dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomic data availability, sequencing motivation and composition Several genomes of Ustilaginaceae and Microbotryum are publicly available on the International Nucleotide Sequence Database Collaboration (INSDC) genome databases (e. g. , https:// www. ncbi. nlm. nih. gov/ genome/). In Ustilaginaceae, the genomes of 28 species from several, phylogenetically dispersed parasitic genera, mainly Ustilago s. l. (15 species; McTaggart et al. 2016)  and Sporisorium (five species), Anthracocystis (three species) and specimens of some other genera are publicly available  (August 2022). For the genetic model organism Ustilago maydis, 38 genome data of several strains and different mating types are available, while for most other species genome data are only available for one or two strains. Many of these are of good assembly quality with limited fragmentation and Anthracocystis panici-leucophaei, Sporisorium graminicola, S.
Haploid Microbotryum genomes contained more genes than haploid Ustilaginaceae genomes. Common orthologous groups contained a similar number of genes in all species, while Microbotryum species contained more taxon and species-specific genes. Orthologous groups were calculated in OrthoFinder v. 2. 5. 2  (Emms and Kelly 2019, 2015)  using the DIAMOND algorithm  (Buchfink et al. 2015). Overlap of orthologous groups between the species was calculated in R 3. 6. 1 (R Core Team 2021) and plotted using the ggplot2 package  (Wickham 2016)  genomes, as it was estimated, for example, that U. maydis genomes did not show signs of extensive gene duplication events  (Kämper et al. 2006). Alternatively, longer retention of duplicated genes in the genomes of Microbotryum species is possible. Using OrthoFinder, we inferred between 522 and 3655 species-specific duplication events in Microbotryum genomes, against only 131 to 1644 such events in Ustilaginaceae.
2021 ) and similar databases incorporate many more species in Ustilaginaceae and Microbotryum alike, as these databases provide annotation based on domain detection rather than few orthologous genes from selected species. Unfortunately, the functional description in these databases is less curated and the available information can differ between entries. To illustrate the possibilities and drawbacks of database annotation approaches under these very different circumstances, we conducted an eggNOG mapper analysis (Huerta-Cepas et al. 2017) on predicted genes of selected high-quality genomes (Table  1 ). The eggNOG mapper uses multiple similarity-based methods to assign a seed orthologue from the database to the input gene. A smaller proportion of Microbotryum genes (43. 3-65. 5%) than Ustilaginaceae genes (90.
65-7. 42%) and even animals (0. 41-2. 54%), plants (0. 63-7. 88%), or bacteria (0. 29-0. 5%). This occurred very rarely in annotations for Ustilaginaceae (Agaricomycotina 0-0. 05%, animals 0-0. 1% and bacteria 0-0. 11%). Most likely, this is i) attributed to a better representation of Ustilaginaceae in the eggNOG database (represented by U. maydis covering 8520 orthogroups; Pucciniomycotina represented by Puccinia triticina and P. graminis covering 5293 orthogroups, accessed August 2022) and ii) an indication of different genome composition of Microbotryum compared to other Pucciniomycotina, but also Ustilaginomycotina. There were no notable differences in the annotation coverage of the seed orthologues for both groups. The COG database  (Galperin et al. 2015 (Galperin et al. , 2019) )  showed the highest annotation success for seed orthologues (up to 95%, Fig.
2B ), which could be due to the broader functional groups proposed in this database or because genes can be represented in the COG database without an annotated function. The COG function of "Replication, recombination and repair" (Wilcoxon p = 0. 01, 6. 2-21. 8% Microbotryum, 3. 0-12. 3% Ustilaginaceae) and "Cell cycle control, cell division, chromosome partitioning" (Wilcoxon p = 0. 001, 2. 71-5. 36% Microbotryum, 2. 05-2. 35% Ustilaginaceae) were overrepresented in Microbotryum seed orthologues. While many COG categories showed statistically significant differences between the groups, these differences were small and could be influenced by the lack of seed orthologues found for Microbotryum. The number of seed orthologues without a COG category as well as "Function unknown" assigned was, however, higher in Ustilaginaceae.
This likely attributes for U. maydis genes being represented in the database independently of an assigned function. For other taxa, genes that remain functionally unassigned might not be represented Fig. 2  Annotation of predicted genes in the eggNOG database. A Percentage of seed orthologues used from different taxonomic groups used for the functional annotation of the predicted genes. Predicted genes from Microbotruym were mostly annotated using seed orthologues from Pucciniomycotina, but more than 25% of the annotated genes were annotated with the help of data from other taxa. In Ustilaginaceae, genes were almost completely annotated with data from Ustilaginomycotina. B Percentage of seed orthologues that could be annotated using the respective databases. In COG  (Galperin et al. 2015 (Galperin et al.
