dataesr/scientific-paragraphs-categorization huggingface dataset:
The oven temperature ramp was 70 • C for 7 min and then 10 • C/min to 330 • C for 4 min (run-length 36. 5 min). The helium constant flow was 0. 7 mL/min in the splitless mode and 1 mL/min in the split mode. The temperatures were as follows: injector-250 • C; transfer line-300 • C; source-250 • C, and quadripole-150 • C. Samples were randomised. An alkane mix (C10, C12, C15, C19, C22, C28, C32, and C36) was injected in the middle of the queue for external retention index calibration. Five scans per second were acquired. Raw Agilent data files were converted to NetCDF format and analysed with AMDIS 32  [99]. A home AMDIS retention index/mass spectra library, built from the NIST, Golm, and Fiehn databases and standard compounds, was used for metabolite identification. Peak areas were determined more precisely using the Target-Lynx software (Waters, France) after converting the NetCDF file to MassLynx format.
Metabolites were normalised to the ribitol internal standard and to dry weight. A subset of metabolites was quantified absolutely using a response coefficient in both injection modes to ensure the absence of saturation. Statistical analysis was carried out with Metaboanalyst 5. 0, a web-based metabolomic processing software, using multivariate and hierarchical clustering statistical approaches  [100]. If a metabolite was not identified in one of the plots but was presented in others, the missing values were replaced by 1 × 10 -15. In other cases, the remaining missing values were replaced by a limit of detection (1/5 of the minimum positive value of each variable). Before the statistical analysis, all data were subjected to sample normalisation via median and autoscaling. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were carried out to determine the metabolomic variability within the experimental plots.
Multi-Omics Data Integration Multi-omics analysis was conducted with the web tool  PaintOmics 4 [101]. The closest to V. cracca model species in KEGG databases, Medicago truncatula L. , was used as a proxy for multi-omics analysis. The Galaxy public server (https://usegalaxy. eu/, accessed from 15 September 2022 to 15 March 2023)  [85]  was used to perform a blastp search of the differentially expressed V. cracca transcripts and proteins against the M. truncatula proteome (retrieved from the Uniprot database, entry UP000002051). The E-value cutoff was set as 0. 001, and the other parameters were used as default. RStudio v. 4. 2. 2 was used to prepare data for the multi-omics analysis. Background data on transcriptomics, proteomics, and metabolomics were provided for PaintOmics 4 software as log 2 FC in irradiated samples compared to controls (pairs K × B, K × L, M × B, and M × L).
From metabolomic data, log 2 FC was only provided for those metabolites with KEGG ID. As relevant features for the PaintOmics 4 tool, we used differentially expressed genes (DEGs) and differentially expressed proteins in both contaminated plots. As relevant metabolites, we chose those significantly different from the control for at least one experimental plot. After selecting and preparing datasets, the following parameters were used to run PaintOmics4: species: Medicago truncatula L. ; databases: KEGG, MapMan, and Reactome; clusters presented in data: generate automatically; metabolite class activity threshold: generate automatically.
Figure5. STRING summary network [29]  for proteins with significantly different abundance tween contaminated and reference plots (homologous proteins of Medicago truncatula L. ). Line th ness indicates the strength of data support (confidence). psaA-photosystem I P700 chloroph apoprotein; cemA-apocytochrome f; ndhH-NAD(P)-quinone oxidoreductase subunit I, chl plastic; AES88244-ribulose bisphosphate carboxylase large chain domain protein; Actin-97-a AES72204-asparagine synthetase; AES87795-large subunit ribosomal protein L16; rps7-rib mal protein S7; petD-cytochrome b; AES69817-ribulose 1,5-bisphosphate carboxylase, large unit; psaC-4Fe-4S ferredoxin; AES63405-F-type H + /Na + -transporting ATPase subunit al atpE-F-type H + /Na + -transporting ATPase subunit beta; petB H + /Na + -photosystem II chloroph apoprotein; AES59115-plastocyanin-like domain protein; AES58557-ribosomal protein AES58553-NADH-ubiquinone oxidoreductase; AES58516-NADH-ubiquinone oxidoreduc chain; AES58550-ATP synthase, subunit alpha; AES58514-ribosomal protein S12C.
