dataesr/scientific-paragraphs-categorization huggingface dataset:
2016). Five million microsporidian spores in 150 ml distilled water were placed on the E. coli lawn. The infection symptoms were checked by Nomarski optics at 48 hr after inoculation. The populations were grown at 23°. Just before starvation, worms were harvested in M9 and centrifuged 5 min at 3000 rpm. A total of 2 ml of pellet was then placed onto a fresh culture. Each uninfected population was thus transferred every 2 days. Each infected population was transferred every 4 or 5 days. The proportion of each strain was quantified at the different timepoints by pyrosequencing on a PyroMark Q96 ID instrument (Biotage/Qiagen), using an SNP identified on the chromosome V between HO1 and the two other haplotypes, HO2 and HO8 (RAD4-chV, T/G at position V: 2,639,853; see Table  S1 ). The allele frequencies were quantified from the height of the pyrogram peaks using the Allele Quantification tool of the PyroMark ID software (Biotage/Qiagen).
Allelic diversity but low haplotypic diversity in each location Our sampling substructure in three locations can be found in the Materials and Methods. We genotyped each of 956 wild individuals using a 2b-RAD sequencing strategy. Our filtered dataset includes 275 SNP sites covering the six chromosomes (Table  1  and Table S3 ), a number consistent with the known level of polymorphism in C. elegans (see Materials and Methods). Over 99% of the individuals were homozygotes and are thus characterized by a haplotype (multilocus genotype). Genotypes for each individual can be found in Table  S5 , Table  S6 , and Table  S7. In each location, we found a strong structure of genetic diversity, where all individuals could be placed in a few homozygous genotype groups (haplotypes), revealing a high level of selfing and linkage disequilibrium.
A total of 95% of the SNPs were present in a worldwide dataset  (Cook et al. 2016 ) (https://elegansvariation. org/) and a majority (57%) were shared between at least two of the three locations (Table 1). Consistent with previous studies  (Barrière and Félix 2005; Andersen et al. 2012) , most of these alleles are likely identical by descent. Of the remaining 14 new polymorphisms, four were singletons separating minor variant haplotypes (such as HO1-1) that are likely to represent new local mutations. Thus, overall, most polymorphisms did not arise locally but corresponded to immigrating alleles. Figure  1A  displays the relationship among the 19 main haplotype groups as a haplotype network (see Figure  S3  for principal component and STRUCTURE, analyses and Figure  S4  for haplotype networks per chromosome).
Despite the pervasive origin of alleles by immigration, the different haplotypes tended to cluster by location, with exceptions such as HO2 and HO7. As observed before  (Barrière and Félix 2005, 2007; Haber et al. 2005; Sivasundar and Hey 2005) , the genetic differentiation between locations (distances. 10 km) was high, with Fst = 0. 52 between the three locations. New in this dataset is the large number of subpopulations sampled in each location. From a hierarchical Fst analysis, we found that the differentiation of samples within locations was even higher, with Fst = 0. 78 over the whole dataset. This high local differentiation was expected from the high rate of inbreeding coupled with bottlenecks in seeding sample-level subpopulations  (Barrière and Félix 2005). We show below that the level of differentiation depends on the very local availability of genotypes within one to a few meters.
A predominant haplotype in Orsay coexists with minor haplotypes over several years In Orsay, one haplotype (HO1) vastly predominated, covering 77% of the individuals and 78% of the C. elegans-positive samples (excluding 2008 collections with a different sample structure). This major haplotype remained predominant over the 7-year span of the survey (Figure  1 , B and C and Figure  2 ). Besides HO1, 12 minor haplotypes could be found. Of these, HO4 was the most abundant (5. 4% of individuals, 10. 8% of samples) and was found on 4 of the 6 years. HO2, HO5, and HO7 were found in 2008 and one other year, whereas HO3, HO6, and HO8-HO13 were found at a single timepoint (Figure  2  and Table  S5 ). The absence of minor haplotypes in our dataset in a given year may only represent insufficient sampling given their low frequency.
