dataesr/scientific-paragraphs-categorization huggingface dataset:
The black solid line is the bisector, while the dashed lines represent the ± 1σ confidence regions. The resulting 1σ uncertainties related to these parameters are 0. 13, 0. 11 and 0. 17 dex, respectively. sub-mm wavelengths. It has been developed especially to fit starforming galaxy SEDs, so that it is well suited for our purposes (see e. g. Smith et al. 2012  for an extensive application of MAGPHYS on Herschel data). Indeed, the basic recipe that this code relies on is a self-consistency between the energy originating from stars, which is partially absorbed by dust, and the infrared light due to reemission by the dust itself. Briefly, the stellar and dust component are computed as follows (see da  Cunha et al. 2008  for details). • The  Bruzual & Charlot (2003)  library of templates is used to compute the integrated light produced by stars in galaxies.
Fitting with MAGPHYS + AGN MAGPHYS does not consider a potential AGN contribution, but only star formation processes are involved in building the SEDs. Since we are interested in probing the possible AGN contribution to the observed SEDs, we make use of a modified version of the code  (Berta et al. 2013) , which accounts also for a possible nuclear heating source. The code by Berta et al. (  2013 ) includes the AGN component and differs from the original MAGPHYS in the model normalization approach. The stellar+dust normalization is not scaled to fit the data, but is allowed to float within a range of normalization values (the same range adopted by the original version of MAG-PHYS), to allow for the additional AGN normalization when fitting the observed SED. This approach results in a three component simultaneous fit, using an overall χ 2 minimization.
Reduced library of templates The set of possible model combinations included in the original version of MAGPHYS is very large (around 10 9 possible configurations, see § 3. 1), as well as the set of physical parameters that the stellar+dust emission models rely on. The range of values spanned by each parameter is physically reasonable, but the computational time taken by the code strongly increases when the AGN component is added to the SED-fitting procedure. For these reasons we take advantage of a reduced grid of templates, as described by  Berta et al. (2013). While the full grid of MAGPHYS templates includes 50000×50000 model configurations, the reduced grid has been built up through a random selection of 1000 stellar templates for each source and 1000 dust-emission templates for each stellar template.
Testing the AGN incidence In order to evaluate the impact of an additional AGN component on a "pure" star-forming galaxy SED, for each source we run both MAGPHYS and MAGPHYS+AGN. As mentioned in § 3. 2. 1, a reduced grid has been adopted in both fitting procedures. First, we run the code version including the AGN and during the random selection of stellar and dust templates their template identification numbers are registered. Once the SED-fitting with MAGPHYS+AGN is accomplished, we run the MAGPHYS code for each source by using the same set of stellar+dust templates previously extracted for the same source. This implementation allows us to test if the fit provided by similar star formation templates improves when accounting for an additional nuclear emission component. In Fig. 5  three different examples of best-fits are shown as a result of this SED decomposition.
(2007, hereafter H07 ) from X-ray data. They used a wide compilation of observed AGN luminosity functions, from the mid-IR through optical, soft and hard X-ray data to build up a self-consistent AGN bolometric luminosity function. H07 adopted a distribution of luminosity dependent bolometric corrections for each selection wave band, as well as a large baseline of intrinsic (i. e. corrected for obscuration) AGN SED shapes (from  Richards et al. 2006; Steffen et al. 2006 ). However, the intrinsic SEDs of  Richards et al. (2006)  also include an additional IR (λ > 1 µm) contribution arising from the torus reprocessed radiation. This IR excess (with respect to a blackbody emission in the Rayleigh-Jeans regime) has to be removed to avoid double counting the input energy of the accretion disc. As already done by  Merloni & Heinz (2013) , we independently evaluated such an excess to be of the order of 33 per cent.
CONCLUSIONS We have presented an estimate of the cosmic evolution of SMBH growth, from z ∼3 down to the present epoch. Our estimate of the BHAD across the cosmic time is the first one derived by using a far-IR selected sample. We selected 4343 PEP sources with 160 µm flux density >3σ detection limit in the GOODS-S and COSMOS fields. The available multi-band photometric coverage allowed us to explore the observed SEDs from the UV to the FIR, in particular thanks to the valuable PACS and SPIRE data from the Herschel satellite. Moreover, the availability of Herschel data both in the GOODS-S and in COSMOS field was essential to explore different and complementary luminosity regimes. Our analysis relies on a robust broad-band SED decomposition performed to decouple the AGN contribution from the host galaxy content in each global SED.
