dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics All data were analyzed using Excel (Microsoft) and GraphPad Prism 6. For each comparison between control and treated groups, normal distribution was determined with GraphPad software (D'Agostino & Pearson omnibus and Shapiro-Wilk normality tests). When applicable (methodology based) investigators were blinded for the condition. Quantification of in vivo survival for every time points, for different positions, was calculated using two-way ANOVA. The overall temporal survival curve shown in Fig. 2R  was obtained by averaging all the analyzed positions from all the animals per stage. The statistical significance in the broadening of the survival curves was assessed via shared control Cummings estimation plot using the DABEST package in R with 5000 bootstraps resamples (https://www.
RNAseq raw data were processed and normalized using subread (version 1. 6. 0) and edgeR (version 3. 28. 0). Statistical data analysis was carried out using the Perseus tool suite for Omics data analysis  [33]. Two-tailed, unpaired permutationbased FDR Students' t test on biological replicates' mean difference was applied (FDR < 0. 05 and 250 randomizations). Log2 fold changes, mean differences, and p values are reported in the Gene expression dataset. The statistical difference of light-induced response ratio between control and treated retinas was calculated using a Wilcoxon rank-sum test. Statistical difference of open field behavioral test was calculated using a two-way ANOVA test.
5A-C ). Increased cone survival was accompanied by amelioration of vision-mediated behavior of rd10 animals injected bilaterally with TSA (Fig. 5D, E ). An improvement in cone function was also detected by electroretinography (ERG). At PN50, a modest but significant increase in photopic b-wave amplitudes was detected in TSA-treated retinas (Fig. 5F, G ). Collectively our data suggest that HDAC inhibition has the potential to support cone survival and function at late stages of photoreceptor degeneration in different RP mouse models.
HDAC inhibition induces global changes in genetranscription patterns in the surviving cones We performed whole transcriptome analysis of flow-sorted rd1 TN-XL cones from retinal explants after seven days in culture (PN19-26). TN-XL-positive cells represented 95% of FASC-sorted cells, suggesting a highly purified cone population (SI Appendix, Fig. S5 ). RNA-seq analysis of differentially expressed genes (DEG) in TSA-treated vs. untreated cones revealed 1845 genes with significantly different expression (enrichment of 1163 and reduction in 682 genes), as indicated in the volcano plot (Fig. 6A , Supplementary gene expression dataset). To identify signature trends for activation or inhibition of downstream pathways, we used protein-coding genes with significant differential expression following the TSA treatment (p ≤ 0.
LINCS analysis indicated that also vorinostat may be repurposed for RP treatment. Notably, the clinical data already available  [71]  would greatly facilitate the repurposing for RP treatment, while the side-effect profile associated with systemic application in cancer therapy would likely be of lesser concern if these drugs were used only locally in the eye. Our previous studies showed HDAC overactivation at the peak of photoreceptor loss in ten rodent models for retinal dystrophies  [15, 35]. Aberrant HDAC activity was detected in photoreceptors regardless of whether underlying mutations affect the phototransduction cascade (rd1, rd10, Rho KO, S334ter, P23H, Cngb1 KO, Cnga3 KO, cpfl1), the photoreceptor outer segment structure (rd2) or the visual cycle (Rpe65 KO). This suggests that increased HDAC activity may contribute to the mutation-related photoreceptor cell death caused by cGMP accumulation  [69] , endoplasmic reticulum stress  [72, 73] , or microglia activation  [74].
Acknowledgements We are thankful to  Klaudija Masarini ,  Norman Rieger ,  Patricia Velasco ,  Coni Imsand  for technical assistance and  Nicolas Casadei  for the help in RNA-seq. We are grateful to  Botond Roska  for inspiring scientific discussions. We thank  Irena Stingl  for graphical design. This work was supported by the  ProRetina foundation , the  Kerstan Foundation, Deutsche Forschungsgemeinschaft  ( DFG TR 1238/4-1 ),  Swiss National Science Foundation  ( 31003A_173008 ),  BMBF  (FKZ:  01EK1613E ), and  GC2018-098557-B-I00  from  MCIU/ AEI/FEDER ,  UE. RGS is a recipient of a JIN grant  RTI2018-098990-J-I00  from  MCIU, Spain. We acknowledge support by  Open Access Publishing Fund of the University of Tübingen.
