dataesr/scientific-paragraphs-categorization huggingface dataset:
The analysis was run assuming a Two-Phase Mutation Model (TPM)  [86]  of microsatellite loci with parameters p s (proportion of one-step mutations) = 0. 88 and D g (average size of non one-step mutations) = 2. 8 as reviewed in  [84]. In M-P-Val, the average observed value of M calculated from the microsatellite dataset was compared with a simulated equilibrium distribution of M for different values of the population parameter h~4N e m (where N e is the pre-bottleneck effective population size, and m is the mutation rate of microsatellites per locus per generation). In Critical_M, values of the critical M threshold (M c ), below which a bottleneck is evident, were also calculated for the different h values. Considering an average mutation rate per locus per generation equal to 0. 0005  [84] ,  [87] , statistical significance was assessed with 10000 simulations carried out with values of h C -{10; 1; 0.
Acknowledgments The authors would like to thank all of those who assisted in sampling the fir populations in Lebanon and Morocco. We are grateful to the  National Council for Scientific Research-Lebanon  as well as the municipalities of Fnaydeq and  Bqaa Safrine in Lebanon  for the logistics support. We also would like to thank  Matthieu Lingrand  ( INRA-URFM ) for the technical support in the laboratory. This is an ISEM contribution number  ISEM 2013-182.
Preprocessing To reduce noise in the corpus we filter out undesirable emails based on several criteria, the first of which is encoding. Messages that are not UTF-8 encoded are removed from the selection. The second criterion is MIME type: we keep single-part plain text messages only, and remove those with HTML or other special contents. In addition, we choose to consider only replies to thread starters. This choice is based on the assumption that the alignment module would have more difficulty in recognizing properly sentences that were repeatedly transformed in successive replies. Indeed, these replies -that would contain quoted text from other messages -would be more likely to be poorly labelled through automatic annotation. The last criterion is length. The dataset being built from a mailing list that can cover very technical discussions, users sometimes send very lengthy messages containing many lines of copied-and-pasted code, software logs, bash command outputs, etc.
The number of these messages is marginal, but their lengths being disproportionately high, they can have a negative impact on the segmenter's performance. We therefore exclude messages longer than the average message length plus the standard length deviation. After filtering, the dataset is left with 6,821 messages out of 33,915 (20%). For building the segmenter features, we use the Stanford Part-Of-Speech Tagger for morpho-syntactic tagging  (Toutanova et al. , 2003) , and the WordNet lexical database for lemmatization  (Miller, 1995).
A total of 708 million P2-positive beads were loaded onto the flow cell for the run, and the output read length was 85 bp, as expected (50 9 35 bp). The three B. anthracis genomes (Sen2col, Sen3 and Gmb1) were sequenced through 3. 2 E + 6, 3. 1 E + 6 and 3. 9 E + 6 barcode reads, which led to 273, 262 and 382 Mb of data, respectively. The global sequencing of these three genomes resulted in 917 Mb of data. For each of the three strains, we performed a mapping against the Ames reference strain though CLC workbench software. For mapping we used relatively stringent parameters (length fraction of 0. 7 and similarity fraction of 0. 8).
Basic genomic data The complete genomic sequences of the six references strains are available on NCBI: Ames  [16]  (NC_003997. 3), Ames Ancestor  [12]  (NC_007530. 2), A0248 (NC_012659. 1), CDC684  [17]  (NC_012581. 1), H9401  [13]  (NC_017729. 1) and Sterne (NC_005945. 1). The sequences of the plasmids pX01 and pX02 from A0248, Ames Ancestor, CDC684 and H9401 are also available on NCBI: NC_012656. 1, NC_012655. 1, NC_007322. 2, NC_007323. 3, NC_012579. 1, NC_012577. 1, NC_017726. 1, NC_017727. 1, respectively. H9401 came from Korea, Sterne came from the UK, and the four other strains came from the USA. Our strains of interest came from Senegal (Sen2Col2 [PRJEB1516] and Sen3 [PRJEB1517]) and Gambia (Gmb1 [PRJEB1518]) and were isolated in 2010. Sen2Col2 was isolated from the lungs of a 6-year-old ostrich.
