dataesr/scientific-paragraphs-categorization huggingface dataset:
Where duplicates were identified, the read with the highest average quality score was retained. Low-quality leading and trailing bases, along with adapter sequences, were filtered out using Trimmomatic v0. 22 with default settings  (Bolger et al. , 2014). Internal low-quality bases (Q < 20) were trimmed from the remaining sequence using a fourbase sliding window. After trimming, any read shorter than 25 bp was removed. Reads with > 90% coverage and > 90% identity to sequences in the nonredundant ribosomal RNA database were removed using riboPicker v0. 4. 3  (Schmieder et al. , 2012).
This also discards transcripts that are so lowly expressed that expression levels are difficult to compare accurately. The reference transcriptome was annotated by BLASTX sequence searching against the National Centre for Biotechnology Information SwissProt databases, restricted to matches with Arabidopsis thaliana and Oryza sativa (E-value threshold = 1e À6 ). Gene ontology (GO) terms were extracted from The Arabidopsis Information Resource  (TAIR, accessed March 2014; Lamesch et al. , 2012)  and the Rice Genome Annotation Project (accessed May 2014;  Ouyang et al. , 2007). Open reading frames (ORFs) were predicted using TransDecoder, with a minimum predicted protein length of 100 amino acids. For each unigene, the contig with the longest ORF was retained for downstream sequence analysis.
Gene ontology enrichment To identify which GO terms were enriched in the differently expressed genes, we conducted an over-representation analysis using ErmineJ v3. 0  (Lee et al. , 2005). These analyses were restricted to GO terms annotated with 10 to 100 unigenes, to avoid overly general GO categories as well as those with low statistical support  (De Wit et al. , 2012). Although rice is more closely related to palms (both being monocots), we used the Arabidopsis data due to a larger number of annotated proteins in the NCBI SwissProt database (i. e. 14 430 Arabidopsis entries vs. 3453 for rice). Furthermore, Arabidopsis GO annotations in the TAIR database are inferred from multiple sources and direct evidence, whereas GO annotations for other species (including The Rice Genome Annotation Project) are generally electronically inferred by BLASTP sequence searching against the Arabidopsis thaliana proteome.
Population genetics To investigate the distribution of genetic variation between the two species, single polymorphic sites (SNPs) were identified from 10 individuals of each species that had the largest amounts of data when pooled across tissues. These included H. forsteriana trees from calcarenite (four individuals) and volcanic (six individuals) soils. Cleaned paired-end reads were mapped onto the reference transcriptome using BWA v0. 6. 1 (q = 20)  (Li & Durbin, 2009) , and SNPs were called with Samtools' v. 1. 18 mpileup function  (Li et al. , 2009)  with the following parameters: base quality value > 30; indels were not called; anomalous read pairs were also used; base calls represented by < 3 independent reads removed. Variant Call Format files were further processed with VCFtools v0.
forsteriana to investigate whether genetic variation was structured over soil type and to assess whether this species could be treated as a single population for further genetic analyses. We also measured genetic differentiation and diversity. Relative differentiation (F ST ) between species was estimated using the R package HIERFSTAT  (Goudet, 2005) , and potential outliers were identified with BayeScan v2. 1  (Foll & Gaggiotti, 2008). Absolute divergence (D xy ) was calculated using the fasta2popgen script (available at https://github. com/LaMariposa/pop-gen_scripts). Heterozygosity was measured using the software 4P  (Benazzo et al. , 2015). Transcriptome-wide and unigene specific nucleotide diversity (p), Tajima's D, Fu and Li's D and Fu and Li's F within and between species were calculated using the mstatspop R package (available from http://bioinformatics.
Detecting selection on coding sequences We used three approaches to search for the signature of selection on coding sequences. The action of selection on a protein-coding DNA sequence can be inferred from x, that is the ratio of the number of nonsynonymous substitutions per nonsynonymous site (d N ) to the number of synonymous substitutions per synonymous site (d S ). Purifying selection is indicated by d N /d S < 1, whereas d N /d S = 1 indicates neutral evolution, and d N /d S > 1 signifies positive selection. We calculated d N /d S for the ORFs using the  Yang & Nielsen (2000)  counting method implemented in yn00, as part of the PAML v4. 7 package  (Yang, 2007). This method estimates d N /d S from a pairwise comparison between sequences, providing a single estimate for the entire ORF, and only considering fixed differences between species and without the need for a closely related outgroup comparison.
