dataesr/scientific-paragraphs-categorization huggingface dataset:
The abundance of β-lactamases in the dewatered sludge and effluent samples was considerably higher than in the infiltration pool and the control soils (Figure  7 ). A total of 1200 and 4,342 reads were characterized as β-lactamases in the sludge and effluent samples, representing 0. 9% and 3. 8% of all integron-associated reads, respectively. In contrast, only six hits were observed in both the SAT soil and the control soils (0. 004% and 0. 006% of all identified reads, respectively). The β-lactamases identified in all of the analyzed samples were further screened against the EX-B database using three levels of stringency: 50-75%; 76-89%, and >90% (SI Tables  S17  and S18 , Figures  S3  and S4 ). The majority of integronassociated β-lactamases in most of the WWTP effluents, the Shafdan sludge and the soil from the Shafdan infiltration pool were >90% identical to clinically derived β-lactamases from the database.
bla OXA and bla GES genes are frequently associated with class 1 integrons. For example, 17. 2% of class 1 integrons (n = 1089) deposited in the integron database  55  (INTEGRALL http:// integrall. bio. ua. pt/?) harbor bla OXA , while 2. 9% of class 1 integrons harbor bla GES. These genes are not observed in class 2 integrons (n = 45) and are only present in a few documented class 3 integrons (n = 8). Therefore, while we cannot validate the source of integrons pinpointed in this study, it is logical that a large fraction of the bla OXA and bla GES β-lactamases identified in this study are associated with class 1 integrons. Validation of integron GC amplicon analyses using qPCR that specifically targeted bla OXA and bla GES genes revealed similar trends in the distributions of these β-lactamases in the integron GC amplicons and qPCR analyses, strengthening the robustness of this method.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acs. est. 6b03188. Relative abundance of molecular classes of β-lactamases in integron gene cassettes in European WWTP effluents and Shafdan samples (Figures  S1  and S5 , respectively); Validation experiments showing agarose gels of PCR and qPCR products for bla OXA and bla GES genes (Figure  S2 ); Distribution of β-lactamase hits in the European effluent and Shafdan samples based on % similarity to EX-B database entries (figures S3 and S4, respectively); Contigs generated from assembly of integron GC reads (Table  S1 ); Validation of integron gene cassette sequences based on cloning and Sanger sequencing (Table  S2 ); Primers and PCR conditions applied in this study (Table  S3 ); Number of high quality reads and βlactamase hits obtained from European WWTP and Shafdan integron GCs (Tables  S4  and S5 , respectively); Prominent integron-associated genes in European WWTP effluents and Shafdan samples based on comparison to the nr database (Tables  S6  and S13 , respectively); Prominent integron-associated genes in European WWTP effluents and Shafdan samples based on comparison to the SEED database (Tables  S7  and S14 , respectively); Prominent integron-associated antibiotic resistance genes in European WWTP effluents and Shafdan samples based on comparison to the ARDB database (Tables  S8  and S15 , respectively); Prominent integron-associated β-lactamases in European WWTP effluents and Shafdan samples based on comparison to the EX-B database (Tables  S9  and S16 , respectively); Classification of β-lactamase genes according to molecular classes (Table  S10 ); and Closest BLAST identities of gene cassette amplicon-associated bla OXA and bla GES in European effluents and Shafdan samples (Tables  S11  and S12 , respectively); Distribution of β-lactamase hits based on % similarity to EX-B database entries in the European WWTP effluent samples and Shafdan samples (Tables  S17  and S18 , respectively) (PDF).
These steps require the usage of specific primers that have to be designed for each isolate and can be very labor-intensive when numerous isolates of many types are studied. In order to facilitate the full-length sequencing of EV-C isolates, a simple method was developed, which allowed the amplification of the whole genome by RT-PCR using four generic primer pairs. Four overlapping fragments were produced by performing four separate PCR reactions on cDNAs generated through a single RT reaction. The four amplicons were then pooled and purified prior to sequencing by high-throughput sequencing methods. The amplification step was assessed on a panel of prototype and field strains whose genome had been previously sequenced by the Sanger method. Sequencing data was analyzed by mapping the reads against reference sequences and by de novo assembly.
Mapping of the Reads to a Reference For each sample, the trimmed reads were mapped against the reference sequence of the corresponding virus. The sequences used as references were the sequences previously determined by the Sanger method. The entire genome of all field isolates were previously sequenced in our laboratory  (Bessaud et al. , 2011) ; for prototype strains, the sequences used as references were retrieved from the Genbank database. Mapping was achieved with CLC Genomics Workbench 8. 5 by using the following parameters: Mismatch cost = 10; Insertion cost = 3; Deletion cost = 3; Length fraction = 0. 5; Similarity fraction = 0. 95.
