dataesr/scientific-paragraphs-categorization huggingface dataset:
This feedback feature allows us to have an estimation of CoBoy's precision in practical use cases. The feedback button is also available for pieces of information requested by CoBoy but not found in databases. The goal is to know whether or not CoBoy was right to request a specific piece of information for a specific medical consultation. In the case of pieces of information on patients displayed by default, this feature is not available, because default pieces of information do not depend on CoBoy's predictions. When the patient is selected, CoBoy uses its rule-based classifier to determine a first subset of pieces of information to provide to the physician. Let us suppose that this only rule is activated: "IF disease=diabetes THEN SEARCH FOR HbA1c", because the patient p is followed up for her/his type 2 diabetes.
Let us suppose that CoBoy computes the following probabilities  (2). P (Blood Sugar = 1 | X p ) 0. 66 (2a) P (HDL = 1 | X p ) 0. 01 (2b) P (LDL = 1 | X p ) 0. 01 (2c) P (Creatinine = 1 | X p ) 0. 34 (2d) P (Microalbumin = 1 | X p ) 0. 42 (2e) In this example, if we use a threshold λ = 0. 5, CoBoy determines that the physician probably needs to obtain the patient's blood sugar history. We can see in this example that, with a more permissive threshold, e. g. λ = 0. 4, CoBoy would also determine that the physician probably needs to know the microalbumin history of the patient. However, using a more permissive threshold may overwhelm physicians with useless pieces of information on the patient. Once CoBoy has determined which pieces of information to provide to the physician, it requests associated raw data in the HCL's database.
In our example, CoBoy has to request raw data for glycated hemoglobin (HbA1c) and blood glucose history concerning the current patient. The HbA1c is a piece of information obtained through laboratory analyses. It is hence a biological analysis on the patient. CoBoy then requests the hundred most recent results concerning the patient's HbA1c from the HCL's database. Once it has obtained these data, CoBoy plots the results history and displays it to the physician (see Fig. 2 ). Concerning the patient's blood sugar history, this piece of information can also be considered to be a biological analysis. However, blood sugar is generally collected by patients themselves using a blood sugar meter. CoBoy then requests the document produced by the patient's blood sugar meter and displays it to the physician (see Fig.
V. LIMITATIONS AND PERSPECTIVES As explained previously, one of the main limitations to the viability of the support provided by our system is its impossibility to provide data coming from laboratories external to the HCL. However, the reasons for these limitations are not due to our decision support system but come from the context of the use of the system. Indeed, although large amounts of data on patients are available and accessible in the HCL's databases, if specific pieces of information necessary to take decisions are not available, the support provided by the system is crippled. A decision support system such as CoBoy must hence be built on a robust and viable structure. Because our system is a prototype not integrated into the current work processes of physicians, it remains difficult to foresee its real impact in practical situations.
ACKNOWLEDGMENTS Works presented in this paper were made during the pandemic wave of SARS-CoV-2. This particularly impacted the clinical trials of the decision support system. We want to address a special thanks to  Pr. Moulin ,  Pr. Raverot ,  Dr. Abeillon ,  Dr. Bouzehouane ,  Dr. Brac De La Perriere ,  Dr. Charriere,  Dr. Lasolle ,  Dr. Moret ,  Dr. Renault , and  Dr. Villar-Fimbel  for their disposal and for allowing this work to be done. This paper is also the result of a long collaboration with the employees of the  Civil Hospitals of Lyon (France). Thanks to all of them.
Data Analysis Data analysis was performed using Agilent's SureCall software. In brief, this included the trimming of adapter sequences and alignment to the human reference genome (hg19/GRCh37) using Burrows-Wheeler aligner. The Agilent SNP caller was used for detecting single nucleotide variants. SNP filtering, mutation classification, and annotation were performed as part of the SureCall analysis workflow. Copy number variants were determined from sequencing coverage data using a genomic analysis toolkit. Single nucleotide variants with an allele frequency > 0. 01 in the general population were excluded. Detected missense, nonsense, frameshift, and splice site mutations were annotated. For prioritization databases, such as HGMD (Human Gene Mutation Database)  [17] , ExAc (Exome Aggregation Consortium)  [18] , and dbSNP (the Single Nucleotide Polymorphism Database)  [19] , as well as polymorphism phenotyping tools/mutation severity predictors such as PolyPhen2  [20]  and combined annotation-dependent depletion (CADD)  [21]  were used.
