dataesr/scientific-paragraphs-categorization huggingface dataset:
e. identical Click-iT labelling, IP and MS/MS protocol) cardiac O-GlcNAcylomic dataset derived from mice that were subjected to NButGT or vehicle treatment 6 h prior to sacrifice (Fig. 5a ). Of the 409 mitochondrial proteins detected in the in vitro reconstitution assay, 85% were also identified as being O-GlcNAcylated in vivo, and among them 122 displayed enhanced O-GlcNAcylation in the two data sets (FC > 1. 2, Table  S1 ). Comparison was also made with data from a previous study performed by Ma et al. in which O-GlcNAc sites on isolated cardiac mitochondria were mapped 12 h after in vivo administration of the OGA inhibitor Thiamet G using a BEMAD labelling method  20. Of the 88 O-GlcNAc-modified proteins identified by Ma et al. , 62 were found to display increased O-GlcNAcylation in our in vitro reconstitution assay, representing a highly significant over-enrichment (Fig.
5b ). The majority of shared proteins across the two datasets were components of the oxidative phosphorylation system, TCA cycle, and fatty acid oxidation pathway (Fig. 5c ), with a few noticeable proteins related to ROS metabolism (SOD2, PRX3) and permeability transition pore (mPTP)/apoptosis (VDAC1, SLC25A4, ENDOG). Within the OXPHOS system, overlap between the two datasets was observed for subunits located in the NADH dehydrogenase (N) and ubiquinone reductase (Q) modules of complex I (NDUFS1, NDUFA7, NDUFA9), the F1 sector of complex V (ATP5O, ATP5B, ATP5A1), the hydrophilic head of complex II protruding in the matrix (SDHA, SDHB) and UQCRC2, a matrix facing subunit of complex III (Fig. 5d  and S2 ). To validate our O-GlcNAcylomic data, immunoprecipitation of O-GlcNAcylated proteins was performed on lysates from control and NButGT-treated mitochondria using an anti-O-GlcNAc antibody, and the resulting immunoprecipitates were probed with antibodies directed against several identified proteins including NDUFS1, ATP5A1, UQCRC2, MTCO1, and SDHB.
As shown in Fig. 6a , exposure to NButGT induced a strong increase O-GlcNAcylation for proteins with a mitochondrial status on the Mitominer database. Red (q < 0. 05) and orange (q < 0. 1) dots represent proteins that were significantly affected in response to NButGT. Number of significantly affected proteins are indicated along with the adjusted p value (q) threshold. Statistical analysis was assessed using a linear regression model (empirical Bayes methods) followed by the Benjamini-Hochberg FDR procedure. d Fold change in the abundance of O-GlcNAc-modified proteins between control and NButGT-treated mitochondria according to their sub-mitochondrial localization. e Relative distribution of mitochondrial proteins (known + predicted) according to their sub-mitochondrial localization. Proteins were ascribed to a particular sub-mitochondrial compartment based on annotations available in the Uniprot and GO databases.
For panels b, e, individual values for each protein identified as well as mean ± sem (n = 3-4 biological replicates) are shown. in global mitochondrial O-GlcNAcylation, which was still conserved after immunoprecipitation. Furthermore, probing with the NDUFS1 antibody revealed a drastic increase in immunoreactivity following exposure to NButGT (Fig. 6b, c ), without any changes in NDUFS1 protein abundance in the lysates (Fig. 6a ). Similarly, O-GlcNAc staining of ATP5A1, UQCRC2, MTCO1, and SDHB (using the antibody mix OXPHOS) was increased following immunoprecipitation with the anti-O-GlcNAc antibody (Fig. 6b-d ). To further validate the specificity of these results, the O-GlcNAc immunoprecipitates were also probed for a nonmitochondrial control protein (e. g. Troponin-I) present in the crude mitochondrial fraction, which was not affected by NButGT in the in vitro proteomics dataset (FC = 0.
Following exposure to NButGT, the activity of complex I was increased by ~50%, while those of complex II, complex IV, and the TCA cycle enzyme citrate synthase were unchanged (Fig. 7i, j ). Of note, respirometry experiments were also performed in presence of the other O-GlcNAc inducer Thiamet G (also an OGA inhibitor), and yielded comparable results (Fig  S3 ). Altogether, these data provided evidence that O-GlcNAcylation increases maximal electron flux through complex I. Furthermore, since the complex I assay measures Fig. 3  Characterization of the pathways over-enriched in response to NButGT in isolated mitochondria. Functional enrichment analysis of overrepresented KEGG a and REACTOME b pathways in NButGT-treated vs control mitochondria performed using g:Profiler. Proteins were input in g:Profiler in order of decreasing q value with a threshold set at q < 0.
