dataesr/scientific-paragraphs-categorization huggingface dataset:
Raw data have been deposited at the European Nucleotide Archive (ENA) under the project number PRJEB23959. Trimmed reads were mapped on CDS using the BWA-MEM package with default parameters  (Li, 2013). Samtools  (Li et al. , 2009)  was used to count mapped reads and the number of reads per kilobase and million reads (rpkm) were then calculated. Statistical analyses of microarray and 454 data were performed as described in  Dussert et al. (2013)  and  Joët et al. (2014). Briefly, differences in transcript accumulation between two consecutive stages were tested using the Limma Package  (Wettenhall and Smyth, 2004 ) and Audic-Claverie statistics (Audic and Claverie, 1997), respectively. In both cases, P-values were Bonferroni corrected to control for false discovery rates. Hierarchical cluster analysis (HCA) was used to group genes according to their transcription profile using the tool developed by  Eisen et al.
(1998). Gene ontology (GO) annotation was performed using Blast2GO software  (Gotz et al. , 2008)  with default parameters. Fisher's exact test was then used to evaluate the significance of GO term enrichment. Biological enrichment analysis was also performed using Arabidopsis homologues of differentially expressed coffee genes and the Classification Superviewer tool (http://bar. utoronto. ca; Provart et al. , 2003)  for Mapman categorisation. The method used for qPCR analysis is described in  Joët et al. (2014). The set of primers which enabled amplification of target genes is detailed in Supp. Table  S1. The level of expression of each gene was normalised to the geometric mean of expression levels of three validated reference coffee genes (Cc08_g05690 Ubiquitin UBQ10, Cc00_g15790 40S ribosomal protein S24, and Cc00_g17460 14-3-3 protein;  Cruz et al.
Production of transgenic coffee somatic embryos overexpressing CaHSFA9 and CaDREB2 genes Coffea arabica HSFA9 and DREB2 cDNAs (Genbank accession numbers JQ687374 and JQ687375, respectively) were used as a template for PCR amplification using the primers described in Supp. Table  S1  and were cloned into the plant overexpression vector pMDC32. Coffee embryogenic calli (C. arabica cv. Caturra) were genetically transformed using recombinant Agrobacterium tumefaciens strain LB1119 containing the recombinant plasmid, as previously described  (Ribas et al. , 2011). About 10 somatic embryos regenerated from each hygromycin resistant callus (independent transgenic lines) and four regenerated plantlets were tested for transformation by PCR amplification of the selection gene, HPTII. Transgenic lines were then tested for both gene expression and desiccation tolerance.
94, https://github. com/ BioInfoTools/BBMap/blob/master/sh/bbduk. sh). For contig generation and scaffolding, the 180bp, 3kb, and 7kb reads were assembled using AllpathsLG (version 50960)  57. The best assembly was obtained by using a random subset of 56 (33%) million reads from the initial 3kb and 7kb libraries with the full set of 162 million reads from the 180bp library, more input data resulted in reduced performance and quality of assembly. AllpathsLG was run with haploidify = true option to compensate for the high degree of heterozygosity present in the P. rapae data. The assembly was composed of 318 million bases contained in 15,799 scaffolds with a N50 of 54,044 bp. Complete conserved single copy ortholog content was assessed at 87% by CEGMA (version 2. 5)  58. A second scaffolding step using SSPACE v2  59  and the 3kb, 7kb, and 40kb libraries together brought up the assembly size and the N50 to 143, 392 bp.
Sample collection and data analysis for GWAS. Host-plant GWAS: The growth chamber conditions for growing Pieris rapae and Arabidopsis thaliana were 23°C day/21 o C night and 60% relative humidity on a 16 hour photoperiod. Three replicates of each of the 96 accessions of Arabidopsis thaliana that are listed in Supplementary Table  1  were grown until they were almost ready to bolt. After taking a picture of each plant, two 5 day old, lab-grown P. rapae larvae were weighed and placed on each plant and then the plant was enclosed in a plastic sleeve bag. After 72 h, the larvae were weighed and a new picture of the plant was taken in order to record plant surface area eaten by the larvae. The weight gained by the larvae and the total surface area eaten were calculated and used as the phenotype data for GWAS (Supplementary data 12).
