dataesr/scientific-paragraphs-categorization huggingface dataset:
Search criteria included carbamidomethylation of cysteine as a fixed modification, as well as oxidation of methionine and acetylation (protein N-terminus) as variable modifications. Up to 2 missed cleavages were allowed. The initial mass tolerance for the precursor was 4. 5 ppm and for the fragment ions was 20 ppm. The DDA files were searched against the human UniProt fasta database (updated 2018-07-01) and the Biognosys' iRT peptides fasta database (uploaded to the public repository). The libraries were generated using the library generation functionality of Spec-troMine with default settings. Machine learning-based training of the LiP-Quant classifier. All HeLa datasets were first analyzed for differentially regulated peptides between the highest drug concentration and vehicle using Spectronaut's statistical testing (one sample twosided t-test with Storey method correction) performed on the modified peptide sequence level using fragment ions as the smallest quantitative units.
This candidate peptide list was filtered based upon q-value < 0. 01 and an absolute log 2 foldchange > 0. 58. Each peptide in this filtered list was then subjected to dose-response correlation testing (using the drc package (https://www. r-project. org)) on all peptides (modified sequence with fragments ions as quantitative units) at every drug concentration to establish a sigmoidal correlation coefficient. As the ground truth (target proteins) was known for the drugs tested in HeLa lysates, each protein identified in each dataset was annotated as either a known target or non-target and from this a contaminant database, or LiP-protein frequency library (PFL), was built. To do so, the same statistically filtered list of differentially regulated peptides as above was used and proteins that were present but not specific for the drug being tested were quantified and assigned a PFL (contamination) score.
For example, a protein that showed differential regulation in 9 of 11 ground truth experiments (several experiments were performed more than once) was assigned a contamination score of 9/11 or 81. 8% (Supplementary Data 14), proteins that never appeared as contaminants in any experiment were not included in the PFL-library. This library enabled the quantitative down-weighting of proteins that were frequently present in LiP experiments but not specific for the drug being tested. We observed high correlation between proteins identified as likely contaminants in the PFL of our LiP-Quant experiments (Supplementary Data 14) and those previously identified as common contaminants in affinity purification mass spectrometry (such as chaperone and structural proteins)  42. To establish the criteria that contribute to the identification of drug targets, we split our dose-response experimental data (filtered based on q-value and log 2 foldchange and PFL annotated as mentioned above) into two independent datasets to train our classifier; training set A included the drugs calyculin A, rapamycin and staurosporine and training set B included FK506, selumetinib and fostriecin (Supplementary Fig.
4A ). For each training set the data was combined and we used linear discriminant analysis (LDA) to build classifiers based upon all potential unique peptide/protein features (e. g. dose-response correlation, PFL frequency, protein coverage, etc). For each training set, known drug targets were selected as a positive training set, resulting in 95 modified sequences for training set A and 33 for training set B. We also randomly sampled 400 background modified sequences as a negative training set from each training set. The features were calculated and stabilized to a defined range between 0 and 1. The LDA-based machine learning was performed five times for each training dataset with resampling of the negative training set each time. The identified criteria were consistent across all LDA analyses (Supplementary Fig.
1B ) and the contribution weights for each of the features from the five LDA analyses was averaged. The relative contributions of each parameter to the LiP-Quant score was very stable across the training sets (Supplementary Fig. 4B ). We termed the linear classifier the LiP-Quant Score in this study. The weights were adjusted such that the combined linear classifier could reach a maximum value of 6. These weightings were incorporated into the analysis pipeline (see below) and verified independently on the other positive control datasets (i. e. training set A was verified on the datasets comprising training set B and vice versa) (Supplementary Fig. 4A ). LiP rankings using both training set analysis parameters were similar across all datasets (Supplementary Fig. 4C ). Using this approach, we established four classifiers that contribute to positive drug target identification (Supplementary Fig.
1B ): (I) correlation of fit with a dose-response binding model, (II) the presence of the identified protein in the LiPprotein frequency library, (III) the number of peptides from an identified protein showing regulation that are in the top ten percent of all peptides ranked by q-value in the Spectronaut filtered statistical test (see above) and (IV) the statistical significance (q-value) of the relative peptide abundances between drug and vehicletreated samples. As training set A contained a larger positive training set (i. e. there were more known drug target peptides identified) the weightings calculated for this training set were used for all subsequent analyses. Automated peptide/protein ranking of LiP-Quant experiments. Using the criteria and weightings established from our training datasets we wrote in-house scripts in R to calculate in an unbiased manner the individual peptide sub-scores for each LiP-Quant experiment.
