dataesr/scientific-paragraphs-categorization huggingface dataset:
Image processing and data analysis Confocal microscopy images were processed with the Leica LAS AF software, ImageJ (2. 0) and Adobe PHOTOSHOP CS5 (12. 0) programs. Images for quantification of autophagosome numbers were obtained from 50 Z stacks consisting of 1 mm depth field multi-layered images with similar settings for all samples. To detect and quantify punctate structures in one channel (green channel or red channel or overlay channel (green channel images superimposed with red channel ones)), the Z stacks were separated into individual images with the ImageJ (2. 0) program and analyzed. The counting procedure was based on a naked-eye detection of punctate structures to avoid cytoplasm noise and dual counting of autophagosomes within the same stack. Histograms were generated with mean of punctate numbers generated from stacks obtained in two independent biological experiments.
Acknowledgements We thank  Elaine Barclay ,  Liliana M. Cano , Frederic Fragniere,  Marina Franceschetti ,  Artemis Giannakopoulou ,  Ricardo Oliva ,  Egem Ozbudak ,  Stephen Whisson  for technical support and/or providing materials. We are grateful to  Zlay Taftacs  and all members of the  Kamoun Lab  for helpful suggestions. This project was funded by the  Gatsby Charitable Foundation ,  European Research Council (ERC) ,  Biotechnology Biological Sciences Research Council (BBSRC)  and the  John Innes Foundation.
In Sect. 3, we introduce the high-multipole ( ≥ 30) likelihood and present its main results. At these smaller scales, we employ a pseudo-C approach, beginning with a numerical Planck collaboration: CMB power spectra, likelihoods, and parameters spherical harmonic transform of the full-sky map, debiased and deconvolved to account for the mask and noise. Section 4 is devoted to the detailed assessment of this high-likelihood. One technical difference between Like13 and the present work is the move from the CamSpec code to Plik for high-results as well as the released software  (Planck Collaboration ES 2015). The main reason for this change is that the structure of Plik allows more fine-grained tests on the polarization spectra for individual detectors or subsets of detectors. We are able to compare the effect of different cuts on Planck and external data, as well as using methods that take different approaches to estimate the maximum-likelihood spectra from the input maps; these illustrate the small impact of differences in methodology and data preparation, which are difficult to assess otherwise.
Multipoles between cut + 1 and max are fixed to the best-fit ΛCDM spectrum when calculating S. This division between varying and fixed multipoles speeds up the evaluation of Eq. (  3 ) through the Sherman-Morrison-Woodbury formula and the related matrix determinant lemma, as described in Appendix B. 1. This results in an orderof-magnitude speed-up compared to the brute-force computation. Table  1. Likelihood codes and datasets. We use these designations throughout the text to refer to specific likelihood codes and implementations that use different input data. A sum of spectra in the description column designates the joint likelihood of these spectra. a "Low-" refers to < 23 for WP, but < 30 for the Planck likelihoods.
The bottom panel of Fig. 1  compares the two χ 2 -based confidence masks. In total, 7 % of the sky is removed by the 2015 confidence mask, compared with 13 % in the 2013 version. The top panel in Fig. 2  compares the marginal posterior low-power spectrum, D ≡ C ( + 1)/(2π), derived from the updated map and mask using the Blackwell-Rao estimator  (Chu et al. 2005)  with the corresponding 2013 spectrum (Like13). The middle panel shows their difference. The dotted  lines indicate the expected variation between the two spectra, σ , accounting only for their different sky fractions. 3 From this, we can compute χ 2 = 29 =2       D 2015 -D 2013 σ       2 , (5) Planck collaboration: CMB power spectra, likelihoods, and parameters and we find this to be 21. 2 for the current data set. With 28 degrees of freedom, and assuming both Gaussianity and statistical independence between multipoles, this corresponds formally to a probability-to-exceed (PTE) of 82 %.