, 2019) )  and PFAM  (Mistry et al. 2021) , almost all seed orthologues were annotated. More specialized databases, such as CAZy  (Drula et al. 2022)  or BiGGR  (Gavai et al. 2015) , supplied annotations for only a small fraction of the genes. Functional annotations were performed using the online version of eggNOG mapper  (Huerta-Cepas et al. 2017 , 2019 , accessed August 2022) with default settings. Plots were created in R 4. 0. 2 (R Core Team 2021) using the ggplot2 package  (Wickham 2016)  ◂ in the databases at all, which would allow annotations of Microbotryum genes with more distantly related, but functionally annotated genes. The Pfam database covered a similar number of seed orthologues as COG, covering up to 94% of seed orthologues with an annotation. Other broad databases, such as GO, BRITE, KEGG KO and KEGG Pathway  (Kanehisa et al.
2021 ), were able to provide an annotation for 32-64% of seed orthologues, while highly specialised databases such as BiGGR (metabolic annotations,  Gavai et al. 2015) , TC (membrane transporter database,  Saier et al. 2021, via KEGG_TC)  and CAZy (CAZyme database,  Drula et al. 2022 ) annotated a small fraction of genes for the two groups (Fig. 2B ). The annotations that can be achieved by database-based approaches such as eggNOG are likely more reliable for Ustilaginaceae, as data is available from a closely related species, which is often not the case for Microbotryum. This makes it difficult to compare these two groups in terms of gene functions and functional annotation of a Microbotryum species are needed to improve the annotation of further closely related species. Microbotryum lychnidisdioicae most likely is best suited as it is already established as a model for molecular host-parasite interaction studies  (Beckerson et al.
, 2012)). Phylogenetic and population studies in both groups have increasingly used molecular markers. Phylogenetics and systematics are mainly based on single marker or multigene datasets. The most common markers amongst these are the rDNA regions of the internal transcribed spacer solely or in combination with other markers such as 26S (e. g. ,  Kemler et al. 2013; Lutz et al. 2005; Stoll et al. 2005; Kruse et al. 2017; Ziegler et al. 2018). Although these markers are still the most common regions for species delimitation and systematics in smut fungi, additional markers have been used, including elongation factor 1 alpha, beta-tubulin and gamma-tubulin (e. g. ,  Freeman et al. 2002; Le Gac et al. 2007; McTaggart et al. 2012). Single or multiple DNA regions are also used to understand the population patterns in both groups (e.
2018 ). The results were aggregated according to  Zhang et al. (2018) : CAZymes had to be annotated by at least two of the three databases included in DBcan2. If an annotation was available from Hotpep  (Busk et al. 2017) , it was used in the aggregation; if this annotation was not available, the result from the dbCAN database was adopted. Plots were created in R 4. 0. 2 (R Core Team 2021) using the ggplot2 package  (Wickham 2016)  wall degradation in plant-pathogenic fungi  (Kubicek et al. 2014 ) and the higher number of glycosyl hydrolases potentially allows the yeasts to metabolise different carbohydrate sources, allowing them to grow on complex substrates. Yeasts of U. maydis for instance grow on artificial media that contains Zea mays tissue as the sole carbon source  (Cano-Canchola et al.
gene predictions were used for Microbotryum Introns Introns/gene Genes (Mb) Reference 37999 3. 55 370. 9 Branco et al. 2018 49707 3. 20 378. 7 Branco et al. 2018 4519 0. 69 346. 89 Schweizer et al. 2018 6311 0. 95 333. 80 Taniguti et al. 2015 5791 0. 92 299. 43 Rabe et al. 2016 4431 0. 70 240. 3 Zhang et al. 2021 9589 1. 27 280. 37 Laurie et al. 2012 3529 0. 55 320. 00 Kämper et al. 2006 genomes NCBI accession Size (Mb) Scaffolds N50 (kb) GC Predicted genes eggNOG seed ortho- logues PRJEB12080 28. 8 131 1517 0. 56 10695 5919 ERZ250707 PRJEB12080 40. 9 225 1035 0. 55 15486 6711 GCA_900015485 GCA_900162835. 1 19 23 782 0. 59 6591 6271 GCA_001010845. 1 20 27 876 0. 55 6676 6268 PRJNA275631 GCA_900080155. 1 21 24 877 0. 52 6288 5850 GCA_020827535. 1 26,4 34 1030 0. 54 6344 5946 GCA_003012045. 1 27 70 1048 0.