Acknowledgements Peta Neale  was supported by an  Australian Academy of Science France and Europe Mobility Grant (Bede Morris Memorial Fund). Lucia Toporova  and  Clémentine Garoche  (both  Institut de Recherche en Cancérologie de Montpellier ) are thanked for assistance in the laboratory. Kambez Akrami  ( Logan City Council ) is thanked for assistance with sampling. The work was partly funded by the  PNRPE  project  PestR  and by the  ANSES  Project  TOXCHEM  (  EST-2018/1/095  ).
Setting and Study Population We conducted a multicenter retrospective cohort study in the ICUs of six hospitals (Dunkerque, Lens, Lille, Roubaix, Tourcoing, and Valenciennes) in the north of France over a period of seven years (1 January 2011 through 1 January 2018). Lille Hospital is an academic hospital. All other hospitals are general hospitals. All consecutive patients treated for ESBL-E BSI during the study period were retrospectively included. Cases were identified using a laboratory database query completed with ICU clinical databased analysis. Cases were defined as adults with blood culture(s) yielding ESBL-E within 24 h prior to ICU admission or during the ICU stay. In the case of several positive ESBL-E bacteremias during the same infectious episode, only the first positive blood culture was considered for analysis.
Patients who had multiple episodes of ESBL-E BSI were included only once in the analysis, and subsequent episodes were considered recurrences or re-infections. Enterobacteriaceae identification and in vitro antimicrobial susceptibility testing were performed with a Vitek 2 system (bioMérieux, Marcy l'Etoile, France). ESBL diagnosis and susceptibility testing were carried out according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints  [8]. Our study was carried out in accordance with national guidelines concerning an observational study conducted retrospectively on collected data (article R. 1121-1-1, Décret no. 2017-884 du 9 mai 2017). The present study obtained ethical approval from the local ethicscommittee ofDron Hospital (comitéd'éthique du Centre Hospitalier Gustave Dron) Ethic code: CNIL 2019-10.
Data Collection and Definitions The following data were recorded: demographic characteristics, indication(s) of ICU admission, underlying clinical conditions, immunodeficiency, and severity of illness at admission. The underlying diseases were classified with criteria proposed by McCabe and Jackson  [9]. Immunodeficiency was defined as neoplasia, neutropenia (neutrophil count <0. 5 × 10 9 cells/L), treatment with glucocorticosteroids, and/or other immunosuppressive therapy, AIDS, as defined by the U. S. Centers for Disease Control and Prevention. Severity of illness was assessed by Simplified Acute Physiology Score II (SAPS II) and Sepsis-related Organ Failure Assessment (SOFA) score  [10, 11]. At the time of BSI diagnosis, we recorded prior antimicrobial therapy within one month before BSI, prior ESBL-E colonization, duration of hospital and ICU stay before BSI, severity of illness, presence of shock, usual biochemical, and hematological tests.
Demographic and Clinical Data In Figure  1 , the flow chart of the study inclusion process is reported (Figure  1 ). Of the 334 critically ill patients selected for having ESBL-E BSI during the study period, 27 were excluded from the analysis. In four cases, data were missing with regard to empirical antimicrobial prescriptions. In 23 cases, patients received aminoglycosides only as definitive treatment. Among the 307 patients included, 169 were treated with aminoglycoside as the empirical treatment and constituted the group with aminoglycoside; 138 did not received aminoglycoside at any time and constituted the group without aminoglycoside. 23 cases, patients received aminoglycosides only as definitive treatment. Among the 307 patients included, 169 were treated with aminoglycoside as the empirical treatment and constituted the group with aminoglycoside; 138 did not received aminoglycoside at any time and constituted the group without aminoglycoside.
Hence, this study will not detail the complex value chain for every single biomass or glycomics, nor to give precise figures in terms of volume or revenues concerning the numerous segmented markets. For analytical reasons, this article is not exhaustive in terms of intermediary molecules and potential applications. Our data are from sectoral reports, scientific journals, and interviews with scientists and representatives of industries and institutions (see Supplemental Material for more details about the qualitative method used to develop this interdisciplinary article).
Box 3: The Automated Glycan Assembly (AGA) niche This value chain relies on the accumulated knowledge on glycan structures, distribution, etc. , and now benefits of the inclusion of this information in many glyco-related databases. The consolidation of databases is in progress due to the overcome of some related technical challenges. Analysis of the structures of individual glycans, their distribution on cells and tissues, their relations to each other and their interaction with proteins and lipids, produce a huge volume of data, high-performance computing, and comprehensive information search and data extraction. A growing number of academic and commercial organizations is working on integrating disparate types of data, from various tools and instruments, and are developing new online searchable and bioinformatics platforms.
