dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure and chromatographic data from known metabolites, obtained from their SMILES and from chemical standards analyzed using a particular LC method, are used to generate a model. It identifies relations between chromatographic reten-tion and the molecular descriptors, theoretically allowing to predict Rt for any metabolite whose molecular descriptors can be calculated  [39]. In the present work, we aim to describe the workflow permitting the generation of a large-scale in-house database of Rt and CCS predicted with published machine learning models. Integration of these data with other sources of information, such as accurate mass, MSe fragmentation, and isotope pattern for facilitating the identification of compounds, is illustrated in an application to toxicology data.
Chemicals and Standards Solutions used were: acetonitrile (UPLC-MS grade, Waters, Milford, MA, USA), methanol (UPLC-MS grade, Waters), Milli-Q purified water (Millipore, Burlington, MA, USA), formic acid (UPLC-MS grade, Honeywell, Charlotte, NC, USA), ammonium formate (Reagent-grade, Sigma-Aldrich, St. Louis, MO, USA), and chloroform (VWR Chemicals, Radnor, PA, USA). Chemical standards (MSMLS) were purchased from Sigma-Aldrich. This library was chosen for the broad chemical and functional diversity of metabolites included. It contains 634 standard metabolites sampled into seven 96-well plates at 5 µg per well, including 37 duplicates. An associated spreadsheet with information, such as metabolite identification, molecular formula, and SMILES was used to build our targeted database. The compounds were dissolved using two different solutions (5% methanol for plates 1-5 and chloroform:methanol:water 1:1:0.
Data analysis of the mixes was semi-automatically performed through the Unifi software (version 1. 9. 4. 053 Waters MS Technologies, Manchester, UK) to obtain the Rt, response, and CCS of the standards after manual verification of the peak integration. Adducts considered were [M+H]+, [M+K]+, [M+Na]+, [M+Cl]-, [M+HCOO]-, and [M+CH3OO]-. Accurate mass, Rt, CCS, and fragmentation patterns were used to build a targeted database of LC and MS properties.
CCS Prediction CCSBase is an electronic interface (https://CCSbase. net) (accessed on 6 May 2022) for accessing the CCS predictive model  [29]. It calculates the predictive CCS values of adducts using SMILES. Original performances of CCSBase were described by Ross et al. [29]  with an R 2 score, a mean absolute error, and a root mean squared error at 0. 991, 3. 83 Å 2 , and 5. 48 Å 2 , respectively. In the study, all adducts considered by CCSBase, namely  and [ M]-, were taken in account. A batch prediction was performed for a dataset of metabolite structures freely available in HMDB v4. 0  [11] , which gathers up to 114,000 human metabolites, covering the majority of untargeted metabolomic data sets. A linear regression was performed comparing predicted and measured CCS of adducts from standard compounds.
Measured CCS were included in this comparison if the standard were listed in HMDB, and if they presented common adducts with predictions. The coefficient of determination R 2 between the predicted and the experimental CCS data and mean absolute error permitted to evaluate the model. The best fit of linear regression was calculated, with an interval of +/-2 standard deviations (SD). All statistical analyses and figure production of this manuscript were conducted under R language and environment [40]. [M+H]+, [M+K]+, [M+Na]+, [M+Na-2H]-, [M+NH4]+, [M]+, [M-H]-,.
Reduction of the Occurrence of False Positive Annotations in Untargeted Metabolomics: Application to Toxicology Data We analyzed LC-HRMS data from an ongoing study assessing the potential toxicity of tobacco cigarette fumes on human bronchial epithelial BEAS-2B cells to demonstrate the relevance of the predicted large-scale database of collision cross-section and retention time to metabolomics. The exposure protocol was adapted from Dusautoir et al. [41]. Briefly, BEAS-2B cells cultured at air-liquid interface was exposed to four puffs of tobacco cigarette emissions or to sterile air (negative control) in four replicates per exposure. Twenty-four hours after exposure, cell metabolism was quenched by the addition of ice-cold methanol:water (80:20, v/v) mixture. Cells were harvested using a cell scraper.
