dataesr/scientific-paragraphs-categorization huggingface dataset:
2008) , particularly over the high latitudes of the SH before the satellite era, this analysis allows evaluating the consistency between the changes in the frontal activity and precipitation from the same reanalysis dataset. The most remarkable features of the normalized change in precipitation from the ERA-40 dataset are a decrease over the central  Pacific, Atlantic, and Indian FIG. 3. Differences between 1982 -2001 and 1962-81  for the frontal activity (cyclonic systems only) (shaded; 8C m 21 s 21 3 10 210 ) and for the zonal anomaly of the 300-hPa geopotential height (Z300, contour interval 50 m; zero contours omitted): (top) JFMA, (middle) MJJA, and (bottom) SOND. Oceans between 358 and 508S and an increase at subpolar latitudes in all seasons. This pattern of change suggests a midlatitude drying and high-latitude moistening and hence a poleward shift of the regions with large precipitation at the extratropical SH, which is in agreement with  Fyfe et al.
(2012) ,  Kang et al. (2011), and Purich et al. (2013). Note also the precipitation increase over central South America during JFMA, which is in agreement with observational studies  (Liebmann et al. 2004 ). During MJJA, enhanced drying conditions are seen across the midlatitudes with a large precipitation decrease over the central and eastern South Pacific Ocean extending over the southwestern coast of South America (with anomalies exceeding one standard deviation) and rainfall increases over central South America. The precipitation change over southeastern Australia and southern Africa suggests wetter conditions, which is not consistent with studies of rainfall trends over those regions  (Taschetto and England 2009; Cai et al. 2012; Purich et al. 2013 ). The rainfall trend calculated for the period 1982-2001 from the ERA-40 dataset was compared with results from the GPCP dataset (not shown) and it was found that for the last 20 years, the ERA-40 agrees well with observations.
7. In addition, and in order to verify the temporal evolution of the precipitation anomalies from the ERA-40 dataset, monthly precipitation data from GPCP and ERA-Interim for the period 1979-2001 have also been included. This analysis allows for verifying the consistency between frontal activity and precipitation on shorter time scales (from interannual to interdecadal) but also for verifying if this consistency arises for an independent source of data. For every season, Fig. 7  suggests a closed consistency between the temporal evolution of the difference between FIG. 5. As in Fig. 3 , but for the normalized difference of precipitation [contours interval 0. 2 (unitless); zero contours omitted. ). Hatched areas indicate where the differences in precipitation are statistically significant at the 95% level.
high and midlatitudes of the precipitation and frontal activity anomalies from year to year. Throughout the period of analysis, peaks of anomalous precipitation have a strong correspondence with peaks of anomalous frontal activity. The linear correlation coefficients calculated between the FA time series and the time series from the various precipitation datasets (shown in Fig. 7 ) confirm the close relationship between the two variables. This consistency is also verified for anomalies at both high and midlatitudes (not shown). During JFMA, a significant positive trend of 0. 46 6 0. 01 decade 21 for the normalized frontal activity difference and a significant positive trend of 0. 63 6 0. 01 decade 21 for the normalized precipitation difference (using 95% confidence intervals) were found. Moreover, the portion of the precipitation trend that is congruent with the FA trend, computed by multiplying the regression coefficient by the FA trend and dividing by the precipitation trend is 64%, suggesting the strong control exerted by the frontal activity on precipitation.
However, the positive trends are significant only for both frontal activity and precipitation at the high-latitude band, suggesting that moistening at high latitudes is strongly associated with the increase in the frontal activity owing to the poleward expansion of the storm track during the austral summer months. At midlatitudes, neither the frontal activity nor precipitation show significant trends over the 1962-2001 period. The temporal evolution of the precipitation anomalies from ERA-40, ERA-Interim, and GPCP for the period 1979-2001 suggests that an overall agreement is apparent; however, rainfall from ERA-40 shows strong discrepancies compared with GPCP. Moreover, ERA-Interim seems to agree better with GPCP than compared with the ERA-40 dataset. However, the consistency between frontal activity and precipitation is also apparent.