Phylogeny, MLVA and orthology. We performed a global genome alignment using MAUVE  [27]  and a multiple alignment with MEGA5  [28]  (using the ClustalW algorithm), followed by a tree reconstruction (distance or neighbour joining method). Using MAUVE and its backbone output file, we calculated the ratio of the size of the core genome to that of the pan-genome to evaluate the closed or open nature of the pan-genome. We were also interested in determining the lineages of the three African strains. For lineage analysis, we used the MLVA8 system  [10] , which is composed of eight VNTR loci: vrrA, vrrB1, vrrB2, vrrC1, vrrC2, CG3, pXO1-AAT and pXO2-AT  [29]. The NCBI and the MLVA databases were also helpful for lineage analysis. Next, we used OrthoMCL  [30]  with default parameters to obtain a list of orthologs and determine the pan-genome composition (core, accessory and unique genes) of B.
anthracis. We also investigated the SNPs present in the core genome by phylogeny, performing a maximum likelihood tree (phyml) with 100 bootstrap iterations. For the core genome tree and the transition/transversion bias, we used MEGA5. Finally, based on the core SNPs tree, we created a time tree based on the Reltime method, developed by Tamura et al. in 2012  [31]. This tree allowed the estimation of the divergence time in millions years and was built using the MEGA 6 software.
General features and phylogeny For each of the three strains, we obtained more than 6 million reads from the SOLiD sequencing. Average coverage was approximately 30, and maximum coverage could reach 3000. The genomes of the three African B. anthracis strains, Sen2Col2, Sen3 and Gmb1, were the same size as the other known strains (5. 23 Mb), and they had the same average number of proteins (approximately 5300) and the same G+C% value. The three genomes were deposited at EMBL with the accession numbers CAVC010000000, CAVD010000000 and CAVE010000000, respectively. The MAUVE alignment revealed no rearrangements and a high conservation among all genomes. Examination of the plcR gene revealed a nonsense mutation typical to B. anthracis  [7]  at position 640 and the four known prophage regions in the three new strains.
Genomic comparison KEGG and COG. The three African strains are identical to each other and to CDC684 at a functional level. Fig. 4  represents the clustering of the strains based only on the difference in the KEGG distribution. Divergences are as follows: one difference with Sterne (ko02020), two with H9401 (ko03010 and ko00300), and multiple differences with Ames and Ames Ancestor (ko00260, ko00300, ko00440, ko02010, ko02020, ko02040), and A0248 (ko00562, ko01040, ko00300, ko00340, ko00440, ko02010, ko02020, ko02040, ko05100). When we examined these details, we noticed two groups: one group contains A0248, Ames and Ames Ancestor, and the other group contains the other strains. There were no differences between the pX01 and pX02 plasmids of the African strains and those of the other strains (data not shown).
anthracis strains available on the NCBI database. Next, we carefully examined the 85 accessory genes. The three African strains and CDC684 contained almost all of the accessory genes. Half of the accessory genes were annotated as hypothetical proteins. The hierarchical clustering again resulted in the same two groups found using COG and KEGG (one with Ames, Ames Ancestor and A0248 and the second with the other strains). Moreover, we calculated the core/ pan-genome ratio and found that the core genome represented 99% of the pan-genome (Table  1 ), again indicating the high rate of conservation among the nine strains. Finally, we studied the SNPs at the core genomic level. We found approximately 3800 SNPs between the nine strains, with 2911 in the core. Among the three African strains, there are 1500 SNPs in the core and 1120 among the six reference strains.
, 2013) , but their potential for evaluation of MH simulations still largely remains to be explored. There have been several attempts to reconstruct MH sea surface temperature (SST) for specific regions (e. g. the North Atlantic:  Kerwin et al. , 1999; Ruddiman and Mix, 1993) , but the Global database for alkenone-derived HOlocene Sea-surface Temperature (GHOST) data set of Mg / Ca and alkenonebased SSTs provides the only global product  (Kim, 2004; Leduc et al. , 2010). Data-model comparisons using the GHOST data set have shown significant mismatches between the modelled and reconstructed SST anomalies  (Schneider et al. , 2010; Hargreaves et al. , 2013; Lohmann et al. , 2013). It has been suggested that these mismatches could reflect organisms whose fossils record the ambient temperature, analytical uncertainties, and/or issues related to the ecology of the sensors which may have resulted in changes in depth and/or seasonal habitat compared to the present day  (Lohmann et al.