1990 IM Transformation Pretreatment method medium ATMT Unknown PEG-mediated Unknown transforma- tion Electroporation Unknown PEG-mediated SCS transforma- tion PEG-mediated SCS, see transforma-Schulz et al. tion, see 1990 Schulz et al. 1990 ATMT Unknown Cultivation medium YPD YMPD YMPD YEPSL CM, YEPS, see Schulz et al. 1990 YPD Cell density 10 8 /ml Unknown 1 × 10 8 /ml Unknown 2 × 10 7 /ml, see Schulz et al. 1990 0. 7 OD600, see Ji et al. 2010 Cell type Sporidia Conidiam hyphae Conidia Protoplasts Protoplasts Haploid basidi- ospores Lineage Microbotry- aceae Ustilaginaceae Ustilaginaceae Ustilaginaceae Ustilaginaceae Ustilaginaceae Species Microbotryum lychnidis- dioicae Moesziomyces antarctica Moesziomyces antarctica Moesziomyces bullatus Sporisorium reilianum Sporisorium scitamineum Function-oriented databases are important tools for similarity-based gene annotation.
Because functional annotation is cumbersome and well-curated information is not widely available, these databases naturally focus on a few model species, making functional annotation of new genomes challenging with decreasing similarity and relation to the model species. For Ustilaginaceae, comparisons are achievable, as the major databases like the Kyoto Encyclopaedia of Genes and Genomes (KEGG; Kanehisa et al. 2021) , Gene Ontology (GO; Ashburner et al. 2000; Gene Ontology Consortium 2021), or the eggNOG database (Huerta-Cepas et al. 2019) contain data from the model organism U. maydis. The Carbohydrate Active Enzymes database (CAZy database, http:// www. cazy. org/; Drula et al. 2022) additionally contains data from U. bromivora, S. reilianum, S. scitamineum and Melanopsichium pennsylvanicum.
In Step 4, genes are converted into the encoded proteins automatically through a functional annotation tool. Pathway mapping and unification (Step 5) refined the results as the final outputs, the ICAM1-associated pathways. Information beyond a subset of the existing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways is retrieved through the workflow. Specifically, integrating single-cell omics data and multiple knowledge bases allows the inference of gene networks containing the components absent from the KEGG pathways. Single-cell omics data analysis adopts a combination of the following standard methods. Quality control (QC) preprocesses the single-cell omics data (scRNA-seq data). Dimensionality reduction (DR) embeds data points in a sparse matrix onto a two-dimensional latent space.
2 ). Step 4: Gene-to-Protein conversion. Step 5: Pathway mapping and unification. The circuits are subpathways transmitting a signal from input receptor nodes to output effector nodes, where the nodes mostly represent proteins such as metabolic enzymes. See also  [15]. Fig. 2. The five cell types for which data were collected. Pulmonary tissue illustrations: Created with BioRender. com. See also  [16].
Removing the spurious edges from DCG correlation networks results in undirected graphical models Next, we obtained gene network models inferred from the data. There are several methods available for gene network inference from single-cell omics data, including regression, mutual information, correlation, Bayesian networks, and a combination of different approaches  [18, 19]. Gene network inference by Bayesian networks has been well investigated; however, this approach is said to be most suitable for small datasets because Bayesian networks infer causality but incur computational costs due to scalability problems such as the variables' rapid combinatorial expansion  [20]. Conversely, simple correlation networks find both direct and indirect relations at a lower cost, but they do not distinguish between causality and correlation  [21].
Corroboration and validation with multiple knowledge bases edit undirected graphical models as dependency graphs Information retrieved from any given dataset, which could have no biological meaning, requires model corroboration and validation with heuristics based on domain knowledge. In this study, we queried multiple knowledge bases, using each knowledge base's application programming interface (API) for model corroboration and validation. Fetching relations between gene pairs in the simple interaction format (SIF) enabled us to convert a subset of undirected edges to directed edges, thereby obtaining dependency graphs (Fig. 4 ). The entire list of relations of dependency graphs with knowledge bases used for model validation can be found in Supplementary Table  S3  online.
Cell types Scores Matched KEGG pathways Infected AT1&2 cells 0 genes (no match) Migratory DCs 40 genes (63. 5% match) NF-κB signaling pathway (hsa04064)  (12) , HTLV-1 infection (hsa05166) (9) TRAM2 23 genes (65. 7% match) Influenza A (hsa05164)  (13) , HTLV-1 infection (hsa05166) (5) MoAM2 31 genes (54. 4% match) NF-κB signaling pathway (hsa04064) (3) All cells 18 genes (64. 3% match) TNF signaling pathway (hsa04668) (3), NF-κB signaling pathway (hsa04064) (3) Unification of the inferred dependency graphs and the KEGG pathways in KEGG Markup Language (KGML) format by Cytoscape 3. 9. 0 [29]  resulted in the final COVID-19-specific ICAM1-associated pathways for each cell type (Fig. 5 ). The rectangular nodes colored blue, yellow, and lime green reflect the proteins on dependency graphs only, the proteins common to both the dependency graphs and KEGG pathways, and the proteins on KEGG pathways only, respectively.
