dataesr/scientific-paragraphs-categorization huggingface dataset:
The close agreement between the data and the mocks is impressive -especially so for the quadrupole, given that the HOD mocks were not built with any requirement to match this moment of the two-point correlation function. Even though we have used simple probabilistic algorithms to mimic colour sub-classes of galaxies, it seems that the resulting galaxy catalogues are reassuringly realistic in their redshiftspace properties. For comparison, the accuracy of the mock red Planck : ΛCDM + GR Blue + Cross Blue galaxies Red galaxies Fig. 14. Dependence on the minimum fitted scale, s min , of the estimate of f σ 8 from the VIPERS PDR-2 data, using a volume-limited sample of luminous blue galaxies (blue circles). We also show for comparison, for the reference s min = 5 h -1 Mpc, estimates based on the auto-correlation of VIPERS luminous red galaxies (red square) and from the blue+cross joint fit (green diamond).
All estimates use the Scoccimarro model. galaxy sample in reproducing the real data is discussed in Appendix E. The results of the joint fit to monopole and quadrupole of the luminous blue galaxies yielded f σ 8 (z 0. 85) = 0. 45 ± 0. 11 , (35) which agrees very well with the complementary VIPERS measurements based on the full PDR-2 sample  (Pezzotta et al. 2017 ), its combination with galaxy-galaxy lensing (de la Torre et al. 2017), and using galaxy outflows from cosmic voids  (Hawken et al. 2017). All these measurements are shown in Fig. 16. Such an agreement provides a posteriori reassurance of the quality of non-linear corrections applied in all these different analyses. We stress that the sample used here differs significantly from the full-survey samples used in  Pezzotta et al. (2017)  and de la  Torre et al.
1997). In this paper we have taken advantage of these differences in order to minimise the systematic uncertainties in the measurement of the growth rate. We have benefited from the VIPERS broad (essentially flux-limited) selection function, which targeted galaxies uniformly and with high sampling rate, independently of type and colour. We used the restframe UV colours to select sub-samples of blue and red galaxies which largely coincide with active and passive galaxy types. We modelled the luminosity evolution of each class to high precision, defining samples that we argue are homogeneously selected in colour and luminosity over the range 0. 6 ≤ z ≤ 1. 0. A clear trend is seen in the data that the red sample has higher velocity dispersion evident by the fingers-of-God feature (See Fig. 7 ).
The corrections seen by  Reid et al. are not significant in comparison with the random errors in the current VIPERS work, which is why we have been content to construct mocks using the simple assumption that central galaxies sit at rest in their haloes. But the challenge of constructing full realistic mocks will only increase as RSD studies move into regimes of higher precision. Applying our current methodology to the VIPERS data, we produced a new measurement of the growth rate at the effective redshift z = 0. 85 using the auto-correlation of the volumelimited blue galaxy sample alone: f σ 8 (z = 0. 85) = 0. 45 ± 0. 11. The estimate is consistent at the 1-σ level with those obtained from the full galaxy sample from the VIPERS dataset, and is fully consistent with the ΛCDM model constrained by the Planck data.
2) where δ m is the density contrast related to the overall matter in the volume V around r. The covariance matrix C i j of the number counts in the measurement bins i and j is defined as C i j = ∆ i ∆ j = 1 N s N s k=1 ∆ k i ∆ k j , (D. 3) where N s is the number of independent realisations and ∆ k i = N k i -Ni is the difference between the measurement in bin i drawn from the k th realisation and the corresponding mean value. The covariance matrix in Eq. (D. 3) results from a combination of two terms C i j = C SV i j + C SN i j. (D. 4) In Eq. (D. 4) C SV i j is the contribution from the sample variance, C SV i j = Ni N j b i b j σ 2 m (V) , (D. 5) where σ 2 m is the variance of the matter density contrast in the volume V. Having at disposal only one realisation of our dataset, we replace Ni with the value directly measured from data.
