dataesr/scientific-paragraphs-categorization huggingface dataset:
When target modification sites are used, the summarizer builds a table with one target site by column. Thus, it is very easy to see which target sites are identifiable or not by a set of proteases. PTMselect Benchmarks. Simulation time depends on the number of proteases and the size of the protein (cf. Supplemental Fig. S2 ). On a Linux 64 bit workstation with one CORE i7 processor the simulation time for 5 digestion settings out of 14, i. e. the simulation of 2379 protease combinations, was <6 sec for Lamin and <12 sec for Citron-kinase. Simulations of parallel proteases digestions. Protein sequences. We used six publicly available protein sequences to evaluate PTMselect (see Supplementary files). PD-1, p53, Huntingtin, Citron-kinase, Cortactin, and Lamin were chosen for their high phosphorylation level, size range, and biological relevance.
Their fasta sequences were obtained from UniProt database  22. PTMselect simulations for six proteins. Parallel protease digestions were simulated for p53, PD-1, Huntingtin, Citron-kinase, Cortactin and Lamin using the default proteases list provided with PTMselect (8 proteases + CNBr). Up to five parallel digestions were simulated with a peptide size range from 7 to 40 amino acids (supplemental files). PTMselect targeted analysis of the TCR signaling pathway. Fasta sequences of proteins of the TCR signaling pathway were downloaded from UniProt database  22. Phosphosite positions for the proteins in this pathway where obtained from reference  17  and PhosphoSitesPlus website  23. For each protein, a text file containing the target site positions was created and used as input in PTMselect (Fig.
1a ). Fasta files and target sites were processed together in PTMselect to produce a score table for each protein. In each score table, target phosphosites identifiable and unidentifiable by any digestion setting were listed. A combination of all score tables was computed automatically by PTMselect summarizer (Fig. 1a ) in a summary table. The summary table was then sorted by number of target phosphosites identifiable in decreasing order, to identify the best digestion settings for MS analysis of the entire TCR pathway (supplemental files). PTMselect prediction of multiple PTMs in a cross-talk example. Fasta sequence of protein H3. 1 (Mus musculus) was obtained from from UniProt database  22. The N-terminal methionine was removed from the sequence. Parallel protease digestions were simulated for H3.
Code availability. PTMselect was developed using the high performance cross-platform Julia language  24  for numerical computing. Files can be accessed at (https://sites. google. com/site/fredsoftwares/products/ptm-select). A manual for using PTMselect to perform phosphosites basic and advanced search can be found in supplemental files. Peptides alignment tool, PepAlign, and lists comparison tool, nwCompare  25  , used to calculate the PTMs concordance are freely available at (https://sites. google. com/site/fredsoftwares/products/pepalign) and (https:// sites. google. com/site/fredsoftwares/products/nwcompare-julia).
Instrumentation The UPLC-MS/MS analysis was conducted using an Acquity UPLC H-Class System coupled to a Xevo TQD Tandem Mass spectrometer (Waters, MA, USA). The ESI was operated in positive ionization mode. Multiple ion monitoring chromatograms were acquired using MassLynx Mass Spectrometry Software 4. 1 (Waters). Chromatographic & mass spectrometer conditions An Acquity UPLC BEH C18 (50 × 2. 1 mm, 1. 7 μm) analytical column was used. Mobile phase A was water: formic acid (100:0. 5, v:v) ammonium formiate 2 mM, and mobile phase B was acetonitrile: formic acid (100:0. 5, v:v). A linear gradient was ramped up from 40 to 95% of solvent B in 2. 5 min at flow rate 0. 6 ml/min. These conditions were maintained during 0. 75 min to clean the column and initial conditions were then restored (95-40% B, 0.
Patients & sampling The clinical protocol was approved by the ethics committee of Grand Ouest (Comité de Protection des Personnes Grand Ouest IV-Eudract number: 2008-007652-10). Ten women with early breast cancer (mean age and SD   63. 4 ± 13. 2) had been diagnosed for estrogen receptor-positive tumors and received TAM therapy 20 mg/day for at least 5 weeks. Blood samples were taken in the morning before ingesting a new dose of TAM, and between 18 and 24 h after the last TAM intake. After centrifugation (1000 × g, 10 min at 4 • C), EDTA plasma was immediately stored at -80 • C until analysis.
