dataesr/scientific-paragraphs-categorization huggingface dataset:
Bacteriophages GAAS analysis showed the presence of bacteriophages in 3 metagenomes (P1, P3, P4) with relative abundances of 4. 74%, 33. 69% and 49. 32%, respectively. The total number of bacteriophage-related reads recovered for P1, P3 and P4 was 34, 91 and 455 sequences, respectively (BLASTX search against the Viral Genome database, E-value,1e 205 ). Enterobacteria phages, Pseudomonas phages, Staphylococcus phages, Streptococcus phages, Pseudomonas phages and Burkholderia phages were among the most represented bacteriophage-related sequences (Table  3 ). Moreover 30 contigs matching the Escherichia phage ECML-117 were assembled from sample P4. Also, sequences related to the bacterial hosts of detected bacteriophages were found, including a few sequences matching 16S rDNA (Table  3 ). Specific PCRs were performed on P1, P3 and P4 samples to verify the presence of the bacteriophage sequences recovered in silico.
(DOC) Table  S5  Anelloviridae detected in the viromes. For each virome, the absolute number of reads matching each identified species of Anelloviridae (BLASTX search against the non-redundant NCBI database, E-value,1e -05 ) is listed. Species are grouped according to the genus to which they belong. (DOC) Table  S6  Reconstructed contigs matching Anelloviridae. For each contig, we list the sample from which it was assembled, the length, the number of reads assembled in the contig and the GC content. The best BLAST hit (BLASTX against the non-redundant NCBI database, E-value,1e -05 ) is also shown, as well as the hit alignment parameters: E-value, percentage of identity, and alignment length. (DOC).
71 175 papillomavirus type 50] 00080 665 4 E2 protein [Human papillomavirus] 4e-56 77. 69 121 00082 771 10 minor capsid protein L2 [Human 7e-128 96. 85 254 papillomavirus type 50] 00089 461 4 L2 protein [Human papillomavirus] 5e-68 90. 26 154 a Number of reads assembled in the contig. b Best BLAST hit according to a BLASTX search against the non-redundant NCBI database, E,1e 205. doi:10. 1371/journal. pone. 0093367. t002.
c Number of contigs matching the corresponding bacteriophage according to BLASTX against the non-redundant NCBI database, E-value,1e 205. d Only the bacterial hosts corresponding to the most abundant bacteriophage types (according to GAAS analysis) are reported. e Presence (''yes'') or absence (''no'') of sequences matching the genome of the corresponding bacterial hosts (MG-RAST annotation based on BLAT search on the SEED database, E-value,1e-05). f Number of sequences matching the corresponding bacterial 16S rRNA (BLASTN against the Greengenes database, E-value,1e-05, minimum alignment length 50 bp). doi:10. 1371/journal. pone. 0093367. t003.
Porites coral-based sea surface temperature (SST) reconstructions are obtained from the measurement of skeleton Sr / Ca ratio. However, the influence of salinity in the incorporation of these trace elements in the Porites aragonitic skeleton is still poorly documented. Laboratory experiments indicate that in three different coral species (not including the widely used Porites genus), salinity does not influence the Sr / Ca thermometer. In this study, we test the salinity effect on Porites Sr / Ca-based SST reconstructions at monthly and interannual timescales in open-ocean environmental conditions. We use a large spatial compilation of published Porites data from the Red Sea and Pacific and Indian oceans. Additionally to those published records, we add a new eastern Pacific coral Sr / Ca record from Clipperton Atoll.
Using two different salinity products (Simple Ocean Data Assimilation (SODA) SSS reanalyses version 2. 2. 4, Carton and Giese, 2008; and instrumental SSS from the Institut de Recherche pour le Développement, France (IRD)  Delcroix et al. , 2011) , we find no evidence of salinity bias on the Sr / Ca SST proxy at monthly and interannual timescales. We conclude that Porites Sr / Ca is a reliable palaeothermometer that is not influenced by salinity variability.
