dataesr/scientific-paragraphs-categorization huggingface dataset:
These perturbations caused by the treatment occur in a planned, scheduled fashion as patients stay in the hospital for several weeks, which enables collecting samples and clinical metadata. The patients receive many drugs including antibiotics that impact the composition and function of the gut microbiome  12, 13. The data also allow us to study how the microbiome composition feeds back on the state of its living host, and address some basic science questions such as how the microbiome influences the dynamics of the human immune system  14. We previously published the first data descriptor of our institutional microbiome dataset of HCT patients (> 10,000 samples from >1,000 patients), where we compiled patients' gut microbiota compositions based on 16S rRNA gene sequencing of fecal samples and its associated metadata  1.
Subsets of this comprehensive dataset were analyzed in a number of publications  9, [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]. Metagenomic shotgun sequencing is more expensive but has advantages compared to 16S rRNA gene sequencing  24  : it not only reveals the composition of the gut microbiome but also the functions encoded by the genes in the microbiome  25, 26. Bioinformatic tools that analyze shotgun sequencing data for different purposes-taxonomic classification of microbial composition  27  , gene abundance prediction of specialty genes such as antibiotic resistance  28, 29  and virulence factors  29  , genome identification of strain-level or species-level metagenome-assembled genomes (MAGs)  30, 31  and metabolic model reconstruction that translate the DNA sequences to biochemical reactions  [32] [33] [34]  -are now readily available.
Some of these tools even work directly with the accession numbers of the sequencing data deposited in public repositories, which greatly facilitates analysis. Here we compile 395 human fecal samples that were analyzed by metagenomic shotgun sequencing, which is a nested subset of samples we compiled previously and analyzed by 16S rRNA amplicon sequencing 1. We present examples of functional analyses, including taxonomic composition, gene functions such as virulence factors and antibiotic resistance and the assembly of genomes from metagenomic data. We first conduct a data validation where we check the data for quality by addressing specific questions: Do the compositions inferred from metagenomic and 16S sequencing data agree? How well does metagenomic sequencing capture antibiotic resistance genes? Can the metagenomic data recapitulate the genomic difference of bacterial pathogens? We display the 395 shotgun samples on a t-SNE map of the >10,000 samples of 16S amplicon sequencing 1.
We then investigate correlations between the consistency of stool samples and the read counts of shotgun samples, and we check the correlation of composition between 16S amplicon sequencing and shotgun metagenomes. We then validate the ability to detect antibiotic resistance genes using an orthogonal detection of the vanA gene for vancomycin resistance using a PCR test. We used the available tools from PATRIC, a publicly accessible database and tool repository for bacterial genome analysis, to do compositional analysis (kranken2), virulence gene (VFDB) and antibiotic resistant gene (CARD) identification. We assembled metagenomically assembled genomes (MAGs) from shotgun reads and compared them with genomes sequenced from isolates of Enterococcus faecium obtained from the same samples  35. We provide Matlab code to compile the output of these metagenomic analysis tools in a Github repository https://github.
Methods Library preparation, shotgun sequencing and human genome decontamination. We compiled 395 of the >10,000 stool samples acquired from allo-HCT patients 1 , extracted the genomic DNA and sequenced on the Illumina HiSeq platform as described previously  14, 16. We removed normal optical duplicates in paired FASTQ files using the clumpify. sh tool from the BBMap package (BBMap -Bushnell B. -https://www. sourceforge. net/projects/bbmap/), producing a pair of read files without duplicates. Using the bbduk. sh script in the BBMap package, we trimmed the right and left side of a read in a pair to Q10 using the Phred algorithm. A pair of reads was dropped if any one of them had a length shorter than 51 nucleotides after trimming. We trimmed 3'-end adapters using a kmer of length 31, and a shorter kmer of 9 at the other end of the read.
One mismatch was allowed in this process, and we allowed adapter trimming based on pair overlap detection (which does not require known adapter sequences) using the 'tbo' parameter. We used the 'tpe' parameter to trim the pair of reads to the same length. We removed human contamination using Kneaddata employing BMTagger. The BMTagger database was built with human genome assembly GRCh38. The paired end read files were uploaded into the Short Read Archive (SRA) of the National Center for Biotechnology Information (NCBI). Taxonomy classification and specific gene mapping for metagenomic reads. We used the services provided by the Pathosystems Resource Integration Center (PATRIC)  2. PATRIC can take input as the SRA accession number of each sample and output the microbiome composition in taxa, as well as genes encoding virulence factors and antibiotic resistances.
