dataesr/scientific-paragraphs-categorization huggingface dataset:
Concerning the configuration using additional aluminum filtration, the physical doses measured by EBT3 indicated an increase of around 8% when 3 mL/well was replaced by 1 mL of cell culture medium. These differences were confirmed at the biological level. Clonogenic assay performed with 1 mL/well led to a decrease of the survival fraction compared to the reference condition. Cells received a higher dose than expected, leading to the shift observed in the survival fraction curve. On the other hand, we obtained an atypical curve for the 9 mL/well condition (compared to the reference curve). Even though we observed a higher cell survival fraction, the linear quadratic part of the curve had a negative P coefficient. A possible explanation is provided in Supplementary data III. According to the literature, the colony size cut-off is set to 60 cells per clone in our clonogenic assay experiments  (Franken et al.
Five isolated colonies of strain 008 were deposited as individual spots on the MALDI target plate. Each smear was overlaid with 2 μL of matrix solution (a saturated solution of alpha-cyano-4-hydroxycinnamic acid) in 50% acetonitrile and 2. 5% trifluoroacetic acid, and allowed to dry for 5 minutes. Measurements were performed with a Microflex spectrometer (Bruker Daltonics, Bremen, Germany). The five 008 spectra were imported into MALDI BioTyper software version 2. 0 (Bruker) and were analysed by standard pattern matching (with default parameter settings) against the main spectra of 4613 bacteria in the BioTyper database and the 25 Bartonella species in our own database. The identification method included the m/z from 3000 to 15 000 Da. For every spectrum, a maximum of 100 peaks was considered and compared with the spectra in the database.
Genome project history The organism was selected for sequencing on the basis of the similarity of its 16S rRNA, ITS, ftsZ, gltA and rpoB to other members of the genus Bartonella. Nucleotide sequence similarities for these genes suggested that strain 008 represents a  new species in the genus Bartonella. A summary of the project information is shown in Table  2. The GenBank accession number is G + CA_900185775, and the entry consists of 12 scaffolds (>1500 bp). Table  2  shows the project information and its association with Minimum Information About a Genome Sequence (MIGS) version 2. 0 compliance. The mate-pair library was prepared with 1. 5 μg of genomic DNA using the Nextera mate-pair Illumina guide. The genomic DNA sample was simultaneously fragmented and tagged with a mate-pair junction adapter.
The profile of the fragmentation was validated on an Agilent 2100 BioAnalyzer (Agilent Technologies, Santa Clara, CA, USA) with a DNA 7500 labchip. The optimal size of obtained fragments was 7. 77 kb. No size FIG. 5. Graphical circular map of chromosome. From outside to centre: genes on forward strand coloured by COGs categories (only genes assigned to COGs), genes on reverse strand coloured by COGs categories (only gene assigned to COGs), RNA genes (tRNAs green, rRNAs red), G + C content and G + C skew. COGs, Clusters of Orthologous Groups database.
Genome assembly The genome's assembly was performed with a pipeline that enabled creation of an assembly with different software programs (Velvet  [30] , Spades  [31]  and Soap Denovo  [32] ), on trimmed (MiSeq and Trimmomatic  [33] ) or untrimmed data (only MiSeq). For each of the six assemblies performed, Gap-Closer  [32]  was used to reduce gaps. Then contamination with Phage Phix was identified (BLASTN against Phage Phix174 DNA sequence) and eliminated. Finally, scaffolds under 800 bp were removed, and scaffolds with a depth value lower than 25% of the mean depth were removed (identified as possible contaminants). The best assembly was selected by using different criteria (number of scaffolds, N50, number of N characters).
Genome annotation Open reading frames (ORFs) were predicted using Prodigal  [34]  with default parameters, but the predicted ORFs were excluded if they spanned a sequencing gap region (contained N). The predicted bacterial protein sequences were searched against the Clusters of Orthologous Groups (COGs) database using BLASTP (E value of 1e-03, coverage 0. 7 and identity percentage 30%). If no hit was found, it searches against the NR database using BLASTP (E value of 1e-03, coverage 0. 7 and identity percentage of 30%). If the sequence length was <80 aa, we used an E value of 1e-05. The tRNAScanSE  [35]  tool was used to find transfer RNA genes, whereas ribosomal RNA genes were found by using RNAmmer  [36]. Lipoprotein signal peptides and the number of transmembrane helices were predicted using Phobius  [37].