The present study provides insights on potential links between functional changes and O-GlcNAcylation of specific proteins of the ETC and TCA cycle. Enzyme activity measurements combined to functional assays revealed that complex I is likely one of the functionally relevant sites of regulation OXPHOS and H 2 O 2 release by O-GlcNAcylation. The N module of complex I consists of several core catalytic subunits containing the FMN prosthetic groups, iron-sulfur clusters, as well as several accessory subunits which altogether oxidize NADH and transfer electrons to the Q and P modules to support proton pumping. Our O-GlcNAcylomic data revealed that subunits within this module displayed greater increases in O-GlcNAcylation compared to other complex I subunits. Similarly, in the Ma et al. 20  dataset, one of the subunits displaying the highest fold change following in vivo treatment with Thiamet G was NDUFS1, located in the N module.
This subunits, which was also hyper-O-GlcNAcylated in our dataset, harbours an NADH-ubiquinone oxidoreductase domain and Fe-S clusters, in addition to containing a number of phosphorylation  27  and acetylation sites, which makes it a potentially important candidate for post-translational regulation of complex I activity. Along the same line, phosphorylation of four other subunits within the N module (NDUFS4, NDUFV1, NFUFV3, and NDUFA12), including one (NDUFSV1) that displayed sensitivity to NButGT, were previously shown to regulate complex I activity and ROS release  [28] [29] [30]. As for the Q module, it encompasses several subunits collectively involved in the reduction of ubiquinone, including NDUFA7 and NDUFA9 which were identified as being O-GlcNAcylated in our dataset, and in the study by Ma et al.
20. Interestingly, recent data indicate that inactivation of NDUFA7 in the heart increases mitochondrial ROS release, and triggers pathological cardiac hypertrophy  31  , although it is still unclear whether this is linked to failed assembly of complex I or to altered activity. Further studies will clearly be required to fully elucidate the mechanism by which acute O-GlcNAcylation stimulates complex I activity and contributes to modulate respiratory capacity and ROS release. In addition to complex I, our data suggest that complex III may constitute another regulatory relevant site affecting ROS release. Our O-GlcNAcylomics data revealed that several subunits of complex III were significantly hyper O-GlcNAcylated following treatment with NButGT (UQCRH, UQCRFS1, UQCR10, MT-CYB, UQCRC1 and UQCRC2).
Among those, three were also hyper O-GlcNAcylated in the Ma et al. dataset (UQCRFS, UQCRQ, and UQCRC2). One possibility is thus that O-GlcNAcylation of one or more of these subunits lowers electron leaks at the ETFQOR site. In addition to these ETC complexes, we cannot exclude the possibility that enhanced respiration and reduced ROS release in presence of pyruvate-malate are also at least partly related to O-GlcNAcylation of other targets such as proteins involved in the TCA cycle. For instance, our O-GlcNAcylomics data revealed that the dihydrolipoamide acyltransferase (DLAT or E2) and dihydrolipoamide dehydrogenase (DLD or E3) subunits of OGDH and PDH were among the proteins found to be hyper-O-GlcNAcylated following exposure to NButGT. Superoxide/H 2 O 2 production by OGDH and PDH was shown to be substantial in presence of their respective substrates (e.
However, irrespective of whether NButGT increases ETC flux or prevents functional inactivation, the mechanisms put forth in this study remain valid: acute O-GlcNAcylation enhances Complex I (CI) activity, increases maximal respiration mainly in presence of pyruvate and malate, and lowers mitochondrial ROS release. Another limitation of in vitro reconstitution assays is that they do not fully mimic the complex conditions encountered in vivo. To tackle this issue, we compared our in vitro data with a methodologically comparable cardiac O-GlcNAcylomic dataset derived from mice injected with NButGT for 6 h before cardiac isolation. Our results reveal a strikingly high degree of overlap (85%) in the O-GlcNAc modified mitochondrial protein identified in the two datasets. Importantly, 122 of these proteins also displayed sensitivity to NButGT.
