are available via http:// bcac. ccge. medsc hl. cam. ac. uk and in supplementary table  S2. Individual-level data are available via the BCAC Data Access Coordinating Committee (bcac@medschl. cam. ac. uk).
The Earthrise Scientific research is increasingly demonstrating the dramatic effects of pollution and climate change on Planetary Health, with a steadily increasing and now incontrovertible production of data and evidence. Despite this, people's behaviors and knowledge are struggling to change, reflecting a gap between scientific evidence and habits. Therefore, this condition slows down the possibility of putting concrete actions into practice to fight climate change, which have broad and lasting involvement and lead to real changes in living habits. Therefore, it is critical to raise awareness regarding issues such as biodiversity, marine and freshwater pollution, air pollution, water scarcity, sustainable transportation, clean energy, and food production. However, in addition to transferring and disseminating knowledge, it is also necessary to engage and empower citizens to change their behavior.
Campaign description and oceanographic parameters Water samples were collected during the TRANSSIZ (PS 92 -ARK XXIX/1) cruise onboard RV Polarstern between 19 May and 28 June 2015. The cruise started in Bremerhaven, Germany, and ended in Longyearbyen, Svalbard (Fig. 1 ), as described in detail by  Peeken (2016). Along the ship track between 19 and 27 May, temperature, salinity and chlorophyll a (Chl a) fluorescence in the surface water layer (6 m depth) were continuously recorded with the FerryBox System of the Helmholtz-Zentrum Hereon, and extracted from the FerryBox database every 2 min. The instrument performs a self-cleaning routine every day, including acid washing and freshwater rinsing. In addition, sensor behaviour is controlled by staff members of Polarstern, although drift of the sensors is rarely observed (for details see  Petersen, 2014).
, 2019) , with the sampling taking place in small leads. All ice stations were 50 to 250 km away from the ice edge and open water  (Dybwad et al. , 2021). A detailed study of nutrients, marker pigments and protist microscopy classified the Yermak Plateau stations  (39, 43, 46)  to be in a pre-bloom phase, while all other stations were in a bloom phase  (Dybwad et al. , 2021). During the ice stations, discrete seawater samples for trace gas and phytoplankton analyses were collected at six water depths between 0. 5 and 50 m depth using 12 L Niskin bottles on the ship-CTD (conductivity, temperature, depth) water-sampling carousel. For trace gases, these samples were transferred to 1 L light-proof glass flasks for direct analysis on board. Values for temperature and salinity were provided by the CTD bottle data file for each station  (Nikolopoulos et al.
Amplicon gene libraries were sequenced using Illumina MiSeq technology in 2 × 300 bp paired-end runs at CeBiTec (Bielefeld, Germany). Raw sequence files have been deposited in the European Nucleotide Archive under accession number PRJEB50492, using the data brokerage service of the German Federation for Biological Data (GFBio) in compliance with MIxS standards. The amplicon analysis workflow is described in Sect. S2 in the Supplement, with bioinformatic code and data files documented under https://doi. org/10. 5281/zenodo. 7609524 (Wietz et al. , 2023). Briefly, after primer removal using cutadapt  (Martin, 2011) , reads were classified into amplicon sequence variants (ASVs) using DADA2  (Callahan et al. , 2016)  and taxonomically classified using the Silva v138 database  (Quast et al. , 2012).
However, a comprehensive understanding of marine trace gas dynamics, including the rapidly changing Arctic, requires further measurements in seawater, sea ice and atmosphere. In conclusion, the reported trace gas concentrations in high spatial resolution provide important insights into climatically and biologically relevant compounds and their connection to microbiology. Code and data availability. Data used in this study are listed below: -Transect data: https://doi. org/10. 1594/PANGAEA. 953917 (Peeken et al. , 2023a). -Vertical profiles: https://doi. org/10. 1594/PANGAEA. 953908 (Peeken et al. , 2023b). -Sequence data: Raw sequence files have been deposited in the European Nucleotide Archive under accession number PR-JEB50492. -Complete amplicon analysis workflow: https://doi. org/10. 5281/zenodo.