The type strain 008 is sensitive to rifampicin, amoxicillin, amoxicillin/ clavulanic acid, oxacillin, nitrofurantoin, doxycycline, linezolid, tobramycin, gentamycin, imipenem, trimethoprim/sulfamethoxazole, fosfomycin and ciprofloxacin and resistant to metronidazole and colistin. The G + C content of the genome is 38. 44%. The 16S rRNA gene sequence and whole-genome shotgun sequence of strain 008 are deposited in GenBank under accession numbers KY555064 and GCA_900185775 respectively. The type strain 008 (CSUR B643, DSM2802) was isolated from the rodent Mastomys erythroleucus trapped in the region of Sine-Saloum, Senegal.
33 Amino acid transport and metabolism F 47 2. 81 Nucleotide transport and metabolism H 63 3. 76 Coenzyme transport and metabolism I 44 2. 63 Lipid transport and metabolism P 57 3. 41 Inorganic ion transport and metabolism Q 15 0. 89 Secondary metabolites biosynthesis, transport and catabolism R 74 4. 42 General function prediction only S 68 4. 06 Function unknown - 603 36. 02 Not in COGs COGs, Clusters of Orthologous Groups database.
Niclosamide and TCSA attenuate damage of intestinal epithelial cells caused by C. albicans. Since Niclosamide and TCSA inhibited the formation of invasive hyphae, we wanted to check whether it conferred a protective activity for host cells against fungal invasion. C. albicans-mediated damage of the human colon epithelial HT-29 cells was quantified based on the LDH release assay in cells treated or not with different concentrations of the two HSA. Our data showed clearly that both antifilamentation compounds significantly reduced, but not completely abolished, the damage to HT-29 enterocytes (Fig. 4E ). The protective effect was perceived at 40 µM of either Niclosamide or TCSA (~20% reduction of enterocyte invasion). At concentration ≥100 µM, Niclosamide and TCSA attenuated HT-29 cell damage by 63% and 49%, respectively.
The sensitivity of C. albicans heterozygous mutants of TIM23 complex including tim23/TIM23, tim17/TIM17, tim50/TIM50, ssc1/SSC1 and mge1/MGE1 was tested toward high concentration of Niclosamide (200 µM). While the WT parental strain had no discernable growth defect, all tested mutants were sensitive when exposed to   1. Gene function and biological process associated with C. Albicans response to Niclosamide. Gene ontology analysis was performed using the Candida Genome Database GO Term Finder. a The p-value was calculated using hypergeometric distribution, as described on the GO Term Finder website. Niclosamide (Fig. 6B ). The Niclosamide-induced haploinsufficiency of the TIM23 complex suggests that the C. albicans translocase machinery of the inner mitochondrial membrane is required to tolerate Niclosamide and/or might be a target of this HSA.
67. Data analysis were carried out using Genespring v. 7. 3 (Agilent Technologies, Palo Alto, CA). Statistical analysis used Welch's t-test with a false-discovery rate (FDR) of 5% and 2-fold enrichment cut-off. Gene ontology (GO) analysis was performed using the Candida Genome Database (CGD) GO Term Finder. The p-value was calculated using hypergeometric distribution with multiple hypothesis correction (http://www. candidagenome. org/cgi-bin/GO/goTermFinder)  68. Descriptions of each C. albicans gene in Table  S2  were extracted from CGD database  69. For quantitative real time PCR (qPCR) confirmation, cell cultures and RNA extractions were performed as described for the microarray experiment. cDNA was synthesized from 1 µg of total RNA using High-Capacity cDNA Reverse Transcription kit (Applied Biosystems).
Acknowledgements We are grateful to  Mike Tyers  and  Susan Moore  ( Université de Montreal ) for providing the yeast bioactive small molecule library. We thank  Martine Raymond  ( Université de Montreal ) for providing C. albicans clinical strains,  René Pelletier  (Research Center of the  CHU de Québec ) for the C. auris strain,  Deborah Hogan  ( Geisel School of Medicine at Dartmouth ) for sharing the Ras1 G13V mutant and  Daniel Kornitzer  ( Technion ) for the hypofilamentous C. albicans strains. Work in Sellam's laboratory  is supported by the  Fonds de Recherche du Québec-Santé (FRQS)  (Établissement de jeunes chercheurs), the  Canada Foundation for Innovation  ( CFI-# 34171 ), the  Fondation de l'Université Laval-Merck  fund and the  Natural Sciences and Engineering Research Council of Canada (NSERC)  discovery grant ( 06625 ).
