dataesr/scientific-paragraphs-categorization huggingface dataset:
The 12 protein-coding genes found in the sequenced and annotated mt genome of D. caninum R166 were translated using translation table 9 representing the echinoderm and flatworm mt code. The amino acid sequences were concatenated and compared to the concatenated amino acid sequences obtained from 53 mt genomes from Anoplocephalidae, Dipylidiidae, Hymenolepididae, Paruterinidae and Taeniidae using MAFFT and resulted in an 80. 6% BLOSUM62 pairwise similarity after removal of all gaps in the alignment. Maximum Likelihood and Bayesian Inferences analysis were used to construct trees as described by Guo  [9]  using Schistosoma japonicum derived data as the outgroup. The 12S rDNA sequence from the newly generated D. caninum feline genotype mt sequence was compared to D. caninum 12S rDNA sequences recently deposited and analyzed by Low et al.
Results Sequencing of the 28S and 18S ribosomal DNA regions. DNA sequence analysis of a 655 bp region of the 28S ribosomal DNA region used in the D. caninum detection PCR (see Table  4 ) resulted in the identification of two unique groups when these DNA sequences were compared to the GenBank reference sequence (Table  5 ). One group exhibited 99. 5% DNA sequence identity towards the published reference sequence (AF023120) and 100% DNA sequence identity towards the canine derived D. caninum isolated at Clinvet (MH040832), whereas the other group (i. e. "feline genotype") exhibited a 93. 5% sequence identity towards the published reference sequence. As described below in detail, the "100% identity group" DNA isolates were almost all of dog and dog flea origin, whereas the "93. 5% identity group" DNA extracts almost all came from cats and fleas collected on cats.
RFLP and hydrolysis probe genotyping assays. Sequence data from 28S rDNA region targeted for the PCR revealed the presence of conserved nucleotide differences when comparing the two genotypes. These conserved differences allowed the design of an RFLP assay using the restriction enzyme StuI to generate a product that would allow direct discrimination between the two groups. The StuI recognition occurs twice in the feline genotype and will result in the fragmentation of the 653 bp feline PCR product into 404 bp, 223 bp and 26 bp, and occurs only once in the reference canine genotype, yielding 629 bp and 26 bp fragments from the 655 bp PCR canine product, thereby allowing a clear discrimination between the two identified groups. RFLP analysis was also performed on the 57 sequence verified samples to validate the RFLP assay for accuracy.
The approx. 13 kb fragment was sequenced using Illumina chemistry resulting in 215 263 reads with a mean read length of 196 bp resulting in > 3000X coverage of the expected mt genome. Mapping the reads to the D. caninum mt genome (AB732959) resulted in assembly of the complete mt genome from D. caninum R166 (representing the "cat genotype") with an mt genome size of 13 598 bp (Figure  2 ; GenBank accession number: MG587892). Nucleotide analysis revealed an overall base composition of 22. 3% A, 8. 8% C, 19. 7% G and 49. 1% T, resulting in a low GC content of 28. 5%. Direct DNA comparison of the complete mtDNA genomes of D. caninum R166 ("feline genotype" MG587892) and the reference D. caninum mtDNA genome (AB732959 and NC_021145; "canine genotype") indicate only a 78. 7% identity on the DNA level.
The DNA sequences of AB732959 and NC_021145 are identical, but they are annotated differently. Analyses of the CDS, rRNA and tRNA regions (36 in total) with the specific associated identities between the two mt genomes are represented in Table  7. The ATP8 coding gene, present in mammalian mt genomes, could not be observed in the D. caninum mt genome, which is in agreement with published tapeworm mt genomes. No STOP codon could be detected for the COX1 protein-coding region from MG587892 and differently annotated COX1 encoding sequences are reported for AB732959 and NC_021145. Both COX1 annotations for the "canine genotype" mt DNA are under review from GenBank (GenBank email communication). analysis. The patristic distance between the D. caninum canine and feline genotypes is larger than the patristic distance between any of the Echinococcus spp.
