dataesr/scientific-paragraphs-categorization huggingface dataset:
Once read quality profiles were validated, the core-denoising algorithm of DADA2 was performed on the forward and reverse reads separately. Amplicon sequence variants (ASV) were inferred and pairs merged. ASVs table was constructed and chimeras were identified and removed only if the parent sequence was at least twice abundant than a left/right-segment recombination. The taxonomic affiliation was assigned using a local BLAST database produced using the lactococcal genomes available on the NCBI database.
MALDI-TOF MS Bacterial Identification MALDI-TOF MS (Biotyper ® , Bruker, Palaiseau, France) identifications were performed on each bacterial isolate studied for their carbohydrate metabolism profile. MALDI-TOF MS analysis was performed by Eurofins Microbiologie Ouest based on their internal procedures (Nantes, France). 2. 4. 3. DNA Extraction, Routine PCR Procedure and Full-Length 16S rRNA Gene Sequencing Full-length 16S rRNA gene sequencing was performed by the Eurofins Genomics Europe Applied (Ebersberg, Germany) according to the company's internal test ID BJ00M Bacterial Species Identification. The obtained sequences were aligned using the MEGA7 program package  [32]  and compared on the NCBI database using the BLAST program (https://blast. ncbi. nlm. nih. gov/Blast. cgi) [33].
Genome Sequencing and Data Analysis 2. 5. 1. Pure Culture DNA Extraction Six L. lactis isolates were chosen following API 50 CH results to perform genotype-phenotype associations. Overnight cultures from these isolates were centrifuged at 13,000× g for 10 min at 4 • C. These. Genomic DNA was extracted using the DNeasy blood and tissue kit (Qiagen, Germany) according to the manufacturer's instruction. Genomic DNA was quantified using the NanoDrop ND-1000 (ThermoFisher Scientific, Illkirch-Graffenstaden, France).
Whole Genome Sequencing and Gene Annotation Whole-genome sequence data were generated by PacBio ® RS II and Illumina ® iSeq sequencing technology in order to produce de novo assemblies. The PacBio ® RS II data were obtained and assembled using the Hierarchical Genome Assembly Process (HGAP) assembly algorithm  [34] , on the Eurofins Genomics platform (Ebersberg, Germany). The Illumina ® iSeq data were produced and assembled using the SPAdes algorithm (Mérieux Nutrisciences, Nantes, France)  [35]. Chromosomal organization of the resulting contigs were predicted by aligning them against the L. lactis subsp. lactis IL1403 reference genome using the MicroScope platform  [36]. The individual genome assemblies were deposited in the NCBI database with accession codes listed in Table  2. Overall genomic relatedness between genomes was assessed using the Orthologous Average Nucleotide Identity (OrthoANI) tool  [37].
purR Gene Sequence Analysis The housekeeping gene purR was then studied to complete the assessment of microbial diversity. It was previously selected after pan-genome analysis for the differentiation of clades beyond the L. lactis subspecies level  [7]. The purR gene encodes for a transcriptional regulator of purine biosynthesis. The use of a single nucleotide inference method highlighted five amplicon sequence variants (ASVs) after raw data processing using the DADA2 algorithm (Table  4 ). A total of 108,455 reads were obtained with an exact purR amplicon size of 435 nucleotides for each clustered ASV  [45]. A close strain similarity was revealed and each ASV variant was represented by a single nucleotide sequence type. Nucleotide variant analysis showed that the most abundant sequence (ASV_1) was 100% identical to the corresponding reference sequence L.
lactis subsp. lactis UC08 (GenBank: CP015903. 1) or UC11 (GenBank: CP015904. 1). A matrix table highlighted nucleotide substitutions between each ASVs, from 1 up to 18 different bases (Figure  1A ). The nucleotide translation into amino acid sequences identified three different ASVs (see matrix table, Figure  1B ). Amino acids sequences of variants ASV_1, ASV_2 and ASV_3 were identical. Variants ASV_4 and ASV_5 showed three amino acid substitutions compared to other ASV (or the reference sequence). A comparison between ASV_4 and ASV_5 showed one amino acid substitution. Substitutions include amino acid changes in position 7: Glu (ASV_1), Ala (ASV_4) and Ser (ASV_5), position 21: Thr (ASV_1) and Pro (ASV_4 and ASV_5) and position 22: Lys (ASV_1) and Gln (ASV_4 and ASV_5). Dendrograms were constructed using nucleotide alignments (Figure  1A ) or protein alignments (Figure  1B ).
