dataesr/scientific-paragraphs-categorization huggingface dataset:
AxioVision or ZEN softwares were used for microscope image analysis (Zeiss). BrdU-incorporation assays. For staining cells in S-phase, BrdU (10 280 879 001, Roche) was added to cell cultures at 20 mM for 1 h. For staining SSB foci undergoing repair, BrdU was added overnight. Cells were then fixed with 4% paraformaldehyde in PBS, permeabilized with 0. 2% Triton X-100 and incubated with 40 U ml À 1 DNase I (M6101, Promega) and 20 U ml À 1 Exonuclease III (M1811, Promega) for 30 min at 37 °C. BrdU was with anti-BrdU mouse IgG (M 0744, Dako) and Rhodamine Red-conjugated anti-mouse IgG (715-296-150, Jackson Immunoresearch laboratories). HPRT assays. Cells were plated at low density (700-1,500 cells per cm 2 ). They were exposed to 100 mM of 6-thioguanine (6-TG; A4882, sigma) twice per day. Cells were fixed in PFA 4% in PBS on days 0, 3 and 6 post-treatment and then stained with 0.
Cells were fixed with 90% ethanol overnight at 4 °C, rinsed in PBS and incubated with 2 N HCl/0,5% Triton X-100 at RT for 30 min. After that, cells were suspended in 0. 1 M sodium tetraborate for 2 min. Cells were incubated with anti-BrdU mouse IgG (M 0744, Dako) for 1 h at 37 °C, washed with PBS and incubated with AlexaFluor 488 anti-IgG Mouse (A-21202, Molecular Probes) for 1 h at RT. Cells were finally incubated with PBS containing 10 mg ml À 1 RNase A and 20 mg ml À 1 propidium iodide for 30 min at 37 °C and then analysed by flow cytometry on BD-FACSCanto II. The results were analysed with the FACSDiva 7. 0 software. Quantitative FISH (Q-FISH). Q-FISH experiments were performed either on metaphases or interphasic nuclei. Metaphase spreads were obtained using a standard method. In brief, cells were incubated 1 h in Karyomax Colcemid (Invitrogen Corporation), trypsinised and incubated in a 60-mM KCl hypotonic buffer.
nci. nih. gov/). Primer sequences were: parp1 forward 5 0 -GCCCTAAAGGCTCAGAACGA-3 0 and reverse 5 0 -CAGAAGG CACTTGCTGCTTG-3 0 ; twist forward 5 0 -GGCTCAGCTACGCCTTCTC-3 0 and reverse 5 0 -TCCATTTTCTCCTTCTCTGGAA-3 0 , slug forward 5 0 -TCGGACCC ACACATTACCTT-3 0 and reverse 5 0 -TGACCTGTCTGCAAATGCTC-3 0 , EAR forward 5 0 -GAGGCTGAGGCAGGAGAATCG-3 0 and reverse 5 0 -GTCGCCCAGGCTGGAGTG-3 0. The PCR protocol was as recommended for the Mx3005P Real-time PCR System (Stratagene). Accumulation of PCR products was measured by SYBR green fluorescence (SYBR Green master mix; Applied Biosystems). Raw data analysis was performed with the MxPro software (Agilent). All target gene transcripts were normalized to the EAR transcripts. All experiments were conducted at least three times with independent RNA isolations.
Acknowledgements This work was supported by the  Centre National de la Recherche Scientifique , the  Universite Ĺille 1 , the  UniversiteLille 2 , the  Ligue contre le Cancer (CA's team: Comitedu Pas-de-Calais and Comitede la Somme ; EG's team is equipe labellisee), the  Association pour la Recherche sur le Cancer , the  Institut Pasteur de Lille , the  SIRIC OncoLille, Grant INCa-DGOS-Inserm   6041 , and the  European  integrated project  RISC-RAD  ( FI6R-CT2003-508842 ). J. N. has a fellowship from the UniversiteLille 2 and from the  Association pour la Recherche sur le Cancer. S. M. had a fellowship from the  Ministry of Research  and from the  Fondation pour la Recherche Medicale. E. D. had a fellowship from the  Institut Pasteur de Lille  and the  Region Nord-Pas-de-Calais. N. M. had fellowships from the  Centre National de la Recherche Scientifique  and the  Region Nord/Pas-de-Calais , from the  Ligue contre le Cancer , and from the  Universite  ´Lille 1.
