dataesr/scientific-paragraphs-categorization huggingface dataset:
Ethical approval This retrospective study (AppendAmbu II) was declared to the Commission National de l'Information et des Libertés (CNIL) (Study DRCI T181). The AppendAmbu study was approved by the "Agence Nationale de Sécurité du Médicament et des produits de santé" (ANSM), the "Comité de Protection des Personnes -Nord Ouest II" (CPP) ethics committee and the CNIL. The study was registered on www. clinicaltrials. gov with the number NCT01839435.
Statistical analysis The characteristics of the study population are described in terms of mean ± standard deviation and median (minimum -maximum) for quantitative variables and frequency and 95% confidence interval for qualitative variables. For analysis of the primary endpoint, the DCS success rate is reported with a 95% confidence interval. For analysis of the secondary endpoints, the unplanned consultation, unplanned readmission and unplanned reoperation rates are reported as percentages. The morbidity (assessed by Clavien-Dindo classification) is expressed as a percentage for each rank. Statistical analysis was performed with SAS® version 9. 2 software (SAS Institute, Cary, NC). A p value ≤ 0. 05 was considered to be statistically significant.
4% in a nationwide cohort in the UK) may also be suitable for DCS. It could be particularly useful in patients with localized peritonitis (pus in no more than 2 quadrants) as recently published by our team in a dedicated video survey study  (21) ), for whom a strategy without postoperative antibiotic therapy is currently being studied (ABAP trial, NCT03688295). However the definition of uncomplicated appendicitis remains not unclear. In our study, it was defined as the absence of abscess, localized or generalized peritonitis, fecaliths or penumoperitoneum. It should be noted that the definition ranged from 0% for a score of 0 to 80% for a score of 5. DCS is therefore proposed to patients with a St Antoine score of 4 or 5. Among these data, CRP level is not usually ordered in the workup of appendicitis, however some data suggest than CRP is valid in distinguishing complicated and uncomplicated appendicitis.
Neurons from these two pathways are morphologically identical but have distinct roles in basal ganglia networks, highlighting the need to deeply characterize their common and specific features. We used RNA sequencing (RNA-seq) and microarray hybridization to identify differential mRNA profiles and analyze the mi-croRNA (miRNA) pool of striatal cell populations, respectively. The analyses of differentially expressed (DE) genes highlight gene expression signatures specific to each population. The integration of these data with miRNA and epigenetic profiles emphasizes complex mechanisms of transcriptional regulation in the striatum involving the activity of selective transcription factors (TFs) and a role for genome tridimensional folding. Finally, analysis of databases on HD underlines differential transcriptomic and epigenetic changes in striatal neurons and glial cells.
, 2014; Han et al. , 2010)  (Figure  2F ). More precisely, high-confidence Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database annotations of the highly enriched (log fold-change [logFC] >1. 5 versus reference) genes in the blue-gene network signature of dSPNs, which includes Trim12a, Usp50 B3gnt3, Hist1h1d, Lsm7, Mrc1, Map3k7cl, and 29 other genes, representing $35% of the 105 genes retained in this signature (including Drd1; LCF = 1. 08), show these genes are implicated in mRNA splicing, extrinsic apoptosis signaling, mRNA splicing, extracellular matrix, and 1-phosphatidylinositol-3-kinase activity (Figure  S7A ). We detected no overlap with previously reported and larger signatures of human striatal neurons  (Kelley et al. , 2018)  (Figure  S7B ). The same appears to apply to the comparison with previously reported and larger RNA-seq signatures of mouse D1 striatal neurons, as inferred from the data made available in this study  (Gokce et al.
, 2012). A single miRNA regulates multiple distinct mRNAs, whereas a single mRNA may be regulated by numerous independent miRNAs, which can be grouped by miRNA families based on their common target genes. We assessed whether the enrichment of mRNA in SPN and glia may be linked to the expression of corresponding miRNA families. According to this hypothesis, strong enrichment of one miRNA in one cell type should lead to limited expression of the corresponding target transcripts in these cells. Therefore, these transcripts should have a greater propensity to be DE in the other cell population (Figure  3C ). Information in miRNA-target databases suggested that SPN-enriched miRNAs (47) could regulate 4,299 predicted target mRNAs (miRTs; Figure  3D ), whereas glia-enriched miRNAs (28) could regulate 1,600 transcripts (Figure  3E ; Table  S4 ).
