dataesr/scientific-paragraphs-categorization huggingface dataset:
Data We have a collection of 40 gated cytometries, Our database is formed by cytometries obtained following the Euroflow protocols using a BD FACSCanto flow cytometer in four different international centres. The size of the cytometry datasets varies from 50,000 cells to 300,000 cells. The samples are from adult male and female individuals, from a variety of age groups and with different conditions (see Table  1 ). C = {C 1,h , C 2,h , C 3,h , C 4,h , C 5,h , C 6,h , C 7,h , C 8,h , C 9,h , C Clearly, there is biological variability, since there are different individuals with different conditions, ages and other different characteristics. Moreover, we have technical variability since we have different centres, different dates of measurement and different incubation times.
Clustering cytometries and template obtention Recall that DB is a subset of 27 cytometries taken from C. We want to compare different methods for clustering the database DB. As a ground truth we use hierarchical clusterings obtained through d learning and dlearning. For a state-of-the-art comparison, we use flowMatch, described in  [7]. Notice that flowMatch is based in a Generalized Edge Cover procedure, a generalization of bipartite matching, where the cost between partitions is given by d(C i , C j ) = 1 k i k j k i k=1 k j l=1 d KL (C i k , C j l ), (11) where d KL is as in (  4 ), or d(C i , C j ) = 1 k i k j ki k=1 kj l=1 d M ahalanobis (N (m i k , S i k ), N (m j l , S j l )), ( 12 ) where d M ahalanobis is the well-known Mahalanobis distance between multivariate normals. In Figure  5  we present hierarchical trees for the database DB that we have obtained using different clustering methods and different distances between cytometries.
Gating and classification We will use the results of optimalFlowTemplates applied to the database DB, introduced in the previous section, as entries to optimalFlowClassification in order to do gating in T S. For the cytometries in T S, we also perform an unsupervised gating given by flowMeans. Results are shown in the first and last columns of Table  3. We also want to compare with a state-of-the-art supervised procedure. In this case we will use deepCyTOF, with some bug corrections and some adaptations to our setting of the github version, implemented in Python with tensorflow 0. 12 and keras 1. 2. 2. In order to use deep-CyTOF we need cytometries with the same number and type of cell types so we use a data set T S = {C 2 , C 5 , C 7 , C 14 , C 15 , C 17 , C 18 }, where we have extracted the common groups from the original cytometries.
Again, in italics we have the cytometry which deepCyTOF uses for learning in each group. We present the best results, as measured by median F-measure  (10) , of applying optimalFlowTemplates + optimalFlowClassification to T S in Table  4. For instance, for C 27 , a 61 year old female with Chronic Lymphocytic Leukemia (see Table  1 ), we have obtained a very satisfactory median Fmeasure of 0. 9313. This value has been obtained through the use of optimalFlowTemplate and then through optimalFlowClassification. What follows is an extended explanation of how to understand the entries of Table  4. For optimalFlowTemplates we have used complete linkage to cluster the cytometries in DB (third column in Table  2 ) and pooling to obtain the templates. This information is provided in the entries Database Clustering and Template Formation in Table  4  corresponding to C 27 (in red).
Database clustering refers to the clustering method used in line 17 in Algorithm 1. Template Formation refers to the method used in line 19 in Algorithm 1. Assigned Cluster refers to the label of the cluster as given in Table  2  to which the new cytometry is assigned. Sample Clustering refers to how we obtain C u in Algorithm 5. Supervised Classification refers to the method used in line 13 in Algorithm 5. Assigned Cytometry refers to the optimal cytometry in the respective cluster that is used for learning (when applicable). From the results shown in Clustering cytometries and template obtention, particularly in Figure  5 , what we see is that optimalFlowTemplates produces trees more similar to the ground truth than the ones given by flowMatch. In particular, we see that optimalFlowTemplates gives a similar structure to the one obtained with dlearning.