Possible recombination events were detected using IMgc  (Woerner et al. , 2007) , and the largest nonrecombining blocks were chosen when necessary. Additionally, a data set of nine loci under divergent selection but with no GO terms related to ecological stresses and flowering time was analysed. These were randomly selected among loci with no evidence of having evolved under positive selection (d N /d S < 1, alpha = 0) between the two species and with similar functional categories to the selection data set. We employed two intron loci (PRK and RPBII) with previously calculated mutation rates in all the IMa2 analyses. Thus, we were able to convert the estimates obtained with IMa2 (which are scaled by a mutation rate specific to each set of analysed data) into absolute time estimates. All analyses were carried out using IMa2, where an ancestral population was allowed to split into two (corresponding to the two extant Howea species) with continuous gene flow  (Hey, 2010) , with preliminary trial runs to estimate the most suitable set of priors.
Knockouts were genotyped and verified as homozygotes using primers designed from the Salk Institute Genomic Analysis Laboratory online T-DNA Primer Design software (accessed March 2014; Table  S4 ) and the REDExtract-N-Amp Plant PCR Kit (Sigma-Aldrich, St Louis, MO, USA) following the manufacturer's protocol and using the following PCR profile: 3 min at 94 °C; 35 cycles of 30 s at 94 °C; 30 s at 55 °C; and 1 min at 72 °C; and then a final extension of 10 min at 72 °C. PCR products were visualized on agarose gels with samples homozygous for the T-DNA insert producing a fragment at least 200 base pairs shorter than wild-type controls (at least one control per marker), and heterozygotes producing fragments of both sizes. In total, 152 of the 161 successfully germinated knockouts were confirmed as homozygous for the T-DNA insert.
Of the 14 576 unigenes, 57% and 31% matched Arabidopsis and rice protein sequences, respectively (Table  S5  describes the full transcriptome annotation and detailed results). The higher percentage of Arabidopsis protein matches is likely due to four-fold more Arabidopsis than rice entries in the Swiss-Prot database (13 037 vs. 3227; Swiss-Prot 2014_07 release note statistics). A total of 79. 3% of unigenes had predicted ORFs longer than 300 bp (Table  S5 ). Raw RNA-Seq data are deposited in the NCBI Sequence Read Archive (accession number SRP041170).
Acknowledgments We thank the  Lord Howe Island Board  and the  New South Wales National Park and Wildlife Services  for granting permission for our research on LHI. We thank  Hank  and  Sue Bower ,  Christo Haselden ,  Peter Weston ,  Larry Wilson ,  Matthew Hill ,  Rex Byrne ,  Louise Wilson  and  Damien Ball  for their help in the field;  Nicolas Ortiz Vaquerizas ,  Martin Selby  and  Tom Smith  for technical support; and  Oliver Windram ,  Mark Chase  and  Tim Barraclough  for comments. The internal CNRS reference ID for this publication is ISEM  2016-096. We thank the  European Research Council , the  UK Natural Environment Research Council  and the  Royal Society  for funding. We would also like to thank  Judith Mank  and the anonymous reviewers for their insightful comments. The paper is dedicated to the late  Elizabeth Brown , bryologist at the  Royal Botanic Gardens, Sydney , who opened so many doors for us in our research on  Lord Howe Island.
2013; POLARBEAR Collaboration 2014a,b; van Engelen et al. 2015). The results in this paper extend our earlier results in Planck Collaboration XVII  (2014). These were based on the March 2013 Planck † nominal-mission data release, which contains approximately 15 months of temperature data alone. The additional information in the full-mission dataset, with 30 months of Planck HFI temperature and polarization data  (Planck Collaboration VII 2016; Planck Collaboration VIII 2016) , allows us to improve our reconstruction noise levels by roughly a factor of two. Approximately half of this improvement comes from lower noise levels in temperature, and the other half from inclusion of polarization data. The improved lensing map is included as part of the Planck 2015 public data release  (Planck Collaboration I 2016) , as well as an estimate of the lensing potential power spectrum and associated likelihoods.
