dataesr/scientific-paragraphs-categorization huggingface dataset:
7 10 -5 , E(CE) = 3 10 -5 and Pm(CE) = 2. 5 are the control parameters of the coupled Earth dynamo model. Through these definitions, A17 introduced the concept of a unidimensional path in parameter space, starting at = 1, and ending at = 10 -7 where reasonable estimates for Earth's core conditions are obtained (A17). The model cases presented here (Table  1 ) range from = 0. 1 to = 3. 33 10 -4 , this latter value defining the model at the middle of the path, which to date is the closest to Earth's core conditions computed in a numerical simulation. The numerical implementation involves a decomposition of u, B and C in spherical harmonics up to degree and order max and a discretisation in the radial direction on a second-order finite-differencing scheme with NR grid points. We use the spherical harmonics transform library SHTns  (Schaeffer, 2013)  freely available at https://bitbucket.
Acknowledgements JA wishes to thank  Thomas Gastine  for insightful discussions and suggestions, and acknowledges support from the  Fondation Simone et Cino Del Duca of Institut de France  ( 2017 Research Grant ). Numerical computations were performed at  S-CAPAD ,  IPGP  and using HPC resources from  GENCI-IDRIS ,  GENCI-TGCC  and  GENCI-CINES  (Grants  A0020402122  and  A0040402122 ). This is IPGP contribution 4022.
Phylogenetic Affinities of Strain Pa Genomes of P. vagans and P. agglomerans strains collections available in GenBank were selected for phylogenomic analysis and the exact phylogenetic position of P. agglomerans strain Pa was inferred (Table  1 ). The genome size was in the range of 3. 52 to 5. 36 mega base pairs (Supplementary Table  S1 ). Genome-to-Genome Distance Calculator (GGDC) analysis revealed four different species sensu Meier-Kolthoff et al. [27]  that was nominated P. vagans and P. agglomerans. Species sensu Meier-Kolthoff et al. [27]  are defined based on the presence of 70% similarity between two genomes, and this cut-off is believed to be suitable for bacterial species discrimination and is considered as the standard threshold allowing species boundaries delimitation (Supplementary Figure  S1A ).
Secondary Metabolite Clusters Programs such as antiSMASH 3. 0  [39] , prediction informatics for secondary metabolomes (PRISM)  [40] , NapDos  [41] , NP. search  [42] , and BAGEL3 bacteriocin specific software  [43]  were used to identify secondary metabolite clusters present in the P. agglomerans and P. vagans genome collection. Figure  11A  and Supplementary Table  S5  show various secondary metabolite clusters using all approaches. Apart from carotenoid and arylpolyene biosynthetic gene clusters, none of the remaining clusters matched already-identified secondary metabolites, suggesting discovery of yet-to-be characterized natural products. Identified secondary metabolite clusters analyzed by the rarefaction approach highlight that saturation was far from being attained (Figure  11C ). All remaining secondary metabolites (SM) were unknown and lied within the different strain's accessory genome.
Using antiSMASH, a clear correspondence between the number of secondary metabolite gene clusters and the size of the accessory genome was demonstrated (Figure  11F ). Approximately 17. 7% of the number of secondary metabolite clusters variance could be interpreted by the size of the genome (Figure  11F ). Correlation between the number of secondary metabolite gene clusters uncovered using antiSMASH and the genome size was clearly established. Up to 20. 3% of the variance in the number of SM clusters could be explained by genome size (Figure  11B ). On the other hand, only 6. 3% of the variance in the number of SM clusters could be interpreted by difference in genome size when using the PRISM software (Figure  11D ). www. mdpi. com/journal/ijms (PRISM)  [40] , NapDos  [41] , NP. search  [42] , and BAGEL3 bacteriocin specific software  [43]  were used to identify secondary metabolite clusters present in the P.
DNA Sequencing and Phylogenetic Analysis The purified amplicons were sequenced in both directions using an ABI 3130 XL available at the iGE3 (Institute of Genetics and Genomics in Geneva, University of Geneva Medical Center (CMU), Switzerland). The exact phylogenetic position has been ascertained by phylogenetic analysis using the multiple sequence alignment web-based MAFFT program  [71] , the maximum-likelihood (ML) algorithm  [72] , MEGA software v. 6  [73] , and the Kimura 2-parameter model  [74]. The validity of branches in the trees was evaluated by bootstrap analysis. (Supplementary Figure  S2 ).
