dataesr/scientific-paragraphs-categorization huggingface dataset:
This subset of spectra seems to be a good representation of a set of spectra identifying the same peptide in the dataset. Indeed, as shown in Fig. 5 , among the 77 spectra identifying pep, only 15 exhibit ∆M values around 0 or 1 Da (referring to an exact match to the expected mass or an incorrect picking of the good isotope, respectively), while the others are distributed over 44 different values of ∆M between -500 and 2065 Da. When examining the alignments suggested by SpecGlobX, we note a large number of non-aligned masses we did not expect (36 out of the 77 alignments returned by Spec-GlobX contain a non-aligned mass), an observation we can extend to the alignments of all PSMs obtained on the complete dataset. Situations combining non-aligned masses including neutral losses and one or several modifications on the same peptide -as previously evaluated with Dsim-seems to be a reality in this dataset.
6  The closeness of spectra identifying peptide DATNVGDEGGFAPNIIENK (HEK293 dataset). In this picture, two vertices representing spectra are linked if the two spectra share at least 26 out of the 50 most intense peaks. The size of each node is related to its degree, the label on each node displays the ∆M value, and colors on the nodes group similar masses and their isotopes for readability. Each link between a pair of spectra is weighted by the number of shared peaks. The arrangement of nodes is done by the directed Force Atlas algorithm so that the topology of the graph represents the closeness of spectra in terms of number of shared peaks highlighting the labile modification is available in Additional file 1). Consistent with that, all the spectra carrying these modifications and identifying pep with a strong confidence share most of their peaks with most other spectra, particularly with spectra associated with PSMs with ∆M = 0 or 1.
Next, we interpreted the 77 spectra identifying pep with MODPlus and MSFragger to evaluate if the behavior on experimental spectra is similar to what we observed on simulated spectra. The answer is clearly yes (the interpretation provided by each software is given in Additional file 2). Even though MODPlus provides the same PSMs as SpecOMS with only two exceptions, the interpretations in terms of modifications are the same for only 19 of these 75 PSMs. On the contrary, MSFragger differs more frequently on the PSM (5 spectra are interpreted as different peptides), but the interpretations of the modifications carried by 40 among 72 PSMs are the same. As already highlighted by simulated spectra, well-known modifications are welllocated by MODPlus (D+ 21. 98 Da, N-Ter+ 43. 01 Da, D+ 53. 92 Da).
However, one surprise is some of the nodes associated with the latter ∆M (Fig. 5 , ∆M = +53. 92 Da for instance) are densely connected to nodes corresponding to mass losses, whereas some other nodes of the same ∆M are close to each other but disconnected from the cluster of nodes. This suggests that some spectra contain a clear signature of the modification with a shift of peaks carrying the modification, while other spectra have partly or totally lost this signature. These findings -which may be related to the significant proportion of non-aligned masses seen in Fig. 4a -imply that non-aligned peaks are more frequent in spectra than previously thought, leading to a lower number of shared peaks if those peaks are not taken into account by search engines. Our small experimental dataset also illustrates that the counterpart of a priori knowledge and ∆M restrictions sometimes have bad effects on the interpretations of spectra (annotated spectrum plots discussed below are available in Additional file 1).
At the same time, the ∆M value is negative, suggesting a loss of amino acids. Even though none of the software has found this most probable interpretation, the usage of SpecGlobX as a decision support tool combined with an expert analysis of the spectrum graph has rather easily led us to the following explanation: the charge state evaluation of the spectrum is wrong (3+ rather than 2+) and the spectrum carries the labile modification of 302. 99 Da (present on a large proportion of the spectra in this sample).
Conclusions SpecGlobX is an easy-to-use software developed to quickly align PSMs. SpecGlobX can be seen as an additional tool to existing OMS software, providing a good decision support tool to highlight complex modifications carried by peptides. We have shown on a large set of simulated spectra (modeled such as to simulate imperfect experimental spectra) that SpecGlobX improves the identification of complex and unanticipated modifications after reprocessing the PSMs lists provided by OMS software. Next, we demonstrated the usefulness of SpecGlobX on a subset of a well-known experimental spectra dataset downloaded from PRIDE, highlighting the presence of a large proportion of non-aligned masses due to neutral losses, charge estimation error, and the presence of dimers. SpecGlobX is freely available to all users, including the source code.