The SNP information for the 96 accessions from the 250K SNP data  70  and the phenotype data were fit using a multivariate linear mixed model in GEMMA (version 0. 94. 1) for association studies  71. The pipeline involves converting the SNP file into a PLINK binary PED file and generating a relatedness matrix file using default parameters. The average initial weight of the larvae was used as a covariate for the analysis. For the functional validation of the candidate genes, knock out T-DNA mutants from SALK and wild type plants were grown and assayed as described above. Herbivore GWAS: 96 P. rapae females were collected from various locations across the US Midwest during June-July 2014 and raised in the lab green house. These butterflies included 57 from around the University of Chicago campus, 19 from Schaumburg, IL, and 16 from North Dakota, 3 from downtown Chicago, and 1 from Carolina Biological Supply.
The growth chamber conditions for growing P. rapae and A. thaliana were 23°C day/21 o C night and 65% relative humidity on a 16 hour photoperiod. Eggs were collected from each female and two 5 day old larvae from each family were weighed and placed together on a Col-0 A. thaliana plant and this was performed in triplicate. Leaf area eaten and weight gain phenotypes were assayed as described for the Arabidopsis GWAS (Supplementary data 13). For genotyping, we selected one random GWAS larva from each family (a total of 96 larvae), sequenced the genome of each sample to a mean coverage of 13. 8X (Supplementary Table  2 ), and analyzed the data using the P. rapae genome assembly. Medium coverage sequencing depth (5-20X) is considered the most cost-effective choice for sample sizes close to 100 individuals  72  and variation in coverage did not introduce spurious population structure.
The PCA plot was generated using the --pca option in PLINK (version 2. 0)  73  (Supplementary Fig. 9 ) and the admixture plot was generated using the default options of ADMIXTURE (version 1. 3) 74 (Supplementary Fig. 10 ). Genomic DNA was extracted from skin tissue of the larvae using the VDRC Drosophila genomic extraction (https://stockcenter. vdrc. at/images/downloads/ GoodQualityGenomicDNA. pdf ) and sequencing libraries were prepared using KAPA Hyper Prep Kits (KR0961 -v1. 14). Barcoded libraries were pooled and sequenced on an Illumina HiSeq 2500 to generate paired-end 100 bp data. The average QPhred score of the 96 samples was 36. 2 and no sample had less than 35 (a Phred score of 20 corresponds to a 1% error rate). Raw reads were further passed through additional quality control software Trimmotatic (version 0.
36)  75  , where all bases in the reads that were below a quality score of 15 were trimmed using a sliding window of 4 bp and all reads less than 36 bp in length were discarded. Trimmed reads were aligned to the P. rapae reference genome using bowtie2  76  , using a -very-sensitive-local option, and the aligned SAM files were prepared for calling SNPs using PICARD tools (version 1. 141, http://broadinstitute. github. io/picard). Three PICARD tools were used with default parameters in the following order: SortSam, AddOrReplaceReadGroups, and MarkDuplicates. GATK (version 3. 4)  77, 78  was then used to call SNPs. During this process, we used BaseRecalibrator to readjust and correct any errors in the quality scores assigned by the sequencing machine, RealignerTargetCreator to locally realign reads reducing the number of mismatches, and IndelRealigner.
Again, the average initial weight of larvae was used as a covariate for the analysis. The statistical significance thresholds for both the Arabidopsis and Pieris GWAS were determined using simpleM  79  , a multiple testing correction method for genetic association studies using correlated SNPs. To test the robustness of the GWAS results, the association tests were repeated 1000 times with randomized sample names in the phenotype file using custom scripts. 99% of the randomized datasets yielded ≤ 3 significant SNPs at our empirical significance threshold and only one randomized dataset yielded more significant SNPs (18 compared to our empirical result of 16, Supplementary Figure  11 ).
These libraries were sequenced on an Illumina HiSeq 2500 to generate SR50bp reads. The DNA sequence data from all three replicates of each treatment were processed with the Trimmomatic (version 0. 36)  75  QC pipeline, where all the bases in the reads that were below the quality score of 15 were trimmed using a sliding window of 4bp. All reads that were less than 25 bp in length were discarded. The trimmed SR50bp reads were aligned to their respective genomes using STAR (version 2. 4. 2) 82 software using default parameters, except for Intron minimum size, which was set to 0 bp for both Arabidopsis and Pieris. In addition, the non-canonical junctions were removed for compatibility with Cufflinks using the RemoveNoncanonical option. After alignment, using the Cufflinks program within the Cufflinks suite (version 2.
