dataesr/scientific-paragraphs-categorization huggingface dataset:
Overview Figure  2  presents the monthly averaged AOD at 500 nm, Ångström exponent between 440 and 870 nm and the FMF (fine mode fraction, the fraction of fine mode AOD to total AOD) in Kashi site from 2013 to 2017. The data are derived from SONET network. The highest AOD occurs in spring, i. e. March and April, while the lowest values occur in summer time, i. e. June and July. The Ångström exponent is positively correlated to the FMF and negatively correlated to the AOD. The lowest mean Ångstöm exponent occurs in March and April, indicating that dust particles are dominant due to the seasonal increase of dust activities in this period  (Littmann, 1991; Qian et al. , 2002). In December and January, the Ångström exponent and FMF increase significantly, which proves that fine particles are an important aerosol source in Kashi.
Computational analytical methods have a rising potential to capitalise on this rich data but they are still at their infancy and are not broadly used for this type of problems. Classical multivariate methods, including Principal Component Analysis (PCA) or Principal Coordinate Analysis (PCoA), have been widely used for single omics datasets, such as 16S or metabolomic data  (Poirier et al. , 2016; Wang et al. , 2017). However, such methods are exploratory and do not fully characterised the microbial communities playing a role in the biological systems being studied. Moreover, these methods are not currently used to integrate information from different omics experiments. Novel computational and statistical methods are becoming available to fully harvest the large amount of data generated in multi-omics experiments  (Lê Cao et al.
, 2009; Singh et al. , 2019; Straube et al. , 2017). These methods aim to extract complementary information from several datasets for a holistic understanding of the interplay between the different levels of information. Several AD studies have already shown the utility of using omics technologies to decipher the anaerobic microbial population  (Amha et al. , 2018; Hassa et al. , 2018). Analyses of a single omics are routinely carried out  (Bize et al. , 2015; Cai et al. , 2016)  but the use of several paired omics on the same samples, and their statistical integration are rare currently. For example, Beale et al. applied metagenomic and metabolomic approaches to obtain new insights on the diversity and activity of the anaerobic population after stress  (Beale et al. , 2016)  but no data integration was conducted between the two approaches.
However, correlation between transcriptomics and metabolomics has already been used to identify important metabolites and genes for example in fungal or plants  (Askenazi et al. , 2003; Hoefgen and Nikiforova, 2008). Our study is the first of its kind in the anaerobic bioprocess field to investigate the link between microbial activity from 16S rRNA sequencing data and patterns of substrate degradation from metabolomics data. We propose a statistical framework to integrate these datasets to identify associations, or correlations, between microbial activity and metabolites specific of substrates degradation. To that end, 27 anaerobic bioreactors were fed with binary mixtures of three substrates of different chemical composition (sludge and grass, or sludge and fish) at different proportions.
The cDNA was quantified using Qubit 2. 0 fluorometer (ssDNA assay kit, Invitrogen, Life Technologies). Archaeal and bacterial hyper variable region V4-V5 of the 16S rRNA gene were amplified as cDNA, and these amplicons were then sequenced according to the protocol described by  Madigou et al. (Madigou et al. , 2019). The raw sequences were deposited in the NCBI databases under the project accession number PRJNA562430. FROGS (Find Rapidly OTU with Galaxy Solution), a galaxy/CLI workflow  (Escudié et al. , 2018) , was used to generate an OTU count matrix. R CRAN software (version 3. 5. 1) was used for alpha diversity analysis with Shannon method using phyloseq R package (version 1. 20. 0). Proportions of archaeal and bacterial OTUs abundances were scaled per sample and OTUs that exceeded 1% in terms of relative abundance in at least one sample, were selected for the remainder of the analysis and square-root transformed.
The detection was performed in full scan over an m/z range from 50 to 500 at a resolution of 100 000. A sample consisting of the supernatant from the digestion of anaerobic sludge was used as a quality control and injected every 10 experiment samples. Blank samples were injected every 10 samples, and an equimolar mix of the samples was injected every 5 samples. The raw data obtained from the LC-HRMS analyses were transformed into mzXML files using MSConvert (ProteoWizard 3. 0). The XCMS R package (version 1. 52. 0) was used to process the data  (Smith et al. , 2006). The method centWave was used to determine chromatographic peaks (region of interest -ROIs) with a m/z error of 10 ppm and a peakwidth between 20 and 50 seconds. The ROIs found in different samples were grouped using the group method with a bandwidth of 30.