Detector combinations Table  7  summarizes the main characteristics of individual HFI detector sets used in the construction of the likelihood function. As discussed in Sect. 3. 1, the likelihood does not use the crossspectra from individual detector-set maps; instead, we first combine all those contributing at each frequency to form weighted averages. As in 2013, we disregard all auto-power-spectra as the precision required to remove their noise bias is difficult to attain and even small residuals may hamper a robust inference of cosmological parameters (Like13). In 2015, the additional data available from full-mission observations enables us to construct nearly independent full-sky Planck collaboration: CMB power spectra, likelihoods, and parameters maps from the first and the second halves of the mission duration.
Beam and transfer function uncertainties The power spectra from map pairs are corrected by the corresponding effective beam window functions before being confronted with the data model. However, these window functions are not perfectly known, and we now discuss various related sources of errors and uncertainties, the impact of which on the reconstructed C s is shown in Fig. 21. Sub-pixel effects. The first source of error, the so-called "subpixel" effect, discussed in detail in Like13, is a result of the Planck scanning strategy and map-making procedure. Scanning along rings with very low nutation levels can result in the centroid of the samples being slightly shifted from the pixel centres; however, the map-making algorithm assigns the mean value of samples in the pixel to the centre of the pixel.
UU Figure  24. Deviations from a white noise power spectrum induced by noise correlations. We show half-ring difference power spectra for 100 GHz half-mission 1 maps (blue lines) of Stokes parameters I (top panel), Q (middle panel), and U (bottom panel). The best-fitting analytical model of the form Eq. (  48 ) is over-plotted in red. Difference between auto and cross-spectra for the 100 GHz half-mission maps, divided by the noise estimate from half-ring difference maps (blue and green lines). Noise estimates derived from half-ring difference maps are biased low. We fit the average of both half-mission curves (black line) with a power law model (red line). The analysis procedure is applied to the Stokes parameter maps I, Q, and U (top to bottom). All data power spectra are smoothed. Since low-frequency noise and processing steps like deglitching leave residual correlations between both half-ring maps, noise estimates derived from their difference are biased low, at the percent level at high-(where it was first detected and understood, see Planck Collaboration VI 2014).
The corresponding white-noise power spectrum is then modulated in harmonic space using the product of the two smooth fitting functions given in Eqs. (  48 ) and (  49 ). Correlated noise between detectors. If there is some correlation between the noise in the different cuts in our data, the trick of only forming effective frequency-pair power spectra from cross-spectra to avoid the noise biases fails. In 2013, we evaluated the amplitude of such correlated noise between different detsets. The correlation, if any, was found to be small, and we estimated its effect on the cosmological parameter fits to be negligible. As stated in Sect. 3. 2. 1, the situation is different for the 2015 data. Indeed, we now detect a small but significant correlated noise contribution between the detsets. This is the reason we change our choice of data to estimate the cross-spectra, from detsets to half-mission maps.
Covariance matrix structure The construction of a Gaussian approximation to the likelihood function requires building covariance matrices for the pseudo-power spectra. Mathematically exact expressions exist, but they are prohibitively expensive to calculate numerically at Planck resolution  (Wandelt et al. 2001) ; we thus follow the approach taken in Like13 and make use of analytical approximations  (Hansen et al. 2002; Hinshaw et al. 2003; Efstathiou 2004; Challinor & Chon 2005). For our baseline likelihood, we calculate covariance matrices for all 45 unique detector combinations that can be formed out of the six frequency-averaged half-mission maps at 100, 143, and 217 GHz. To do so, we assume a fiducial power spectrum that includes the data variance induced by the CMB and all foreground components described in Sect.
In the case of dust, we used priors similar to those used on data. Furthermore, in the following the dust amplitude parameter is named gal ν×ν 545. We then ran an MCMC sampler to derive the cosmological and foreground parameters posterior distributions for all dataset realizations. For each simulation, we computed the shift of the derived posterior mean parameters with respect to the input cosmology, normalized by their posterior widths σ post. When a Gaussian prior with standard deviation σ prior is used, we rescale σ post by [1-σ 2  post /σ 2 prior ] 1/2 ; this is the case for τ and for the Galactic dust amplitudes gal ν 545 in the four cross-frequency channels used. In Fig. 27 , we show histograms of the shifts we found for all 300 simulations for the six baseline cosmological parameters, as well as the FFP8 CIB and galactic dust amplitudes.