Some examples of these databases include GlycoCosmos (https://glycosmos. org/), Glyco3D  (Perez et al. , 2017) , GlycoStore  (Zhao et al. , 2018) , Kegg Glycan (https://www. genome. jp›kegg›glycan) and UniCarb (https://unicarb-db. expasy. org). These improvements are easing data interpretation for less experienced glycoscientists. New developments are emerging where software applications based on machine learning trained on both experimental and computational data will allow the transition of glycoanalytics to high-throughput use and faster detection of novel drug-diseases relationships and will facilitate the implementation of precision medicine. Free access to data online are re-shaping scientific and industrial practices. The challenge by firms in a sustained open innovation system is how to deal with the apparent contradictions related to contributing to and exploiting open data, and protecting their business  (Price, 2015).
The libraries were combined with other libraries from unrelated projects and sequenced on an Illumina NextSeq 500 instrument (150 bp cycles, paired ends). Raw sequencing datasets are available in the Sequence Read Archive (SRA) database under accession number PRJNA556544. The sequencing data were processed following a pipeline described elsewhere  (Lequime et al. 2017). In brief, sequencing reads with a quality score less than thirty were trimmed using Trimmomatic v0. 36  (Bolger, Lohse, and Usadel 2014). Reads were mapped to the host reference genome (GCA_001876365. 2, VectorBase) using Bowtie2 v2. 3. 4. 3, and remaining reads were subjected to de novo assembly with Ray v2. 3. 1-mpi followed by BLAST search and selection of viral contigs in Geneious v. 10 (http://www. geneious. com/) (Boisvert, Laviolette, and Corbeil 2010; Langmead and Salzberg 2012).
A seeded de novo assembly with IVA v1. 0. 3 was applied to verify previously obtained contigs  (Hunt et al. 2015). Consensus CFAV genome sequences were constructed by scaffold mapping with Bowtie2 v2. 3. 4. 3. The 3 0 and 5 0 ends of the consensus CFAV genome sequences from the isolated viruses were verified by rapid amplification of cDNA ends (RACE) using 5 0 /3 0 RACE kit, second Generation (Roche), following manufacturer's instructions with prior Poly(A) addition to 3 0 end of RNA using E. coli Poly(A) Polymerase (New England Biolabs). CFAV full-genome sequences are available in the European Nucleotide Archive (ENA) database (PRJEB33690: LR694076, LR694077, LR694078). For all viruses, single-nucleotide variant (SNV) frequencies and coverage were calculated using LoFreq v2. 1. 3. 1 and bedtools v2.
Reads were assembled using SPAdes v3. 7. 0 and contigs were annotated using BLAST  (Altschul 1997; Bankevich et al. 2012). The preassembled genome sequences were used in a second step to map the reads using CLC Genomics Assembly Cell v5. 1 (https:// www. qiagenbioinformatics. com). Read mapping files with CFAV reference sequences are available in the SRA database under accession number PRJNA556544. For the mosquito pool from Guadeloupe, the CFAV genome sequence was obtained using the NetoVIR protocol for viral metagenomics  (Conceicacão-Neto et al. 2015). In brief, whole mosquitoes were homogenized, centrifuged, and filtered to enrich for viral particles. The filtrate was treated with Benzonase (Novagen) and Micrococcal Nuclease (New England Biolabs) to digest free-floating nucleic acids. DNA and RNA were extracted (QIAGEN Viral RNA mini kit), reverse transcribed, and randomly amplified using a slightly modified Whole Transcriptome Amplification 2 (WTA2) Kit procedure (Sigma-Aldrich).
WTA2 products were purified and used to prepare libraries using the Nextera XT Library Preparation Kit (Illumina). Libraries were sequenced on an Illumina NextSeq 500 instrument (300 bp cycles, paired ends). The reads were cleaned with Trimmomatic (Bolger, Lohse, and Usadel 2014) and de novo assembled into contigs using metaSPAdes  (Nurk et al. 2017) , followed by annotation using DIAMMOND  (Buchfink, Xie, and Huson 2015). Raw reads were remapped to the closest available CFAV genome sequence from Macapa, Brazil (CFAV-Macapa2  (Fernandes et al. 2018 )) using BWA  (Li and Durbin 2009). A 20-nucleotide gap and a few ambiguous nucleotides were resolved by PCR and Sanger sequencing. The primers and PCR-verified positions are provided in Supplementary material S1. The raw sequencing dataset of the mosquito pool from Guadeloupe is available in the SRA database under accession number SAMN10762280.