It uses the algorithm Kraken 2  27  for taxonomic classification, and the algorithm KMA  36  to align the metagenomic reads to non-redundant databases. The virulence factor composition analysis is based on the Virulence Factor Database 29 and the antibiotic resistance composition is based on the Comprehensive Antibiotic Resistance Database (CARD)  28. The taxonomy, virulence factor and antibiotic resistance table for each of the 395 samples are provided as text tables. Genome assembly. We adapted a recently published pipeline to assemble the genomes of bacteria from shotgun sequenced samples  37. Briefly, the pipeline first assembled contigs using metaSPAdes  38. Then, it binned the contigs into MAGsusing three different methods: Metabat2 30 CONCOCT 31 and Maxbin2 39. The results were then aggregated using DASTool which implements a dereplication, aggregation and scoring strategy  40  to produce the strain-level genomes.
Data Records The shotgun sequenced samples were deposited in the NCBI/SRA as paired-end fastq files decontaminated of human reads. We updated the data table tblASVsamples. csv in Figshare (https://doi. org/10. 6084/m9. figshare. 12016983. v8) that we had previously published as part of our microbiota compilation 1 : We added a new column to the table, 'AccessionShotgun', which lists the SRA accession record for each of the 395 samples presented here. All other samples were left with an empty entry in column 'AccessionShotgun'. The table can be updated in the future as new shotgun sequences become available. We compiled the additional tables for each sample as comma-separated value (csv) files in Figshare (https://figshare. com/account/home#/projects/120102) as following: • ReadCounts. csv: list the 395 samples used this study for shotgun ○ SampleID: Name of samples ○ Readcount: Number of reads for each sample after decontamination of human reads.
• Abundance: A Kraken 2 report provides information of the bacterial taxa in each sample. ○ Kindom, Phylum, Class, Order, Family, Genus: Each column contains name of taxonomic classification of each sample ○ ColorOrder: Numeric data representing the order that each taxa was plotted in our manuscript. ○ HexColor: Color code in hex format for each taxa ○ 395 columns using sample names as column names: Numeric data as the relative abundance of each sample. Every column should sum to 1. ■ NOTE: The (U)nclassified reads from kraken2 are not included in the calculation. ■ The sample names of those columns are modified to be compatible with the format requirement in matlab. To convert the names back to match the SampleID in the rest of the files, 's' at the beginning of each column name should be removed, and underscore ('_') needs to be converted to period (eg.
Technical Validation The nested subset of shotgun-sequenced samples explores various microbiome states experienced by patients. Out of the >10,000 samples with 16S rRNA gene sequencing 1 , a total of 395 samples were sequenced using metagenomic shotgun technique for the purposes of different projects. Using a t-SNE map generated from Bray-Curtris dissimilarity matrix of 16S rRNA gene sequencing (Fig. 1a ), we highlighted the samples with shotgun sequencing data available, which are distributed across the map (Fig. 1b ). The nested subset captures a wide range of microbiome states, representing many states found in the original dataset of >10,000 samples. For example, both Enterococcus-dominated "dysbiotic" states (dark green portion of t-SNE projection in  The stool consistency from these patients varies widely.
g. Enterobacteriales was renamed to Enterobacterales), sharing the same sequencing of tested 16S region (Escherichia and Shigella are the same in 16S gene sequencing), and poorly studied taxa (e. g. 'CAG-352' in 16S sequencing). The second is the detection variation. The taxa missing entirely either in 16S or in shotgun overall have very low median abundance even detected in the other pipeline -only 'Incertae Sedis' and 'CAG-352' found in 16S are between 1 to 4 percent, while all other missing taxa show less than 1% median relative abundance in the other pipeline where they are found. The third reason could be differences between the databases used for taxonomy detection. To systematically compare 16S and shotgun sequencing, we calculated correlation of relative abundance between the two pipelines in taxonomic classification.