The Human Microbiome Project (HMP) identified the 16S rRNA gene sequences of 'most wanted' taxa -prevalent in the healthy human microbiota but distant from previously known sequences. Since 2012, few of the corresponding genomes have been isolated and sequenced, and only through advanced isolation techniques. We demonstrate that the genomes of the most wanted taxa can be identified computationally through their correlation in abundance across multiple public metagenomic data sets. We link over 200 most wanted sequences with nearly complete genome sequences, including half of the taxa identified as high-priority targets by the HMP. The genomes we identify have strong similarity to genomes reconstructed through expensive isolation techniques, and provide a more complete functional characterization of these organisms than can be extrapolated from their 16S rRNA gene.
Introduction The majority of organisms inhabiting our bodies and our world cannot be readily cultured. As a result, most microbial communities can only be explored through culture-independent molecular techniques. Among these, two major approaches dominate: PCRbased amplification and sequencing of hypervariable regions of the 16S rRNA gene, and random sequencing of whole-community DNA. The former approach was pioneered in the 1980s by Norm Pace  (Olsen et al. , 1986) , and has resulted in the creation of an extensive catalog of 16S rRNA sequences comprising today over two million distinct entries. These data are widely available through three main databases: RDP  (Cole et al. , 2009) , Silva  (Quast et al. , 2013)  and  Greengenes (DeSantis et al. , 2006). The random sequencing of community DNA is being increasingly applied to the study of environmental  (Tyson et al.
, 2004; Rusch et al. , 2007)  and human-associated microbial communities  (Gill et al. , 2006; Qin et al. , 2010; Human Microbiome Project C, 2012). Unlike 16S rRNA-based studies, whole-community metagenomic projects suffer from the relative dearth of extensive and broad reference databases as the genomic data in public databases (in terms of both genes and genomes) is primarily derived from the relatively small number of genomes that have been grown in culture. The public data are also biased in their phylogenetic distribution  (Wu et al. , 2009). Recent studies have begun to develop new reference catalogs based on the genes and genomes found in environmental  (Yooseph et al. , 2007)  and hostassociated  (Qin et al. , 2010; Human Microbiome Project C, 2012)  metagenomic data sets and have already added millions of new genes to public databases such as MG-RAST  (Wilke et al.
, 2016)  and IMG/M  (Markowitz et al. , 2008). Missing from these catalogs are reliable connections to the wealth of information already accumulated in 16S rRNA studies. Creating a link between 16S rRNA amplicon studies and whole-community metagenomic data is complicated by the fact that the 16S rRNA gene is frequently misassembled or left unassembled even in isolate genome assemblies, let alone metagenomic mixtures. A testament to this difficulty are extensive efforts undertaken during the past couple of years to isolate or culture the organisms associated with the 'most wanted' 16S rRNA gene sequencessequences without close relatives in public databases yet found in high abundance in multiple samples from the HMP project  (Fodor et al. , 2012). To date, just two of these organisms have been isolated and sequenced  (Ma et al.
Genus-level classification of OTUs The databases SILVA, NCBI nt and NCBI WGS (Whole-Genome Shotgun) were used to retrieve the genus annotation for the OTU validation set. For the most wanted OTUs, we also relied on the RDP taxonomical annotation from  Fodor et al. (2012). A threshold of 98% identity and 98% alignment coverage were used to define the genus annotation level in the different databases. For the RDP annotation, we only considered genera with classification confidence ⩾ 80%, and discarded any assignments that disagreed with the information from other databases.
OTU data The metadata and sequences for the most wanted OTUs (priority group, max fraction body habitat, RDP assignment and so on) were downloaded from the HMP website at http://www. hmpdacc. org/most_wanted/. The sequences and Qiime-processed  (Caporaso et al. , 2010)  abundance table for the HMP OTUs were downloaded from http://hmpdacc. org/HMQCP/.
MGS genome metadata The MGS metadata and sequences (genes, proteins, draft genomes) were downloaded from https://www. cbs. dtu. dk/projects/CAG/. Note that the MGS genomes were reconstructed from metagenomic data using a coabundance clustering approach similar to the one described here. They were further refined through sample-specific reassembly and the resulting assemblies were validated for completeness and correctness. For more details see  Nielsen et al. (2014).