ACKNOWLEDGMENTS This paper uses data from Herschel's photometers PACS and SPIRE. PACS has been developed by a consortium of institutes led by MPE (Germany) and including: UVIE (Austria); KU Leuven, CSL, IMEC (Belgium); CEA, LAM (France); MPIA (Germany); INAF-IFSI/OAA/OAP/OAT, LENS, SISSA (Italy) and IAC (Spain). This development has been supported by the funding agencies BMVIT (Austria), ESA-PRODEX (Belgium), CEA/CNES (France), DLR (Germany), ASI/INAF (Italy), and CICYT/MCYT (Spain). SPIRE has been developed by a consortium of institutes led by Cardiff Univ. (UK) and including: Univ. Lethbridge (Canada); NAOC (China); CEA, LAM (France); IFSI, Univ. Padua (Italy); IAC (Spain); Stockholm Observatory (Sweden); Imperial College London, RAL, UCL-MSSL, UKATC, Univ. Sussex (UK); and Caltech, JPL, NHSC, Univ.
The helicase fragments were directly sequenced in both orientations and in triplicate (MegaBACE sequencer; GE Healthcare, Buckinghamshire, UK). The sequences were aligned with previously published sequences from GenBank by using ClustalW (www. clustal. org) and manually aligned by using MEGA software version 4. 1 (www. megasoftware. net). Modeltest software (www. molecular evolution. org/software/phylogenetics/modeltest) was used determine which model of evolution was most appropriate for our analysis. Optimal alignment of the predicted highly conserved helicase amino acid sequences showed several amino acid substitutions in the mimivirus amplicons we derived compared with other available sequences (Figure  3 ). Nine of the 12 sequenced amplicons showed high identity among themselves and with the APMV sequence (GenBank accession no.
HQ336222). The other 3 amplicons showed a high identity with Megavirus chilensis (GenBank accession no. JN258408), a giant virus isolated in 2011 from seawater off the coast of Chile  (15). A neighbor-joining phylogenetic tree constructed on the basis of the helicase gene revealed that all the amplicons we derived clustered with mimivirus isolates; however, according to the sequences alignment analysis, 3 of them clustered directly with the Megavirus chilensis group (Figure  2 ). The sequences have been deposited in GenBank. Concomitantly with molecular analysis, the pools of samples were submitted to a virus neutralization (VN) test to detect mimivirus neutralizing antibodies. VN was used rather than ELISA because the secondary antibodies required for an ELISA for monkey species were unavailable.
Acknowledgments We thank  João Rodrigues dos Santos ,  Gisele Cirilo , and their colleagues for excellent technical support. We also thank  Milton F. Souza-Júnior  for providing some samples used in this study. This study was supported by the  Conselho Nacional de Desenvolvimento Científico e Tecnológico ,  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior , the  Fundação de Amparo à Pesquisa do Estado de Minas Gerais , the  Ministério da Agricultura, Pecuária e Abastecimento , and the  Pro-Reitoria de Pesquisa da Universidade Federal de Minas Gerais. F. P. D. was supported by a fellowship from the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. E. G. K. , C. A. B. , G. S. T. , and P. C. P. F. are researchers of the  Conselho Nacional de Desenvolvimento Científico e Tecnológico.
Table. Sources and test results for serum samples from wild and domestic animals analyzed for presence of mimivirus, Brazil* Figure 2. Consensus bootstrap phylogenetic neighbor-joining tree of helicase gene from nucleocytoplasmic large DNA viruses showing alignment of mimivirus and megavirus isolates obtained from Cebus apella (CA) and bovids (Bos) in Brazil. Tree was constructed by using MEGA version 4. 1 (www. megasoftware. net) on the basis of the nucleotide sequences with 1,000 bootstrap replicates. Bootstrap values >90% are shown. Nucleotide sequences were obtained from GenBank. Scale bar indicates rate of evolution. Real-time PCR, helicase gene States where samples were Total no. Total no. No. (%) negative No. (%) VN >90%, no. Species collected samples pools pools positive pools (%) pools Black howler monkeys Tocantins 91 21 21 (100.
