dataesr/scientific-paragraphs-categorization huggingface dataset:
Our primary analysis is of the shift in best-fit cosmological parameters as determined from: (1) a prior on the value of τ (as a proxy for low-polarization data) and PlanckTT 3  data restricted to < 800; 4 and (2) the same τ prior and the fullrange ( < 2500) of PlanckTT data. Taking the former data set as a proxy for WMAP, these are the parameter shifts that have been of great interest to the community. There is of course a degree of arbitrariness in the particular choice of = 800 for defining the low-data set. One might argue for a lower , based on the fact that the WMAP temperature maps reach a signal-to-noise ratio of unity by 600, and thus above 600 the power spectrum error bars are at least twice as large as the Planck ones. However, we explicitly select = 800 for our primary analysis because it splits the weight on ΛCDM parameters coming from Planck so 2 Although the South Pole Telescope and Atacama Cosmology Telescope had already measured the CMB T T power spectrum over this multipole range (e.
g. ,  Story et al. 2013; Das et al. 2014 ), Planck's dramatically increased sky coverage leads to a much more precise power spectrum determination. that half is from < 800 and half is from > 800,  5  Addressing the parameter shifts from < 800 versus > 800 is a related and interesting issue, and while our main focus is on the comparison of the full-results to those from < 800, we compute and show the low-versus high-results as well. Additionally, in Appendix A we perform an exhaustive search over many different choices for the multipole at which to split the data. In addition to the high-Planck temperature data, inferences of the reionization optical depth obtained from the low-Planck polarization data also have an important impact on the determination of the other cosmological parameters. The parameter shifts that have been discussed in the literature to date have generally assumed a constraint on τ coming from Planck LFI polarization data  (Planck Collaboration XI 2016; Planck Collaboration XIII 2016).
During the writing of this paper, new and tighter constraints on τ were released using improved Planck HFI polarization data  (Planck Collaboration Int. XLVI 2016; Planck Collaboration Int. XLVII 2016). These are consistent with the previous ones, shrinking the error by about a factor of 2 and moving the best fit to slightly lower values of τ. To make our work more easily comparable to previous discussions, and because the impact of this updated constraint is not very large, we have chosen to write the main body of this paper assuming the old τ prior. This also allows us to more cleanly isolate and discuss separately the impact of the new prior, which we do in a later section of this paper. Our focus here is on the results from Planck, and so an in-depth study comparing the Planck results with those from other cosmological data sets is beyond our scope.
Nevertheless, there do exist claims of internal inconsistencies in CMB data  (Addison et al. 2016; Riess et al. 2016) , with the parameter shifts we discuss here playing an important role, since they serve to drive the PlanckTT best fits away from those of the two other CMB data sets, namely the Planck measurements of the φφ lensing potential power spectrum  (Planck Collaboration XVII 2014; Planck Collaboration XV 2016)  and the South Pole Telescope (SPT) measurement of the T T damping tail  (Story et al. 2013). Thus, we also briefly examine whether there is any evidence of discrepancies that are not just internal to the PlanckTT data, but also when comparing with these other two probes. The features we identify that are driving the changes in parameters are approximately oscillatory in nature, a part of them with a frequency and phasing such that they could be caused by a smoothing of the power spectrum, of the sort that is generated by gravitational lensing.
We thus investigate the role of lensing in the parameter shifts. The impact of lensing in PlanckTT parameter estimates has previously been investigated via use of the parameter "A L " that artificially scales the lensing power spectrum (as discussed on p. 28 of Planck Collaboration XVI 2014 and p. 24 of Planck Collaboration XIII 2016). Here we introduce a new method that more directly elucidates the impact of lensing on cosmological parameter determination. Given that we regard the < 2500 Planck data as providing a better determination of the cosmological parameters than the < 800 Planck data, it is natural to turn our primary question around and ask: what is it about the < 800 data that makes the inferred parameter values differ from the full -range parameters? Addressing this question, we find that the deficit in low-multipole power at < ∼ 30, the "low-deficit," 6  plays a significant role in driving the < 800 parameters away from the results coming from the full -range.