Methods Animal care. For in vitro studies, all experiments on animals were approved by the University of Ottawa Institutional Animal Care Committee and conducted according to the directives of the Canadian Council on Animal Care. Eight weekold rats (Wistar, male) were euthanized by thoracotomy following ketaminexylazine anaesthesia. For in vivo dataset, experiments were approved by the Animal Research Committee of the Université catholique de Louvain and conformed to the American Heart Association Guidelines for Use of Animal in Research. Twelve week-old mice (C57BL/6 N, male) from Janvier Labs, housed with a 12 h/12 h light/ dark cycle, had free access to water and standard chow. Preparation of isolated cardiac mitochondria. Heart mitochondria were prepared as described previously  32. Hearts were rapidly excised and immersed into ice-cold isolation medium (buffer A, in mM: 300 sucrose, 10 Tris-HCl, 1 EGTA, pH 7.
The peptides were subjected to Nano-Spray-Ionization source followed by tandem MS/MS in a tribrid Fusion Lumos Orbitap analyser coupled online to the nano-LC. Spectra were acquired by a data dependent scan routine with ion precursor detection in the Orbitrap and daughter ions in the Iontrap. The resulting MS/MS data were processed using Sequest HT search engine within Proteome Discoverer 2. 4 against a rat protein database obtained from Uniprot (29 953 entries). Trypsin was specified as cleavage enzyme allowing up to 2 missed cleavages, 4 modifications per peptide and up to 5 charges. Mass error was set to 10 ppm for precursor ions and 0. 1 Da for fragment ions. Oxidation on methionine, carbamidomethyl on cysteine were considered as variable modifications. False discovery rate (FDR) was assessed using Percolator and thresholds for protein, peptide and modification site were specified at 1%.
The filtered Sequest HT output files for each peptide were grouped according to the protein from which they were derived and abundance was evaluated by label-free quantification within Proteome Discoverer from area under the curve of MS1 intensities. Following such procedure, we were able to identify 2534 putative O-GlcNAcylated proteins. LC-MS/MS data analysis. The analysis was conducted in R (R Core Team, 2020). The expression values were log2 transformed but not subjected to normalization given the data acquisition technique involving an enrichment of O-GlcNAc proteins through immunoprecipitation prior to MS analysis. To maximize stringency, only proteins reliably detected in all experimental replicates (n = 4) from control and NButGT-treated mitochondria were considered. Of the 842 proteins identified, mitochondrial proteins (409) were selected according to their mitochondrial status from the Mitominer database.
Prior to differential expression analysis and based on exploratory analysis, NButGT-treated replicate n°3 was removed as it did not respond to treatment (assessed by O-GlcNAc immunoblotting). The differential expression between control and NButGT-treated groups was statistically assessed through linear models using empirical Bayes methods for variance modelling, as implemented in the R/Bioconductor limma package  42, 43  where the group effect was the only one included in the modes. P-values for the group effect were adjusted for multiple testing with the Benjamini-Hochberg FDR procedure. Proteins having a group effect with an adjusted p-value (q) < 0. 1 and q < 0. 05 were arbitrary considered differentially expressed between control and NButGT-treated mitochondria with either high or very high confidence score, respectively.
Pathway analysis and bioinformatics. To identify the pathway annotations of the mitochondrial O-GlcNAcylome, the list of proteins displaying significantly increased O-GlcNAcylation was ranked according to q values and uploaded into the g:Profiler  44  platform, and an ordered query was performed using the Rattus norvegicus database. KEGG (metabolism, cellular processes, human diseases) and REACTOME (biochemical pathways) terms annotating 350 proteins or less were considered in order to filter out large annotations that provide limited interpretative value  45. The g:SCS (shortest common superstring) algorithm was used for multiple hypothesis testing corrections using a default alpha threshold of 0. 05  44. The ggplot2 R package was used to generate bubble plots in which pathway enrichments were expressed as Rich Factors, which represents the ratio of the number of proteins observed for a given pathway term to the total number of proteins for this term.