For the period 1979-2001, the difference of frontal activity between high and midlatitudes yields a trend of 0. 5 6 0. 02 decade 21 , the GPCP precipitation dataset yields a trend of 0. 8 6 0. 05 decade 21 , and the ERA-Interim precipitation dataset yields a trend of 0. 9 6 0. 4 decade 21. In all cases the trends are significant at the 95% confidence level. The corresponding FIG. 6. As in Fig. 3 , but for the normalized difference of precipitation [contour interval 0. 2 (unitless); zero contours omitted] superimposed on the normalized difference of FA (shaded; unitless). percentages of precipitation trends due to the FA trend are 49% and 54%, respectively. The precipitation trends discussed here agree with  Fyfe et al. (2012) , who showed consistent trends in sea level pressure and related poleward-shifting surface westerlies and extratropical storm track as drivers of the rainfall trends observed during recent decades.
7. However, the trends are much weaker when evaluating over the 1979-2001 period. The percentage of the precipitation trend from the ERA-40 dataset attributed to the FA trend is 74% for the 1962-2001 period. In comparison, the portion of the precipitation trend from GPCP and ERA-Interim due to FA is 40%. Overall, results displayed in Fig. 7  highlight the strong correspondence between frontal activity and precipitation, not only in terms of the trends over the last 40 years, but also in terms of the variability. Moreover, it was shown that a large amount (more than 50%) of the zonal mean precipitation trends is congruent with the FA trend during both JFMA and SOND. This is a robust result that suggests that understanding the mechanisms leading to the frontal activity changes will certainly help to understand the patterns of precipitation change.
Summary and conclusions The focus of the research presented here is to explore the consistency between the poleward shift in the midlatitude westerlies and storm tracks during the last decades of the twentieth century in the SH, reported in previous studies, with the poleward shift of frontal activity. Moreover, the analysis also allowed for exploring the correlation between fronts and precipitation, suggesting that the changes in precipitation are strongly tied to changes in frontal activity at the subtropical and subpolar latitudes of the SH. Frontal activity, a proxy for the dynamics of the lower troposphere, is defined in terms of the temperature gradient times the relative vorticity, the main features associated with the passage of midlatitude fronts, using the ERA-40 dataset during the period from 1962 to 2001.
ERA-40 and ERA-Interim data have been provided by the European Centre for Medium-Range Weather Forecasts from their website at http://data-portal. ecmwf. int/data/d/era40_daily/ and at http://data-portal. ecmwf. int/data/d/interim_daily/, respectively. GPCP precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, has been downloaded from their website at http://www. esrl. noaa. gov/psd/. The authors also thank two anonymous reviewers whose comments greatly helped to improve the manuscript.
FC was measured on rs-fMRI data using Pearson correlation between the time courses of brain regions. We quantified the correlation between SC alone and FC as a reference, and also fed SC to generative neurodynamical models of increasing complexity: a spatial autoregressive (SAR) model  [36] , analytic models with or without conduction delays  [28] [29] [30] [31] 37] , and biologically constrained models  [29, 32]. Importantly, all these models were used in their stationary regime in the strong sense, since their parameters were not changed during the simulations. Of these models, only the SAR is explicitely associated with a stationary FC; other, more complex models, generate dynamics that are compatible with a non-stationary FC. We computed FC from data simulated by these models and compared the results to empirical FC.
Predictive power of models In agreement with H1, SC explained a significant amount of the variance of whole-session FC for all subjects, as did all generative models (permutation test, p,0. 05 corrected) (Figure  1 , panel A). Generative models predicted FC better than SC alone (paired permutation test, p,0. 05 corrected). Predictive power obtained with the average subject ranged from 0. 32 for SC alone to 0. 43 for the SAR model (Table  1 ). For a given model, predictive power was reproducible across subjects. Contrary to our hypothesis H2, generative models had similar performance, and complexity was not predictive of performance. The results remained unchanged when no global signal regression was applied (Figure  S1 ). Also, findings were found to be similar for SC alone and the SAR model at finer spatial scales (N = 461 and N = 825 regions, Figure  S2 ) and consistent with a replication dataset (Figure  S3 ).