For the time being, however, we propose a stepwise approach with targeted panel sequencing as the first step in genetic diagnostics. For patients, in whom no mutation could be detected, further workup, including whole exome sequencing, should be considered especially in the case of young age and positive family history and if further family members are available for sequencing. In the future, whole exome sequencing (WES) will probably substitute panel sequencing in the diagnostic setting as both WES and whole genome sequencing and the ensuing data analysis are becoming cheaper and quicker and databases with annotation of variants are improving. The continued improvement of sequencing technologies and bioinformatics analysis will further facilitate the discovery of single nucleotide variants and indels (insertions/deletions) and other structural rearrangements in the future.
Funding Open Access funding enabled and organized by Projekt DEAL. Financial support for this research came from the  German Ministry of Education and Research (BMBF , grant no. 01E01303 ; sys-INFLAME grant nos. 01ZX1306F  and  01ZX1306A ), from the  E-rare program  of the  European Commission EURO-CMC  ( 01GM1502 ), and from the  German Center for Infection Research DZIF  ( 8000805-3  and  TTU-IICH_07. 801 ). This study was supported by the  German Research Foundation (DFG)  under  Germany's Excellence Strategy "CIBSS" (EXC-2189 ) (Project  ID   390939984 ) and " RESIST" (EXC 2155 ) (Project  ID   39087428 ).
ACKNOWLEDGEMENTS This work is dedicated to the memory of  Prof. André Capron. We would like to acknowledge support from our funding agencies  Institut National de la Santé et de la Recherche Médicale , the  Center national de la Recherche Scientifique , the  University of Lille , the  Pasteur Institute of Lille , the  région des Hauts-de-France, state of Minais Gerais/FAPEMIG  (Franco-Brazilian call 2014-2015, FLUMICROBIOT), and l' Agence Nationale de la Recherche  ( ANR-17-CE15-0020-01 ,  ACROBAT  and  ANR APP Flash   COVID-19  ( AM-CoV-Path ). V. S. and M. G. M. received salary support (Ph. D. fellowship) by  Lille University  (V. S. and M. G. M. ) and by the  Fondation pour la Recherche Médicale  (V. S. ). F. T. received salary support by the  CNRS.
5. RNA-seq and data analysis procedures, as well functional gene analysis, and information on data repository, are detailed in the Supplementary Material. Each RNA-seq analysis was validated by RT-qPCR (see below), with YWHAZ, PPIA, or Pdk1 used to normalize gene expression in infected relative to non-infected HepG2, HuH7 and PMH, respectively. Pearson correlation analysis was applied to the log2 FC of 14 significantly deregulated genes obtained by RNA-seq and RT-qPCR, as shown in the Supplementary Material, and indicates a very strong correlation. Reverse Transcription-Quantitative PCR RNA was extracted from cultured cells and depleted for DNA, as described above. RNA concentration and purity were assessed using a NanoDrop spectrophotometer (Thermo Scientific). One microgram of total RNA was used for reverse transcription with the LunaScript ™ RT SuperMix Kit (NEB).
e. , Ccl5, Ifit3b, Ifr7, Isg15, Zbp1, Ifi44, Ifit1, Ifit3, Oas3, Apol9b, Oas2l, Oas1h, Tgtp1, Rsad2) (Supplementary Table  S6 ). Indeed, 166 (37%) and 126 (33%) of the upregulated DEGs in HepG2 and PMH, respectively, were mapped as ISGs in the interferome database  (Rusinova et al. , 2013) , of which 56 were common to both HepG2 and PMH datasets (Supplementary Table  S8 ). In agreement with this, Gene Ontology of Biological Processes (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that genes which were upregulated by infection in HepG2 and PMH cells significantly clustered in functions associated with viral infections and IFN responses (Supplementary Table  S9 ). In contrast, upregulated DEGs in Huh7 cells were associated with entirely different biological processes and pathways, the most significant being "cell division" (GOBP) and "cell cycle" (KEGG) processes.