End-to-End simulations While the previous section validated our methodology, our approximations, and the overall implementation, this does not yet give the sensitivity to residual systematic uncertainties undetected by data consistency checks. These are by their very nature very much more difficult to address realistically, since, when an effect is detected and sufficiently well understood, it can be modelled and is corrected for, in general at the TOI-processing stage; only the uncertainty of the correction needs to be addressed. Still, HFI has developed a complete model of the instrument which contains all identified systematic effects and enables real-istic simulation of the instrumental response. We have therefore generated a number of full-mission time streams which we have then processed with the DPC TOI processing pipeline in order to create map datasets as close to instrumental reality as we can in order to assess the possible impact of low-level residual instrumental systematics, the effects of which might have remained undetected otherwise.
End-to-end simulations are computationally very costly, (typically a week for each simulated mission dataset) and hence only a few realizations were generated). As explained in Section 5. 4 of Planck Collaboration VII (2016), the end-to-end simulations are created by feeding the TOI processing pipeline with simulated data to evaluate and characterize the overall transfer function and the respective contribution of each individual effect on the determination of the cosmological parameters. Simulated TOIs are produced by applying the real mission scanning strategy to a realistic input sky specified by the Planck Sky Model (PSM;  Delabrouille et al. 2013 ) containing a lensed CMB realization, galactic diffuse foregrounds, and the dipole components. To this sky-scanned TOI, we add a white-noise component, representing the phonon and photon noises.
The very low-temporal-frequency thermal drift seen in the real data is also added to the TOI. The noisy sky TOI is then convolved with the appropriate bolometer transfer functions. Another white-noise component, representing Johnson noise and read-out noise, is also added. Simulated cosmic rays using the measured glitch rates, amplitudes, and shapes are added to the TOI. This TOI is interpolated to the electronic HFI fast-sampling frequency. It is then converted from analogue to digital using a simulated non-linear analogue-to-digital con-verter (ADC). Identified 4 K cooler spectral lines are added to the TOI. Both effects (ADC and 4 K lines) are derived from the measured in-flight behaviour. The TOI finally goes through the data compression/decompression algorithm used for communication between the Planck satellite and Earth.
The simulated TOI is then processed in the same way as the real mission data for cleaning and systematic error removal, calibration, destriping, and map-making. Some limitations of the current end-to-end approach follow. No pointing error is included, although previous (dedicated) simulations suggest that this has negligible effect. In addition, this effect was included in the dedicated simulations performed to assess the precision of the beam recovery procedure. The first step of the TOI processing is to correct the ADC non-linearity (ADC NL). For the flight data, the ADC NL was determined by using HFI's measured signal at the end of the HFI mission, with the instrument's cooling system switched off and an instrument temperature equal to 4 K. This determination relied on supposing the signal to be perfect white noise and therefore to correspond to the distortions brought in by the ADC.
In the current implementation, we assume perfect knowledge of ADC NL and 4 K lines. This is of course not true for the real data and future endto-simulations, accompanying Planck's next data release, will improve our model of this effect. After ADC NL correction, the signal is converted to volts. Deglitching is then performed by flagging glitch heads and by using glitch tail time-lines. This enables the creation of the thermal baseline which is used for signal demodulation. The thermal baseline and glitch tails are subtracted, the signal is converted to watts, and the 4 K lines are removed. The resulting signal is then deconvolved by the bolometer transfer functions. We do not include uncertainties in the glitch tail shape used in the deglitching procedure, i. e. , the templates are the same for the simulations and the processing; but here again, previous studies suggest any difference is a small effect.
53), when using these nested subsamples of the baseline data we expect shifts of the order of 0. 5, 0. 4, and 0. 8 σ respectively, in units of the standard deviation of Planck collaboration: CMB power spectra, likelihoods, and parameters the max = 999 results. We further note that the value of θ for max 1197 is lower compared to the baseline value. In particular, at max = 1197, its value is 0. 8 σ low, while the expected shift is of the order of 0. 7 σ, in units of the standard deviation of the max = 1197 results. The value of θ then rapidly converges to the baseline for max 1300. Figure C. 8 in Appendix C. 3. 3 also shows that these shifts are related to a change in the amplitude of the foreground parameters. In particular, the overall level of foregrounds at each frequency decreases with increasing max , partially compensating for the increase in ln(10 10 A s ) and Ω c h 2.