Skeletal morphology and microstructure Prior to further characterizations, the aragonitic nature of the skeleton of Acropora millepora was confirmed by Raman and FTIR spectra (Figure  S1 ) in order to ensure the absence of recrystallization in calcite, or the presence of traces of vaterite. In addition, EDS spot analyses were performed on different points (transects, and points taken randomly, data not shown) for checking the elemental homogeneity of the skeleton. They confirmed that the atomic percentages of minor elements (Mg, Na, Sr, S) did not vary significantly, demonstrating that the skeletal parts were fresh from top to bottom and also in their diameter (see Figure  1 A ). This check allowed us to assert with confidence that the fragments used for the subsequent matrix extraction were not submitted to detectable early diagenetic processes, affecting the mineral phase.
Acknowledgments The authors would like to thank  Lotte Huisman  and  Prof. David Miller  ( James Cook University, Australia ) for providing the skeleton samples of Acropora millepora, and  Dr. Isabelle Zanella-Cleon  ( Institut de Biologie et de Chimie des Proteines (IBCP) Lyon, France ) for the mass spectrometry analysis. The authors also thank the three anonymous reviewers who contributed in improving the manuscript.
Next Generation Sequencing Bioinformatics Illumina HiSeq paired-end (23 90 bp) fastq data sets were trimmed and filtered with Trimmomatic (version 0. 30;  Lohse et al. , 2012)  to remove primers and low quality bases and reads. Resulting reads were then mapped to a bespoke reference constructed from telomeric regions 17p and XpYp using the MEM algorithm of BWA (version 0. 7. 4; Heng, 2013)  with -M flag deployed to mark secondary mappings, which were then removed using SAMtools  (version 0. 1. 19; Li et al. , 2009)  using the view -F 256 command. MergeSamFiles. jar, part of Picard toolkit and API (version 1. 102; http://picard. sourceforge. net), was then used to merge the resultant SAM files, and sorted, indexed BAMs were created from these using SAMtools. Having mapped the data, an in-house Java script, based on the Picard API, was deployed to filter for paired-end reads that straddled the 17p and XpYp telomeric regions and thus likely to describe interchromosomal fusion events.
ACKNOWLEDGMENTS We thank  Professor Keith Caldecott  ( University of Sussex ) for providing the LIG3 shRNA. This work was funded by  Cancer Research UK  ( C17199/ A13490 ) and the  National Institute for Social Care and Health Research (NISCHR) Cancer Genetics Biomedical Research Unit. The work in the Hendrickson laboratory was funded, in part, by grants from the  NIH  ( GM088351 ), the  National Cancer Institute  ( CA15446 ), and a research contract from  Horizon Discovery.
When forests are converted to grasslands, and grasslands turned into agricultural lands, a sharp switch from one type of soil microbial community to another one occurs. Since the ability of an ecosystem to withstand serious disturbances may partly depend on its microbial component(s), characterizing bacterial community composition and/or structure might help to better understand and manipulate ecosystem processes. The aim of the present study was to investigate the effects of soil characteristics and different agricultural managements on soil bacterial community in two seasons. Sampling was carried out in spring and autumn 2007, when the plant cover-growing season usually starts and ends. A combination of culture-based and molecular techniques along with statistical analysis of data obtained was applied to interrogate the diversity, function, and ecology of soil bacterial communities.
The measurements were performed on air-dried soils, pre-conditioned by a 10-d incubation in open glass jars, at -33 kPa water tension, and 30uC. The incubation was employed for restoring, within limits  [27] , the microbial activity of air-dried soils to that of soils in the field. Four replicates of each soil sample were used. Average values are given in mg C kg -1 of soil. For measuring microbial respiration, 20 g (oven-dry basis) of moist sample were placed in 1 L stoppered glass jars. The CO 2 evolved was trapped, after 1, 2, 4, 7, 10, 14, 21, 28 days of incubation, in 2 ml 1 M NaOH and determined by titration of the excess NaOH with 0. 1 M HCl  [32]. Non-linear least square regression analysis was used to calculate parameters affecting C mineralization from daily CO 2 evolution data (Stat Win 4.