The Cytoscape (version 3. 8. 0) stringApp plugin was used to import protein-protein interaction data from STRING  46. For this analysis, an interaction score of 0. 7 (high confidence based on default active interaction sources) was set as minimum. Clustering of the STRING network was performed with the Markov Cluster (MCL) algorithm with a granular parameter set at 4. The Auto-annotate plugin  47  of Cytoscape was used to identify pathways/processes corresponding to these protein clusters based on Stringdb description and GO annotations. To compare overlap between distinct O-GlcNAcylomic datasets, Venn diagrams were generated using the Eulerr R Package. Hypergeometric tests were used to determine the statistical significance of the actual vs expected number of shared hyper-O-GlcNAcylated proteins between datasets.
Statistics and reproducibility. For functional analyses, values are reported as mean ± sem for a minimum of 3 biological replicates, with 2-3 experimental replicates per experimental groups. Data are graphically represented as histograms. Paired two-sided t-tests (GraphPad Prism 8. 4. 3) were used to determine statistical difference when two means were compared, with a significance threshold set at p < 0. 05. For proteomics, values reported were obtained from 3-4 biological replicates. Data are depicted as volcano plots or histograms of mean Log2 FC ± sem with values for individual proteins represented by dots. Difference in the abundance of individual proteins between groups was determined by linear models using empirical Bayes methods for variance modelling. For each p value obtained, a corresponding FDR was calculated according to the Benjamini and Hochberg method.
Acknowledgements In Canada , this work was supported by grants from the  Canadian Institutes of Health Research  ( MOP 136999 ), and the  Natural Sciences and Engineering Council of Canada  ( RGPIN 2016-09932 ). In Belgium, this work was supported by grants from the  Fundamental Research of excellence in Strategic areas-Walloon Excellence in Life Sciences and Biotechnology FRFS-WELBIO (Belgium) , the  Fonds National de la Recherche Scientifique FNRS (Belgium) ,  Action de Recherche Concertée from Wallonia-Brussels Federation  and thanks to the mobility partnership between France and the  Wallonia-Brussels Federation of Belgium Hubert Curien Partnership Tournesol (PHC Tournesol). JD is  FRIA (Fund for Research Training in Industry and Agriculture, Belgium)  grantee. SH is senior research associate at the FNRS.
Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026495. Source data for the graphs and charts is available as Supplementary Data 1 and any remaining information can be obtained from the corresponding author upon reasonable request: Yan Burelle (yburell2@uottawa. ca) and Luc Bertrand (luc. bertrand@uclouvain. be). All requests will need to specify how the data will be used and will require approval by co-investigators.
Simulations Study Datasets were simulated using R version 3. 5. 2. This methodology was applied to two different models from the literature. The performance evaluation of the proposed methodology was performed on 5000 subjects simulated by the models described in the corresponding articles. Usually, it is necessary to perform K simulations for each subject; a subject being defined by its sampling times t j and its covariate values A. Since we simulated patients with exactly the same sample times and covariate values, K = 5000 simulations could be used for simulated patients. For the rest of this work, we focus on clearance that controls exposure and use only one concentration to estimate individual clearance as per hospital practice. For each drug tested, the sampling times were chosen in line with the model itself (i.
The case of calcium phosphate apatite KS Calcium phosphate in the form of carbapatite (CA) is a very common crystalline phase in urinary stones. Taking into account a large data bank including 50,000 stones acquired over the past three decades, CA has been identified by selective infrared analysis in 80% of all stones in both sexes, in most cases as a minor component  [75]. It is worth underlining that despite its low proportion, CA is often clinically relevant as an initiating phase of the lithogenic process, as observed in stones developed from a Randall's plaque  [76] [77] [78] [79] [80] [81]. XRD as well as PND experiments have been performed on CA typical of physiological (bones) as well as pathological (Randall's plaque) calcifications  [85] [86] [87]. Moreover, using atomic force microscopy, Eppell et al.
Acknowledgments This work was supported by the  Physics and Chemistry Institutes of CNRS  and by contracts  ANR-09-BLAN-0120-02 ,  ANR-12-BS08-0022 ,  ANR13JSV10010-01 , convergence UPMC  CVG1205 ,  Labex Matisse, Labex Michem  and  CORDDIM-2013-COD130042. The authors are grateful to the  SOLEIL Synchrotron Facility  and the  Leon Brillouin laboratory  for beam time allocation.
