dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank the  Drosophila community  for fly stocks and antibodies;  National Centre for Biological Sciences, Fly Facility  for stocks; JNCASR Imaging facility and our laboratory members for valuable inputs and suggestions. Schematics created with BioRender. com. This work was funded by the  Indo-French Centre for the Promotion of Advanced Research (IFCPAR/CEFIPRA)  grant to MSI and LW; MSI's work was also supported by  SERB  grant,  J C Bose  award project and  Jawaharlal Nehru Centre for Advanced Scientific Research.
ERK2 was used as loading control. i WM9 melanoma pretreated with 5 μM VitD3 were incubated with resting or activated PBMCs. Melanoma cell death was quantified using Incucyte. Results show as mean+/-SD of one representative experiment done in triplicate Furthermore, increased GZMB and IFNγ mRNA expression upon activation of PBMCs with PMA/ionomycin was abrogated by rITBL1 (sup. fig. 2A ). Taken together these observations support an inhibition of immune cell activation by rITGBL1. To gain insight on the mechanism by which MITF regulated ITGBL1, we focused our attention on RUNX2 that was reported to regulate ITGBL1 expression in breast cancer cells  [10]. Analysis of the TCGA melanoma database revealed a significant increase in ITGBL1 expression in tumors with high RUNX2 expression, whereas MITF was decreased in these same patients (sup.
Molecular analyses DNA from the largest sized mouse tumor, adjacent treated skin and a kidney was sequenced, patterns of single nucleotide variation, insertion and deletions in tumors and skin were analysed and oncoplots generated of the top 20 frequently mutated genes by sex and histology to identify patterns of mutations. Paired-end RNA sequencing was performed from fresh whole DMBA/TPA treated skin from the back and normal skin from the abdomen. Human single cell RNA-seq data from male and female keratinocytes was analysed from a study of squamous cell carcinoma  (17) , downloaded from GEO database (GSE144236).
Personalized medicine approaches stratify cancer patients by genotype; however, to date, the potential for cancer stratification and therapy by sex has not been explored. Further work will be necessary to identify the potential regulators of sex-linked cSCC dimorphism. exome sequencing was performed by Novogene (Novogene UK). Exome capture was performed with Agilent SureSelect Mouse All Exon Kit and sequenced on the Illumina HiSeq platform. Sequencing reads were trimmed using Trimmomatic(2), aligned to the mm38 reference genome using BWA(3) and duplicate reads were marked using Picard Tools (http://broadinstitute. github. io/picard). Somatic mutations were called using the Varscan 2 pipeline using reads with mapping quality >=20, and tumor and normal coverage >=10(4). Variants within the targeted capture genes were kept for further analysis and were annotated using Variant Effect Predictor  (5).
RNA-seq analysis To identify sex specific autosomal responses analysis was performed on protein coding genes with the X and Y chromosomes removed from analysis. Reads counts of genes were filtered by removing any gene with less than 10 counts across all samples. Analysis of normal and treated skin between sex was performed independently. For pathway enrichment analysis genes that were significantly differentially expressed between sexes in normal or treated skin (FDR p-value < 0. 2) were compared against the Hallmark Database using the Molecular Signatures Database v7. 0 (Msigdb, Broad Institute, https://www. gseamsigdb. org/gsea/msigdb/index. jsp). Immune cell populations were inferred from RNA-seq data using ImmuCC  (10).
Data Analysis Statistical analysis was performed using GraphPad Prism (Version 7. 01). Mann-Whitney U test was used to compare significant difference between groups. Human single cell RNA-seq data from a study of squamous cell carcinoma(1) was downloaded from GEO database (GSE144236) and analysed in the Seurat R package  (11)  following standard pipeline and authors analyses. Cell populations were measured as a percent representation of all cells detected within the sample. Differential expression in the normal tumor-adjacent keratinocytes between sex (female n=3, males n=6) was performed with the FindMarkers function after subsetting samples for normal keratinocytes. Pathway analysis was performed as described above.
AV:  Wellcome  Beit Fellow, personally funded by a  Wellcome Intermediate Fellowship  ( 110078/Z/15/Z ), additional work funded by  Cancer Research UK  ( A27412 )  Leo Pharma Foundation  and  Royal Society   RGS\R1\201222. SJF:  European Commission  ( FP7-PEOPLE-2013-IEF -6270270 ) and the  Royal College of Surgeons in Ireland StAR programme. The  Irish Centre for High End Computing  (https://www. ichec. ie/)provided HPC infrastructure. CC-BY 4. /funder, who has granted bioRxiv.
