dataesr/scientific-paragraphs-categorization huggingface dataset:
Seed classes According to germination data extracted from automatic computer-vision germination device as well as the growth data, the cowpea seeds were then classified into different categories based on ageing, germination status, seedling condition and speed of germination. Based on ageing process practiced initially on the seeds, they were first classified either into two groups (Non-aged & Aged) or into five subgroups (Non-aged, AA24, AA48, AA72 and AA96) corresponding to seeds aged for 24, 48, 72 and 96 h, respectively. Based on the data extracted from the automatic computer-vision germination device, seeds were classified into two groups (Germinated and Non-Germinated). Also, based on the start of germination and commencement of radicle protrusion within 48 h after sowing, seeds were classified into three classes (Early germinated, Medium Germinated & Dead).
These white objects represent the main regions of interest (ROIs) that define the locations of the seeds achieved by thresholding the spatial dimension of band λ780 nm as described by Eq. (  1 ): where λ corresponds to a wavelength of 780 nm, and an empirically determined threshold T was used at this wavelength band. The resulting binary image was used as a mask to identify the seed pixels in the image as the main regions of interest (ROIs). This mask was applied for all bands (from λ375 to λ970 nm) in the multispectral image highlighting only the seeds in black background in all bands. Next, all seeds were collected in a blob database from which different attributes of the seeds such as color, dimensions, texture, shape and main spectral features of all individual seeds appeared in the image could be extracted.
Thus, the recognition rates for new samples in the validation set, defined as the proportion of samples identified correctly to that of the total number in the validation set, were computed for each discriminant model using the following equation. Image processing and spectral data extraction were performed using the VideometerLab3 software version 1. 6 (Videometer A/S, Hørsholm, Denmark) and the multivariate analyses and related statistics were performed using an in-house written script by Matlab ® version 7. 7. 0. R2008b (The Mathworks Inc. , Natick, Massachusetts, USA).
Discrimination between 'Germinated' and 'Non-Germinated' seeds As a complementary work of discriminating aged seeds from the non-aged seeds, it was very important to differentiate between the germinated seeds (i. e. viable seeds) and the non-germinated seeds that could be decayed, dead of delayed in germination under the effect of ageing. The time series images acquired by the automated computer-vision germination device showed clearly which seeds were germinated (live) and which ones failed to germinate during the first 4 days after sowing. Hence, a new linear discriminant analysis model was built to discriminate between germinated and non-germinated seeds in training data set under cross validation (n = 401) and the resulting model was used to classify the seeds in an independent validation set (n = 100).
Discrimination between 'Normal' and 'Abnormal' seedlings Table  4  shows the performance of another LDA model built for classifying cowpea seeds into two categories: (1) seeds that are able to produce normal seedlings and (2) seeds that produced abnormal seedlings due to deformed essential structures or missing one or more of their essential structures. The results revealed that the accuracy of the model was moderate with overall classification accuracy of 68. 08, 64. 34 and 62. 00% in training, cross-validation and validation datasets, respectively. To improve the performance of such a model, more seeds should be involved to include all possible variation in considerations.
An abnormal seedling is defined when one or more of the essential seedling structures is impaired or missed. Such abnormal seedlings are incapable of giving normal growth and are therefore incapable of developing into healthy plants in the field. In this regard, the results revealed that the accuracy of the LDA model designated to discriminate between seeds that produce normal seedling and seeds that developed abnormal seedling (Table  4 ) was moderate with overall classification accuracy of 68. 08, 64. 34 and 62. 00% in training, cross-validation and validation datasets, respectively. In fact, this model needs some improvements by utilizing some other models such as quadratic discriminant model or non-linear models could be examined to enhance the overall classification accuracy. Also, extracting some textural features of the seeds at certain wavebands could have positive effect in increasing the accuracy of the classification models.
Acknowledgements Authors would like to significantly acknowledge the financial support from  STDF-IRD-AUF  joint research project provided by  Egyptian Science and Technology Development Fund (STDF) ,  Institut de recherche pour le développement (IRD)  and  Agence Universitaire de la Francophonie (AUF). Also, the support of the  European Union AgreenSkillsPlus fellowship  (grant agreement  N° FP7-609398 ) provided under the framework of  EU 's COFUND- FP7 People Programme  are highly appreciated. Special thanks to  Jean-Pierre Renou , the director of IRHS unit,  French National Institute for Agricultural Research (INRA, France)  and the Instrumentation and plant imaging platform PHENOTIC which hosted the project.