5 ± 0. 1 to 23. 7 ± 0. 1 (Table  1 ). Following the RT step with random primers performed on viral RNA extracted from infected cell culture supernatants (or from crushed brains of mice infected by CV-A1 or CV-A19), the PCR performed with the four primer pairs produced gel bands at the expected size for all the viruses (data not shown). Some additional bands were also observed for certain RT-PCR products. Among our panel, CV-A19 NIH-8663 RNA gave the weakest bands on ethidium bromide-stained gel after the RT-PCR step. It is not possible to exclude the hypothesis that the four primer pairs had a low efficiency in amplifying the genome of this virus through PCR because of mismatches. Nonetheless, since the RNA of this virus was extracted from a mouse brain collected more than 20 years ago, this result is more likely due to the low amount of full-length virus RNA in this sample.
Bioinformatics Analysis Raw data generated by high-throughput sequencing was analyzed by two different methods commonly used for virus characterization. The first method consists in mapping the reads against a reference sequence. This method can be used to re-sequence isolates or to detect single-point mutations that appear when a given virus is propagated under different conditions (e. g. , various a These percentages indicate the proportion of total reads that matched with the reference sequence. b These percentages indicate the proportion of total reads that were included in the virus contig through de novo assembly. host species, various cell lines or presence of antiviral molecules, for example). The second method consists in constructing contigs through de novo assembly (i. e. , without any reference sequence to drive the assembly).
In conclusion, we developed a set of generic primers for the synthesis of RT-PCR products that span the whole genome of EV-C isolates. This method was evaluated by using a panel of viruses already characterized by Sanger-based methods to allow the comparison of the sequences generated by our technique with those obtained previously. For de novo assembly, the raw data were analyzed in real conditions, i. e. , with no reference to the sequences already obtained by Sanger-based methods. After assembly, the consensus sequences generated in this way were identical to the Sanger consensus sequences. In this work, the RT-PCR products were sequenced by the Illumina sequencing technology but could be sequenced on any high-throughput sequencing platform. Thus, the sequencing of DNA amplicons covering the whole genome of enterovirus A71 isolates on an Ion Torrent Personal Genomic Machine System was previously reported  (Baronti et al.
1B ;  Earley et al. 2006 Earley et al. , 2010;; Pontvianne et al. 2010 ). Unlike VAR1 genes, which become selectively silenced in emerging leaves of seedlings  (Earley et al. 2010) , the VAR2, VAR3, and VAR4 rRNA genes are constitutively expressed in leaves (Fig. 1B ). However, VAR3 gene expression is elevated in hda6 mutants (Fig. 1B ), suggesting that a subset of VAR3 genes is subjected to chromatin-mediated silencing. We searched for rRNA gene sequence variation within pre-rRNA transcription units by analyzing short read genome sequence data for potential single-nucleotide polymorphisms (SNPs) relative to a consensus 45S rRNA gene reference sequence (Supplemental Fig. S2 ). Candidate polymorphisms, supported by multiple independent reads, were examined further using PCR amplification of the region and sequencing of multiple independent clones.
Thirteen lines had the reciprocal genotype, with the top of chromosome 2 derived from Bur-0, and the top of chromosome 4 derived from Col-0. Eleven of these lines yielded strong signals for both VAR2 and VAR3. Collectively, these results show that the majority of VAR3 rRNA genes in Col-0 cosegregate with VAR2 genes at NOR4, but a subset of VAR3 genes is located at NOR2. Ecotypes Col-0 and Ler have VAR1 and VAR3 genes but differ with respect to VAR2, allowing VAR2 genes to be mapped in yet a third RIL population (Fig. 3D ). C o l -0 B u r -0 B B B B B C C C C C C C C C B B B B B B B B B B B B B C B B B B B B B B B B B B B B C C C C C B B C C C B B B B C C C B B B C B B C B B B B C C C B B B B C C C C C C B C C C C C C B C C C B B B B U C C B B B C C C B C C C C B B B B B B B 2 8 5 2 8 9 2 9 0 2 9 2 2 9 4 2 9 6 2 9 8 3 0 0 3 0 1 3 0 2 3 0 3 3 0 7 3 0 9 3 1 0 3 2 1 3 2 2 3 2 3 C C C C C C C C C C C C C C B B B C C C C C B B B B C B B B B B B B B C C B B B B B C C B C C C C B B C B B B B C C C B B B C C C C B B B B B C C C C C B C C C C C C C B B B C C C C C C C C C C C C B B C C C C B B B C C C C C C C C C C C 3 U C C L L C C C L L C C C C C C C C C C L L L C C C C C L L L L L L L L L L L L C C C L L L L L C C U C C L L C C C C C L L L L L L L L C C C C C L L L L L L L C C C C U 5 C C C L L U L L L L C C C C L C C C C C C C C C C C C C L L L L L L L U C C L L L L L C C L L L L L L L L L L L L L L L C C C L L L L L L L L L L C C L L L C C L L L L L 1 6 0 1 6 1 1 6 7 1 7 3 1 8 0 1 8 8 1 9 3 1 9 4 1 9 9 2 3 1 2 3 2 2 3 7 2 4 5 2 5 3 2 6 4 2 9 5 2 9 7 RIL mapping results, these data show that VAR2 genes map primarily to NOR4.