The combined LiP-Quant score enables direct comparison of LiP peptides with each other and allows more robust discrimination of genuine targets from random hits. Ranking on the protein level was performed using the best LiP-score per protein, only. All half maximal effective/inhibitory concentrations (EC 50 /IC 50 ) were calculated using the drc package (https://www. r-project. org). The necessary output files from Spectronaut are outlined in the docstring at the start of the R script. Criteria used for establishing a LiP threshold score. Aggregating results from five positive control experiments (rapamycin, calyculin A, selumentinib, FK506 and fostreicin) conducted in HeLa lysate and analyzed with our LiP-Quant pipeline, we found that LiP scores show a bimodal distribution. Staurosporine was excluded from the threshold calculation as it shows a level of promiscuity (binding potentially hundreds of kinases) that is rare among drugs, making it difficult to ascertain if low scoring peptides are genuine targets that were not detected or kinases that are not bound by the drug.
As this difficulty in interpreting non-target peptides could bias the threshold calculation the dataset was excluded. Peptides from known target proteins show a clear enrichment in the high-scoring peak of the distribution (LiP-Quant score > 1. 5), whereas all other peptides are enriched in the low-scoring peak of the distribution with a median of approximately 0. 8 (Fig. 1b ). We defined a threshold score of 1. 5 by taking the median LiP-Quant score from the aforementioned experiments, plus three standard deviations, to ensure minimal (<1%) nontarget peptide presence (Supplementary Data 3). Although the approach ensures a strong enrichment for genuine targets, it should be noted that some peptides from these targets are expected below a LiP-Quant score of 1. 5 as both LiP-Quant and non-LiP-Quant peptides can be expected from genuine target proteins.
This could also be, in principle, used as a discriminating factor under the assumption that drug targets with low EC50 values should be indicative of a strong binding interaction between protein and compound, and a compound that binds with high affinity (e. g. nM or lower) is more likely to be an effective drug in vivo. However, we normally do not exclude candidates with EC50 close to the uM range a priori, as compounds that weakly bind the target and have a phenotypic effect can be further refined during pre-clinical drug development. Structural models. The amino acid conservation in the structural model of calyculin A bound to the PP1-gamma catalytic subunit has been calculated using the ConSurf algorithm (Landau 2005). Structures of Botrytis cinerea protein-drug targets were modeled using homolog kinases with high sequence similarity for which experimental structural data was available using the Swiss-model.
Benchmarking of the LiP-Quant classifier. The LiP-Quant staurosporine experiment was selected to provide an estimation of the method FDR because of the known binding promiscuity of the compound. Published datasets of staurosporine proteome-wide binding profiles obtained with TPP 6 and kinobeads  23  were used to compare the predictive power of the three chemoproteomic methods. We used LiP-Quant scores for LiP-Quant, -log 10 (adjusted p-values) of the R 2 correlation indices of the melting curves for TPP reporting both replicates analyzed in the original publication  6  , and the number of spectral counts (PSM) for kinobeads as ranking criteria. For the comparison of LiP-Quant, TPP and kinobeads in the venn diagram of Fig. 2b , we considered as TPP and kinobeads hits, those proteins defined as hits by the authors in the original publications.
3 ) and consider only the protein hits that have multiple high-scoring LiP-Quant peptides. We chose the top 3 LiP-Quant peptides of a protein-drug target candidate among the 15 highestranking peptides by LiP-Quant score of the whole proteome (Supplementary Data 15). Only one protein target candidate typically fulfilled these criteria in all analyzed experiments. LiP-Quant peptides measured with rapamycin, FK506, selumetinib, staurosporine, fostriecin and calyculin A in HeLa proteome extracts were analyzed. We analyzed the staurosporine dataset assayed using a 4 h long LC gradient. We calculated the position of drug binding sites using the center of mass of all atoms assigned to the aforementioned top 3 LiP-Quant peptides of the main candidate target. Structural models and geometric calculations were performed using PyMol 2.