Funding Research partially supported by  FEDER ,  Spanish Ministerio de Economía y Competitividad , grant  MTM2017-86061-C2-1-P  and  Junta de Castilla y León , grants  VA005P17  and  VA002G18. Research partially supported by DEEL IRT. We thank the  AI interdisciplinary institute ANITI , grant agreement number  ANR-19-PI3A-0004  under the French investing for the future  PIA3 program. The funding bodies did not play any role in the design of the methodology, creation of the algorithms, analysis and interpretation of data, or in writing the manuscript.
SOLPS-ITER is a code package for tokamak plasma boundary modelling comprising the B2. 5 plasma fluid code and the EIRENE neutral kinetic code. The form of the momentum balance equation, used in the SOLPS-ITER code before version 3. 0. 6 (and in all previous versions of SOLPS codes), was simplified due to numerical reasonsthe electric field term was replaced with the electron gradient pressure term, friction and thermal force terms were derived using the trace impurity assumption. It has now been decided to switch back to the general Braginskii version of this equation. Derivation of the new form of the parallel friction and thermal force terms has been required to allow the use of the general form of the equation and allow more accurate modelling of multicomponent plasmas. The new form is based on the theoretical description of multicomponent plasma derived by V.
The ITER divertor required intense numerical studies [3 and references therein]. These simulations have been largely performed with the SOLPS-4. 3  [4]  code package which comprises a fluid plasma code describing plasma transport in the SOL and divertor region (the B2 code in the case of SOLPS-4. 3) coupled to a Monte Carlo kinetic code (the EIRENE code  [5] ) for the neutral dynamics. The very first version of the fluid part of the code was developed by B. Braams in 1987  [6] , and consisted of a Braginskii solver for multifluid plasma dynamics parallel to the tokamak magnetic field. Since then, the fluid code has been coupled to EIRENE and has been developed notably to include solution of the current continuity equation and addition of drift terms  [7]. The most modern version of the code, SOLPS-ITER  [8]  was launched in 2015 by the ITER Organization and is being continuously updated.
It comprises the most up-to-date versions of the fluid code (B2. 5). The most recent update in 2016  [9]  includes a generalization of the parallel momentum balance equation for ions (PMBE) used in the fluid component  [10]. This generalization required a change of the treatment of friction and thermal force terms, the detailed physics description of which is the subject of the present paper. The SOLPS-ITER equations are given in toroidal geometry, with the assumption of toroidal symmetry, and do not include any variation of the variables in the toroidal (z) direction. Coordinates are denoted as follows: x -for poloidal direction, y -for radial direction, z -toroidal coordinate (absent in the equations and used only in the geometry description). The coordinate system is orthogonal and for its description in the code gathering equations metric coefficients are used.
2 1 1 0 2 2 _ , (2) ) ( 1 || || 2 2 1 0 , 0 , 0 _ ,                V V n n z z m S s n Z Z p p OLD m fr         (3) Here  , a z is the charge number of the species α and β,  This form of the friction and thermal force terms is the modified form of the classical expression for these terms in  [12] , the modification was made in order to match equation (1). In the SOLPS-ITER code, the full Braginskii form  [12] en z x T n h b h g h y g h h g h x g h t V n m                                      || || 1 1 (4) This equation is written in the form in which it is implemented in the code. Here For a full description of other variables and of the specific formulation used in the SOLPS code suite, see  [7]. Note that all ion species in eq  (2)  and in all following equations are assumed to share a common temperature T i , which is the fundamental assumption in the SOLPS-ITER physics model.
Background: Host-associated microbiomes, the microorganisms occurring inside and on host surfaces, influence evolutionary, immunological, and ecological processes. Interactions between host and microbiome affect metabolism and contribute to host adaptation to changing environments. Meta-analyses of hostassociated bacterial communities have the potential to elucidate global-scale patterns of microbial community structure and function. It is possible that host surface-associated (external) microbiomes respond more strongly to variations in environmental factors, whereas internal microbiomes are more tightly linked to host factors. Results: Here, we use the dataset from the Earth Microbiome Project and accumulate data from 50 additional studies totaling 654 host species and over 15,000 samples to examine global-scale patterns of bacterial diversity and function.