7609524 (Wietz et al. , 2023 ; https://github. com/ matthiaswietz/transsiz, last access: 6 February 2023). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-20-851-2023-supplement. Author contributions. VG, RSE, BB and IP designed the study. BB, VG and RSE performed trace gas measurements prior to the campaign; VG and RSE performed trace gas measurements on-board. IP coordinated all TRANSSIZ work, and supervised the biological sampling onboard as well as subsequent performed pigment analyses. AN coordinated the oceanographic sampling and water mass classification. KM set up the AUTOFIM sampling system and supervised DNA extraction. MW performed bacterial community analyses. VG, BB, IP and MW wrote the paper. All co-authors have read and contributed to the paper.
Acknowledgements. We are thankful to the captain, crew and scientists from the  TRANSSIZ expedition  ( ARK XXIX/1 ;  PS92 ), carried out under grant number  AWI_PS92_00. We thank  Francois Truong  and  Jean-Eudes Petit  for help with data processing, as well as  Josephine Rapp  and  Halina Tegetmeyer  for help with amplicon sequencing. We would like to thank the three anonymous reviewers who provided very useful comments that helped to improve the paper. Financial support. Ilka Peeken ,  Matthias Wietz  and  Katja Metfies  are funded by the  PoF IV program  " Changing Earth -Sus-taining our Future " Topic 6. 1 of the  Helmholtz Association. The publication is part of the  FRAM Observatory  under EPIC number hdl:10013/epic. 912d6e80-8865-431d-bc3b-a016f99166e9. Financial support was also provided by  AWI ,  CNRS  and  CEA.
Sequence analysis was performed using the QIIME2 software  [48, 49]. Reads were denoised using DADA2 resulting in a list of Amplicon Sequence Variants (ASVs)  [50]. Taxonomic affiliations were obtained using the SILVA 138 reference database  [51, 52]. ASVs represented by a single sequence or matching with "Archaea", "Eukaryota" and "Unassigned" were finally removed. The rarefaction analysis showed that two skin mucus and one gill mucus samples had lower sampling depth than the others (with 264, 386 and 449 reads). These three samples were discarded, and the data were rarefied to 13,900 sequences. Unfortunately, after this rarefaction, there was no skin mucus samples left for O. melanura during summer (Additional file 1: Table  S1 ).
Data and statistical analyses All statistical analyses were performed using the rarefied ASV table. Bacterial alpha diversity was calculated using Shannon and Faith's phylogenetic indices with the R package phyloseq  [53]. A General Linear Model (GLM) was applied to identify which variables (fish species, tissue-skin and gill mucus and water communities -and season) influence bacterial diversity (lme4 package). Differences between groups were then identified using estimated marginal means comparisons using the emmeans package. The effect of fish species, tissue and season, as well as the possible interactions between these factors on bacterial communities was assessed by Permutational Multivariate Analysis of Variance (PERMANOVA) and pairwise comparisons for both weighted Unifrac and Bray-Curtis indices (adonis2 function, R package vegan).
Considering that some bacteria from the surrounding environment can be found on the fish tissue by chance (e. g. , caused by water currents), these analyses were also performed without considering sequences identified in water samples (i. e. , on the "specific" gill and skin mucus microbiota). The number of shared ASVs among seasons was calculated and represented using a Venn diagram for skin and gill mucus for each fish species. Bacterial taxa (i. e. , phylum, class, order, family and genus) contributing the most to the differences between skin and gill mucus bacterial communities across seasons were assessed with a Linear discriminant analysis Effect Size (LEfSe) and LDA scores  [54]. To investigate the relationships between bacterial composition and environmental factors, we also performed Canonical Correspondence Analyses (CCA) with the R package vegan.
The influence of environmental variables (temperature, salinity, oxygen O 2 , NH 4 , NO 3 , NO 2 , PO 4 , SiOH 4 , chlorophyll a and suspended particulate matter (Additional file 1: Table  S2 ), measured by the SOMLIT (Service d'Observation en Milieu Littoral, https:// www. somlit. fr/)) were tested on both skin and gill mucus bacterial communities with an ANOVA using the R package vegan. ANOVA (or Kruskal-Wallis rank sum tests when data were not normally distributed) and Tukey post hoc tests (or post hoc Conover-Iman tests) were performed to analyze the influence of seasonality on abundance and Shannon diversity index  [55]  of Lamellodiscus species in each fish species. Associations between parasitic abundance (total abundance and abundance of each Lamellodiscus species) and temperature were quantified using negative binomial generalized linear models (MASS package in R).