Data collection and quality control We have compiled site-based SST reconstructions made using the alkenone unsaturation index, the Mg / Ca palaeothermometer, and statistical reconstruction techniques for dinocysts and planktonic foraminifera assemblages, covering all ocean basins (Supplement Table  1 ). This is the same set of sensors as used in the MARGO LGM synthesis  (Kucera et al. , 2005a) , except that we do not include records based on diatom and radiolarian transfer functions because of lack of available harmonised data sets. Most of the Mg / Ca and alkenone reconstructions are from the GHOST database  (Kim et al. , 2004; Leduc et al. , 2010) , but additional Mg / Ca and alkenone records, and the census counts of planktonic foraminifera and dinocysts, were obtained from public archives (e.
g. Pangaea, NOAA-NGDC World Data Center for Paleoclimatology) or provided by the original author. The data set is a selection of the available records from each ocean basin. Only sites that met the following data quality criteria were included in the compilation: 1. The individual records have at least 10 data points between 0 and 10 ka BP, and at least one data point in the 5. 5-6. 5 ka BP time window. 2. The sedimentation rate is at least 2 cm per 1000 years to ensure that individual samples represent no more than the investigated 1000 years time window, assuming no impact of bioturbation. 3. The chronology was based on at least two radiocarbon dates or other stratigraphic markers within the interval between 0 and 10 ka BP. We generated new SST reconstructions based on assemblage counts for planktonic foraminifera and dinocysts, using the methods adopted by the MARGO project for the LGM  (de Vernal et al.
Reconstructions based on planktonic foraminifera The planktonic foraminifera census counts were initially screened for taxonomic consistency and counting method, and assessed for the effect of carbonate dissolution. Only records that passed this pre-screening were used for further statistical analysis. We did not identify any records from the Indian Ocean that were suitable. The data set therefore includes 57 planktonic foraminifera-based SST records (Supplement Table  1 ), with 14 from the North Atlantic, 2 from the equatorial South Atlantic, 15 from the Mediterranean Sea, and 26 from the Pacific. The average resolution across the MH interval is 4 samples per 1000 years, with a range of between 1 and 21 samples per core. The planktonic foraminifera census counts were converted into SST estimates using the multi-technique approach described by  Kucera et al.
(2005b). This approach is based on the simultaneous application of the Modern Analogue Technique (MAT) and the Artificial Neural Network (ANN) methods. The calibration data set was derived from the MARGO LGM project  (Kucera et al. , 2005b)  and uses six regional calibrations against seasonal means of SST at 10 m water depth from the 1998 version of the World Ocean Atlas (WOA98:  Conkright et al. , 1998). The MAT approach searches the calibration data set for samples with assemblages that most resemble the fossil assemblage. We used the 10 best analogues, identified using the squared chord distance measure, in the Atlantic and Pacific, and the 5 best analogues in the Mediterranean Sea. The ANN method estimates SSTs by mapping the foraminifera census counts onto a highly recursive system of equations iteratively optimised on the training data.
Reconstructions based on dinocysts The data set includes 28 dinocyst-based SST records (Supplement Table  1 ), with 24 sites from the North Atlantic and 4 from the Mediterranean Sea. The average resolution across the MH interval is 6 samples per 1000 years, with a range of between 1 and 20 samples per core. The dinocyst-based reconstructions were made using the MAT, as described in detail by  de Vernal et al. (2005 de Vernal et al. ( , 2013)). The modern reference database includes 940 sites from the North Atlantic, North Pacific, Arctic Ocean, and adjacent epicontinental seas. The reference sites cover a wide range of environments, from cold to sub-tropical domains, neritic and open ocean conditions, and brackish to fully marine settings. Reconstruction uncertainties were calculated by retaining one-fifth of the data for verification independent of the original calibration.
Reconstructions based on Mg / Ca thermometry There are 38 Mg / Ca-based MH SST records in the data set (Supplement Table  1 ), with 19 records from the Pacific, 12 from the North Atlantic, 5 from the Indian Ocean and 2 from the South Atlantic. Most of these records came from the GHOST database  (Leduc et al. , 2010 ), but we excluded 3 GHOST records because they did not meet our quality criteria and added 9 records. The average resolution across the MH interval is 6 samples per 1000 years, with a range of between 1 and 24 samples per record. The Mg / Ca temperatures are based on measurements on different planktonic foraminifera species at the different sites. Furthermore, the samples are prepared using different cleaning methods  (Barker et al. , 2003; Boyle and Rosenthal, 1996; Boyle and Keigwin, 1985; Boyle et al.