Retention times from the same ROI groups were unified across samples using the orbiwarp method. A second grouping was carried out using a bandwidth of 25. Finally, missing ROIs in the samples were filled using the fillPeaks method. Initial metabolite identification was performed based on the comparison of the accurate molecular mass measured by LC-HRMS with the corresponding values found in the online databases HMDB, LipidMaps, and PubChem  (Fahy et al. , 2007; Lee et al. , 2018; Wishart et al. , 2018). In addition, for selected compounds, a confirmation of the metabolite identification was performed by MS/MS fragmentation, and followed by the comparison of the acquired MS/MS spectra with the theoretical spectra from the online databases HMDB and MassBank  (Horai et al. , 2010).
Statistical analysis and data integration Multivariate data analysis was carried out using the mixOmics R package , using a suite of component-based methods to identify groups of microorganisms and metabolites degradation with coordinated patterns. First, each dataset was analysed independently using an exploratory (unsupervised) approach, or a supervised approach to identify key OTUs and metabolites prior to data integration as follows. Unsupervised analysis on the 16S data. Sparse Principal Component Analysis (sPCA,  (Shen and Huang, 2008) ) was applied on all the samples of the microbial dataset (16S rRNA, 22 samples) to evidence main sources of variation and identify OTUs that most contribute to each principal component. Briefly, sPCA is based on PCA that includes a LASSO penalty for variable selection.
, 2017). Clustering. Once selected, the abundances of the OTUs and the metabolites selected with sPCA and sPLS-DA respectively were clustered using hierarchical clustering (using Ward's method and Manhattan and Euclidean distances respectively for each data type), then represented in heatmaps (heatmap. 2 function from gplots R package, version 3. 0. 1). Data integration of the selected OTUs and metabolites. The aim of this analysis is to identify patterns of different variables (microbes and metabolites) characteristics of the different conditions. Thus, this analysis included all 22 samples representative of the different feeding conditions. We calculated the metabolite degradation rate data as the ratio between the metabolites abundances at days 14, 21 or 28, relative to day 0. Both datasets were then integrated using Partial Least Square (PLS) regression method with a canonical mode  (Lê Cao et al.
, 2009)  to identify groups of OTUs and degraded metabolites that are highly correlated. Briefly, PLS weights variables from both datasets optimally using loading vectors, so that their linear combination (called PLS components) is maximally correlated and thus extract similar patterns across datasets (see supplemental figure  S2 A  and B ). Loading vectors correspond to the weight assigned to each variable to define the PLS components. We subsequently applied hierarchical clustering on the loadings vectors to identify clusters of metabolites and OTUs variables with similar patterns of degradation rate or activity across the different types of substrate mixtures. As the correlation structure between high-throughput datasets can be highly spurious, we then further assessed the strength of the association between the variables assigned to each cluster using the proportionality distance (R package propr  (Quinn et al.
Sparse PCA highlighted the main sources of variability in the 16S rRNA data set, while identifying key OTUs driving this variation. We selected 43 OTUs, which in combination explained 67 (first component) to 85% (two components) of the total microbial community variance in the data. The sample plot in Figure  3  showed the strong influence of the feeding composition on the microbial community. The samples were regrouped according to the major co-substrate, and regardless of the sample collection time. This result suggests that the variance due to time within conditions is low, and active microbial community over time is relatively stable. The relative abundance of the selected microorganisms was compared between the different mixtures (Figure  4 ). We observed differences in the active archaeal community depending on the feeding type.
