dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017). To address the issue of large size DB, different strategies were developed such as pseudo-metagenome approach  (Heyer et al. , 2017) , partial searches against smaller sub-DB  (Muth et al. , 2015a; Tanca et al. , 2016) , or the two-round DB searching method  (Jagtap et al. , 2013). The two-round DB searching method consists in searching experimental MS against a refined database composed of the protein sequences identified in a preliminary error tolerant search, allowing significant increase in the total number of identified proteins. This strategy was extensively used in recent metaproteomics studies  (Russo et al. , 2016; Serrano-Villar et al. , 2016; Deusch et al. , 2017; Gallois et al. , 2018). Regarding metagenomic data, both assembled  (Teeling et al. , 2012)  and non-assembled  (Herbst et al.
Databases Creation and Protein Identification Protein searches were performed with ProteinPilot (ProteinPilot Software 5. 0. 1; Revision: 4895; Paragon Algorithm: 5. 0. 1. 0. 4874; AB SCIEX, Framingham, MA, United States) (Matrix Science, London, United Kingdom; v. 2. 2). Paragon searches 34 were conducted using LC MS/MS Triple TOF 5600 System instrument settings. Other parameters used for the search were as follows: Sample Type: Identification, Cys alkylation: Iodoacetamide, Digestion: Trypsin, ID Focus: Biological Modifications and Amino acid substitutions, Search effort: Thorough ID, Detected Protein Threshold [Unused ProtScore (Conf)]>: 0. 05 (10. 0%). Three DBs were created using the same metagenome (EMBL-EBI Project number: ERP009703, Ocean Sampling Day 2014, sample: OSD14_2014_06_2m_NPL022, run ID: ERR771073) (MiSeq Illumina Technology) and were generated with mPies v 0.
9, our recently in house developed mPies program freely available at https://github. com/johanneswerner/mPies/ (Supplementary Presentation 1;  Werner et al. , 2019). The three DBs were: (i) a non-assembled metagenome-derived DB (NAM-DB), (ii) an assembled metagenome-derived DB (AM-DB), and (iii) a taxonomy-derived DB (TAX-DB) (Table  1 ). Briefly, mPies first trimmed sequencing raw reads with Trimmomatic  (Bolger et al. , 2014). For NAM-DB, mPies directly predicted genes from trimmed sequencing reads with FragGeneScan  (Rho et al. , 2010). For AM-DB, mPies first assembled trimmed sequencing reads into contigs using metaSPAdes  (Nurk et al. , 2017)  and subsequently called genes with Prodigal  (Hyatt et al. , 2010). For TAX-DB, mPies created a pseudo-metagenome using SingleM  (Woodcroft, 2018)  to predict operational taxonomic units from the trimmed sequencing reads and retrieved all the taxon IDs at genus level.
Database Choice Affects the Total Number of Protein Identification The two-rounds search strategy commonly used in recent metaproteomics studies  (Russo et al. , 2016; Serrano-Villar et al. , 2016; Deusch et al. , 2017; Gallois et al. , 2018)  significantly reduced the size of protein search DBs, which in turn increased the total number of identified proteins with both AM-DB and NAM-DB (Table  1 ). Overall, the total number of identified proteins was found to be consistent with other metaproteomics studies conducted in marine oligotrophic waters  (Morris et al. , 2002; Sowell et al. , 2009; Williams et al. , 2012 Williams et al. , , 2013;; Dong et al. , 2014). NAM-DB led to greater protein identifications (gelbased: 714, gel-free: 1,131) than AM-DB (gel-based: 277 and gel-free: 549) and TAX-DB (gel-based: 434 and gel-free: 464) for both proteomics approaches.
CONCLUSION Metaproteomics enables to progress beyond a mere descriptive analysis of microbial community diversity and structure, providing specific details on which bacteria, and which pathways of those key players, are impacted by possible perturbations. Nevertheless, using this powerful tool without fully apprehending the limitations could lead to significant misinterpretations, especially in the case of comparative metaproteomic studies. This study clearly evidenced the implications of critical decisions in metaproteomic workflow. Our findings lead to the general recommendation of diversifying when possible the protein search database as well as protein fractionation, especially if only one condition/ecosystem was studied. A robust diversified workflow allows crossing information from multiple metaproteomes in order to accurately describe the functioning of microbial communities.
