dataesr/scientific-paragraphs-categorization huggingface dataset:
In both candidate regions, we morphologically characterized myelinated axons in the Sapap3-KO mouse model, measuring both axon caliber and myelin thickness as the determinant parameters of axon conductivity. To do so, we took advantage of the open-source software AxonDeepSeg  51  , which automatically segments axon and myelin compartments using deep learning methods and performs various morphometric measurements (axon caliber, myelin thickness, g-ratio). AxonDeepSeg is fast (a few seconds per image), allowing the analysis of hundreds of images in only a few minutes. As myelin coverage is done by oligodendroglial cells, i. e. the glial cells that wrap around the fibers to produce the myelin sheaths, we additionally screened this cell type in the same regions of interest for potential abnormalities.
Acknowledgements All animal work was conducted at the  PHENO-ICMice facility. This work furthermore benefitted from the equipment and services from the  iGenSeq core facility  at the  ICM  for the genotyping of the animals. All electron microscopy imaging was performed using the equipment of the ICM. QUANT facility, with particular regards to all the advice and help provided by  Dominique Langui. All fluorescent microscopy was conducted using the equipment at the  CELIS core facility. Moreover, sample preparation and immunohistochemistry were possible using the equipment at the  HISTOMICS core facility. We thank  Drs. G. Feng  and  Ann M. Graybiel  for kindly providing founders of the Sapap3-KO colony,  Christian Perone  for helping us apply AxonDeepSeg,  Dr. Dorien Maas  for her guidance with immunohistochemistry protocols,  Sami Beaumont  for all the advice on analytical and statistical tools, as well as  Marine Euvrard  for her help with animal experiments.
This work was realized with the following funding:  Agence Nationale de la Recherche  ( ANR-16-INSERM-SINREP ,  ANR-19-ICM- DOPALOOPS ) (EB), the  ICM Big Brain Theory Program  ( BBT-ACTIMYEL ; EB,  BN-O ), the  L'Oréal-UNESCO  Fellowship 2016 (CS), the  Canada Research Chair in Quantitative Magnetic Resonance Imaging  [ 950-230815 ], the  Canadian Institute of Health Research  [ CIHR FDN-143263 ], the  Canada Foundation for Innovation  [ 32454 ,  34824 ], the  Fonds de Recherche du Québec -Santé  [ 28826 ], the  Natural Sciences and Engineering Research Council of Canada  [ RGPIN-2019-07244 ], the  Canada First Research Excellence Fund  ( IVADO and TransMedTech ) and the  Quebec BioImaging Network  [5886,  35450 ]. The core facilities were supported by " Investissements d 'avenir"  ( ANR-10-IAIHU-06  and  ANR-11-INBS-0011-NeurATRIS ) and " Fondation pour la Recherche Médicale ".
for visualization of our data, two open-source software packages available on GitHub. Cluster analysis was done by fitting a Gaussian mixture model with two components (2 clusters) to our data 81. 78. Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18, 529 (2017). 79. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676-682 (2012). 80. Ho, J. , Tumkaya, T. , Aryal, S. , Choi, H. & Claridge-Chang, A. Moving beyond P values: data analysis with estimation graphics. Nat. Methods 16, 565-566 (2019). 81. Patel, E. & Kushwaha, D. S. Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model. Procedia Comput. Sci. 171, 158-167 (2020).
1995; Mathur et al. 2008; Eff-Darwich et al. 2008; Marques et al. 2013; Benomar et al. 2015; Eggenberger et al. 2019a; García & Ballot 2019). In this work, we explore the chemical and rotational evolution of low-mass stars on the PMS and the MS for different stellar masses and metallicities that cover the range of Galactic open clusters with ages between 5 Myrs and 4 Gyrs. We study the effects and the relevance for these stars of different transport processes that we already explored and validated for the specific case of the solar-type stars (Paper I), and that depend on both mass and metallicity. In Sect. 2, we present the observational data that we used to constrain model predictions. In Sect. 3 we describe the input physics of the stellar models and the different processes implemented in the evolution code STAREVOL used for this work.