Substrates degradation dynamics We studied the temporal dynamics of degradation of the different substrate mixtures by analysing the metabolic fingerprint of the degradation in the bioreactors using an HPLC-ESI-HRMS instrument. After data examination with XCMS, a total of 267 regions of interest (ROI) were detected. ROI designate chromatographic features (m/z) in the spectrum. Sparse PLS-DA identified 70 ROIs out of the 267 ROIs specific to each substrate (sludge, grass, fish) initially present at day 0. The classification error rate from the sPLS-DA was 0, indicating that all samples could be perfectly assigned to their respective groups using leave-one-out cross validation based on these 70 ROIs. The ROIs were then clustered into four groups according to their intensities at day 0. The degradation pattern of the selected ROIs was then examined for every mixture across all time points.
This can be explained as some metabolites may not be easily degraded, or may be a product of degradation of other metabolites. for bioreactors containing fish, 14-21 for bioreactors containing grass and 14-21-28 for bioreactors containing sludge. For each waste mixture and date, triplicates bioreactors numbered 1, 2 and 3 are indicated. Intensity ranges from blue (low) to red (high). The putative identification of the ROIs based on their m/z is given in the Table  S2. Out of the 70 selected ROIs, 19 metabolites were identified, whilst the rest could not be assigned. The metabolites assignment step in MS metabolomics analysis is a traditional bottleneck  (Longnecker et al. , 2015). Our study was further hampered by the high structural diversity of the compounds found in bioreactors, the lack of databases specific for anaerobic digesters, and the absence of relevant literature of metabolomics analysis on anaerobic bioreactors.
Correlation between microbial activity and substrates degradation pattern Statistical data integration analysis was carried out between the microbial and metabolites datasets to identify potential degraders of specific metabolites. As far as we know, this type of analysis has never been applied in the anaerobic digestion field. Correlation studies have been used to relate metabolite profiles to microbiome in gut studies  (Han et al. , 2019; McHardy et al. , 2013). For example,  Han et al (2019)  estimated the correlation between cecal compounds and bacteria profiles acquired respectively by LC/MS and 16S RNA sequencing analyses using Spearman's correlation. However, in datasets containing a large number of variables, such approach is highly likely to result in spurious correlations. One novelty in our study is to use component-based approaches, such as multivariate PLS method that are well suited for a high correlation structure within and between datasets, whilst being able to highlight key associations between variables from each dataset.
As described in the methods section, we considered the metabolites degradation rates relative to day 0 in our analysis. The sample (ordination) plots from PLS (Figure  S2 A-C ) show similar patterns for microbial and metabolites datasets, suggesting that an underlying correlation structure between the two datasets exists and can be extract with such method. The correlation circle plot (Figure  S2 D ) projects the variables into the space spanned by the PLS components to visualise simultaneously the active microorganisms and the metabolites responsible for this ordination of the samples  (González et al. , 2012). In particular, active microorganisms and metabolites with a high degradation rate projected close to each other on this graph are correlated, as they contribute equally to define the PLS components.
Funding This work was supported by the  National Research Agency  (grant number  ANR-16-CE05-0014 ) as part of the  Digestomic  project. Kim Anh Lê Cao ,  Olivier Chapleur  and  Laëtitia Cardona  scientific travels were supported in part by the  France-Australia Science Innovation Collaboration (FASIC)   Program Early Career Fellowships  from the  Australian Academy of Science  (grant number  39417TM ). Kim Anh Lê Cao was supported in part by the  National Health and Medical Research Council (NHMRC) Career Development fellowship  (grant number  GNT1159458 ).
The analytical description of the topside ionosphere included in the NeQuick model is studied in detail. First, the modeled scale height behavior is analyzed at infinity and for the lowest part of the topside region; in the latter case, the analysis is done through an expansion in Taylor series near the F2-layer peak. Moreover, the significant influence of the three NeQuick topside parameters in the modeling of the topside profile is investigated in detail and, in particular, it is shown that for the lowest part of the topside the model assumes a linearly increasing trend of the topside scale height. Second, the topside formulation is inverted to derive a fully analytical expression of the topside scale height as a function of the electron density and F2-layer peak parameters. This expression has been applied to a selected and very reliable dataset of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)/FORMOSAT-3 radio occultation profiles.
