dataesr/scientific-paragraphs-categorization huggingface dataset:
Percentages are based on the number of patients in the safety set in the specific treatment group. *Eight patients who were randomised to the T&E ranibizumab alone group or the PRN ranibizumab group, supposed to be noted with laser treatment equal to 0, actually received laser treatment in the study eye. These patients were assigned to T&E ranibizumab+laser group in the safety analysis set, although laser was not considered as the study treatment because of the initial randomisation, and as such, these patients were not considered in the analysis of the number of study laser treatments. Because the laser treatment of these eight patients was not the randomised study medication, these cases were recorded in the concomitant medication dataset. †Treatment visits do not include protocol-mandated intermediary visits.
Acknowledgements The authors would like to thank  Mohammed Najeeb Ashraf  from  Medical Operations (Global Business Services-Global Medical and Clinical Services, Novartis Healthcare, India ) for his medical writing and editorial support and  Stephanie Blick  ( Novartis Pharma AG, Basel, Switzerland ) for her administrative, technical and logistic support towards the development of the manuscript. Collaborators Group members are listed in online supplementary file S1. Contributors Conception and design of the study: CP, VB, SP and AW. Analysis and interpretation: CP, FF, SM, FR, KH, JS, VB, SP, WJS, AW and JF. Critical revision of the article: CP, FF, KH, FR, SM, WJS, AW, SP and JF. Final approval of the article: CP, FF, SM, FR, KH, JS, VB, SP, WJS, AW and JF. Data collection: VB, SP, WJS and AW.
Data sharing statement Full trial protocol can be assessed at: http://www. novctrd. com/ctrdWebApp/clinicaltrialrepository/displayFile. do?trialResult=11543 Open Access This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4. 0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons. org/ licenses/by-nc/4. 0/.
The numbers of contigs before and after cleaning are shown in Table  1. Functional annotation was carried out on cleaned contigs using tools from RAST (4, 5) (http://rast. nmpdr. org/) and from the MicroScope platform (6) (http://www. genoscope. cns. fr/agc /microscope/mage/). RAST annotation (Table  1 ) showed the genes involved in antibiotic resistance, and interestingly, the gene encoding the ␤-lactamase was present in the three strains. The knowledge of the complete sequence of these strains will allow us to better understand the impact of these bacteria on the intestinal epithelium. Nucleotide sequence accession numbers. The draft genome sequences have been deposited at DDBJ/EMBL/GenBank under the accession numbers listed in Table  1.
ACKNOWLEDGMENTS This work was supported by the  European Research Council  and from grants from  Danone Research and Yakult Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Tomoaki Naito  is an employee of  Yakult Honsha Co. , Ltd. High-throughput sequencing was performed on the Genomics Platform, a member of the  France Génomique consortium  ( ANR10-INBS-09-08 ). We thank  Philippe Bouvet  and  Sylvain Brisse  for helpful discussions.
We complement our previous analysis of a sample of z ∼ 1-2 luminous and ultra-luminous infrared galaxies ((U)LIRGs), by adding deep VLA radio observations at 1. 4 GHz to a large dataset from the far-UV to the sub-mm, including Spitzer and Herschel data. Given the relatively small number of (U)LIRGs in our sample with high S/N radio data, and to extend our study to a different family of galaxies, we also include 6 well sampled near IR-selected BzK galaxies at z ∼ 1. 5. From our analysis based on the rad-tran spectral synthesis code GRASIL, we find that, while the IR luminosity may be a biased tracer of the star formation rate (SFR) depending on the age of stars dominating the dust heating, the inclusion of the radio flux offers significantly tighter constraints on SFR. Our predicted SFRs are in good agreement with the estimates based on rest-frame radio luminosity and the Bell (  2003 ) calibration.
g. U et al. 2012). The work by  Bressan et al. (2002)  was mainly focused on understanding the nature of the FIR-Radio correlation, particularly for a local sample of obscured starbursts, by making use of stateof-the-art models of star forming galaxies. They used GRASIL to model the IR emission of galaxies and extended its predictions beyond the sub-millimetre regime, up to 316MHz. They showed that the delay of the non-thermal emission with respect to the IR and thermal radio can give rise to observable effects in the IR to radio ratio as well as in the radio spectral slope, potentially allowing the analysis of obscured starbursts with a time resolution of a few tens of Myr, unreachable with other SF indicators. Vega et al. (2008)  also included radio modelling (with at least 3 data points in the radio regime) in their analysis of a sample of 30 local (U)LIRGs, with the aim of studying their starburst nature and disentangling the possible AGN contribution.