Acknowledgements. We acknowledge the crucial contribution of the  ESO  staff for the management of service observations. In particular, we are deeply grateful to  M. Hilker  for his constant help and support of this programme. Italian participation to VIPERS has been funded by  INAF  through  PRIN 2008 , 2010, 2014 and 2015 programs. LG, FGM,BRG and JB acknowledge support from the  European Research Council  through grant n. 291521. OLF acknowledges support from the  European Research Council  through grant n. 268107. JAP acknowledges support of the  European Research Council  through the  COSFORM ERC Advanced Research Grant  (#  670193 ). GDL acknowledges financial support from the  European Research Council  through grant n. 202781. RT acknowledges financial support from the  European Research Council  through grant n.
202686. AP, KM, and JK have been supported by the  National Science Centre  (grants  UMO-2012/07/B/ST9/04425  and  UMO-2013/09/D/ST9/04030 ). EB, FM and LM acknowledge the support from grants  ASI-INAF I/023/12/0  and  PRIN MIUR 2010-2011. LM also acknowledges financial support from  PRIN INAF 2012. SDLT and MP acknowledge the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the  "Investissements d'Avenir" French government  programme managed by the  ANR. TM and SA acknowledge financial support from the  ANR Spin(e)  through the  French grant   ANR-13-BS05-0005. The Big MultiDark Database used in this paper and.
These ionic current changes are dependent on the physical properties of the nucleic acid bases and allow determination of the nucleotide sequence  [6]. Compared to other available devices, the MinION™ sequencer presented a low capital cost, was miniature (3. 5 × 10. 5 cm), portable and directly connected to a computer to perform data acquisition and analyses in real time. These features made the MinION instrument a new attractive tool with potential new opportunities and benefits for management of viral infections. In 2014, ONT opened the MinION Access Program (MAP) framework to approximately 1,000 users  [6]  to assess potential applications, performance and limitations of the device for their own research. As a selected laboratory, we aimed to investigate the ability of the MinION nanopore long-read single-molecule sequencing system as an alternative to traditional cloning and Sanger sequencing procedures for studying the viral population genetic diversity using individuals infected with the hepatitis B virus (HBV) as a model.
MinION™ runs Prior to sequencing, the MinION™ device was connected to a computer using a USB 3. 0 cable and port. A MinION™ R7. 3 flow-cell was inserted and a quality control was run using the MinKNOW control software to assess pore activity (quality and number of functional pores). The MinION™ flow-cell was then loaded with 6 μL of the library mixed with 140 μL of 1X SQK-MAP004 EP buffer, or 75 μL 1X SQK-MAP005 Running Buffer + 66 μL nuclease free water, and 4 μL of SQK-MAP004 Fuel Mix or 3 μL SQK-MAP005 new Fuel Mix. All sequencing runs were performed for 18 hours on average using the standard 48-hour sequencing protocol (MAP_48H_Sequencing_Run) without reloading of the library during the run. Each flow-cell was used to run a single sample.
MinION™ raw sequencing data analysis Each read generated during a sequencing run is stored in an individual fast5 file. The raw MinION™ sequence data are uploaded in real-time for base calling with the software Metrichor™ Agent using the R7 2D Basecalling workflow (v 2. 26. 1 to v 2. 38. 3). MinION™ sequencing generates up to three types of read for each DNA molecule: "Template" corresponding to the forward strand, "Complement" corresponding to the reverse strand and "2D read" corresponding to a consensus sequence of the Template and Complement reads. Metrichor Agent returns files into two folders: "pass" and "fail". "pass" contains only fast5 files where 2D base-calling was successful and the mean quality of the 2D read is ! 9 Phred score. The MinION™ "pass"-labeled reads (encompassing together template, complement and 2D reads) from each sample were then aligned against HBV reference genomes, plus the 3.
Genetic recombination analysis Breakpoint positions for genomic recombination in HBV whole genome sequences were determined by using the jumping profile hidden Markov model (jpHMM-HBV) available at the Department of Bioinformatics of the Go ¨ttingen University, Germany (http://jphmm. gobics. de/submission_hbv) [17]. Sequence position numbers are provided according to an HBV genotype A reference sequence (Genbank AM282986).