Diketopiperazines 6-12 In addition, seven diketopiperazines were isolated (Figure  4 ). Their planar structures were easily identified by their spectroscopic data in comparison with those found in the literature, while the absolute configurations of their stereogenic carbons were determined using Marfey's analyses. Therefore, after hydrolysis and derivatization with the L-enantiomer of Marfey's reagent, the obtained derivatives were analysed by HR-ESIMS-HPLC. On the basis of the retention times of their respective Marfey's derivatives, the absolute configuration of the stereogenic carbons of the seven diketopiperazines were deduced as depicted in Figure  4. As for the cyclo (Pro-Ser)  (10) , only the proline residue was determined as L-Pro. during the extraction process, but its structure was not studied in depth because it did not reveal any activity in our panel of assays.
Diketopiperazines 6-12 In addition, seven diketopiperazines were isolated (Figure  4 ). Their planar structures were easily identified by their spectroscopic data in comparison with those found in the literature, while the absolute configurations of their stereogenic carbons were determined using Marfey's analyses. Therefore, after hydrolysis and derivatization with the L-enantiomer of Marfey's reagent, the obtained derivatives were analysed by HR-ESIMS-HPLC. On the basis of the retention times of their respective Marfey's derivatives, the absolute configuration of the stereogenic carbons of the seven diketopiperazines were deduced as depicted in Figure  4. As for the cyclo (Pro-Ser)  (10) , only the proline residue was determined as L-Pro.
Chromatography columns (CC) were performed using silica gel (200~400 mesh; Merck, Darmstadt, Germany) and Sephadex LH-20 (Amersham Pharmacia, Uppsala, Sweden). The Marfey's experiments were performed using a Thermo LTQ Orbitrap XL mass spectrometer coupled to a Thermo Ultimate 3000 RS system (Thermo Fisher Scientific Spa, Rodano, Italy), which included solvent reservoir, in-line degasser, ternary pump, column thermostat, and refrigerated autosampler. LC-MS data were recorded and analyzed using the software Thermo Xcalibur 2. 07 (Thermo Fisher Scientific Spa). The samples (5 µL) were applied on to an analytical reversed-phase column (Phenomenex Kinetex C18, 100 × 2. 1 mm, particle size 5 µm), which was eluted at 200 µL/min. The elution procedure consisted of an isocratic profile of acetonitrile-water (5:95, v/v) for 3 min, followed by a linear gradient from 5% to 60% ACN/H 2 O over 20 min, a linear gradient from 60% to 90% ACN/H 2 O over 1 min, and an isocratic profile over 5 min.
Quantum Mechanical Prediction of 1 H-1 H Coupling Constants of epi-Tetradehydrohalicyclamine B (1a) Initial conformational search was performed using molecular dynamics simulations at different temperatures, performed in vacuo in the CFF91 force field using the Insight II/Discover package (BIOVIA: San Diego, CA, USA). The resulting conformers (42 conformers within 4 kcal/mol from the lowest-energy conformer) were used as starting structure for quantum mechanical calculations with the Gaussian 09 program  [13]. Geometries were optimized at the B3LYP/6-31G(d) level of theory and the continuum-solvent (PCM) model for MeOH, and 1 H-1 H coupling constants were calculated for each conformer at the B3LYP/6-31G(d,p) level of theory according to the suggestion by Bally and Rablen  [11] , i. e. , considering only the Fermi contact contribution to J and scaling the calculated value In DKP cyclo(Pro-Gly) (7), L configuration was found for proline residue (Figure  S1 ).