The agreement between 16S and metagenomic shotgun were generally high and decreased for lower taxonomic ranks (Fig. 3 ), indicating that the two approaches have different sensitivities for taxonomy. The few samples with low correlation tended to be those sequenced at a lower read depth, but some samples sequenced deeply could also have low correlation (Fig. 3a-e ), indicating that other factors than read counts may affect the taxonomic mapping of metagenomes. One possible explanation is that discrepancies between the database of bioinformatic pipelines may become especially visible for highly diverse samples, leading to low correlations. We therefore calculated the alpha-diversity as determined by the Shannon index for each taxonomic classification. Then we divided the values into three groups: high diversity (top 33%), middle diversity and low diversity (bottom 33%).
Acknowledgements, Author Contributions & Competing Interests Acknowledgements. This work was supported by the  National Institutes of Health (NIH)  grants  U01 AI124275  to EP and JBX and  R01 AI137269  to JBX and YT. This work could not have been possible without our longtime collaborator Eric Littman. Eric passed away in June 2021 when we were starting to prepare this manuscript and he is not on the author list only because of the need to have all authors read and agree on the final version. We acknowledge Eric's invaluable contribution to this dataset and his analyses of shotgun sequencing data that inspired some of the examples presented here.
Code Availability The customized analysis code (Matlab 2020a) used for the examples provided below is available in the GitHub repository (https://github. com/joaobxavier/shotgun_scientific_data). with each part in a separate directory: • Figure  1 : Figuer1/scFigure1. m • Figure 2: Figuer2/scFigure2. m • Figure 3: Figuer3/scFigure3. m • Figure 4: Figuer4/scFigure4. m • Figures 5: Figuer5/scFigure5. m.
csv: Taxa absent in shotgun sequencing but present in 16S gene sequencing in our 395 samples ○ TaxaName: Name of missing taxa ○ Taxa: Classification level of taxa names (eg. Genus) ○ frequencyPresentIn16S: Counts of non-zero abundance found in 16S (non-zero) / total number of samples ○ PercentageInShotgun: metagenomic sequencing in our 395 samples ○ medianRelAbdIn16S: Median of relative frequency in 16S sequencing of that taxa ○ Template: Hit of resistant genes in CARD ○ Accession: NCBI accession number of the template ○ Genome: Strain names where the template gene is found ○ Species: The species of the strain ○ resistGene: Gene name if available. ○ resistMechanism: Mechanism of resistance interpreted by CARD ○ Zoliflodacin-unknown (multiple columns): Antibiotics whether the gene.
The results showed that the thermal performance intensified with increasing the NF flow rate, ', and inlet temperature, as shown in Fig. 17 , achieving the maximum heat transfer enhancement of 46% with 2. 5 vol. % SiO 2 /water NF. Hussein et al. [142]  examined the thermal performance of the automotive cooling system using SiO 2 / and TiO 2 /water at ' of 1 -2 vol. % experimentally. In addition, a statistical model for cooling components based on the input and output parameters was  Ray and Das studied the cooling of cross-flow, mixed (air)/ unmixed using Al 2 O 3 , CuO, and SiO 2 in ethylene glycol/water (60EG/ 40 W wt. /wt. ) mixture computationally  [144]. The authors coded the effectiveness-number of transfer unit method in MATLAB with variable ' of 1 À 6 vol. %. The results showed superior cooling performance was obtained at 1 vol.
time. When the pole piece ring is fixed, relation (5) yields the load angle φ. To evaluate this angle, the two angular position sensors are used and their data processed. To measure the total flux in a pole piece, an electromotive force sensing coil was placed around two pole pieces (shown in Fig. 6 ). Total flux can be determined by integrating the electromotive force. The relation between average flux density axial component in a pole piece B (q) av and axial flux in a pole piece ϕ (q) HPN is given in Eq. (  6 ).