Acknowledgments We thank  Lise Frézal ,  Sarah Marsh ,  Patrick Phillips , and  Robert Luallen  for their help with sampling, and  Henrique Teotonio  and  Amandine Veber-Delattre  for discussions. This work benefited from the expertise of the high-throughput sequencing platform of  Institute for Integrative Biology of the Cell  and we are grateful to  Erwin van Dijk ,  Yan Jaszczyszyn , and the other members of the platform for troubleshooting the 2b-RAD protocol with us. We acknowledge  Amhed Vargas Velazquez ,  Pierre Vincens , and  Annabel Whibley  for their help in sequence analysis,  Marine Stefanutti  and  Clémence David  for their help in genomic DNA extraction and genotyping, and  Hervé Gendrot  for culture plate preparation. This work was supported by the  Centre National de la Recherche Scientifique , a " Coup d'Elan " 2011 grant from the  Bettencourt-Schueller Foundation , grant  ANR-11-BSV3-013  from the  Agence Nationale pour la Recherche , and grant  DEQ20150331704  from the  Fondation pour la Recherche Médicale.
Massilibacteroïdes vaginae' sp. nov. strain MV12 T is the type strain of 'Massilibacteroïdes' gen. nov. , a new genus within the family of Porphyromonadaceae. This strain was isolated in Marseille from a patient with vaginosis. 'Massilibacteroïdes vaginae' is a Gramnegative, aero-anaerobic bipolar bacillus. Here we describe the phenotypic characteristics and complete genome sequence of this bacterium. The EMBL accession number is FXDJ00000000 (FXDJ01000001-FXDJ01000009), and its genome consists of nine scaffolds. The strain was deposited in two culture collections under the numbers CSUR P1477 and DSM 100704.
Twenty separated colonies were deposited on a MTP 384 MALDI-TOF target plate (Bruker). Each spot was covered with 2 μL of matrix solution (saturated solution of α-cyano-4-hydroxycinnamic acid) in 50% acetonitrile and 2. 5% trifluoroacetic acid, then left to dry for 5 minutes at room temperature. The spectra were imported into the MALDI BioTyper software (version 2. 0, Bruker) and analysed using standard pattern matching (with default parameter settings) against the main spectra of 6335 bacteria in the Bio-Typer database. A score made it possible to identify or not identify the tested species: a score of >2 with a validated species enabled identification at the species level; a score of >1. 7 but <2 enabled identification at the genus level; and a score of <1. 7 did not enable any identification.
For strain MV12 T , the highest score reached was 1. 34, which does not enable any identification. Therefore, this isolate could not be ranged within any known genus. The reference mass spectrum, generated by comparing 20 spectra obtained from 20 individual colonies of strain MV12 T , was integrated into our database (Fig. 1 ). Because MALDI-TOF MS identification failed, we performed identification by amplifying and sequencing the 16S rRNA gene, as previously described  [9]. The strain MV12 T exhibited a 93. 5% 16S rRNA gene sequence similarity with Parabacteroides goldsteinii, the closest related species with a validly published name  [10]. This similarity percentage is lower than the threshold recommended to delineate a new genus without carrying out DNA-DNA hybridization  [11, 12]. The genus Parabacteroides belongs to the family of Porphyromonadaceae  [13] , which contains 18 different genera.
Of them, the genus found to be the most closely related to 'Massilibacteroïdes vaginae' strain MV12 T , according to the comparison of the 16S rRNA gene sequences, is first Parabacteroides, followed by Tannerella, Porphyromonas and Dysgonomonas. Phylogenetic analysis was performed, and in the inferred phylogenetic tree obtained by comparing the 16S rRNA gene sequence obtained for strain MV12 T with the closest related species, it forms a distinct lineage close to the genus Parabacteroides (Fig. 2 ). Sequences were aligned using CLUSTALW software, and the phylogenetic tree was built using MEGA software  [14]. The strain was incubated at several growth temperatures (25, 28, 32, 35, 37, 40 and 50°C). The bacterium grew between 25 and 40°C, with optimal growth at 37°C. No growth was evident at 50°C.
Sequences were aligned using Muscle  [9]  and phylogenetic tree was built using MEGA6 software  [10]  and maximum likelihood method. at an operating voltage of 60 kV. Using the electron microscopy, we determined that the cells had a mean length of 2. 06 μm (range 1. 27-3. 21 μm) and a width of 0. 73 μm (range 0. 62-0. 81 μm) (Fig. 4 ). The growth of the bacterium was tested in different atmospheric conditions using GENbag anaer and GENbag microaer systems (bioMérieux). The bacteria multiplied in aerobic and anaerobic atmospheres. It also multiplied in microaerophilic atmosphere supplemented with 5% CO 2. However, optimal growth was shown in anaerobic conditions. Colonies also grew on brain-heart infusion (BHI) agar and on BHI agar supplemented with 1% NaCl. This strain exhibited oxidase and catalase activities.