Functional annotation A functional annotation of the MGS proteins to the eggNOG groups (COG) was performed in two steps. First, the MGS proteins were assigned to the eggNOG 3. 0 database with blastp (v. 2. 2. 29+), using an e-value threshold of 1E -5. The first 20 hits for each protein were then considered for pairwise alignment to the MGS proteins using Clustal Omega  (Sievers et al. , 2011). The sequence identity and coverage was extracted from the alignment. Finally, the COG was assigned from the hit that shared the highest sequence identity and coverage. The PICRUSt COG prediction was performed with picrust-1. 0. 0  (Langille et al. , 2013)  according to the authors' recipe for metagenome prediction.
Genes from each MGS were ordered by decreasing connectivity (gene coabundance network based on Spearman's correlation and a threshold of 0. 7). The 50 most connected genes were selected and used to compute the mean tracer vector for each MGS. This reduced data set was used for further analyses. These transformations were performed using the momr package  (Prifti and Le Chatelier, 2014). 'Validation set' and 'most wanted' OTU abundance table creation V3-V5 16S rRNA abundance table and taxonomic annotations were downloaded in a Qiime  (Caporaso et al. , 2010) -formatted spreadsheet from: http:// downloads. hmpdacc. org/data/HMQCP/otu_table_psn_ v35. txt. gz. The abundance table for the HMP most wanted OTUs was generated by mapping the shotgun sequences generated in the HMP project to the consensus sequences of the OTUs using Bowtie  (Langmead et al.
, 2009)  with parameters '-q-v 1k 1-suppress 1, 2, 4, 5, 6, 7, 8'. The resulting alignments were converted into an abundance table using custom software available from http://www. cbcb. umd. edu/software/MGS_correlation/. Correlation analysis for linking OTUs to MGS sequences Correlation analysis was performed in R using custom software available from http://www. cbcb. umd. edu/soft ware/MGS_correlation/. Briefly, abundance tables for OTUs and MGSs were normalized by conversion to ratios and were log-transformed to control variation and reduce the effect of extreme values. A pseudocount of 1E -17 was added to avoid underflow. For every MGS or most wanted OTU, we identified the OTU or MGS, respectively, whose abundance profile correlated best in terms of the Pearson's correlation value. We used Pearson's correlation to be consistent with the procedure used to construct the MGS clusters.
Results We derived a gene abundance table by mapping the shotgun reads from 138 stool samples sequenced in the HMP project  (Human Microbiome Project C, 2012)  to the 3. 9M gene catalog constructed in the MetaHIT project  (Qin et al. , 2010). These gene-level abundances were used to estimate the abundance of 741 MGS. We similarly derived abundance tables for 45 411 OTUs generated by the HMP from the V3-V5 hypervariable region of the 16S rRNA gene. We then identified correspondences between the metagenomic species and 16S rRNA OTUs by computing the correlation between the corresponding abundance profiles. We focused on the MGS correlated to 1468 OTUs ('most wanted' OTUs) representing 16S rRNA gene sequences that were poorly characterized in public databases, but prevalent in the HMP human samples  (Fodor et al.
The PICRUSt prediction based on the sequence of the most wanted OTU only recovers a small fraction of the functional profile of this genome, highlighting the limits of prediction based on 16S rRNA sequences for organisms poorly represented in public genome database. To further demonstrate that the bioinformatically identified genome sequences capture valuable biological information, we compared the MGS sequences we found to be associated with the most wanted OTUs to related genome sequences from public databases. Specifically, we identified 58 genomes containing 16S rRNA genes highly similar to those of 'most wanted' OTUs (with ⩾ 98% identity over 100% length, see Supplementary Table  2 ). These 58 genomes were mainly isolated and sequenced by the HMP project and through another advanced culturing technique called culturomics  (Lagier et al.
Discussion The increased availability of metagenomic data sets spanning hundreds of samples provides an unprecedented opportunity for the development of novel comparative methodologies. Nielsen et al. (2014)  have recently shown that the abundance profile of metagenomic genes across multiple samples can be used to organize the data into metagenomic units, representing clusters of covarying genes. Here we have leveraged this information to provide a first correspondence between 16S rRNA-based and whole-metagenome surveys of the gut microflora, as well as to link the information constructed in the European MetaHIT project with that generated in the US NIH-led HMP project. When applied to a collection of 16S rRNA OTUs determined by the HMP to be 'most wanted' because of their high prevalence in the HMP samples and their relative absence from public databases, our method was able to identify corresponding MGS sequences for a large number of OTUs, including roughly half of the high-priority organisms from the human stool.
