dataesr/scientific-paragraphs-categorization huggingface dataset:
The MAG taxonomy was further refined by placing all 16S rRNA genes recovered from the bins into the phylogenetic framework of the current SILVA reference database, which allowed classifying most of the MAGs down to genus level (Table  S2 ). When we compared the taxa represented by the MAGs to the distribution of the corresponding taxa in amplicon libraries of the bacterial gut microbiota of a representative selection of higher termites that were classified using the same framework  (Lampert, Mikaelyan & Brune, 2019) , we found a high level of congruence between the datasets. The MAGs represented 15 of the 19 bacterial phyla in the amplicon libraries that comprised >0. 1% of all reads, including all core phyla (represented in >80% of all host species) with the exception of Verrucomicrobia (Fig.
3 ). A high representation in the amplicon libraries of the taxa represented by MAGs was confirmed at all taxonomic ranks down to the genus level (Table  S4 ), underscoring that the present dataset covers the majority of lineages that colonize the higher-termite gut. We computed the relative abundance of each MAG. These abundances ranged from 0. 005% to 4. 03% (Table  S2 ), with a mean value of 0. 19%. These values indicated that the present dataset includes major taxonomic groups of the termite gut microbiota, which was confirmed when we looked at the taxonomic distribution of the MAGs. Considering the MAG relative abundance and not only their presence within samples, we could observe an effect of the host diet on the taxonomic distribution (Fig. 4 ). Indeed, similarities were observed when we compared taxonomic patterns of the MAG relative abundance with previously published 16S rRNA gene amplicon-based surveys (Abdul  Rahman et al.
Archaea The archaeal domain was represented by members of the phyla Euryarchaeota and Bathyarchaeota (Fig. 5 ; Fig. S3 ). Euryarchaeota were represented by 23 MAGs that were classified as members of the genera Methanobrevibacter (family Methanobacteriaceae; three MAGs) and, Methanimicrococcus (family Methanosarcinaceae; three MAGs), and members of the family Methanomethylophilaceae (16 MAGs), nine of them in the genus Candidatus Methanoplasma. MAGs assigned as Euryarchaeota encompassed three (Methanobacteriales, Methanosarcinales, and Methanomassiliicoccales) of the four orders of methanogens found in termite guts  (Brune, 2019) ; Methanomicrobiales were absent from the present dataset. This genomic resource will be extremely valuable for a better understanding of the genomic basis of methanogenesis in the termite gut and more generally for investigating the functional role of archaea in arthropod guts.
Firmicutes Firmicutes was by far the phylum with the highest number of MAGs, but also the phylum with the highest average relative abundance (33. 5%) in 16S rRNA gene-based surveys (Fig. 3 ). The 237 MAGs (40% of the total dataset) represented three classes (Bacilli, Clostridia, and Erysipelotrichia) and ten families, including four members of Streptococcaceae (Bacilli) and three members of Turicibacteraceae (Erysipelotrichia). S4 ). Interestingly, among the Defluviitaleaceae, the genomes were mainly recovered from the P1 compartment (53 MAGs, i. e. , 79% of the family members) whereas Ruminococcaceae were predominantly recovered from the P3 compartment (59 MAGs, i. e. , 62% of the family members). Further studies should investigate the potential metabolic specialization of these two families in relation to the gut physicochemical properties.
, 2015a) , one might hypothesize the existence of different arsenals of lignocellulolytic enzymes, potentially reflecting the host diet specificity (balance between cellulose, lignin, and hemicelluloses). More generally, Firmicutes and especially Ruminococcaceae are also abundant and metabolically important in rumen systems  (Svartström et al. , 2017; Söllinger et al. , 2018; Stewart et al. , 2018). At a broader scale, our dataset will allow comparative studies between intestinal tract microbiota of ruminants and phytophagous or xylophagous invertebrates, which would allow a better understanding of plant polysaccharide degradation across the tree of life.