ACKNOWLEDGMENTS The authors acknowledge the use of de. NBI cloud and the support by the  High Performance and Cloud Computing Group at the Zentrum für Datenverarbeitung of the University of Tübingen. They want to personally acknowledge  Jules Kerssemakers  ( German Cancer Research Center),  Manuel Prinz , and  Katrin Leinweber  ( Technische Informationsbibliothek, TIB. eu ) for code review, critical thoughts, and software publication advice. This manuscript has been released as a  Pre-Print at bioRxiv  (doi:  10. 1101/697599 ).
ACKNOWLEDGMENTS This work was funded by  Sorbonne Université ,  CNRS , the  FP7-People program  ( CIC -ID: 618643 ) of the  European Union , the  Labex  " MiChem " ( ANR-11-IDEX-0004-02 ), the  Region Ile de France  through the framework DIM Nano-K, and the  French National Research Agency  (project  ANR-17-CE07-0012-01 ).
Arginase is important for parasite survival: deleting the arginase gene is auxotrophic for polyamines  [14]. Being arginase distinct from the mammalian target  [11]  and absolute necessity of the enzyme for survival of the pathogen, it represents a key parasite drug target  [15]. Oxadiazoles have been reported as potent anti-leishmanial agents  [16, 17]. Potent antimicrobial and anti-leishmanial activity are also documented for indolizine-containing compounds  [18, 19]. The MetIDB database was screened in previous work with in silico procedures and ten promising flavonoids were proposed as anti-leishmanial candidates  [20]. In this study, a series of novel oxadiazoles and indolizine containing compounds were synthesized, screened in silico with the Electron Ion Interaction Potential/Average Quasi Valence Number (EIIP/AQVN) filter, followed by ligand-based virtual screening and molecular docking to Leishmania arginase.
From this, many of the same spectral features are visible between both aerosolization modalities, particularly within the phospholipid region of 600 to 800 m/z; suggesting that both modalities follow similar mechanisms of  ion formation, as previously described  20. This is further supported by putative identifications, based on accurate mass measurements, from the LIPID MAPS database  28  , of phospholipids within the mass spectra, Fig. 1b, c , showing the same features are present. These include common microbial cell membrane lipids including phosphatidic acids, phosphatidylethanolamine, and phosphatidylglycerols. Microbial Speciation using Optimised ALDI-MS Analytical Set-Up. Following optimisation of the laser operation parameters, the full repertoire of microbial isolates was analysed, with five analytical measurements taken per isolate, in line with previously reported studies  19, 24.
Each replicate took approximately five seconds, allowing each isolate to be analysed in less than 45 seconds. Acquired spectral data was subjected to background subtraction, mass drift correction, total ion count normalisation, and mass binning to 0. 1 Da bins, as detailed in the supporting information. Preliminary analysis using the MetaboAnalyst 3. 0 platform  29  of the 600 to 1000 m/z range showed that clear and significant separation was evident between the major microbial groupings, mainly bacteria and yeast, and Gram-negative and Gram-positive morphologies (Fig. 4 ). Although PCA modelling is a useful approach to visualise sample groupings in multivariate datasets, we have utilised the Random Forest machine learning algorithm to construct classification models validated using a leave-one-isolate-out cross validation approach.
Use of previously published electrical Diathermy ReIMs Data. Here, ALDI-MS data is compared to previously published electrical diathermy REIMS data  19. This data was collected as previously described using the same mass spectrometry instrument and operating parameters as detailed for the ALDI-MS analysis shown here. This dataset consisted of 25 microbial species, ten of which were analysed using ALDI-MS in this study. The raw data for the subset of ten species used in this ALDI-MS study were extracted and processed using the parameters detailed below. Data and statistical Analysis. To correct for mass drift and remove background mass spectral signals, all. RAW data files were processed using MassLynx software (V4. 1, Waters) through the Accurate Mass Measure tool employing the Automatic Peak Detection option with all options chosen, with lock mass correction against a mass of 554.
2615 with a mass windows of +/-0. 500, and averaged over a total of ten scans. Resulting processed mass spectra were used in subsequent intensity analysis calculations, identification of most intense spectral peaks, and m/z values for accurate mass identification of metabolites. For intensity analysis, mean and standard deviations were calculated from individual intensity values taken for five identified analytical repeats identified through total ion count peaks within the acquisition file. Additionally, raw mass spectral data files were processed using the Offline Model Building (OMB) software (Version 1. 1. 29. 0, Waters) to perform background subtraction, mass drift correction against leucine encephalin lock mass compound (negative mode m/z = 554. 2615), and mass binning to 0. 1 Da within chosen restricted mass ranges.