Bacterial Genome Sequencing Assembly and Annotation The genome of strain Pa was sequenced using the facilities available in the iGE3 genomics platform of the University of Geneva (http://www. ige3. unige. ch/genomics-platform. php). De novo assembly resulted in 45 contigs. P. agglomerans strain Pa genome sequence has been deposited in the genome sequence database (accession no. MUJJ00000000. 1).
Average Nucleotide Identity Analysis Average nucleotide identity (ANI) values of P. vagans and P. agglomerans strains were estimated using the server EzBioCloud, available at http://www. ezbiocloud. net/tools/ani [38, 76, 77]. 4. 6. 4. Genome-to-Genome Distance Calculator (GGDC) Analysis DSMZ software, available at http://ggdc. dsmz. de [27] , was used to assess in silico genome-to-genome distance values.
LC-MS Analysis: HPLC-HRMS Instrumentation and Conditions HRESIMS data were obtained using a Thermo LTQ Orbitrap coupled to an HPLC system (PDA detector, PDA auto-sampler and pump). All conditions were according to Slama et al. [17]   Raw mass data files were sliced into two data sets as positive and negative based on ionization mode using MassConvert tool from Proteo Wizard. As the negative mode was not high resolution, only positive mode files were considered for further process. Positive mode sliced data files of all samples and sample blanks belonged to six different media were imported and processed using MZmine 2. 37, following the steps and predefined parameters explained in  [82]. The sequence of steps involved peak detection (mass detection and chromatogram builder), chromatogram deconvolution, deisotoping, filtering, alignment, gap filling, adduct and complex ion search, and finally formula prediction.
Final data output from the MZmine was exported as a CSV file and further clean-up was done manually by removing adducts and peaks present in both samples and their medium blanks as explained in  [82]. Additionally, the formulae which did not follow the nitrogen rule were also eliminated. The CSV file was cross-checked with Xcalibur 3. 1 using raw mass data files to eliminate unreliable formulae, and the unidentified peaks by MZmine were also evaluated for reliable formulae and the intensity before elimination.
Dereplication Process to Identify Known and New Hits The CSV file, after the clean-up process, was aligned in ascending order with the peak retention times. Predicted formulae were searched using databases such as Antibase 2012, SciFinder, Dictionary of Natural product 27. 0, DEREP-NP 2013, and REAXYS online databases. This was followed by a thorough search of the fragmentation patterns of the precursor ions on Xcalibur 3. 1 with the aid of a fragmentation tool available with ChemDraw professional 17. 0 (PerkinElmer Informatics, Cambridge, UK) to make lists of best possible hits from each medium. Finally, all six lists were combined to produce a comparison table with tentative identification, chemical structure, and reported biological importance of the predicted hits (Supplementary Table  S5 ).
Metabolomics. GC-MS analyses were carried out as in ref. 50 , using gas chromatography coupled to mass spectrometry (GC-MS), via methanol:water extraction followed by derivatization with methoxylamine and N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) in pyridine. Data were extracted using the online processing software Metabolome Express. Flux calculations. 33 S,  13  C isotopic data (absolute contents in mmol m -2 and %) were used to compute the flux of leaf sulfate reduction, in µmol m -2 s -1. Equations used and model description are provided in Supplementary Notes 2 and 3. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
2a, b ). SmcHD1 peaks in the latter region accord with a previously published dataset analysing SmcHD1 occupancy in XY neuronal stem cells  37. We went on to analyse Xi transcription in SmcHD1 mutant compared to wt cells using allelic chromatin RNA-seq (ChrRNAseq), which enriches for nascent unprocessed mRNAs, thereby maximising the number of informative SNPs due to inclusion of intron sequences. The results are summarised in Fig. 1d-h. In wt cells, Xi expression was detected for a small number of genes (28/ 403), largely corresponding to genes previously reported to escape X inactivation (Fig. 1g , Supplementary Fig. 2c ). However, in SmcHD1 mutant cells, Xi expression was seen for the majority of Xi genes (254/403) (Fig. 1d-f ). For 67 SmcHD1-dependent genes, Xi expression was in a similar range to that found on Xa, with a further 163 genes showing partial or low-level activation (Fig.