Literature Search We conducted a literature search of key research-specialised databases using keyword search terms, both in English and French. The keywords in English (not provided here, but equivalent in French) were: "sport policy(ies)", "sport politics", "politics and sport", "policy-making and sport", "sport federation(s)", "sport governing body(ies)", "national sport organisation", and "sport development". The search was conducted from April to October 2022. The objective was to identify the literature providing elements contributing to the understanding, analysis, description (exploration) and even conceptualisation around sport policy. This was to review the issues and the boundaries and to be able to better understand them for a national sport federation. In other words, as long as the references included elements that allowed for a better understanding of the issues of any sport policy, and more generally, of the development of sport from a local to international dimension, they were taken into account.
Data Analysis The process of data analysis was conducted through the review of the full text of each reference. The data analysis process for the 109 publications continued, following Miles and Huberman's recommendations, which suggested dividing data into intellectual bins that ultimately reflect common themes between books and articles  [25]. Under the overarching subject 'sport policy', two streams were distinguished: governmental sport policy and sport policy from non-governmental national sport organisations. Then, each stream was analysed and categorised into four levels. The first stream (governmental sport policy) was split into two fields of study, i. e. , development of and through sport, while the second stream (non-governmental sport policy) was split into two ways of exploring sport policy, i.
Accession differences were associated mainly with DMCs in CG context. Hierarchical clustering of samples based on total mCG profiles revealed a perfect clustering of samples by accession identity, irrespective of light conditions. Using microsatellite information as a benchmark of genetic divergence within the clonal lineage, we show that genetic divergence between accessions correlates strongly with overall mCG profiles. However, our results suggest that environmental effects that do occur in CG context may produce a heritable signal that partly dilutes the genealogical signal. Our study shows that methylation information in plants can be used to reconstruct microevolutionary genealogy, providing a useful tool in systems that lack genetic variation such as clonal and vegetatively propagated plants.
(2019) , as the average methylation difference across all cytosines between two samples. A non-metric MDS was used to visualize epigenetic distances with the function isoMDS of the R-package 'MASS'  (Venables & Ripley, 2002). The statistical significance of isoMDS clustering was determined through a permutational multivariate analysis of variance with the adonis function from R-package 'vegan'  (Oksanen, 2007). A hierarchical cluster analysis was realized with hclust function from 'stats' R-Package Team (2020) based on the pairwise distance matrix for cytosine methylation in CG context and based on eight microsatellite loci (SSR;  Kirschner et al. , 2016; Wilschut et al. , 2016)  using Nei distance with the UPGMA linkage method. Mantel test analyses were performed to determine correlations between SSR-based and methylation-based distance matrices.
| DNA methylation correlations with genetic structure Consistent with the observation that accession differences were observed in CG context (Table  1 ), hierarchical clustering of all experimental samples based on total CG methylation profiles revealed a clustering of samples by accession identity (Figure  5a ). In all cases, samples were separated according to their accession identity. The two replicates of each accession always clustered closely together, even though they were grown under different light conditions. There was no clear clustering based on geographic origin, but we observed that different accessions that were originally collected from the same field location (which presumably share a common ancestor much more recently than accessions collected from different regions or countries do) always clustered close together (Figure  5b ).
DATA AVA I L A B I L I T Y S TAT E M E N T The demultiplexed sequencing data generated in this work can be accessed through the European Nucleotide Archive (ENA) under project PRJEB56325. The raw sequencing data, R scripts and configuration file used for the epiGBS pipeline, as well as the output generated from it, are deposited into Zenodo (https://doi. org/10. 5281/ zenodo. 7566483). The source code and details used are freely available at https://zenodo. org/badge/ lates tdoi/53555 6610.
Radiotherapy Plan Radiotherapy treatment using volumetric modulated arc therapy (VMAT) was undertaken with a RapidArc (Varian ® , Le Plessis-Robinson, France) machine. Radiotherapy plans (RT-plan) were computed using Eclipse ® TPS. Complexity indexes were obtained from RT-plan using Raystation ® TPS and an in-house python code available at: https://github. com/AurelienCD/ DeepHybridLearning_RadiotherapyQA_Depository_ManuscriptID_Diagnostics  [16]  and "Com-plexity_index. py" (accessed on 1 February 2023). To achieve an easy solution applicable to routine clinical settings, only six complexity indexes were used: Leaf Sequence Variability (LSV), Aperture Area Variability (AAV), Modulation Complexity Score (MCS), Leaf Travel (LT), combination of LT and MCS (LTMCS) and Small Aperture Score (SAS10)  [5].