Plates were read in a Victor2 Perkin plate reader (excitation 340 nm/emission 665 nm). Materials. Details of all materials used in these studies are provided in Supplementary Data 16. All chemicals, enzymes, peptides and compounds were purchased from Sigma-Aldrich unless specified otherwise. BAYE-004 was produced by Bayer Crop Science (Supplementary Note 1 and Supplementary Figs. 10, 11). MBP-CK1 recombinant enzyme was cloned in-house by Bayer Crop Science. Frozen HeLa cell pellets were purchased from Ipracell (Belgium) and live HeLa cells for culture were purchased from Sigma-Aldrich. Strains of S. cerevisiae were obtained from the European Saccharomyces Cerevisiae Archive for Functional Analysis (Euroscarf) or subcloned from them (Supplementary Data 16). B. Cinerea was provided by Bayer Crop Science (Supplementary Data 16), over-expression construct for CK1-His was cloned in-house by Bayer Crop Science.
Acknowledgements The authors thank the  Haering laboratory (EMBL Heidelberg ) for providing yeast strains. P. P. is funded by a  Personalized Health and Related Technologies (PHRT)  grant ( PHRT-506 ), a  European Research Council Consolidator (ERC-CoG ) grant ( ERC-CoG 866004 ) and a  Sinergia grant  from the  Swiss National Science Foundation  (SNSF grant  CRSII5_177195 ). P. P. receives funding from the  European Union 's  Horizon 2020 research and innovation programme  ( ERC  grant  866004  and also grant  823839 ), a  Personalized Health and Related Technologies (PHRT)  grant ( PHRT-506 ) and a  Sinergia grant  from the  Swiss National Science Foundation  ( CRSII5_177195 ).
Data availability All mass spectrometry proteomics data have been deposited at ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD018204 and PXD015446. The source data underlying Figs. 1a, 2, 4b, d, f , and Supplementary Figs. 2b, c, 3, 4b, e, 6a, b and 8a are provided as a Source Data file. Source data underlying Figs. 1d, 2c, d, 4c , and Supplementary Figs. 4a, b , e, 5c, 7a, b and 9a are included in the PRIDE repository with the dataset identifier PXD019902. All other relevant data are available from the corresponding authors on request. UniProt fasta databases for organisms were accessed on January 1st, 2018 via the UniProt databases download page (https://www. uniprot. org/downloads). Protein structures for E2AK2 kinase (PDBID: 2a19  25  ), MAPK2K1 (PDBID: 4u7z  44  ), K66A3 kinase (PDBID: 4nus  45  ), FKBP1A (FK506) (PDBID: 1fkj  46  ), FKBP1A (rapamycin) (PDBID: 2dg3  47  ) and PP2A (PDBID: 1it6  48  ) were downloaded in 2019 from the Protein Data Bank website (https://www.
Prediction of mutation effect in Erg11p The impact of mutations in ERG11 was predicted by using different software and the 5v5z entry  39  from the Protein Data Base (https://www. rcsb. org/) as a reference for Erg11p 3D structure. The solvent accessibility of amino-acids within Erg11p and the prediction of protein thermodynamic stability (∆∆G) and thermostability changes (∆Tm) upon single-site mutations were evaluated using PoPMuSiCv3. 1  40  and HoTMuSiCv0. 5. 41 The global effect of mutations were evaluated using SNPMuSiC. 42 All output files were generated using MuSiCv3. 0r3 available online at https://soft. dezyme. com/. The amino-acid solvent accessibility ranged from 0 (buried) to 100 (fully accessible). The mutation effect on protein stability was classified as destabilizing (∆∆G>0 and ∆Tm<0) or stabilizing (∆∆G<0 and ∆Tm>0).
Newly identified mutations impact Erg11p structure. In order to evaluate the putative effect of newly identified amino-acid substitutions in Erg11p, we explored the effect of each mutation using structural prediction with the newly described C. albicans CYP51/Erg11 structural model protein 5v5z as reference. 39 The impact of the 3 newly identified amino-acid substitutions (P30S h , Y64H, P236S) identified in Erg11p was evaluated and compared with the results obtained for mutations already known to be involved in phenotypic fluconazole resistance. The PoPMuSiCv3. 1 and HoTMuSiCv0. 5 software were used to predict ∆∆G, ∆Tm and solvent accessibility of amino-acids and the SNPMuSiC software was used to predict the overall effect of mutations. The heterozygous substitution D9V h could not be evaluated due to absence of the starting positions information in the protein 5v5z entry.