For inclusion in the formula groups, infants had to be exclusively formula-fed at the time of randomization. Exclusion criteria included the following: history of neonatal health problems; clinical evidence of chronic illnesses or gastrointestinal disorders; diagnosed metabolic or immune disorder; consumption of formula for special medical purposes; oral antibiotic treatment at the time of enrollment. Concomitant antibiotic treatment during the study was recorded and was not significantly different between formula groups. This clinical trial was prospectively registered in clinicaltrials. gov under the name "The Combiotic-Study (GOLFIII)" (NCT02221687) and carried out in accordance with the Declaration of Helsinki and the Good Clinical Practice standards as far as they apply to nutritional trials.
16S rRNA gene amplicon data processing The raw fastq files were processed at the level of molecular strains (ZOTUs) using the UNOISE3 method  [30]  from USEARCH11 (64-bit version)  [31]  as implemented in IMNGS  [32]  with settings of side trimming of 10 bases, amplicon size minimum >300 and maximum <500, minimum ASV (amplicon sequence variant) size ¼ 4, trim score ¼ 20, and other default settings. Denoised sequences were aligned and taxonomically classified with SINA  [33]  using SILVA release 138 as reference  [34]. The taxonomic classification was further manually refined for sequences missing family and higher classification using EzBioCloud  [35]. For analysis at the level of operational taxonomic units (molecular species; sOTUs), the denoised sequences (ZOTUs) from the previous step were clustered at 97% sequence similarity using USEARCH11.
The aligned sequences of both ZOTUs and sOTUs were used to determine phylogenetic trees with the Neighbor-Joining method in MEGAX  [36]. To identify the ZOTUs corresponding to the probiotic used for the intervention, all sequences were BLASTed against the reference sequence of L. fermentum CECT5716. ZOTU83 returned 100% sequence identity and formed a monophyletic cluster with 5 other molecular strains (Zotu404, Zotu288, Zotu672, Zotu514, and Zotu186) that all shared >98% sequence identity and were thus considered to represent the target. The contingency table summarizing the occurrence of molecular species (i. e. , counts of quality-checked and assembled sequences), their taxonomy, and associated metadata about the samples are provided in Supplemental Table  2.
Downstream sequence data analyses Processed 16S rRNA gene amplicon data was analyzed further in R using Rhea (v1. 1. 5) [37]. The first step of data analysis was normalization, either by division to minimal sampling size for cases where counts were required (e. g. , β-diversity) or relative to 100 for statistics on taxa composition. Diversity within samples (α-diversity) was measured as richness (count of sOTUs) and its variant effective richness (i. e. , count of species above 0. 25% relative abundance independent of sequencing depth)  [38]. Diversity between samples (β-diversity) was calculated using the generalized UniFrac method (α ¼ 0. 5)  [39]. Ensuing dissimilarity matrices were visualized as either multidimensional scaling (MDS) plots or hierarchical clustering trees. Decomposition of groups of microbial profiles to reveal their intrinsic organization and the calculation of their β-diversity distances to reference points were performed with DivCom  [40].
Statistical analysis Statistical analyses of microbiota data and gut milieu parameters were conducted as described in Rhea (v1. 1. 5) [37]. Differences in β-diversity were tested using permutational multivariate analysis of variance within the R package "adonis". Differences in gut milieu parameters and the relative abundance of taxa between groups were tested using the Kruskal-Wallis rank sum test, followed by pairwise comparisons using the Wilcoxon rank sum test. Differences in prevalence (number of positive samples) were evaluated using Fisher's exact test. Prior to statistical testing of microbial taxa, thresholds of prevalence and relative abundances were applied to reduce the number of tests on underpopulated and lowly abundant taxa. These thresholds were as follows: prevalence >30% within at least one group (e.