Deproteinization was performed by adding the same methanolic mixture, vortexing, and centrifuging at 14,000× g at +4 • C for 15 min. Supernatants were concentrated to dryness with speedvac and reconstituted before injection in a water:methanol (90:10, v/v) mixture. After metabolomic analyses, LC-HRMS data were analyzed with Progenesis QI (Nonlinear Dynamics, UK) for feature extraction. Data normalization and statistical analyses were conducted under the R environment [40] on the features detected in ESI+ and ESI-. When searching against HMDB, two identification strategies were evaluated:  (1)  with an m/z (tolerance set at 5 mDa), isotope and fragmentation match only, and (2) with an m/z, isotope, fragmentation, CCS, and Rt match of the created predicted large-scale database.
Analysis of Standard Compounds and Generation of an In-House Database A total of 542 standards were originally used in the candidate database. Each set of data was manually examined for errors. Figure  1  describes the workflow used for the targeted database construction. The candidate sorting step has allowed the detection of 266 and 301 compounds in ESI+ or ESI-, respectively, corresponding to a total of 380 different metabolites. Accurate mass, Rt, CCS, and fragmentation were used to build a targeted database of LC and MS properties. ducted under the R environment [40] on the features detected in ESI+ and ESI-. When searching against HMDB, two identification strategies were evaluated:  (1)  with an m/z (tolerance set at 5 mDa), isotope and fragmentation match only, and (2) with an m/z, isotope, fragmentation, CCS, and Rt match of the created predicted large-scale database.
Analysis of Standard Compounds and Generation of an In-House Database A total of 542 standards were originally used in the candidate database. Each set of data was manually examined for errors. Figure  1  describes the workflow used for the targeted database construction. The candidate sorting step has allowed the detection of 266 and 301 compounds in ESI+ or ESI-, respectively, corresponding to a total of 380 different metabolites. Accurate mass, Rt, CCS, and fragmentation were used to build a targeted database of LC and MS properties. Metabolites belong to different chemical classes and are mainly carbohydrates, carboxylic acids, lipids, nucleotides, and organoheterocyclic compounds (Figure  2 ) involved in different pathways that reflect the metabolic status in biological matrices of interest.
Metabolites belong to different chemical classes and are mainly carbohydrates, carboxylic acids, lipids, nucleotides, and organoheterocyclic compounds (Figure  2 ) involved in different pathways that reflect the metabolic status in biological matrices of interest. namics, UK) for feature extraction. Data normalization and statistical analyses were conducted under the R environment [40] on the features detected in ESI+ and ESI-. When searching against HMDB, two identification strategies were evaluated:  (1)  with an m/z (tolerance set at 5 mDa), isotope and fragmentation match only, and (2) with an m/z, isotope, fragmentation, CCS, and Rt match of the created predicted large-scale database.
CCS Prediction and Creation of a CCS Database The CCS database was generated according to the workflow described in Figure  3. Predictions were performed for almost 114,000 metabolites from the HMDB v4. 0 database, generating 916,104 CCS adduct values. Results were validated with a validation set composed of 501 measured CCS adduct values from 297 standard compounds in both ionization modes. x FOR PEER REVIEW 6 of 13.
CCS Prediction and Creation of a CCS Database The CCS database was generated according to the workflow described in Figure  3. Predictions were performed for almost 114,000 metabolites from the HMDB v4. 0 database, generating 916,104 CCS adduct values. Results were validated with a validation set composed of 501 measured CCS adduct values from 297 standard compounds in both ionization modes. Figure  4  illustrates the match between predicted and experimentally determined CCS. Outliers were kept as part of the data set in the absence of any evidence that they were the result of an error. The R 2 score of the linear regression achieves 0. 938 and the mean absolute error was calculated at 3. 94 Å 2 , while the SD reaches 6. 11 Å 2 or 3. 36%. The predicted CCS = 0. 95 × measured CCS + 7. 92. The resulting output table of CCS allowed us to build our large-scale in-house reference database.