The value of τ is thus more effectively determined by its prior and shifts downward by 0. 59 σ with respect to the baseline. The value of Ω c h 2 shifts upward by 1. 7 σ (cf. 0. 8 σ expected from Eq. 53). Whether this change is just due to a statistical fluctuation is still a matter of investigation. However, since parameter shifts are correlated, we evaluated whether the ensemble of the shifts in all cosmological parameters between the max = 999 and min = 1000 cases are compatible with statistical expectations. In order to do so, we computed the χ 2 ∆ statistic of the shift as χ 2 ∆ = i j ∆ i Σ -1 i j ∆ j , (54) where ∆ i is the difference in best-fit value of the ith parameter between the max = 999 and min = 1000 cases and Σ is the covariance matrix of the expected shifts, calculated as the sum of the parameter covariance matrices obtained in each of the two cases, ignoring correlations between the two datasets.
8σ with respect to runs performed with CAMB. Nevertheless, we found that for all other parameters, and in all other likelihood tests presented in this Section, this problem did not arise, since the explored parameter space was entirely contained in the PICO interpolation region so as to guarantee accurate results, as also detailed in Section C. 5. Furthermore, this inaccuracy does not change any of the conclusions of this paper. We therefore decided to keep in Fig. 35  the results obtained with PICO but we have added results for the > 1000 case obtained with CAMB (case "CAMB, lmin=1000"). we do not find evidence for a discrepancy between the two datasets. A more precise and extended evaluation and discussions of these shifts, based on numerical simulations, will be presented in a future publication.
This is precisely what happens (see the blue dashed curve in Fig. 8 ). It is of course possible -though unlikelythat systematics are only showing up in the E channel, leaving B modes unaffected. Indeed, this possibility is further challenged by the fact that we do not detect anomalies in any of the six polarized power spectra; as detailed in Fig. 7 , they are consistent with a ΛCDM signal and noise as described by the final 70 GHz covariance matrix. These tests are specific to Planck and aimed at validating the internal consistency of the datasets employed to build the likelihood. As a further measure of consistency, we have carried out a null test employing the WMAP data, detailed in Sect. 2. 6. In brief, we have taken WMAP's K a , Q and V channels and cleaned them from any polarized foreground contributions using a technique analogous to the one used to clean the LFI 70 GHz maps, employing the Planck 353 GHz map to minimize any dust contribution, but relying on WMAP's K channel to remove any synchrotron contribution.
Figure25. Difference between auto and cross-spectra for the 100 GHz half-mission maps, divided by the noise estimate from half-ring difference maps (blue and green lines). Noise estimates derived from half-ring difference maps are biased low. We fit the average of both half-mission curves (black line) with a power law model (red line). The analysis procedure is applied to the Stokes parameter maps I, Q, and U (top to bottom). All data power spectra are smoothed.
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. We further acknowledge the use of the  CLASS Boltzmann code (Lesgourgues 2011 ) and the  Monte Python package  (Audren et al.
In whorl 3 of Viridiflora, ectopic expression of RcAPETALA2 (RcAP2) along with the down regulation of RcPI, RcAP3, and RcSEP3 is associated with loss of stamens identity and leaf-like structures formation. In whorl 4, the ectopic expression of RcAP2 associated with a down-regulation of RcSEP3 and of the C-class gene RcAGAMOUS correlate with loss of pistil identity. The latter also suggested the antagonist effect between the A and C class genes in the rose. Together, these data suggest that modified expression of the ABCE flower organ identity genes is associated with the phyllody phenotype in the rose Viridiflora and that these genes are important for normal flower organs development.