In DKP cyclo(Pro-Ala) (8), both alanine and proline residues were found to have L configuration (Figure  S2 ). In DKP cyclo(Pro-Val) (9), the valine residue was found to have L configuration while the proline residue was found to have D configuration (Figure  S3 ). In DKP cyclo(Pro-Ser)  (10) , L configuration was found for proline residue while it was not possible to establish the configuration for the serine residue, probably because of the low amount of diketopiperazine (Figure  S4 ). In DKP cyclo(Pro-Ile)  (11) , the isoleucine residue was found to have L configuration while the proline residue was found to have the D configuration (Figure  S5 ). In DKP cyclo(Pro-Tyr)  (12) , the tyrosine and the proline residues were found to have the L configuration (Figure  S6 ). Initial conformational search was performed using molecular dynamics simulations at different temperatures, performed in vacuo in the CFF91 force field using the Insight II/Discover package (BIOVIA: San Diego, CA, USA).
The resulting conformers (42 conformers within 4 kcal/mol from the lowest-energy conformer) were used as starting structure for quantum mechanical calculations with the Gaussian 09 program  [13]. Geometries were optimized at the B3LYP/6-31G(d) level of theory and the continuum-solvent (PCM) model for MeOH, and 1 H-1 H coupling constants were calculated for each conformer at the B3LYP/6-31G(d,p) level of theory according to the suggestion by Bally and Rablen  [11] , i. e. , considering only the Fermi contact contribution to J and scaling the calculated value by 0. 9117. The averaged coupling constants shown in Table  2  were obtained using populations calculated from the CFF91 energies, and including in the calculations only the conformers populated by more than 1%. It is interesting to note that using quantum mechanical calculations to evaluate energies and populations of conformers produced a remarkably worse fit with the experimental values.
049M citric acid monohydrate + 0. 1M Na 2 HPO 4 •2H 2 O + 1 mL H 2 O 2 30%/L) were added as a substrate and after 15 min incubation at room temperature, the reaction was stopped by addition of 100 µL 1 N sulfuric acid. Optical density (OD) was measured at 490 nm using a plate reader (BioTek Instruments, El 800, Gen 5 software, Winooski, VT, USA). Amyloidβ-42 quantification was calculated using standard curves, which were prepared with synthetic Aβ-42 HFIP treated (JPT Peptide Technologies, Berlin, Germany) and Aβ-42 specific polyclonal antibody. Curve fitting was performed using a 4 parameters sigmoid equation (SigmaPlot, Systat, Sigma). Results are expressed as fold change ± s. d. All experiments were performed in triplicate.
Acknowledgments: The authors are grateful to  Professor J. -C. Braekman  for the gift of the sponge samples,  A. Blond  and  A. Deville  ( MNHN, Paris, France ) for some NMR spectra, and  A. Marie  and  L. Dubost  ( MNHN, Paris, France ) for MS measurements. Others NMR spectra were performed at the " Laboratorio di Tecniche Spettroscopiche ",  Dipartimento di Farmacia, Università di Napoli Federico II , the assistance of  Paolo Luciano  is gratefully acknowledged.
A multi-segment approach was employed as part of this process to minimize impacts of gradients in T/RH/P along slanted paths with lengths greater than 1 km. In these cases, a chord (observational line of sight) was divided into multiple segments, each segment was assigned different estimates of T/RH/P using a distance-weighted average of the surrounding weather stations, and a value of P was computed on a per chord basis using a lapse rate equation to account for changes in height above sea level at each chord segment. The weather data was obtained from weather stations located at each transceiver, and others, owned and operated by local institutions or individuals, scattered throughout the area of interest. Prior to ingesting these data, the provided temperature, %RH and surface pressure from each was evaluated against local standard for quality assurance and control.
Post-Deployment Calibration of Chord Concentration Data The initial real-time estimates of column concentrations were computed using version 12. 2 of LBLRTM, spectroscopy coefficients developed based on HITRAN 2008  (Rothman et al. , 2009) , and on-and off-line wavelengths that were measured and adjusted periodically over the period of the campaign. Version 12. 2 of LBLRTM employs a Voigt line shape function at user-defined atmospheric levels, a model of the continuum that includes self-and foreign-broadened water vapor as well as continua for CO2, O2, N2, O3, and extinction due to Rayleigh scattering. The initial spectroscopy database included updates to the CO2 line parameters and coupling coefficients derived from the work of  Devi et al. (2007a)  and  Devi et al. (2007b). While every effort was made during deployment to calibrate the system and maintain a stable baseline, no real-time independent data were available for cross-comparison.