Discussion The molecular characterization of D. caninum isolates collected from dogs, cats, and in infected fleas collected either from dogs or cats allowed the identification of two distinct genotypes that clearly differ from each other. East et al. , 2013, collected D. caninum proglottids from six spotted hyena  [7]. Initial PCR amplification and sequencing of the 314 bp fragment indicated identical sequence data for the partial 12S mt rDNA region from all six proglottids. Comparison of 314 bp sequence data with two published D. caninum sequences revealed a high (99%) similarity to one sequence from Europe (accession number L49460. 1) but a considerably lower similarity (89%) to one sequence from Asia (accession number AB031362. 1). They selected one of the six samples and PCR amplified and sequenced 1176 bp of the 12S mt rDNA (accession number KF202097).
orientis (26 specimens) and Rhipicephalus sanguineus sensu lato (120 specimens) were collected from 29 dogs. PCR utilizing the primers we published in 2014  [3]  was performed to amplify the partial 28S rDNA gene region of D. caninum. They found 2% of cat fleas and 10% of cat lice infected by D. caninum. They indicated that the representative 28S rRNA sequence isolated from their flea and louse specimens (accession no. KY751956) demonstrated 95% sequence similarity with that of D. caninum (accession no. AF023120), and they suggested the existence of a second distinct species from the one available in GenBank. This 5% divergence of the approx. 650 bp region of the 28S rDNA is consistent with data reported in this study. PCR amplification and sequencing of the partial 12S rDNA gene region indicated that the 12S rDNA sequences (accession no.
Acknowledgements. The authors would like to thank the research personnel from both Clinomics and  Clinvet International (Pty) Ltd  for their assistance in conduct of the research. The study was funded by  Merial SAS, France. There were no conflicts of interest. Special thanks to  Prof. M. Lappin ,  North Carolina State University, USA ;  Prof. Piyanan Taweethavonsawat ,  Chulalongkorn University, Bangkok, Thailand ;  Prof. Elias Papadopoulos ,  Thessaloniki Veterinary Faculty, Greece ;  Prof Smaro Sotiraki ,  Hellenic Agricultural Organisation, Greece ; and  Dr Maureen Forsyth ,  Auckland, New Zealand , for the collection and shipment of fleas and Dipylidium sp. tapeworms.
The secondary objectives of the PEARL study are to: identify S. pneumoniae serotypes in nasopharyngeal and blood samples; identify the antimicrobial susceptibility profiles of the pathogenic bacteria isolated from nasopharyngeal and blood samples and compare these profiles with antibiotic prescriptions; examine the association between respiratory viral infections and invasive pneumococcal infections; identify risk factors for CAP in this population, especially those that may be modifiable (crowding, tobacco smoking, domestic sources of smoke produced by cooking or heating, etc. ), and thus provide data for prevention programs; provide current data on the incidence and severity of CAP in vulnerable populations in Lebanon; provide a unique transcriptomics dataset as regards the sociodemographic profile of the patients and spectrum of diseases; compare microbiological and transcriptomic methods in estimating viral vs bacterial attributable fractions of LRTI; and assess the operational capacity of rapid, nuclear acid-based point-of-care diagnostic tests in a humanitarian crisis.
Are the datasets clearly presented in a useable and accessible format? Not applicable Competing Interests: No competing interests were disclosed. Reviewer Expertise: Infectious epidemiology, paediatric pneumonia I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Reviewer Report 14 January 2020 https://doi. org/10. 21956/gatesopenres. 14036. r27382 © 2020 Page A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comment: The analytic methods planned for the primary objective are fairly restricted. I would encourage the authors to learn from the approaches taken in studies such as PERCH and get the most they can out of the data. This can still be done even though recruitment has been completed. Action/Answer: Methodologies such as partially latent class models (if this is what Dr Howie refers to) have been considered for PEARL study. Nevertheless, they can poorly be applied to our dataset given the restricted range of samples in our study: nasopharyngeal swab mainly, some sputum, and blood cultures -that are expected to have a low yield in primary health care and will therefore be poorly informative. In comparison with PERCH, where information from a much wider range of biological samples taken from severe cases was used, our dataset would be poorly suitable for complex models.
" Comment: What does the protocol mean when it specifies "particular attention to pneumonia with a breathing rate > 50 breaths per minute'? Was this a de facto entry criterion? Action/Answer: No, it just means that we measured the breathing rate twice in children, in order to evaluate its pertinence in our own dataset. In the manuscript, we therefore changed the sentence as follows: "Moreover, in children under five years of age, we will assess the breathing rate twice, since it has been shown that the cut-off of > 50 breaths per minute, assessed twice, is more specific for pneumonia than the standard IMCI recommendations for children 1-5 years old (40 breaths/min), and was successfully adopted in the ALMANACH protocol. " Comment: What does the protocol mean when it says that 'relevant' results will be communicated to clinicians.