These five ASVs sequence reads were all affiliated to the species level (100% identity) as L. lactis using BLAST searches against a local database including any sequences of lactococcal genomes part of the NCBI database (built on 10 February 2020). Interestingly, the phylogenetic analysis of purR did not identify L. lactis to the subspecies level, as it did for Frantzen et al. [7]. In their work, primers designed by Frantzen et al. were used for relative quantification of the microbial community in starter cultures. Medium amplicon size was then required. Therefore, the purR amplicon size (435 nucleotides) was shorter than the full-length purR gene (816 nucleotides), identified in the wholegenome sequences. The targeted amplicon purR showed an alignment between position 347 to 782. A BLAST search performed on the full-length purR gene affiliated the genome sequences to the subspecies level (100% identity) as L.
These copies harbor intra-genomic heterogeneity, which makes it difficult to differentiate bacteria to the species/subspecies level using 16S rRNA amplicon  [45, 48]. ASV methods better discriminate ecological patterns capturing all biological variation  [31]. Furthermore, DNA sequences of protein-coding genes are more effective than 16S rRNA genes when distinguishing between very closely related bacteria  [7, 49]. The targeted amplicon comparison highlighted a noteworthy difference between results obtained with fragments of the 16S rRNA gene These five ASVs sequence reads were all affiliated to the species level (100% identity) as L. lactis using BLAST searches against a local database including any sequences of lactococcal genomes part of the NCBI database (built on 10 February 2020). Interestingly, the phylogenetic analysis of purR did not identify L.
[14, 53]. Phenotypic data of the type strain IL1403 was extracted from Siezen et al. [54]. A genotype-phenotype association was devised to study possible relations between genes and phenotypes observed in the API 50 CH systems. A representative set of six L. lactis isolates were selected for WGS studies: three isolates were part of the A group (L1, L2, L3), one isolate was part of the B group (L8), one isolate was part of the C group (L14) and one isolate was part of the D group (L9). An Illumina iSeq ® platform (L1, L2, L8, L9, L14 genomes) and a PacBio ® RS II technology (L3 genome) were used for genome assemblies. The role of L. lactis as an important industrial starter strain is mainly due to the rapid conversion of lactose to lactic acid. The KEGG pathways (https://www. genome. jp/kegg-bin) of Lactococci cell metabolism summarize two possible catabolic ways for lactose utilization: (i) hydrolysis of lactose to give α-D-glucose and D-galactose performed by a β-galactosidase (EC 3.
These genomic changes could be worsened To further examine isolate 14 antimicrobial activity, CFSs of each batch culture were collected during these growth slowdowns. Their antimicrobial activity was assessed by the agar well diffusion assay. Thus, CFSs were collected between 26 and 34 h of growth for S. aureus DSMZ 13,661 assays (mono and co-cultures). Their CFSs largest inhibition halo was obtained after 32 h of growth. Similarly, CFSs were collected between 8 and 20 h of growth for S. ser. Typhimurium CIP 104,115 assays (mono and co-cultures). Their CFSs largest inhibition halo was obtained after 16 h of growth (data not shown). Table  8  summarizes inhibition halo values (mm) of CFSs both obtained from mono and co-cultures after 32 h of growth for S. aureus assays and after 16 h of growth for S.
Acknowledgments: We thank  Stéphane Aymerich  for the L. lactis IL1403 strain. The  INRAE MIGALE  bioinformatics platform is thanked for access to computational resources and data storage facilities. The  LABGeM (CEA/Genoscope  and  CNRS UMR8030 ), the  France Génomique and French Bioinformatics Institute  national infrastructures (funded as part of  Investissement d'Avenir program  managed by  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013 ) are acknowledged for support within the MicroScope annotation platform. This work was financed by  Flores de terroirs company and "Association Nationale Recherche Technologie " (convention  CIFRE N • 2017/0745 ).