For statistical analysis, including principal component analysis (PCA), the MetaboAnalyst 3. 0 29 platform was used. After processing of mass spectra, a data matrix was exported from the OMB software and uploaded to the MetaboAnalyst 3. 0 online analysis pipeline where it underwent additional processing for log transformation and Pareto scaling (mean-centred and divided by the square root of standard deviation of each variable). For PCA analysis, the first and second principal components were used in two dimensional plots, with 95% confidence regions displayed using colour shading. For Random Forest analysis, a mass spectral matrix after data pre-processing in the Offline Model Builder Software was imported into the machine learning scikit-learn package  31  , via a bespoke graphical user interface.
Hirshfeld Surface Calculations Hirshfeld surfaces analyses  [11]  and their associated twodimensional fingerprint plots as a powerful tool leads not only to study the solid-state behavior of molecules, but also to qualify the different types of inter-molecular inter-actions in the title compound. Molecular Hirshfeld surfaces, which were generated by the Crystal Explorer program  [12, 13] , were constructed from CIF file in order to analyze crystal structures. Molecular Hirshfeld surfaces are built based on the distribution of electrons calculated as the sum of spherical atom electron densities  [14, 15]. Moreover, the d norm normalized contact distance property between molecules in 3D space based on both d i and d e (The distance from Hirshfeld surface to the nearest nucleus inside and outside, respectively) and the van der Waals radii (ri dvW and re dvW) of the atoms given by the following equation:  [16, 17].
X-ray Crystallography A new single crystal was mounted at 293 K on a Bruker ApexII Kappa CCD diffractometer using AgKa radiation (λ = 0. 5608 Å). A number of 28,517 reflections were measured, out of which 4650 were independent and 4211 had d norm = d i -r vdw i r vdw i + d e -r vdw e r vdw e intensity I > 2σ(I). The crystal structure was solved by direct methods using the program SHELXS-2013  [22]. However, the antimony atoms were determined by the method of Patterson with SHELXS-2013 program  [22] , while the other atoms such as the organic moieties were found from successive Fourier calculations using SHELXL-2013  [23]. The hydrogen atoms of the CH, NH and NH 2 groups were fixed geometrically by the appropriate instructions of the program (AFIX 43, AFIX 43 and AFIX 93, respectively)  [23].
Calculation of solar positions In the case of a perfectly oriented instrument, the suntracking can be done by calculating its positions during the day. A Matlab program using the algorithm of Jean Meeus is used  [23, 24]. Zenithal and azimuthal angles of the Sun are obtained in a topocentric coordinate system. The calculation is made from the geographical coordinates of the tracker, the date, time and offset relative to Coordinated Universal Time (UTC). The zenithal angle θ 2 is measured relative to the local vertical, that is to say the altitude alt = 90 • -θ 2. The azimuthal angle θ 1 is measured in the tangent plane to the Earth from North and eastward (Figure  3 ). This reference is named topocentric coodinate system and is sketched on Figure  3. The algorithm calculates the position of the Sun between -2000 and 6000 years, with an uncertainty of 0.
Thus unlike all other instruments presented in the literature (for which the spectrometer should be placed just next to their servo control captor), there are ≈14 m from our servo system up to our FTIR spectrometer. Our heliostat has enabled us to record atmospheric spectra during 10 h without interruption (no optical realignment, no anarchic response at the zenith), and with a 0. 15 cm -1 resolution. One of those spectra is presented on the top of Figure  10 , and compared with simulations in the 900 cm -1 -1200 cm -1 range. The simulated spectra were obtained with the 4A/OP radiative transfer calculation software using several database notably the GEISA 2011 database  [28, 29]. It has been used to estimate the radiance at ground level. However, it may differ slightly from experimental spectrum because 4A/OP provides two default types of profiles (pressure, temperature, main molecules concentration), corresponding to tropical and polar environments.
Acknowledgements This work was funded by the  ANR   ASTRID  Project # ANR 12-ASTR-0028  called  QUIGARDE  ( 2013-2016 ). The authors are also indebted to the  Délégation Générale de l'Armement  and the  Centre National de la Recherche Scientifique  for financial support of the PhD of  Marie-Hélène Mammez.