Accession numbers All MinION nanopore data supporting the results of this article have been submitted to the SRA NCBI archive with the study number SRP129826 [accession number of each sample: B5584 _HBV genotype D1/E recombinant virus_ Full genome_Clone (SAMN08367587), B5584 _HBV genotype D1/E recombinant virus_ Full genome_ PCR product (SAMN08367588), B6260_ HBV genotype D2_Full genome_Clone (SAMN08367589), B6260_ HBV genotype D2_Full genome_ PCR product (SAMN08367590), B6505_ HBV genotype A2 _ Full genome_ PCR product (SAMN08367591)] and BioProject number PRJNA430224. The Ion Torrent dataset was first preprocessed to remove duplicates and sequences of low quality (minimum quality score of 18), followed by further screening and removal of humanderived sequences using DeconSeq version 0. 4.
MinION™ sequencing of a HBV recombinant strain Nanopore sequencing of amplified DNA from sample B5584 produced 4,893 sequence reads from 193 of 512 flow cell channels including 472 corrected pass reads out of 735 initial pass reads (15%). All 472 corrected pass reads aligned to the HBV consensus sequence previously obtained with the Sanger method (Table  1 ). These individual corrected reads were 2,432 nt long on average (range: 50-3,201 nt), including 213 reads that cover ! 95% of the HBV genome with an average percent identity of 87% (range: 69-98%) with the Sanger sequence. As previously observed with clones, the deduced MinION consensus sequence showed >99% nucleotide identity compared to the Sanger sequence. The 472 nanopore sequences were analyzed with the jpHMM-HBV software (Fig  1 ).
Identification of deletion HBV variants Sequencing of amplified products from sample B6260 produced 3,844 reads from 413 of 512 nanopore channels. Only 72 reads (1. 9%) categorized as pass reads and 30 corrected reads aligned to the B6260 Sanger sequence (Table  1 ). These corrected reads were 2,082-nt in length on average (range: 213-3,015 nt) with a mean pairwise nucleotide identity of 89% (range 77-94%). Due to a very low number of reads, the MinION consensus showed only 95% nucleotide identity compared to the Sanger sequence. However, all 30 complete and partial corrected sequences were tested by BLASTn analysis using the NCBI viral database and were matched with HBV genotype D reference sequences. Similarly, shorter reads were also observed in samples B5584 and B6505 (Table  1 ). The alignment of two ~1,960 nucleotides long reads from sample B5584 with the full HBV sequence identified a large 1,229-nt deletion in the Pol/PreS1 region that made them potential HBV spliced variants with known splice donor and acceptor sites located at positions 2,471 and 489  [21].
Of note, the recombination breakpoints at positions 850 and 1,494 mentioned above were also found in the spliced sequences. Sequencing an independent PCR product with the Ion Torrent Proton system confirmed the presence of this spliced variant (SAMN08376066). The analysis of sample B6505 reads revealed a diverse population of smaller genomes with 21 reads (range: 1,052-2,167 nt) showing a 1,049-nt deletion potentially associated with a previously described spliced variant with breakpoints at positions 2,447 and 282  [21].
Four other distinct reads (range: 3,065-3,092 nt) with a shorter 96-nt deletion between positions 2,131 and 2,226 were also observed in sample B6505 Core region sample. No cryptic consensus splicing signals (5'-AGGURUGU--YNRCURAC--Yn--YAGG -3') were observed at 2,131 and 2,226 breakpoints (SAMN08367591).
Kravitz et al. , 2015)d under the Australian Research Council's Special Research Initiative for the Antarctic Gateway Partnership (project SR140300001). Shingo Watanabe was supported by the Integrated Research Program for Advancing Climate Models, MEXT, Japan. The Earth Simulator was used in his simulations. Jin-Ho Yoon was supported by the National Strategic Project -Find particle of the National Research of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), the Ministry of Environment (ME), and the Ministry of Health and Welfare (MOHW) NRF-2017M3D8A1092022. Description of the 11 models participating in this study. Column 1 gives the standard model name. Columns 2 and 3 give the defaultand perturbed surface ocean albedo, defined as upward shortwave divided by downward shortwave radiative flux at the surface, both averaged over ocean regions and over years 11-50 of simulation.
Acknowledgements. We thank  Jón Egill Kristjánsson , who tragically passed away, for invaluable comments on an earlier version of this manuscript. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank all participants of the  Geoengineering Model Intercomparison Project  and their model development teams, CLIVAR/WCRP Working Group on Coupled Modeling for endorsing GeoMIP, and the scientists managing the Earth System Grid data nodes who have assisted with making GeoMIP output available.