3D finite element model As opposed to modeling the concentric magnetic gear structure  [3] , it is impossible to carry out the computation using the 2D finite element method  [18]. Since induction features an axial component in pole pieces for the transverse flux magnetic gear, a 3D magnetostatic computation is necessary. These experimental results will therefore be compared with the magnetostatic results from the 3D finite element model (3D FEM). The software enabling this study is COMSOL 5. 2a  [19]. The 3D FEM mesh begins with 5000 triangles discretizing the sides of   and c ) the mesh at the other extremity, i. e. on the HPN ring side. Fig. 8. Induction distribution computed from the 3D finite element method using COMSOL 5. 2a. Fig. 9. Average torque evolution vs. load angle obtained from the 3D FEM computation and experimentation on the prototype presented in Table  1 , at 50 rpm for the low speed rotor.
Data collection and extraction For the purposes of quantifying the costs associated with IAS in the Mediterranean basin, we combined information from databases linked to the InvaCost project, the first global effort to systematically compile and synthesise the monetary costs of invasive species  (Diagne et al. 2020a ) (Fig. 1 ). InvaCost is a living database, meant to be updated on an ongoing basis by authors and future users  (Diagne et al. 2020a ). We used the cost entries available at the time of writing  (November, 2020; 4,793 entries, Ballesteros-Mejia et al. 2020; Diagne et al. 2020b) , which were the result of both systematic and targeted searches, conducted through standardised English-language search strings in Web of Science, Google Scholar and Google. Targeted searches allowed opportunistic addition of supplementary cost entries, in both English and French.
These searches were conducted in a number of different ways which span from examining the content of relevant web pages to contacting national and international experts for obtaining published or unpublished documents. Further methodological details regarding the search strategies, search terms used, material included, the screening process and the inclusion criteria, can be found in  Diagne et al. (2020a). These data were further complemented with 5,212 cost entries extracted from literature in 15 languages other than English  (Angulo et al. 2020 (Angulo et al. , 2021)). These cost estimates were collated through a) a standardised literature search that used the InvaCost protocol described in  Diagne et al. (2020a)  and b) a more targeted opportunistic search through national databases, web pages of national institutions, NGOs and other organisations, as well as through contacts with regional national experts  (Angulo et al.
2021). We filtered the cost entries compiled (n = 10,005) to select only costs of IAS in the 26 countries having a coastline on the Mediterranean Sea (or countries within these countries, i. e. Andorra, San Marino, Vatican City), or costs in the Mediterranean Sea. Costs of IAS explicitly occurring in overseas territories of these countries (e. g. French Guiana) were excluded from our analyses. Prior to analyses, all cost entries in our database were expanded so that each entry was annualised (i. e. corresponding to a single year), given that original cost estimates may have corresponded to either a cost realised over a single year, a period of less than a year, or a cost reoccurring over a series of years. For the purpose of expanding these original cost entries, we used the expandYearlyCosts function of the 'invacost' R package  (Leroy et al.
2020) , based on the difference between the probable starting and ending years of each cost entry presented in the database. Note that this process removed any cost entries (including one for Israel, Morocco and Tunisia) that occurred over an unspecified time period following the procedure described in  Diagne et al. (2020a). Our analysis is therefore based on the 4,786 "expanded" cost entries resulting from this process and occurring up until 2017 (the last complete year included in all systematic searches). These mostly originated from the following 15 Mediterranean countries: Albania, Bosnia and Herzegovina, Croatia, Cyprus, Egypt, France, Greece, Israel, Italy, Libya, Malta, Montenegro, Slovenia, Spain and Turkey. All cost estimates were standardised to 2017 equivalent US dollars (US$) using the market exchange rate (World Bank), and accounting for inflation (Consumer Price Index of the year the cost was estimated for in each study)  (Diagne et al.
To assess temporal trends of invasion costs in the Mediterranean over time, we considered 5-year means since 1990 (the first year with invasion costs in our database). We examined costs as a function of the "Impact year", which reflects the time at which the invasion cost likely occurred based on probable starting and ending years  (Leroy et al. 2020). This allowed for an estimation of annual average costs over the entire reported period. In addition to the data included in our cost database, we collected complementary elements from the Centre for Agriculture and Bioscience International (CABI 2020) to obtain information on the geographic origin of each invasive species causing observed damage costs in the studied area, including their presence in each country, pathways of introduction, impacts and uses (if any).