In Sect. 4, we compare the observations for the Hyades and Praesepe open clusters with the predictions for Li, Be, and surface rotation of our non-standard model including meridional circulation, shear-induced turbulence, atomic diffusion, overshoot, and parametric viscosity and turbulence. In Sect. 5, we compare the model predictions with Li and rotational periods (P rot ) data in a sample of open clusters over a broad range in age and metallicity. In Sect. 6, we discuss model predictions for the internal rotation and compare to asteroseismic measurements. We summarise our results and conclude in Sect. 7.
Surface and internal rotation We compared the surface rotation predicted by our models with the observational data set gathered by  Irwin & Bouvier (2009) ,  Gallet & Bouvier (2015)   1 ). We constrained our model predictions for internal rotation using the asteroseismic measurements obtained by  Benomar et al. (2015)  for a sample of MS field stars observed by Kepler. We selected a sub-sample of four stars with metallicities close to that of the Hyades.
Input physics We used the same updated version of the stellar evolution code STAREVOL as in Paper I (for general information and previous versions see  Siess et al. 2000; Palacios et al. 2006; Decressin et al. 2009; Lagarde et al. 2012; Amard et al. 2019 ), which we refer to for details and references. The models presented in this work were computed with the same inputs physics (equation of state, opacities, nuclear reactions, model atmosphere, and mass loss). We used the same values for the mixing length parameter (α MLT , assuming the Schwarzschild criteria for convective stability) and the initial abundances that resulted from model calibrations on the Sun that were carried out for classical models (no transport of chemicals beyond convection), and for models including both rotation and atomic diffusion (respectively models C and R1 from Paper I).
The Hyades and Praesepe Here we focus on the well-studied Hyades and Praesepe for which Li, Be, and surface rotation data are available. These two open clusters are close in age (0. 72 Gyr and 0. 75 Gyr, respectively) and metallicity ([Fe/H] = +0. 13±0. 05 and +0. 16±0. 08 dex, respectively) according to the references quoted in Table  1. Figure  2  shows the observed Li abundances and rotation velocities (V sin i) as a function of the effective temperature from  Cummings et al. (2017). Both clusters exhibit similar patterns, namely the so-called Li dip between ∼ 6'400 and 6'800 K, the Li-T eff decrease in G-type stars on the cool side of the dip, and the well-know break in rotation velocity (≈ 6'400-6'500 K) for dwarf stars later than the F4 spectral type  (Schatzman 1959 (Schatzman , 1962;; Kraft 1967; Boesgaard 1987 ).
Model predictions for Li and surface rotation -Comparison to open clusters of different ages and metallicities In this section, we compare the predictions of models ν R1 T 6. 42 A and M ν ′ R2 T 6. 42 A to rotation rates and surface Li abundances for a sample of open clusters with different ages and metallicities (see Table  1 ). Be cannot be used here, as there are not enough available data for clusters other than the Hyades and Praesepe. In the ν R1 T 6. 42 A case, we computed models for the actual [Fe/H] of the individual clusters and for three initial rotation rates (slow, median, and fast), while in the M ν ′ R2 T 6. 42 A case, we used the models at the metallicity of the Hyades and Praesepe ([Fe/H] = +0. 15 dex), and for median rotation discussed previously. The values of A(Li), T eff , and P rot predicted by the M ν R1 T 6.
In these models, the convective envelope is thinner due to the lower metallicity and the penetrative convection, which is the main process responsible for the Li depletion on the PMS and it is less efficient than at higher metallicity. This can be directly seen when comparing the panels showing NGC 6633 and UMa clusters as well as the Hyades and Praesepe clusters, which have similar ages but different metallicities. This suggests a stronger metallicity dependence of the Li depletion in cool stars compared to that predicted. Data for the cooler stars in the most metal-poor and oldest cluster (NGC 2243) would be required to confirm this trend at older ages. We see in Fig. 6  that the Li dip is not present in the youngest clusters, and that its depth increases with time along the MS (see also  Wallerstein et al.
A , assuming a similar dependence between ν ′ add and T eff as assumed to fit the Hyades and Praesepe, would predict an even stronger Li depletion and would be incompatible with the data of NGC 2243. We thus reach similar conclusions as in Sect. 4. 2, for a relatively large range of metallicities and ages. Model ν R1 T 6. 42 A initially developed to reproduce surface and internal constraints for solar-type stars nicely explains the general trend of the data for both the rotation and Li behaviours over the mass and the metallicity ranges probed in this study, except for the Li dip. On the other hand, model M ν ′ R2 T 6. 42 A with turbulent viscosity for the transport of angular momentum parametrised to fit the dip in the Hyades and the Pleiades predicts rotation rates that are too slow for the lower mass models.