DATA SAMPLE & OBSERVATIONS This work is intended to be complementary to the physical analysis performed in our previous paper (BLF13) and aimed at testing BLF13 estimates, in particular those concerning M and SF R, against observations through the investigation of the radio emission of high-z star forming galaxies. For this reason we first apply our analysis to the same sample of 31 high-z (U)LIRGs whose far-UV to sub-mm properties have been deeply investigated in BLF13 and then, in order to strengthen our conclusions, we extend the analysis by including a small but well representative sample of 6 BzKselected star forming main sequence (MS) galaxies at z∼1. 5 for which also high S/N radio observations are available. These two sample benefit from a full multiwavelength coverage from far-UV to Radio including Spitzer and Herschel (PACS+SPIRE) data.
BzK data sample The six BzK galaxies analysed here have been selected from the original sample studied in  Daddi et al. (2010, D10 hereafter). These are NIR-selected galaxies, in GOODS-N, with K < 20. 5 (Vega scale; or K < 22. 37 AB), to which the BzK color selection criterion of  Daddi et al. (2004)  has been applied, together with the requirement of a detection in deep Spitzer 24 µ m imaging, in order to select a sample of star forming galaxies at 1. 4 < z < 2. 5. This small sample of BzK galaxies has the advantage of having, in addition to the availability of a spectroscopic redshift, also a very rich photometric dataset including full far-UV to NIR coverage, Spitzer (IRAC, MIPS + 16 µ m InfraRed Spectrograph peak-up image  Teplitz et al. (2011 ), Pannella et al. (2014) ) and Herschel (from both PACS and SPIRE) observations and high S/N radio detections at 1.
4 GHz  (Morrison et al. 2010). All the BzKs have redshift in the range 1. 4 z 1. 6, that is in the lowest redshift regime probed by BzK selection. This is due to the requirement, by  Daddi et al. (2010) , of a radio detection coupled with the choice of observing the CO[2-1] transition. Its rest-frame frequency of 230. 538 GHz can be observed with the PdBI only up to z = 1. 87. Among the six targeted galaxies, five redshifts were obtained through the GOODS-N campaigns at Keck using DEIMOS  (D. Stern et al. 2010). The redshift for BzK-12591 was instead derived by  Cowie et al. (2004). This galaxy, showing a strong bulge in the Hubble Space Telescope (HST) imaging, has also a possible detection of [N eV ]λ 3426 Å emission line, suggesting the presence of an AGN. For these objects, thanks to the availability of data from MIR to mm wavelengths,  Magdis et al.
2011). This has been interpreted as a further indication that most of the stellar mass forms in continuous mode of SF. Under this picture high-z LIRGs and (U)LIRGs seem to mostly reflect the high SFR typical for massive galaxies at that epoch, so they are not brief stochastic starbursts as their local counterpart. They simply represent the early gas-rich phase of smoothly declining SFH of L galaxies, as demonstrated by our analysis. The availability of radio data has allowed us to put stronger constraints on our solutions, particularly in terms of the SFH and current SFR of these objects. Moreover being the SFR and M of a galaxy related one to each other and dependent on the age distribution of stellar populations and giving the radio constraints also on the fraction of very young stars contributing to the NT radio luminosity through core-collapse SNe explosions, the analysis performed in this work goes in the direction of strengthening our predictions also in terms of the stellar content of these galaxies.
SUMMARY & CONCLUSIONS In this paper we have extended the thorough SED analysis performed by BLF13, by including also the modelling of radio emission for a sample of high-z (U)LIRGs and BzK-selected galaxies for which a full multiwavelength dataset from far-UV to sub-mm was already available. In BLF13 we have found that the values of M and SFR estimated through our approach can be, in some cases, significantly different with respect to previous works based on more classical methods (e. g. hyperz-mass). In particular we have found higher stellar masses for the most dust obscured galaxies and lower SFR by a factor ∼ 2 for the best fits requiring a significant contribution by intermediate-age stars (with ages older than 10-90 Myr on average) to the NIR and the FIR (through cirrus emission). We have investigated here the origin and relevance of the cirrus component contributing to the FIR emission of our z ∼ 1 -2 (U)LIRGs by complementing their far-UV to sub-mm data with VLA radio observations at 1.