Linearity The calibration curves were obtained by plotting the ratio of the peak area of studied analytes to each specific internal standard against the respective concentration. All calibration curves proved to be linear over the concentration range of 1-500, 0. 1-50 and 0. 2-100 ng/ml for both TAM and NDTAM, 4OHTAM and ENDO, respectively when evaluated by weighted (1/×) linear regression. Tables  2  and 3  show the mean deviation (RE) and the RSD at each calibration level, calculated using data obtained on six consecutive runs, for the four analytes. The criterion for accepting any curve was that all data points should have an RSD and an RE of less than 15%. The results of the lowest standard point were -3. 2 and 8. 4%; -7. 3 and 1. 6%; -3. 0 and 6. 7%; -7. 7 and 6. 4% for RE and RSD, for TAM, NDTAM, 4OHTAM and ENDO, respectively.
Precision & accuracy Within-run and between-run precision and accuracy were determined with QC samples at different concentrations as described in the experimental section. Data for within batch and between batch precision and accuracy of the method are presented in Table  4. Within-run precision ranged between 4. 1 and 11. 0%, 4. 5 and 12. 4%, 4. 2 and 12. 2%, 6. 1 and 11. 1%; betweenrun precision ranged between 5. 3 and 9. 0%; 5. 6 and 9. 3%; 4. 7 and 10. 3%, and 5. 1 and 8. 9 % and the range of accuracy was -2. 7 to 4. 7, -5. 2 to 4. 1%, -0. 2 to 10. 1% and -2. 0 to 2. 9%, for TAM, NDTAM, 4OHTAM and ENDO, respectively. No carryover effects were observed.
Acknowledgements This work is supported by the  Swedish Research Council. ET is also supported by the  Swedish Research Council Formas. The work acknowledge funding from the  CaPPA project (Chemical and Physical Properties of the Atmosphere)  funded by the  French National Research Agency  through the  Programme d'Investissement d'Avenir  under contract  ANR-11-LABX-0005-01 , the  Regional Council Nord Pas de Calais-Picardie  and the  European Funds for Regional Economic Development (FEDER) , and  Hauts de France council and European Regional Development Fund (ERDF)  through the  CPER Climibio. This work was performed using HPC resources from  GENCI-TGCC  (Grant  2017-A0010806820  and Grant  2018 -A0030806820 ).
β-actin was used as loading control. Ratio of COX-2/β-actin was calculated after densitometry analysis using ImageJ software. Data are represented as mean ± SEM (n = 4), *p < 0. 05 versus LPS (One-way Anova followed by the Dunnett's test) COX-2 activity was also assessed on BMDM treated for 30 min with aspirin to inhibit basal COX activity prior to the addition of 10 ng/mL LPS for 24 h which induces COX-2. These cells were then treated with 10 and 50 µM of derivatives and further incubated with AA. PGE 2 production revealed that compound A5 inhibited strongly COX-2 activity with an IC 50 of 13. 3 µM, whereas moderate effect was observed for compounds A2, A3 and A4 (Fig. 2b ). Indeed, the assay used for COX-2 activity cannot exclude an effect on mPGES-1. In parallel, we assessed the effect of these compounds on the production of IL-6 and NO measured by its breakdown product nitrite.
Data analysis IL-6, nitrite and PGE 2 measurement were determined from 3 to 5 independent experiments and expressed as percentage of LPS alone and expressed as mean ± SEM. COX-1 activity for the compounds were expressed as percentage of PGE 2 measured in cells exposed to vehicle and AA, and as percentage of PGE 2 in cells treated with LPS, ASA and AA for COX-2. Curve fitting and calculation of the IC 50 values were done using Grafit7 Software (Erithacus software, Staines, UK) and GraphPad Prism 6. Images of western blot were analyzed using ImageJ Software (version 1. 52a, NIH, MA). Ratio of COX-2 to β-actin was determined and the results are expressed as fold of LPS signal. Statistical analysis was performed using one-way ANOVA, followed by Dunnett's multiple comparisons test. Differences were considered significant when p < 0.