The Activity of Selective TFs Is Predicted to Modulate the Cellular Specificity of Gene Expression We next analyzed the enrichment of specific transcription-factor-binding sites (TFBSs) in the cell-type-enriched genes based on regulatory motif over-representation across genes or chromatin immunoprecipitation sequencing (ChIP-seq) datasets  (Zhou et al. , 2017). We identified 23 to 35 TFs per cell population, each showing significant enrichment of TFBSs among cell-typeenriched genes (Figure  4A ; Data S1). Some identified TFs are potential regulators of a large number of cell-type-enriched genes, such as RUNX1, which potentially binds to 570 genes among the 759 enriched in microglia. We then evaluated the relationship between the cell-type enrichment of TFs and the enrichment of TFBSs in DE genes in the same population.
, 2015; Hodges et al. , 2006; Langfelder et al. , 2016). We extracted genes significantly up-or downregulated in the striatum compared to healthy controls (p % 0. 001, according to Hodges et al. ; Figure  S4A )  (Hodges et al. , 2006). We retrieved 1,984 upregulated and 1,626 downregulated mouse orthologous genes. Genes significantly upregulated in HD were more highly represented in glia than SPNs (Figure  6B ), whereas we observed the opposite pattern for downregulated genes (Figure  6C ). We observed the same trends when considering each cell population separately (Figures  6D  and 6E ). In addition, we obtained similar results when intersecting our database with RNA-seq data generated in the striatum of R6/1 mice at the symptomatic stage  (Achour et al. , 2015)  (Figure  S4B ; Table  S6 ), thereby showing that neuronal and glial cells preferentially contributed to down-and upregulated genes, respectively.
(E) TF-binding site prediction obtained for the DEGs belonging to EPUs with a ratio of DEG to the total number of genes >50% for the SPN and glia contrasts. The profiles are consistent with the results obtained for total DEGs, suggesting that the transcriptional control of genes belonging to EPUs containing a significant number of cell-type-enriched genes may be mediated by TFs with a high probability of regulating the DEG. See also Table  S5. Finally, we performed a comparison of the network signatures obtained in our database to the whole striatum network signatures previously obtained in the allelic series of HD knockin mice (Hdh)  (Langfelder et al. , 2016). Such a comparison showed that several cell-type-enriched genes may be strongly deregulated in a polyQ-and age-dependent manner in the striatum of Hdh mice (Table  S3 , sheet 2, last columns after selecting ''True'' for all columns under a specific cell type).
Application of Our Database to Study the HD Epigenetic Signature We next studied the acetylation state of H3K27 (H3K27ac), which has been linked to gene downregulation in HD mouse striatum  (Achour et al. , 2015; Le Gras et al. , 2017) , to further understand how mHTT might alter transcriptional regulation in a cell-type-specific manner. We performed H3K27ac ChIP-seq using the striatum (e. g. , caudate nucleus) of grade 2 HD patients and control individuals. H3K27ac ChIP-seq data were also generated using their cerebellum, a tissue spared by the disease (Figures  S5A-S5C ). As expected, H3K27ac was specifically enriched at striatal markers (e. g. , PPP1R1B/DARPP32) in striatal samples and enriched at cerebellar markers (e. g. , NEUROD2) in cerebellar samples (Figure  S5D ). We then assessed regions that were differentially enriched in H3K27ac in HD versus control striata using a method that allows computing single H3K27ac values per gene.
Glial-specific genes were enriched in cluster A (Figure  7C ), which contained genes linked to functions related to exosomes, transcription, and myelin (Figure  S5F ). In contrast, neuron-specific genes were not enriched in cluster A, but they were enriched in cluster C (Figure  7C ), which displayed a significant neuronal signature (Figure  S5G ). Accordingly, clusters A and C were enriched in up-and downregulated genes in HD patients, respectively (Figure  7D ). More specifically, upregulated genes originating from both astrocytes and microglia were strongly enriched in cluster A, whereas downregulated genes originating from both dSPNs and iSPNs were strongly enriched in cluster C (Figure  7E ). We then performed a similar analysis, but integrating our database with the H3K27ac ChIP-seq and RNA-seq datasets previously generated using R6/1 striatum  (Achour et al.