Artificial Intelligence Algorithms 2. 4. 1. Machine Learning (ML) Input data of the ML models were six quantitative variables obtained from the RT plans which represents the complexity of the radiotherapy treatment. LSV, AAV, MCS, LT, LTMCS and SAS10 are sensitive to the variation of the leaf position, number of monitor unit and area of irradiation. The aim of the ML models was to be able to predict, from these six complexity indexes as input, the two classes of conformance and non-conformance QC (0 or 1, respectively) as output. ML classification models were used for the low to medium complexity RT plans of tumors localized in the brain and thorax. The models were developed with hyperparameter optimization and cross-validation using the Sklearn python library  [17]. This library provides a selection of efficient tools for ML and statistical modeling including classification, regression, clustering and dimensionality reduction.
Training and testing were proportionally assigned to 80% and 20%, respectively. Several ML models were tested and compared by evaluating their prediction scores: Linear Discriminant, Linear Regression, Ridge, Gaussian NB, Decision Tree, Support Vector Classifier, Stochastic Gradient Descent and Random Forest Classifier. ML model hyperparameters were optimized using gridsearch and the cross-validation tools of the Sklearn library. As presented in the code available online, for each ML model classifier, the hyperparameters were tuned to obtain a higher validation score and area under the curve (AUC). Following model optimization, Receiver Operating Characteristic (ROC) curves and confusion matrices were computed for the best models. This was conducted to find the optimal probability threshold for obtaining as few as possible false positives.
This optimization gave 2 dense layers of 658 neurons and an output layer of two neurons with an L2 regularization function and a learning rate of 0. 005. The activation function and dropout were relu, 0%; selu, 60% and softmax in order. The model was built with a binary cross-entropy loss function and binary accuracy optimization metrics on 100 epochs. As with the majority of medical datasets, our data were imbalanced with an important proportion of conformance QC in comparison to non-conformance QC; to avoid the negative impact of this characteristic, the Imblearn python library and Keras class weight was used  [19]. In addition, to manage the imbalance data, weights of the model were adjusted regarding the proportion of classes. This optimization gave 2 dense layers of 658 neurons and an output layer of two neurons with an L2 regularization function and a learning rate of 0.
005. The activation function and dropout were relu, 0%; selu, 60% and softmax in order. The model was built with a binary cross-entropy loss function and binary accuracy optimization metrics on 100 epochs. As with the majority of medical datasets, our data were imbalanced with an important proportion of conformance QC in comparison to non-conformance QC; to avoid the negative impact of this characteristic, the Imblearn python library and Keras class weight was used  [19]. In addition, to manage the imbalance data, weights of the model were adjusted regarding the proportion of classes. All the DHL codes with detailed documentation are available at: https://github. com/ AurelienCD/DeepHybridLearning_RadiotherapyQA_Depository_ManuscriptID_Diagnostics and "Deep Hybrid Learning. ipynb" (accessed on 1 February 2023).
However, these approaches need to use the machine to obtain the log files or have to be completed after patient treatment as post-treatment patient-specific QA. As QA of the prediction model itself, we add to the annual QC of the TPS a QC of the script used in routine for complexity indexes extraction and prediction constancy for the API. Ten constancy plans for different tumor locations are used for this control: the script is run, the given metrics are compared with the reference (i. e. , metrics given at the first run of the script) and the prediction conformance of the QC is also compared to the reference prediction. A significant number of studies have developed AI models to accelerate the patient QA process in RT. However, very few studies have implemented the models clinically. A study was implemented clinically, but the results obtained for the AUC were at best 0.