This striking difference was also highlighted by the very limited overlap of upregulated DEGs between the three systems, with only 17 common genes (Figure  2D ). 16 of these 17 genes were nonetheless categorized as ISGs (Table  1  and Supplementary Table  S8 ). With respect to downregulated DEGs, only 69 were common to all three datasets (Figure  2E ). However, intersecting DEGs from only two datasets yielded a significant number of genes commonly downregulated (i. e. , 380 DEGs common to HepG2 and Huh7 and 360 common to Huh7 and PMH). Functional analysis of each individual or intersecting dataset (Supplementary Table  S10 ) highlighted the "coagulation and complement cascades" as one of the top 3 KEGG pathways most significantly perturbed by infection (Figure  2F ). Ingenuity Pathway Analysis (IPA) also identified the "coagulation system" and "complement system" among the top 15 statistically significant canonical pathways altered by long-term Listeria infection (Figure  2G ).
Consistent with the fact that most complement proteins and several coagulation factors are acute phase proteins (APPs), the "acute phase response (APR) signaling" pathway was also significantly deregulated. It should be noted that in these pathways, the C1S complement component and SERPINA1 are encoded by a single gene in the human genome and multiple paralogs in the mouse genome. We thus included these two genes to the overlapping DEG datasets, leading to 71 genes (Huh7- HepG2-PMH, Supplementary Table  S11A ), 380 genes (Huh7-HepG2, Supplementary Table  S11B ) and 362 genes (Huh7-PMH, Table  S11C ), whose under-expression could be used as markers associated with Listeria persistent infection in hepatocytes (Supplementary Table  S11 ). In summary, the transcriptional signature of long-term Listeria infection is characterized by the robust activation of interferon response genes in HepG2 and PMH cells, and the inhibition of APR genes in all three hepatocyte models, with the downregulation of a large number of genes involved in the complement and coagulation cascades.
Different Interferon Responses to Listeria Infection in HepG2, Huh7, and PMH Cells The Listeria-induced interferon responses in HepG2 and PMH, strikingly absent in Huh7, prompted us to further examine the expression of IFN genes. We first analyzed the RNA-seq data to have information about the absolute expression levels (fragments per kilobase of transcript per million mapped reads, FPKM) of IFN transcripts coding for either type I IFNs (IFN-I: IFN-a subtypes, IFN-b, -ϵ, -k, -d, -z, -w), type II IFN (IFN-II: IFN-g), or type III IFNs (IFN-III: IFN-l1, -l2, -l3). A threshold FPKM value of 0. 3 was applied to define detectable expression above background, as previously described  (Ramskold et al. , 2009). No IFN gene transcripts were detected in non-infected samples of any of the three models, nor in infected Huh7 samples (Figure  3A ).
, 2004; Wollam and Antebi, 2011; Yin et al. , 2011). This was confirmed at the protein level, as infection decreased the amount of secreted AHSG and apolipoproteins APOA1, APOE and APOH, in both HepG2 and Huh7 conditioned media, as well as of APOB, APOC3 and SERPINF2 in Huh7 (Supplementary Table  S12 ). In addition, genes involved in bile acid and cholesterol biosynthesis pathways, namely bile acid-CoA:amino acid N-acyltransferase (BAAT) and 3-hydroxy-3methylglutaryl-CoA synthase 2 (HMGCS2), respectively, and several apolipoprotein genes (APOA1, APOA4, APOC1, APOE, APOH) were downregulated PMH as in Huh7 cells (Supplementary Table  S11 ). It was particularly striking that cholesterol metabolism-related pathways were the most highly significantly altered pathways in HepG2 and Huh7 transcriptome datasets, as shown by KEGG (Figure  3G ) and IPA enrichment analysis (Figure  3F ).
01, see Supplementary Material). Proteins with significant decreases, expressed as log2 (I/NI) SC ratio, were then selected on the basis of a decrease in RNA level of the corresponding gene in the transcriptome dataset, resulting in the identification of 25 and 82 proteins in HepG2 and HuH7, respectively (see Supplementary Table  S12 ), of which 17 and 41 were APPs. (a) For eight proteins with SC=0 in the Huh7 infected condition, an arbitrary value of SC=1 was used to allow the calculation of a log2 ratio (in this case, the value is preceded by "<"). pathway were also downregulated, including the rate limiting enzyme cytochrome P450 family 7 subfamily A member 1 (CYP7A1). Together, these results suggest that long-term Listeria infection disturbs the expression of cholesterogenic-, lipogenic-and bile acid-associated genes.