Reconstructions based on alkenone unsaturated ratio There are 89 alkenone-based MH SST records in the data set (Supplement Table  1 ), with 39 records from the Pacific, 26 from the North Atlantic, 6 from the Indian Ocean, 8 from the Mediterranean Sea, and 10 from the South Atlantic. The average resolution across the MH interval is 5 samples per 1000 years, with a range of between 1 and 33 samples per record. Most of the alkenone records have been obtained from the GHOST database  (Kim, 2004; Leduc et al. , 2010). We excluded 11 of the GHOST records because they did not meet our quality criteria and added 9 new records. Rosell-Melé et al. (2001)  examined the analytical precision and reproducibility of alkenone-based temperature estimates gener-ated by different laboratories, and found that inter-laboratory differences were on average ±1.
They showed that primary producers, one generic term that includes the alkenone-synthesising coccolithophores, preferentially thrive during summer at high latitude because of light limitation, and during winter at low latitudes when increased surface ocean mixing brings nutrients to the photic zone  (Schneider et al. , 2010). Different foraminifera species also occupy different ecological niches, and their representative season may vary from place to place, depending on the species analysed downcore (see e. g. Lombard et al. , 2011). Assigning foraminifera-derived SST to summer temperatures and alkenone-derived SST to mean-annual temperatures hence provides an overly simplified template for SST databases, and much progress must be done into this direction to reduce the uncertainties associated with an SST database derived from multiple sensors.
To test the impact of the choice of baseline climate on the reconstructed SST anomaly patterns, we examined the effect of using the updated version of the World Ocean Atlas (WOA09;  Locarnini et al. , 2010)  and the Hadley Centre Sea Ice and Sea Surface Temperature data set, which covers the period of 1900 to 2000 (HadiSST;  Rayner et al. , 2003). We also examined the impact of using a long-term core-top average to calculate the anomalies, by comparing data from the GHOST database (which includes a "modern" reference based on the 1000year core top average) with the anomalies from WOA98. The average of the absolute difference in the MH mean annual SST anomalies based on WOA98 and WOA09 is 0. 3 • C (Fig. 1a ), while the average absolute difference between WOA98 and the HadiSST data set is 0.
However, it is questionable how different the SST signal would actually have been even when considering a chronological error of 1000 years, considering the results of the simple exercise where we used two different definitions of the early Holocene time window. If, as we believe, the early Holocene SST signal was weak, then chronology alone is unlikely to explain the observed difference lest we have made such large error as to compare Holocene and Glacial sediments. Our analyses were greatly facilitated by the fact that much of the primary data and the SST reconstructions are archived at, for example, Pangaea (http://www. pangaea. de) or NOAA's National Climatic Data Center (http://www. ncdc. noaa. gov/data-access/paleoclimatology-data). However, target data sets for model evaluation need to be comprehensive because regional and/or zonal signals could be significantly affected by data gaps.
Acknowledgements. This research was supported by the  Australian Research Council , grant no. DP1201100343. We thank  Daniel Ariztegui ,  Lucilla Capotondi ,  Basil Davis ,  Valentino Di Donato ,  Maria Geraga ,  Zhimin Jian ,  Yoshimi Kubota ,  Baohua Li ,  Gema Martinez Méndez ,  Tarek Melki ,  Andrea Piva ,  Imene Rouis ,  Liang-Jian Shiau ,  Yurika Ujiie , and  Rong Xiang  for contributing data to this compilation. We also would like to thank  Richard Telford  and an anonymous referee for reviewing our manuscript and  Brad Rosenheim  for his short comment, with all comments contributing to the improvement of the manuscript. This paper was initiated during the  COMPARE (Comparing Ocean Models with paleo-Archives) workshop held in Bremen, Germany 2012 , which was supported by  Past Global Changes (PAGES).