The capacity of biomolecular NMR to determine structures has also continuously improved. Higher field strengths, cryogenic probes and optimal isotope labeling have boosted sensitivity and resolution. More efficient and automated data treatment facilitates and accelerates structure determination. 5 Magic-angle spinning solid-state NMR (MAS NMR) has continued improving its capacity to determine structures, in particular due to faster spinning and proton detection. NMR stays an important technique also for determining structures of nucleic acids and their complexes. Nonetheless, structure determination by NMR a is not expanding; in contrast to XRD and EM, the number of NMR structures deposited in the Protein Data Bank per year has in fact been slightly decreasing over the last decade to about 400, compared to about 10000 by XRD and >800 by EM.
Context. Disks around pre-main-sequence stars evolve over time by turbulent viscous spreading. The main contender to explain the strength of the turbulence is the Magneto-Rotational-Instability (MRI) model, whose efficiency depends on the disk ionization fraction. Aims. We aim at computing self-consistently the chemistry including PAH charge chemistry, the grain charging and an estimate of an effective value of the turbulence α parameter in order to find observational signatures of disk turbulence. Methods. We introduced PAH and grain charging physics and their interplay with other gas-phase reactions in the physico-chemical code ProDiMo. Non-ideal magnetohydrodynamics effects such as Ohmic and ambipolar diffusion are parametrized to derive an effective value for the turbulent parameter α eff.
Teague et al. (2016)  found a turbulence value of (0. 2 -0. 4) c s in the TW Hya disk, while  Flaherty et al. (2018)  constrained α to be between 0. 04 c s and 0. 13 c s. Flaherty et al. (2015)  provided a low upper limit of v turb < 0. 03 c s for the turbulence in the HD 163296 disk. Earlier studies with the IRAM Plateau de Bure interferometer provided limits of v turb ≤ (0. 3 -0. 5) c s  (Dartois et al. 2003; Piétu et al. 2007). Hughes et al. (2011)  constrained the value v turb ≤ 0. 1 c s for the TW Hya disk and v turb ≤ 0. 4 c s for the HD 163296 disk using data obtained by the Smithsonian Millimeter Array. The accuracy of the estimated values is limited by the uncertainties in inverting the protoplanetary disk thermal structure from the observations. Hartmann & Bae (2018)  argue that a low viscosity disk model driven by hydrodynamic turbulence is compatible with the observed disk mass accretion rates and the measured low turbulence width.
In this paper, we explore further the effects of a detailed treatment of the physics and chemistry of PAHs and grain charging on the disk ionization. For this purpose, we implemented MRIturbulence heating and cooling in the photo-chemical disk code ProDiMo. We considered far-ultraviolet from the star and the accretion excess, X-ray, and cosmic rays as sources of ionization. We used CO isotopologue rotational lines as potential tracers of turbulence in disks because CO is the most abundant molecule in disks after H 2 , is widespread through the disks, and because its chemistry is well understood. The numerical study of MHD processes in triggering disk turbulence is beyond the scope of this paper. Instead, we parametrize the onset and effects of turbulence on the gas by simple empirical formula.
This study focuses on the role of the gas and grain chemistry in the disk's ionization, which in turns can affect the non-ideal MHD coefficients. The scope of the ProDiMo code is to be able to derive disk parameters such as α by matching high signal-to-noise observations carefully considering the relevant disk physico-chemical processes. The paper is organized as follows: the ProDiMo code is introduced in Sect. 2. 1; a brief discussion on the gas-phase and PAH chemistry are given in Sect. 2. 2 and 2. 3; the dust charging physics implemented in the code is described in Sect. 2. 4; the prescription of our MRI-driven turbulence model is provided in Sect. 3; analytical results on the influence of non-ideal MHD are shown in Sect. 4; the protoplanetary disk model and the model results are presented in Sect.