In F2 individuals that inherited NOR4 from Col-0, VAR4 genes are present (e. g. , see F2 individuals A12 and A72 that are homozygous for NOR4 of Col-0 and NOR2 of Sha). Conversely, VAR4 genes are not detected in lines in which NOR2 was inherited from Col-0 (e. g. , F2 individuals A2, A11, A68, and A81). Collec-tively, these data indicate that VAR4 genes are located at NOR4. C C C C C C C C L L L L L L L C C C L L U C C L L U C C L L C C C C C C C C C C L L L L L L C C L L 3 We next mapped the genomic locations of rRNA genes bearing the various SNPs that define active versus silenced rRNA gene subtypes (see Fig. 1D ). For these analyses, DNA isolated from RILs was tested using CAPS or dCAPS assays as in Figure  1D. NOR association mapping data for polymorphic nucleotides mapped in Col-0 × Bur-0 RILs are shown in Supplemental Figure  S9  and summarized in Figure  4B.
Collectively, these analyses showed that SNPs that map to NOR2 of Col-0 include insertion 6978+CAT (present in VAR1d,e), 2982A (present in haplotype A), and 4456G (present in haplotype B). Importantly, none of these NOR2-linked polymorphisms are detected in RNA isolated from leaves of wild-type plants, but all are expressed if silencing is abrogated in the hda6 mutant (see Fig. 1D ). In contrast, SNPs that map to NOR4 include 4302C (VAR2 a/b), 4565T (present in VAR3c), and 7122A (present in VAR3c), all of which are expressed in both wild-type plants and hda6 mutants. Two of the SNPs that are present in the VAR3c haplotype mapped to both NORs (2943A and 3439C), whereas other SNPs present in VAR3c mapped exclusively to NOR4 (4565T and   7122A). These mapping data are explained by deduced haplotype VAR3x, which has the 2943A, 2989A, and 3439C SNPs but not the 4565T or 7122A SNPs (see Fig.
Sequencing of rRNA gene intervals Genomic DNA extracted from 2-wk-old leaf tissue was used to amplify the 3 ′ ETS region using primers described above. The amplified region was then cloned into pGEM-T Easy cloning vector (Promega). An Applied Biosystems 3730 automated sequencing system and BigDye Terminator v3. 1 cycle sequencing kit (Applied Biosystems) were used to sequence each clone. Sequences were analyzed and aligned using Geneious software (http:// www. geneious. com).
Acknowledgments We thank  Ram Podicheti  for assistance with DNA sequence analyses, and  Scott Michaels  for seeds of the A. thaliana line that contains ecotype Sf-2 NOR4 introgressed into Col-0. This work was primarily supported by  National Institutes of Health  grant  GM60380  (to C. S. P. ) and support to C. S. P. as an Investigator of the  Howard Hughes Medical Institute  and the  Gordon and Betty Moore Foundation. C. C. was supported in part by a  Carlos O. Miller Fellowship  awarded by  Indiana University. T. B. was supported in part by a  Ruth L. Kirschstein National Research Service Award  from the  National Institutes of Health. C. C. performed the analyses of Figures 1-3 and  4B  and all associated Supplemental Figures related to the genetic mapping of rRNA gene subtypes except VAR4.