The paper is organized as follows. Section 2 introduces the shifts seen in parameters between using Planck < 800 data and full-data. Section 3 describes the extent to which the observed shifts are consistent with expectations; we make some simplifying assumptions in our analysis and justify their use here. Section 4 represents a pedagogical summary of the physical effects underlying the various parameter shifts. We then turn to a more detailed characterization of the parameter shifts and their origin. The most elementary, unornamented description of the shifts is presented in Sect. 5. 1, followed by a discussion of the effects of gravitational lensing in Sect. 5. 2 and the role of the low-deficit in Sect. 5. 3. In Sect. 5. 4 we consider whether there might be systematic effects significantly impacting the parameter shifts and in Sect.
Note that the constraints are generally in good agreement, with the full Planck data providing tighter limits on the parameters; however, the best-fit values certainly do shift. It is these shifts that we seek to explain in this paper. A prior τ = 0. 07 ± 0. 02 has been used here as a proxy for the effect of the low-polarization data (with the impact of a different prior discussed later). As a comparison, we also show results for WMAP T T data combined with the same prior on τ (grey). slightly shifted. It is these shifts that we seek to explain in the later sections. Fig. 1  also shows constraints from the WMAP T T spectrum. As already mentioned, these constraints are qualitatively very similar to those from Planck < 800, although not exactly the same, since WMAP reaches the cosmic variance limit closer to = 600.
Comparison of parameter shifts with expectations In light of the shifts in parameters described in the previous section, we would of course like to know whether they are large enough to indicate a failure of the ΛCDM model or the presence of systematic errors in the data, or if they can be explained simply as an expected statistical fluctuation arising from instrumental noise and sample variance. The aim of this section is to give a precise determination based on simulations, in particular one that avoids several approximations used by previous analyses. One of the first attempts to quantify the shifts was performed in appendix A of Planck Collaboration XVI (2014), and was based on a set of Gaussian simulations. More recent studies using the Planck 2015 data have generally compared posteriors of disjoint sets of Planck multipole ranges (e.
Description of Simulations To calculate the expected shifts, we generate a suite of simulated Planck data and, for each data set, compute a likelihood and numerically maximize it to obtain the best-fit parameters, subject to various multipole range cuts. The difference in bestfit parameters between different cuts builds up a distribution of the expected shifts, which can be compared to the shifts seen in real data. The goal of these simulations is to be as consistent as possible with the approximations made in the real analysis (as opposed to, for example, the suite of end-to-end simulations described in Planck Collaboration XI 2016, which aim to simulate systematics not directly accounted for by the real likelihood). In this sense, our simulations are a self-consistency check of Planck data and likelihood products.
Note that although visually the > 800 data appears to be a better fit with < 30, the χ 2 is worse by ∆χ 2 = 3. 2. Middle: Same as panel (5a) of Fig. 7 , but with dashed lines showing the responses with the gravitational potential fixed. Bottom: Same as panel (5a) of Fig. 7 , but with an additional free parameter, A L , shown in yellow. This added degree of freedom tracks reasonably well the oscillatory residuals, leaving smaller shifts for the other parameters and a reduced low-deficit. Table  3. Comparison of the expected dispersion ("Exp. ") and observed ("Obs. ") parameter shifts between pairs of datasets. We show results for the case where we use all the lowest multipoles, where we excise the < 30 multipoles, and where we also fix the lensing potential, as described in Sect. 5. 2. The shifts are shown in units of standard deviation of the respective < 800 runs for each case.
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES , and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planck-collaboration. Part of the analysis for this paper was run on computers operated by  WestGrid  (www. westgrid. ca)and  Compute Canada  (www.
computecanada. ca). This work was also supported by the  Labex ILP  (reference  ANR-10-LABX-63 ). We thank all of the users of  Cosmology@Home  for donating computing time in support of this work, and in particular the top contributors, MaDcCow (Thomas Wooton), 25000ghz ( Roberto Piantoni ), and  Rally1965 , as well as the top team, BOINC. Italy.