ProDiMo ProDiMo is a code built to model the gas and dust grain physics and chemistry  (Woitke et al. 2009; Kamp et al. 2010; Woitke et al. 2016). It has been used to model disk Spectral Energy Distributions (SEDs,  Thi et al. 2011) , water deuteration chemistry  (Thi et al. 2010 ) CO rovibrational emissions including UVfluorescence  (Thi et al. 2013) , and many Herschel observations from the GASPS large programme. X-ray physics are implemented  (Aresu et al. 2012; Meijerink et al. 2012; Aresu et al. 2011). ProDIMo has been designed to run within a few CPUhours per model such that automatic fittings of the observed continuum emission and of thousands of gas lines are feasible. Such fitting procedure requires running thousands of models, which would be too time-consuming with a full 3D non-ideal MHD radiation chemical code.
(35) However, we did not have anions in the chemical network at this current stage. Charge exchange reactions will result in the positive charges being carried by the species with the lowest ionization potential, whose value is lower than the energy required to detach an electron from a neutral grain. The cations with low ionization potential will preferably recombine with free electrons and not with negatively-charged grains because of the velocity and also on the extra work required to remove an electron from a grain (∼2. 7 eV). (Z d = -1, 1 µm) 2. 7 charged grain (Z d = 1, 1 µm) 8. 002 Ref. NIST database (Linstrom & Mallard 2005).
Acknowledgments FUNDINGS: -CENAPD at  UNICAMP-SP, Brazil  for the computational resources. - CNPq  (grant number  400953/2016-1 ). - Fundação Araucária  ( Code 006 -09/2016 ). - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil  ( CAPES) -Finance Code 001. - Ministère de l'Enseignement Supérieur et de la Recherche Scientifique, Algeria.
Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it.
This scenario makes development of analysis software or a specific usage of public databases very challenging. In a typical MS-based metabolomics/lipidomics pipeline, samples are analyzed by liquid or gas chromatography, coupled to MS (LC-MS/GC-MS), or by direct infusion (DIMS). Measurement of molecular intensity is typically done via software that detects features formed from isotopic patterns (or single peaks) along the time axis. For LC-MS, ionization can be performed in either positive or negative mode to produce protonated or deprotonated ions. It is also common for ion adducts to be formed, including metal adducts (Na + , K + ), which have the same time elution profile but different m/z values. Many software packages perform adduct grouping, such that quantification values are reported both for individual features, as well as for the summed abundance across different adduct forms assumed to have come from the same starting molecule.
For quantification across different samples, software may perform retention time alignment to ensure that the same features are quantified in each sample. In GC-MS, analysis is performed on volatile molecules and, in some cases, a derivatization step is applied to increase the volatility of compounds of interest. Molecular identification remains challenging in metabolomics. Typically, some combination of the following steps can assist with identification via searching a pre-existing library or database: accurate neutral mass, the relative abundance of isotopomers, the retention time, masses of fragmentation products (MS/MS and MS n spectra), collisional cross section for platforms with ion mobility, etc. (see the reviews in refs 2-4 for more details). In the case of MS/MS and MS n fragmentation, the spectra can be compared against an in-house spectral library or databases storing reference spectra for molecules including Metlin,  5  The Human Metabolome Database,  6  Global Natural Products Social Molecular Networking 7 (GNPS), MassBank,  8  and others (see ref 9 for a review) or analyzed by in silico identification software.
For public data sharing, there are several databases that host data sets in support of publications or community data sets, including the European Bioinformatics Institute (EMBL-EBI) MetaboLights database  15  and the NIH Metabolomics Workbench. 16 n this work, we describe a data standard for MS-based metabolomics analytical pipelines, called mzTab-M, which captures the downstream results of analysis (i. e. , excluding raw data), suitable for statistical analysis, result visualization, or submission to a public repository in support of a publication. The standard has been developed in a joint and open process between members of the Metabolomics Standards Initiative (MSI),  17  the Metabolomics Society Data Standards Task Group, and the Proteomics Standards Initiative (PSI), which had originally developed the mzTab format on which it is based.