Acknowledgements We would like to thank all of the participants in this study, who provided their time and biological samples. We are very grateful to all health care workers, nurses, medical doctors and laboratory technicians at the participating health facilities, in particular:  Ahmad Al Hallak ,  Anas Altabaa ,  Ali Rida ,  Asma Allouch ,  Danielle Chaaya ,  Hossam Al Nazer ,  Hussein Madie ,  Khaled Hallak ,  Majdeddine Mouzawak ,  Marianne Abi Fadel ,  Maryam Menhem ,  Nahed Elboosh ,  Souraya Nasser ,  Taha Abdou ,  Yasmine Amraoui ,  Zeina Jbara , and the other individuals who helped to set up this protocol. Khaled S. Aoun ,  Kamile , and especially  Shafiq Bassil  from  Bioteck company  deserve a special note for their kind collaboration in the transportation of biological samples.
INTRODUCTION Cell adhesion to the extracellular matrix (ECM) is not only central to tissue assembly and repair, but also to immune surveillance and response  (Ley et al. , 2007). Adhesion kinetics and strength are currently measured with atomic force microscopy (AFM)  (Benoit et al. , 2000; Lehenkari and Horton, 1999; Li et al. , 2003) , surface plasmon resonance technology (SPR)  (Kim et al. , 2011) , fluid-flow devices  (Lu et al. , 2004) , shear-spinning disks  (Boettiger, 2007; Friedland et al. , 2009; Garcia et al. , 1998) , centrifugation methods  (Reyes and Garcia, 2003)  or micropipette aspiration  (Hogan et al. , 2015). Accurate measurements of celladhesion kinetics and strength absolutely require large and well-defined datasets, in order to grasp the heterogeneity in cell behavior of cell subsets and to capture rare events.
The piezoelectric element attached to the flow chamber was driven with a function generator (Siglent, SDG830) providing oscillating current amplified by a RF-amplifier (SCD, ARS 2_30_30, 50 Ω impedance, 10 W maximum output power). A maximum driving voltage of 42 Vpp was applied in these experiments at 14. 3 MHz for not more than a few seconds, this did not lead to sample heating of more than two degrees. Part of the data was acquired using a LUMICKS AFS Stand-alone instrument. Images were acquired with a bright field inverted microscope (Nikon Eclipse Ti). Here, an LED light source (Thorlabs, M660L4) was used in combination with an air objective lens (Nikon, CFI Plan Fluor 10X/0. 30 NA, field of view 1. 8 mm 2 ). To regulate the sample z-position, a piezo translation stage (PI, P-517. 2CL) was mounted on the microscope and driven by a digital piezo-controller (PI, E-710.
0 nm x and y, and 72 nm in z (Figure  S2 ). The tracking software is available at http://figshare. com/articles/AFS_software/1195874. For the real-time tracking of 300 cells in parallel, a fast PC (including two Xeon E5 2643v2 processors) running National Instruments LabView is used. Additionally, a moving ROI was implemented to adjust all the ROIs, frameto-frame, bases to the previously determined xy-position. Separate calibration of cells takes only a few minutes, including data processing.
ACKNOWLEDGMENTS The authors thank  Andrea Candelli  for helpful discussions (LUMICKS B. V. ) and  Jenneke Klein Nuland  for providing lab facilities needed for cell culture (ACTA, Amsterdam). This work is part of the research program of  Future & Emerging Technologies (FET)  (E. J. G. P. and G. J. L. W. ), which is part of the  EU Horizon 2020 program , and supported by the  Institut Pasteur through its Transversal Research Program  ( PTR 424 ).
Aphids were weighed at Day 7, the time point of transition to adulthood in aphids reared on plants. Ten aphids per diet were isolated and followed individually for fecundity: the number of newborn nymphs was counted, their size measured with a Leica MZFLIII (Leica, Wetzlar, Germany) microscope using an F-view camera link to the CellF software (Soft Imaging 197 System, Tokyo, Japan) and they were checked for any visible morphological phenotype.