Data Twenty-one right-handed healthy volunteers were recruited within local community (11 males, mean age 2262. 4 years). Data were acquired using a 3 T Siemens Trio TIM MRI scanner (CENIR, Paris, France). For acquisition and preprocessing details, see Text S1. For each subject, the preprocessing yielded three matrices: one of SC, one with the average fiber lengths, and one of empirical FC. These matrices were also averaged across subjects ('average subject').
For each type of connections and each model, we represented the individual predictive powers (bar chart representing mean and associated standard deviation), as well as the predictive power of the average subject computed using the original SC (diamonds), or after adding homotopic connections (circles). (EPS) Figure  S3  Performance of computational models on the replication dataset. The replication dataset was from the study of Hagmann and colleagues  [55]. Brain network was defined at low anatomical granularity (N = 66 regions), and connectivity measures were averaged over five healthy volunteer subjects. (A) Predictive power for all connections and when restricted to intra/ interhemispheric, direct/indirect connections. For each type of connections and each model, we represented the individual predictive powers (bar chart representing means and associated standard deviations), as well as the predictive power for the average subject computed using the original SC (diamonds), or after adding homotopic connections (circles).
Acknowledgments The authors are thankful to  Olaf Sporns  ( Department of Psychology, Princeton University, Princeton, USA ) and  Christopher J. Honey  ( Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA ) for providing the neural-mass model; to  Gustavo Deco ,  E ´tienne Hugues  and  Joanna Cabral  ( Computational Neuroscience Group, Department of Technology, Universitat pompeu Fabra, Barcelona, Spain ) for providing the Kuramoto and rate models as well as the spike model; and to  Olaf Sporns  and  Patric Hagmann  ( Department of Radiology, University Hospital Center and University of Lausanne, Lausanne, Switzerland ) for sharing their data for replication. We would also like to thank them for fruitful discussions. The authors are grateful to  Stephane Lehe  ´ricy and his team ( Center for Neuroimaging Research, Paris, France ) for providing them with the data, and especially to  Romain Valabre  `gue for his help in handling coarse-grained distributed parallelization of computational tasks.
This work was performed using HPC resources from  GENCI-CINES  (Grants No. 2012-076353  and No. 2013-076353 ),  HPC-Europa2  (Contract No. 228398 ), and the  PRACE-2IP  ( FP7 RI-283493 ) project NANODROPS. LM acknowledges funding by the  Institut National de la Santeet de la Recherche Medicale (INSERM) , JB is funded by  iViv (UniversiteParis Diderot, France). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Background: Continuous efforts to estimate actual diversity and to trace the species distribution and ranges in the natural environments have gone in equal pace with advancements of the technologies in the study of microbial species diversity from microscopic observations to DNA-based barcoding. DNA metabarcoding based on Next Generation Sequencing (NGS) constitutes the latest advancement in these efforts. Here we use NGS data from different sites to investigate the geographic range of six species of the diatom family Leptocylindraceae and to identify possible new taxa within the family. Methodology/Principal Findings: We analysed the V4 and V9 regions of the nuclear-encoded SSU rDNA gene region in the NGS database of the European ERA-Biodiversa project BioMarKs, collected in plankton and sediments at six coastal sites in European coastal waters, as well as environmental sequences from the NCBI database.
All species known in the family Leptocylindraceae were detected in both datasets, but the much larger Illumina V9 dataset showed a higher species coverage at the various sites than the 454 V4 dataset. Sequences identical or similar to the references of Leptocylindrus aporus, L. convexus, L. danicus/hargravesii and Tenuicylindrus belgicus were found in the Mediterranean Sea, North Atlantic Ocean and Black Sea as well as at locations outside Europe. Instead, sequences identical or close to that of L. minimus were found in the North Atlantic Ocean and the Black Sea but not in the Mediterranean Sea, while sequences belonging to a yet undescribed taxon were encountered only in Oslo Fjord and Baffin Bay. Conclusions/Significance: Identification of Leptocylindraceae species in NGS datasets has expanded our knowledge of the species biogeographic distribution and of the overall diversity of this diatom family.
danicus and L. minimus. Here we use DNA metabarcoding, NGS data from several sites across European seas collected in the frame of the ERA Biodiversa project BioMarKs (http://www. biomarks. eu) as well as sequences deposited in the public nucleotide sequence database GenBank, with the aim of assessing the diversity of leptocylindracean species and exploring their biogeographic range. As marker we chose the nuclear-encoded SSU rDNA because of the availability of an ever-growing dataset of taxonomically validated reference sequences, covering virtually every known eukaryotic lineage  [21]. As sequence length is still a restricting factor in NGS approaches, we sequenced two short variable regions in the SSU rDNA, namely the V4 and V9 tags, for which universal amplification primers are available.