Microscopy. Z stack images were acquired on a Zeiss AX10 microscope equipped with AxioCam MRm charge-coupled device camera using AxioVision software (Carl Zeiss International, UK). Best exposure time for each field and channel was manually determined and kept fixed among experiments. Further image editing and refinement was achieved through Fiji/ImageJ. RNA extraction. Cells grown on T25 flasks (Nunc) were washed twice in PBS and lysed directly with 1 ml of Trizol reagent (Life Technologies). Samples were incubated for 5 min at 25 °C, cellular lysates were collected and transferred into 1. 5 ml RNase-free Eppendorf tubes. 0. 2 volume of chloroform was added to each sample and mixed by vigorous shaking for 15 s. Samples were incubated for 2 min at 25 °C and centrifuged at 12,000 × g for 5 min at 4 °C.
The cross-linking was quenched by addition of 1. 25 mL of 2. 5 M glycine. After 5 min at RT, plates were placed on ice for an additional 15 min. Cells were finally harvested by a cell lifter, transferred to a 15 mL falcon tubes, and centrifuged at 800 × g for 10 min (4 °C). The cell pellet was snap-frozen in liquid nitrogen, and stored at -80 °C until use. Hi-C libraries were then sequenced on a HiSeq4000 (paired-end reads of 100 bases each). Two biological replicates of wt and SmcHD1 mutant MEFs were sequenced in separate lanes, each yielding ~350 million reads per replicate. Allele-specific alignment. All NGS data were obtained from interspecific FVB-CAST/EiJ cells, which enabled allele-specific analysis. Assigning of reads into one of the parental genomes was performed in two stages. First reads were mapped to mm10 reference genome with the aligner optimal for the assay.
Differential expression analysis was performed with DESeq2 69 , a package from the R Bioconductor project. Results were further processed, analysed and visualized with custom R scripts using commonly used base and Bioconductor packages. ChIP-seq analysis. ChIP-seq reads were mapped with bowtie2 with SNP Nmasked genome index and sorted with SNP-split to separate subsets of reads originating from parental genomes. For SmcHD1, CTCF and Rad21 ChIP-seqs, macs2 was used to detect significant enrichments (narrow peaks, q-value < 0. 05). Peak calling was performed on unsorted reads. Allele-specificity of distinct peaks was determined by calculating the ratio of allele-specific reads overlapping the peak in maternal and paternal genome. Peak were considered exclusively specific to either paternal or maternal chromosome if they contained more than 90% of all input corrected reads.
Chromosome-wide SmcHD1 and H3K27me3 ChIP-seq data were obtained by averaging log2(IP/input) values calculated for 500 kb bins within 10 kb intervals. H3K27me3 depletion domains were defined as regions with intervals of mean log2(IP/input) < 0, adjacent regions with negative values separated by no more than 20 kb were merged. Hi-C analysis. Hi-C reads were mapped with HiCUP pipeline 70 using bowtie2 indexes based on the FVB-Cast SNP N-masked mm10 genome. Valid sets of Hi-C ditags were obtained after removing uninformative reads (re-ligations, dangling ends, etc), as well as exact duplicates. Hi-C ditags were then sorted based on confident SNP content with SNPsplit into following groups: FVB-FVB, FVBunassigned, Cast-Cast, Cast-unassigned, FVB-Cast, unassigned-unassigned. For allele-specific analysis subsets "FVB-FVB" and "FVB-unassigned" were merged into one "FVB" and similarly subsets "Cast-Cast" and "Cast-unassigned" were merged into subset "Cast".
For comparative analysis of the wt and mutant cells, Hi-C data interaction number were down-sampled to the size of the smallest sample and further equalized per chromosome to normalize for differences in DNA copy number. Interactions were further binned into 1 Mb intervals genome-wide and 100 and 50 kb bins chromosome-wide with juicer tools  71. Matrices with interactions for distinct chromosomes were balanced separately with the Knight-Ruiz balancing algorithm implemented in juicer tools, that ensures that each row and column of the contact matrix sums to the same value. TAD borders were called with TADtool  72. Insulation scores were calculated with the matrix2insulation. pl script developed in Dekker Lab based on 50 kb balanced matrices with the following options: -is 500000 -ids 200000 -im imean).
Eigenvectors for compartment analysis were obtained with matrix2compartment. pl script based on interaction matrices binned into 250 kb using standard settings apart from changing -ca parameter to 0. 005. Both matrix2insulation. pl and matrix2compartment. pl scripts are available from https://github. com/dekkerlab/cworld-dekker. Heatmaps and sums of interaction numbers for specific parts of chromosomes were generated based on balanced Hi-C matrices with HiTC Bioconductor package  73. Whole-genome bisulfite sequencing analysis. Whole-genome bisulfite libraries were mapped with Bismark 74 , and deduplicated. Further reads were allelespecifically sorted and processed with bismark_methylation_extractor to retrieve methylated cytosines. Cytosines covered by at least three reads in allele-specific alignment were used for further analysis.