Microarray Experiments and Data Collection The "INRA-BF2I_A. pisum_Nimblegen-ACYPI_4x72k_v1" microarray for the pea aphid was developed in collaboration with Roche NimbleGen using the pea aphid genome v1. 0 assembly  (International Aphid Genomics Consortium, 2010). This NimbleGen 385K 4-plex (4 × 72 000 probes) high-density array can accommodate 4 samples that are hybridized onto a section of the array containing 72 000 60-mers oligonucleotide probes, representing 24 011 pea aphid transcripts (corresponding to 23855 genes) as described in  Rabatel et al. (2013). The microarray design can be found in the ArrayExpress database (Accession No. A-MEXP-1999). Labeling (using the NimbleGen One-Color DNA Labeling Kits and Cy3 Random Nonamers), hybridization on the arrays (at 42 • C for 16-20 h) and scanning (using MS 200 Microarray Scanner and the MS 200 Data Collection Software) were carried out by Roche NimbleGen, as described in the NimbleGen arrays user's guide for gene expression arrays.
Microarray Data Analysis: Differential Expression Microarray data were normalized, using the RMA method  (Irizarry et al. , 2003) , and then transformed into log 2 for subsequent analyses. Differentially expressed genes (DEGs) were predicted using one-way between groups ANOVA analyses [Limma package  (Ritchie et al. , 2015)  from the R Bioconductor project v3. 2]. Moderated t-test P-values were adjusted using a false discovery rate [FDR,  (Dudoit et al. , 2003) ] threshold of 0. 05. Tissue-enriched genes were detected using the control diet (AP3) samples and only the genes harboring a |log 2 (Fold-Change)| > 2 were kept for further analyses. Pairwise comparisons between the two diets at each time point and for each tissue were also performed to identify the DEGs in response to a Tyr and Phe depleted diet.
Microarray Data Analysis: Functional Annotation Analysis As the microarray was originally designed on the v1. 0 of the pea aphid A. pisum genome, we filtered the dataset based on the recently released pea aphid genome v2. 0 assembly to exclude both outdated genes and genes with cross-hybridizing probes using PatMaN  (Prüfer et al. , 2008)  with a threshold of three mismatches. Using this filter, 13 668 genes were excluded among which 13 646 (99. 8%) that were unannotated. Only the remaining genes (10 343) were taken into account for the functional annotation analysis. The Gene Ontology (GO) analysis was first performed using the pea aphid annotation v2. 1b. In order to have more detailed results and given the more extensive functional annotation of genes in Drosophila melanogaster, we decided to perform a second GO analysis using the corresponding D.
melanogaster genes for each differentially expressed A. pisum gene. For simplicity's sake, we refer to these genes as "putative homologs" throughout the text. To perform this analysis, we retrieved putative D. melanogaster homologs for each differentially expressed A. pisum gene using BLASTP  (Altschul et al. , 1990)  and we carried out a GO analysis based on the annotations collected in FlyBase version FB2017_01  (Gramates et al. , 2017). Enrichment analysis of the DEG sets was performed using BINGO v3. 0. 3  (Maere et al. , 2005)  with an FDR threshold of 0. 01. Lists of enriched GO terms were further refined using REVIGO  (Supek et al. , 2011)  with a similarity threshold of 0. 5 and the D. melanogaster database.
Microarray Data Validation To validate the transcriptional differences identified with the microarray analyses, we conducted quantitative reverse transcription-PCR (qRT-PCR) experiments for a selection of genes (seven) corresponding to different functional classes and spanning a broad range of expression differences as determined with the microarrays. Three genes were differentially expressed in the gut samples (ACYPI000653, ACYPI001701, and ACYPI010105), three genes were differentially expressed in the bacteriocytes (ACYPI003338, ACYPI004647, and ACYPI006800), and one gene was differentially expressed in both tissues (ACYPI001281). Expression of these seven genes was assessed for all the time points used in the microarray experiment (a total number of 35 sample replicates for the guts and 16 sample replicates for the bacteriocytes).
Primers to target transcripts (Supplementary Table  S2 ) were designed with the Oligo6 software  (Rychlik, 2007). For data normalization, two genes were tested in the different tissues and time-points analyzed: rpl7 (ACYPI010200) and rpl32 (ACYPI000074). Real-time RT-PCR data were analyzed using the BestKeeper © software tool  (Pfaffl et al. , 2004)  and the rpl7 gene was retained as the best candidate for data normalization. Internal standard curves were generated for each gene using serial dilutions (from 2000 to 0. 002 fg/µl) of purified PCR products amplified from a pool of cDNA. The PCR reaction to prepare the control sample for the standard curve was carried out starting from 1 µl of reverse transcription product using UptiTherm DNA Polymerase (Interchim, Montluçon, France), according to the manufacturer's instructions.