These libraries were sequenced on an Illumina HiSeq 2500 to generate SR50bp reads. In addition, one library from each of the host-plant species and two libraries from of each butterfly species (the egg and a larval stage) were sequenced with PE100bp in order to generate a reference transcriptome for each species. The number of reads generated from each library is listed in Supplementary Table  9. The SR50bp and PE100bp reads were processed with the Trimmomatic QC pipeline (version 0. 36)  75. All trimmed PE100bp reads and the trimmed SR50bp reads were assembled together for each host-plant and herbivore system to generate a reference transcriptome with the Trinity package (version r20140717)  83. After excluding different isoforms, as predicted by Trinity, the number of unique coding sequences found in each plant species was: A.
thaliana, 18,105; M. sativa, 43,843; C. microcarpa, 20,367; and P. oerstedii, 22,073. The number of unique coding sequences in each butterfly species was: P. rapae, 12,037; C. eurytheme, 16,277; P. polytes, 15,176; and H. cydno, 19,914. Trimmed   SR50bp reads from each species were aligned to their respective reference transcriptome and differential expression analyses were performed with the Trinity package, which includes bowtie2  76  for alignment, RSEM  84  for transcript quantification and edgeR  85  for differential expression analysis. Default parameters and an FDR-adjusted p-value of 0. 001 were used for analyses. We visualized the correlation among samples and treatments by generating PCA plots of the RNA-seq expression data from all four plant and butterfly species using the PtR option in the Trinity package  83  (Supplementary Figs.
12  and 13 ). These analyses showed that the RNA-seq expression data clustered in biologically meaningful ways. For instance, we found that biological replicate samples grouped together, as did samples from similar experimental treatments (butterfly eggs vs. larvae, leaf tissue exposed to eggs vs. leaf tissue exposed to larvae). For both Pieris rapae and Arabidopsis thaliana, we analyzed the RNA-seq data two different ways, using Cuffdiff 67 and Trinity  83. These different analyses produced similar numbers of differentially expressed genes, as revealed by comparing Fig. 2i  & j to Fig 4. b & c. We explored this further by comparing the identity of genes across the two datasets and we found that while some differentially expressed genes were unique to one dataset or the other, many were found to be differentially expressed by both methods (Supplementary Table  10 ).
Osiris 9 paralog detection. All Osiris 9 protein sequences from Bombyx mori, Drosophila melanogaster and Danaus plexippus were extracted from the Osiris protein database  39. Using them as the query, the homologous sequences were extracted from the de novo transcriptomes of all the four butterfly species and the P. rapae genome using tblastn similarity searches. A multiple sequence alignment of all extracted Osiris 9 protein sequences, including the Osiris 11 and Osiris 14 from B. mori, was generated using MAFFT (version 6. 847b)  89  with the L-INS-i algorithm. A phylogenetic tree was reconstructed using FastTree (version 2. 1. 10)  90 , which infers approximately maximum likelihood phylogenies from alignments of protein sequences. All the default options and the JTT+CAT model were used for tree inference.
Acknowledgments We thank  E. Westerman ,  R. Marquez ,  L. Southcott ,  A. Russell  and  G. Garcia  for assistance with experiments and  D. Samac  for generously providing Medicago sativa seeds. N. Saleh  helped with generating the inbred P. rapae material for the sequencing the initial genome assembly. We also thank  Cristina Sahagun  for assisting with the butterfly photography, and we thank reviewers for very helpful feedback on the manuscript. This project was funded by  NIH  grant  GM108626  and  NSF  grant  IOS-1452648  to MRK, funds from and  University of Pittsburgh , the  Pew Charitable Trust and Neubauer  funds from the  University of Chicago.
Data availability All the DNA-seq and RNA-seq raw reads generated during this study have been deposited in the NCBI SRA database under the study accession: SRP134094. The assembled Pieris rapae genome (v1_HIRISE) is available on the Lepidopteran genome database, Lepbase, and all eight de novo transcriptome assemblies have been deposited in the DDBJ/EMBL/GenBank TSA database, under the following accession numbers: Arabidopsis thaliana (GGJX00000000), Medicago sativa (GGKA00000000), Citrus x microcarpa (GGJQ00000000), Passiflora oerstedii (GGJO00000000), Pieris rapae (GGJY00000000), Colias eurytheme (GGJZ00000000), Papilio poytes (GGKB00000000) and Heliconius cydno (GGKC00000000).