In addition, we show how it is possible to analytically invert the semi-Epstein formulation (on which the NeQuick topside is based) to derive the topside scale height as a function of measured parameters. This new methodology has been applied to a dataset of selected Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO profiles to calculate the topside scale height and then to model it by considering a linear dependence on the altitude. The linear dependence, validated against COSMIC topside profiles, is shown to be appropriate to model the lower part of the topside but fails to capture the behavior of the topside at high altitudes.
A. COSMIC/FORMOSAT-3 RO Dataset Selection COSMIC/FORMOSAT-3 is a constellation made up of six microsatellites launched on April 15, 2006 into a circular orbit (with 72°of inclination) at about 800 km altitude and a separation angle of 30°in longitude between neighboring satellites  [32]. The mission is a collaborative project between the National Space Organization in Taiwan and the University Corporation for Atmospheric Research in the United States. Each satellite carries a global positioning system (GPS) RO receiver capable of measuring the phase delay of radio waves from GPS satellites as they are occulted by the Earth's atmosphere and thus providing an accurate determination of the ionospheric vertical electron density profile  [33] ,  [34]. The calculation of the topside scale height requires very reliable topside profiles.
In order to perform our investigation using topside RO profiles under the best possible colocation conditions with corresponding ionosondes, in space and time, we have compiled a very reliable dataset by selecting RO COS-MIC profiles collocated (within 1°in latitude and longitude) and simultaneous (within 7. 5 min) with ionosonde measured profiles for which both NmF2 COSMIC ≈ NmF2 Ionosonde and hmF2 COSMIC ≈ hmF2 Ionosonde matched within 5%. Ionosonde data used in this study were downloaded from the Digital Ionogram DataBASE by means of the SAO Explorer software developed by the University of Massachusetts, Lowell  [35]. To maximize the accuracy, only manually scaled ionograms were used to obtain the electron density profiles. In standard RO data inversion, the inversion error is systematically accumulated from the top of the RO inverted profile to the bottom; thus, when NmF2 COSMIC matches NmF2 Ionosonde , it is expected that the full topside of the RO profile is correctly estimated  [36].
A. Statistical Validation of the Topside Scale Height Linear Approximation For each of the selected COSMIC RO profiles, tTEC is calculated by integrating topside electron density values from hmF2 to the satellite height as follows: tTEC measured = h COSMIC hmF2 N e,COSMIC dh. ( 28 ) Moreover, each topside RO profile provides measured values of NmF2 and hmF2 that are used to model the topside profile through  (1)  with H Linear values obtained applying  (25)  to H Epstein values calculated through  (24)  for the considered RO profile, as described in Section III-B. tTEC modeled values are then calculated as follows: tTEC modeled = h COSMIC hmF2 N e,Epstein Linear dh. ( 29 ) Then, tTEC root mean square error (RMSE) and normalized RMSE (NRMSE) values were calculated, expressed in TECU (1 TECU = 10 16 el/m 2 ) and in percentage, for the entire selected COSMIC dataset where N = 382 and runs on the selected COSMIC profiles.
By considering a selected dataset made by 382 COSMIC RO profiles, H 0 and g values have been derived through a linear fit of retrieved topside scale height values, as the corresponding intercepts and slopes; this approach allows us to get very reliable H 0 and g values from COSMIC (or other missions') RO profiles and also from ISR topside profiles. When considering only the lowest topside region (from hmF2 to about 800 km of altitude), this study showed that the linear approximation of the topside scale height, embedded in NeQuick, is very reliable. However, to describe the entire topside profile the use of higher order terms is essential. With regard to this issue, in the NeQuick model, the topside behavior for altitudes well above hmF2 is taken into account through the inclusion of the r parameter.
However, retrieving information on the r parameter from COSMIC RO profiles is not feasible because higher order terms have a very little importance at COSMIC probed altitudes. This article, studying in depth some features of the NeQuick topside formulation, complements the existing literature about the model and consequently it is considered beneficial for NeQuick and IRI users interested in the topside modeling. Thanks are due to the COSMIC/FORMOSAT-3 team for making freely available Radio Occultation data by means of COSMIC Data Analysis and Archive Center (CDAAC). 2  This publication uses data from ionospheric observatories made available via the public access portal of the Digital Ionogram Database 3 of the Global Ionosphere Radio Observatory in Lowell, MA. The authors are indebted to the observatory directors and ionosonde operators for the significant investments of their time, effort, expertise, and funds needed to acquire and provide measurement data to academic research.