CCS Prediction and Creation of a CCS Database The CCS database was generated according to the workflow described in  Figure  4  illustrates the match between predicted and experimentally determine CCS. Outliers were kept as part of the data set in the absence of any evidence that the were the result of an error. The R 2 score of the linear regression achieves 0. 938 and th mean absolute error was calculated at 3. 94 Å 2 , while the SD reaches 6. 11 Å 2 or 3. 36%. Th predicted CCS = 0. 95 × measured CCS + 7. 92. The resulting output table of CCS allowe us to build our large-scale in-house reference database.
Rt Prediction and Creation of an Rt Database The Rt prediction workflow is described in Figure  5. In total, 204 compounds from the developed method were selected for the QSRR model; 114 were detected in both ESI+ and ESI-, while 90 were detected only in one ionization mode (45 for each ionization mode). Seven compounds were excluded due to incomplete data in molecular descriptors. Support vector regression (SVR) algorithm based on 113 molecular descriptors presented the best performances, with the R 2 score at 0. 999 and the mean absolute error at 0. 10 min for the training set. The validation set tested by cross-validation (n = 10) validated the model with the following performances: mean of cross-validation R 2 score 0. 898, mean absolute error 0. 81 min, and standard deviation of the mean absolute error 0.
Seventy-six percent of hits were filtered using the predicted large-scale database (Figure  6b ). The percentage of features with only one hit significantly increased with the additional CCS and Rt match (+53%), while the percentage of features with more than 10 hits decreased in the same conditions (-39%) (Figure  6c ). For further identification, possible candidates for each compound are ranked by Progenesis QI on an overall score based on the m/z match, isotope similarity, fragmentation score, CCS, and Rt error (data not shown).
Discussion LC-HRMS is an uncontestably powerful analytical approach employed in both targeted and untargeted metabolomics. We used an UPLC-IMS-QTOF to create an accurate in-house database using a commercial library of metabolite standards. Based on experimental results, we used two existing prediction models (CCSBase and QSRR Automator) to predict CCS and Rt values of a large-scale database to increase confidence in metabolite annotation. Associating Rt and CCS is relevant as it can provide complementary information coming from chromatographic and ion mobility separation or even replace other orthogonal properties (isotope similarity and fragmentation score) for putative compound annotation. Moreover, for all metabolites, predictions were performed for protonated and deprotonated ions as well as adducts, each having the same Rt but a different CCS  [42].
The co-occurrence of adducts is common when analyzing heterogeneous biological samples  [43]. Gathering predictions for multiple metabolic features of the same metabolite is valuable information, allowing for cross-validation of identification. The predicted database is presented in Table  S2. The comparison of mass spectra, Rt, CCS, and accurate mass of a feature with experimental data acquired from standard compounds measured under the same analytical settings permits achieving the highest level of identification confidence. An in-house database of 380 different metabolites was generated, allowing a confident and definitive identification (level 1 according to the MSI  [9] ). Metabolites were separated using an Acquity UPLC HSS T3 column that possesses superior polar-compound retention and aqueous mobile phase compatibility compared to more classical stationary phases.
Predictions using machine learning are data-driven approaches providing predictions for metabolites with corresponding properties  [45]. After the training of the model, predictions could be generated immediately for other compounds. For CCS predictions, CCSBase is a machine learning-based prediction model built from a combined database, enabling to cover an important variety of structural compounds, participating in the transferability of this model. Indeed, large-scale CCS predictions were validated with our experimental data with a low bias and a high R 2 at 0. 938. A CCS index filter defined as mean error +/-2 SD, i. e. , maximum 16. 16 Å 2 , could be used as the threshold for excluding false positives. This match tolerance, reflecting the deviation of analytes or family of analytes or type of adducts, is relatively large compared to other work demonstrating that median relative errors as low as 3 to 5% are reachable using other models  [22, [24] [25] [26] [27].