18 There are several related and complementary efforts, which include efforts to define minimum reporting requirements for different aspects of metabolomics. 11, 19 There is also general agreement among standards groups (MSI, Metabolomics Society) to promote the use of the PSI's mzML format for raw data storage. 20 mzML is an XML-based standard for MS data, either for profile data as recorded directly from the instrument or for centroided data (peak picked in the m/z domain). The freely available ProteoWizard software embeds software libraries from several vendors of MS instruments, enabling the conversion of vendor raw files into mzML. 21 For NMR metabolomics, the recently released nmrML standard follows a design principle similar to that of mzML, capturing NMR spectra and some metadata within an XML-based standard.
22 For the description of study design, experimental metadata, and sample processing parameters, the ISA framework,  23  while generally applicable to all types of experimental design, has been particularly taken up by the metabolomics field. The PSI previously developed the mzTab format (version 1. 0) to act as a simple format for quantified and/or identified peptides and proteins in MS workflows. 18 mzTab version 1. 0 also has a section to allow smallmolecule data to be captured. However, the data model was rather simple and did not cover some important use cases for metabolomics/lipidomics and, as a result, it has not been extensively used for small molecules or lipids. The development of mzTab-M has thus branched off from the original mzTab format development, and we report it here as a new standard for metabolomics called mzTab-M ("version 2.
■ METHODS The mzTab-M format was designed in a process that was open to any interested parties. All associated materials and code for processing and validating files are fully open source and are hosted on GitHub (https://github. com/HUPO-PSI/mzTab). mzTab-M started from the design of mzTab version 1. 0 format but was further developed to support the specific needs of metabolomics (see the Supporting Information for more details on the relationship). The development took place via face-to- face workshops and regular conference calls. The specifications have been submitted to a formal document process for anonymous review, overseen by an editor commissioned jointly between the PSI and the Metabolomics Society. The mzTab-M format is defined by the specification document and example files that demonstrate how to encode certain features (see the GitHub repository).
The specification document describes the overall structure of the format: what tables must be present, what columns and rows must be present in those tables, and what terminology is allowed as data values. For various aspects of metadata, the standard enforces (and can be checked by validation software) that controlled vocabulary (CV) terms are used (e. g. for names of software, databases, parameters, statistics, etc. ), which can be sourced from the PSI-MS CV  26  (https:// www. ebi. ac. uk/ols/ontologies/ms), as well as other CVs where appropriate. ■ RESULTS AND DISCUSSION mzTab-M has been designed to act as a single data format for metabolomics and lipidomics, including an appropriate level of detail about the "final" results: i. e. , molecules quantified across samples. The format also contains the ability to represent "intermediate" or supporting data, including the evidence trail for identifications from software (scores or statistics), as well as the quantification values derived directly from MS: i.
e. , prior to any adduct grouping or summarization steps. The format is represented as tab-separated text, meaning that it can be loaded directly into a spreadsheet editor or into statistical software such as R or SPSS for downstream analysis and visualization, without any need for coding, and can thus replace the use of tables (e. g. , in pdf or Excel format) of supplementary data in support of publications. It is also relatively straightforward for informatics groups to develop software to add support for the standard to existing software. The mzTab-M format consists of four cross-referenced data tables (Figure  1 ): metadata (MTD), small molecule (SML), small molecule feature (SMF) and the small molecule evidence (SME). The MTD and SML tables are mandatory, and for a file to contain any evidence about how molecules were quantified or identified by software, all four tables must be present.
The tables must follow the order MTD, SML, SMF, and SME, with a blank line separating each table. The structure of each table, in terms of the rows and columns that must be present, is tightly specified, as explained in the following sections and formally in the mzTab-M specification document. Metadata (MTD) Table. The metadata table has multiple rows and exactly three columns (Figure  1A ). Each row must contain (1) "MTD", (2) a parameter name, and (3) the parameter value. The types of parameters that must or may be present are described in the specification document, and allowed values from CVs are defined in a mapping file. The MTD table must report at least a simple specification of the experimental design, in terms of the number of different measurements (i. e. , usually the count of MS runs) and the groupings of those MS runs (i.