Functional annotation of the PRM predicted transcriptome derived from the draft genome The final assembly of the PRM genome (Accession Number: QVRM00000000. 1) contains 7171 contigs with an N50 value of 278,630 bp and an overall genome GC content of 44. 6%. The assembled genome size is ~959 Mb containing 14,608 predicted protein coding genes, for which BLAST hits against the NCBI non-redundant (nr) database (July 2018) were obtained for 13,840 genes  [11]. The genome assembly is significantly larger than many other mite genomes identified to date, for example those of Tetranychus urticae (90. 8 Mb), Psoroptes ovis (63. 2 Mb), Sarcoptes scabiei (56. 3 Mb), Dermatophagoides farinae (53. 5 Mb), Varroa destructor (294. 1 Mb) and Metaseiulus occidentalis (151. 7 Mb) and more similar in size to tick genomes, i.
Interactive web-based presentation of the entire PRM genome and stage gene expression facilitates interrogation of individual genes and their stage-specific expression profiles The full annotation of the PRM genome has been made publicly available via the Online Resource for Community Annotation of Eukaryotes (OrcAE: https:// bioinformatics. psb. ugent. be/orcae/overview/Degal [87] ). To maximise the utility of this information for researchers, for each gene we created a gene-specific page, describing the full annotation available for that gene, including information relating to: gene function, GO, Pfam protein domains, protein homologues and significant BLAST hit data, gene structure, coding sequence, protein sequence and, where available, transcript evidence based on associated ESTs/cDNA data.
Expression profiling of genes across the PRM lifecycle Multiple collections of stage-sorted PRM were pooled and total RNA purified for each stage. Total RNA yields of > 7. 5 μg and RNA integrity numbers (RIN values) of greater > 7. 2 (range 7. 2-9. 4) were obtained for each stage. Illumina sequencing resulted in 42-67 million raw sequence reads for each of the six independent sequencing libraries (one for each of the six D. gallinae lifecycle/adult sex stages). For each stage a set of expression estimates (transcripts per million, TPM) was generated from the trimmed reads, using the transcript quantification tool Kallisto (Version 0. 46. 2)  [10]  and the predicted transcriptome derived from the PRM genome  [11] , with both sequencing depth and gene length considered in the expression estimate.
Network analysis and clustering of stage-enriched gene expression in PRM The network analysis of the entire PRM transcriptome (Fig. 3 ) was performed in order to determine genes expressed in either single or multiple stages of PRM. Genes sharing similar signalling pathways and biological functions often display similarities in their patterns of expression and therefore regulation  [96]  and a similar expression pattern across multiple samples may indicate that they could be involved in similar biological processes, i. e. guilt-by-association  [46]. The PRM lifecycle expression network was generated in the Graphia version 2 package  [29]  using the count data derived from Kallisto. A Pearson correlation cut-off value of ≥0. 97 was applied, resulting in a final gene network containing 13,967 nodes (genes) linked by 45,230 edges.
Clustering with a Markov Cluster Algorithm (MCL) cut-off of ≥1. 2 resulted in the generation of 44 MCL clusters. MCL clusters sharing similar expression patterns across the stages were further merged, resulting in a total of 17 superclusters (Table  1 ). The distribution of genes across each MCL cluster and supercluster are shown in Supplementary File 1. The genes within each supercluster were mapped back to the original PRM genome annotation and a Gene Ontology (GO) analysis was performed within the Blast2GO/OmicsBox package to identify associated GO terms for molecular function, biological process and cellular component attributed to each supercluster.
To allow comparison of the functions of these highly expressed genes between the individual stages, each transcript was assigned to a broad category indicative of their biological function, which was based on the associated annotation (comprehensive assessment of data from Blastp homology, associated GO annotations and InterPro terms) and is summarised in Supplementary File 4. 2725 (1)    Adult Males (AM) 292 (13)    Deutonymphs (D) 295 (12)    Protonymphs (P) 165 (17)    Larvae (L) 1907 (3)    Eggs (E) 1052 (4) E/AF 2480 (2) E/D 178 (16)    E/L 743 (6)    L/AF 688 (8)    L/AM 201 (15)    L/D 382 (11)    L/D/AF 236 (14)    P/D 695 (7)    D/AF 899 (5) D/AF/AM 414 (10)    D/AM 593 (9)  Genes most abundant in multiple life stages and sexes Examining arms of the Venn diagram with transcripts enriched in more than one stage can be informative for identifying genes associated with stage-specific traits.