In the latter order, 54 MAGs recovered from the P1, P3, and P4 compartments of wood-, litter-, humus-, and soil-feeding hosts were assigned to the termite-specific cluster Treponema I  (Ohkuma, Iida & Kudo, 1999; Lilburn, Schmidt & Breznak, 1999)  and represent the first genomes of this cluster from higher termites. Indeed, to date only two Treponema I genomes are available, and both were recovered from isolates, namely Treponema azotonutricium and Treponema primitia, from the hindgut of the lower termite Zootermopsis angusticollis  (Graber, Leadbetter & Breznak, 2004). Thus, our dataset significantly expands the genomic resources for this taxonomic group. Subclusters of this clade have been identified on a dedicated genome tree (Fig. 6 ). The genome tree topology is in agreement with a previous phylogenomic Spirochaetes study  (Gupta, Mahmood & Adeolu, 2013).
Regarding Spirochaetes classification, our tree topology suggests that the genus Treponema could be elevated at least to the family rank due to the presence of distinct Treponema clusters (Fig. 6 ). This observation is also in agreement with the recent Genome Taxonomy Database, which now proposes a Treponemataceae family and a Treponematales order  (Parks et al. , 2018; Chaumeil et al. , 2019).
S8 ). This family, among others, is composed of various sulfate-reducing bacteria; this functional group has already been identified in different termite species  (Kuhnigk et al. , 1996). Thus, these MAGs could provide new genomic resources to further investigate this metabolism in the termite gut. Our dataset comprises 33 MAGs of Bacteroidetes (Fig. S9 ), including members of the families Cluster V (four MAGs), RC9 gut group (six MAGs), Paludibacteraceae (two MAGs, both assigned to the Paludibacter genus), Rikenellaceae (nine MAGs), Marinilabiliaceae (one MAG), and Prolixibacteraceae (one MAG). These Bacteroidetes were found in the P1, P3, and P4 compartments and in wood-, litter-, humus-, and soil-feeding termites. In Blattodea guts, different clusters of Alistipes (Bacteroidetes) have been found in a 16S rRNA gene survey  (Mikaelyan et al.
S10 ). Their function in termite gut remains unclear, but Chloroflexi, including Dehalococcoidia, were found to be enriched in lignin-amended tropical forest soil  (DeAngelis et al. , 2011) , where oxygen concentration and redox potential are highly variable, as in the termite gut  (Brune, 2014). Therefore, their ability to use oxygen as final electron acceptor and their potential involvement in lignin degradation could be investigated by comparative genomics. Minor phyla were also present in our dataset. Two MAGs assigned as Cloacimonetes (Fig. S14 ) and five MAGs assigned as Kiritimatiellaeota were recovered from the P3 compartment of the two humus-feeding termites N. taracua and T. hospes (Fig. S15 ). Kiritimatiellaeota have been reported to be present in the digestive tract of various animals  (Spring et al.
Recovery of metagenome-assembled genomes (MAGs) from the 30 termite gut metagenomes analyzed in this study. The host termite, its mitochondrial genome accession number, dietary preference, and the originating gut compartments are indicated. C crop (foregut), M midgut, P1-P5 proctodeal compartments (hindgut). The sample codes used for the metagenomes are the combination of host ID and gut compartment.
Funding This study was funded by the  Deutsche Forschungsgemeinschaft  in the collaborative research center SFB  987  ( Microbial Diversity in Environmental Signal Response ) and by the  Max-Planck-Gesellschaft. The work conducted by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. Petr Stiblik  and  Jan Šobotník  were supported by grant " EVA4. 0 ,"No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000803  financed by  OP RDE. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In particular, we processed time series of at least 10 000 spectra acquired in continuous (containing at least 1000 amplified emission spectra) from the two samples of cells, i. e. , C6 glioma and Astrocyte. The random nature of amplified emission spectra also causes large intensity fluctuations, even between consecutive shots. Spectra are grouped in sets based on peak intensity, and then analysed to retrieve similarities within the same set and among different cells of the same stock. In particular, given two datasets, respectively for cancerous and healthy cells, average spectra are calculated for each dataset and used to extract principal component (PC) loadings. Loadings are in turn used to calculate PC scores for each single-shot spectrum. The resulting scores of the first two PCs (i.