ACKNOWLEDGMENTS We are very grateful to  Alexandra Gruss ,  Mélanie Hamon ,  Anne Gabory  and  Sébastien Bloyer , for helpful discussions, and to platforms PAPPSO (for proteomics),  @Bridge  (for Bioanalyzer) and IERP (for animal facilities) of  Inrae, Jouy-en-Josas. We also wish to thank the high-throughput sequencing facility of I2BC ( Gif-sur-Yvette ) and the  Genotoul bioinformatics platform Toulouse Occitanie  (http://bioinfo. genotoul. fr)for providing computing and storage resources. HB wishes to acknowledge  INRAE-MICA division , the  Fonds France-Canada pour la recherche (FFCR) ,  the iXcore Foundation for Research, French Agence Nationale de la Recherche (ANR)  and programme d'investissements d'avenir (PIA).
FUNDING This work was funded by grants from  FFRC FCRF  (grant  2017 ) to HB and LG,  ANR PERMALI  ( N°ANR-20-CE35-0001-01 ),  ANR TheraEpi  ( N°ANR-20-PAMR-0011 ) and iXcore Foundation (2015) to HB, and  INRAE-MICA division  ( AAP 2019 ) to CA.
DATA AVAILABILITY STATEMENT The RNA-seq data presented in the study are deposited in the Gene Expression Omnibus (GEO) repository (https://www. ncbi. nlm. nih. gov/geo/), accession numbers GSE184697, GSE184729 and GSE184808. The MS proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository (https://www. ebi. ac. uk/pride/), accession number PXD027154.
Sample collection Tumor samples and retrospective clinical data from 40 patients were obtained from multiple national and international collaborating centers and collected at the Department of Neuropathology of the University Hospital Heidelberg (Germany). Sample selection was based on unsupervised visualization of genome-wide DNA methylation data that revealed a molecularly distinct group of tumors (n = 40) forming a cluster separate from all established entities. A proportion of data was generated in the context of the Molecular Neuropathology 2. 0 study. Analysis of tissue and clinical data was performed in accordance with local ethics regulations. Clinical details of the patients are listed in Supplementary Table  1  (online resource).
DNA methylation array processing and copy number profiling Genome-wide DNA methylation profiling of all samples was performed using the Infinium MethylationEPIC (EPIC) BeadChip (Illumina, San Diego, CA, USA) or Infinium HumanMethylation450 (450k) BeadChip array (Illumina) according to the manufacturer's instructions and as previously described  14. Raw data were generated at the Department of Neuropathology of the University Hospital Heidelberg, the Genomics and Proteomics Core Facility of the German Cancer Research Center (DKFZ) or at respective international collaborator institutes, using both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue samples. All computational analyses were performed in R version 3. 6. 0 (R Development Core Team, 2016; https://www. R-project. org).
Copy-number variation analysis from 450k and EPIC methylation array data was performed using the conumee Bioconductor package version 1. 12. 0  16. Raw signal intensities were obtained from IDAT-files using the minfi Bioconductor package version 1. 21. 4. Illumina EPIC and 450k samples were merged to a combined data set by selecting the intersection of probes present on both arrays (combineArrays function, minfi). Each sample was individually normalized by performing a background correction (shifting of the 5% percentile of negative control probe intensities to 0) and a dye-bias correction (scaling of the mean of normalization control probe intensities to 10,000) for both color channels. Subsequently, a correction for the array type (450k/EPIC) was performed by fitting univariable, linear models to the log2-transformed intensity values (removeBatchEffect function, limma package version 3.
30. 11). The methylated and unmethylated signals were corrected individually. Beta-values were calculated from the retransformed intensities using an offset of 100 (as recommended by Illumina). All samples were checked for duplicates by pairwise correlation of the genotyping probes on the 450k/EPIC array. To perform unsupervised non-linear dimension reduction, the remaining probes after standard filtering  14  were used to calculate the 1-variance weighted Pearson correlation between samples. The resulting distance matrix was used as input for t-SNE analysis (t-distributed stochastic neighbor embedding; Rtsne package version 0. 13). The following non-default parameters were applied: theta = 0, pca = F, max_iter = 10,000 perplexity = 20.
Fastq files from transcriptome sequencing were used for de novo annotation of fusion transcripts using the deFuse  18  and Arriba (v1. 2. 0)  19  algorithms with standard parameters. All further analysis was performed in R (version 3. 6. 0; R Core Team, 2019) using the DESeq2 package (v1. 28. 1)  20. Principal Component Analysis (PCA) was performed after variance stabilizing transformation of the count data and normalization with respect to library size, based on the selection of the top 1,000 most variable genes with relative log expression normalization. Similarities between samples were determined by computing Manhattan distances on the variance stabilized data followed by unsupervised hierarchical clustering. Differential expression testing was performed on raw count data after fitting a negative binomial model.