The modelling of the radio emission includes both the thermal and nonthermal components, as described in B02. -We are able to reproduce, well within a factor of two, the far-UV to radio emission for all the (U)LIRGs with RMS 3-4 σ, and 4/6 BzKs in our sample. The inclusion of the radio data in the SED fitting has required a different best fit for the sources U4812 and U5152 with respect to BLF13, with a different geometrical distribution for stars and dust for the former, and the addition of a late burst of SF for the latter, to enhance the radio emission. The extensive spectro-photometric coverage of our high-z galaxy sample up to radio allows us to set important constraints on the star formation history (SFH) of individual objects. For essentially all (U)LIRG and BzK objects we find evidence for a rather continuous SFR and a peak epoch of SF preceding that of the observation by a few Gyrs.
This seems to correspond to a formation redshift of z∼ 5-6. We underestimate the radio data of the two BzKs, BzK-12591 and -25536. The first one has confirmed AGN  (Cowie et al. 2004)  and shows low value of qT IR = 2. 06. The latter does not have confirmed AGN contributing to its SED but also shows the same q value. The low q observed for this galaxy may be indicative of a hidden AGN. -We have compared our predicted SFR averaged over the last 10 Myr, SF R10, to the values derived from radio luminosity using the  Bell et al. (2003)  calibration, and from the IR luminosity using the  Kennicutt (1998)  calibration. We find very good agreement between our SF R10 and the SF R Bell 1. 4GHz derived from the rest-frame radio luminosity. The estimated SF RK are instead systematically off-set by a factor 1.
And this could be different from the simple assumption of a same scaling factor for all the stellar populations. The results discussed in this paper mostly rely on VLA observations that were not completely matched in sensitivity to our Herschel and Spitzer data, and consequently only a few (U)LIRGs had reliable radio detections. Our analysis points towards the radio flux as an essential information for interpreting star-forming galaxies at high redshifts and for recovering reliable SFH. The currently operational and much improved sensitivity of J-VLA promises to add important data as soon as the relevant cosmological fields will be surveyed at faint enough limits. And, of course, forthcoming facilities like Meerkat, or future ones like SKA, will provide the full extensive radio coverage needed for all the relevant use.
ACKNOWLEDGMENTS This paper uses data from Herschel's photometers SPIRE and  PACS. PACS has been developed by a consortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KU Leuven, CSL, IMEC (Belgium) ;  CEA ,  LAM(France) ; MPIA (Germany) ;  INAF-IFSI/OAA/OAP/OAT ,  LENS ,  SISSA (Italy) ;  IAC (Spain). This development has been supported by the funding agencies  BMVIT (Austria) ,  ESA-PRODEX (Belgium) ,  CEA/CNES (France) ,  DLR (Germany) ,  ASI/INAF (Italy) , and  CICYT/MCYT (Spain). SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including:  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ.
Massive telomere reorganization upon carbon source exhaustion To investigate telomere organization in live cells, we monitored the subnuclear distribution of the telomeric protein Rap1 fused to green fluorescent protein (GFP)  [22]  at different stages of a liquid culture, from glycolysis to late respiration to SP. We observed dramatic changes in the distribution of the Rap1-GFP signal during this time course (Fig. 1a, b ). In agreement with previous reports  [6, 22] , Rap1-GFP formed three to five foci during the logarithmic phase, quantified using our custommade software (Fig. 1c ; adapted from  [22] ). In cells undergoing respiration (after 2 days in culture), Rap1-GFP foci were fewer and brighter, with 50 % of the cells showing two foci and 30 % of the cells having only one focus (versus 23 % and 10 %, respectively, during fermentation).
c Quantification of the distribution of intensity and number of foci of Rap1-GFP images from experiment shown in (b) with our in-house software. Pie charts represent percentages of cells with 0 (white), 1 (red), 2 (orange), 3 (green) and 4 (blue) foci. Box plots: white = fermentation (Ferm), light gray = respiration (Resp), dark grey = stationary (Stat). Median (line) and mean (cross) are indicated. For each condition, more than 1000 cells were analyzed. Statistical tests were carried out using the Mann-Whitney non-parametric test (****p < 0. 0001; ***p < 0. 001; **0. 001 < p < 0. 01; *0. 01 < p < 0. 05; ns = p > 0). d Colocalization of telomeres with Rap1 foci. ImmunoFISH with Y' probes was performed on a WT strain yAT1684 at SP. e Representative fluorescent images of the telomere-associated protein Rap1 tagged with GFP in SP WT and sir3Δ cells.