Bacterial 16S ribosomal RNA gene amplification, sequencing, and metagenome prediction The 16S content of BAL fluid DNAwas characterized either by quantitative PCR using previously reported primers specific to pan bacteria or by major phyla (Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria, and Fusobacteria), or Illumina MiSeq sequencing using primers targeting the V1-V2 region, as previously described. 11 Sequences were processed and the resulting table of operational taxonomic units was used for metagenome prediction, using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software  12  (see detail in the Methods in this article's Online Repository).
Principal component and statistical analysis To analyze the distribution of BAL cell gene expression profiles, the target gene/reference gene copy number ratio was log-transformed and principal component analysis was performed using prcomp (scaled) routine in R (R Foundation, Vienna, Austria). 13 Multiple-group comparisons were performed using Kruskal-Wallis test with Dunn post hoc analysis and Grading of pulmonary allograft rejection according to guidelines of the International Society for Heart and Lung Transplantation. 9 àConducted in a subset (n 5 106) of samples. Virological investigations ranged from CMV only to more extensive testing, as per case requirement. posttransplantation differences in the relative abundance of the remodeling gene expression profiles were compared using chi-square test (R package PMCMR).
To this end, we employed 2 complementary approaches (experimental scheme detailed in Fig  1, A ). A subset of 9 BAL samples, that represented an inflammatory, remodeling, or intermediate gene expression profile in our first-round real-time PCR, was analyzed by RNA sequencing. Data were filtered on the basis of adjusted P value (. 05 threshold) and ranked by fold-change. In parallel, we selected 6 GO terms linked to remodeling (see Fig  1, B  for details) and interrogated the GO database using the application AmiGO version 2. 3, 10 generating a list of 627 candidate genes. Table  II  presents the restricted list of 11 remodeling genes obtained after combining the 2 approaches, and further assessing potential candidates on the basis of a literature survey on the remodeling-fibrosis axis, in the context of various respiratory conditions.
Moreover, when considering all of those samples associated with a clinical suspicion of infection, we found a striking difference in the bacteria identified by culture and/or type of detected dysbiosis, depending on the remodeling context (Fig 4  , D ). Hence, combined with the information we obtained on the kinetics of gene expression profiles (Fig 2  , D  and E ) and microbiota states (Fig  3, E  and F ), this dataset indicated that the early acquisition of a catabolic remodeling profile and dysbiosis driven by highly stimulatory bacteria, is linked to inflammation and a more frequent association with clinical infection. In contrast, the clinical implications of the coincident anabolic remodeling profile and dysbiosis driven by low stimulatory bacteria, which are likely to be expressed later following lung transplantation, remain to be defined.
This ensured consistent cell viability levels across the different experimental conditions, in spite of a median 19% decrease in the presence of S aureus and P aeruginosa versus P melaninogenica and S pneumoniae (see Fig E2 in the Online Repository). In this setting, bacteria identified in lung transplant recipients on a catabolic remodeling background (a mix of S aureus and P aeruginosa) induced a globally distinct remodeling gene expression profile in macrophage-fibroblast cocultures, as compared to the representatives of the community linked to anabolic remodeling (P melaninogenica and S pneumoniae)( Fig 5  , A ). Specifically, Staphylococcus and Pseudomonas induced maximal expression of MMP7, -9, and -12, while Prevotella and Streptococcus were linked to higher THBS1 expression, 2 features aligning with catabolic and anabolic gene expression profiling in BAL cells of lung transplant recipients, respectively (Fig 5  , B  Consequently, this dataset underscored the critical role of bacterial stimulation in the maintenance of homeostatic matrix turnover, suggesting that the detrimental impact of airway microbiota dysbiosis following transplantation may consist of exaggerated anabolic or catabolic remodeling processes.
39, 40 ur in vitro dataset indicates that S aureus and P aeruginosa, typically associated with proinflammatory microbial communities, trigger a stronger catabolic remodeling profile, in comparison to P melaninogenica and S pneumoniae. This is consistent with recent observations linking the 2 former bacterial species to increased BAL levels of matrix glycosaminoglycans, as compared to those associated with S pneumoniae, in acute exacerbation and stable COPD, respectively. 41 There are different ways bacteria could modulate matrix production in our in vitro system. Fig  5, A-D  indicates that this occurred through stimulation of fibroblasts and macrophages, by shifting the gene expression balance between matrix components and metalloproteinases. In addition, bacteria could have impacted remodeling through direct degradation of the matrix.