Genome annotation Open reading frames (ORFs) were predicted using Prodigal  [15]  with default parameters. Functional annotation was performed using comparative genomics by BLASTp search against the GenBank nonredundant (nr) and Clusters of Orthologous Groups (COGs) databases  [16] , with an E-value threshold of <1e-3 for amino acid sequences >80 aa and <1e-5 for sequences of <80 aa. The transfer RNAs were searched for using tRNAScanSE tool  [17] , ribosomal RNAs by using RNAmmer  [18]  and BLASTn against GenBank. The ORFans were identified as sequences which were larger than 80 aa without a significant hit, which was defined as an E value of >1e-03 against the nr database. Artemis was used to visualize genomic features  [19]. Pairwise genome comparisons were performed using Proteinortho software  [20]  to detect best BLASTp reciprocal hits between M.
73 μm. Positive for phosphatase alkaline, leucine arylamidase, trypsin, phosphatase acid, naphtol phosphohydrolase, β-galactosidase, α-glucosidase, β-glucuronidase, N-acetyl β-glucosaminidase, arginine dihydrolase, β-galactosidase, β-galactosidase 6 phosphate, α-glucosidase, β-glucosidase, α-arabinosidase, N-acetyl-β-glucosaminidase, fermentation of mannose, fermentation of raffinose, glutamic decarboxylase acid, α-fucosidase, reduction of nitrates, phosphatase alkaline, arginine arylamidase, leucyl glycine arylamidase, leucine arylamidase, alanine arylamidase, glutamyl acid and glutamic arylamidase. Strain MV12 T has susceptibility to penicillin G, cefotetan, imipenem, metronidazole, vancomycin and amoxicillin/clavulanic acid. The 16S rRNA gene sequence was deposited in GenBank database under the accession number LN866992.
The European Molecular Biology Laboratory (EMBL) accession number for the complete genome sequence is FXDJ00000000 (FXDJ01000001-FXDJ01000009), and it consists of nine scaffolds. The strain was deposited in two culture collections: the Collection de Souches de l'Unité des Rickettsies (CSUR) under number CSUR P1477, and the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ) under number DSM 100704.
6 [G] Carbohydrate transport and metabolism 139. 0 4. 5 [E] Amino acid transport and metabolism 139. 0 4. 5 [F] Nucleotide transport and metabolism 56. 0 1. 8 [H] Coenzyme transport and metabolism 82. 0 2. 7 [I] Lipid transport and metabolism 44. 0 1. 4 [P] Inorganic ion transport and metabolism 68. 0 2. 2 [Q] Secondary metabolites biosynthesis, 18. 0 0. 6 transport and catabolism [R] General function prediction only 189. 0 6. 2 [S] Function unknown 72. 0 2. 4 COGs, Clusters of Orthologous Groups database.
The European Molecular Biology Laboratory (EMBL) accession number for the complete genome sequence is FXDJ00000000 (FXDJ01000001-FXDJ01000009), and it consists of nine scaffolds. The strain was deposited in two culture collections: the Collection de Souches de l'Unité des Rickettsies (CSUR) under number CSUR P1477, and the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ) under number DSM 100704. Positive for phosphatase alkaline, leucine arylamidase, trypsin, phosphatase acid, naphtol phosphohydrolase, β-galactosidase, α-glucosidase, β-glucuronidase, N-acetyl β-glucosaminidase, arginine dihydrolase, β-galactosidase, β-galactosidase 6 phosphate, α-glucosidase, β-glucosidase, α-arabinosidase, N-acetylβ-glucosaminidase, fermentation of mannose, fermentation of raffinose, glutamic decarboxylase acid, α-fucosidase, reduction of nitrates, phosphatase alkaline, arginine arylamidase, leucyl glycine arylamidase, leucine arylamidase, alanine arylamidase, glutamyl acid and glutamic arylamidase.
Extraction of Total RNA and Reverse Transcription of RNA Samples The total RNA extraction procedure was carried out as described in  Emboulé et al. (2009). Total RNA quantification was performed by Nanodrop 2000c (Thermo Scientific), and total RNA samples were pooled in RNase-free water at a final concentration of 0. 5 µg/µL for further analysis. E. ruminantium genomic DNA (gDNA) contaminant was evaluated in each RNA sample by performing the pCS20 PCR using AB128-AB129 primers as described previously with a limited number of cycles (25 cycles)  (Martinez et al. , 2004). In the RNA samples, no signal was obtained by the pCS20 PCR (data not shown), indicating limited E. ruminantium gDNA contaminant. Afterward, the RNA samples were reverse transcribed by random priming with Superscript II (Invitrogen) according to the manufacturer's instructions.