We have analysed the agreement between the theoretical predictions of these two sets of models and observational data for 7 The expression used is similar to Eq. (  18 ) from Paper I, but starting the integration at the outer edge of the convective core when present: Ω rad = M BCE M TCC r 2 Ω dm/ M BCE M TCC r 2 dm, with M TCC being the mass coordinate of the top of the convective core and M BCE being the mass coordinate at the base of the convective envelope. Li, Be, and surface rotation rates available in a sample of open clusters of different ages and metallicities. We also compared the predicted internal rotation profiles with asteroseismic constraints in field MS stars with [Fe/H] close to that of the Hyades. Both ν R1 T 6. 42-4. 5 A and M ν ′ R2 T 6. 42 A explain the main general trends observed between Li depletion and stellar mass, age, rotation, and metallicity covered in this study.
Both the ν R1 T 6. 42 A and the M ν ′ R2 T 6. 42 A models predict mean angular rotation velocity in the radiative interior that are compatible with the values inferred from asteroseismology in field MS with the same metallicity as the Hyades and the Pleiades. Additional asteroseismic data to probe the internal rotation of young MS stars over a large metallicity range would be valuable. Reflecting on the modelling of turbulent shear, we have to conclude that none of the combinations for the prescriptions of the horizontal and vertical turbulence (D h and D v ) we used is able to reconcile all the surface constraints used in this study. Model ν R1 T 6. 42-6. 5 A , tailored for solar-type stars, includes the  Mathis et al. (2018)  prescription for the horizontal shear diffusivity D h , which is the only prescription including the contributions of both the horizontal and vertical shear on the horizontal turbulent transport, and the  Zahn (1992)  prescription for the vertical shear diffusivity D v , which does not take into account the effects of thermal and molecular diffusivity on the vertical turbulent transport, but it is the only one that has been validated by direct numerical simulations  (Prat & Lignières 2013; Garaud et al.
Acknowledgements. We thank  J. W. Ferguson  for providing us with lowtemperature opacity tables adapted to the solar mixture we adopt in this work,  E. Semenova  for providing us observational data for NGC 2420 open cluster,  J. D. Cummings  and collaborators for providing the log g values for their Hyades and Praesepe sample,  L. Amard  and  P. Eggenberger  for fruitful discussions, and the anonymous referee for constructive comments on the manuscript. This work was supported by the  Swiss National Science Foundation  (Project  200020-192039 PI C. C. ), by  CNES  (funding of AP at LUPM) focussed on the PLATO mission, and by the  Agence Nationale de la Recherche  grant  POPSYCLE  number  ANR-19-CE31-0022. This research has made use of  NASA's Astrophysics Data System Bibliographic Services.
The isotope cluster for peptides remains strictly bound to the natural monoisotopic ion, leading to a large increase in the intensity of the monoisotopic ion of peptides and proteins. This allows for better precision in the monoisotopic mass measurement, with higher identification scores and protein sequence coverage 1 in mass spectrometry-based experiments. This method, initially developed for signal processing and quantifying the incorporation rate of  12  C into peptides, was based on a multistep process that was difficult to implement for many laboratories. It involved (1) extracting the intensities of isotopologues using the commercial software Progenesis QI for metabolomics, (2)  aligning the "Features" files with the peptide identification files (Mascot files), (3) calculating the ratio of the monoisotope intensity to the intensity of all isotopologues (called R iso ), (4)  establishing the equation of the theoretical R iso values for each peptide sequence, calculated using the MIDAs application  7  as a function of  12  C enrichment, after determining the elemental composition of each peptide and therefore the exact number of carbons it contains, and (5) calculating the abundance of  12  C in each experimental peptide by fitting its experimental R iso to the closest theoretical R iso.