However, in respiratory diseases associated with acute inflammation or fibrogenesis, local conditions linked to a catabolic and anabolic remodeling profile, respectively, promote the growth of specific bacteria, which in turn differentially impact matrix regulation. There were limitations to this study. First, although our in vitro dataset revealed a link between remodeling gene expression levels and activities, we acknowledge that remodeling in the transplanted lung cannot be inferred solely from the identification of BAL cell gene expression profiles, given the importance of posttranscriptional regulation in this process. Nonetheless, the different remodeling gene expression profiles we identified correspond to distinct microenvironmental conditions, as corroborated by specific features in the lung microbiota composition and the underlying clinical state.
E1 Custom oligonucleotide primers and probes (Table  E1 ) were designed using National Center for Biotechnology Information (Bethesda, Md) primer blast software and were purchased from Microsynth (Balgach, Switzerland). Amplification was carried out using iQ Multiplex Powermix and a CFX96 Real-Time detection system. Absolute quantification was performed using the CFX Manager software 2. 1 (all from Bio-Rad) based on values obtained with a set of purified amplicons used as standards.
Bacterial metagenome prediction The operational taxonomic units table obtained from 16S rRNA gene sequencing data was normalized relative to the 16S rRNA gene copy number, and the metagenomic content was predicted using PICRUSt software. The predicted metagenome was exported as KOs for principal coordinate analysis on Bray-Curtis distance, using the cmdscale function in R. To investigate the potential functional capacity of the bacterial metagenome in respect to host remodeling, a subset of 159 KOs related to matrix interactions (search terms chondroitin*, collagen*, fibrinogen, fibronectin, hyaluron*) or protein/peptide degradation (search terms protease, proteinase, peptidase, *lysin, *pain, *staphin) were selected. All of these KOs were previously reported to be linked to bacterial genomes.
For image acquisition, we used a motorized inverted fluorescence microscope (Axio Observer Z1, Carl Zeiss, Oberkochen, Germany) with 203 objective. A consistent predetermined pattern of 8 images were acquired, both within and across experiments. The process was automated using the Mark&Find function in AxioVision imaging software (release 4. 7. 1, Carl Zeiss MicroImaging, Goettingen, Germany). The fluorescence area was quantified and cell nuclei were counted with ImageJ (version 1. 50b) software (National Institutes of Health, Bethesda, Md).
This work was supported by  European Commission FP7  (grant no. 305457 ,  SysCLAD Consortium ),  Swiss National Science Foundation  (grant no. 156897 , L. P. N. ),  Fondation de la Banque Cantonale Vaudoise  (J. -D. A. , E. B. ),  Fondation Juchum  (S. M. , A. K. ), and  Fondation Professeur Placide Nicod  (C. Pa. ). The Cohort of Lung Transplantation was financially supported by the  Association Vaincre La Mucoviscidose , the  Association Gr egory Lemarchal , the  R egion Pays de La Loire , the  Agence de Biom edecine , and the  Institut Hospitalo-Universitaire CESTI. This work was realized in the context of the  IHU-Cesti  project thanks to French government financial support managed by the  National Research Agency  via the  Investment into the Future program  ( ANR-10-IBHU-005 ). The  IHU-Cesti  project was also supported by  Nantes M etropole and R egion Pays de la Loire.
Identification of a set of remodeling genes. A, Schematic outline of experimental approach. B, GO criteria used in candidate gene selection process. C, Principal component (PC) analysis, and associated eigenvectors, based on quantitative PCR determination of expression of the restricted list of 11 remodeling genes (see TableIIfor details) in the initial subset of 9 BAL samples (dots). AB 203 BAL 112 patients, 0. 5-14 mo Category Gene Ontology term Associated gene number Growth factors Transforming growth factor 175 First-round qPCR Platelet-derived growth factor 33 Remodelling Extracellular matrix 415 Inflammation Intermediate 9 representative BAL for 6 remodeling-related Gene Ontology terms Tissue remodeling 8 Remodeling screening analysis (detailed in Panel B) Matrix components n t i c e e n g a l o r l o C b i F n 3 3 1 0 2 Total RNA Gene Ontology sequencing database screening C p-value adjustment Fold change ranking remodeling genes 627 candidate IGFBP2 THBS1 PDGFD Literature survey Remodeling Fibrosis Respiratory conditions Restricted list of 11 remodeling genes PC2 (20.