, 2013). On the other hand, instrumental temperature measurements contain observational bias corrections  (Rayner et al. , 2003)  or uncertainties due to SST data interpolation in some database such as HadISST  (Rayner et al. , 2003)  and ERSST  (Smith et al. , 2008)   (Smith et al. , 2006; Maupin et al. , 2008; Pfeiffer et al. , 2009; Hetzinger et al. , 2010; Scott et al. , 2010; Deser et al. , 2010; Flannery and Poore, 2013; DeLong et al. , 2014). Coral δ 18 O reflects SST as well as changes in seawater δ 18 O. Coupled measurements of Sr / Ca and δ 18 O in coral samples have been used to estimate past variations in the seawater oxygen isotopic composition (δ 18 Osw)  (McCulloch et al. , 1994; Gagan et al. , 1998; Ren et al. , 2003). δ 18 Osw and SSS are affected by the same processes in the tropics (the precipitation to evaporation ratio, vertical and horizontal advections) therefore, the combination of Sr / Ca and δ 18 O measurements in corals can theoretically provide information on SST and qualitative SSS changes through time  (Gagan et al.
In this paper we extend the spatial coverage of available Sr / Ca data by presenting a newly obtained eastern tropical North Pacific coral record from Clipperton Atoll. The compilation of these coral records permits a better geographical and spatial investigation of the salinity influence on the coral Sr / Ca palaeothermometer in multiple open-ocean conditions at seasonal and interannual timescales. In February 2005, a sampling expedition led by the Institut de Recherche pour le Développement, France (IRD), collected a 1. 94 m long Porites core (labelled CL3) at 10 m water depth with a hydraulic drill. CL3 was cut in several slabs of 1 cm of thickness with a circular saw, along the main growth axis to allow the continuous sampling of corallites. The first two slabs (44 cm length in total) were cleaned with deionized water in an ultrasonic bath for 10 min and then air dried at room temperature for 24 hours.
Coral database We compiled all the publicly available seasonally and annually resolved Porites Sr / Ca records from the NOAA and Pangaea palaeoclimatology databases (http://www. ncdc. noaa. gov/data-access/paleoclimatology-data and http: //www. pangaea. de/, respectively). These seasonal to annual resolution records cover various intervals of 5 to 25 years within the last 3 decades (Table  1  and Fig. 1 ). Our database is composed of 18 records: one in the eastern tropical North Pacific Ocean (this paper), three in the central equatorial Pacific Ocean  (Palmyra, Fanning and Christmas; Nurhati et al. , 2010) , six in the western tropical South Pacific Ocean (Rarotonga;  Linsley et al. , 2000; Vanuatu; Kilbourne et al. , 2004; Tahiti; Cahyarini et al. , 2008; Fiji; Linsley et al. , 2006; Wu et al.
Data processing To compare Sr / Ca to SST, a site-specific calibration is usually necessary to obtain the best-possible fit between the two variables (see  Corrège 2006  for a compilation of published calibrations). This approach yields variable calibration slopes, indicating that the effect of other factors (vital effects, environmental influences, analytical bias  (Hathorne et al. , 2013) , instrumental SST data set and statistical methods used for the calibration  (Kawakubo et al. , 2014) ; see  Corrège, 2006 , for a review) might be locally incorporated in the process. To free the records from the influence of these other factors on the Sr / Ca in corals, we used a single Sr / Ca-SST calibration equation that we applied to each coral record. The calibration equation corresponds to the average of the 38 linear regressions compiled by  Corrège, 2006  (Sr / Ca = -0.
0607 × SST + 10. 553; hereafter C06). However, the intercept of the C06 equation is representative of a mean SST of 25 • C, which is not the mean SST at all coral sites of our database. In order to minimize the site-specific mean SST effect, we elected to work with SST anomalies rather than absolute SST, using only the slope of C06 (SST = Sr / Ca anom / -0. 0607). We also use the rescaled bio-smoothing Sr / Ca-SST slope of  Gagan et al. (2012)  (-0. 084 mmol mol -1 • C -1 ) for a comparison. Moreover, in order to have a consistent instrumental reference, we chose to perform the Sr / Ca-SST calibrations using the OISST monthly product (version 2,  Reynolds et al. , 2002)  for all Sr / Ca records. The OISST product blends instrumental data from different sources and is regularly updated. Additionally, this product is spatially complete due to the use of satellitederived SST estimates  (Deser et al.