Reverse transcription and PCR amplification of corresponding cDNA were done using KpnIprimers as previously described  (Emboulé et al. , 2009). Moreover, the efficiency of reverse transcription was checked using real-time PCR (qPCR) targeting the E. ruminantium 16S rRNA gene by processing the RNA and cDNA samples simultaneously. The primers used for qPCR were 16S-F: 5 ′ AGC GCAACCCTCATCCTTAG 3 ′ and 16S-R: 5 ′ AGCCCACCCTAT AAGGGCC 3 ′. The final concentration of these primers was 900nM. SyberGreen Master Mix was used according to the manufacturer's instructions (Applied Biosystems, France). The PCR program was 10 min at 95 • C and 45 cycles with 30 s at 95 • C, 30 s at 60 • C and 1 min at 72 • C. The difference of Ct between the RNA and cDNA samples was always higher than 5 cycles, indicating low contamination by gDNA in the RNA samples (data not shown).
For data analyses, only probes that are specific for Senegal and Gardel strains were used. The microarrays contained a total of 16,199 probes (60-mer), with three replicates per probe, including 83 bovine genes as negative controls. The selection of E. ruminantium probes gave 14,747 specific probes (91%), with Tm between 80 and 92 • C containing 30 to 50% GC. These rules were progressively relaxed to get the additional 964 probes for a subset of missing sequences (Tm between 70 and 92 • C and a GC % between 20 and 60). Analysis of specificity indicated that 46,696 oligos had 100% identity on 60 bases, 73,513 had 95% identity on 60 bases and 84,767 had 95% identity on at least 50 bases. Cross-reactivity of probes between the strains was assessed using this latter criterion (95% identity on at least 50 bases).
Microarrays: cDNA Labeling, Scanning and Data Analyses Two hundreds nanograms of cDNA were randomly amplified and labeled with Cy3-dUTP using the SureTag DNA Complete Labeling Kit (Invitrogen, France). Following purification with reaction purification columns (Invitrogen, France), quantification of Cy3-dUTP incorporation was performed by absorbance measurement at 550 nm. The yield of cDNA labeling ranged between 3 and 5 µg, and the specific activity ranged between 20 and 25 pmol per µg of cDNA, according to the manufacturer's instructions (Invitrogen, France). Microarrays were incubated for 24 h at 65 • C, with a rotation of 20 rpm, in a hybridization chamber. After hybridization, stringent washings were performed according to the manufacturer's instructions (Invitrogen, France). Arrays were scanned using an Agilent C microarray scanner and extracted with the Agilent Feature Extraction program (version 10.
10). The obtained images were saved in TIFF format and data with the signal intensities of all spots on each image were saved as ". txt" files for further analysis. Normalization of microarray data was performed using the Limma package  (Smyth, 2004)  in R (R Core Team, 2013), available from Bioconductor (http://www. bioconductor. org). Inter-slide normalization was performed using the quantile method. Mean log 2 fold change (FC) between the virulent and attenuated strains was calculated for each probe and a B test was performed. Probes that had low or no signals were excluded from the analysis. For each probe, the expression values of the biological replicates for the virulent and attenuated strains were compiled. The average FC per probe was calculated by taking the average of the log 2 values of the virulent strain from the average of the log 2 values from the attenuated strain.
ACKNOWLEDGMENTS We would like to thank  Isabel Marcelino  for providing the biological samples. We would also like to thank  Sandra Fourré  for her technical assistance during the preparation and analyses of the microarrays. We wish to acknowledge the excellent support received from the  Nice-Sophia Antipolis Functional Genomics Platform. Finally , we would like to thank  Christophe Noroy  very much for creating the Circos genome diagram.
9 to 157. 8°using a laser operating at 664. 5 nm and a refractive index detector (Wyatt Optilab). Bovine serum albumin dissolved in phosphate-buffered saline (PBS) pH 7. 4 with a concentration of 5 mg/mL was used for calibration. The data were analyzed using ASTRA software. Also, to be able to calculate the M w of micelles using the Zimm plot method,  33  the refraction index increment (dn/dc) of the polymers was measured in water by injection of 600 μL of precisely weighted samples in the range of 6-15 mg/mL and using a flow rate of 0. 6 mL/min in an Optilab Rex detector (Wyatt Technology). Critical Micelle Concentration (CMC) Determination. The CMC of the different block copolymers in water was determined using pyrene as a fluorescent probe. 34, 35 Samples were prepared by dissolving the polymers in THF at different concentrations, of which 500 μL was added to 4.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acs. langmuir. 8b03576. Detailed data on mPEG5k-ABCPA-mPEG5k macroinitiator, polymer and Micelle characterization, including the effect of different parameters on mPEG5k-bp(HPMA-Bz)9. 6k and mPEG5k-b-p(HPMA-Bz)4. 7k polymers (PDF).