Data and methodology Here we give a brief overview of the procedure that we use to measure the CMB lensing potential and its power spectrum from the Planck maps. We defer the detailed technical aspects of our pipeline to Appendix A. Our main results are based on a foreground-cleaned map of the CMB synthesized from the raw Planck 2015 full-mission frequency maps using the SMICA code  (Planck Collaboration IX 2016). This foreground-cleaned map combines all nine frequency bands from 30 GHz to 857 GHz with scale-dependent coefficients chosen to provide unit response to the CMB with minimal variance; for more details see Planck Collaboration IX (2016) ‡. On the small scales where lensing effects are most important (multipoles above = 1000), most of the CMB information in the foreground-cleaned maps originates with the 143 GHz and 217 GHz channel data.
In Fig. 1  we plot the lens reconstruction noise levels for these estimators. The most powerful estimator is T T , although the T E and EE estimators are also useful on large angular scales. The quadratic lensing estimators take inverse-variance filtered CMB multipoles as input. We obtain these using a filter that masks the Galaxy and point sources, and also bandpass filters the data in harmonic space to 100 ≤ ≤ 2048. For our baseline analysis, we start with the masks used in the analysis of Planck Collaboration XVII (2014), with a Galaxy mask that removes 30. 2 % of the sky and a point-source mask that removes an additional 0. 7 % of the sky. This removes the brightest Sunyaev-Zel'dovich (SZ) clusters as described in Planck Collaboration  XVII (2014) ; contamination of the lensing reconstruction from residual unresolved SZ is expected to be at the percent level for Planck  (van Engelen et al.
Lensing B-mode power spectrum The odd-parity B-mode component of the CMB polarization is of great importance for early-universe cosmology. At first order in perturbation theory it is not sourced by the scalar fluctuations that dominate the temperature and polarization anisotropies, and so the observation of primordial B-modes can be used as a uniquely powerful probe of tensor (gravitational wave) or vector perturbations in the early Universe. A detection of B-mode fluctuations on degree angular scales, where the signal from gravitational waves is expected to peak, has recently been reported at 150 GHz by the BICEP2 collaboration  (Ade et al. 2014). Following the joint analysis of BICEP2 and Keck Array data (also at 150 GHz) and the Planck polarization data, primarily at 353 GHz (BICEP2/Keck Array and Planck Collaborations 2015), it is now understood that the B-mode signal detected by BICEP2 is dominated by Galactic dust emission.
The matter power spectrum also scales with the primordial amplitude A s ; keeping A s fixed, but increasing k eq , means that the entire spectrum shifts sideways so that lenses of the same typical potential depth Ψ lens become smaller. Theoretical ΛCDM models that keep eq ≡ k eq χ * fixed will therefore have the same number (proportional to k eq χ * ) of lenses of each depth along   13 ). Solid coloured contours show 68 % and 95 % constraints when additional information is included: BAO data from SDSS and 6DF  (Anderson et al. 2014; Ross et al. 2015; Beutler et al. 2011; blue) , and the same but fixing the CMB acoustic-scale parameter θ MC to a CMB power spectrum fit (red; θ MC = 1. 0408). Solid black contours show the constraint from the Planck CMB power spectra (Planck temperature+low-polarization).
Constraints on extensions to the ΛCDM model The CMB power spectra only weakly constrain extended models that provide multiple ways to combine late-time parameters to give the same observed angular diameter distance to recombination. The main Planck CMB power spectrum constraints on these parameters are driven largely by the impact of lensing smoothing on the acoustic peaks, which weakly breaks the geometric degeneracy. Fig. 11  Constraints on the curvature and rms lensing deflection angle from Planck TT+lowP CMB power spectra (samples, colour coded by the value of the Hubble constant), showing that the closed-universe models favoured at the 2 σ level have high lensing power that is inconsistent with the lensing reconstruction result. The combined lensing result (black contours) is much tighter, and provides additional information on top of the constraint when adding BAO data (red contours); both joint constraints, and the combined filled contours, are consistent with a flat universe.