Availability of data and materials This published article and its supplementary files give all information required to reproduce the simulated dataset DSim. The HEK293 spectra dataset supporting the conclusions of this article was downloaded from the PRIDE repository,  PXD001468 , file b1922_293T_proteinID_02A_QE3_122212. raw https:// www. ebi. ac. uk/ pride/ archi ve/ proje cts/ PXD00 1468 Declarations.
The joint estimation is advantageous, as it explicitly considers magnetization exchanges between the macromolecular and the free pools in the two-pool model for a more accurate T 1 and MPF estimation. 11 owever, two important features are typically not considered in common SPGR-based qMT methodology: • First, on-resonance saturation effects induced by the readout pulses on the macromolecular lineshape are usually ignored. Of interest, Mossahebi et al. 10  have argued that on-resonance saturation effects of the SPGR readout pulse at the macromolecular pool level may have a nonnegligible effect on the qMT parameters' estimation. This was further demonstrated by ensuing work in the context of magnetic resonance fingerprinting  12  and VFA-T 1 mapping. 13, 14 It is therefore worth investigating whether these saturation effects (referred hereafter to as on-resonance saturation [ONRS]) may bias the estimated values of qMT parameters if not accounted for in the joint VFA-SPGR and MT-SPGR data modeling.
29 Because dipolar order effects manifest as a reduced RF saturation rate of the macromolecular pool,  28  which varies with the preparation pulse's power and frequency,  21, 25, 30, 31  biases can be expected in the estimated qMT parameters from SOFS-based Z-spectra analyzed with the two-pool model. Conversely, an alternative approach involving symmetric dual-offset frequency saturation (DOFS; RF irradiation applied on both sides regarding the resonance frequency) pulses, which effectively decouples the dipolar orders from their associated macromolecular Zeeman orders,  32, 33  would make the two-pool model more accurate for Z-spectrum data analyses. In this work, qMT simulations and experiments were performed to confirm the existence of biases in quantitative MPF and longitudinal relaxation derived from SOFS-based Z-spectra analyzed with the classical two-pool model.
Additionally, we propose an alternative framework based on DOFS pulses to cancel out dipolar order effects and a two-pool model that accounts for ONRS effects when deriving quantitative MT parameters from combined VFA-SPGR and MT-SPGR data in the human brain. The proposed framework was repeated for reproducibility assessment and further extended in an adapted joint single-point qMT methodology  7  for simultaneous estimation of MPF and apparent free pool T 1.
2. 5. 1 Joint fitting of VFA and Z-spectrum data Three frameworks of data analysis were used to derive the quantitative maps of MT parameters (T 2,f , T 2,b , T 1 , MPF, and the exchange rate R), combining VFA data with (i) SOFS Z-spectra and fitted by the qMT-noONRS model (SOFS-noONRS; "A"), (ii) SOFS Z-spectra and fitted by the qMT-ONRS model (SOFS-ONRS; "B"), and (iii) DOFS Z-spectra and fitted by the qMT-ONRS model (DOFS-ONRS; "C"). Hence, only the DOFS-ONRS framework is conceptually consistent, because it combines a model that is adapted to the input data (no dipolar order). All reconstructions included transmit-field inhomogeneity correction of all pulse-related quantities in Eqs. (  5 ) and (  6 ), as provided by the estimated and resampled relative B 1 + map. Comparison of the three frameworks was achieved by performing linear regression and Bland-Altman analyses on quantitative parameter values derived in WM and DGM regions.
12, 36 owever, bandwidth effects of the finite readout pulse are not considered in the calculation of W b RO , and its subsequent effect on the parameters' estimation may still depend on the readout pulse power, shape and duration, as the absorption lineshape varies rapidly around the resonance. As such, we chose a rather spectrally concentrated and spatially nonselective Hann-shaped pulse for readout with a reasonable duration (τ RO = 1 ms) instead of seemingly often used short rectangular pulses that may exhibit a large spectral response with nonnegligible sinc ripples. In the proposed DOFS-ONRS framework, VFA data provided the necessary sensitivity to longitudinal relaxation. Although SPGR sequences are attractive for their simplicity, acquiring high-resolution images requires an important acquisition time increase, for which advanced acceleration methods such as compressed sensing  73  should be beneficial.