Strains and plasmids The Saccharomyces cerevisiae strain with which we assayed HMBS variant libraries (MATa ts-hem3::KanR his3D1 leu2D0 ura3D0) was kindly provided by Drs. Guihong Tan, Charles Boone, and Brenda Andrews. For yeast expression, we used the Gateway-compatible destination vector pHYC-Dest2 (CEN/ARS-based, ADH1promoter, and LEU2 marker). 24  The HMBS open reading frame (ORF) clone (Ensembl: ENST00000652429. 1, GenBank: NM_000190. 4) was obtained from the Human ORFeome v. 8. 1 library. 27  Wild-type (WT) reference or mutated disease-associated versions of the HMBS ORFs were transferred into pHYCDest by Gateway LR reactions. After confirmation of ORF identity and expected mutations by Sanger sequencing, the expression clones were transformed into the appropriate yeast temperature-sensitive (ts) strain in parallel with an ''empty'' expression vector control (bearing the counterselectable ccdB marker controlled by a bacterial promoter).
Separate sequencing runs performed for each isoform with an Illumina NextSeq 500 via a NextSeq 500/550 High Output Kit v. 2 achieved an average sequencing depth of $2,000,000 paired-end reads per tile. Sequencing reads were demultiplexed with bcl2fastq v. 2. 17 (Illumina) and processed with the previously described TileSeq strategy. 28  In brief, variant frequencies in each condition were determined with Python scripts (https://github. com/RyogaLi/tileseq_ mutcount, see web resources), which incorporated Bowtie2  30  as part of the pipeline to align the sequence of each read pair to the reference template. Following alignment, variants were called when Q scores (based on both reads in the pair) indicated the variant to have a posterior probability exceeding 90%. Read counts were then normalized on the basis of sequencing depth to yield variant frequency data for each condition and replicate.
Processing of raw read count data was carried out with the R package tileseqMave (https://github. com/jweile/tileseqMave; see web resources). 26, 28  Briefly, to account for PCR and sequencing error and the possibility of a bottleneck effect from non-selective pool sampling, variants for which the number of read counts within the selective or non-selective library fell below a chosen threshold (10) or for which the frequencies fell below a custom percentile (90th) of variant frequencies observed in the WT library were filtered out. The effect of PCR and sequencing errors were reduced by subtracting WT from variant frequencies in both the non-selective and selective pools. Next, an enrichment ratio (F) was calculated for each variant with the ratio of adjusted frequencies in the selective to the non-selective library.
Phylogenetic comparison of different models for hypercomplementation To assess whether variants exhibiting "hyper-complementing," i. e. , higher-than-WT, growth in the yeast complementation assay are likely to be advantageous, deleterious, or neutral in humans, we performed phylogenetic analysis as described previously. 28, 31  Briefly, we first normalized each variant's score relative to the WT score for that position to avoid penalizing the WT variant itself in cases where its score is slightly greater than 1. Additional processing and imputation steps removed low-quality data and imputed likely values for missing data points, respectively. Three sets of scores (s) were generated from this dataset, each testing a different way of relating variant score for amino acid a at site r (s r,a ) to the preference for amino acid a (defined as p r,a ¼ sr;a S a 0 s r;a 0 ) across a set of aligned homologous sequences.
In the first The American Journal of Human Genetics 110, 1-18, October 5, 2023 3 Please cite this article in press as: van Loggerenberg et al. , Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation, The American Journal of Human Genetics (2023), https://doi. org/10. 1016/j. ajhg. 2023. 08. 012 (''advantageous'') model, p r, ¼ s r,a. In the second (''neutral'') model, if s r, > 1 then s r,a ¼ 1. In the third (''damaging'') model, if s r, >1, we transformed it to a value of 1/s r,a , otherwise p r,a ¼ s r,a. For a set of 60 Ensembl homologs having at least 85% sequence identity to the human protein (Table  S10 ), we applied the phydms software package (https://github. com/jbloomlab/phydms; see web resources) and determined the quality of fit to the phylogeny (as measured by the Akaike information criterion) under each of the three preference models.
The code used for these analyses is located at https://github. com/ jweile/maveLLR. We next calibrated the relationship between LLR values and evidence strength categories within the ACMG/AMP framework, using an approach adapted from Tavtigian et al. 32  Briefly, LLRs for descending evidence levels are modeled to decrease by a factor of 2 at each level starting from a fixed LLR, the "pathogenic very strong" (PVSt) level, which is chosen such that all ACMG/AMP evidence combination rulesets result in posterior probability of pathogenicity that satisfy the following constraints: >99% for all pathogenic (P) rules, >90% for all likely pathogenic (LP) rules, <10% for all likely benign (LB) rules, and <1% for all benign (B) rules. Here, we followed Tavtigian et al. in modeling a global prior probability of 0.