To improve our analysis and interpretation of invasion costs, we also extracted information on several country indicators from the World Bank (2020) (Suppl. material 2: Table  S2 ) to further assess whether costs in each country could be correlated to key socioeconomic variables. To that aim, the ggcorr function of the 'GGally' package in R 4. 0. 0 was used. We found significant correlations between some of these indicators (Suppl. material 2: Fig. S1 ). However, since we aimed to study the relation of each indicator with the observed costs independently, we estimated Spearman rank correlations between each extracted indicator and country-level expenditures and damage costs using the 'ggpubr' package in R 4. 0. 0.
Network analysis of costs Spatial and taxonomic aspects of Mediterranean invasion costs were concurrently examined using a bipartite network of two types of nodes: (1) countries and (2) taxonomic groups (excluding studies reporting costs on diverse taxonomic groups, or in other words costs for species belonging to different taxonomic groups that were reported together). For taxa, broad groupings were created from combinations of habitat and animal taxonomic group (e. g. "terrestrial mammal", "aquatic arthropod") or plant guild e. g. ("terrestrial forb" or "aquatic floating") to facilitate broad-scale taxonomic linking among countries. The taxonomic groupings used can be found in Suppl. material 1: Mediterranean database. In brief, links were produced among nodes where a group had a cost in a given country, and the link thicknesses and node sizes were attributed to respective cost totals.
As such, the size of the nodes, and thickness of the links, correspond to the magnitude of cumulative economic costs incurred for the 1990-2017 period. The network was illustrated in Gephi 0. 9. 2 using the ForceaAtlas2 algorithm  (Bastian et al. 2009 ). We applied the Map Equation community-detection algorithm (version 0. 19. 12, www. mapequation. org; Rosvall and Bergstrom 2008, Rosvall et al. 2009 ) to examine clusters of countries which exhibited similar combinations of invasion costs. Clusters within this network reflect groups of nodes sharing costs (e. g. , an invasive group that impacted multiple countries, or multiple groups that impacted altogether one to several countries). The network analysis was performed using the 'biogeonetworks' R package  (Leroy et al. 2019; Leroy 2020) , and based on the Map Equation algorithm optimised for a two-level partition of the network with 1,000 trials.
Overview of invasion costs Between 1990 and 2017, the total cost of IAS in the Mediterranean basin was estimated at $27. 31 billion (in 2017 US$ values). The majority of the costs for the Mediterranean in our database were published after the mid-2000s (orange line, Fig. 2 ). The number of costs occurring per year exhibited a general increase over time, especially after 2006 (red line, Fig. 2 ) The vast majority (87%) of total costs for the region were derived from expectations or predictions (Potential, $23. 73 billion), rather than empirical observations (Observed, $3. 59 billion). However, these potential costs correspond to a relatively small number of database entries (n = 279) with the majority of entries corresponding to empirical observations (n = 4,507, Fig. 3 ). Additionally, close to 98% of the cost entries for the Mediterranean basin (n = 4,672), corresponding to $25.
Spatial distribution of costs Between 1990 and 2017, the majority of Mediterranean invasion costs were recorded in the western part of Europe: Spain ($12. 47 billion, n = 3,367), France ($10. 85 billion, n = 1,237) and Italy ($680. 76 million, n = 107). Costs were also high in Libya ($593. 04 billion; n = 8). The sum of costs in the remaining 11 countries for which data were available (i. e. Albania, Bosnia and Herzegovina, Croatia, Cyprus, Egypt, Greece, Israel, Malta, Montenegro, Slovenia and Turkey) were found to be relatively low, corroborating low numbers of cost entries (Fig. 4 ).
Documents per year Cost entries per year Note the different scales for the two vertical axes. All data shown here reflect costs occurring in 2017 or earlier, as used in our analysis (note that some of these costs were published after 2017). The numbers inside or adjacent to each balloon correspond to the sample size (also indicated by shading). When "Observed" costs were considered, France ($780. 71 billion, n = 1,036), Italy ($502. 9 million, n = 94), and Libya ($339. 77 million, n = 4) were the top three countries, with Turkey ($325. 84 million, n = 11) ranking fourth and Spain ($234. 48 million, n = 3,320) fifth. Our dataset contained no costs for the following 11 countries: Algeria, Andorra, Gibraltar, Lebanon, Monaco, Morocco, Palestine, San Marino, Syria, Tunisia and Vatican City.