We analyze microbiomes from non-captive hosts sampled from natural habitats and find patterns with bioclimate and geophysical factors, as well as land use, host phylogeny, and trophic level/ diet. Specifically, external microbiomes are best explained by variations in mean daily temperature range and precipitation seasonality. In contrast, internal microbiomes are best explained by host factors such as phylogeny/immune complexity and trophic level/diet, plus climate. Conclusions: Internal microbiomes are predominantly associated with top-down effects, while climatic factors are stronger determinants of microbiomes on host external surfaces. Host immunity may act on microbiome diversity through top-down regulation analogous to predators in non-microbial ecosystems. Noting gaps in geographic and host sampling, this combined dataset represents a global baseline available for interrogation by future microbial ecology studies.
A large study examining skin surface microbiomes across 205 amphibian species found strong correlations with bioclimatic factors  [5] , while digestive microbiomes of mammals were influenced by diet and gut morphology, and indeed distinct from environmental microbiomes  [6]. A substantial role of biotic interactions in shaping microbial communities was also obvious from a strong bacterial-fungal antagonism revealed by global patterns in topsoil and ocean microbiomes  [7] , a pattern also revealed on hosts  [8] [9] [10] [11]. These studies are foundational for understanding large-scale microbial ecology patterns. Here, we use data of host-associated bacterial communities from the Earth Microbiome Project and 50 additional studies that meet our criteria for inclusion to produce a large dataset for analysis through a standardized pipeline (Fig.
Host selection also was found to be more important than diet or captivity status in avian guts  [34]. Some key questions for microbiome meta-analyses are featured in Table  1 , and while host-microbiome datasets are accumulating with time (Fig. 2 ), knowledge gaps are identified including gaps in regions sampled (Fig. 1b ) and host groups with unstudied microbiomes (Fig. 3 ). Importantly, the eukaryotic and viral components of the microbiome remain a research frontier. The bacterial microbiome dataset and metadata accumulated here is a public resource and may provide future eco-evolutionary, veterinary, or medical insights. As an example, we investigate the bioclimatic correlates of the abundance of an arthropod symbiont that is increasingly important for disease vector control (Wolbachia  [59, 60] ) across the global dataset we assembled.
Results After combining microbiome datasets targeting the V4 region of 16S rRNA gene obtained from Illumina platforms and using a standard analysis pipeline (Fig. 1a )  [61]  to identify sub-Operational Taxonomic Units (sOTUS), or unique sequence variants  [27] , we were able to compare data from 654 host species distributed globally. Samples ranged from 46 countries in 7 biomes and from hosts including marine sponges 490 m below the sea surface to bar-headed geese at 3955 m elevation in the Himalayas. While representing a global sampling of hosts, visual inspection of the map of global distribution of samples suggests that hosts from much of Africa and Asia are underrepresented (Fig. 1b ), and many eukaryotic clades have not been sampled, thus providing ample opportunities for future research (Fig.
A principal coordinates analysis illustrates clustering of internal microbiome by host class and depicts the higher microbial diversity found in samples of mammals, amphibians, birds, and non-avian reptiles (crocodile and iguana) as compared to insects and carnivorous plants (Fig. 6a ; Additional file 1: Tables S2, S3). External microbiomes were structured most strongly by bioclimatic factors (bioclim2 and bioclim15 explaining 60% and 7% of variation, respectively), as well as external habitat type (5%) as compared to host class (not significant; Additional file 1: Table  S2 ). Bioclim2 is a measure of mean diurnal temperature range and most significantly structured the external microbiome (Fig. 6b , Additional file 1: Figure  S3 ). Because our dataset was heavily filtered to standardize sampling among host species and locales, we verified that we retained power to detect previously described patterns.
For example, four human populations included in the dataset recapitulated previously described patterns including greater gut microbiome diversity in developing countries (  [64] ; Additional file 1: Figure  S4 ). Table  1  Outstanding questions in host-microbiome research. Host-microbiome research is an emerging field. Knowledge gaps include the eukaryotic and viral components of the microbiome  [35] [36] [37] , novel bacterial clades and uncultured microbes  [38] [39] [40] , and large gaps in the geography and host taxa sampled for microbiome studies. Most studies to date have focused on human or other mammalian gut microbiomes, agricultural plants, and fish studies focused on aquaculture, leaving other vertebrate and invertebrate hosts underrepresented. Wild samples are needed to overcome alterations due to captivity  [41, 42].