These findings suggest the need for a better characterization of the organisms found in the healthy human microbiome, some of which may be important for maintaining health. These results indicate that valuable biological insights can be obtained through data integration across multiple experimental platforms, despite experimental limitations such as PCR amplification biases and other technical challenges (both experimental and bioinformatic). At the same time, our study was hampered by ambiguous and incorrect taxonomic labels in public databases, particularly within the Lachnospiraceae family, underscoring the need for further development and refinement of taxonomic databases, particularly keeping in mind the need of large-scale automated computational analyses of the data. Despite our initial success, it is important to also note several limitations of the approach used.
Video Microscopy The channels were then immediately placed in a closed chamber maintained at constant temperature (33 • C). Few minutes after centrifugation had been stopped the concentration waves of bacteria started to propagate inside the channels (Fig  1 ). The observations were performed with a Leica MZ16F stereomicroscope equipped with two fluorescence sets: a green one, GFP2 (Leica) Ex480/Em510 and a red one, G (Leica) Ex546/Em590. Images were recorded on a CCD camera (CoolSnapHQ, Roper Scientific) at a frame rate of one image per minute (switching every minute from one fluorescence channel to the other). The image stacks were then post-processed using ImageJ and Matlab.
Background: CATI is a French initiative launched in 2010 to handle the neuroimaging of a large cohort of subjects recruited for an Alzheimer's research program called MEMENTO. This paper presents our test protocol and results obtained for the 22 PET centres (overall 13 different scanners) involved in the MEMENTO cohort. We determined acquisition parameters using phantom experiments prior to patient studies, with the aim of optimizing PET quantitative values to the highest possible per site, while reducing, if possible, variability across centres. Methods: Jaszczak's and 3D-Hoffman's phantom measurements were used to assess image spatial resolution (ISR), recovery coefficients (RC) in hot and cold spheres, and signal-to-noise ratio (SNR). For each centre, the optimal reconstruction parameters were chosen as those maximizing ISR and RC without a noticeable decrease in SNR.
Background CATI is a French platform funded in 2010 with the aim of supporting multicentre clinical trials involving neuroimaging (cati-neuroimaging. com). The main project devoted to CATI was to handle the multimodality imaging aspect of a large cohort of subjects included in a research program on Alzheimer's disease (AD). This program, called ME-MENTO, is a longitudinal study aiming at better understanding the natural history of Alzheimer's disease. It has enrolled 2300 subjects with either light cognitive deficits or cognitive complaints that will be followed up for at least 5 years (http://clinicaltrials. gov/show/NCT01926249). The imaging aspect of the MEMENTO study includes the acquisition of PET cerebral glucose metabolism and amyloid distribution, with MRI studies on anatomy, diffusion, and resting state.
In this paper, we focus on the optimization of PET quantitative values to the highest possible per site, while reducing, if possible, the variability across centres for the MEMENTO cohort. Phantom data were acquired during a technical visit to the PET centres, prior to patients' participation in the trial. The same phantom data were also acquired during follow-up visits after 18 months. The present study only addresses the data acquired during the site technical set-up. Several large cohort studies on AD have been launched since the early 2000s. Among them, ADNI-1 was a forward-thinking, imaging-based multicentre clinical trial that involved 50 centres in North America, with the aim of identifying biomarkers of AD. For the PET imaging carried out in this study, the ADNI PET core determined reconstruction parameters for each scanner model and 3D-Hoffman phantoms were acquired with a standard protocol.
Acknowledgements We sincerely thank the staff from all departments of Nuclear Medicine who participated in the MEMENTO cohort study for their availability and their patience in spite of our numerous requests. We also are grateful to  Alain Giron  and  Marion Houot  for their help with statistical analyses, and to the staff from Esprimed (www. esprimed. net),who were in charge of phantom acquisitions, for their efficiency and professionalism. Finally, we greatly appreciate the support of  Philips ,  GE Healthcare , and Siemens engineers in determining and setting up the optimized reconstruction parameters.
Habitus photographs. The photograph in Fig. 3  was obtained from an image-stack taken with the aid of a Leica MZ 16 with a camera (Canon EOS 450D) attachment and Combine ZP software, and subsequently processed with Adobe Photoshop CS3. The photograph was taken from a specimen preserved in ethanol which had to be stabilized with fine-grained sand to obtain the desired strictly dorsal view. Depository. ES: Laboratório de Ecologia Subterrânea/ ISLA: Coleção de Invertebrados Subterrâneos da UFLA-Universidade Federal de Lavras, Brazil.