The light collected by the objective passes through the dichroic mirror and was partially reflected by a 50:50 beam splitter onto an Ocean Optics HR4000 Spectrometer with resolution 0. 15 nm. Spectra were recorded in continuous mode with an acquisition time of 100 ms, and successively preprocessed and analyzed using PCA statistics toolbox in Matlab. The light transmitted by the beam splitter passes through a tube lens and was imaged on a CCD (DCU224M CCD, Thorlabs). Simulations: 3D-FDTD simulations of light scattering from a cell were performed using FDTD Solutions (Lumerical Inc. ). Cells were modelled as spheres with a core-shell structure (12 µm in diameter), core of index 1. 5 and a shell with index varying between 1. 33 and 1. 47. The core diameter was fixed to one third of the cell diameter in all simulations and symmetry boundary conditions were employed to significantly reduce the computation time.
Tuning of model parameters is no guarantee of model performance in an evolving environment that has not been observed yet. Climate change may reshape North Atlantic phytoplankton populations and primary production  (Balaguru et al. , 2018)  due to increasing surface temperature, shoaling mixed layer depths, and increasing upper ocean stratification  (Edwards and Richardson, 2004). Predicting and quantifying the impact of these changes requires robust model formulations, not models tuned to present climate conditions. Code and data availability. All code and data are available at https://github. com/mara-freilich/grazing_functions_bg (DOI: 10. 5281/zenodo. 4282657) co-authors. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. The authors would like to acknowledge funding from an NDSEG fellowship and Martin fellowship to MAF.
Open Practices The data and analysis script, which can be used to reproduce the results reported below, are available at https://osf. io/ w8qtv/. Materials -including an exhaustive list of items and procedures that could be used to reproduce the methodsare available at the same OSF page, as is a pre-registration of the methods and analyses. Four changes were made to the pre-registered analysis plan. First, as noted below, we neglected to describe one of the core hypotheses of the project (and, indeed, one of the core findings of the project we are replicating) in the pre-registration document. Second, we did not gather strength ratings based on participant photographs after seeing that the quality of the photographs were not sufficient for assessing strength. Third, we did not ask participants to report their fighting ability.
Limitations and future directions Naturally, multiple limitations apply to the current findings. We discuss three notable ones. First, data were collected from a relatively affluent sample of young Dutch participants. As noted above, some of the relationships observed in the current study might not be generalizable to other populations. Second, the single-item measures of emotion based on posed facial expressions are noisy. Imprecision in this measure might attenuate effect size, and results using this type of measure might not generalize to other measures of emotion (e. g. , measurements of facial expression; verbal self-reports). Third, participants reported hypothetical responses to hypothetical moral violations. The extent to which these responses -in terms of emotion or aggression -would generalize to behaviors in more ecologically valid conditions is an open question.
Plant neighborhood characterization The plant species spatial distributions in the mesocosms changed over time due to the ongoing community dynamics. To take these dynamics into account, occurrences of plant individuals were, therefore mapped in all mesocosms after 2y and 3y of experimental community cultivation (i. e. early spring 2011 and 2012), using an 80 × 80 cm squared lattice centered on the mesocosm (Fig. 1 , called plot from here on). The use of this 80 × 80 cm lattice aimed at avoiding an edge effect within mesocosms. We recorded presence/absence data in 5 × 5 cm cells of the lattice based on aboveground coverage (i. e. 256 cells in total per lattice). A plant species was considered as present when at least one individual rooted within the cell, with each individual belonging to a single cell.
Data trimming and contingency matrix preparation Trimming, filtering, clustering, OTU identification and taxonomic assignments were performed as described elsewhere (e. g. Ben Maamar et al. 2015, Lê  Van et al. 2017). To summarize the strategy, short sequences (<200 bp), sequences with homopolymers (>8 nucleotides) or ambiguous nucleotides, sequences containing errors in the multiplex identifier or primer, were deleted from the dataset. Chimeric sequences were detected using UCHIME  (Edgar et al. , 2011)  and were deleted. After these steps, and from the two replicates, only sequences displaying 100% identity were kept (sequences are available under the accession number PRJEB22724, European Nucleotide Archive). The remaining sequences were grouped into OTUs using DNAclust  (Ghodsi et al. 2011 ) with a 97% sequence identity threshold, and a contingency matrix was built.