The data were then filtered to eliminate outliers based on monoisotopic intensity, peptide size, and  12  C composition derived from the adjustment. Protein abundance was calculated on the basis of the abundance of their three most intense peptides (Top-3). The absence of an automated processing tool contributed to limiting the widespread diffusion of the method. To overcome these limitations, we established the basis of a new theoretical background to process the experimental data in SLIM-labeling experiments and developed the appropriate procedures based on open-source resources, using, in particular, dedicated OpenMS modules  8  for peptide identification in conjunction with a modified version of FeatureFinderIdentification. 9  This solution provides a Gaussian fit of the intensities of every isotopologue chromatographic trace (mass trace) for peptides with a validated identification, allowing extraction of the relevant information.
The only experimental data required for high-quality quantification are the abundance of M 0 and M 1 , the monoisotopic ion, and the +1 isotopologue of the peptides identified with high confidence. Computation of the  12  C abundance of peptides and the molar fraction of peptides from the NC and  12  C conditions in multiplexing experiments is performed by implementing appropriate calculation modules in a KNIME working environment. 10, 11 We also present the theoretical basis for establishing appropriate filters to obtain high-quality processed data from experimental datasets. These new integrated tools provide a convenient framework that enables wider use of the bSLIM-labeling strategy.
Definition of the Practical Computation Parameters We assessed the theoretical ranges of the various parameters used to process the experimental datasets. The workflow is composed of two distinct components for (i) peptide identification and feature extraction (Figure  3A ) and (ii) computation of P′( 12 C) and log 2 (R) (Figure  3B ). Peptide Identification and Feature Extraction. This pipeline is based on OpenMS (v2. 6. 0) plug-in nodes used inside the KNIME (v4. 2. 2) environment. Experimental mass spectrometry. raw files were converted to mzML files using the MS-convert tool from Proteowizard. 15, 16 he S. cerevisiae protein sequence database consisted of the UniProt UP000002311 reference proteome augmented with shuffled entries produced using the DecoyDatabase node of OpenMS, In the present workflow, we use X!Tandem 17 as a protein sequence database search engine, with XTandemAdapter linked to the PeptideIndexer, peptide spectrum matches (PSM) Feature extractor, PercolatorAdapter, and Epifany  18  for FDR filtering and IDFilter OpenMS nodes.
The X!Tandem E-value threshold was set to 0. 001 and FDRs were computed at 1%, both at the peptide and protein level, using the posterior error probability (PEP) scores of the peptide spectrum matches (PSMs). The resulting idXML file was processed, together with the initial mzML file, using a modified version of the FeatureFinderIdentification (FFId) node. 9 This version allows modeling the mass trace of every isotopologue by applying a Gaussian fit (or Exponential Modified Gaussian fit) to its chromatographic elution profile. This allowed extraction of the features (height of the peak, area, and detailed statistics) of five isotopologues on every isotope cluster, associated with quality scores. These data were retrieved from the FFId output files in featureXML format. We then used XPath to generate two.
tsv files, one containing all of the features associated with every identification, the second containing the corresponding protein ID and accession number. To process batches of experimental files, we made use of KNIME's ZipLoopStart and ZipLoodEnd nodes. Computation of P′( 12 C) and log 2 (R). The features-and "ProtID"-containing files were joined to restore the association between the peptide and protein identifications. We defined a first KNIME "metanode" with a series of filters to select the peptides with high-quality modeled features, flagged as "0(valid)" by FFId for M 0 and M 1. The other filters are database search engine-specific and can be easily edited depending on the preferred peptide identification strategy. In the present workflow, based on XTandemAdapter, we transformed certain post-translational modifications into a custom one-letter code: "(oxidation)" as O, "(acetyl)" as B, and "(phospho)" as Z.
The last metanode determines the molar fractions of normal carbon (NC) and  12  C-labeled peptides in multiplexing experiments by implementing eq 6. Next, the ratio NC 12C is calculated and log 2-transformed. Integration of bSLIM-Labeling Quantification Data at the Protein Level. To edit variations of protein abundance, we integrated an R script into the workflow through the KNIME Rsnippet node. This script allows the grouping of peptides per protein and calculation of the mean and median of the log 2 of the NC C 12 ratios, both for all of the quantified peptides and using only the three peptides with the highest M0_model_area intensity (Top-3). The final results are written into an Excel file for further biological interpretation. The overall SLIM-labeling data processing workflow is presented in Figure  2A, B.