DISCUSSION Multicellular interactions in the CNS are at the basis of complex and high-level functions. Understanding this rich cellular environment requires standardized and sensitive procedures. Breakthroughs in high-throughput profiling methods, from that of DNA to proteins, are at the basis of large international initiatives aiming to characterize specific brain regions  (Hawrylycz et al. , 2012; Thompson et al. , 2014)  (http://portal. brain-map. org/). However, approaches to define the roles and characterize individual CNS cell populations in adult mammals are still challenging. Here, we provide a method combining existing technologies to dissect independent cell populations in the adult CNS. Application of this method to the mouse striatum produced the first mRNA and miRNA database for the four major striatal cell types, allowing the possibility to investigate the regulation mechanisms of celltype-specific gene expression and the differential involvement of these cells in physiological and pathological conditions.
, 2016) , providing insight into the signaling pathways that may distinguish cell types. Here, we provide a novel resource for probing the molecular diversity and studying the biology of specific cell types in the mouse striatum, covering dSPNs, iSPNs, astrocytes, and microglia. Importantly, our data include transcriptomic signatures that are retained by our network analysis (i. e. , spectral decomposition), in which data are analyzed against probabilistic functional networks (i. e. , MouseNet) and the risk of false positives and false negatives is reduced by virtue of the integration of orthogonal datasets. Spectral decomposition of gene expression levels against probabilistic functional networks may retain genes not retained by traditional analysis of transcriptomic data, and vice versa.
Nuclear-Related Transcriptional Control: The Importance of Combining Multiple Levels of Analysis We took advantage of our transcriptomic database to assess potential mechanisms leading to these cell-type-specific patterns. Cell-type-specific miRNA profiles did not predict mRNA enrichment profiles. This result is consistent with previous studies showing the absence of a correlation between miRNA and mRNA expression levels in cancer cells  (Li et al. , 2015). It has been shown that other crucial steps for miRNA activity occur before loading into the RNA-induced silencing complex (RISC), such as the control of RISC loading by the endonuclease dicer (DICER) complex or the removal of the passenger strand to generate a mature complex, as well as the regulation of miRNA activity by other noncoding RNA (for review, see  Ha and Kim, 2014).
In particular, it has been previously demonstrated that DNA conformation is a strong regulator of TF activity and access to the promoter and that smaller EPUs can be distinguished based on DNA interactions  (Dixon et al. , 2012; Patel et al. , 2018; Zaytseva and Quinn, 2018). Indeed, we found the DNA structure in EPUs could be linked to cell-type-enriched genes in our data using previously published databases. This is coherent data showing that samples from different organs can display different EPU profiles and suggests a smaller and more integrated level of regulation by DNA folding  (Dixon et al. , 2012). Our dataset on striatal celltype-specific transcriptome in adult glial and neuronal subpopulations provides an opportunity to model and partially explain the transcriptional profiles by integrating current knowledge about TFs, their interactions with each other, and the DNA conformation that regulates TF binding at promoter levels.
1% Triton X-100. Slices were then washed three times, for 10 minutes each, in 1 x PBS and mounted on Superfrost+ slides, in Vectashield Fluorescence mounting medium with DAPI. Images showing the specificity of transgenic mice were acquired on an inverted Zeiss LSM 510 confocal microscope with LSM software (Zeiss, Carl Zeiss Microscopy GmbH, Go ¨ttingen, Germany). Images showing the loss of eGFP following direct freezing and the preservation of eGFP fluorescence with partial 4% PFA perfusion or our new procedure were acquired with a Zeiss AxioVision microscope (Zeiss, Carl Zeiss Microscopy GmbH, Go ¨ttingen, Germany).
miRNA microarray One hundred nanograms total RNA of each sample was labeled according to the instructions of the Agilent miRNA Complete Labeling and Hybridization Kit (Agilent Technologies, Basel, Switzerland). The labeled RNAs were hybridized to Agilent Mouse miRNA Microarrays (Release 19. 0, 8x60K; Agilent Technologies, Basel, Switzerland) with 1,247 mouse miRNAs represented, for 20 h at 55 C with rotation. After hybridization and washing, the arrays were scanned with an Agilent microarray scanner using high dynamic range settings as specified by the manufacturer. Agilent Feature Extraction Software v 10. 7. 3. 1 was used to extract the data. Samples were processed on two different microarray slides at six-month intervals, with one sample present on both batches to be used for batch-effect correction.