Statistical analysis Statistical analysis was performed using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA). Data on survival could be retrospectively retrieved for ten patients. Distribution of time to progression or recurrence (TTP) after surgery was estimated by the Kaplan-Meier method and compared between groups with the log-rank test. Patients lost to follow-up are censored at date of last contact in analysis of TTP. P-values below 0. 05 were considered significant.
Acknowledgements For excellent technical support we sincerely thank the  Microarray Unit of the German Cancer Research Center (DKFZ) Genomics and Proteomics Core Facility , as well as  I. Leis  and  M. Schalles  ( Department of Neuropathology, Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany ). We also thank the  German Childhood Cancer Foundation  for funding (" Neuropath 2. 0 -Increasing diagnostic accuracy in paediatric neurooncology " ( DKS 2015. 01 )). This study was supported by the  Hertie Network of Excellence in Clinical Neuroscience. P. Sievers is a fellow of the  Hertie Academy of Excellence in Clinical Neuroscience. S. C. Henneken  received scholarships of the  Mildred-Scheel doctoral program  of the  German Cancer Aid  and the  German Academic Scholarship Foundation.
S. Brandner was partly funded by the  National Institute of Health Research (NIHR) UCLH/UCL Biomedical Research Centre. A subset of the human tissue was obtained from  University College London NHS Foundation Trust as part of the UK Brain Archive Information Network (BRAIN UK , Ref:  19/001 ) which is funded by the  Medical Research Council and Brain Tumour Research UK. U. Schüller  was supported by the  Fördergemeinschaft Kinderkrebszentrum Hamburg.
In this work we utilise a set of 662 infant metagenomes coupled with viromes to identify and characterise the temperate phages in the gut at 1 year of age and evaluate degree of induction. The samples originate from subjects of the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) cohort, an ongoing mother-child cohort study followed since pregnancy and throughout early life with exhaustive phenotyping and sample collection. By using the paired metagenome and virome data we are able to address the role of prophages in this system; we were able to estimate if these phages are actively induced and whether this is affected by external variables such as antibiotic usage, and start to address the question of what their potential function might be through the analysis of accessory genes.
Metagenome and Virome Sequencing and Assembly. The same samples were used for both metagenome and virome sequencing. For virome sequencing they were filtered through a 0. 45 µm pore size PES filter (Minisart® High Flow Syringe Filter, Sartorius, Göttingen, Germany). For the metagenome sequencing DNA was extracted using the method of Mortensen et al  [42]  and the DNA was sequenced, and genomes assembled as described in Li et al  [43]. Sequencing reads have previously been deposited in the SRA under the accession: PRJNA715601. DNA for sequencing of the virome was extracted and sequenced as previously described in Deng et al  [44]. The virome sequencing reads will be deposited upon submission.
Putative Prophage Contig Identification, and classification. Putative prophage sequences were identified using a combination of two methods: DeepVirFinder (v1. 0)  [45]  and VIBRANT(v1. 0. 1)  [46]. Assemblies from all 662 metagenomes were concatenated into a single pool and filtered to those above a minimum length of 4 kb. These assemblies were run through DeepVirFinder (v1. 0) using default parameters after creating models from all known phage genomes, downloaded from the Millardlab database in September 2019  [47]. Resulting contigs were filtered to include only those with a p-value of <0. 05 after FDR correction. The assemblies were also run through VIBRANT (v1. 0) using the nucleotide input and parallelisation options. Only those predicted phages of 'medium quality' and above were used further.
Both sets of output were compared to the Refseq+plasmid database with a cut off of 95% identity using MASH (v2. 2)  [48]  and any contigs with matches that might be contamination were removed. Resulting contigs from both methods were then combined and dereplicated at 95% ANI with dedupe2. sh [49]. This set was also run through CheckV(v0. 7. 0)  [47]  to give the details required for the minimum information about uncultivated viral genomes (MIUViG)  [50]. Whilst measures have been taken to remove potential bacterial contamination in these sequences by applying appropriate cut-offs with the tools used, there is always the possibility that some bacterial data still remains and this should be considered in any future analysis. Additionally, whilst the phages identified in this work are referred to as prophages, it is important to remember that this assignment is putative, as the discovery of virulent phages is still possible from metagenomic data.