These data indicate that in LIF + FBS medium, OTX2 is sufficient, without the contribution of FGF2, to induce an expansion of the primedlike ESC compartment as observed in FGF2-primed WT ESCs and NanogKO ESCs in LIF + FBS medium. Then, we analyzed the response to FGF2 of NanogKOS, DKO, and Otx2KO ESCs. Indeed, on the basis of their identity in LIF + FBS, the NanogKOS and DKO ESCs should be primed more efficiently than Otx2KO ESCs. Consistently, when compared with WT, NanogKOS and DKO cells exhibited higher induction of OCT6 and lower expression of CH, while Otx2KO cells induced OCT6 in fewer cells and retained a higher number of NANOG + cells (Figure  S7F ). OTX2 or GFP were detected in most of the NanogKOS and Otx2KO cells and virtually in all DKO cells (Figure  S7F ). We next analyzed the identity of Tx-treated pPyCAGOtx2-ER ESCs in chimeric blastocysts.
ACKNOWLEDGMENTS We thank the staff of the IGB animal house facility, the  IGB  integrated microscopy facility, the  IGB FACS facility , and the  Tigem NGS facility. We also thank  D. Graniero  for typing and formatting the manuscript and  S. Arbucci  and  V. Mercadante  for technical support. This work was supported by the  Italian Association for Cancer Research (AIRC)  (Project  IG-14152 ), the  POR MOVIE of Regione Campania  ( B25C13000240007 ), and the  PRIN  project ( 20157JF8P5_004 ) to A. S. and the  Medical Research Council  to I. C.
Foraging Behavior We delimited albatross foraging tracks based on visual observations of departure and arrival times from twice-daily nest checks during incubation, and hourly nest checks from dawn to dusk during brooding. To remove unlikely Argos locations, tracks were filtered using the Iknos Toolkit (Y. Tremblay, unpublished program) for Matlab (The MathWorks, Natick, MA), following  Kappes et al. (2010). First, a speed filter of 80 km h -1 was applied to transit rates between successive locations (following  Hyrenbach et al. , 2002; Suryan et al. , 2006)  to remove unrealistic flight speeds  (Spear and Ainley, 1997). Next, the maximum change in azimuth was set to 170 • to remove track spikes between successive locations that are likely to be erroneous  (Keating, 1994; Freitas et al. , 2008).
, 2000) , and landing rates of wandering albatrosses (Diomedea exulans) are correlated with field metabolic rates  (Shaffer et al. , 2001a). We used an algorithm (Iknos toolkit for Matlab; Y. Tremblay, unpublished program;  Kappes et al. , 2015)  to identify landings based on rapid changes in temperature, and stable periods associated with sitting on the sea surface  (Wilson et al. , 1995) , for those individuals equipped with temperature records. We defined daylight hours based on civil twilight (sun no more than 6 • below the horizon) using NOAA's solar calculator in the maptools package in R (Lewin-Koh and Bivand, 2010) and temporally-matching to tracking locations.
ACKNOWLEDGMENTS We thank  Jill Awkerman ,  Sarah Chisholm ,  Peter Kappes ,  Nicolas Mignot ,  Scott Seganti , and  Jean-Baptiste Thiebot  for invaluable assistance in the field, and  Lindsay Young  for conducting genetic sexing. We are grateful to the  Hawaiian Islands National Wildlife Refuge, U. S. Fish and Wildlife Service ,  Department of the Interior , for logistic support and permission to conduct research on Tern Island; to L' Institut Polaire Français  and  Terres Australes et Antarctiques Françaises  for supporting field activities on Amsterdam Island; and to the  Fulbright Commission  for supporting research on Amsterdam Island, and while in residence at  Centre d'Études Biologiques de Chizé (CNRS).
Statistical Analysis Summary descriptive statistics are reported as mean and standard deviation, median (inter quartile range), or counts (%), as appropriate. We used the t test and Mann-Whitney test to evaluate the difference of continuous variables as appropriate. Fisher exact test was used for variance comparison. For score differences, the paired t test was used. Finally, the correlation coefficient was calculated between the results of SG and MCQs using the Pearson R 2 correlation test. All analyses were performed with SPSS 21. 0 (IBM Inc), R 3. 3. 1 (R Project for Statistical Computing) and Prism GraphPad 7. 0 (GraphPad Software Inc).