The dataset we assembled provides a broad overview of factors driving host microbiome structure, function, and diversity (Figs. 4, 5, and 6). It also provides a resource for comparative microbial ecology. As an example of its utility, we scrutinized the dataset for global trends in the distribution of Wolbachia, a genus of common Gram-negative bacteria known to be reproductive parasites and serving as mosquito disease vector control agents; our data confirm these bacteria being mainly present in insects (Table  3 ) and find them most abundant in cool environments of the globe (Fig. 10 ). At a global scale, we thus describe novel patterns and lay the groundwork for future mechanistic studies on hostmicrobiome interactions.
Host immune complexity, though strongly correlated with host phylogeny, appears to influence both internal and external microbiomes in terms of diversity and community structure (Figs. 8  and 9 ). Indeed, internal microbiome phylogenetic diversity had a weak direct path from host phylogeny, but a strong Fig. 3  Phylogenetic tree of selected eukaryotic hosts at the class level. Numbers adjacent to black circles indicate the number of species included in our dataset from that class. Groups' missing microbiome data are evident; however, only studies focusing on the V4 region of the rRNA gene were included. Tree was retrieved from TimeTree (http://www. timetree. org), which aggregates taxonomic and phylogenetic information from published literature. Interact with this tree at IToL: https://itol.
Nottingham et al. [69]  found that both plant diversity and soil microbiome diversity follow temperature (and elevation) gradients at a regional scale with more species under warmer conditions. At a global scale, the external plant microbiome richness was also negatively correlated with elevation (Pearson's correlation, N = 85, r = -0. 324, P = 0. 002), but mean annual temperature was not significantly correlated with these plant microbiomes (r = -0. 174, P = 0. 111). Path analysis of our external microbiome dataset containing 33 plant host species did not show a significant effect of temperature on microbiome richness, but rather greater diversity with lower NDVI (Fig. 5 ). While host factors did not play a significant role in external microbiomes at the global scale, they can be important divers of host microbiomes locally (e.
Our analysis also revealed several correlations that at first glance were counterintuitive, such as microbiome diversity increasing with host immune system complexity, that suggest previously unrecognized top-down regulating effects. The analysis of Wolbachia occurrence and diversity across hosts and environments exemplifies the data mining potential of the metadata set assembled for this study. We anticipate that the identified patterns will be instrumental in deriving testable hypotheses and therefore have great potential to stimulate exciting experimental tests to elucidate the underlying mechanisms.
Sequence acquisition To investigate global patterns of microbiome diversity and structure across host systems, we performed standardized bioinformatics analysis on combined datasets and procured environmental and host-associated metadata. We focused on studies with targeted 16S rRNA gene sequence data obtained from an Illumina platform. Insecta had a substantially higher number of average Wolbachia reads than any other class. Most other Wolbachia-positive samples were rare and found in organisms where insects are a substantial portion of the diet (e. g. , amphibia, bats, carnivorous plants), and as some of the host samples were derived from the gut, this is to be expected. Interestingly, Demospongiae were positive for Wolbachia, which may indicate that there were marine arthropods living within the sponges that were Wolbachia-positive b Insecta showed the highest number of unique Wolbachia sOTUs present within each class.
Amphibia was high in the number of unique sOTUs, and many of these amphibian samples were taken from the gut c Calculating the relative percentage of sOTUs found within each class that are also found within class Insecta indicated that the majority of sOTUs attributed to other host samples can be found in Insecta (likely as part of the diet of the host), and the remaining sOTUs are likely insects that are not present as directly collected samples in the dataset that were also prey items to their hosts. A heatmap showing mean abundance of each Wolbachia sOTU is presented in Additional file 1: Figure S8 A literature search was conducted to identify relevant studies with data accessible from public databases. The majority of sample sequences were downloaded from the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI), the European Nucleotide Archive (ENA), MG-RAST, and Qiita repositories.