Processing of paired-end reads The raw data from each 3C experiment were processed as follows First, PCR duplicates were collapsed using the six Ns present on each of the custom-made adapters. Reads were then aligned using Bowtie 2 in its most sensitive mode against the S. cerevisiae reference genome  [50]. Paired-end reads were aligned as follows: for each read the length of the sequence mapped was increased gradually from 20 bp until the mapping became unambiguous (mapping quality >40). Paired reads were aligned independently.
Multi-dimensional scaling is then applied to recover the optimal 3D coordinates from this distance matrix. To allow direct comparison between the structures obtained in different conditions we first re-scaled them to equalize the volume occupied by their associated convex hull. We then scaled the distances in each structure to account for the measured difference in size between nuclei in G0 and G1 daughter cells (1. 5 and 1. 7 μm, respectively; data not shown and  [52] ). Telomere pair distances were then directly computed from the structures to assess telomere re-organization.
Acknowledgments We thank  Patricia Le Baccon  for assistance with microscopy,  M. H. LeDu  for providing recombinant Rap1, and  Sheldon Decombe  for his help on Fig. 6b. We also thank  Geneviève Almouzni  for critical reading of the manuscript and the members of the  Taddei laboratory. AT and MG thank  Isabelle Sagot  for discussion and sharing unpublished results. RK and MM thank  Alain Jacquier  and  Gwenael Badis-Breard  for their constructive suggestions and comments and their help in the preparation of BY quiescent strains. M. Guidi was supported by a doctoral fellowship from the  Association pour la Recherche sur le Cancer  and the  Institut Curie. M. M. is the recipient of an  Association pour la Recherche sur le Cancer  fellowship 20100600373. The research leading to these results has received funding from the  European Research Council  under the European Community's  Seventh Framework Program  (FP7/2007 2013/ European Research Council  grant agreement  210508  to AT and  260822  to RK) and the investissement d'avenir ( Labex DEEP) Agence Nationale de la Recherche  to A.
Names of epiRILs numbered 1-6 in each panel are in Materials and Methods. Data of epiRILs were obtained from GEO (GSE37284  [46] ). doi:10. 1371/journal. pgen. 1005154. g007 and 7F ) and two epiRILs with lower level of hypomethylation (epiRIL260 and epiRIL480) are shown. Global hypomethylation indexes of epiRIL98, epiRIL260, and epiRIL480 are 0. 38, 0. 04, and 0. 09, respectively. "WT" data are from the parental wild-type Col plant used to generate the epiRILs. (B) CHG methylation levels in the genes that were not methylated in WT but methylated in epiRIL98 (methylation level < 0. 1 in WT and ! 0. 1 in epiRIL98: n = 232). For these transcription units, distributions of the methylation levels were compared among the parental WT, the parental An increase in non-CG methylation is also seen in mutants of the histone demethylase gene IBM1.
DNA methylation analyses For the 1G and 9G ddm1 plants and their controls, genomic DNA was isolated from rosette leaves using the Illustra Nucleon Phytopure genomic DNA extraction kit, and genome-wide bisulfite sequencing was performed as described previously  [65]. Raw sequence data were deposited in the DDBJ (DNA Data Bank of Japan) Sequence Read Archive (DRA; accession nos. DRA002545, DRA002546, DRA002548, DRA002549, DRA002551, DRA002554, DRA002555, DRA003018, DRA003019 and DRA003020). The adaptor sequences were clipped out using the FASTX-toolkit (http://hannonlab. cshl. edu/fastx_toolkit/). Reads were trimmed to 90 nucleotide length (45 nucleotide for the data obtained from GEO-GSE39901) and mapped to reference genomes (Release 10 of the Arabidopsis Information Resources) using the Bowtie alignment algorithm  [66]  with the following parameters, "-X 500-e 90-l 20-n 1".