The δ 34 S measurement required the ground up seagrass tissue to be weighed into tin capsules along with vanadium pentoxide. Metabolites were extracted, analysed and annotated as described in Hasler-Sheetal et al 2016. In brief, we extracted metabolites from freeze-dried seagrass tissue and analysed by GC-qtof-MS after derivatisation. The data was inspected, annotated and analysed using MassHunter and Mass Profiler Professional (Agilent Technologies, Santa Clara, USA). Yearly horizontal production rates were determined through the measurement of the horizontal internodal lengths (the length between leaf scars) on the horizontal ramets  (Duarte et al. , 1994) , in order to determine the leaf plastochrome interval (PI). All ramets were dried at 60 °C for 48 hours. The measurements were performed on a Zeiss model Discovery.
Statistical analyses Before statistical testing, we carried out normality and homogeneity checks in SPSS (IBM Corp. , 2017) which included using Cochran's Q test. Two-way Analysis of Variance (ANOVA) in SPSS was used to test for differences between stations and seasons. Principal component analyses (PCA) were carried out using the R 'stats' package  (R Core Team, 2013)  and MetaboAnalyst 4. 0  (Chong et al. , 2018)  in order to identify differences between stations or seasons. Missing values in the metabolomics dataset were calculated using a K-nearest neighbour algorithm, variables with over 50% missing values were discarded. Data was normalised by median, log transformed, and auto-scaled (mean-centred and divided by the standard deviation of each variable). The most influential metabolites in separating the seasons and stations along the principal components (PC) were identified by inspecting the loadings for each PC.
Acknowledgments The study was funded by the  ACES Erasmus Mundus JMD  (Grant No. 553648-EPP-1-2014-1-UK-EPPKA1-JMD-MOB ) and the  EU H2020  project  TAPAS  (Grant Agreement No. 678396 ). We thank  I. Glampedakis ,  C. Escobar-Sierra  and  G. Hatzigeorgiou  for assistance with sampling,  A. Giuliani  for the 3D graphic and useful discussions, and  Katrine Clement Kirkegaard ,  I. Stavrakaki ,  E. Dafnomili ,  S. Zivanovic  and  X. Vlata  for helping with laboratory analysis.
Statistical Analysis Statistical analyses were performed using Stata software (version 15, StataCorp, College Station, USA). Continuous data were expressed as means and standard-deviations (SD). The normality of the distribution was checked with a Shapiro-Wilk test. To analyze longitudinal data (variations in energy intake and macronutrient, variations in biochemical markers and blood pressure) and to evaluate the effect of fibers and nutritional factors (energy intake, protein intake, complex carbohydrate, simple carbohydrate, saturated fat, monounsaturated fat, polyunsaturated fat, cholesterol intake, fiber intake, water intake) on health outcomes (central fat, weight, BMI, fat mass, fat free mass, waist, blood glucose, HbA1c, insulimenia, Homa-IR, total cholesterol, triglycerides, HDL, LDL, Hs-CRP, SBP, DBP), marginal models, estimated using a generalized estimating equation, were used to study group (Re, rE, re) and time-point evaluation effects, and their interactions, taking into account, between and within, participant variability.
Acknowledgments: This work was supported by the  Laboratory of Metabolic Adaptations to Exercise under Physiological and Pathological Conditions  ( AME2P EA3533 ), by the  Institut de Médecine du Travail  from the  Faculty of Medicine of Clermont-Ferrand , and by the thermal baths of  Chatel-Guyon  and the  Omental Thermalia, Chatelguyon, France.
Data processing and computational platform We performed flat-/dark-fields correction on raw data and each tomographic projection was normalized with the average value of background outside the object. The phase contrast retrieval algorithm  (Paganin et al. , 2002)  was applied to each XPCT projection. After the phase retrieval procedure, we used the resulting set of data for 3D reconstruction of the object. Tomographic reconstruction was done with Filtered Back Projection method (FBP). A post processing procedure has been applied to enhance visibility of calcifications and soft tissue of the PG. Standard tools and plugins of the open-source image program ImageJ/Fiji were used  (Schindelin et al. , 2012). In particular, average intensity and maximum intensity projection options of the standard tool "Z Project" of ImageJ/Fiji were used.
Data pre-processing, artifact removal, phase retrieval and FBP reconstruction were done with the open-source software toolkit SYRMEP Tomo Project (STP)  (Brun et al. , 2017; van Aarle et al. , 2015). Maximum projection through the XPCT volume with a thickness 10 µm has been used for the comparison with the histology, which is a 2D technique and the information in depth of histological section is averaged within 10 µm. Both maximum and average z-projections through XPCT volume with a different thickness has been applied to highlight specific 3D features in the samples.