"ns" means not significant (p > 0. 01). The significant correlations are significant at the 99 % confidence level. n is the number of values for each record at both resolutions. value at seasonal and interannual resolution (Fig. 2a  and Table  2 ; Fig. 2b  and Table  3 , respectively). As with SST, we used the closest SSS grid point for each location (Table  S1 ). The SODA SSS is a reanalysis product involving modelling and data assimilation, but unfortunately, it lacks salinity data in several oceanic regions. However, the SODA SSS product is the only global ocean gridded SSS database currently available, making it the only possible choice for this study. To test the potential limitation of the seasonal and interannual SSS variability in the SODA SSS product at our sites, we investigated the correlation between T and SSS using the 1 • × 1 • gridded instrumental IRD SSS product (covering 120 • E-70 • W, 30 • N-30 • S;  Delcroix et al.
, 2011)  at 12 tropical Pacific sites of our database (Fig. 1c  and Table  S2  and 3 ). The IRD SSS product data set is made freely available by the French Sea Surface Salinity Observation Service (http://www. legos. obs-mip. fr/observations/sss/). As for SODA SSS, we resampled the corresponding IRD SSS monthly average for the five at a 2-month resolution and the eight-times-per-year coral records.
(2013) , even though working with Sr / Ca anomalies minimizes these offsets. Thus, we strongly support the conclusion drawn by  Hathorne et al. (2013)  stating that future Sr / Ca coral data should always be corrected using an international standard. We then look at each individual records at interannual resolution using both a Hanning filter and an annual average method. No significant relationship between SST and SODA SSS is found when using the annually averaged time series. However, when using the Hanning filter, the Kavieng, Tahiti, Christmas Island and Fanning locations present significant relationships between SST and SODA SSS/IRD SSS (r = 0. 53 (SODA), r = 0. 48 (SODA), r = 0. 52 (IRD), r = 0. 66 (SODA) and r = 0. 61 (IRD), respectively; p < 0. 01; Table  3 ). Significant correlations can also be noted between instrumental SST and SSS at several locations, mostly with Hanning filtered data.
Acknowledgements. The authors would like to thanks  Antoine Grémare ,  Bruno Malaizé  and  Thibaut Caley  for their comments and statistical advice. This article greatly benefited from the thorough reviews of  C. Saenger  and two anonymous reviewers, and from comments from the editor  Nerilie Abram. This research was funded by the  INSU , through the  Imago program VADE RETRO  awarded to  Thierry Corrège. Edited by:  N. Abram.
BICEP1 is a millimeter-wavelength telescope designed specifically to measure the inflationary B-mode polarization of the Cosmic Microwave Background (CMB) at degree angular scales. We present results from an analysis of the data acquired during three seasons of observations at the South Pole (2006Pole (  to 2008)). This work extends the two-year result published in  Chiang et al. (2010) , with additional data from the third season and relaxed detector-selection criteria. This analysis also introduces a more comprehensive estimation of band-power window functions, improved likelihood estimation methods and a new technique for deprojecting monopole temperature-to-polarization leakage which reduces this class of systematic uncertainty to a negligible level. We present maps of temperature, E-and B-mode polarization, and their associated angular power spectra.
Map Results Applying the mapmaking algorithm described above to the three-year dataset yields T , Q, and U maps at 100 and 150 GHz (available in the data release), which are qualitatively similar to those in Figure  2  of C10. We compare the noise level in these maps, estimated from noise simulations, to that of the two-year maps from C10. When using the same central region, which encompasses 27% (19. 2%) or 305 (203) square degrees of the 100 (150) GHz maps, we find that the rms noise is 0. 68 (0. 50) µK-degree. This is consistent with the 52% increase in integration time found in §3. 2. An alternate, and perhaps more natural, measure of map depth is obtained by calculating the weighted standard deviation of a jackknife map (see §6. 2), using the weight map described in §4. 3. The jackknife map, chosen to be the seasonsplit temporal jackknife, has the same noise level as the threeyear map, but no signal.
This noise model more closely follows the analysis philosophy of faithfully simulating the real data and then treating the simulated and real timestreams symmetrically, including the filtering step. Additionally, by deriving the noise model from a full scanset length timestream, instead of a large number of halfscan length segments, we capture the low-frequency atmospheric noise, which persists over many half-scans and is heavily correlated between detectors. A direct comparison of the noise model used in this analysis with that of C10 is presented in §6. 1. 2. We generate 499 independent realizations of BICEP1 noise. Each realization consists of timestream data for all detectors across all three years of observation. These simulated timestreams pass through the low-level processing and filtering operations, are binned into maps, and reduced to power spectra.