Computational detail The stable structures of the CX  [4] -TFSI -complexes have been optimized with the DFT method by using the global hybrid Generalized Gradient Approximation (GGA) functional B3LYP  [15, 16, 17]. This method together with the empirical Becke and Johnson damping dispersion corrections D3BJ in combination with the 6-31þG(d) basis set  [18, 19]  is implemented in Gaussian 09 program  [20]  and Gauss View  [21]  as a visual program. The binding energies of CX  [4] -TFSI -complexes are calculated including the BSSE (Basis Set Superposition Error) counterpoise (CP) corrected energy of Boys and Bernardi  [22] , which considers the fragments relaxation terms and the changes of the conformations in such geometric form. The binding energies are given by the following formula: ΔE CX[4]-TFSI-¼ E CX[4]-TFSI--E CX[4] -E TFSI-þ BSSE where E CX  [4]  , E TFSI À , and E CX  [4] -TFSI -are the total energies of the host and the guest or the host-guest molecules.
Spectroscopic imaging is expanding thanks to new instrumental concepts, technological developments, easier use and lower costs. This leads to collect and to handle huge hyperspectral databases. Among data providing useful information, there may also be unwanted information (i. e. , either biased or irrelevant with respect to the information one wants to retrieve from the analysis). The classical approach aims at building a spectra library, which contains both informative spectra and outliers, so as to train learning algorithms. However, reference spectra cannot always be acquired, especially for samples prone to ageing or changes when exposed to humidity, temperature. Thus, building a library with reference spectra is not always possible. To handle this issue, a new supervised method (SSMS for Supervised Selective Method based on SIMPLISMA) has been designed and is described in this article, to identify and exclude unwanted spectra from the resolution process when no library is available.
Introduction Recent developments in advanced techniques for material analysis, such as characterizations in 3D  [1, 2]  or in complex environments  [3] , and an easier access to cutting-edge techniques lead to deal with a larger and larger amount of data (large scanned areas and volumes, monitoring of the evolution of a sample vs. time, large number of replicates. ). In particular, the use of spectroscopy techniques (e. g. , Raman  [4] , NMR  [5]  or terahertz  [6] ) requires to handle hyperspectral databases. More specifically, Fourier-Transform Infrared (FT-IR) microscopy is employed to obtain chemical mapping of ma-terials and has known very interesting developments in the past few years  [7, 8, 9]. Along with spectra providing useful information on the samples composition, these hyperspectral databases may also contain biased data (outof-measurement ranges, noise, artifacts.
) or unwanted information (e. g. , linked to mounting resin or stand). Therefore, such big data analysis requires a thorough pre-processing stage to exclude non useful information and keep only the relevant spectra. Data, which are out-of-range or characterized by a too low signal-to-noise ratio, are relatively easily removed from databases. However, the identification of misinformative or unwanted information is based on preconceptions on their spectral characteristics or on their detection by learning algorithms (e. g. Random Forest classifier, Support Vector Machines, artificial Neural Networks). The latter approach is highly efficient but requires the algorithms to be trained on an available database. Such IR spectral libraries including relevant spectra and outliers are scarce, especially for inorganic materials.
In this work, a home made selective robust method, called SSMS (Supervised Selective Method based on SIMPLISMA), has been developed to eliminate non useful spectra based on statistical considerations without needing any training database. SSMS enables to identify irrelevant spectra without any a priori knowledge of the material. Such an automatic treatment becomes essential to enable the analysis of large databases. SSMS algorithm is detailed and illustrated on the obtaining of chemi-cal maps issued from FT-IR microscopy acquisitions. The use of a particular set-up, called static attenuated total reflection (s-ATR), is shown to be an efficient tool for the mapping of inorganic materials. A granular composite used for construction applications, containing a very sensitive ettringite binder, was successfully studied avoiding cross-contamination and surface degradation issues.
Acquiring one FT-IR spectrum per loca- tion, a 3D data matrix D 3D of dimensions [x × y × m] is constructed [19]. x and y are the numbers of acquisitions in the two spatial directions ( x, y). m is the number of wavenumbers for which absorbance values are recorded. As most chemometrics tools are not designed to directly handle 3D matrices, the two spatial indexes are combined to store the data in a 2D matrix: [x, y, m] → [n, m], n ∈ [1; x × y] (3) D 3D [x×y×m] → D [n×m] (4) Finally, the application of the Beer-Lambert law to the matrix of data leads to consider the following bilinear problem: D [n×m] = C [n×p]. S t [p×m] + E [n×m] (5) S is the matrix of spectra relative to each compound, C the matrix of concentrations and E the matrix of residual errors  [20].