Computational details (MD simulation setup) To examine variant-specific impacts on the dynamic behavior of HMBS and its movements during catalysis, we next performed MD simulations. Starting structures were obtained by applying necessary changes to the high-resolution X-ray crystal structure (Protein Data Bank [PDB] ID: 5M6R). 33  Specifically, to study the apoenzyme system, the co-factor (DMP) and substrate (PBG units) were removed. Mutations were introduced by PyMol (The PyMOL molecular graphics system, version 1. 3, Schro ¨dinger, LLC. ), and the protonation states of ionizable amino acids at pH ¼ 7. 4 were checked with Hþþ server. 34  For simulations, we used AMBER parm14SB  35  parameters and the TIP3P model for solvent. 36  The nonstandard PBG structure was first energy minimized with the B3LYP DFT functional in combination with the 6-31G(d) basis set.
[37] [38] [39]  Partial atomic charges were obtained from the electrostatic potential with the Gaussian 16 (Revision C. 01). 40  The remaining parameters were obtained from standard parm10 and GAFF2 parameter files, via the ANTECHAMBER module of the Amber18 software suite. 41  All missing heavy atoms and hydrogen atoms were added with the LEaP module of Amber18. 41  All systems were first neutralized with Na þ ions and solvated in an explicit TIP3P water box with at least 1. 0 nm from the edge of the enzyme. 36  Covalent bonds involving hydrogen atoms were constrained with the SHAKE algorithm  42  and the particle mesh Ewald  43, 44  algorithm was used for long-range electrostatic interactions. The water molecules and ions were relaxed with 1,000 steps of steepest descent and 2,000 steps of conjugate gradient minimization, while the protein was constrained with a 500.
Thermostability calculations We calculated protein thermostability (DDG) values to relate these to our map scores and thereby identified functionally important HMBS residue positions. Calculations of DDG were carried out with DDGun3D version 0. 0. 2 (https://github. com/biofold/ ddgun), as previously described. 46  The PDB entry 3ECR for HMBS satisfied the following conditions: an X-ray determined structure with resolution 2. 2 A ˚or better, monomeric structure, and no missing or non-standard residues. 47  We defined stabilizing amino acid substitutions as those for which DDG R À1, and a destabilizing substitution for DDG < À1.
Structure modeling and protein positional features We used the Pymol software to place map scores in the context of a solved HMBS crystal structure (PDB ID: 5M6R). Using the InterfaceResidues. py script (https://pymolwiki. org/index. php/ InterfaceResidues), we defined interfacial residues as a change in accessible surface area (DASA) greater than 1 A ˚2 between the complex and single-chain structures. We used the FreeSASA program (https://freesasa. github. io/) to calculate the relative solvent exposure of residue positions. After examining the distribution of relative solvent exposure values and it's fit to a mixture of Gaussians (Figure  S10 ), we established thresholds corresponding to the high and low peaks. Residues with surface area values exceeding 40% were considered exposed, while those below 20% were classified as buried.
Relating variant functional scores to population allele frequencies Variants in HMBS were retrieved from UK Biobank (OQFE version of whole-exome VCF files; application ID: 51135), which includes sequencing data from $450,000 participants, as were non-overlapping gnomAD v. 2 and v. 3 datasets (https://gnomad. broadinstitute. org/). We then calculated odds ratios to evaluate the extent of allele depletion for variants with either damaging or neutral scores in our combined map.
Systematic maps of HMBS missense variant functional impact By coupling an efficient functional complementation assay with the TileSeq framework for multiplexed assays of variant effect, we sought to measure the functional consequences of all possible missense HMBS variants. 28  First, we constructed libraries of HMBS variants (for both erythroid-specific and ubiquitous isoforms) by using our previously described POPCode mutagenesis method. 28  To balance the objective of having substantial representation of each variant in the library against the objective of having roughly one amino acid substitution per clone, we generated two separate full-length libraries, with mutagenesis affecting the N-and C-terminal halves of the protein, respectively. Variant libraries were initially generated as a pool of amplicons.