The three consensus CFAV sequences obtained by untargeted viral metagenomics were ultimately verified by read mapping with Bowtie2 v2. 3. 4. 3  (Langmead and Salzberg 2012). All three CFAV sequences from untargeted metagenomics of A. aegypti are available in the ENA under accession numbers (PRJEB33690: LR694079, LR694080, LR694081). SNV frequencies and coverage were calculated using LoFreq v2. 1. 3. 1 and bedtools v2. 25. 0, respectively  (Quinlan and Hall 2010; Wilm et al. 2012). The coverage and SNV frequency results were visualized using ggplot2 v3. 2. 0 and cowplot v0. 9. 4 packages in R v3. 5. 2 (R Development Core Team 2013; Wickham 2016; Wilke 2019).
Survey of published CFAV genome sequences The oldest wild-type CFAV full-genome sequence is available in GenBank under accession number GQ165810 and was obtained from a strain named Rio Piedras02  (Cook et al. 2009 ) initially detected by RT-PCR in mosquitoes from Puerto Rico in 2002  (Cook 2006). In 2011, CFAV was isolated from the first laboratory generation of A. aegypti mosquitoes from Axochiapan, Morelos, Mexico and the full polyprotein sequence is available in GenBank under accession number KJ476731. In 2012, a fullgenome sequence was obtained from a CFAV isolate derived from an A. aegypti laboratory colony originating in Galveston, Texas, USA  (Bolling et al. 2015)  and is available in GenBank under accession number KJ741267. A full-genome sequence available in European Nucleotide Archive under accession number LR596014 was obtained from a CFAV strain recently isolated from an A.
aegypti laboratory colony originating in Kamphaeng Phet, Thailand in 2013  (Baidaliuk et al. 2019). Two nearly full CFAV genome sequences derived from RNA-seq data of A. aegypti specimens caught in Cairns, Australia and Bangkok, Thailand in 2014 and 2015, respectively  (Zakrzewski et al. 2018) , were requested directly from the authors. CFAV genome sequences were remapped to obtain a consensus sequence and calculate SNV frequencies and coverage as described above. Two nearly full CFAV genomes were detected in wild-caught A. aegypti specimens from Miami, USA in 2016  (Metsky et al. 2017). Reads were de novo assembled to generate consensus genome sequences and calculate coverage and SNV frequencies as described above. The consensus sequences from both studies are now available in the ENA (PRJEB33801: LR694072, LR694073, LR694074, LR694075).
Another set of nearly full-genome sequences of CFAV was detected in RNA-seq libraries of mosquitoes from Macapa, Amapa, Brazil, not only in A. aegypti but also in Culex spp. pools in 2017  (Fernandes et al. 2018 ) and were requested directly from the authors. In addition to mosquitoderived CFAV sequences, the CFAV strain persistently infecting the Aag2 cell line was sequenced on four separate occasions and the genome sequences are available in GenBank under accession numbers M91671, KU936054, MH237596, and MH310082  (Cammisa-Parks et al. 1992; Maringer et al. 2017; Di Giallonardo et al. 2018; Weger-Lucarelli et al. 2018). The main phylogenetic analyses relied on full or nearly full CFAV genomes, but additional analyses incorporated several partial CFAV genome sequences that are available from Github (https://github.
CFAV phylogenetic analyses All CFAV genome sequences used in this study and their genetic annotations according to  Blitvich and Firth (2015)  are available from Github (https://github. com/artembaidaliuk/CFAV-cISF-phy logeny-2019. git). Nucleotide alignments were prepared using local pairwise alignment (L-INS-i) in MAFFT v. 7. 407  (Katoh 2002)  for the three subsets of sequences: (i) only sequences with full or nearly full ORF available, (ii) sequences that were used to test phylogenetic congruence in SE Asia, and (iii) full ORFs and partial sequences. Full-ORF alignment was subjected to recombination tests in RDP4 v. 4. 95  (Martin et al. 2015). Alignment positions with >20 per cent gaps, if any, were trimmed in the initial alignments using Goalign v. 0. 2. 7 (https://github. com/evol bioinfo/goalign).