These datasets suggest that multiple kinases and proteases participate in the processes of egress and invasion during blood-stage growth of malaria parasites. In conclusion, the results of this study demonstrate that PfSERA5 is phosphorylated in P. falciparum schizonts by PfCDPK1 following a rise in intracellular Ca 2ϩ , which has been shown previously to trigger egress. Phosphorylation enhances the protease activity of PfSERA5. Although this is one of the first reports to show that the activity of a parasite protease can be regulated by a kinase, many human proteases have been shown to be regulated by different kinases  (33). The evidence discussed above thus clearly provides data about a link between PfSERA5 protease activity and its phosphorylation status, which is essential for the timely egress of merozoites from infected erythrocytes.
1% formic acid in 95/5 acetonitrile/water) to 50% solvent B over 60 min. For recombinant proteins, a relatively smaller gradient was used. The eluted peptides were injected into the Orbitrap Velos Pro MS, and MS1 data were acquired using a mass range from 350 -2000 Da in full scan mode at 60,000 resolution. The top 20 precursors were allowed to fragment using collision-induced dissociation in an ion trap with a collision energy of 35. For phosphopeptide analysis, precursors were fragmented using high-energy collision dissociation and detected in an Orbitrap at a resolution of 7500. The raw data were analyzed using Proteome Discoverer 1. 4 (Thermo Fisher Scientific) with the SEQUEST algorithm against the P. falciparum database downloaded from PlasmoDB and UniProt. The identification of peptides was validated using Percolator.
Merozoite egress inhibition assay To determine the effect of inhibition of PfSERA5 phosphorylation by inhibiting PfCDPK1 on P. falciparum merozoite egress, egress inhibition assays were performed as described previously  (41). Briefly, late-stage schizonts (42-44 hpi) were treated with different concentrations of purfalcamine (0. 5-10 M), 50 M BAPTA-AM, and appropriate solvent controls for 8 -10 h in duplicate wells. Following incubation, parasites from treated and control wells were collected, washed, and stained with ethidium bromide (10 mg/ml) and ethidium bromidepositive cells were scored by flow cytometry on a FACSCalibur (BD Biosciences) using CellQuest software. The fluorescence signal (FL-2) was detected with a 90-nm band pass filter using an excitation laser of 488 nm collecting typically 100,000 cells per sample.
Post-acquisition data were analyzed using FlowJo software (Tree Star, Inc. ) by determining the proportion of FL-2-positive cells representing schizonts. Inhibition of egress of merozoites was calculated as follows: % inhibition of merozoite egress ϭ 100 ((T-C)/(I-C)), where I is the frequency of schizonts (percent) at the start of the assay, T is the frequency of schizonts (percent) after treatment, and C is the frequency of schizonts (percent) in the control.
org/10. 14264/uql. 2017. 436. AF networks of microbial evolution. We first inferred phylogenomic networks based on a data set of 2,783 completely sequenced microbial genomes (2,618 bacterial genomes and 165 archaeal genomes [total of 9,582,718,896 bases]) downloaded from NCBI on 31 January 2016 (see Data Set S1 in the supplemental material), including plasmid sequences if present. Where two or more genomes had identical contents of 25-mers (D 2 S distance ϭ 0), only one was retained. We also removed edges for which the D 2 S distance was Ͼ10; these genomes share Յ0. 01% of 25-mers with any other genome. Following this filtering step, we took 2,705 genomes forward into subsequent analyses. For each network, we systematically assessed the number of nonsingleton nodes (c) (i. e. , the number of nodes with one or more edges), the size of the maximal clique (i.
Remarkably, chromosomal sequences, apart from rRNA genes, although usually representing more than 99% of the genome sequences, contribute little to overall phylogenetic signal. Core k-mers of microbial genera. We define a core k-mer in a group of interest as a k-mer that is present in every genome within the group, e. g. , a core 25-mer in Proteobacteria is present in all proteobacterial genomes in our database (see Materials and Methods). We identified core 25-mers for each genus in our 2,783-genome data set. Of these 699 genera, 497 are represented by only a single genome isolate, and a further 51 consist of highly divergent genomes for which no core 25-mers were identified; we exclude these data from this part of analysis. The remaining 151 genera for which core 25-mers were identified are listed in Table  S2.
Core functions of microbial phyla. To relate the shared k-mers to biological functions, for all 25-mers in these 2,783 genomes we organized the genome coordinates of each instance, and the biological function annotated for the gene product encoded at those coordinates, in a relational database. Functional annotation was based on Clusters of Orthologous Groups (COGs)  (43). Then, using the list of core 25-mers described above, we grouped these 25-mers by taxon, focusing on proteincoding sequences (i. e. , rRNA sequences were discarded; see Materials and Methods). This yielded a set of core 25-mers for 112 genera in 16 phyla; the corresponding COG functional categories for these core 25-mers are shown in Table  S3. The noninformative functional categories R (general function prediction only) and S (function unknown) were excluded from subsequent analyses.