05 were considered statistically significant. The group of breastfed infants, which was not blinded and randomized, was used as the reference for assessing the effects of the formulae. Besides the aforementioned analyses performed in Rhea, the effects of intervention groups and visit time points were also analyzed using generalized linear models using the lme4 package in R. In these analyses, random slopes were not included as there were not enough time points for all individuals to support such a fit. Furthermore, random effects of study center were not considered as a number of centers had low numbers of participants (i. e. , <5), and therefore random effects by center were unlikely to be well estimated. To make postfit estimations, the R package emmeans was used to estimate marginal means or least-squares means, predicted from model fit for the subgroupings as required (i.
Development of the intestinal ecosystem over time After raw data processing, the final dataset contained ~111 million high-quality, assembled sequences (84,650 AE 20,438 per sample), representing a total of 319 sOTUs (66 AE 21 per sample, with a length of 432 AE 11 bases), which was used as a foundation for all other analyses. As observed in other cohorts  [7, 39] , phylogenetic profiles of the fecal microbiota in all infants (HM, IF, and CF) depended on age (month 4, M4; month 12, M12; and month 24, M24) (Figure  1A  and Supplemental Figure  2A ). Of note, the microbiota profiles were not affected by sample origin, i. e. , the study center (data not shown). The specific microbial features in the feces of the infants at 4 months of age were accompanied by significantly higher concentrations of acetate, secretory IgA, and calprotectin; higher fecal water content as a marker for the softness of the stool; and lower pH and butyrate concentrations.
Acknowledgements We are very grateful to our colleagues at  Biofortis  for their great support with study coordination, site care, and lab work and methods. We are also thankful to all study sites for excellent participant care, especially to the participating families, for their willingness to join the study and their compliance. We want to thank  Dr. Julia Günther  and  Dr. Kirsten Berding-Harold  ( HiPP GmbH ) for their excellent support in editing the manuscript. The authors' responsibilities were as follows; HP and CH: designed research (project conception, development of overall research plan, and study oversight). IL and EI: analyzed data, performed bioinformatics analyses, and generated data and readouts. JPKR: performed statistical analyses. IL and TC: interpreted the data. MS and TC: coordinated the project and data analysis.
Data Availability The raw amplicon data generated in the context of the present study have been submitted to the European Nucleotide Archive and are available under the project accession number PRJEB47935. Funding TC and IL received funding from HiPP GmbH & Co. Vertrieb KG, Pfaffenhofen, Germany, to perform data analysis. TC received funding from the DFG (German Research Foundation), Project 403224013 "SFB1382" and Project 460129525 "NFDI4Microbiota. " CH and MS are employees at HiPP GmbH & Co. Vertrieb KG, Pfaffenhofen, Germany, Research and Development. HP received consultation fees from HiPP and supervised the study. JPKR is an employee of Staburo GmbH, Munich, Germany, subcontracted by HiPP GmbH for statistical services. EI reports no conflicts of interest.
| Shark species and size assessment During treatment, the victim's wounds were photographed along with a metric scale bar. The general and specific characteristics of the bite wounds were analyzed and compared with anatomical data compiled for each species of large shark capable of inflicting such wounds on a human. 4 We used the characteristic tooth marks left by unfinished and superficial bites to assess the size of the animal. The centre of each tooth mark was used to measure the interdental distance (IDD) between two tooth tips; these measurements were averaged and we used a published total length (TL) to IDD regression algorithm to determine the size of the animal. 8.
French Polynesia Since 2005, CRIOBE has maintained a shark bite incident database for French Polynesia. Data characterizing the victim's profile, their ocean activity, and the circumstances of the incident are collected from press (written and televised) reports and the register of medical evacuations from remote islands compiled by the Central Hospital of the capital Tahiti. This information is further supplemented with onsite interviews by the first author aimed at documenting the history of shark bites at islands throughout French Polynesia, as well as by the existing literature on shark bites in this region. 9  The main wound severed the right leg at the level of the lower third of the tibia (Figure  2A, B ). Characteristic shark tooth marks on the lateral-internal portion of this circular wound (Figure  2C ) allowed an average interdental distance (IDD) of 19 mm to be estimated (Figure  2D ,D′).