qRT-PCR analyses were done following essentially the same strategy described in  Dallas et al. (2005). aRNA samples were treated with DNase I (Promega, Madison, WI, United States) and reverse-transcribed in cDNA using the SuperScript TM III First-Strand Synthesis System for RT-PCR (Thermo Fisher Scientific), with random primers and oligodT, according to the manufacturer's instructions. RT-PCR was performed with a LightCycler 480 Instrument (Roche Diagnostics, Basel, Switzerland) using either 2. 5 µl of cDNA (at around 1. 5 µg/µl), diluted at 1/5, or water (for negative control reactions) in a total PCR reaction final volume of 10 µl. Quantitative RT-PCR data were analyzed using the REST software 1   (Pfaffl et al. , 2002). The correlation analysis between microarray and qRT-PCR data was done with (i) a regression analysis using a linear model (R-squared of 0.
pisum genes belonging to different functional classes was monitored using qRT-PCR to validate the microarray data (Supplementary Table  S2 ). qRT-PCR assays were performed on the same RNA samples used for the microarray experiment (all time points from the gut and bacteriocytes tissues of AP3 and YFØ aphids). A Pearson's coefficient of determination of 0. 91 (P < 2. 2 × 10 -16 ) was observed between the qRT-PCR and microarray datasets showing a high degree of reliability for the microarray results (Supplementary Figure  S3  and Supplementary Table  S3 ). We next identified gut-and bacteriocyte-enriched genes using data from all time points of aphid samples reared on the complete AP3 artificial diet. Genes were considered tissue-enriched using a fourfold change threshold in the gut/bacteriocyte contrast.
Note that a large proportion of genes for the two tissues are poorly annotated and were therefore categorized in a class named "function unknown. " Differentially expressed genes from YFØ aphids compared to AP3 aphids were identified using a one-way between groups ANOVA (Limma) applied at each time point independently. Of the 10 343 transcripts analyzed, 857 (8. 29%) were identified as being significantly differentially expressed during nymphal development of the pea aphid reared on a Tyr/Phe depleted diet. Strikingly, most of the transcriptional changes induced by Tyr/Phe nutritional stress occurred in bacteriocytes (771 DEGs) whereas limited transcriptomic changes were observed in gut samples (89 DEGs) (Table  1  and Supplementary Table  S5 ). These differences in transcriptional response to nutritional stress define the bacteriocytes as a prominent mediator of metabolic homeostasis.
Tyr/Phe Biosynthesis Is Not Regulated in the Bacteriocytes Given that bacteriocytes together with B. aphidicola are essential for the production of tyrosine and phenylalanine, we first hypothesized that the expression of the genes involved in their biosynthesis would be altered to accommodate for the lack of the amino acids. To test this hypothesis, we analyzed all identified bacteriocyte DEGs using the functional annotation available in the ArthropodaCyc database  (Baa-Puyoulet et al. , 2016) , which also contains the global reconstruction of the metabolic network of the pea aphid. The most striking result was the absence of genes related to Tyr and Phe biosynthesis among the bacteriocyte DEGs (Table  2 ), despite the tyrosine biosynthetic pathway being crucial for the symbiotic metabolism of the pea aphid and B.
Therefore, we argue that the differences seen in bacteriocyte gene expression are due to the nutritional stress imposed by the absence of Tyr/Phe in the artificial diet. The observed bacteriocyte response to metabolic changes with massive transcriptional reprogramming points to a more complex physiological role of these specialized cells central to symbiosis. Our results indicate that bacteriocytes use alternative strategies to cope with metabolic stress different from the classic autophagy-based ones  (Mizushima and Klionsky, 2007). Instead of triggering cell death (our dataset does not show induction of apoptotic and autophagy-related pathways under nutritional stress), we discovered that aphids display a defined temporal sequence of molecular events that includes an extensive transcriptional reprogramming of the bacteriocyte cells through chromatin remodeling.
Acknowledgments We are grateful to  Fanny Coulpier  for help in high-throughput sequencing. We thank the genomic platform of the IBENS, supported by  France Ge  ´nomique national infrastructure and funded as part of the  Programme "Investissements d'Avenir" (PIA)  managed by the  Agence Nationale de la Recherche  ( ANR , contract  ANR-10-INBS-0009 ).