Genes encoding putative allergens, including venom allergen 5, a homologue of the Lepidoglyphus destructor mite allergen 7 like and a house dust mite, Dermatophagoides farinae allergen group 27 like serpin  [2]  were also enriched in this stage. Initial formation of the peritrophic membrane in preparation for a blood meal is evidenced by a putative peritrophic membrane chitin binding protein largely found in peritrophic matrixes, which contains the chitin binding protein domains IPR002557 and IPR0365508. Analysis of the top 100 most highly expressed genes in each stage (Fig. 4 ) showed 38 transcripts in larvae, which were not present in the top 100 expressed genes of known function for the other stages (termed "L38" transcripts below) and that the largest functional categories of transcripts in larvae were energy metabolism and cuticle proteins with 6 transcripts in each (Supplementary File 4).
Dermanyssus gallinae putative allergens Currently, a total of 39 allergen groups have been classified for house dust mites (HDM) by the WHO/International Union of Immunological Societies Allergen Nomenclature Subcommittee (WHO/IUISAN http:// www. allergen. org/) based on their predicted immunoreactivity and function. BLASTp homology searching (with a cut off of E < 10 -05 ) of the inferred proteome of PRM with selected archetypal mite allergens from the HDMs, Dermatophagoides pteronyssinus, D. farinae and Bloomia tropicalis and the Astigmatid mite Psoroptes ovis identified homologous PRM proteins. Confirmation of the expression of these genes was acquired by analysis of associated transcript data for all homologues. In addition, conservation of functional domains and active sites for the major allergen groups 1 and 2 were confirmed by bioinformatics analysis.
Transcripts lacking these conserved functional elements (e. g. non-functional homologues and pseudogenes) were discarded. Using these selection criteria, homologous proteins belonging to 32 of the 39 defined allergen groups were identified in the predicted PRM proteome. The PRM genes representing these putative allergens with the top BLASTp hit against the allergens from the other mite species, and which met the inclusion criteria for each allergen group (as described above) are described in Table  3. No PRM protein homologues were identified for the allergen groups: 5, 7, 19, 21, 36 and 38. In addition, a BLASTp search for Group 17 allergens could not be performed as the sequence was not available in the WHO/IUISAN database or literature. The expression profiles of the genes encoding the top BLASTp hits for the 32 allergen groups encompassed all stages of PRM (Fig.
A large number of the differentially regulated genes in our analysis relate to this transition. Genes encoding two related proteins: DEGAL6771g00070 and DEGAL6824g00220 were identified in the region of the Venn/Eular analysis representing the blood feeding stage overlap (i. e. protonymphs, deutonymphs and adults). Both of these genes were highly expressed in all blood feeding stages and with the exception of DEGA L6824g00220 in deutonymphs, they were the most abundant transcripts in all blood feeding stages that were represented in the blood-feeding overlap group. The proteins encoded by both transcripts are mucinperitrophin like salivary proteins containing a characteristic chitin binding peritrophin-A domain (IPR036508/ PF01607) and are predicted to be glycosylated, particularly DEGAL6824g00220, which is predicted to be heavily glycosylated, typical of mucin-peritrophin proteins  [80].
ovis (groups 8, 9, 15, 27, 28, 29 and 33) and E. (See figure on previous page. ) Fig. 5  Heatmaps depicting the expression profiles of the Dermanyssus gallinae homologues of house dust mite allergen groups across the 6 stages: eggs (E), larvae (L), protonymphs (P), deutonymphs (D), adult females (AF) and adult males (AM). The darker red shading indicates a higher transcript per million value (TPM). Panel a depicts the expression level of the top Blastp hit for each of the 39 house dust mite allergen groups classified by the WHO/International Union of Immunological Societies Allergen Nomenclature Subcommittee. No allergen homologues were detected for groups  5, 7, 17, 19, 21, 36, and 38. Panel b depicts the expression (TPM, transcripts per million) of the 25 Dermanyssus gallinae homologues of the Dermataphagoides farinae group 1 allergen Der f 1 maynei (groups  1, 3, 6, 9, 15, 18, 27, 28, 29)   [12, 73]  and is representative of the difficulty in identifying the true orthologous allergen proteins using an in silico approach alone.