Only the top five COG categories and the corresponding numbers of core k-mers are shown for each phylum; in most cases, the top five categories account for Ͼ50% of the annotated core k-mers. The categories that are significantly enriched in a phylum (P Յ 0. 05) are noted with an asterisk (*). Each edge between two nodes represents the number of connections between isolates from the two phyla; the thickness of each edge is proportional to the number of shared k-mers. The five representative Proteobacteria groups are labeled with the corresponding Greek characters. tree-like structure. We introduce for the first time the concept of a k-mer similarity network and two types of AF networks, the I-and P-networks. We show that by combining a k-mer approach with the use of a relational database, biological information can be accessed efficiently for large-scale data.
MATERIALS AND METHODS Data. In total, 2,785 completely sequenced genomes of Bacteria and Archaea were downloaded from NCBI on 31 January 2016 (Data Set S1); two of these were identified as "multispecies" and "multi-isolate" and were thus excluded. Functional annotation of the remaining 2,783 genomes was obtained through the corresponding RefSeq records. Genes encoding ribosomal RNAs were identified based on annotation. Genomes with no annotation information were excluded from our rRNA-gene network. Of the 2,783 isolates, 921 contained plasmids; these plasmid genomes were used in the plasmid-only network. Relational database of k-mers and genome features. We extracted 10,059,526,408 distinct 25mers from the genomes of 4,401 bacterial and archaeal isolates (present as of 31 January 2016 in NCBI RefSeq), of which 2,783 genomes were complete and included in our subsequent analysis (see above).
We organized these k-mers, and their genomic locations and features (based on RefSeq annotations), in a relational database using SQL, following the method of Greenfield and Roehm  (37). Tables in the database contain a list of isolates, lists of genes and their sequences, coherent taxonomic information for each isolate, an indexed list of all 25-mers, an indexed list of gene-by-gene comparisons for each pair of genes, and an indexed list of genome-by-genome comparisons for each pair of genomes. Alignment-free (AF) network. We followed the method of Bernard et al. (29)  in generating the AF networks. We first computed pairwise comparisons for the 2,783 isolates and generated for each comparison the corresponding D 2 S distance (15) value d, using 25-mers across parallel central processing units (CPUs).
For a pair of genomes a and b, we transformed d into a similarity measure S ab , where S ab ϭ 10 Ϫ d. For instance, considering two highly similar genomes of a and b for which distance d ab ϭ 0. 001, the similarity measure S ab ϭ 9. 999. Likewise, considering two highly dissimilar genomes of a and b for which d ab ϭ 9. 925, S ab ϭ 0. 075. We ignored any edge for which d Ͼ 10 (i. e. , for which the S value was negative), as the corresponding pair of sequences shares only Յ0. 01% of 25-mers (i. e. , 25-mers capture little evidence of homology). We then generated the networks using JSON files containing the S values as input for a Javascript script using the D3 library (https://d3js. org/). Here, we present two types of AF networks. For a phylum-level depiction of the network (P-network), we grouped all sequences of the same phylum as a single entity prior to calculating the distance; each phylum is represented by a node in the network.
ACKNOWLEDGMENTS This project was supported by an  Australian Research Council  grant ( DP150101875 ) awarded to M. A. R. and C. X. C. and by a  James S. McDonnell Foundation  grant awarded to M. A. R. This work was supported by computational resources of the  National Computational Infrastructure (NCI) National Facility  systems through the  NCI Merit Allocation Scheme  (Project  d85 ). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. G. B. implemented the analysis workflow, conducted the experiments, and prepared the first draft of the manuscript. G. B. and C. X. C. prepared all figures and tables. P. G. provided the k-mer database and contributed to analyses using this database. G. B. , C. X. C. , and M. A. R.
Threshold No. of nonsingleton nodes, c Density, D Size of the maximal clique, z No. of cliques, n 0 745 0. 025 48 20,557 1 718 0. 021 46 13,272 2 680 0. 017 45 3,925 3 648 0. 014 39 1,406 4 601 0. 011 34 800 5 556 0. 009 30 589 6 499 0. 006 25 368 7 439 0. 004 13 122 8 353 0. 002 11 26 9 245 0. 001 9 14 complete genomic datasets 1. 0 rRNA genes only chromosomal sequences Network density (D) 0. 4 0. 6 0. 8 without rRNA genes plasmid sequences only 0. 2 0. 0 0 1 2 3 4 5 6 7 8 9 less Similarity threshold (t) more stringent stringent.