, 2004). The nacre proteins were equally proven to play a primary role in controlling the formation of interfacial structure and biocompatibility with bone as well as the stability of biogenic tissues  (Kim et al. , 2002). While, it was also argued that the nacre molecules efficient in bone cell differentiation were probably more related to peptides  (Rousseau et al. , 2008)   (Rousseau, 2003). To date, more than 50 proteins and 50 peptides from nacre have been identified (UniProt protein database, see www. uniprot. org). Though, the nacre osteoinductive compounds have not yet been identified. Ethanol extraction process for biomolecules has been involved previously  (Huebner and Bietz, 1993)   (Esen, 1986)   (Colvin, 1961) , but very recently, ESM (Ethanol Soluble Matrix), an organic extract from nacre powder of Pinctada (P.
In the future, we will have to develop a more sensitive activity-screening tool to test the compounds present in small amount. A pre-test is firstly conducted in MC3T3-E1 cells. F5 and F6 are observed to have induced a formation of mineralization nodules (data not shown). Afterwards, F5 and F6 are prepared at classic concentration and treated onto the two cell models systematically to validate their osteoinductive capacity (see part 3. 6). Before that, some observations of the purified ESM fractions under Scanning Electron Microscopy are performed (see part 3. 5).
A SEM observation into the ESM fractions and elemental analysis by EDS ESM, and the most important ESM fractions, ESMc, ESMa and F6, are observed under SEM, with the purpose to compare the exterior appearance at micro scale, revealing directly or indirectly some characters of the osteoinductive compounds. Simultaneously, the topical elemental analysis at several positions of the samples is performed by EDS to supply a view on the chemical composition of ESM fractions. The SEM micrographs are presented in Fig. 8. The results of EDS analysis is shown as supplementary data 2. SEM images (Fig. 8 ) show that ESM, ESMc and F6 are all a mixture of organic matrix and mineral crystals (Fig. 8a , c, g), but organized differently. In ESM, the crystals are embedded into the organic   (a,b) , COMPO mode for (c to h), 7.
Methods Research publications on the incidence and mechanisms of HCC development due to occupational exposure to non-infective risk factors of the past 45 years were searched for in three databases: Medline (www. ncbi. nlm. nih. gov/pubmed), Scopus (www. scopus. com/) and Cumulative Index to Nursing and Allied Health Literature (www. ebscohost. com/nursing/products/cinahl-databases/the-cinahl-database). The MeSH term Unique ID D006528 was used to identify the pathology with the following entry terms: Carcinomas, hepatocellular; hepatocellular carcinomas; liver cell carcinoma, adult; liver cancer, adult; adult liver cancer; adult liver cancers; cancer, adult liver; cancers, adult liver; liver cancers, adult; liver cell carcinoma; carcinoma, liver cell; carcinomas, liver cell; cell carcinoma, liver; cell carcinomas, liver; liver cell carcinomas; hepatocellular carcinoma; hepatoma; and hepatomas; together with occupational, work and occupational disease.
However, PBB have been detected as impurities in PBDE. Residues remaining in and around facilities that formerly manufactured, processed, or produced PBB-containing goods are current sources of exposure. Workers at companies that manufactured PBB may have been exposed by skin contact, inhalation or unintentional ingestion. The conclusions of the IARC meeting on PBB are summarized in a Lancet Oncology report  (279). The full report will be issued as volume 107 of the IARC monograph series. There was 'inadequate' epidemiological evidence for PBB carcinogenicity, however, 'sufficient' evidence in experimental animals, as well as data indicating that their toxic effects and carcinogenic potential involve a number of the same pathways as PCB, resulted in their classification as 'probably carcinogenic to humans' (Group 2A)  (280).
Ocean microbes drive global-scale biogeochemical cycling 1 , but do so under constraints imposed by viruses on host community composition, metabolism, and evolutionary trajectories  [2] [3] [4] [5]. Due to sampling and cultivation challenges, genome-level viral diversity remains poorly described and grossly understudied in nature such that <1% of observed surface ocean viruses, even those that are abundant and ubiquitous, are 'known' 5. Here we analyze a global map of abundant, double stranded DNA (dsDNA) viruses and viral-encoded auxiliary metabolic genes (AMGs) with genomic and ecological contexts through the Global Ocean Viromes (GOV) dataset, which includes complete genomes and large genomic fragments from both surface and deep ocean viruses sampled during the Tara Oceans and Malaspina research expeditions 6, 7.