The combined dataset is made available here (Additional file 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12), and accession numbers and DOIs for all published studies are indicated in the mapping (metadata) files described in Additional file 1. Datasets were selected that followed the Earth Microbiome Project (EMP) protocols and standards (http:// www. earthmicrobiome. org/protocols-and-standards/). Studies were first screened to ensure certain criteria were met, including (1) collected from a "wild setting," where hosts were not exposed to any experimental treatments; (2) ethanol preserved or frozen until DNA extraction; (3) targeted the V4 region of the 16S rRNA bacterial gene region, using primers 515f and 806r  [85] ; and (4) sequencing on Illumina MiSeq or Illumina HiSeq platforms. Once a study passed our inclusion filters, we downloaded fastq files and obtained metadata for each study.
The compiled dataset is comprised of 15,790 samples from 51 studies including all host-associated data from the Earth Microbiome Project  [4]  and includes 16 host classes (Fig. 3 ) from plants to corals to vertebrates (including 4 human populations; Additional file 1: Figure  S4 ,  [9, [50] [51] [52] [53] [54] [55] [56] [57] [58] ).
Sequence preparation Sequence data were quality filtered using Quantitative Insights into Microbial Ecology (QIIME) defaults and classified into sub-Operational Taxonomic Units (sOTUS, or unique sequence variants) using the Deblur workflow  [27]. Within Deblur, reads were trimmed to 90 bp to correspond with the shortest read length in the combined dataset. Taxonomy was assigned using the RDP Classifier with GreenGenes 13-8 as reference through a custom bash script  [122, 123]. Then, sOTUs with fewer than 90 reads, taxonomically assigned as "mitochondria" and "chloroplast," or not identifiable at the Kingdom level were removed. A phylogenetic tree was built with fasttree2  [124]. Samples were rarefied at 1000 sequences per sample to retain most samples, normalize read counts across samples, and reduce computational demands.
General analysis framework and datasets We employed multiple statistical approaches, including general linearized models, permutational multivariate analysis of variance, and path models, to explore the main drivers of host-associated microbial diversity, community structure (i. e. , beta diversity), and PICRUStpredicted functionality. Tests also targeted hypotheses surrounding how immune function may shape diversity and composition of host microbiomes and how the distribution of Wolbachia varies across bioclimates. For our analyses, the data were divided into 3 main subsets: (i) an internal microbiome dataset comprised of 741 samples derived from internal host habitats or whole-body samples, (ii) an external microbiome dataset comprised of 1193 samples derived from external host surfaces, and (iii) a marine dataset comprised of 266 external surface samples from marine organisms.
Sample metadata and predictor variables For each sample, we tabulated a comprehensive set of abiotic and biotic predictor variables for testing targeted hypotheses and inclusion in model-based analyses of our datasets. Associated metadata included in the mapping file was gathered by downloading the metadata for each study, taken directly from the paper corresponding to the study, or obtained from publicly available databases. Table  2  provides a description of each metadata field. Elevation data was extracted from Google Earth using latitude/longitude coordinates. Current bioclimatic variables representing temperature and precipitation (1 km resolution) were extracted for each unique geographic location from the WorldClim2 database. These 19 bioclimatic variables were extrapolated from a global network of stations collecting data from 1970 to 2000  [125].
Eighteen marine and geophysical variables were extracted from the MARSPEC database at the same 1 km resolution  [126]  for the marine dataset models. The variables of interest are defined in Additional file 1: Table  S1  and below. Because of expected high correlation between many Worldclim bioclimatic variables, we filtered these predictors to a least correlated subset. From the 19 bioclim variables, we selected the 8 least correlated ones (caret package in R  [127] ) based on a threshold of r < 0. 7. These least correlated variables included Mean Diurnal Temperature Range (Bio2), Isothermality (Bio3), Max Temperature of Warmest Month (Bio5), Mean Temperature of Driest Quarter (Bio9), Precipitation of Driest Month (Bio14), Precipitation Seasonality (Bio15), Precipitation of Warmest Quarter (Bio18), and Precipitation of Coldest Quarter (Bio19) and were included in model selection procedures described below.