However, excluding false positive identifications with a CCS match higher than the defined threshold remains of great importance when considering the number of possible matches when using m/z match, isotope similarity, and fragmentation score only. Moreover, this additional separation process participates in better detection of compounds presenting contaminant mass spectra due to the co-elution or a poor abundance. For Rt predictions, the workflow was different as we trained and validated an accurate machine-learning model based on compounds with various physicochemical properties. The training set allowed the model to be trained, while the test set made of unknown data for the trained model allowed the model to be validated. With this strategy, the model was estimated with small error differences in favour of minimum overfitting.
An Rt index filter defined as mean error +/-2 SD, i. e. , maximum 1. 11 min, could be used as a threshold for eliminating the majority of misidentified compounds. Outliers could be due to software bias, random noise in the data used or errors in the attribution of standards. Naylor et al. described the QSRR Automator's original performances on various chromatographic columns. They showed errors in predictions within 1 min for the majority of predictions, and within 2 min for almost all predictions  [38]. QSRR here performs comparably to previously published methods  [34, 36]. In relatively short run time methods, as in our method, many metabolites have very close Rt, including isomers with Rt that fall within 10 s of each other. Our database does not permit the distinction between those metabolites but is adequate to differentiate between clearly separated compounds of the same mass and reduce false positive identifications, leading to an advanced biological interpretation of results.
Even if the generated model was based on compounds with various physicochemical properties separated and identified with an optimized method, particular attention should be dedicated to avoiding inaccurate results, including (i) compounds not retained in the column (ii) compounds retained after the observed Rt of the training data (iii) compounds with physicochemical properties that differ from the training set. For example, the in-house database presented here was generated from a large variety of chemical standards dedicated to metabolomics analyses but did not include complex high molecular weight compounds. Biased predictions for those metabolites should be excluded. The Rt database that we created is strictly related to our chromatographic conditions, so it can be directly useful only for those who decide to strictly adapt our choice of column, mobile phases, and flow rates.
Such a comparison has already been described elsewhere  [22, [24] [25] [26] [27]. Instead of that, the chosen strategy consisted of emphasizing the usefulness of our workflow with concrete application on biological data. Finally, by associating Rt predictions with CCS predictions, the generated large-scale database is strictly related to the instrumental configuration, but the workflow could be largely generated to other experimental conditions. Most prediction models or workflows previously reported discuss one or the other predicted property (CCS or Rt), while only a few associate multi-dimensional information for metabolite annotation  [47] [48] [49] [50]. Interestingly, all of them are dedicated to lipids or exogenous compounds, while our workflow predicted a database including small molecules found in the human body, including water-soluble or lipid-soluble endogenous metabolites and exogenous compounds.
Regarding the tremendous interest of the scientific community in metabolomics, providing a practical workflow is of large importance for analytical chemists or biologists who cannot develop machine learning models but who want to improve the biological interpretation of their metabolomics data. The usefulness of our combined large-scale predicted database was demonstrated with an application of biological data generated from a toxicology study on tobacco cigarettes. The results demonstrated that the introduction of CCS and Rt values for metabolite identification could significantly reduce false positive identifications, with the benefit of narrowing the search scope and improving the identification accuracy.
Conclusions In this study, a workflow was introduced to remove false positive annotations in nontargeted metabolomics studies. The procedure includes the implementation of a combined CCS and Rt-restricted database starting from a commercial library of metabolite standards. This experimental database has been used to predict CCS and Rt of a large-scale dataset using existing machine learning tools. As illustrated by an application on a metabolomic study on tobacco cigarette toxicity, the presented workflow reduces the occurrence of false positive annotations in untargeted metabolomics and adds confidence to the identification of metabolites. This database has been integrated into the protocol used in our laboratory for untargeted metabolomics analyses and is freely downloadable. When making the assumption that the created database could be a representative subset of compounds present in the human metabolome, biological interpretation of metabolomics data is notably improved, giving new insights into biomarker research or mechanisms that generate a specific phenotype.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/metabo13020282/s1, Figure  S1 : Performances of the SVR model using QSRR Automator algorithm; Table  S1 : Linear regression of Predicted Rt (min) using QSRR Automator algorithm by measured Rt (min) of compounds used from the developed method. This figure was generated by QSRR Automator from all the data available for model construction (including training data); Table  S2 : Large-scale in-house reference database.