2 with an augmented CO2 line parameter database, version 12. 8 with version 3. 6 of the line parameter database, and version 12. 8 that included CO2 line parameters from HITRAN 2016  (Gordon et al. , 2017). Version 12. 2 was used primarily as an internal engineering validation of the algorithm process and to assess the methodological differences between the original retrieved values and reprocessed data. Retrievals based on version 12. 8 were denoted as the current baseline, and the alternative set of retrievals performed using line parameters from HITRAN 2016 were constructed to assess changes due to current spectroscopy. The baseline spectroscopy includes CO2 line parameters from HITRAN 2012  (Rothman et al. , 2013)  with updates from  Devi et al. (2007a) , as well as weak-band CO2 line parameters in the 5929-6392 cm -1 band provided by  OCO-2 ABSCO v5.
0 (Payne, 2017). The primary objective behind repeating the correction and retrieval process using both RT model parameters derived from HITRAN 2012 and 2016 was to assess whether or not one or the other parameterization provided a significantly better overall fit to the data for the two nominal wavelengths of interest. While the RT model parameterization is continuously being refined by groups worldwide, changes to the database may or may not have a positive impact on the resulting retrievals that rely on observations at two distinct nearly monochromatic wavelengths.
However, compound identification still remains as the major bottleneck of non-targeted analysis, not only because it is a time-consuming process, but also due to the current low number of metabolites that are unequivocally identified  [22, 23, 24]. Compound identification mainly relies on the comparison between the observed chromatographic retention time (Rt) and mass spectra, as well as on the information included in metabolite databases such as the Human Metabolome Database (HMDB) or METLIN  [25, 26]. Nevertheless, a wide number of the detected features are finally not identified  [2, 27]. Compound identification based on mass spectra can be difficult if only a monoisotopic peak is detected or if the formation of adducts prevents fragmentation, as well as MS/MS data may be insufficient to distinguish between different structural isomers and/or stereoisomers.
Within this framework, several CCS databases have been reported with the aim of integrating the CCS as an identification parameter in non-targeted metabolomics workflows  [36, 37, 38, 39, 40, 41]. Nevertheless, consensus about CCS values is still required in order to build reference CCS libraries that can be used globally in metabolomics studies  [29]. In this sense, it is also necessary to extend the existing databases to a wider number of parent compounds and metabolites. Threshold criteria for CCS measurements must also be established before this parameter may be accepted as an identification parameter to support metabolomics or other type of analysis. Paglia et al. have originally proposed an acceptable deviation of 2% between CCSs measured in samples and reference CCS values in databases  [34, 42].
In the trap cell, wave velocity and height were established at 311 m s -1 and 4. 0 V, respectively. In the case of the transfer cell, these parameters were set at 219 m s -1 and 4. 0 V, respectively. IMS DC bias and trap DC bias were set at 3. 0 and 47. 0 V, respectively. For analyses carried out under ESI+ mode, IMS wave velocity and height were fixed at 1000 m s -1 and 40. 0 V, respectively, whereas these parameters were set at 550 m s -1 and 40. 0 V when the system was operated in ESI-mode. Quadrupole resolution was established to 12. 5 for MS/MS analyses. MS data were acquired in the range m/z 150-1200 at 2. 5 Hz. The TOF analyzer was operated in high resolution mode. Lock mass standard was supplied at 20 µL min -1 and MS data was acquired each 15 s at 5 Hz (3 scans to average). LockSpray capillary voltage was fixed at 3 kV and 2.
Results and discussion In 2017, we developed the first large TW CCSN 2 database for steroids because the lack of databases is view as the main drawback for the integration of this molecular characteristic in metabolomics workflows for peak annotation  [39]. For the creation of this database, steroid standards were analyzed by ESI-TWIMS-TOF-MS and the CCS of detected molecular ions was measured in triplicate. As a proof of concept, the CCS of the protonated molecule of nandrolone was also measured in urine samples. In this sense, urine matrix did not show any effect on its CCS (∆CCS = 0. 2%). Deeper insight is still needed to discard any effect of the matrix on CCS measurements taking into consideration that first studies about this topic are now beginning to be reported  [43, 48]. In addition, more studies are required before being confident in the use of CCS databases for the identification of compounds, especially for those molecules that may involve legal actions such as the analysis of steroids in the fields of antidoping and chemical food safety.