However, in case of complete digestion in GI fluids, local exposure (e. g. in the upper GI tract) needs to be considered. 13) Only nanomaterials that do not quickly degrade, e. g. by dissolution, under digestive tract conditions are considered for testing under Step 1, which involves gathering the available information as well as data from a set of in vitro studies. In particular, information on carcinogenic, mutagenic, reprotoxic (CMR) properties of the nanomaterial or its components is considered at Step 1. A degradation test under simulated lysosomal conditions is also carried out (see Section 6. 2. 2), along with a battery of relevant in vitro toxicity tests including genotoxicity, in consideration of the specific properties of nanomaterials. In this regard, the bacterial reverse mutation assay (Ames test) is not considered suitable for nanomaterials owing to the inability of bacterial cells that internalise particles.
Data and methodologies Primary references of particular relevance were identified by the EG members (up to 18 April 2018). Also considered were publicly available guidance documents and reports relevant to risk assessment of nanomaterial in agri/food/feed and produced by European Commission non-food Committees, international authorities such as the FDA, WHO and JRC, ECHA and EMA. A draft of this Guidance underwent a public consultation from 12 January to 4 March 2018. The comments received were considered and have been incorporated where appropriate. For construction of this Guidance, a problem formulation approach was followed for nanomaterials. As a result, this Guidance highlights nanospecific issues only, and will be applied in conjunction to the existing EFSA Guidances for conventional materials.
The EU project NanoDefine (www. nanodefine. eu 15  ) has developed guidance for the selection of appropriate techniques and interpretation of results. Respective publications, technical reports and protocols are available from http://www. nanodefine. eu/index. php/nanodefine-publications. The project addresses the recommended European Commission nanomaterial definition, i. e. size and size distribution as well as volume specific surface area, where applicable. It provides a decision-flow scheme and is supported by an e-tool and methods manual. (see http://www. nanodefine. eu/index. php/nanodefiner-e-tool). The decision as to whether the material has to be considered for nanospecific risk assessment under this Guidance can be supported by using this flow scheme. The flow scheme is based on a tiered approach.
Standardised methods Preference should be given to standardised methods where available and applicable. While a number of standard methods are available for particulate materials in pure solid state (e. g. powders), there are hardly any standard methods available for the characterisation of nanomaterial in complex matrices. Appendix C provides an overview of currently available standard methods at the time of issuing this Guidance. It is recommended to search the ISO and CEN databases 18  for the most up-to-date and appropriate methods. In cases where no standard methods are available, the applicant is responsible for providing methods for the physicochemical characterisation and quantification of the nanomaterial for which approval is sought that are appropriate both for the pristine state and in matrices.
, 2015; ECHA, 2016 ; ECHA/JRC/RIVM, 2016; OECD, 2016a,b). The scientific justification for read-across should demonstrate that the physicochemical characteristics of source and target (nano)materials are similar enough to allow the prediction of the toxicological effect of the target nanomaterial. At present, however, the scientific basis for allowing substantiation based on existing data for read-across is limited. This is because, unlike conventional chemicals, the current database on physicochemical and toxicological parameters of nanomaterials is too limited. It is, nevertheless, relevant to assess if the existing data on a nanomaterial can be useful for grouping/read-across of other variants of the nanomaterial with the same chemical composition, and to identify what would be needed to substantiate the use of existing data for a grouping/read-across approach.
e. developmental neurotoxicity and developmental immunotoxicity). According to OECD Guidelines (TG 443 (2012a)), the selected parameters to be measured in the EOGRTS fall into the following categories: À reproductive endpoints À developmental (prenatal and postnatal) endpoints À specific endpoints (developmental neurotoxicity, immunotoxicity and endocrine disruption). With the additional parameters evaluated in the F1 generation in the EOGRTS, it is expected that the F2, with their limited parameter assessments, would seldom affect the hazard characterisation for risk assessment  (Piersma et al. , 2011). When predicted human exposures are considered adequately characterised, however, this may be factored into the decision to require the assessment of an F2 generation. In devising appropriate Step 3 additional testing, a case-by-case approach should be adopted with careful consideration given to all available data as well as animal welfare issues.