Acknowledgements The authors are grateful to  Maryse Leconte ,  Jacky Ezagal  and  Jean-Eudes Bavay  ( INRAE Université de Tours, UMR BOA, France ) for their technical assistance, to  Christelle Hennequet-Antier  for expert advice on statistical analysis, and to  Philippe Didier  for the care of experimental birds at  INRAE Poultry Experimental Facility  (2018, https ://doi. org/10. 15454/1. 55723 26250 88729 2E12 ). The research was supported by a research grant from  Institut Carnot "France Futur Elevage ", and was part of the  PhD project of Audrey Gloux.
Statistical Analysis All calculations were made using SAS for Windows (Version 9. 4; SAS Institute Inc), with the level of statistical significance set at P <. 05. Descriptive data analysis was conducted depending on the nature of the considered criteria. For quantitative data, this included number of observed (and missing, if any) values, means, standard deviations, medians, first and third quartiles, and minimums and maximums. For qualitative data, this included the number of observed (and missing, if any) values and the number and percentage of patients per class. Comparison between unpaired variables was performed using the chi-square or Fisher exact test for categorical variables and the Student or Wilcoxon test for quantitative variables (normality of variables checked). Comparison between paired variables was assessed using the chi-square or Mantel-Haenszel test for categorical variables and the paired Student test or Wilcoxon signed rank test for quantitative variables.
received research support, consulting fees, speaking fees, and royalties from  Arthrex. J. -M. F. has received research support, consulting fees, and speaking fees from  Arthrex. M. T. has received research support, consulting fees, and speaking fees from  Arthrex. A. S. has received consulting fees from  Arthrex. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto. Ethical approval for this study was obtained from the  CPP Sud-Est III Comite de Protection des Personnes  (Research ethics committee approval No. 2016-007B ).
The densities of pure CO 2 (ρ C02 )and H 2 O (ρ H2O ) are approximated at a limited P-T range using tables produced by NIST Chemistry WebBook (https://webbook. nist. gov/chemistry/). Equation (  2 ) is subjected to two boundary conditions: concentration gradients are equal to zero at the outer surfaces of the cell mimicking symmetry of the system. At r = R(t) volatiles in magma are in chemical equilibrium with the bubble. Thus, c s ¼ c eq s p; T; x b CO2 À Á. We use D-compress software  40  in order to calculate equilibrium concentrations. D w ¼ c w Á exp À8:56 À 19110 T À Á ; D c ¼ exp À13:99 À ð17367þ1:945P g Þ T þ c w Áð855:2þ0:271P g Þ T ð5Þ The nucleation time of bubbles t n from a supersaturated melt is related to the bubble number density BND via the nucleation rate I(m -3 s -1 ): t n = BND/I According to classical nucleation theory  41  , I increases extremely fast with oversaturation pressure ΔP: I $ expðÀ1=ΔP 2 Þ.
(  3 ) and (4) allows to calculate all parameters on the bubble-melt interface. Then, concentration distribution in the whole domain is calculated during backward substitution. We found this method stable and computationally efficient in comparison with explicit methods that require extremely small timesteps for stability reasons. Estimation of magma composition. In order to estimate magma compositions in the deep magma reservoir we used "Petrolog" software  45. Reverse crystallization from a more evolved magma (sample 12KY-108-1, 1987 AD eruption  46  ) was performed. The starting pressure is set to atmospheric level and magma is H 2 Osaturated. Our simulations reveal total amount of mineral phase of 20% for the starting composition, which is in a good agreement with the measurements on the samples  47.
Acknowledgements We thank  Yuri Taran ,  Patrick Allard ,  Roberto Moretti , and  Daniel Neuville  for helpful discussions as well as  Brian Shirofor  his very helpful revision of the manuscript. This study was supported by the  Russian Ministry of Education and Science  (Grant no. 14. W03. 31. 0033 ), and by the  European Research Council (ERC)  under the  European Union   Horizon 2020 Research and Innovation Programme  (Grant agreement 787399-SEISMAZE).