052 +0. 03 -0. 02 (Planck TT+lowP; 68 %), (26) and the joint constraint Ω K = -0. 005 +0. 009 -0. 007 (Planck TT+lowP+lensing; 68 %). ( 27 ) This measurement of curvature from the CMB alone has subpercent precision. External data can also break the geometric degeneracy; the joint BAO contours are shown in Fig. 11  and are consistent. Since the BAO results are independent of fluctuation amplitude, the lensing-reconstruction joint constraint adds significant additional information, shrinking the allowed parameter space compared to Planck+BAO alone. In addition to curvature, CMB results for dark energy models also suffer a geometric degeneracy, and lensing results can similarly provide additional information; for applications to a variety of dark energy models and modified gravity see Planck Collaboration XIV  (2016).
Consistency and null tests With multiple frequency bands and the combination of both temperature and polarization data, the Planck full-mission data set provides many opportunities to test internally the lens reconstruction. In this section we present consistency and null tests for the MV lensing potential presented in Sect. 3. For consistency tests, we perform lens reconstruction using different data/analysis choices than those used for our fiducial MV reconstruction, obtaining an alternative measurement of the lensing potential power spectrum Ĉφφ L | test , which we then compare to the baseline MV reconstruction. To compare the test and MV reconstructions more quantitatively, we use a χ 2 statistic for the difference between the bandpowers, which is calculated as χ 2 test = B L i Ĉφφ L test -Ĉφφ L MV Σ -1 test i j B L j Ĉφφ L test -Ĉφφ L MV , (30 ) where Σ test is a diagonal covariance matrix for the difference obtained from simulations of the two lens reconstructions.
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. Some of the results in this paper have been derived using the HEALPix package. We acknowledge support from the  Science and Technology Facilities Council  [grant number  ST/L000652/1 ].
, 1991; Carter, 2009; McInerney et al. , 2011) , or used as a dating tool  (McClaran and Umlauf, 2000; Piperno and Stothert, 2003; Parr and Sullivan, 2005; Piperno, 2006). Motivated by anthropogenic emissions of carbon dioxide (CO 2 ) (Mauna Loa Observatory; NOAA-ESRL data at http://www. esrl. noaa. gov/) and their direct association with climate change, a set of recent studies has advanced the idea that many monocotyledonous crop species (bamboo, sugarcane, maize, rice, etc. ) as well as grasslands in general (among the largest ecosystems in the world -  Suttie et al. , 2005)  may play a significant role in C sequestration through a newly evidenced mechanism: CO 2 biosequestration in grass Published by Copernicus Publications on behalf of the European Geosciences Union. biosilica particles  (Parr and Sullivan, 2005, 2011; Parr et al.
In the functional Magnetic Resonance Imaging (fMRI) data analysis, detecting the activated voxels is a challenging research problem where the existing methods have shown some limits. We propose a new method wherein brain mapping is done based on Dempster-Shafer theory of evidence (DS) that is a useful method in uncertain representation analysis. Dempster-Shafer allows finding the activated regions by checking the activated voxels in fMRI data. The activated brain areas related to a given stimulus are detected by using a belief measure as a metric for evaluating activated voxels. To test the performance of the proposed method, artificial and real auditory data have been employed. The comparison of the introduced method with the t-test and GLM method has clearly shown that the proposed method can provide a higher correct detection of activated voxels.
The main limitation that GLM method suffers from is that it ignores correlation between voxels in both time and space dimensions that are present in fMRI times series  [9]. Clustering fMRI time series has emerged as a possible way to detect brain activity. It has been used as an exploratory data analysis technique in fMRI time series. In this case, several approaches based on c-means have emerged  [10]  such as spatiotemporal clustering analysis of fMRI data  [11]  and Fuzzy clustering analysis (FCA)  [12]. Therefore, hierarchical clustering analysis (HCA)  [13]  has been gained its place with its ability to produce connectivity map in fMRI data. Recently, clustering time series  [14, 15]  has newly concerned a considrable amount of research. They have shown their efficiency because they form partitions based on similarity of voxel values in the fMRI time series where each partition is represented by the cluster centroid that is sufficient for the analysis and investigation of fMRI time series  [16].