Definition of the query datasets Two types of SSU rDNA datasets were used in this study. The first consisted of sequences of the V4 region (390 bp), obtained through 454 pyrosequencing, and of the V9 region (130 bp), obtained through Illumina sequencing, gathered from planktonic and benthic samples collected within the ERA Biodiversa project BioMarKs (http://www. biomarks. eu/). The second comprised the nucleotide sequences deposited in GenBank (http://www. ncbi. nlm. nih. gov/genbank/) which, in addition to sequences from cultivated strains, also includes environmental sequences of known geographic provenance. Regarding the BioMarKs dataset, we examined sequences obtained from coastal stations at six localities along the European coasts. These included Naples (Long Term Ecological Research station MareChiara, LTER-MC, Tyrrhenian Sea) and Blanes Bay (Blanes Bay Microbial Observatory, BBMO) in the Mediterranean Sea; Oslo Fjord, Roscoff (station SOMLIT-Astan, Western English Channel) and Gijon (Spain), on the North-eastern Atlantic coast; and Varna on the Black Sea (see Table  S1  in Tables  S1 for  metadata ).
Plankton and sediments samples were collected at all these stations during 2010. Additional plankton and sediment samples from the stations in Naples and Oslo Fjord were obtained during the fall of 2009. BioMarKs protocols for sampling, sequencing and processing of reads have been described in Logares et al. [22]. Briefly, seawater samples were taken with Niskin bottles from near the surface (subsurface, 1 m) and at depth  (20-40 m). Irrespective of the presence of a chlorophyll maximum, the latter samples were indicated as DCM (Deep Chlorophyll Maximum) in the dataset. Plankton samples were size-fractioned into 0. 8-3 mm, 3-20 mm and 20-2000 mm samples on polycarbonate filters (142 mm and 47 mm diameter). Immediately upon filtration, filters were flash-frozen in liquid N 2 and stored at 280uC.
Sediment samples were gathered using sediment corers and small aliquots from the surface layer were frozen at 280uC. Total DNA and RNA in plankton samples were extracted from the same filter using the NucleoSpin RNA kit (Macherey-Nagel, Hoerdt, France). Total DNA and RNA were extracted from sediment samples using the RNA Power Soil Total Isolation kit combined with DNA Elution Accessory kit (MoBio Laboratories). Extracted RNA was reverse transcribed to cDNA using the RT Superscript III random primers kit (Invitrogen, Carlsbad, CA, USA). The V4 sequence dataset was generated from each of the three size fractions of each plankton sample (at two water depths) and from sediment samples for all sites and collection dates, with sequences originating from the DNA as well as from the cDNA. The V4 region was PCR-amplified using eukaryote-specific primers  [13]  and sequenced at the CEA Genoscope in Evry (France) using a GS FLX emPCR Genomic Lib-L kit according to the manufacturer's protocol (Genome Sequencer FLX Titanium, 454 Life Sciences from Roche, Brandford, CT, USA).
For protocols of V4 PCR, sequencing, quality filtering and curation of sequences refer to Logares et al. [22]. This was the first dataset produced in BioMarKs, and some samples, namely those from Oslo Fjord 2009 and Naples 2009, were sequenced multiple times. The V9 dataset was generated from three size fractions of surface samples and largely cDNA as template, and both DNA and cDNA templates of sediment samples (Table  S4  in Tables  S1 ). The V9 region was PCR-amplified using the universal forward primer 1389F and the eukaryotic-specific reverse primer 1510R  [23]. Amplifications of the V9 region were done in triplicate using the following PCR program: initial denaturation step at 98uC for 30 sec, followed by 25 cycles of 10 sec at 98uC, 30 sec at 57uC, 30 sec at 72uC, followed by 15 cycles of 10 sec at 98uC, 30 sec at 48uC, 30 sec at 72uC and final elongation step at 72uC for 10 minutes.