Summary of qMT parameters of the reproducibility experiment estimated by the DOFS-ONRS framework and evaluated by Bland-Altman and linear regression analyses. The coefficient of variation between test and retest experiments are provided. Representative axial views of MPF (A,B) and T 1 (C,D) maps from the DOFS data and qMT-ONRS model (DOFS-ONRS framework) using the full Z-spectrum data analysis (A,C) and the joint single-point MTw analysis (B,D), generated with Δ = 4 kHz and B 1,RMS SAT = 3. 73 μT.
DATA AVAILABILITY STATEMENT The prototype MT-prepared SPGR sequence "vibeMT" used in experiments is available on the constructor Siemens C2P platform. A MATLAB (The MathWorks, Natick, Massachusetts, USA) implementation of the simulations is made available at https://archive. software heritage. org/browse/directory/e6ecf00ebd02ffaf5522053fa e189c58e11fa339/). A processing code for the joint estimation of MPF and T 1 based on the DOFS-ONRS framework is available at https://github. com/lsoustelle/qMT. The.
T A B L E 2 7 27 Summary of qMT parameters of the reproducibility experiment estimated by the DOFS-ONRS framework and evaluated by Bland-Altman and linear regression analyses. The coefficient of variation between test and retest experiments are provided. Representative axial views of MPF (A,B) and T 1 (C,D) maps from the DOFS data and qMT-ONRS model (DOFS-ONRS framework) using the full Z-spectrum data analysis (A,C) and the joint single-point MTw analysis (B,D), generated with Δ = 4 kHz and B 1,RMS SAT = 3. 73 μT.
Molecular dynamic calculations (AMBER 12) Calculations on molecules (7c, 7d, 8c, and 8d) were carried out according to the protocol: 0. 5 ms with 2 fs steps molecular dynamics simulations were calculated in explicit solvent (chloroform; CHCl 3 box) without any ROE constraints under constant pressure periodic boundary conditions, pressure relaxation time of 0. 5 ms, and constant temperature 300 K, using the package of molecular dynamics simulation programs AMBER 12. The starting molecules were constructed using MarvinSkectch (ChemAxon), Antechamber and Xleap from AMBER program suite. Molecular simulation was done with Sander program with general AMBER force eld (GAFF) and included amino acid parameters (ff99SB). Ptraj was employed for the analysis of 25 000 structures. 53, 54 nthesis of N-tert-butyloxycarbonylaminophtalimide (2) Phthalic anhydride (1.
Molecular dynamic calculations of 2:1-[a/aza]-trimers In order to investigate the major conformation(s), as well as the possible intramolecular hydrogen bonds in the 2:1-[a/aza]oligomers (trimers and hexamers), molecular dynamic calculations for the four molecules (7c, 7d, 8c and 8d) were carried out according to the protocol described in experimental section using AMBER soware package. Each trajectory carry 25 000 structures for each molecule. AMBER module Ptraj was employed for their analysis in order to assess the possible existence of hydrogen bond and the average values of the torsion angles. It has been reported that the secondary structure of azapeptides is based on the structural backbone elements which composed of the hydrazine and an urea constituent, in which the two parts are described by the torsion angles f and c, respectively (ESI, Fig.
Acknowledgements M. C. Averlant-Petit and Loic Stefan acknowledge the  Centre National de la Recherche Scientique (CNRS)  for funding. M. I. A. Ibrahim acknowledges Erasmus and  Université de Lorraine  for funding, acknowledges  NIOF (Egypt)  and  Hiroshima University (Japan)  for the scientic collaboration. The authors acknowledge  Mathilde Achard  for supporting in HPLC and MS analyses of the synthesized peptides, and  Olivier Fabre  for NMR experiments. The authors acknowledge  Mathilde Achard  for supporting in HPLC and MS analyses of the synthesized peptides, and  Olivier Fabre  for NMR experiments.