Acknowledgements We would like to express our sincere thanks to our colleagues  Marconi Souza Silva  (UFLA) and  Luiz Felipe Moretti Iniesta  for helping with collections, and to our colleagues at the  Museum für Naturkunde, Berlin :  Manfred Uhlig  for the male habitus photograph (Fig. 3),  Andreas Wessel  for assistance in the layout of figures, and  Jason Dunlop  for language editing. Alexandr Emeljanov ,  Zoological Institute, Russian Academy of Science ,  St Petersburg , and  Manfred Asche ,  Museum für Naturkunde, Berlin , gave valuable taxonomic advice and provided constructive comments on the manuscript. The work on which this manuscript is based was funded by the  Conselho Nacional de Pesquisa (CNPq) , process nº 477712/2006-1, and to  R. L. F. ( CNPq  grant nr. 304682/2014-4 ).
Food consumption data The types and quantities of foods that made up the average Burban total diet' and the average Bsemi-rural total diet^are based on the results of previous studies, which are published elsewhere  (Reshmany 2010; Nasreddine et al. 2010; Raad et al. 2014). Briefly, in each of the areas under investigation, food consumption data were collected by conducting individual food consumption surveys, following the same methodology, using the same dietary assessment instrument, and targeting the same population group, i. e. , adults aged 25-54 years. The age and sex distribution of the study samples (n = 444 in Beirut and n = 200 in Keserwan) were proportionate to that of the baseline population, according to the National Survey of Household Living conditions (Central Administration for  Statistics et al.
2006). In each area, the sample was drawn from randomly selected households. One adult from each household was invited to participate. The survey's design and conduct were approved by the Institutional Review Board of the American University of Beirut, and informed consent from adults was obtained prior to enrolment in the study. Food consumption data were collected by means of a culture-specific semi-quantitative food frequency questionnaire (FFQ)  (Nasreddine et al. 2006). A reference portion, expressed in household measures or grams, was specified for each food item listed on the questionnaire. The average individual's daily ration, including drinking water and other drinks, was estimated at 3036. 5 g/day for the urban area and of 3706. 1 g/day for the semi-rural one.
The appropriate amount of each raw, prepared, or cooked food item to be included in its composite food group was determined based on the respective food consumption data (Tables  1  and 2 ). Food items of each group were combined and homogenized, while frozen, using a stainless steel blender. Therefore, for the urban diet, 21 composite food groups per market basket x three market baskets = 63 composite food group samples were prepared for analysis. Similarly, and following the same procedure for the semi-rural diet, 63 composite food group samples were prepared, thus amounting to 126 samples prepared for analysis in this study. Composite samples were then stored at -20 °C overnight in sealed disposable aluminum foil containers for solid foods or in glass bottles for liquids, until their delivery in ice to the laboratory for chemical analysis, the next morning,.
Analytical quality control All data were subjected to strict quality control procedures, such as the analysis of procedural and instrumental blanks in addition to spiked samples with each set of analyzed samples. The target pesticide quantification was based on peak area ratio of the target ion divided by the internal standard (PCB 153). Surrogate standard (PCB 52) recoveries were calculated to monitor the analytical process's performance, and the obtained values were higher than 60 %. For all pesticide residues under investigation, the limit of detection (LOD) was of 3. 33 μg/kg of sample matrix except for beta endosulfan, parathion, and heptachloro-endo-epoxide, for which the LOD was of 13. 33 μg/kg. For all pesticide residues under investigation, the limit of quantification (LOQ) was of 10 μg/kg of sample matrix except for beta endosulfan, parathion, and heptachloro-endo-epoxide, for which the LOQ was of 40 μg/kg.
Calculation of dietary exposure Calculation of dietary exposure was performed for those pesticide residues that were detected or quantified in at least one food sample in at least one of the areas under study (Beirut or Kesrwan). The deterministic approach was applied for the calculation of the dietary exposure to pesticide residues. Given that the percentage of censored data (results reported below LOD and/or LOQ) exceeded 60 % for all the pesticide residues, two scenarios were adopted: (1) the lower bound (LB) approach by replacing the results below LOD by zero and results below LOQ by LOD and (2) the upper bound (UB) approach by replacing the results below LOD by LOD and results below LOQ by LOQ (GEMS/Food-Euro 1995; EFSA 2010). To take into account the variability that exists in food consumption patterns and to provide data on the exposure levels' distribution within the studied population, the distributions of food intakes as provided by the individual dietary surveys were combined with the average concentration for each pesticide residue under study.