We combine time series for the seismicity rate in the Reelfoot region, verticalcomponent GPS displacements at site PTGV, Mississippi river stage data from a river gauge at New Madrid, GRACE-observed loading and calculated stress changes induced by the GRACE loads on the principal faults, and assess the presence of common temporal modes in these data. A strong correlation is observed between all time series, with the correspondence between GRACE loading and Mississippi river stage confirming the hydrological origin of the loading signal. We also see a good correlation between the peak in seismicity rate, and the peak in the predicted stress amplitudes on the fault system, as derived from GRACE data. This demonstrates that despite the long wavelength of the GRACE observation data, it is sufficient to image the causative load changes capable of driving the variation in seismicity on an individual fault system.
Annual stacks reveal a strong correlation between GPS displacement and seismicity, with a peak in JASO and a trough in JFMA, and an anti-correlation between the seismicity, and river stage and GRACE loading, with minimum load corresponding to maximum seismicity. To probe beyond the initially identified annual signal, we apply Multi-channel Singular Spectrum Analysis (M-SSA)  44, 45  to the four observational datasets (seismicity rate, vertical-component displacement at PTGV, river stage, GRACE equivalent water height; see Methods) for the 10-year period (2002. 6-2012. 6) where they overlap. This form of principal component analysis simultaneously takes advantage of the spatial and temporal f Earthquake frequency histogram, stacked on an annual timescale (as in Fig. 3d ). g-j, as in b-e, but stacked on an annual timescale.
Methods Seismicity analysis. We use the seismic catalogue for the New Madrid region maintained by the Center for Earthquake Research and Information (CERI) at the University of Memphis  27. While this catalogue extends back to 1974, we limit our data set to a sub-catalogue extending from the 1 January 2000 through to 31 December 2015, in order to maximise the uniformity of the catalogue, and to limit our susceptibility to any biasing due to changes in instrumentation or data quality. Over this monitoring period, the stations distribution and operation have remained reasonably stable (see Supplementary Fig. 1 ), and so should not lead to any major changes in the detection sensitivity or biasing in the catalogue due to network evolution. The complete CERI earthquake catalogue is declustered prior to regional selection using the routines of Reasenberg  54.
The confidence intervals shown are calculated based on applying the same process to 10,000 randomly generated seismicity catalogues with the same magnitude-frequency distribution, and taking envelopes that encompass the maximum (or minimum) 9,500 and 9,900 of the resultant ratios in each magnitude band for the 95 and 99% limits, respectively  8. The jack-knife analyses shown in Figs. 3c, f and 4c, f are determined by repeating the ratio-based analysis of Figs. 3b, e and 4b, e, but removing one calendar year of data from the seismic catalogue in each test, after declustering (if used) and regional selection have been applied. The residuals shown in Figs. 3c, f  and 4c , f are then determined by differencing the observed ratio and the 95% confidence interval for each test, such that positive values indicate datasets and magnitude bands where the confidence interval has been exceeded.
Full details of each individual jack-knife are shown in Supplementary Figs. 2 3 4 5. GPS data. We use GPS position time series from the Central and Eastern USfocused study of Craig and Calais  29  , where the full processing routine is described in detail. GPS data, most critically those of the Mid-America GPS Network  55  , are publicly available from the UNAVCO and CORS archives (http://www. unavco. org/ and http://www. geodesy. noaa. gov/CORS/, respectively), and were processed using the GAMIT-GLOBK software package  56. Time series shown in Figs. 5  and 6  (and Supplementary Fig. 6 ) are weekly position solutions determined from the combination of seven daily position solutions within each GPS week, initially tied to the International Terrestrial Reference Frame (ITRF-2008)  57. The data shown have been subjected to the removal of offsets associated with instrumentation changes (by allowing for a one-off three-component offset at the time of instrumentation change) and then to a linear detrend.