For example, we can not guarantee that our method would produce the same results using datasets measuring variations in protein expression levels directly, which requires further analysis, such as reversed-phase protein array (RPPA). Given the above limitation, future work will include the following two tasks. One is calibration experiments on multilayered models extended from viral dynamics models  [37]  with multiomics data incorporating distinct layer datasets  [38]. The current results rely on the transcriptome data only, making it challenging to check the upper layer of the biological hierarchy. Consequently, we can consider a multilayered model, such as a combination of a metabolic flux model and Hoffmann's NF-κB model  [39]. The other is to produce a simulation for the explicit use of direct transcription factor perturbations (knockout or overexpression)  [40] ; for example, one such study significantly improved prediction accuracy for downstream targets  [41].
e. , signaling pathways, metabolic pathways, or protein-protein interactions  [43]. Third, even though the correspondence between genes and transcripts is not one-to-one due to exon gaps or splicing invariance, 94% of functionality can be determined from transcriptome data  [44]. The single-cell omics data (GSE155249_supplement. h5ad) used in this study originate from a primary source  [45]. Manually inputting the search term ((COVID-19 OR SARS-CoV-2) AND gse[entry type]) AND "Homo sapiens" AND h5ad to the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) DataSet  [46]  provided the data. The data included the gene expression profiles of bronchoalveolar lavage fluid samples isolated from 10 patients with severe COVID-19 and two negative controls via high-throughput single-cell RNA sequencing.
Quality control For further analysis, the data were processed through quality control, including filtering, scaling, and normalization by Scanpy  [47]. The data originally contained 77,650 cells × 24,714 genes. Given cell quality, one can regard the cells with overexpressed mitochondrial RNA per data count tagged by a unique molecular identifier (UMI)  [48]  as dead or broken cells. Likewise, cells with many genes per data count tagged by UMI could be identified as doublets. The filtering process excluded 8,916 cells with over 5,000 expressed genes, 700 genes that were detected in fewer than three cells, mitochondrially encoded genes, and cells with a low percentage (< 10%) of mitochondrial genes. After filtering, the dataset contained 68,734 cells × 24,001 genes. The count data were scaled with regression on total UMI counts and normalization per feature based on standard deviation.
Single-cell omics data were isolated from bronchoalveolar lavage fluid samples collected from 10 patients with severe COVID-19 and two negative controls. The data were processed and annotated in a sparse matrix of mRNA expression levels in a single-cell population. The primary article classified the cell types of clusters based on cell-type-specific marker genes. The input data underwent dimensionality reduction, clustering, and statistical tests for each cell type. This analysis resulted in a hash table of DEGs, including the cluster number corresponding to the cell type, gene name string, and log fold change. Then, the DEGs were extracted from 21,819 genes among the SARS-CoV-2-infected 15,481 cells (cells with SARS-CoV-2 transcripts detected); infected alveolar type 1 and 2 cells, CCR7+ migratory dendritic cells, CCL2+ monocyte-derived alveolar macrophages, and CCL4+, CCL20+, CXCL10+, CXCL11+, IL1B+, TNFSF10+, DEFB1+ tissue-resident alveolar macrophages.
Data availability statement Supplementary figures are provided in the Supplementary Information. All the datasets derived from this study, including Supplementary figures, have been deposited in the figshare repository under the URL: https://doi. org/10. 6084/m9. figshare. c. 5756363. Other data are available from the corresponding author (odaka[at]nii[dot]ac[dot]jp) on reasonable request. Single-cell RNA sequencing data supporting the findings of this study were obtained from the NCBI Gene Expression Omnibus (GEO) with the accession code GSE155249.
The aim of this study is to establish linear measurements of local goat kids at birth and their factors of variation, as well as their possible correlations with birth weight. Additionally, the study aims to design statistical models and barymetric functions to predict birth weight of kids based on their morphometric data. The database includes data on 128 goat kids born to 89 goats and 9 bucks in the experimental goat herd at the El GORDHAB station of IRA. Average BW, withers height (L1), heart girth (L2), rump height (L3) and body length (L4) of all kids were 2. 45kg, 32. 66cm,30. 56cm, 33. 41cm and 31. 21cm respectively. Results show that in general, local goat kids are small in size and weight at birth, which varies depending on sex, dam age and type of birth. Highest and positive correlation coe cient value between birth weight and heart girth were observed (r = 0.