DE gene analysis RNaseq read counts were log2 transformed and normalized using the TMM algorithm from the edgeR package. The resulting data were transformed with the voom function from the limma package to fit a binomial-like distribution and the mean-variance bias was determined for further correction. MicroRNA expression levels were log2 transformed and normalized with the quantile normalization from the preprocessCore package (https://github. com/bmbolstad/preprocessCore). In both cases, the impact of normalization and transformation was determined by comparing the data distribution before and after processing with the custom scripts. Correct data distribution was essential before performing the next steps. We used principal component analysis (PCA) and hierarchical clustering to cluster samples based on their expression levels.
PCA was performed on all detected samples (16,058 mRNAs and 371 miRNAs) using the prcomp function with unscaled parameters. The data representation of the PCA was generated using the ggplot2 package (https://ggplot2. tidyverse. org/). Euclidean distances between the cell types were determined for the top 20% mRNA and miRNA with the highest variation coefficients across cell types and plotted using the heatmap. 2 package. This analysis allowed us to detect a batch effect between the two different microarray slides for miRNA measurements. We thus used the limma package (duplicateCorrelations function) to create a linear model taking into account cell population membership and the slide batch to correct expression values. Hierarchical clustering and PCA after correction revealed a strong reduction of the batch effect and were therefore used for data representation.
For both mRNA and miRNA profiles, a linear model was created to determine group membership of the samples (dSPN, iSPN, astrocytes, microglia) with the limma package  (Ritchie et al. , 2015). We determined the transcripts enriched in one cell population compared to all the others with the following contrasts: dSPN = dSPN À ðiSPN + Astrocytes + MicrogliaÞ 3 iSPN = iSPN À ðdSPN + Astrocytes + MicrogliaÞ 3 Astrocytes = Astrocytes À ðiSPN + dSPN + MicrogliaÞ 3 Microglia = Microglia À ðiSPN + dSPN + AstrocytesÞ 3 Given that dSPN and iSPN are two members of the same neuronal class, we performed an additional contrast to compare the SPN with glial cells: SPN = dSPN + iSPN 2 À Astrocytes + Microglia 2 The resulting data were transformed following the empirical Bayes (eBayes) algorithm and a fold change (in log2, logFC) and p value were computed for each gene for each contrast.
Variance scree plots showed most of the variance to be explained by the four first components. We therefore pairwise plotted each principal component and demonstrated that no sample clustering occurred based on the mice being of the C57BL/6 or FvB strain. For the evaluation of the impact of our method on cellular stress, we used the list of genes supplied with the Mouse Stress and Toxicity RT2 Profiler PCR array (QIAGEN). We used RSEM tool to compute FPKM from our raw data and compared the FPKM values with expression values obtained by Zhang and collaborators  (Zhang et al. , 2014). An heatmap was made to compare the expression levels of the selected genes between LCM and FACS method.
Sample correlation and comparison with the Allen Brain Atlas Following RNaseq data preprocessing, we computed the pairwise Pearson correlations between each sample for the expression levels of all detected mRNAs to determine the reproducibility between our samples. We then used the heatmap. 2 package for graphical representation of correlations, without sample clustering. We next evaluated the reproducibility of our method with published transcriptome studies. We separated the detected genes into low, medium, and highly expressed genes based on the quartile distribution of averaged log2 transformed and normalized expression levels between all cell populations. We randomly selected 100 genes of each of three categories and compared their expression by linear regression with probe intensity data obtained by the Allen Brain Study in mouse striatum to demonstrate the reproducibility of our database with already published expression profiles (http://mouse.