Cluster analysis and diversity indices Quantity One software package (Bio-Rad) and Phoretix 1D PRO software (Phoretix International, Newcastle upon Tyne, United Kingdom) were used for CD-DGGE profile analysis. The cluster analysis and dendrogram generation were carried out by using the Phoretix 1D Pro software according to the manufacturer's instructions (Phoretix International, Newcastle upon Tyne, United Kingdom). Bands of CD-DGGE patterns were aligned and normalized using reference lanes. Background noise was subtracted by rolling ball algorithm with a radius of 50 pixels; the automatic band detection was performed with a minimum slope of 200 and a noise reduction of 10, and peaks smaller than 2% of the maximum peak were discarded. Bands were manually corrected and matched to create an absent/present binary matrix.
The similarity between the band patterns was calculated using the Dice coefficient and the clustering analysis was performed with the unweighted pair group method with arithmetic averages (UP-GMA) to generate a dendrogram by using mathematic averages algorithm programs integral to the Phoretix 1D Pro software. Coefficient of cophenetic correlation was used to measure the consistency of clusters. DGGE banding data were used to estimate three diversity indices by treating each band as an individual operational taxonomic unit (OTU). The number of DGGE bands present in each sample was used to measure the Richness index (R). The Shannon-Weaver index of general diversity (H9)  [48]  and the Simpson index of dominance (D)  [49]  were calculated from the number of bands present and the relative intensities of each band (P i ) in each lane.
Relative signal intensities of detected bands, in each gel track, were determined by using the Quantity One software package (Bio-Rad) and calculated from the peak area of the densitometric curves. The Shannon-Weaver diversity (H') was calculated using the following equation: , ~{ X P i ð Þ(log e P i ), where P i (the proportion of abundances of the i th band) is measured as: P i ~ni =N, where n i is the peak height of a band, and N is the sum of all peak heights in the densitometry profiles. The Simpson index (D) was calculated with the formula: D~X (P i xP i ); it measures the strength of dominance because it weights towards the abundance of the OTUs and varies inversely with species diversity  [50].
Sequencing reactions were prepared from PCR products using an Applied Biosystem Big Dye Terminator sequencing kit version 3. 1, according to the manufacturer's instructions and analysed using a 3730 DNA Analyzer Applied Biosystem apparatus. The sequences were compared with those in the GenBank databases by using the BLAST program and Seqmatch tool of the RDP (http://www. ncbi. nlm. nih. gov/ BLAST/and http://rdp. cme. msu. edu/, respectively) and aligned with the closest relatives with the Clustal W function of the BioEdit package  [51]. Bacterial identification by 16S rRNA gene sequences assignment was performed using the RDP Classification Algorithm (http://rdp. cme. msu. edu/classifier/classifier. jsp).
Distances among samples were calculated using ''Bray-Curtis'' distance implemented in ''vegan'' package as: i~sample i ; j~sample j , BC ij ~2C ij = S i zS j À Á , where C ij = sum of the smaller value for species in common between samples i and j, and S i and S j = total number of species in samples i and j, respectively  [52]. Variation of biodiversity indexes of cultured bacteria was inspected using ANOVA analysis. Biodiversity indexes were first divided into groups depending on sampling season and different managements of soils and then the analysis was performed. Clustering analysis (UPGMA) on biochemical parameters was performed. Each parameter was first divided in groups, in the same way of previous ANOVA analysis, and averaged. Then, each result obtained was normalized using the maximum-minimum normalization technique in order to make the data comparable.
Taxa responses to land use determined by phylogenetic affiliation of soil bacterial isolates. Taxonomic affiliation was investigated on a total of 203 bacterial isolates. In detail, 20-21 colonies recovered from each soil sample at each season and showing different morphologies (for a total of 101 bacterial isolates in spring and 102 in autumn) were subjected to DNA extraction and PCR amplification of 16S rRNA gene. An amplicon of the expected size (about 1500 bp) was obtained from each isolate, and its nucleotide sequence was determined and submitted to GenBank (Table  S6 ). Although it is generally accepted that not all bacteria, including types of soil bacteria, are culturable, the isolation of bacteria by agar plate cultivation and subsequent phylogenetic analysis permit to isolate and identify previously uncultured representatives or even new members of certain bacterial species for further analysis of their metabolic function.