When processing a 1. 53 Gb mzML file with the S. cerevisiae protein sequence database, the overall process from peptide identification to protein quantification is executed in an average of 1355 s per file of experimental data (on Intel Xeon Gold 6134, 8 Cores, 3. 2-3. 7 GHz Turbo, 24. 75 Mo Cache and 16 Go 2666 MHz DDR4 RAM). Most of this time was dedicated to the peptide identification and filtering steps including Percolator and Epifany (343 s), FFId (305 s), and features and protein ID extraction (707 s). The complete workflows are provided as Executable files E1 and E2. The files can be directly imported into a valid KNIME instance and run as provided, with the appropriate adjustments of certain parameters (protein sequence database, ion intensity threshold, auxotrophies) specific to the user's experimental conditions.
Experimental Validation of the Bottom-Up Quantification Pipeline We collected experimental datasets using the cell extracts from the yeast cultures grown under NC or 12C conditions to test and validate our data processing pipelines. The first two datasets consisted of a series of samples of (i) S288c and (ii) BY4742 peptides. In both cases, peptides were analyzed as 1:0, 0. 75:0. 25, 0. 5:0. 5, 0. 25:0. 75, and 0:1 vol/vol mixtures of identical amounts of total protein extracts prepared from the NC and 12C conditions, respectively. For BY4742, we expected lower  12  C incorporation rates, as the His, Leu, and Lys residues remain unlabeled. The third dataset consisted of an analysis of 1:1 mixtures of identical amounts of protein from S288c and BY4742 extracts prepared under the 12C or NC condition (S288C-12C vs BY4742-NC and S288c-NC vs BY4742-12C, with biological duplicates and technical replicates).
This allowed quantitative analysis of proteome differences between two yeast strains, often referred to as "wild-type" strains, in a very stringent condition, as only a few proteins are expected to vary significantly. The quantitative results obtained were analyzed using a nonsupervised Bayesian clustering algorithm AutoClass@IJM. 23, 24 he information content of the various classes was evaluated using the Saccharomyces Genome Database  25  (SGD) annotation files and GO-term analysis tools (SGD and BiNGO 26 ). Figure  4  shows the inferred  12  C abundance in the various samples as box plots and the correlation with the theoretical values expected from an incomplete labeling of the peptides, due to the presence of nonlabeled amino acids in the growth media.
■ RESULTS AND DISCUSSION Here, we describe an easy-to-use and straightforward workflow to process bSLIM-labeling quantitative proteomics data. The workflow (Figure  4A, B ) is implemented in the KNIME environment and uses open-source resources from the OpenMS suite and dedicated R scripts. The workflow integrates a series of nodes for peptide identification through a database search engine, the calculation of appropriate FDR metrics, feature detection for highconfidence identified peptides, and extraction of the intensity of every isotopologue in every isotope cluster of these peptides. Although we used X!Tandem in this study, other search engines are supported by OpenMS, such as MS-GF+, Mascot, or OMSSA. These search engines may be used with ad hoc parameters and produce the idXML files required for feature detection.
Typically, we identified approximately 25 000 peptides with X!Tandem E-values better than 1E-3 at a 1% FDR. A key node in this workflow is OpenMS FeatureFinder-Identification (FFId). This node was originally developed for label-free quantitative proteomics, with an efficient algorithm to extract the raw data (m/z and intensity) of peptides at the MS1 level, find chromatographic peaks (features), and model peptide elution by a Gaussian fit. A control algorithm provides a quality score for each feature. Here, we used a modified version of FFId that allows extraction of the data (raw and modeled) from every isotopologue trace of each identified peptide isotope cluster. All of the analyses were performed using the "Model Area" values, a metric commonly used in modern label-free quantification software.
27 Since most of the developments of the SLIMlabeling quantification method presented here rely on accurate M 0 and M 1 intensity measurements, we selected only the data that passed the quality control step (flagged as "0 = valid") for further processing. Applying this stringent filter reduces the total number of entries to an average of 14-16 000. All of the datasets were acquired using an Orbitrap Q-Exactive Plus mass spectrometer. Although this instrument is remarkable in terms of mass accuracy, there may be some concerns about the precision of ion intensity measurements in isotope clusters  [28] [29] [30]  using the Orbitrap as a mass detector, with errors above 20% in the M1 intensity measurement for low-abundance ions. We attempted to overcome this limitation by running samples at different AGC target values (from 1E5 to 5E6) and different resolutions (from 7.