Main text The fundamental bottleneck preventing the incorporation of viruses of microbes into predictive ecosystem models is the lack of quantitative surveys of viral diversity in nature. This is because (i) most naturally-occurring microbes and viruses resist being cultured, and (ii) viruses lack a universally conserved marker gene, which precludes barcode surveys of uncultivated viral diversity 5. While viral metagenomics was introduced to circumvent these issues, low-throughput sequencing technologies initially yielded highly fragmented datasets suitable only for strongly database-biased descriptions  11  , and gene-level analyses of environmental viral communities (reviewed in ref. 5 ). Subsequent improvements in experimental methods, sequencing technologies, and analytical approaches progressively enabled viral population ecology through the availability of genomic information  5, [12] [13] [14].
For example, 1,148 large viral genome fragments captured in a fosmid library of Mediterranean Sea microbes revealed remarkable viral diversity in a single sample, with some of these genomes seemingly globally distributed based upon the six viral metagenomic datasets available at the time 12. Similarly, 69 viral genomes assembled from single-cell genomic datasets provided reference genomes that illuminated the ecology, evolution and biogeochemical impacts of viruses infecting an uncultivated anaerobic chemoautotroph 14. Beyond these 'omics-enabled experimental advances, metagenomic approaches have matured to be quantitative 5 and informative enough, at least for dsDNA templates, to themselves provide genomic information on viruses that infect both abundant and rare hosts. For example, the analysis of 43 surface ocean viral metagenomes (viromes) comprising the Tara Oceans Viromes (TOV) dataset revealed the global underlying structure of these communities, and identified 5,476 viral populations, only 39 of which were previously known 10.
Here we further identify ocean viral populations, characterize the most abundant and widespread types of ocean viruses, and analyze new viral-encoded AMGs and their distributions to expand our understanding of how viruses modulate microbial biogeochemistry. We do so on the basis of a new Global Oceans Viromes (GOV) dataset, which augments TOV with 61 new samples to better represent the surface and deep oceans, and now totals 104 ocean viromes representing 925 Gbp of sequencing.
(Supplementary Table  1 ). Beyond better sample coverage, analytical approaches including cross-assembly  15  and genome binning  16  make the GOV dataset a much improved genomic representation of the sampled viruses. From 1,380,834 contigs generated, which recruited 67% of the reads, we identified 15,280 viral populations (Fig. 1A , Supplementary Fig. 1 ). This expands the number of known ocean viral populations nearly 3-fold over the prior TOV dataset  10  , while also improving the genomic context for these TOV-known populations by a 2. 5-fold increase in contig length on average (Supplementary Table  2 ). Rarefaction analyses show that while mesopelagic viral communities remain undersampled, epipelagic viral communities now appear near-completely sampled (Extended Data Fig. 1A ). Because bathypelagic communities were underrepresented due to cellular contamination of these viromes, we focused the remaining analyses on the 15,222 non-bathypelagic viral populations.
However, recent work has demonstrated the predictive power of sequence-based approaches such as similarities between (i) viral genomes and host CRISPR spacers  20  (ii) viral and microbial genomes due to integrated prophages or gene transfers  12  and (iii) viral and host genome nucleotide signatures (here, tetranucleotide frequencies)  9. We applied all 3 methods to the GOV dataset and predicted hosts at the phylum level, or class level for Proteobacteria (Supplementary Table  4 ). These results were then summarized at the VC level, leading to host range predictions for 392 of 867 VCs, and for which confidence was assessed by comparison to a null model (Supplementary Fig. 1 ). The hosts of the 38 globally abundant VCs were largely restricted to abundant and widespread epipelagic-ocean microbes identified from miTag-based OTU counts in Tara Oceans microbial metagenomes  21.
3A )  30, 31. DsrC, specifically, dictates sulfur metabolism rates, as it provides the sulfur substrate to DsrAB-sulfite reductase for processing  32. A conserved C-terminal motif with two cysteine residues CysBX10CysA is essential for this function. Outside of energy metabolism, a second class of dsrC-like genes (also known as tusE) lack CysB and are instead involved in tRNA modification  33. In the GOV dataset, four clades of dsrC-like sequences were similar to TusE (DsrC-1 to DsrC-4), whereas the fifth (DsrC-5) was most similar to bona fide DsrC involved in sulfur oxidation (Extended Data Fig. 5 , Extended Data Table  1 , Supplementary Fig. 2 ). Second, 4 soxYZ genes from the sox operon were identified on viral contigs (Extended Data Fig. 6 )  30, 31. Like DsrC, SoxYZ is an important sulfur carrier protein harboring a sulfur interaction motif identified in GOV SoxYZ proteins (Fig.