Thus, a first step is needed to determine the number of compounds and their relative spectra. The resolution of this step will be described in section 4. 1. It permits to obtain an over-determined system, which can then be solved to find the maps of relative concentrations, as detailed in section 4. 2. In this work, all data preprocessing and resolution techniques were implemented in a home-made algorithm encoded with Matlab R2015a using Curve Fitting, Global Optimization and Statistics & Machine Learning toolboxes.
However, the use of the s-ATR set-up induces artifacts (Table  1 ), resulting in biased spectra, which would not be identified using this classical approach. Due to their singularity, these biased spectra would be assimilated to fictive compounds. This could lead to misjudge the number of compounds p and hence the matrix S of spectra. To deal with this problem, the algorithm Supervised Selection Method for Spectra (SSMS) was developed to solve the whole issue at once: solving the two steps (i) and (ii) all together including the detection of s-ATR artifacts and the exclusion of related spectra. SSMS uses SIMPLISMA, an iterative algorithm designed by Windig et Guilment  [22]  to select the most singular spectra out of a database. In SIM-PLISMA, at each iteration, the purest spectrum is selected as the one which forms the "most orthogonal" hypercube with the spectra already selected.
ii Application of SIMPLISMA to select a number P 0 ≈ αP all of spectra out of the preprocessed database (i. e. , initial database -outlier spectra) (Fig. 3 , Step 5 : purple). The coefficient α ensures that the number P 0 of selected spectra is small enough to be easily and accurately checked by the operator, but large enough to contain at least one spectrum representative of each actual compound and of each unwanted spectrum. iii Examination of the P 0 spectra, supervised selection and exclusion of the P 1 spectra presenting s-ATR artifacts among P 0 (Fig. 3 , Step 6 : half light blue, half purple symbolizing the spectra first selected within P 0 (purple ones) and then excluded by the operator). At this stage, it is also possible to select and exclude spectra, which do not present any interest for a specific study (e.
g. , those of a mounting resin). iv Back to the preprocessed data matrix (i. e. , initial database -outlier spectra): all unwanted spectra, presenting a correlation coefficient > 0. 95 with the aforementioned P 1 spectra are identified by statistical considerations in the preprocessed data matrix (i. e. , initial database -outlier spectra) and excluded from the bilinear problem (Fig. 3 , Step 7 : light blue). At this stage, a reduced matrix is obtained, which only contains relevant spectra with respect to the study. v PCA on the reduced database (i. e. , obtained after Step 7 ) to find p (Fig. 3 , Step 8 : green). vi Selection of the p most different spectra using SIMPLISMA on the remaining P 0 -P 1 spectra (Fig. 3 , Step 9 ). vii The p spectra selected previously form the first estimation of the matrix of spectra S ((Fig.
Iterative process: Under these constraints, the iterative process consisted in successive evaluations of C and S until convergence was achieved: • Evaluation of C: min C D -C. S t    0 -2 ≤ C ij ≤ C local rank,ij + 2 1 -3 ≤ p j=1 C ij ≤ 1 + 3 (9) • Evaluation of S: min S D -C. S t 0 -1 S ij L sensor + 1 (10) • Evaluation of the quality of the reconstructed data (Lack of Fit): LoF = D -C. S t 2 D 2 (11) In this study, the function lsqlin in Matlab R2015a was used to deal with the constrained linear least-squares problem. If spectra normalization had been chosen instead of imposing closure, the function fmincon could have been used to deal with non linear constraints.
SSMS applied on a granular composite with an ettringite binder The new methodology developed in this work is illustrated step by step on a s-ATR map (48 X 82 pixels, i. e. 3936 spectra) acquired on a mineral  It has also led to exclude 1654 spectra (42. 02% of the database) because of a too low signal to noise ratio (Fig. 3  step 3 , Fig. 4. g). Then, using a PCA study on the remaining spectra, P all was found to be equal to 4 (Fig. 3  step 4 ). To make sure that at least one spectrum for each actual compound and one spectrum for each possible artifact was selected, α was set to 4. Hence, using SIMPLISMA, P 0 = 16 spectra have been selected in the database (Fig. 3 step 5  ). Thus, with α equal 4 (same order of magnitude as P all ), it became possible for the operator to accurately examine each of the P 0 = 16 spectra out of the 3936 initial spectra.