Only uniquely mapped reads were used. Clonal reads were removed except one with the best quality. Any read with three consecutive methylated CHH sites were eliminated. The level of methylation of cytosine in a genomic region was calculated using the ratio of the number of methylated cytosine to that of total cytosine. For the three epiRILs and two parental lines, whole-genome bisulfite sequencing was described previously  [46]  and the data are in GEO (GSE62206). DMRs (differentially methylated regions) were defined by comparing the methylation level of 100-bp windows throughout the genome between two genotypes. The windows with at least 20 sequenced cytosines were used for the comparison. The level of methylation was calculated using the weighed methylation level of each genotype  [67]. The windows were selected as DMRs when difference of methylation level was 0.
Processing ChIP-seq data ChIP-seq data of various histone modifications  [72]  in GEO (GSE28398) were used for our analysis. The coordinates were remapped onto TAIR10 annotation using a script in TAIR  [73]. Enrichment of histone modification in a DMR was calculated by the density of ChIP-seq reads, and normalized by the mean and the standard deviation of the density of reads in 100,000 windows randomly chosen across the genome.
Processing MeDIP-chip data of epiRILs The MeDIP-chip data of 123 epigenetic recombinant inbred lines (epiRILs), ddm1 and WT are in GEO (GSE37284). The regions that were methylated (M) in WT and unmethylated (U) in ddm1 were selected as targets of ddm1 mutation using the values for HMM (hidden Markov model) status (M (methylated) or I (Intermediate) or U (Unmethylated))  [46]. Global hypomethylation index of an epiRIL was calculated as the genome-wide average of the values for HMM status of probes on the chip (M = 0, I = 0. 5, U = 1) in the target regions of ddm1 mutation. The data of inference of inherited haplotypes were shown in the previous study  [46]. Following are names of lines numbered 1-6 in.
This work was supported by grants from the  Mitsubishi Foundation  (to TK), the  Japanese Ministry of Education, Culture, Sports, Science and Technology  ( 19207002  and  19060014 , to  TK) Systems Functional Genetics  Project of the  Transdisciplinary Research Integration Center ,  Research Organization of Information and Systems, Japan  (to YT, AT, AF and TK), the  European Union Seventh   Framework Programme Network of Excellence EpiGeneSys  (Award  257082 , to VC), and the  Investissements d'Avenir   ANR-10-LABX-54   MEMO LIFE as well  as  ANR-11-IDEX-0001-02   PSL Ã Research University  (to VC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Search strategy An exhaustive search strategy will combine comprehensive database searches with forensic or 'expansive' searches. These searches will be iterative and will evolve as the review progresses because their purpose is to build our knowledge of recommendations and guidance in conducting and reporting meta-ethnography rather than to answer a tightly-defined research question  [20]. This will combine browsing of texts (publications) with periods of more focused systematic searching rather than a linear search process  [20]. To identify relevant literature we will start with seminal methodological and technical publications known to our international expert academic advisors and the project team including Noblit and Hare's  [13]  seminal original book on Fig. 2  eMERGe mixed methods research design meta-ethnography and detailed worked examples of metaethnographies.
Comprehensive database searches to identify methodological publications Bibliographic databases will be searched including: MED-LINE, SCOPUS, PsycARTICLES, PsycINFO via Health Source: Nursing/Academic Edition; Pubmed; CINAHL; the International Bibliography of the Social Sciences; Sociological abstracts; Web of Science Core Collection; British Education Index; ERIC (Educational Resources Information Center); Australian Education Index; and ERA (Educational research abstracts) Online. The draft search terms are shown in Table  1  these will be refined following piloting. The final search strategy will be available from the corresponding author after completion of the project. Reviewers will also hand search reference lists in texts that meet the inclusion criteria for the review for other relevant studies not already identified.
Data extraction and analysis Data will be extracted from each included text by only one reviewer because this is a qualitative review in which the key principles are transparency and consensus, not independence and inter-rater reliability. The completeness of the data extraction will be checked by a second reviewer for five texts per reviewer (10 in total). A template using Microsoft Excel will be developed and piloted to record characteristics of publications including details such as the authors, publication year, title, aim, topic focus, academic discipline, and type of publication. The initial data extraction categories will be informed by the project team's earlier methodological review  [18]  and will be refined following piloting. Extracted data will be analysed qualitatively. The qualitative analysis software NVivo 10.