Network analysis of cell-type specific mRNA-seq data The raw sequencing reads were pre-processed to discard both adaptor sequences and low-quality reads using the Trimmomatic. The filtered reads were then mapped to the mouse reference genome mm10 (UCSC 2013 release) using Bowtie2 (http:// bowtie-bio. sourceforge. net/bowtie2/index. shtml). Read distributions were calculated using the featureCounts tool. We used DESeq2, available as an R package in Bioconductor (www. bioconductor. org), to generate the log fold-change (logFC) values. The ratio used to generate the logFC values is the ratio between gene expression levels in individual cell types and the average gene expression levels across cell types. We generated the networks that describe each cell type by performing spectral decomposition of the RNA-seq signal (logFC values) against the probabilistic functional network Mousenet v2, as previously described  (Tourette et al.
, 2014). Briefly, this approach maps entire gene expression datasets onto Mousenet v2 to identify sub-networks of closelyderegulated genes, reducing the risk of false positives and false negatives in raw gene expression data and highlighting bio-topological essentiality and centrality in these data. We then generated artificially-permuted datasets by randomly permuting the pairs of logFC values from the original logFC distribution to assess the specificity of the sub-networks generated using spectral decomposition of the signal. Using this approach, the global distribution of logFC values in permuted datasets remained the same compared to the original data. The number of permuted genes was set from 10% to 100% by 10% increments. For each permutation, we constructed the corresponding sub-networks and compared them to the original sub-networks by assessing the number of edges in common between the original and permuted sub-networks.
Such permutation analysis showed the sub-networks generated using Mousenet v2 for spectral decomposition of the signal to be specific to the RNA-seq datasets analyzed herein (data not shown). We explored the biological content of the network signature obtained for each cell type using the Enrichr database, selecting for GO annotations (Biological processes, KEGG pathways) and Reactome annotations. Annotations supported by a minimal number of nodes set to N > 9 and a P value < 10 À6 were considered optimal. These analyses were complemented with PubMed searches.
Cross-analysis with HD expression profiles We compared our cell-type enriched expression profiles with a published database of transcriptional alterations in HD patients  (Hodges et al. , 2006). Based on the publication, we selected significant genes with a p value < 0. 001 and classed them as increased (logFC > 0) or decreased (logFC < 0) in HD. We next converted the RefSeq IDs of significant probes to ENSEMBL IDs in each group using biomaRt. For probes without a RefSeq ID, we used the HUGO gene symbol for conversion to a RefSeq ID and, finally, to an ENSEMBL ID. Probes without RefSeq or HUGO gene IDs were discarded. Human ENSEMBL IDs from each group were then converted to mouse ortholog ENSEMBL IDs using biomaRt. As two microarray slides were used, we removed the duplicated genes at the end of the conversion to keep only single DE genes in HD with murine IDs.
Finally, we crossed these two lists with those of cell-type-enriched mRNAs to determine cellular enrichment of increased and decreased genes in HD. Volcano plots were built using the ggplot2 package to represent the proportion of altered genes in HD patients enriched in SPN or glia in our study. Heatmaps were generated with the heatmap. 2 package, as described above. Finally, we grouped potential cell-type enriched altered genes in HD by GO with the goseq package, as previously described in the package vignette, with a FDR threshold of 0. 1. Treemaps were created using the REVIGO graphical tool (http://revigo. irb. hr/).
ChipSeq data analysis For human samples, libraries were prepared using the NEXTflex ChIP-Seq Kit (V12. 10 2012) and sequenced on an Illumina Hiseq 2500 sequencer as single-end 50 bp reads following the manufacturer's instructions. Image analysis and base calling were performed using RTA v1. 18. 61 and CASAVA v1. 8. 2. Adaptor dimer reads were removed using DimerRemover v0. 9. 2 (https:// sourceforge. net/projects/dimerremover/). For the mouse samples, library preparation was described in  (Achour et al. , 2015). For human samples, reads were mapped to the human genome (hg19) using Bowtie1 v1. 0. 0 with the following arguments: -m 1-stratabest -y -S -l 40 -p 2. Peak calling was performed using the SICER. sh script of SICER v1. 1 with the following parameters: species: hg19, threshold for redundancy allowed for chip reads: 1, threshold for redundancy allowed for control reads: 1, window size: 200 bps, effective genome size as a fraction of the reference genome of hg19: 0.