0) to compute load Love numbers and Green's functions for horizontal and vertical displacements caused by unit, harmonic loading functions. We then convolve the Green's functions with the spatially and temporally varying surface load derived from GRACE from 2002 to 2012 (the period from 2012 to 2016 contains large gaps in the gravity data time series), to compute model surface displacements at the location of the set of cGPS stations in the NM region. We also compute the full time-varying stress tensors on the Reelfoot and Cottonwood grove faults, assuming their geometry  30, 47, 48  , at a 20 km depth, from the three-dimensional full load derived from GRACE. Note that considering the wavelength of the load, calculated stresses will not vary significantly within the thickness of the crust.
Acknowledgements T. J. C. thanks the  Royal Commission for the Exhibition of 1851  for financial support through a  Research Fellowship. This work was partly funded by the  French Investment Program SINAPS  project through the  Commissariat à l'Énergie Atomique  ( CEA/DASE/ LDG ) and the  Institut de Radioprotection et Sûreté Nucléaire (IRSN). Initial stages of this work were hosted at the  Yves Rocard Joint Laboratory (ENS, CNRS , and  CEA/DASE). We thank  Damian Walwer  for his assistance with MSSA analysis, and  Laurent Bollinger  and  Luce Fleitout  for useful discussions.
Data availability. All data used in this study are publicly available. The seismic catalogue used in this study is maintained by the Centre for Earthquake Research and Information at the University of Memphis, Tennessee, and can be accessed at http://www. memphis. edu/ceri/seismic/catalog. php. GPS data are available though UNAVCO (http://www. unavco. org/) and CORS (http://www. geodesy. noaa. gov/ CORS/) Data Archives. Gravity data are available through the Groupe de Recherche en Géodésie Spatiale, France, at http://grgs. obs-mip. fr. River stage data are available from the US Army Corps of Engineers, online for initial data (http:// rivergages. mvr. usace. army. mil), and upon request from USACE for qualitycontrolled data. All river data remain preliminary and are subject to change.
Background Contamination is a well-known and ancient problem in molecular biology research. Most people who have worked in a molecular biology laboratory for a while have at least once observed an extra band on a gel, or obtained DNA sequence data originating from an unexpected species. Projects involving a polymerase chain reaction (PCR) step are particularly sensitive to contamination because initially small amounts of foreign DNA can accidentally be amplified by PCR, and transferred from tube to tube. A number of published results obtained by Sanger sequencing were subsequently demonstrated to most likely result from laboratory contamination  [1] [2] [3] [4]. Massive sequencing projects and databases based on next-generation sequencing (NGS) technologies are far from immune from contamination issues  [5] [6] [7] [8].
A couple of recent studies, however, have revealed that both crosscontamination  [14]  and environmental contamination  [15]  can cause serious problems even in standard NGS projects, that is, when target DNA is a priori thought to be much more abundant than contaminant DNA  [16]. These studies ring an alarm bell and call for a systematic examination of the prevalence of contamination in past, present and future NGS datasets. Recently, we conducted a multispecies population genomic project in which one to ten individuals from each of >100 non-model species of animals were subjected to RNA sequencing (RNAseq), leading to a number of scientific publications  [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30]. The co-occurrence in the same laboratory of many samples from many distinct species in a relatively short period of time provides an ideal situation for investigating the effect of crosscontamination in a molecular ecology project.
Our goals in this study were multiple. First, we aimed to quantify the prevalence of cross-contamination, identifying at which steps of the experimental protocol it most often happens, and if possible delivering guidelines on how to avoid it. Second, we wanted to check the robustness of our published results to the problem of contamination, and if possible identify solutions to this problem. Two distinct, complementary approaches were taken. Regarding between-species contamination, cytochrome oxidase 1 (cox1) was used to detect the occurrence of foreign cDNA sequences in a sample and trace their likely sources. cox1 is a high-expressed gene, and is therefore expectedly prevalent in RNAseq data. It is the standard DNA barcoding tool in animals, so a huge database of cox1 reference sequences from many distinct species of animals is available.