Realizations of these signal simulations are generated from the theory spectra using the synfast utility included in the HEALPix package 21. At this stage, the CMB signal is smoothed by convolution with a Gaussian beam; for each simulated sky, two sets of T , Q, and U maps are produced, corresponding to BICEP1 100 GHz and 150 GHz beam widths. The resolution of the HEALPix maps is 0. 11 • (Nside = 512). Using the pointing data from the telescope, we simulate timestreams for every detector by performing a second order Taylor expansion interpolation from the nearest pixel center to the actual detector pointing. Signal-only timestreams are processed through the analysis pipeline, passing through the same filtering and map-making steps as the real data. For each of the two classes of signal simulation, we apply this process to 499 independent sky realizations drawn from the same underlying CMB power spectra to produce sets of simulated signal-only maps.
Relative Gain Mismatch For the C10 analysis, relative gain mismatch caused by imperfectly differenced detector pairs was judged the leading source of possible BB contamination, estimated to produce a bias on r ≤ 0. 17 (T10). Although this exceeded the r = 0. 1 benchmark set, it remained small compared to the statistical uncertainty of the two-year result. With the addition of a third year of data and additional detectors, we are motivated to use the relative gain deprojection technique described in §4. 4 (and further detailed in  Aikin et al. in preparation)  to keep the potential contamination from relative gain leakage well below the statistical uncertainty. To quantify the remaining leakage after deprojection, we compare three sets of simulated data: the first two include relative gain mismatch at the level observed for BICEP1 and either do, or do not, employ deprojection to remove the resulting temperature-to-polarization leakage.
Upper Limit and Confidence Intervals The left panel of Figure  10  shows the BICEP1 likelihood function for r calculated using both the direct method (simulation based) and alternate method (via Hamimeche & Lewis bandpower likelihood approximation). The most notable difference is that the alternate likelihood calculation extends to negative non-physical values of r. Because of this feature, we choose to derive the maximum likelihood estimate of r from the alternate likelihood calculation, as the likelihood peak will always exist, regardless of whether the data contain a high or low noise fluctuation. For the specific case of the BICEP1 three-year results, the likelihood peaks at a slightly positive value, r = 0. 03, and the two likelihood calculations agree on the peak position; this agreement is generally quite good for all simulated results with maximum likelihood r above zero.
The upper limit from the direct likelihood is more conservative than the same limit calculated from the alternate likelihood, both for the specific case of the BICEP1 three-year data and also for simulations of that data. We believe that the direct likelihood is more accurate, though the agreement in the region of the likelihood peak shows that the Hamimeche & Lewis (HL) bandpower likelihoods are an excellent choice for most purposes. The 95% confidence upper limit from three years of BICEP1 observations is r < 0. 70. The right panel of Figure  10  shows this limit along with the distribution of upper limits obtained from simulations. The median upper limit, a useful benchmark of experimental sensitivity, is r < 0. 65 at 95% confidence. For both likelihood methods (direct and HL bandpower likelihood), we estimate the Monte-Carlo uncertainty by repeating the above calculations for the first 250 and the last 249 realizations separatly and find that the 95% confidence upper limits and the maximum likelihood estimate differ by ∆r < 0.
The dashed black line indicates the median of the distribution (-0. 27). The solid black line indicates the value of this shift for the real data (-0. 10). natural way. Figure  11  shows the probability distribution for ρ as a function of the theory, with both the Bayesian upper limit and the Feldman-Cousins confidence interval shown. It is a feature of the Feldman-Cousins construction that the 95% confidence interval for a bounded theory can be either onesided or two-sided depending on the data; for the BICEP1 three-year result, we obtain a one-sided 95% confidence interval, r < 0. 62.
The differences due to the change of analysis pipeline, noise model, bandpower window function calculation, and data selection are within expectation. Second, jackknife null tests confirm the internal consistency of the data and analysis. The most important results of BICEP1 are the CMB power spectra bandpowers. Overall, the spectra are consistent with the ΛCDM cosmological model. We detect E-mode power in the first acoustic peak at 15σ, the most significant such detection to date (Figure  9 ). We confirm the T E superhorizon fluctuations, first detected by WMAP  (Kogut et al. 2003) , at 8σ. The total detection significance for non-zero EE power is 18σ, and 14σ for T E power. The primary goal of BICEP1 is to search for the inflationary B-mode signal. The B-mode spectra are consistent with zero, and we place the strongest upper limits to date in the 50 < ℓ < 200 region where the signal from inflation is expected to peak (Figure  13 ).