Therefore, even if the metagenome sequencing is becoming the most powerful tool to investigate microbial communities, the ability to isolate indigenous strains actually remains the unique way to further characterize and select soil bacteria showing interesting properties. Additionally, standard cultivation techniques have been shown to be able to capture members of the soil rare biosphere which could not be detected by metagenome sequencing  [63]. The 203 sequences obtained were compared with those present in the GenBank databases by using the BLAST  [64]  program. Results showed that most of 16S rRNA gene sequences matched NCBI database sequences at 99-100% of similarity at the genus level, with Arthrobacter, Bacillus, Stenotrophomonas, Pseudomonas and Burkholderia as the most representative genera.
So, identification at the genus level was achieved in 103 isolates and at the species level in 72 isolates, while 12 isolates were only affiliated to taxa level higher than genus and 16 remained unidentified (Table  S6 ). Further comparison with GenBank databases by using the Seqmatch tool of the RDP indicated that 16S rRNA gene sequences were affiliated to four phyla: Proteobacteria (classes a, b and c), Bacteroidetes (classes Flavobacteria and Sphingobacteria), Actinobacteria and Firmicutes. Even though the RDP analysis does not permit to affiliate a bacterial isolate to a given species, it allowed to assess the genus of almost all isolates, including those not identified through the BLAST search. In fact, a boostrap value of at least 80% is satisfactory for RDP requirement. Almost all our sequences gave rise to 100% boostrap, 12 ranged from 89% to 99%, and only two of them only resulted in low values (24 and 54% respectively, referred to two spring isolates).
Acknowledgments We are grateful to  P. P. Roggero  ( University of Sassari ) and his team ( G. Seddaiu ,  G. Urracci  and  L. Doro ) for their contribution to collect the Sardinian soils and for providing data on experimental area. The experimental site was chosen on the basis of vegetation and soil surveys made in collaboration with  S. Madrau ,  S. Bagella ,  R. Filigheddu ,  M. C. Caria , and  E. Farris  ( University of Sassari ). We thank  R. Pastorelli  for her help in soil sampling,  S. Tabacchioni  and  L. Chiarini  for bacterial isolation,  C. Cantale  and  M. Sperandei  for figure preparation,  M. T. Rubino  for chemical-biochemical analysis, and  S. Cesarini  and  L. Pirone  for their valuable suggestions. The research was carried out in the context of the  FISR SOILSINK  research project coordinated by  R.
This research was funded by  MIUR (Integrated Special Fund for Research -FISR)  in the frame of the  Italian National Project SOILSINK ''Climate change and agro-forestry systems, impacts on soil carbon sink and microbial diversity '', and partially supported by  MIUR (Research Department of Italian Government)  in the framework of the  Agreement Program ENEA-CNR (Articolo 2 , comma 44, Legge 23. 12. 2009 n. 191 -Legge Finanziaria 2010). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction As reported by Cisco [1], the Internet traffic has increased more than fourfold over the past five years. Consequently, the processing speed of network devices such as switches and routers, has grown to let devices process incoming packets at line rate, and pass them to user-space processes for carrying out further analysis -such as intrusion detection, flow management, traffic classification and monitoring, accounting, policying, etc. Two main trends are keys to this evolution. On one hand, modern Network Interface Cards (NICs) hardware can effectively handle packet rates in the order of tens of Gbps. On the other hand, independent approaches have been proposed to overcome the severe software bottlenecks that affect network stacks of standard operating systems (OS). Examples include PF_RING with Threaded NAPI  [18]  and variants  [13] , Netmap  [27, 28] , PacketShader  [19]  and PFQ  [11].
System Model The system model we consider in this paper builds over the main lessons learned in  [31]  concerning flow management, that we describe with the help of Fig. 1. In Fig. 1 , our multi-thread system sniff packets, placing them in a flow manager structure, that fires classification operations over batches of packets. A traffic analyzer would have a similar structure, with one or more analyzer modules replacing the classification one. Note that,. Instead, we achieved line rate performance (14. 2Mpps) by using two separate processes, each of which processed the output of one of the multiple RSS queues exported by the sniffer tools (1I-2Q-2P). Locking issues explain this significant performance gap, and they have to be avoided to allow for sustained system operation  [31]. Alternatively, we could avoid locking by replicating the flow manager data structure, letting each thread access a different structure.