Acknowledgements This work of the  Interdisciplinary Thematic Institute InnoVec , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ) and SFRI (STRAT' US  project) under the framework of the  French Investments for the Future Program. he doctor r nt or n nced the n t re de ' En e nement Supér eur, de Recherche et de  'Innov t on We thank  Delphine Garnier  and  Estefania Oliva  ( PACSI, GDS 3670 ) for NMR analyzes and HRMS measurements, and  Lydia Karmazin  and  Corinne Bailly  ( GDS 3648 ) for X-ray analyses.
Acknowledgements Thanks are expressed to  Mr. Pierre Sánchez  for his help in obtaining mass spectra, to  Aurélien Lebrun  for recording some NMR spectra. We thank the  Ministère de l'Enseignement Supérieur et de la Recherche  for providing a research grant for the PhD thesis project of E. G. Computational resources were provided by  Calcul Québec and Compute Canada. R. R. is grateful to  FRQNT for a Graduate Scholarship  ( B1X/B2X ),  Hydro-Québec  and  Université de Sherbrooke  for a doctoral scholarship, and  NSERC  for a  Canada Graduate Scholarship  ( CGS M ) as well as a  Vanier Canada Graduate Scholarship.
[9] , questions whose solutions are based on a limited number of choices are often ineffective for self-assessment. According to their estimations, about a third of students simply try different suggested choices instead of turning to the course or remediation materials when they do not know the answer. It should also be noted that some questions need smooth grading. A typical example concerns the demand to prepare a chemical structure corresponding to a given SMILES string. The binary assessment results in a grade equal to zero in case of any minor error, whereas the smooth assessment distinguishes the level of students as a function of the number of mistakes in the answer. Here, we describe a novel software tool able to perform a smooth grading of chemical structures using the Moodle platform.
Development A workflow used by the developed plugin is shown in Fig. 1. In this implementation, we use an in-house correction algorithm hosted on a REST server. It computes the similarity between the student's and teacher's chemical structures. A REST server is, in our opinion, the most relevant technology in this context. It can be managed as suited by the end user-for instance, it can be installed on the same server as Moodle, encapsulated in a virtual machine or a different machine. The server uses little computing power. It doesn't store any data and communicates exclusively with the Moodle server. Data is exchanged using the JSON (JavaScript Object Notation)  [12]  format, which is a standard in web applications and server transfers. The user interface is built using Chemdoodle Web Component  [13] , an open source JavaScript library providing a sketcher to draw chemical structures.
Implementation The plugin implementation involves three main steps: (i) formulating a question, (ii) answering a question, and (iii) displaying a teacher's feedback. In this plugin, the teacher inputs from 1 to N deemed correct answers, allowing for several alternative structures (the plugin does not allow the teacher to define "inexact" answers). For instance, for the question "what is the structure of glucose?" both furanose and pyranose forms of glucose can be accepted as answers. Thus, for each answer, the teacher needs to draw the expected structure using the Chemdoodle Web Component Ketcher, then click on the 'Insert given structure as answer/update the answer with the structure' button in order to insert this structure (Fig. 2 , top). In this case, Chemdoodle JSON is used to encode a given chemical structure in MOL format.
Both the Molfile and the Chemdoodle JSON will be saved as an answer in JSON format to the Moodle database. Apart from chemical structures, the teacher can prepare instructions and feedback for the students. Two kinds of feedback are possible: "general" grade-unrelated feedback and "specific" feedback, displayed if a grade is inferior to 1, aiming to help students improve their answers. Upon taking a test, a student follows the teacher's instructions in order to prepare the required chemical structure (Fig. 2 , bottom). Once submitted, the answer is processed using the same procedure as for the teacher's question (see above). Then, the answers of both the teacher and the student are sent through a cURL [20] request to the correction REST API, written in the Pascal Object language  [21]. Connections to the REST API are authenticated using the JWT standard.
This work could be enhanced by the addition of several features, such as the possibility to consider wrong answers to give specific feedback to the students (for example, when requested to draw the major product of a reaction, it is desirable to consider the minor product as a wrong answer to provide some specific explanation to the student). The creation of a dedicated tool to automate the editing of questions using a set of chemical structures could be a useful addition-by generating questions in XML format that can be imported into Moodle, for instance. Other options to tune the soft grading are also desirable. For instance, it could be possible to let the teacher build the grade as a weighted sum of the structural similarity and the stereochemistry score. Another improvement could be to let the teacher decide if the module should standardize the protonation of the chemical structures.