OTUs affiliation and clades selection A total of 3471 fungal OTUs for the 406 samples were obtained. A large proportion of these OTUs were rare (i. e. >70% of the OTUs represented by less than 25 reads). To avoid over representation of rare OTUs and limit chimeric OTUs contaminants in the dataset (e. g. Mysara et al. 2017) , OTUs occurring in less than 1% of the samples were removed. The resulting dataset contained 2057 fungal OTUs. All the statistical analyses were performed at three taxonomic levels: (i) all fungi, (ii) within the three most abundant phyla (i. e. Ascomycota, Basidiomycota, Glomeromycota), and (iii) within the most abundant class in each phylum (i. e. Sordariomycetes, Glomeromycetes, and Agaricomycetes) (i. e. seven datasets in total). Phyla and classes were thus selected according to their respective dominance in the entire assemblage and within the phyla.
Diversity indices calculation We based our analyses on indices describing fungal community richness, diversity and evenness. We calculated the relative abundances of OTUs as the proportion of reads attributed to the OTU in a sample, and then calculated the indices for different clades following a down scale approach (from the entire assemblage to the phyla and classes). We calculated the indices at the plot scale (i. e. , the five samples from each plot pooled) and at the sample scale (i. e. , based on the sample from the center of the plot only) for the seven fungal datasets (see above). For both scales (alpha and gamma diversity), we calculated the OTU richness (S) giving the number of co-occurring species, and Pielou's evenness indice (J) that informs on competitive equilibrium in the community.
The evenness was calculated on OTU relative abundances computed from the normalized number of reads per OTU. We used Pielou's evenness because strong correlations (i. e. > 90%) between Shannon diversity, Simpson diversity, and Pielou's evenness were found. The Shannon and Simpson indices were thus discarded before further analyses. No strong correlations (i. e. > 90%) were found between the richness and evenness regardless of the taxonomic level analyzed. Indices were calculated using the VEGAN package  (Oksanen et al. 2013 ) in R (R Core Team, 2015).
Statistical analyses To determine whether the fungal community structure was influenced by the past and present plant neighborhoods at the sample and plot scales, we used multiple regression analyses with plantspecies abundances as explanatory variables in linear models with the function "lm" in R (3. 3. 0). These analyses were performed on the seven fungal datasets (from the entire assemblage to the three phyla and the three classes). At the plot scale, we tested the influence of past and present plant composition on the fungal OTU richness using the total abundances of the plant species over the lattice, for each date (see above section Plant neighborhood characterization). We thus constructed two models (past and present) for each taxonomic level analyzed (i. e. 14 models in total), which were optimized using a backward stepwise selection procedure of the explanatory variables based on the Akaike's Information Criterion (AIC,  Burnham and Anderson 2002).
Acknowledgements A. K. B. , P. V. and C. M. conceived the ideas and experimental design. A. K. B. and C. M. did the experiments. N. V. did the data analyses. N. V. , A. K. B. , P. V. and C. M. did the interpretations and writing of the publication. This work was supported by a grant from the  ANR program , by a grant from the  CNRS -EC2CO program  ( MIME  project) and by the  French  ministry for research and higher education. We thank  Kevin Potard  for advice on statistical analysis. We are also grateful to  D. Warwick  for English editing, helpful comments and suggestions on a previous version of the manuscript. We also thank the  Institut National de la Recherche Agronomique  ( INRA, Montpellier, France ) for providing M. truncatula seeds. We also thank  Marine Biget  and  Sophie Michon-Coudouel  as well as the  "Human and Environmental Genomic Plateforme of Rennes" (France)  for performing molecular analyses.
Diversity Array technology sequencing (DArTseq) genotyping Genotyping by sequencing was performed for the PVA-QPM inbred lines with a high-density whole-genome profiling of DArT services  [11]. The genotyping services were provided by the Integrated Genomic Service and Support (IGSS) platform of BecA-ILRI in Kenya deploying 44,391 DArTseq codominant markers. Data generated were analysed with the DArTsoft (DArT P/L, Canberra, Australia) software as described by DArT Pty Ltd. , Australia  [40].