One might naively expect the upper limit to improve by a larger factor when adding 52% more data. We have confirmed that the relatively small decrease is not a result of the set of analysis refinements discussed in §6. 1. The change in mapmaking pipeline, noise model, bandpower window function calculation, and deprojection each produce small shifts in bandpowers and error bars as described above. But when we apply all of these changes together to the original C10 data set, the resulting r constraint derived using the same offsetlognormal likelihood approximation as C10 is r < 0. 71. In other words, the net effect of these changes on the r upper limit is close to zero for this dataset. The relatively small decrease in the new upper limit is explained by two factors. First, the offset-lognormal likelihood approximation used in C10 resulted in a negative bias on the upper limit for those specific BB bandpowers (we find that the r constraint derived from offset-lognormal bandpower likeli-hoods is biased low for some cases and high for others).
Applying the more accurate direct likelihood calculation to the reanalyzed C10 dataset shifts the upper limit in this case from r < 0. 71 to r < 0. 80. Second, upon including the new data in this analysis the upper limit fluctuates somewhat high compared to the average of simulations. With an r = 0 input model, simulated datasets run through our final analysis yield upper limits on r that decrease by a median of 0. 27 when including the additional data of the full three years (Figure  12 ). The corresponding decrease seen in the real data is only 0. 10 (from 0. 80 to 0. 70). Although this decrease is smaller than average it is not an unlikely result; 17% of the simulations saw even less of a decrease, and in 7% of the simulations the upper limit actually increases when adding the additional data.
Interesting constraints can be placed on cosmic birefringence from the BICEP1 T B and EB spectra, which are predicted to by zero by the ΛCDM model. This topic will be explored in detail in  Kaufman et al. (2013). Measurement of CMB B-mode polarization remains the most promising approach for testing the inflationary paradigm. BICEP1 has provided the lowest upper limits on inflationary B-modes to date. In this data release, we demonstrate a deprojection technique that will enable future experiments to cope with the increasingly important temperatureto-polarization leakage and develop a direct likelihood calculation for converting bandpower results into constraints on r without approximations. BICEP2 has completed three years of observation with an order of magnitude better mapping speed at 150 GHz than BICEP1.
H3K9ac levels follow H3K4me3 at Cfp1 regulated regions independent of gene induction Cfp1 influences H3K4me3 deposition at many genomic locations spread throughout the mouse ES cell genome and particularly at active gene promoters  [28]. We tested whether Cfp1-deficiency affects histone acetylation at non-doxorubicin-inducible promoters by examining H3K4me3 patterns at the most active genes in wt ES cells as identified in our RNA-Seq dataset (n = 2500, Additional file 6). The results confirm that lack of Cfp1 leads to severely reduced H3K4me3 levels, mainly located downstream from the TSS (Figure  4A, B, E ) and that this is accompanied with a reduction in H3K9,K14ac as determined by ChIP-Seq (Figure  4A, B, E ). Similar results were obtained for this gene set when comparing doxorubicin-treated wt and Cfp1 -/-ES cells (Additional file 7: Figure  S5A, S5B  and S5E ).
Antisense transcription is also a feature of gene expression thought to be affected by H3K4 methylation  [44] [45] [46]. Active mammalian transcription units produce small sense and antisense RNA molecules at their TSS and Transcription End Sites (TES)  [47, 48]. We investigated levels of these transcripts at genes regulated by doxorubicin in wt and Cfp1 -/-ES cells using strand specific RNA-Seq datasets. As expected, upon induction sense RNA accumulated both downstream of the TSS and upstream of the TES (Figure  5C, D, G  and H ) and profiles were strongly similar when comparing doxorubicin treated wt and Cfp1 -/-ES cells. Induced genes also displayed increased divergent antisense transcription at both TSS and TES, albeit at much lower abundance, but this was strikingly elevated in Cfp1 -/-ES cells (Figure  5C, D, G  and H ).