In Fig. 14 , we see that most n = 4 potentials have a long and steep tail for φ < φ * , with a kink around φ * -0. 4 (in natural units). This shape generates a significant running on the largest observable scales, while preserving a smaller running on smaller scales. With such a feature in the scalar primordial spectrum at large scales combined with a nonzero contribution from tensor fluctuations, the best fit model for n = 4 has a temperature spectrum very close to that of the minimal ΛCDM model for > 40, but not for smaller multipoles. The amplitude of the Sachs-Wolfe plateau is smaller. This allows the large-scale data points from Planck to be fitted slightly better. However, the case r = dn s /d ln k = 0 still lies at the edge of the 95% CL, and the minimum effective χ 2 of this model is smaller than in the n = 2 case by only 3.
, 2009). The forthcoming Planck polarization data will prove very useful in this regard. Additionally, since strong deviations from power law behaviour typically indicate nonlinear physics, these models generically also predict a non-Gaussian signal potentially observable in the bispectrum (Planck Collaboration XXIV, 2014). However, the best fit wiggles and step inflation models have oscillations with a frequency too high to be accessible to bispectrum analysis at present.
22  and included in the summary Table  12. The power spectra P RR (k), P RI (k), and P II (k) are normalized according to the primordial values of the fields R(x) and I(x) defined above. It is interesting to consider how much isocurvature power is allowed expressed as a fraction of the power in three bands spanning the CMB temperature spectrum observed by Planck. To this end, we define the following derived quantities The results for α RR (2, 2500) show that the nonadiabatic contribution to the temperature variance can be as large as 7% (9%, 5%) in the CDI (NDI, NVI) model (at 95% CL). These results are driven by the fact that on large scales, for ≤ 40, the Planck data points on average have a slightly smaller amplitude than the best fitting ΛCDM model. Hence the data prefer a significant amount of anticorrelated isocurvature modes, leading to a reduction of amplitude of the Sachs-Wolfe plateau and to a decrease of the effective χ 2 by up to 4.
This possibility is strongly disfavoured by the data, which are consistent with the peak location predicted by a pure adiabatic model. Hence in the NVI case we obtain slightly stronger bounds and a smaller reduction of the effective χ 2. The fact that the data prefer models with a significant contribution from CDI or NDI modes should be interpreted with care. The detection of a shift in the phase of acoustic oscillations would bring unambigous evidence in favour of isocurvature modes. With Planck data, we are not in this situation. The evidence is driven by a small deficit of amplitude in the Sachs-Wolfe plateau, which could have several different possible explanations (such as a deficit in the large-scale primordial power spectrum, as already seen in the previous sections). However, multi-field inflationary scenarios can produce the mixture of curvature and isocurvature fluctuations which we have found to provide a good fit to the Planck data.
Conclusions This paper establishes the status of cosmic inflation in the context of the first release of the Planck cosmological results, which includes the temperature data from the first 2. 6 sky surveys. CMB polarization as measured by Planck will be the subject of a future release. We find that standard slow-roll single-field inflation is compatible with the Planck data. This result is confirmed by other papers of this series. Planck in combination with WMAP 9-year large angular scale polarization (WP) yields Ω K = -0. 006 ± 0. 018 at 95% CL by combining temperature and lensing information  (Planck Collaboration XVI, 2014; Planck Collaboration XVII, 2014). The bispectral non-Gaussianity parameter f NL measured by Planck is consistent with zero (Planck Collaboration XXIV, 2014). These results are compatible with zero spatial curvature and a small value of f NL , as predicted in the simplest slow-roll inflationary models.
A key Planck result is the measurement of the scalar perturbation spectral index. Planck+WP data give n s = 0. 9603±0. 0073 (and n s = 0. 9629 ± 0. 0057 when combined with BAO). This result disfavours the Harrison-Zeldovich (HZ) n s = 1 model at more than 5σ. Even in extended cosmological models, the HZ spectrum cannot be reconciled with the data. Allowing a general reionization scenario yields ∆χ 2 eff = 12. 5 with respect to ΛCDM for Planck+WP data. When the primordial helium abundance or the effective number of neutrino species are allowed to vary, the best fit of the HZ model to a combination of Planck+WP and BAO data is still worse by ∆χ 2 eff = 4. 6 and 8. 0, respectively. We find no evidence for Planck data preferring a generalization of a simple power law spectrum to include a running of the spectral index (dn s /d ln k = -0.