Acknowledgements We thank members of the  Brockdorff and Klose labs  for helpful discussions and advice. We would like to thank  Vladimir Benes  and  EMBL Gene Core  for ChrRNA-seq and WGBS,  Amanda Williams  from  Oxford University Zoology Dept. sequencing, the  High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics  (funded by  Wellcome Trust  grant reference  090532/Z/09/Z  and  MRC Hub  grant  G0900747 91070 ) for the generation of the H3K27me3 Sequencing data,  Miguel Branco  for advice on whole genome bisulfite sequencing, and  Michal Maj ,  Dunn School Flow Cytometry Facility  for assistance in flow-sorting for Repli-seq. N. B. is funded by  Wellcome  ( 103768 ) and the  European Research Council  ( 340081 ). J. D. is an investigator of the  Howard Hughes Medical Institute  and was funded in part by a grant from the  National Human Genome Research Institute  ( HG003143 ).
Phenolics Profiles in Response to P. phytofirmans PsJN In order to deeper investigate the quantitative and qualitative modifications of phenolic compounds of grapevine plantlets inoculated with P. phytofirmans PsJN, metabolic changes in roots and leaves in response to PsJN were evaluated by comparison with control plantlets. The extracts obtained from roots and leaves of vitroplantlets were analyzed by UHPLC-UV/DAD-MS QTOF and showed a more complex composition of the obtained extracts for roots than leaves. Indeed, the analysis data processing has led to integrating 85 and 32 peaks for roots and leaves, respectively, on the chromatogram at 280 nm (Supplemental Figure  S2a, b ). The retention times and intensity of signals were reproducible and stable along the analytical process, indicating the reliability of.
Phenolics Profiles in Response to P. phytofirmans PsJN In order to deeper investigate the quantitative and qualitative modifications of phenolic compounds of grapevine plantlets inoculated with P. phytofirmans PsJN, metabolic changes in roots and leaves in response to PsJN were evaluated by comparison with control plantlets. The extracts obtained from roots and leaves of vitroplantlets were analyzed by UHPLC-UV/DAD-MS QTOF and showed a more complex composition of the obtained extracts for roots than leaves. Indeed, the analysis data processing has led to integrating 85 and 32 peaks for roots and leaves, respectively, on the chromatogram at 280 nm (Supplemental Figure  S2a, b ). The retention times and intensity of signals were reproducible and stable along the analytical process, indicating the reliability of metabolomic investigation.
The control and bacterized samples were mainly separated by the PC1, which represented 48. 6% and 47. 6% of the variation among the samples, respectively, in roots and leaves (Figure  6a, b ). This figure represents both the data obtained in the first and second biological repetitions. The two independent biological repetitions of the experiment showed similar chromatographic profiles and discriminations between the two conditions for both roots and leaves, strengthening these results. A PLS-DA (partial least squares discriminant analysis) was also performed on the data, allowing to highlight the most discriminant metabolites in each dataset (variable importance for the projection, VIP score >1). The statistical test revealed that relative amounts of 32 and 17 compounds in roots and leaves, respectively, were significantly modified by the beneficial bacterium.
A PLS-DA (partial least squares discriminant analysis) was also performed on the data, allowing to highlight the most discriminant metabolites in each dataset (variable importance for the projection, VIP score >1). The statistical test revealed that relative amounts of 32 and 17 compounds in roots and leaves, respectively, were significantly modified by the beneficial bacterium. A smaller number of metabolites were modified by PsJN in leaves compared to the roots. However, discriminant compounds highlighted in the leaves represented 53% of the detected metabolites against around 38% for the roots, as the composition detected for the obtained both extracts was initially different.