, 2001). Last but not least, this coupled model is also a unique tool to document the origin of the chemical species measured in cloud water and rainwater (including organics containing up to four carbon atoms), which is usually not available from measurements  (Leriche et al. , 2007). In fact, the contributions of particle scavenging, mass transfer and aqueous reactivity to simulated aqueous concentrations can be retrieved for all the species described in the model, thus providing highly valuable information to interpret observations. The model was evaluated against the unique database built on a long-term basis at the Global Atmosphere Watch (GAW) Puy de Dôme (PUY) station to document the cloud water chemical composition, as discussed by  Deguillaume et al. (2014). The originality of the present work is to include organic species up to C 4 in the comparison between the simulated and measured concentrations; some attempts were per-formed in the past, but with a main focus on inorganic and short-chain organic compounds, and they were moreover often measured during specific short-term field campaigns.
The chemical composition of the particles was prescribed according to the measurements conducted between 28 February and 1 March 2000 by  Sellegri et al. (2003)  in a moderately polluted air mass (see Table  3 ). Particles were sampled using a low-pressure cascade impactor (13-stage ELPI impactor, commercialized by Dekati Inc. ) and were further analyzed using ion chromatography. The mass fraction of iron was derived from particle-induced X-ray emission. More details on the sampling procedure and data analysis can be found in  Sellegri et al. (2003). The mass distribution of the dicarboxylic acids derived from these measurements is shown in Fig. S1  in the Supplement. As expected, oxalic acid is the most abundant and the major fraction of the diacids found in the two accumulation modes (0.
Microphysical evolutions Figure  3  shows the time evolution of the simulated cloud liquid water content (LWC) and mean cloud droplet radius. It should be noted that the appearance of the cloud was scheduled around noon, when the photolysis rates are at their maximum, to favor enhanced photochemistry. Apart from the condensation and evaporation phases, the LWC is close to 0. 4 g m -3 , with an average value of 0. 3 g m -3 calculated from the whole simulation. A constant cloud droplet radius of 9. 5 µm is obtained all along the stable phase of the cloud lifetime, between 12:20 and 13:40 LT. These values are in good agreement with the typical values measured at the PUY station from 2001 onwards, which can be found in a database including 110 cloud events that is available online at http://www.
obs. univ-bpclermont. fr/SO/beam/data. php. Indeed, based on this large dataset, the LWC measured at the PUY station exhibits rather limited variation, with an average value of 0. 28 ± 0. 12 g m -3 , while the droplet radius is on average approximately 10 µm  (Deguillaume et al. , 2014). Deguillaume et al. (2014)  classified the cloud samples using a statistical analysis methodology that takes into account the physicochemical parameters of the cloud together with the back-trajectories of the sampled air masses. Cloud events in the westerly and northerly-northwesterly air masses were the most frequent, representing 72 % of the air masses sampled at the PUY station, the majority of which were categorized as "marine" or "highly marine". These two categories of cloud events displayed high pH values compared to other types, namely "polluted" and "continental" (mean pH 5.
Acknowledgements. The authors acknowledge  Karine Sellegri  for data availability on aerosol particles and for helpful discussions. This work was supported by  CEA/CNRS  through contract  CEA 12-27-C-DSPG/CAJ -CNRS 77265. The authors are also very grateful to the  Agence Nationale de la Recherche (ANR)  for its financial support through the  BIOCAP  project ( ANR-13-BS06-0004 ). Edited by: David Topping Reviewed by: two anonymous referees.
Peak integrals from TopSpin were exported to MatLab where a single decaying exponential function was used to t the R 1 and R 2 relaxation data. Errors were calculated by a Monte Carlo error estimation. A random number between 0 and 1 was multiplied with the integral error, which was based on the noise level compared to the signal for each peak, and added to the recalculated integrals. The tting was then repeated 2000 times with a new random number between 0 and 1 generated each time. Two times the standard deviations of the R 1 or R 2 values received from the ts for each residue were used as errors. The correlation times (s c ) for TXB aqueous and TXB DPC were calculated from the  15  N T 1 and T 2 relaxation times and the  15  N resonance frequency (n N ), 71 MHz at a 16. 4 T NMR magnet (700 MHz 1 H Larmor frequency) using eqn (1): s c z 1 4pn N ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 6 T 1 T 2 À 7 r (1) which is generally used for rigid protein molecules in the limit of slow molecular motion (s c [ 0.