Data recording and studied traits The data used in present study were collected from a total of 128 kids (63 males and 65 females; 43 born simple and 85 born double), the progeny of 9 sires and 89 dams. The kidding was the subject of a precise statement, which included the number of the dam, the date of birth, the total number of kids born. An examination of the two quarters of the udder was carried out in order to identify the presence of an imbalance of the udder, a large teat, mastitis or the absence of colostrum. At birth, all kids were weighed (BW) and tagged. Body dimensional measurements were taken at birth. The body measurements using a tape included the following traits: withers height (L1 : measured as the distance from the top of midline between the hips to the ground), heart girth (L2 : is a circumferential measure taken around the chest just behind the front legs and withers.
Statistical analysis Data tabulation was done with the Excel program. Descriptive analysis was performed using mean, standard deviation (SD), minimum and maximum of the data. Correlation and regression model analysis between body measurements and birth weight were performed using SPSS program. Linear and multiple linear regression model used were as follows : Model (1) BW ijkl = µ + SEX i + TB j + S k +DA l + α 1 (L1) + e ijkl Model (2) BW ijkl = µ + SEX i + TB j + S k +DA l + α 2 (L2) + e ijkl Model (3) BW ijkl = µ + SEX i + TB j + S k +DA l + α 3 (L3) + e ijkl Model (4) BW ijkl = µ + SEX i + TB j + S k +DA l + α 4 (L4) + e ijkl Model (5) BW ijkl = µ + SEX i + TB j + S k +DA l + α 1 (L1) + α 2 (L2) + e ijkl Model (6) BW ijkl = µ + SEX i + TB j + S k +DA l + α 1 (L1) + α 3 (L3) + e ijkl Model (7) BW ijkl = µ + SEX i + TB j + S k +DA l + α 1 (L1) + α 4 (L4) + e ijkl Model (8) BW ijkl = µ + SEX i + TB j + S k +DA l + α 1 (L1) + α 2 (L2) + α 3 (L3) + e ijkl Model (9) BW ijkl = µ + SEX i + TB j + S k +DA l + α 1 (L1) + α 2 (L2) + α 4 (L4) + e ijkl Model (10) BW ijkl = µ + SEX i + TB j + S k +DA l + α 1 (L1) + α 2 (L2) + α 3 (L3) + α 4 (L4) + e ijkl Where m is the mean of the trait evaluated, SEXi is the i th xed effect of sex of kids (i = males, females), TB j is the j th xed effect of type of birth (j = simple, double), DA k is the xed effect of k th dam age category (k = < 3years, ≥3years), α 1 (L1) is L1 as a covariate, α 2 (L2) is L2 as a covariate, α 3 (L3) is L3 as a covariate, α 4 (L4) is L4 as a covariate , and e ijkl is the residual error.
We reported alpha-hemolysin translocation ATP-binding protein (HlyB) as a candidate protein. Homology searches were carried out using NCBI's Conserved Domain Database (CDD)  [23]. According to subfamily domain architectures, the candidate protein (Cb122_00594) belongs to the ABC_6TM exporters' superfamily (cl38913, E-value 6. 31 × 10 -27 ). This family represents a subunit of transmembrane (TM) helices typically found in the ATP-binding cassette (ABC) transporters known as exporters (Figure  S1 ). A detailed annotation of C. burnetii isolates is shown in Figure  S2. Moreover, protein modelling of CB122_00594 was performed  [24]. In total, 174 residues have been modelled with 99. 9% confidence by the single highest scoring template. Tridimensional prediction of the Cb122_00594 protein confirmed the structure of a hemolysin of a secretion system hlyb/d complex, with significant scores (confidence of 99.
This is the first time that the role of C. burnetii strains isolated from placenta from women with Q fever has been assessed in vivo and ex vivo. Firstly, we analysed the sequencing data of five placental isolates of C. burnetii and we compared them with NM, reference strain, and three other strains (Netherlands, Guyana and Germany). The pan genome analysis showed that placental isolates share a large proportion of genes and the greater number of unique genes was found in Cb93 and Cb122 isolates of C. burnetii. Our study focused on the Cb122 strain, as this isolate appeared as a unique branch of the genomic-based phylogenetic tree of the nine investigated strains. Among the genomic specificities of the Cb122 isolate, we reported the presence of an alpha-hemolysin translocation ATP-binding protein (HlyB).