Analyses by UHPLC-UV/DAD-MS ESI QTOF Phenolic compounds analyses were performed on a UHPLC Agilent 1290 coupled to a UV-vis Diode Array Detector (Agilent 1290 Infinity series) and an Accurate-Mass Q-TOF 6530 spectrometer (Agilent Technologies, County of Santa Clara, CA, USA) with an electrospray ionization source (ESI, Agilent Jet Stream Technology, County of Santa Clara, CA, USA). The equipment was managed by using the Mass Hunter Workstation Data Acquisition software B. 07. 00 (Agilent Technologies, County of Santa Clara, CA, USA). For chromatographic separation, a Poroshell 120 EC-C 18 column (3 × 150 mm, particle size 2. 7 µm, Agilent technologies, County of Santa Clara, CA, USA), equipped with a Poroshell C 18 pre-column (3 × 5 mm, particle size 2. 7 µm, Agilent technologies, County of Santa Clara, CA, USA), was used at a constant temperature of 60 • C and with injections of 3 µL of sample solution.
, 2012; Yaksi and Friedrich, 2006)  to obtain a temporally more accurate estimate of actual neuronal firing rates by reverse-filtering the slow decay dynamics of calcium signals and smoothed with a sliding window filter (190 ms) to reduce fast noise. The resulting averaged response profiles were then submitted to a hierarchical clustering  (Deneux et al. , 2016b)  (see Materials and methods) to group together cells with similar response profiles and thereby identify different functional response types. In order to better appreciate the variety of responses, we overclustered the data, based on the same homogeneity threshold for all datasets, so that most response types are represented by multiple clusters (Figure  1C-E ). The clustering also identified a number of weakly or non-responsive cells that were not included in the analysis.
We obtained 19, 31 and 21 functional clusters explaining 52. 3 ± 7. 2%, 51. 9 ± 6. 4% and 51. 3 ± 6. 5% of the total variance (mean ±STD) for V1-projecting, layer 5 and layer 2/3 cells, respectively. These high fractions of explained variance and the strong homogeneity of the clusters as seen in response correlation matrices (Figure  1C  and Figure  1 -figure supplement 1) indicate that the clustering provides an accurate dimensionality reduction of the dataset. Within these clusters, we identified a maximum of 9 response types (Figure  1C-E ), including onset (ON), offset (OFF) and sustained (Tonic) responses with loud or quiet intensity tuning, and also clusters combining loud OFF and sustained responses (labeled as 'loud OFF +tonic') or quiet and loud ON responses (labeled as 'ON'), as described previously  (Deneux et al.
The shifts in the distribution of sound envelope features that we observed across layers and cell subpopulations is an interesting case of coding bias in different neural populations of auditory cortex (Figure  1 ). As observed earlier, in layer 2/3, there is a balance between the main neuron types that signal the beginning and the end of sounds with equivalent fractions of neurons responding to quiet or loud onsets and loud offsets (Figure  1F , note that 'tonic +loud OFF' neurons are included in this count. Quiet offset neurons are rare and not robustly detected across datasets. Detection of tonic responses by clustering is also variable. ). In upper layer 5, the distribution becomes more biased toward loud onsets at the expense of offset neurons, a trend that is further increased in the layer 5located V1-projecting cells in which half of the neurons respond to loud onsets and 10% of the cells to loud offsets.
After band-passing, the measured signals around 0. 1 Hz, and determining their phase in each pixel and for each condition, both the preferred bar location and the hemodynamic delay could be determined for each pixel, yielding azimuth maps as in (Figure  2E ). Similarly, elevation maps were obtained in a subset of the animals using a horizontal bar drifting vertically. These maps coincided with those obtained in previous studies  (Marshel et al. , 2011) , so we could determine V1 border as the limit between pixels displaying and not displaying retinotopy. To localize the auditory cortex, the reflected light was collected at 20 Hz by a CCD camera (Smartek Vision, GC651M) attached to a custom-made macroscope. The focal plane was placed 400 mm below superficial blood vessels. A custom-made Matlab program controlled image acquisition and sound delivery.
Eye tracking and trial filtering Left eye measurements (eye size, pupil diameter, pupil movement) were made by tracking the eye using a CCD camera (Smartek Vision, GC651M). A Python software was used to capture images from the camera at 50 Hz, synchronized with the cortical recordings. These movies were analyzed off-line using custom automatic Matlab programs that traced the contours, first of the eyelid, second of the pupil. The eye lid shape was approximated by two arcs and involved the estimation of 6 parameters (four for the coordinates of the two points where the arcs join, two for the y-coordinates of the crossings of the arcs with the vertical line at halfway between these two points; see Figure  2C , bounds for the parameters were set by hands and appear in yellow). The pupil shape was approximated by an ellipse, described by four parameters (center x and y, radius and eccentricity).