Amplicons were then pooled and purified using the NucleoSpinH Extract II kit (Macherey-Nagel, Hoerdt, France). To obtain a similar number of reads for each sample, purified amplicons were quantified with the Quant-iT PicoGreen dsDNA kit (Invitrogen) and then mixed in equal concentrations. Bridge amplification and sequencing were performed using a Genome Analyser IIx system (Illumina, San Diego, CA, USA) at Genoscope -Centre National de Sequencage (Evry, France). Overlapping sequencing reads were merged using an internal script based on the fastx library (http://hannonlab. cshl. edu/fastx_toolkit/index. html). Forward and reverse primer sequences were detected in the reads and the region between them was extracted. Extracted sequences were quality filtered and chimeras were detected using the chimera search module of the usearch program  [24] , looking for chimeras with respect to a reference database, and also within each sample.
Gathering leptocylindracean sequences from the datasets Bacillariophycean sequences were retrieved from the BioMarKs V4 and V9 datasets according to the following procedure. We used two reference datasets derived from the PR2 database  [21]  and truncated to the boundary of the targeted amplicon (V4 or V9), excluding the primer sequence. Environmental sequences were searched against the corresponding reference dataset using a global alignment program (ggsearch36) (http://faculty. virginia. edu/wrpearson/fasta/CURRENT/) from the fasta package. For each query sequence, we retained in this initial assignation the assignation of the best hit (based on percentage of identity of the global alignment), or the assignation of the last common ancestor of the best hits in case of equality. Environmental sequences with assignation matching Bacillariophyta formed our initial dataset.
The BioMarKs V4 bacillariophycean sequences were clustered with V4 reference sequences of two bolidophyceans used as outgroups and 102 diatoms, including Leptocylindraceae, as in  [7] , with the CD-HIT-EST-2D module of CD-HIT suite (http:// weizhong-lab. ucsd. edu/cdhit_suite/cgi-bin/index. cgi). Likewise, the bacillariophycean BioMarKs V9 sequences were clustered with V9 reference-sequences of two bolidophyceans and 95 diatoms. The settings of the clustering procedure were as follows: sequence identity cut-off value set at 0. 90 (see Methods S1 for rationale of choice); do not compare strands; use global sequence identity; cluster sequence to the best cluster that meets the threshold; use bandwidth of 20. Alignment coverage parameters and length coverage parameters were set to defaults. All sequences in the query dataset with similarity $0.
90 to any of the references were matched with the most closely related reference with CD-HIT-EST-2D. Clusters of sequences grouping closer to non-leptocylindracean references were eliminated, whereas clusters that grouped closer to any one of the leptocylindracean references were re-clustered at the $0. 97 similarity level (other parameters, as before). The secondary clusters of sequences with similarity $0. 97 are hereafter referred to as Operational Taxonomic Units (OTUs). OTUs represented by a single sequence (singleton) or two sequences (doubleton) were removed from the V4 and V9 datasets because these sequences have a high probability of representing sequence errors. Putative leptocylindracean sequences were retrieved from the nucleotide collection in GenBank using the megaBLAST option (http://blast.
Taxonomic validation To confirm that the obtained OTUs belonged to Leptocylindraceae, the sequences gathered as described above were aligned and analysed phylogenetically. One dataset included the V4 reference-sequences of leptocylindracean species and of other diatoms and of bolidomonads, as well as single representatives of putative leptocylindracean BioMarKs OTUs and all putative leptocylindracean sequences from GenBank. Only one representative sequence per BioMarKs OTU was included to keep the size of the dataset manageable. A V9 dataset was generated similarly. A third dataset was prepared with the full-length nuclear SSU rDNA sequences of all diatom and bolidomonad reference sequences used in  [7]  and putative leptocylindracean sequences retrieved from GenBank. Sequence alignment of these three datasets was done on the T-REX web server  [25]  using the slow and iterative refinement method (FFT-NS-i)  [26]  of the Multiple Sequences Alignment (MSA) programme MAFFT v6.