Transcriptome Assembly and Annotation High-quality transcript sequences were obtained by de novo transcriptome assembly of the RNA-seq reads, using Trinity software (version: r20140413p1; Langmead and Salzberg, 2012) with default parameters. Open reading frame (ORF) identification was identified using Transdecoder  (Grabherr et al. , 2011)  with default parameters. Homology sequences search was performed against the UniProt databases by ncbi-blast-2. 2. 27+ with an E-value of 10 -6. The GO terms were assigned to each assembled sequences based on the UniProt databases. The all information of transcriptome data is available in the database 1.
Gene Expression Analysis and DEG Identification The read counts for each transcript were calculated after aligning the RNA-Seq reads on the assembled transcriptome using Bowtie2 2. 1. 0 (Langmead and Salzberg, 2012). To accurately measure gene expression level, we only retained pairs of reads having both ends matching on the same transcript. The expression level of each transcript was normalized as the fragments per kilobase of exon per million fragments mapped (FPKM), which is analogous to single-read RPKM for single reads  (Mortazavi et al. , 2008). The P-value were calculated according to the statistical R package DEGseq  (Wang et al. , 2010)  using MA-plot-based method with random sampling model. Then, the differentially expressed genes were identified using fold-change >3 and a P < 0.
Validation of Differential Expressed Genes The sequences of the genes used for qRT-PCR validation were provided (details listed in Supplementary Data Sheet S1). Genespecific primers (Supplementary Table  S1 ) were designed using Primer3 2. Total RNAs were extracted from leaves and from sepals, petals, stamens, and pistils at 8-10 mm flower buds development stage using TRIzol RNA purification kit (TaKaRa, China). One microgram of total RNA was used in reverse transcription in a total reaction volume of 20 µL in the presence of 6-mer random primers and an oligo primer according to the protocol provided by manufacturer TaKaRa  (Yan et al. , 2011). The standard curve for each gene was obtained by real-time PCR with five dilutions of cDNA. The reactions were performed in 20 µL volumes each containing 10 µL 2× SYBR Green 2 http://bioinfo.
ut. ee/primer3/ Mastermix (TaKaRa), 300 nM of each primer and 2 µL of 10-fold diluted cDNA template  (Yan et al. , 2014). The PCR reactions were run in a Bio-Rad Sequence Detection System. Three biological replicates were performed for each analysis. RhGAPDH (AB370120) was used as control. Quantification of gene relative expression in different organs was performed using the delta-delta Ct method as described by  Livak and Schmittgen (2001). All data were expressed as the mean ± standard deviation (SD) after normalization.
Twenty bud grafting experiments were performed for each. These data showed that all Old Blush buds with Viridiflora as rootstock bloomed normally and flower organs phenotype was identical to non-grafted Old Blush control plants (Figure  2B ). At the same time, all Viridiflora flowers with Old Blush as rootstock exhibited phyllody phenotype similar to the none grafted Viridiflora control plants (Figure  2D ). It should be noted that flowers of Old Bush used as rootstock, showed no phyllody phenotype. These data suggest that phyllody phenotype in Viridiflora is not a result of phytoplasma infection. In agreement with these data, transmission electron microscopy or PCR experiments identified no trace of phytoplasma or phytoplasma DNA respectively, in young buds of Viridiflora and Old Blush (Supplementary Figures  S1  and S2 ).
Flower Buds Transcriptome Comparison in Viridiflora and Old Blush To investigate further the molecular basis of the phyllody phenotype, we compared the transcriptome in flowers of Viridiflora and Old Blush. Total RNA was prepared from flower buds at 8-10 mm development stage and then used to build two libraries for high-throughput sequencing. A total of 40 and 44 million reads were generated from Viridiflora and Old Blush samples, respectively. The transcriptome assembly sequences of R. chinensis cv. Viridiflora has been deposited at the Database of Transcriptome Shotgun Assembly (TSA) at DDBJ/EMBL/GenBank under the accession GETJ00000000. The transcriptome data are available in the database 1. In total, five gigabase for each library were produced. Paired-end reads were assembled into transcript sequences and yielded 68,565 unisequences with an average length of 887 bp (Figure  3A ).