, while 90 were detected only in one ionization mode (45 for each ionization mode). Seven compounds were excluded due to incomplete data in molecular descriptors. Support vector regression (SVR) algorithm based on 113 molecular descriptors presented the best performances, with the R 2 score at 0. 999 and the mean absolute error at 0. 10 min for the training set. The validation set tested by cross-validation (n = 10) validated the model with the following performances: mean of cross-validation R 2 score 0. 898, mean absolute error 0. 81 min, and standard deviation of the mean absolute error 0. 15 min. Detailed results are presented in TableS1and FigureS1. Rt predictions were performed for almost 114,000 metabolites from HMDB v4. 0. The resulting output table of Rt allowed us to build our large-scale in-house reference database.
OBSERVATIONS AND DATA REDUCTION We re-analysed the NaCo Elias2-24 observations in 𝐿 -band from July 15, 2018 (J21), using the same pre-processed Angular Differential Imaging (ADI) cube, stellar point spread function and parallactic angles acquired by J21 and the same platescale and true north calibrations. After bad frame removal, the cube contained 414 frames with total on-source integration time of 82. 8 minutes and full-width at half-maximum (FWHM) of 0. 116". After subtracting the median background, we post-processed the cube using median ADI, fullframe Principal Component Analysis PCA-ADI and PCA-ADI in concentric annuli using our NaCo data reduction pipeline  (Hammond et al. 2022) , based on the Vortex Image Processing package ( , Gomez  Gonzalez et al. 2017). For PCA-ADI in concentric annuli, we used a 1-FWHM rotation angle threshold, a 1-FWHM inner mask and 12-pixel (0.
CONCLUSIONS (i) We detect a 12 CO counterpart of the planet candidate presented by J21, and we recover the companion with independent reduction of the NaCo data. If confirmed this would be the first simultaneous planet detection via disc kinematics and direct imaging. (ii) The CO data shows a velocity shift, broadened linewidth, and increased disc temperature at the location of the planet candidate, all of which are predicted by models of planet-disc interactions. (iii) These kinematic structures can be explained by a ≈ 8M Jup planet or a 3M Jup accreting planet. (iv) Cloud contamination prevents an accurate planet mass estimation. Higher-J and/or less abundant isotopologues/molecules can alleviate this, and together with the measured 𝐿 flux, or improved data from e. g. VLT/ERIS, can provide the first simultaneous dynamical and luminosity constraints on a forming planet candidate.
ACKNOWLEDGMENTS We made use of ALMA data ADS/JAO. ALMA#2016. 1. 00484. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with  Chile. The  Joint ALMA Observatory  is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the  National Science Foundation  operated under agreement with Associated Universities, Inc. Simulations were performed on ozSTAR, funded by  Swinburne University  and the  Australian Government. C. P. , V. C. and D. J. P. acknowledge  Australian Research Council  funding via  FT170100040 ,  DP18010423  and  DP220103767. L. P. and S. J. gratefully acknowledge support from  ANID BASAL  project  FB210003 ,  ANID -Millennium Science Initiative Program   -NCN19_171 , and  ANID FONDECYT 1221442.
DATA AND SOFTWARE AVAIBILITY Raw data sets are publicly available on the ALMA and ESO archives. Calibrated ALMA data sets are available at alma-dl. mtk. nao. ac. jp/ftp/alma/lp/DSHARP. Calibrated NACO data and synthetic models are available at 10. 6084/m9. figshare. 21915000. Our NaCo pipeline is available at: github. com/ IainHammond/NaCo_pipeline. and are publicly available at github. com/danieljprice/phantom and github. com/cpinte/mcfost. The scripts used to make the figures are available at https://github. com/cpinte/elias2-24.