Agronomic data collected The number of days to 50% anthesis (DA) and silking (DS), and plant height (PLHT) were recorded. Plant aspect (PASP) scores were obtained using a scale of 1-9, where 1 denoted excellent overall appearance of plants and 9 extremely poor overall appearance of plants. Ear aspect (EASP) was also rated on a 1-9 scale, where 1 indicated well-filled ears with no insect and disease damages and 9 represented plots with ears having only one or no kernel. Anthesis-silking interval (ASI) was computed as the difference between DA and DS, while the number of ears per plant (EPP) was derived as the number of ears harvested per plot divided by the number of plants in the same plot. Stay-green characteristic (STGR) was rated under low-N at 70 days after planting (DAP), that is, soft dough development stage  [43] , on a 1-9 scale where 1 = less than 0.
Statistical analysis DArTseq markers with > 80% call rate were retained prior to statistical analysis. Thereafter, markers with > 10% missing rate were filtered out using the TASSEL software version 5. 2. 12  [44]. Minimum and maximum frequencies of 0. 05 and 0. 95 respectively, were also considered for the filtering of markers to finally retain 8171 markers for all subsequent analyses. Summary statistics including gene diversity, heterozygosity, polymorphic information content (PIC) and major allele frequency were computed with PowerMarker version 3. 25  [45]. Gene frequency, and frequency based genetic distance matrix consisting of the 70 inbred lines were estimated for the DArT-seq data using the Nei  [24]  method implemented in PowerMarker version 3. 25. Using the frequency based genetic distance estimates, the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and 1000 nonparametric bootstrapping across different loci were applied in PowerMarker to construct a dendrogram to visualize patterns of genetic dissimilarities in the panel of 70 lines.
Availability of data and materials The datasets used and/or analysed for the present study are available to authorized users at the International Institute of Tropical Agriculture (IITA) maize improvement programme data repository as follows: Phenotypic data of 70 early PVA-QPM maize hybrids evaluated under optimal conditions at Ile Ife and Mokwa, 2016 & 2017. DOI: https://doi. org/10. 25502/tenz-sh65/d Phenotypic data of 70 early PVA-QPM maize hybrids evaluated under low N conditions at Ile Ife and Mokwa, 2016 & 2017. DOI: https://doi. org/10. 25502/6r18-0206/d Genotypic data of 70 early maturing PVA-QPM maize lines for diversity study. DOI: https://doi. org/10. 25502/m5e9-yc02/d.
1. Two parallel groups of 70 patients will receive either FLYP or normal saline in addition to usual treatment. The primary endpoint is the International Normalized Ratio (INR) at hospital admission. Secondary endpoints are transfusion requirement, length of stay in the intensive care unit, survival rate at day 30, usability and safety related to FLYP use, and other biological coagulation parameters. Conclusion: With this trial, we aim to confirm the efficacy of FLYP in TIC and its safety in civilian prehospital care. The study results will contribute to optimizing guidelines for treating hemorrhagic shock in civilian settings. Trial registration: ClinicalTrials. gov, NCT02736812.
Data collection The main variables to be collected by the prehospital BLS and ALS teams are as follows: patient demographics and history; circumstances, mechanism, type, and site of the injury; clinical and biological status (SBP, diastolic blood pressure (DBP), mean blood pressure (MBP), HR, SI, respiratory rate, Glasgow Coma Scale score, body temperature, pulse oximetry, and hemoglobin and lactate levels when available); treatments and products administered (e. g. , hemostatic device use (tourniquet, QuikClot®); volume expansion via IV or intra-bone infusion; sedation; use of vasopressor, tranexamic acid, or labile blood products; airway management (i. e. , spontaneous breathing or mechanical ventilation)). The main variables collected during hospitalization will be clinical and biological data, treatments administered, and patient outcomes.
The statistician, as well as the outcome assessor, will be blinded to patient randomization and group allocation. Analyses for the phase III trial primary outcome and secondary analyses are intention-to-treat. A per-protocol analysis will be carried out considering that some patients in the FLYP arm may not have received the treatment or may have received it only partially. Additionally, a priori subgroup analysis will be processed in a subpopulation of randomized patients with a confirmed diagnosis of hemorrhagic shock. No interim analysis is planned. The quality of randomization will be assessed by ensuring that the distribution of characteristics in the two groups is balanced. Data will be summarized as medians with interquartile ranges for continuous variables and as counts with percentages for categorical variables.