Statistics The results of this study were expressed as the mean ± standard deviation. Normality was tested using the Shapiro-Wilk test. A student's test was used to compare treatments with and without biochar. A oneway analysis of variance (ANOVA) was performed on the data to compare treatments without biochar against control treatments, as well as treatments in the presence of biochar against control treatments. We also performed principal component analysis (PCA) to assess the effects of different treatments on the detected biomarkers using R software (R Core Team, 2015) and the FactoMineR and Factoshiny packages. Heatmap correlation matrices were generated using the Corrplot and ggplot2 packages.
a. ; sweep gas was not applied. The orbitrap was calibrated prior to analysis, and a lock mass list composed of previously identified contaminants was used during injections for better accuracy (phthalates with m/z 279. 1591, 301. 1410, and 391. 2843 in positive-ionization mode and trifluoro-acetic acid with m/z 248. 9604 in negative-ionization mode  [29] ). Data were acquired from 1 to 18 min in full scan HRMS (FullMS)/data-dependent MS/MS (ddMS 2 ) mode. The FullMS acquisition range was m/z 150-520 with a resolution of 70,000 at full width at half maximum (FWHM) at m/z 200; the automatic gain control (AGC) target was 10 6 and the maximum injection time (IT) 200 ms. Up to five ddMS 2 scans were triggered, with a dynamic exclusion of 2. 0 s and an intensity threshold of 10 4 , for each FullMS scan depending on a priority inclusion list of putative metabolites based on in silico predictions and the metabolic fate of α-MT analogues  [16, 17, [30] [31] [32] [33] [34] [35] [36] [37]  (Supplementary Table  S1 ).
Data Mining LC-HRMS data were processed with Thermo Scientific Compound Discoverer (v. 3. 1. 1. 12), using a partially automated targeted/untargeted approach, as previously described with minor modifications  [28]. Briefly, the ions detected in HRMS were compared to a list of theoretical metabolites based on in silico predictions, the metabolic fate of α-MT analogues, and postulation, and generated using combinations of the following transformations: desaturation (2H > ø), dihydrodiol formation (ø > 2O 2H), ketone formation (2H > O), oxidation (ø > O), oxidative deamination to alcohol (2H N > H O), oxidative deamination to ketone (3H N > O), reduction (ø > 2H); acetylation (H > 2C 3H O), glucuronidation (H > 6C 9H 6O), glycine conjugation (H O > 2C 4H N 2O), glutathionylation (ø > 10C 17H 3N 6O S), methylation (H > C 3H ), and sulfation (H > H 3O S); in-source amine loss, abundant for α-MT, was added to the phase I transformation list (3H N > ø); the maximum number of dealkylations was 2, the maximum number of phase II reactions was 2, and the maximum number of transformations was 4.
LC-HRMS intensity threshold was 5 × 10 3 and HRMS mass tolerance 5 ppm. The HRMS/MS spectra and theoretical elemental composition of the ions were compared to mzCloud (Drugs of Abuse/Illegal Drugs, Endogenous Metabolites, and Natural Products/Medicines libraries), ChemSpider (Cayman Chemical and DrugBank libraries), and HighResNPS online databases: intensity threshold, 10 5 ; HRMS mass tolerance, 5 ppm; HRMS/MS mass tolerance; 10 ppm. The chromatographic peaks detected in controls with a similar or higher intensity than that of the peaks detected in the samples were disregarded. Molecules with a signal intensity lower than 0. 5% of that of the metabolite with the most intense signal were also disregarded.
Her research involves two larger software projects: Peacemaker and Travis. While the former program is gradually gaining more and more attention for predicting physical and chemical properties of liquids based on high-level quantum chemical calculations, the latter allows a systematic characterization of molecular simulation trajectories, and is used broadly in the international scientific community. In 2017 she held the Inman Lecture of the Molten Salts and Ionic Liquids Discussion Group of Royal Society of Chemistry (UK). In 2019 she was awarded the Ruth Lynden-Bell Award at the ILMAT V in Paris (France) and in the same year she was an Invited Professor of the University of Strasbourg (France). Eva Perlt graduated from the University of Leipzig where she also completed her PhD in 2014. After research stays at the University of Bonn, the University of California, Irvine, and the Free University Berlin, she is currently a Junior Research Group Leader at the Otto Schott Institute for Materials Research at the Friedrich Schiller University Jena (since 2021).
2000) ; other processes such as sorption may also contribute to the observed phenomena. Note the paired dataset is insufficient to determine the association or statistical significance of the direct relationship between metribuzin and its TPs. Similarly, the parent compound fipronil, with a relatively short half-life in water on the order of 10s of days  (Chopra and Kumari, 2009) , generally had lower concentrations than its more toxic metabolites fipronil sulfon and fipronil sulfide  (McMahen et al. 2016) , again suggestive of relative high degrees of fipronil processing. The relationship between fipronil and fipronil sulfide is not significant at the 0. 05 level (data insufficient to determine the relationship between fipronil and fipronil sulfon). The co-occurrence of parent compounds with TPs is geographically focussed around the Varanasi samples (G21 and G22), although it remains unclear if the transformation processes were occurring locally (either before or after run-off into the Ganga) or if the observed compounds reflected processes further upstream.