g. HDM and P. ovis  [54, 59, 91]  and are likely to have similar digestive function in PRM. The major group 2 allergens (and the related minor allergen groups 7, 22 and 35) are lipid-binding proteins that have a characteristic Ig-like E set domain fold (IPR014756), MD-2related lipid-recognition (IPR003172) and a Neimann-Pick C2 intracellular cholesterol transporter 2 (NPC2) domain (IPR039670). Two PRM transcripts (DEGA L3867g00080 and DEGAL4453g00010) had significant homology to Der p 2 and possessed the characteristic lipid-binding functional domains. An additional group 2 allergen homologue (DEGAL6668g00010) was identified with homology to the Pso o 2 allergen from P. ovis, but not Der p 2, and was the most abundant transcript in the 6-way Venn analysis adult male clade and the 3rd most abundant transcript in adult males overall.
Bioinformatic analysis Base calls were made using the Illumina CASAVA 1. 8 pipeline. Post-sequencing, read quality of raw FASTQ files was checked with FastQC v0. 11. 7. The CLC Genomics Workbench (Version 12, Qiagen Ltd) was used for adapter, quality, ambiguity, and length trimming. For alignment of the read data, we employed the draft genome assembly for PRM, which is a ∼ 956 Mb genome containing 14,608 predicted protein-coding genes  [11]. Pseudo alignment of the read data to the D. gallinae genome (Accession ID: QVRM00000000) was performed in Kallisto (Version 0. 46. 2)  [10]  generating read count data for each transcript across all RNA-seq samples, which were used as input for the network clustering within the Graphia package (Version 2. 0, Kajeka, UK) and for the analysis of differentially expressed genes between PRM stages.
Functional annotation analysis The transcriptome sequencing results were loaded into OmicsBox (Version 1. 3. 11, Biobam, Spain) before applying "Blast", "InterProScan", "Blast2GO Mapping", and "Blast2GO Annotation" functions. Gene ontology (GO) analysis were then generated at ontology level 2, 3 and group within 17 stage-specific superclusters. Three aspects of GO were displayed in the charts consisting of biological processing, cellular component, and molecular function. In addition, functional metabolic pathways were mapped using the Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway database within OmicsBox.
Interactive web-based presentation of the D. gallinae genome and stage gene expression The graph visualisation and analysis package, Graphia (Version 2. 0, Graphia Technologies Ltd. , UK) was used to display the gene network expression graph. TPM data for all predicted PRM genes (n = 14,608) across all stages (× 6) was used as the input dataset. The network graph was generated using a Pearson correlation cut-off value of ≥0. 97 with k-nearest neighbours (k-NN) of 5 edge reduction applied. The resulting graph was then clustered using a Markov Clustering Algorithm (MCL) cut-off of ≥1. 2, and all components with less than 5 edges (genes) were removed. Finally, gene expression MCL clusters sharing similar expression patterns across all 6 life cycle stages were merged manually into superclusters.
Assessment of the most abundantly expressed genes for each D. gallinae stage The transcripts for each PRM stage were ordered by transcript abundance (i. e. based on the estimated TPM value data from Kallisto). However, over half of the transcripts (range 51-72 for all life stages) in the top 100 most abundant genes were of ribosomal origin or had little or no associated annotation (Supplementary File 2 -Unfiltered). Therefore, transcripts were prefiltered to remove ribosomal genes and those genes with no known function, obtaining more meaningful gene lists of the top 100 most abundant transcripts for each stage (Supplementary File 2 -Filtered). Venn/Euler analysis was performed with the 6 top 100 abundant (pre-filtered) gene identifier datasets using InteractiVenn  [37].
Determination of differentially expressed genes Pairwise comparison of differentially expressed genes (DEGs) was performed using the NOISeq package (Version 3. 11) with the TPM data from Kallisto for all stages. As the RNA-seq data from the PRM stages was generated in singlicate from large pools of mites, we used the non-parametric, NOISeq package (Version 3. 11) to estimate replicates for each of the stages prior to the determination of DEGs  [88, 89]. Using the NOISeq-sim method we simulated a total of 5 replicates for each stage based on a replicate size of 0. 2 and a variability value of 0. 02. We applied an initial counts per million (CPM) cut-off, > 10 reads in all stage samples and data was normalised via the trimmed mean of M (the log2 ratio of two comparisons) values (TMM), where D describes the TPM difference for each gene.