Final single-gene alignments were concatenated into a single alignment. All single-gene alignments as well as a full-ORF alignment of the first subset of sequences were used for nucleotide substitution model search by ModelFinder in  IQ-TREE v. 1. 6. 3 and v. 1. 6. 10 (Nguyen et al. 2015; Kalyaanamoorthy et al. 2017 ). The best model for each alignment was chosen based on the bayesian information criterion (BIC) score for the most comprehensive dataset (i) (Supplementary material 2). In data subsets (i) and (iii), an edgeunlinked partition model with a separate substitution model for each genome region was tested; however, none of the partition schemes resulted in a better model than a single model for the full ORF according to BIC scores. The test was performed using the ÀMFþMERGE option in IQ-TREE.
The best substitution model for the ORF alignment of subset (i) and the concatenated alignment of subset (iii) was GTRþFþG4 and two almost equally best models for the ORF alignment of subset (ii) were GTRþF þ I and GTRþFþG4, therefore, the latter was chosen for the following tree reconstruction of all three data subsets. Consensus phylogenetic trees were reconstructed in IQ-TREE from 1,000 ultrafast bootstrap maximum likelihood (ML) tree replicates. The trees were built as unrooted and visualized with midpoint rooting because including the closest possible outgroup sequences did not allow reliable rooting. In data subset (ii), constrained trees were built following different clustering scenarios and tested for the differences in topology with an unconstrained tree using approximately unbiased (AU) test implemented in IQ-TREE  (Shimodaira 2002).
cISF phylogenetic analyses All full or nearly full-ORF cISF sequences available in GenBank (March 2019) and the novel CFAV sequences were used for recombination and phylogenetic analyses. The full data subset of cISF sequences, their annotations based on available sources, and metadata are available from Github (https://github. com/ artembaidaliuk/CFAV-cISF-phylogeny-2019. git). Amino-acid alignments of each protein were obtained by pairwise alignment (L-INS-I, BLOSUM62) in MAFFT v. 7. 407. After trimming positions containing >20 per cent of gaps with Goalign v. 0. 2. 7, both the amino-acid and back-translated nucleotide alignments were kept for further analyses. Amino-acid guided nucleotide alignments of all genes were concatenated into a full-ORF alignment and subjected to recombination tests in RDP4 v4.
95. The protein alignments containing three outgroup sequences (dengue virus 1, tick-borne encephalitis virus, and Rio Bravo virus) are available from Github (https://github. com/artembaidaliuk/ CFAV-cISF-phylogeny-2019. git). They were concatenated into structural (C-prM-E) and nonstructural (NS1-NS2A-NS2B-NS3-NS4A-NS4B-NS5) protein alignments as well as a full polyprotein alignment. The best substitution model according to BIC scores was selected by ModelFinder in IQ-TREE v. 1. 6. 12. For all three alignments, LGþFþR5 was the best model, and it was used to reconstruct consensus ML trees from 1,000 ultrafast bootstrap replicates in IQ-TREE based on structural and nonstructural fractions of the polyprotein. In all phylogenetic analyses, Biopython was used for data manipulation and visualization  (Cock et al.
In addition to the six novel CFAV sequences that we generated in this study, we assembled two nearly full genomes of CFAV (CFAV-FL-05-MOS and CFAV-FL-08-MOS) that had been previously detected in cDNA libraries from pools of wild-caught A. aegypti in FL, USA  (Metsky et al. 2017). We also remapped two CFAV genome sequences from Thailand and Australia (named CFAV-Bangkok and CFAV-Cairns, respectively) that were previously obtained from RNA sequencing of wild-caught A. aegypti pools  (Zakrzewski et al. 2018). We first examined the patterns of minority genetic variants in all the CFAV deep-sequencing datasets available (Supplementary Fig. S1 ). With the exception of CFAV-PP, CFAV isolates (CFAV-Zik, CFAV-KC, and previously reported CFAV-KPP  (Baidaliuk et   All the novel CFAV consensus sequences described above were combined with other published CFAV sequences, including four distinct CFAV genome sequences derived from the Aag2 cell line (https://github.
We selected a subset of the CFAV full-genome sequences that included the four sequences from Cambodia, both sequences from Thailand, three sequences from the Americas (Puerto Rico, Mexico, and USA), and the single sequence from Africa (Uganda). This subset of sequences was chosen to exclude sequences from metagenomics datasets with potentially mixed infections, and to minimize the number of alternative topologies. We used this subset of wild-type CFAV sequences to build ORF phylogenetic trees that had a constrained topology between the clades and unconstrained topology within the clades. Using an AU tree topology test performed on unrooted trees, we found that alternative tree topologies (sequences from Cambodia and Thailand constrained to group together, or sequences from Cambodia and the Americas constrained to group together) were significantly less likely than the unconstrained topology (p ¼ 0.