Acknowledgements The bilateral project  CNR/RFBR (2018-2020 ) -accordo  CNR-RFBR delle Relazioni Internazionali  ( CUP B86C17000210005  & Russian number  18-52-7819 ), the national project RFBR (number  18-29-26028 )-Russian Federation, the  FISR  Project " Tecnopolo di nanotecnologia e fotonica per la medicina di precisione " (funded by  MIUR/CNR ,  CUP B83B17000010001 ) and the  TECNOMED  project (funded by  Regione Puglia ,  CUP B84I18000540002 ) are acknowledged for financial support. We also thank the  Ministry of Science and Higher Education of Russian Federation within the State assignment FSRC «Crystallography and Photonics» RAS  for financial supporting and for the use of equipment of the  Shared Research Center "Structural diagnostics of materials"  (project  RFMEFI62119X0035 ). M.
2018) , it was demonstrated that quasars can be used as robust tracers of the underlying matter field, extending growth rate measurements to redshift  ∼ 1. 5. The eBOSS program concluded observations on March 1st 2019. This work is one of a series of papers presenting an analysis of the final eBOSS data release 16 (DR16) quasar sample, which approximately doubles the number of quasars of the previous DR14 release. The DR16 quasar catalogue is presented in  Lyke et al. (2020). The clustering catalogue used for this analysis is  described in Ross et al. (2020). The quasar mock challenge used to assess modelling systematics is described in  Smith et al. (2020). N-body simulations for assessing systematic errors are presented in  Rossi et al. (2020)  for LRG,  Alam et al. (2020)  and  Avila et al.
Auto-and cross-velocity power spectra RESPRESSO provides the prediction for the auto matter power spectrum. However, the full modelling of the power spectrum for the RSD effects on large scales requires the input of the cross spectrum for the matter and velocity divergence,    , as well as the 3 We modify the RESPRESSO python package into a F version. auto-power for the velocity divergence field,. An alternative approach to the perturbative calculation is to model the velocity power spectra using empirical relations measured from N-body simulations. Bel et al. (2019)  performed a study based on a set of Dark Energy and Massive Neutrinos Universe (DEMNUni) Nbody simulations  (Carbone et al. 2016) , in the presence of massive neutrinos. The velocity field was reconstructed from the cold dark matter particles using a Delaunay tessellation.
CONCLUSIONS In this paper we presented the full-shape and BAO-only analysis of the eBOSS DR16 QSO clustering sample. We measured the two-point correlation function of the quasar sample, which we decomposed into Legendre multipoles,  ℓ (), with ℓ = 0, 2, 4. In our full-shape analysis, we incorporated a new recipe to describe the correlation function. The matte power spectrum is calculated using RESPRESSO  (Nishimichi et al. 2017) , whose original python code was implemented in F. The power spectra that involve the velocities were computed using the fitting formulae provided by  Bel et al. (2019). In the final data release, we doubled the number of objects and the survey area compared to the DR14 sample, leading to a 6 detection of the BAO signal in configuration space (consistent with the Fourier space analysis of  Neveux et al.
This manuscript presents a benchmark problem for the simulation of single-phase flow, reactive transport, and solid geometry evolution at the pore scale. The problem is organized in three parts that focus on specific aspects: flow and reactive transport (part I), dissolution-driven geometry evolution in two dimensions (part II), and an experimental validation of threedimensional dissolution-driven geometry evolution (part III). Five codes are used to obtain the solution to this benchmark problem, including Chombo-Crunch, OpenFOAM-DBS, a lattice Boltzman code, Vortex, and dissolFoam. These codes cover a good portion of the wide range of approaches typically employed for solving pore-scale problems in the literature, including discretization methods, characterization of the fluid-solid interfaces, and methods to move these interfaces as a result of fluid-solid reactions.
Chombo-Crunch Chombo-Crunch is a code suite for the solution of flow, reactive transport and geometry evolution at the pore scale developed since 2010 by Trebotich and co-workers  [64] [65] [66]. Flow, transport, and geometry evolution processes are implemented using the Chombo software package  [3, 25] , while geochemical reactions are implemented in the CrunchFlow code  [101]  which is coupled to Chombo via a custom interface. Geometric multigrid solvers from Chombo and algebraic multigrid solver from PETSc  [7]  are available at runtime. The governing equations are discretized directly on a Cartesian grid using an embedded-boundary (EB) finitevolume method. Chombo-Crunch solves the transient incompressible Navier-Stokes equations (Eqs. 33 and 34)  or the transient Stokes equations (as is the case in this benchmark).