The heterozygous substitution P30S h was predicted to have a neutral impact by SNPMuSic and a putative protein stabilizing effect (∆∆G= -0. 27 and ∆Tm=0. 35) using HoTMuSiC and PoPMuSiC. These results were not in favor of a mutation involved in resistance. However, the 2 homozygous substitutions (Y64H and P236S) showed a low level of amino-acid accessibility < 20% (5. 85 and 17. 94) and an assumed protein destabilizing effect (∆∆G=2. 03 and 1. 41; ∆Tm= -2. 48 and -1. 6 for Y64H and P236S, respectively). These results were corroborated using the SNPMuSic software that predicted a deleterious effect for both mutations. For comparison, the mean values obtained for substitutions already known to be involved in fluconazole phenotypic resistance and found in this study (T123I, Y132H, R157K, S405F, G448E, G450E, R467I) were ∆∆G=1.
Funding E. S. was the recipient of a Ph. D. grant from  DIM Malinf -Région Ile-de-France (DIM Malinf-Région Ile-de-France   2014  to M-E. B. and A. P. ). This work has been supported by the  French Government 's  Investissement d'Avenir program  ( Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases ,  ANR-10-LABX-62-IBEID  to C. d'E).
Nowadays, information and communication technologies (mobile phones, connected objects) strongly occupy our daily life. The increasing use of these technologies and the complexity of network infrastructures raise issues about radiofrequency electromagnetic fields (Rf-Emf) exposure. Most previous studies have assessed individual exposure to Rf-Emf, and the next level is to assess populational exposure. In our study, we designed a statistical tool for Rf-Emf populational exposure assessment and mapping. This tool integrates geographic databases and surrogate models to characterize spatiotemporal exposure from outdoor sources, indoor sources, and mobile phones. A case study was conducted on a 100 × 100 m grid covering the 14th district of Paris to illustrate the functionalities of the tool. Whole-body specific absorption rate (SAR) values are 2.
The tool was designed under the software (version 2. 18), which is linked with Python programming language [27]. All geoprocessing QGIS methods necessary to the approach were run on this software and, also on R (version 3. 3. 2) via the "Processing" framework  [27]. The linkage between R, QGIS, and Python provides data processing methods that are not directly available on each software. As part of this study, we designed a statistical tool with a graphical user interface (GUI) that provides automated geospatial data processing to assess and map aggregated exposure to Rf-Emf sources (Figure  2 ). It integrates data that characterize an analysis area and allows one to define parameters for exposure scenarios. To be useful to entities in charge of the risk assessment, to epidemiologists, and to the general public, the developed toolboxes were designed to be as simple as possible.
The tool was designed under the software (version 2. 18), which is linked with Python programming language [27]. All geoprocessing QGIS methods necessary to the approach were run on this software and, also on R (version 3. 3. 2) via the "Processing" framework  [27]. The linkage between R, QGIS, and Python provides data processing methods that are not directly available on each software. The aggregated exposure assessment was conducted on the 14th district of Paris, which is densely urbanized, to illustrate the functionalities of the statistical tool. The aggregated exposure assessment was conducted on the 14th district of Paris, which is densely urbanized, to illustrate the functionalities of the statistical tool.
In total, 1100 measurement points were recorded during the trial to cover the entire district. Each grid cell was 2. 4 points on average. To assess the possibility of extracting exposure information from sparse data, we used the full dataset in combination with ordinary kriging. Since downlink exposure was log-normally distributed, a logarithmic transformation was applied to obtain normally distributed data. Ordinary kriging was calculated by considering only interpolation points in a local neighborhood of the point to be predicted, thus obtaining a local varying mean, which has been chosen equal to four times the variogram range. Estimated exposure was then back-transformed into linear scale by applying the following operations (1 and 2)  [29, 30] : 𝑧(𝒙) = 𝑒 (𝑧 ̃𝑙𝑜𝑔 (𝒙) + 𝜎 𝑙𝑜𝑔 2 (𝒙) 2 ⁄ ) , (1) 𝜎 2 (𝒙) = 𝑒 2 × 𝑧 ̃𝑙𝑜𝑔 (𝒙) + 𝜎 𝑙𝑜𝑔 2 (𝒙) × (𝑒 𝜎 𝑙𝑜𝑔 2 (𝒙) -1) , (2) where () and  2 () are, respectively, the predicted value and the kriging variance at point x, ̃  () is the predicted value in natural logarithmic scale, and   2 () is the kriging variance in logarithmic scale.