Note that when only the main Ganga samples are considered (Fig. S5 ), the compound specific relationships with EEM proxies become much less apparent (except for two significant associations with HIX). This suggest that the overall relationships observed within the combined dataset likely reflect the broader range of surface water chemistry encountered when the tributaries are considered, and thus the sensitivity of EEM proxies for potential predictive power may only be suitable for discriminating lower and higher risk sites for EOC contamination. Detailed investigation of the potential added value of selected fluorescence and absorbance measurements as indicative proxies and as a potential predictive tool to inform EOC monitoring is a subject of ongoing investigation by co-authors.
Acknowledgements This research was supported by a  Department of Science and Technology (DST, India) -Newton Bhabha -Natural Environmental Research Council (NERC,UK) -Engineering and Physical Sciences Research Council (EPSRC,UK)   Indo-UK Water Quality Programme award  ( NE/R003386/1  and  DST/TM/INDO-UK/2K17/55 (C) & 55(G);  2018 -2021  to DP et al. ; see http://www. farganga. org/) and a  Dame Kathleen Ollerenshaw Fellowship  (to LR). Field campaigns were undertaken as part of a wider sampling initiative by the collective Team Saptanadi (http://www. saptanadi. org/) whose further contributing projects are gratefully acknowledged:  NE/R003106/1  and  DST/TM/ INDO-UK/2K17/30  to Reynolds et al. ), and  NE/R000131 /1 to Jenkins et al. , with particular thanks to  Darren Reynolds ,  Bethany Fox  and  Robin Thorn  ( University of the West of England ) of the Water Quality -TEST project, and  Mike Bowes  and  Daniel Read  ( UK Center for Ecology and Hydrology ) for the wider cross-project collaboration.
In parallel, V exc must be chosen to be big enough to avoid uncertainty on the demodulated signal, but small enough to avoid the saturation of the LIA. To determine the best experimental conditions, the effect of the sinewave parameters (f exc and V exc ) on the QTF parameters (f 0 and Q) was measured. The results of the commercial QTF are reported in Figure  3. For each data point, the demodulated signal is recorded for 60 s at a rate of 1 Hz. The mean value and the standard deviation are calculated. The acceptable error intervals for f 0 and Q calculated in the previous section are represented by shaded areas. In Figure  3a , V exc is arbitrary fixed to 0. 1 V pp and f exc is chosen to have |f 0f exc | variation between 5 and 50 Hz. In Figure  3b , |f 0f exc | = 20 Hz and V exc ranges between 3 and 300 mV pp.
Sensors 2023, 23, x FOR PEER REVIEW 5 of 10 T = 1 |f 0 -f exc | , (8) V out (t) = V exc × e -πtf 0 Q. ( 9 ) For instance, fexc must be chosen to be close enough to f0 to obtain a good accuracy on the demodulated signal, but far enough to avoid f0 = fexc, leading to huge values of T. In parallel, Vexc must be chosen to be big enough to avoid uncertainty on the demodulated signal, but small enough to avoid the saturation of the LIA. To determine the best experimental conditions, the effect of the sinewave parameters (fexc and Vexc) on the QTF parameters (f0 and Q) was measured. The results of the commercial QTF are reported in Figure  3. For each data point, the demodulated signal is recorded for 60 s at a rate of 1 Hz. The mean value and the standard deviation are calculated. The acceptable error intervals for f0 and Q calculated in the previous section are represented by shaded areas.
Spectral Preprocessing To improve the quality of the spectra and Raman shift bands and to compare the different spectra, all the received spectra were preprocessed before the machine learning classification analysis. All preprocessing steps were performed using our in-house code (written in Python). In the first step, the spectra were cut to the 1800-600 cm -1 range and smoothed using the Savitsky-Golay algorithm with 5 points, aiming to reduce the instrumental noise (of the device) and improve the spectral information. In the next step, baseline correction was performed to eliminate the differences between the spectra resulting from fluorescence and spectral shifts in the baseline. The process was carried out in several stages  [39] ; the full (1800-600 cm -1 ) region was subdivided into two sub-regions, 1800-1201 cm -1 and 1200-600 cm -1.