OpenFOAM ® Darcy-Brinkman-Stokes The OpenFOAM-DBS method solves the micro-continuum model formed by Eqs. 13-17. The solver is implemented in the open-source simulation platform OpenFOAM ® 5. 0 (http://www. openfoam. org). OpenFOAM ® is a C++ library which solves partial differential equations using the finite volume method on an unstructured grid. OpenFOAM-DBS benefits from all the features offered by the OpenFOAM ® library, including code parallelization, and an entire set of tools including discretization schemes and geometricalgebraic multigrid solvers. For the benchmark problem (Section 6), the computational domain is discretized using a Cartesian grid. The code also works on all kinds of unstructured grids. Equations 13-17 are solved using a sequential coupling strategy. The solution algorithm is presented in detail in  [98].
The code uses Runge-Kutta methods, of order 2 or 4, for the solution of this large set of ODE's. The velocity u is computed from the vorticity ω by taking the curl of the stream function ψ solution of the Poisson equation -∇ 2 ψ = ω. The Poisson solvers are gridbased solvers, either the fast Fourier transform (FFT) solver FISHPACK using cyclic reduction  [2, 106]  or the AMG solver MUDPACK  [1] , while the curl and gradient operations are performed by 4th order finite differences (FD) schemes on Cartesian grids. A time-splitting algorithm is used to split transport and reactions on the one hand (Lagrangian feature), and the differential operators and coupling terms on the other hand (Eulerian feature). The communications between grids and particles are performed by kernel interpolations defined by Eq.
In contrast, Chombo-Crunch and dissolFoam must have an explicit description of the grain surface. For Chombo-Crunch, the mask image was saved as a binary file (0 for pore and 255 for solid) and processed with a dilate filter followed by an erode cycle from the imageJ software  [92]. The resulting grayscale image-with a smoother grain surface-was read by the code, which then used a level set algorithm  (19)  with a threshold value of 128 to generate the implicit functions that define the embedded boundary. The lattice Boltzmann code did not participate in part III. DissolFoam took advantage of the octagonal shape of the grain to initialize the domain with an exact octagonal perimeter rather than using the image directly. Further, dissolFoam also took advantage of the ability to remesh pore spaces externally (Section 5.
However, the staircase outline for OpenFOAM-DBS is an artifact of the visualization software after a threshold is applied to the output porosity (ε) field. These initial differences likely have an effect on the different evolution of the grain for the different codes. In spite of this, however, the agreement is in general very good and the simulated evolution of the shape is qualitatively similar between codes (Figs. 12). As in part II, the dissolution of the trailing edge of the grain predicted by Chombo-Crunch is slower than for other codes. In this case, it also it dissolves faster in the leading edge. There is however good agreement among all codes in capturing the width of the evolving grain. The vortex code predicts a slightly wider grain than the other codes. In the direction perpendicular to the plane shown in Fig.
12 ), the top and bottom boundaries were considered non-slip boundaries. Because flow is faster in this midplane and slower towards the boundaries, dissolution is slightly faster here. As a result, the grain surface recedes faster in this area, especially in the leading edge of the grain (Fig. 13 ). The predictions for the trailing edge by the different codes diverge more significantly. While the vortex code predicts a similar pattern as in the leading edge, dissolFoam and OpenFOAM-DBS predict instead that trailing edge remains a vertical wall (only slightly concave for OpenFOAM-DBS), and Chombo-Crunch predicts a convex evolution. Although there is no experimental data to ascertain the evolution, additional simulations with Chombo-Crunch did not change this prediction. This result however explains the longer trailing edge for this code discussed earlier as Fig.
Acknowledgements This material is based upon work supported as part of the  Center for Nanoscale Control of Geologic CO 2  , an  Energy Frontier Research Center  funded by the  US Department of Energy, Office of Science, Office of Basic Energy Sciences  under award  DE-AC02-05CH11231. Chombo-Crunch simulations used resources of the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  supported by the  Office of Science of the US Department of Energy  under contract  DE-AC02-05CH11231. Development of dissolFoam was supported by the  US.