In total, 1100 measurement points were recorded during the trial to cover the entire district. Each grid cell was 2. 4 points on average. To assess the possibility of extracting exposure information from sparse data, we used the full dataset in combination with ordinary kriging. Since downlink exposure was log-normally distributed, a logarithmic transformation was applied to obtain normally distributed data. Ordinary kriging was calculated by considering only interpolation points in a local neighborhood of the point to be predicted, thus obtaining a local varying mean, which has been chosen equal to four times the variogram range. Estimated exposure was then back-transformed into linear scale by applying the following operations (1 and 2)  [29, 30] : z(x) = e ( z log (x)+σ 2 log (x)/2) , (1) σ 2 (x) = e 2× z log (x)+σ 2 log (x) × e σ 2 log (x) -1 , (2) where z(x) and σ 2 (x) are, respectively, the predicted value and the kriging variance at point x, z log (x) is the predicted value in natural logarithmic scale, and σ 2 log (x) is the kriging variance in logarithmic scale.
Near-Field Exposure Mobile phone exposure is cumulative with outdoor and indoor exposures. This exposure reflects two main usages of mobile phones, i. e. , voice calls and data transfers via base stations or Wi-Fi (Figure  1 ). Other parameters affect the exposure such as call time, laterality, and posture for voice mode  [11, 58, 59] , volume of data traffic for data mode  [11] , and uplink transmitted power (P x ). For both modes, physiological data were also used (body weight, head weight, age  [60] ). Some of these parameters were calibrated with Mobi-Expo data  [9, 10]. P x values were measured in the 14th district through Nemo Handy trace mobile phones while uploading 100 MB files on an FTP (File Transfer Protocol) server repeatedly. Whole-brain and whole-body SAR transfer functions were computed by using equations from the RF-IEM (RF-EMF Integration Exposure Model) tool developed within Geronimo and CREST (RF exposure induced by novel use and new technologies from mobile communication systems) projects and exposure was estimated with the equations from the LEXNET study that are providing the whole-body and whole-brain SAR per unit of time and W/m 2 for downlink exposure and per watt emitted from the devices  [11, 27]   (6).
Assessment of Population in Buildings (Residents and Workers) The first step in the calculation procedure is the estimate of the population in buildings according to their employment, i. e. , resident or occupational activity. A building typology was constructed using data from the MAJIC (Cadastral Information Update) database (Cerema, https://datafoncier. cerema. fr/ donnees/fichiers-fonciers) to locate buildings and define their function. The population in buildings, named P i was estimated for each age class and category by a surface ratio using IRIS (Grouped Blocks for Statistical Information) data from the 2015 national census (National Institute of Statistics and Economic Studies, INSEE) (7): P i = n j=1 S ij S j P j , (7) where S ij is the housing area intersecting the IRIS, P j the residential population in the IRIS, and S j the total housing area in the IRIS.
The final exposure distributions were then segregated into 20 percentiles, using the NumPy module (version 1. 11. 3, https://numpy. org/, [64] ). It is important to note that all aggregated exposure estimates were given in SAR (mW/kg). As some exposure distribution parameters, especially for outdoor and Wi-Fi distributions, were computed in volt per meter (V/m), these distributions were converted into SAR using the RF-IEM tool  [26].
Inner Brussels and Melbourne, which are more densely populous, display similar median outdoor estimates, each one reaching 0. 59 V/m  [75, 76]. Outdoor exposure was quantified by an ordinary kriging method, and the quality of the interpolation was cross-validated by leave-one-out method. In a different approach, Isselmou et al. used kriging with external drift. They combined measurement data with a geostatistical model output as the external drift  [7]. In our case, the geostatistical model could be improved with Cartoradio measurement data (https://www. cartoradio. fr/index. html). Measurements would also serve as control points for the goodness of interpolation. Meanwhile, outdoor exposure time-variation was assessed from dedicated Rf-Emf sensor networks with multiple sensor nodes in relative proximity (100 nodes per km 2 ) which, to our knowledge, are only found in Santander and Antwerp  [18, 32, 33].