Therefore, clustering techniques can be used to separate activated voxels from non-activated ones. However, methods based on clustering for fMRI data analysis suffer from limitations and relevant problems that need being addressed. Their main drawback lies on their reliability where it is established only with the number of iterations and repeated runs  [17]. Accordingly, several methods based on probability theory of Bayesian spatiotemporal model approaches that incorporate spatial correlation among brain responses have been recently found as successful applications in the analysis of fMRI data  [18]   [19]. An alternative method has been recently proposed by Bowman et al. [20]  Gaussian Markov random field priors on the regression coefficients of a general linear model. Another research has investigated spatiotemporal modalities by hierarchical Bayesian approaches and incorporated the estimation of the HRF  [21].
A. Artificial data This section describes a form of artificial data used by Francois et al. [11]. In general, fMRI signal is a stochastic process. So, a synthetic three-dimensional fMRI dataset (64, 64, 64) has been generated. The number of slices is 64 and each signal is generated by the following formula: A(t) × e iʘ(t) + n c (t) (17) The above function is a complex signal where A(t) stands for the amplitude. Let M be the levels of activation and let φ be the Gaussian random delay distributed with zero mean and unit variance. Let ω be the frequency of the signal and selected to be π/10 because the fMRI signal is relatively weak. The amplitude on such a basis is defined by a sinusoidal function as follows: A(t) = M × sin(ωt + φ) (18) We consider ʘ(t) = π/4 where the real and imaginary channels play a symmetric role and n c (t) are the complex Gaussian white noise centered with unit variance.
B. Real fMRI Dataset This section reports the result of proposed method tested on a real fMRI dataset that concern an auditory stimulus. This data was collected by Geriant Rees et al. and are available in http://www. fil. ion. ucl. ac. uk/spm/data. These whole brain BOLD/EPI images were acquired on a modified 2T SIEMENS MAGNETOM Vision system. Each acquisition is composed of 64 contiguous slices (64x64x64 3mm x 3mm x 3mm voxels) where any acquisition occurs in 6. 05s, with the scan to scan repeat time (TR) set arbitrarily to 7s. So that, 96 acquisitions were made (TR=7s), in blocks of 6, giving 42s blocks. Starting with rest, the condition for successive blocks alternated between rest and auditory stimulation that was bi-syllabic words presented binaurally at a rate of 60 per minute. In this experiment, the authors mentioned that the functional data starts at acquisition 4.
C. Comparison with GLM method This section provides a comparison of the GLM results with the obtained results of the introduced framework. Firstly, the GLM results realized by SPM tools assumes that the fMRI time series correspond to the realization of an identically independent stochastic process and divides data into two groups, obtained during on (activation) and off (no activation) periods. This separation is done by p-value (0. 05) and (0. 001). The GLM results have shown the different projection (axial, coronal and saggittal) as well as the design matrix (see Fig. 12 ). Another important feature that distinguishes the GLM method is the long time needed for completing the job. In contrast to GLM, the method based on Dempster-Shafer theory is not difficult to understand and it is easy to implement but it needs to have prior knowledge about the experiment conditions.
Participants Participants were two groups of Carnegie Mellon University students recruited through an online psychology participant pool. Thirty-two English-speaking participants (17 female, 15 male) between the ages of 18 and 22 (median 19 years old) were in the egocentric experimental condition. Thirty-two participants (23 female, 9 male) between the ages of 18 and 21 (median 19 years old) were in the exocentric condition. The data from one 60-year-old participant were discarded in response to a reviewer's request for our sample to match the customary age ranges used in RT experiments in the cognitive psychology literature; this removal did not affect the overall results. All participants were right-handed with self-reported normal hearing and provided written informed consent prior to testing in accordance with procedures approved by the Carnegie Mellon University Institutional Review Board.