Genes were then ranked based on the following calculation (-sign(M)*sqrt(M^2 + D^2)) to provide a set of ranked differentially expressed genes. Also, log2 ratio and fold changes were calculated in all genes in each pairwise comparison. Identification of putative D. gallinae allergens PRM homologues of the current 39 house dust mite allergen groups, classified by the WHO/International Union of Immunological Societies Allergen Nomenclature Subcommittee (WHO/IUISAN http://www. allergen. org/) were identified by Blastp searching of the predicted PRM proteins database (DEGAL_PROT) hosted on OrCAE  [11]  using the BLO-SUM62 matrix with a gapped alignment and an expect cutoff value of E < 1. 0E -05. The Blastp query sequence for each allergen group was obtained from published Dermatophagoides pteronyssinus or Dermatophagoides farinae sequences; however, allergen sequences from Psoroptes ovis and Blomia tropicalis were used as query sequences when a Dermataphagoides spp.
sequence was unavailable or lacked significant homology with PRM. Homologous sequences from D. gallinae were only considered if there was at least one piece of evidence supporting gene expression identified in the associated RNA-seq datasets: mixed stage 454  [4]  and PacBio Iso-Seq  [11]  and the stage Illumina RNA-seq data (presented in this manuscript). Additional selection criteria was applied to the major allergen groups 1 and 2. Group 1 allergen homologues were only considered if they possessed the conserved elements essential for folding and function: catalytic residues (QCHN), active site domains, semi-conserved cysteine and asparagine residues important for di-sulphide folding and glycosylation  [18, 79]. Likewise, group 2 homologues were only considered if they possessed the characteristic Ig-like E set domain fold (IPR014756), MD-2-related lipid-recognition (IPR003172), Neimann-Pick C2 intracellular cholesterol transporter 2 (NPC2) domain (IPR039670) and a putative valine-rich cholesterol binding site identified in HDM  [71].
Expression patterns of putative allergen homologues across the PRM stages were visualised by plotting the estimated TPM data using pheatmap package (version 1. 0. 7) [47]  in R (version 4. 0. 2)  [69]. QVRM00000000. The full annotation of the D. gallinae genome has been made publicly available via the Online Resource for Community Annotation of Eukaryotes (OrcAE) via the following link: https://bioinformatics. psb. ugent. be/orcae/.
Data collection A structured questionnaire was developed to gather demographic data on human residents as well as food animals present in the farm on the visit date. To minimize recall bias, we enquired about consumption of AAIs over the latest 90 and 7 days for humans and animals, respectively. In all visits, the investigators requested to inspect the cabinets used to keep medicines of humans and animals. This was taken as an opportunity to discuss information on types, doses and concentration of any AAIs identified. All medicine products containing AAIs were singled out after reviewing the label or the prescription (for human medicines). AAI were classified based on the WHO list of antimicrobial agents. 20 The concentration of AAIs contained in these products was described and was expressed in mg/tablet (human products) and mg or mL per kg of product (animal products).
1 DDD kg per kg of standing body mass. Such individual contributes to a body-mass time denominator of 365 % 80 " 29 200 (kg-days). The treatment intensity for each species was calculated as the ratio of the total number of doses to the kgdays. For humans, the quantities of AAIs consumed were estimated from the actual doctors' prescriptions where available. For animals, they were inferred from the preparation instructions of each product consumed. The estimated number of ADD kg was multiplied by the 'technical dose' of each of the AAI consumed. The technical dose values for each AAI were inferred from the products' preparation instructions (as written in the label) and from the prescription data in the case of human antimicrobial products. For animals, the technical ADD kg was defined as 75% of the treatment daily dose for 1 kg of live animal bodyweight and was obtained from the preparation instructions as indicated in the products' labels and consumption estimated from the weight (inferred from age) of the animal.
Animal weights were derived from age based on previous studies. 9, 22, 23 Respectively, 0. 225 and 0. 120 L were used as daily water intake and 0. 063 kg and 0. 037 kg as daily consumption of feed of 1 kg of poultry (any species) and pig. To account for individuals that reported using medicine but did not remember whether it included AAI or not, we assumed that a fraction of them did actually consume an AAI. This was taken as the proportion consisting of an AAI among all medicine products. In cases where participants were sure of the consumption of AAIs, but did not remember for how many days, we assigned the average of the remaining observations. For individuals where AAI concentration or dosage data were missing, we extrapolated from similar products used in the same species. Cuong et al.