In order to reinforce the application of our CCS database for steroids identification, the CCS of glucuronides and sulfate conjugates (n = 25) was examined in presence of different urine samples over four months (n = 9-10). Under this context, it is also shown the improvement on sensitivity and selectivity achieved by the implementation of TWIMS in a LC-ESI-TOF-MS method intended for the analysis of phase II steroid metabolites. Finally, a practical approach is presented in order to highlight the benefits of including IMS in nontargeted workflows.
Robustness of CCS measurements in urine samples In this work, different urine samples were selected in order to evaluate the influence of this matrix on the CCS of steroid conjugates. Samples were treated and fortified with a steroid mixture standard solution at 2 µg mL -1 in urine according to the procedure described in Section 2. 2. This concentration level ensured that all steroids were detected under both ESI+ and ESI-modes. Despite conjugated steroids are usually analyzed in negative ionization conditions because it provides higher signal sensitivity  [15, 18] , sample analysis was also performed in positive mode with the aim of carrying out a more comprehensive study of the CCS of steroids in urine matrices. Moreover, the published CCS database for steroids does not include information related to the negative ionization of androgens.
On the other hand, urine samples were diluted 10-fold before their analysis by LC-ESI-TWIMS-TOF-MS in order to reduce and/or avoid the matrix effect affecting the ionization of steroids. In addition to fortified samples, blanks of each urine sample and standard solutions were also analyzed. Standard solutions were used as quality control and as a part of internal reproducibility studies for inhouse validation of the CCS database for steroids  [39]. Selected steroid standards have been characterized in terms of CCS over four months (9 measurements for each steroid) and compared with the TW CCSN 2 values reported in the database. In total, 297 CCS measurements were carried out taking into account positive and negative ionization conditions. Differences between the mean measured TW CCSN 2 values and the TW CCSN 2 values in the database were lower than 0.
9%. These results fully fall within the threshold of 2% widely applied to CCS measurements  [34, 49] , showing the robustness of the database over the time. Consequently, the reported CCS database was used with confidence for the CCS characterization of steroids in presence of biological matrices. Initially, the TW CCSN 2 of steroids was measured in different calve urine samples (n = 5) that were directly fortified at 2 µg mL -1   prior to their dilution and further analysis. After the first week of experiments, urine samples from four calves and one adult bovine were filtered, spiked at the same concentration level, homogenized and kept at -20°C. Aliquots (n = 7) of these samples were treated and analyzed within the following four months with the aim of obtaining a more detailed vision of the interactions between the matrix and these metabolites as a function of time.
As previously observed for estrogen compounds  [39] , the TW CCSN 2 is similar for the protonated and deprotonated molecules of androgens presenting both species under ESI+ and ESI-conditions, respectively. Surprisingly, the [M+H] + ion of epitestosterone glucuronide possesses a TW CCSN 2 much smaller than its related [M-H] -ion (i. e. 206. 0 Å 2 and 218. 5 Å 2 , respectively). In comparison to its epimer (i. e. testosterone glucuronide, Table  1 ), it seems that this molecule compacts when is protonated. Consequently, it provides evidence justifying why the protonated species of epitestosterone glucuronide is more compact than other steroids presenting similar m/z  [39]. CCS differences within the range ±0. 50% were observed for the averaged TW CCSN 2 of steroid metabolites in urine samples compared to the TW CCSN 2 values reported in the database (Table  1 ), except for DHEA sulfate (∆CCS = +0.
51%), 5α-androstan-3α,17β-diol 17-sulfate (∆CCS = +0. 64%) and estriol 3-sulfate (∆CCS = +0. 84%) analyzed in positive mode. Therefore, urine matrix does not seem to have any relevant influence on the CCS of phase II steroid metabolites. Such conclusion was also previously reported for other compounds and matrices  [43, 48]. Nevertheless, important results can be obtained when the CCS measurements for each compound in urine are examined in detail. In total, 1806 CCS measurements were carried out taking into account the number of analytes studied, the number and type of urine samples that were analyzed within four months, and both negative and positive ionization modes. High accuracy was generally achieved for the measurement of the TW CCSN 2 of the vast majority of steroid ions. As indicated in Figure  1A , more than 77% of CCS measurements matched the database TW CCSN 2 values within ±0.