Acknowledgements The research leading to these results has received funding from the  Bill & Melinda Gates Foundation  (grant  OPP1160394 , and  OPP1132809 ),  European Research Council  under the European Union's  Seventh Framework Programme  ( FP/2007-2013)/ERC Grant Agreement 639907. J. R. L. also acknowledges funding from  EPSRC  Grant  EP/L025906/1 ,  BBSRC  Grant  BB/L022761/1 & BB/R010218/1. A. J. L. , J. A. T. , A. C. C. and C. G. D. acknowledge funding from  AMR Cross Council  funding  MR/N002679/1. C. Ö. and G. P. W. acknowledge funding from the  European Union  under a  Marie Curie Initial Training Network FP7-PEOPLE-2012-ITN Grant  Agreement Number  316630 CAS-IDP. We thank  Peter Güntert  for providing the CyLib soware and  Tobias Madl  for providing Gd(DTPA-BMA). Molecular graphics and analyses were performed with the  UCSF Chimera package.
We call this the backward model (see Fig. 3a ) and it requires a trained forward model to provide a 1D sequence representation that it can learn in going from structure to sequence. Once trained, the backward model can then predict a likely sequence representation from structure alone. We now provide details of how these models were trained and the biologically meaningful results that they generate. The forward model was fit using a training dataset along with a validation dataset that was used to check for convergence and to avoid over-fitting (see Methods). With the fitted forward model, a test set of sequences was then given as input, resulting in a set of predicted local contact maps. We found a Pearson correlation of 0. 61 between the individual pairs of predicted and experimental contact maps in the test set.
3b ) may be indicative that the principles of chromatin folding learned by our models are are not the primary drivers of conformation in these locations. Our findings thus highlight two aspects: First, 2D contact maps can be efficiently encoded into a 1D vector and decoded back (our backward and forward model effectively work as an auto-encoder, similarly to  [39] , with the difference that our embedded feature vector is the chromatin sequence). Second, a backward model that predicts sequence from structure can be used to identify locations in the genome where sequential and structural datasets likely differ from one other. One could imagine this to be a powerful technique to analyze phenotype-to-genotype linkages by identifying regions where chromatin states are likely to vary by using contact maps from cells of differing tissue, developmental time or disease.
Specifically, for each pair of sites i and j in the genome separated by a distance d = |j -i|, we calculated ∂P(c ij )/∂σ k for the data in which (i, j) appear centred in the sequence w ( for an example, see the pairs of sites (1) and (2) in Fig. 4a ). Then we averaged all pairs of sites in the test data that meet these criteria, obtaining a sequence of average gradients for centred sites situated at a distance d. In Fig. 4b  we show the ∂P(c ij )/∂σ k d at each distance d as a function of relative position k. We find that making the regions between contacting sites more inactive tends to favor more contact ( ∂P(c ij )/∂σ k d < 0). This is strongest at shorter distances of contact (d < 300 kbp) and becomes weaker as the distance increases. On the other hand, making the sites of A B Fig. 3  Detecting potential discrepancies between sequential and structural datasets.
Hi-C data Chromatin structural information comes in the form of genome-wide contact maps that were obtained from the publicly available Hi-C experiments done by Schuettengruber et al. [51]  (GSE61471), performed on 3000-4000 Drosophila melanogaster embryos, 16-18 hours after egg laying. The contact map is an array whose elements n ij are the number of times a particular pair of genomic sites were found to be in contact. The contact map was built using a set of N non-overlapping sites of a fixed size (we used a size of 10 kbp which gave N = 9663 for all the autosomal chromosomes of D. melanogaster). The counts, n ij , between all pair of sites i and j in the contact map were determined by counting up all sequenced pairs from the Hi-C measurement that fell into a given pair yielding an N × N symmetric matrix (and each unique sequence pair was only counted once to reduce experimental bias, as suggested in  [8] ).