1F ). Expression of the smooth muscle cell marker genes Acta2, Actg2, and Myh11 did not change upon damage in our dataset (Fig. 1F ). Asterisks indicate significance (Student's t test: n. s. , not significant, P ≥ 0. 05; *P < 0. 05; ***P < 0. 001). (F) Bulk mRNA sequencing strategy and gene expression levels of different cardiac cell types identified in the purified Ki67-RFP + populations. Cardiomyocytes were enriched in neonatal hearts. Hematopoietic cells were enriched 3 and 7 dpi, while resident lineages such as endothelial cells and fibroblasts were more abundant 14 dpi. CF, cardiac fibroblasts; CM, cardiomyocytes; EC, endothelial cells; HC, hematopoietic cells; SMC, smooth muscle cells.
S3 ). Based on differential gene expression of some well-characterized markers, we found that all major cardiac cell types were present in the single-cell dataset (Fig. 2  C-E and SI Appendix, Fig. S3 ): clusters 8 and 9 comprised 437 cardiomyocytes enriched for transcripts of Myh6, Actc1, and Tnnt2; clusters 6 and 7 contained a total of 282 cardiac fibroblasts expressing Pdgfra, Col1a1, and Tcf21; 458 hematopoietic cells enriched for Ptrpc, Mrc1, and Sirpa were assigned to clusters 1 and 4; 705 endothelial cells expressing Pecam1, Cdh5, and Fabp4 were present in clusters 2 and 3; and 57 cells in cluster 5 were enriched for smooth muscle genes Acta2, Myh11, and Actg2. Of note, while SIRPA has been reported to be expressed by human embryonic stem cell-derived cardiac progenitors (32), we only found significant Sirpa expression in hematopoietic cells in our dataset.
To validate that sequenced Ki67-RFP + cells were proliferative, we used the cyclone algorithm  (33)  to assign cell cycle stages to each individual cell in our filtered dataset (Fig. 2F ). While present throughout the cell cycle (and absent from quiescent cells arrested in G 0 )  (22) , Ki67 is most highly expressed in the G 2 stage  (34, 35). Consistent with these previous studies, cells in S and G 2 /M phase were enriched in the fraction of Ki67-RFP + cells (Fig. 2G ; χ 2 test, P < 0. 001), confirming that they were actively cycling. Most Ki67-RFP + neonatal cells were identified as cardiomyocytes, while no Ki67-RFP + cardiomyocytes were found in the datasets compiled from either homoeostatic or injured adult hearts (SI Appendix, Fig. S4A ). In homoeostatic adult heart, Ki67-RFP + cells mapped to noncardiomyocyte lineages, in particular to the hematopoietic and endothelial cell lineages (SI Appendix, Fig.
S4A ). Ki67-RFP + cells present in the infarcted region 14 dpi were mainly fibroblasts and hematopoietic cells (SI Appendix, Fig. S4A ), while Ki67-RFP + cells in the remote region additionally represented mostly the endothelial and hematopoietic lineages (SI Appendix, Fig. S4A ). Collectively, these data show that there is a significant number of proliferative cardiomyocytes in neonatal hearts, but not in homoeostatic or injured adult hearts. Since we did not find evidence for a distinct CSC population expressing proposed markers such as Abcg2 (side population marker), Kit (encoding c-KIT), and Ly6a (encoding SCA-1), we investigated the presence of cells expressing at least one transcript of these markers within all cardiac cell types (Fig. 3 A-C ). We identified 225 cells expressing Abcg2, which were scattered within all main cardiac cell types found in our dataset (Fig.
3  A and B)  (13, 14). Most of the 145 Kit-expressing cells were endothelial cells (Fig. 3  A and B)  (15) , in contrast to earlier reports  (4, 11). Some fibroblasts, which are usually not excluded when purifying putative c-KIT + CSCs  (4, 11, 36) , also expressed Kit (Fig. 3 A  and B ). About 32% of all cells (613 cells of a total of 1,939 cells) in our dataset expressed Ly6a (Fig. 3A ). Ly6aexpressing cells were assigned to all cardiac cell types, while absent in neonatal cardiomyocytes (12) (Fig. 3 A  and B ). Cells positive for a putative CSC marker (i. e. , Abcg2, Kit, or Ly6a) were identified in both Ki67-RFP + and Ki67-RFP -cardiac cell populations (Fig. 3C ). Gata4, a marker of regenerating cardiomyocytes in zebrafish  (37) , was expressed in most cardiac lineages in a total of 214 cells (SI Appendix, Fig.