dataesr/scientific-paragraphs-categorization huggingface dataset:
All tests were adjusted for age, sex, BMI and the 10 first genetic PCs of the genotyping matrix. Note that for this genetic screening we only applied the adjustment on the outcomes. Two-sided adjustment has already been used for mean effect tests in GWAS to account for relatedness  20  , but would require further investigation to be extended in the MANOCCA test. Following the results from our simulations for genetic variants with a MAF 5% or larger, the type I error will remain robust only for up to 50 principal components (Fig. S4c ), resulting in 10 GWAS per dataset. MANOCCA did not detected any genome-wide significant signals at a stringent Bonferroni corrected threshold (P = 5 x 10 -9 , Fig. S9 ). Yet, 46 genetic variants from eleven loci show suggestive significance association (P = 5 x 10 -7 , Table  S5 ).
Discussion Covariance is a fundamental feature of omics data. Covariances might be explained by multiple factors, including shared biological mechanisms, shared environmental risk factors, or causal effects between the outcomes measured. However, our understanding of the factors involved in covariance has been very limited, partly due to the lack of adapted methodologies and software allowing for systematic screening of large-scale omics datasets. Here, we present MANOCCA, a robust and computationally efficient approach for the identification of predictors associated with the covariance of a multivariate outcome. We show that MANOCCA outperforms existing covariance methods and that, given the appropriate parametrization, it can maintain a calibrated type I error in a range of realistic scenarios when analysing highly multidimensional data.
The application of MANOCCA to the Milieu Intérieur dataset demonstrates the validity and relevance of our approach, identifying multiple health-related and lifestyle factors significantly associated with the covariance of blood biomarkers and immune phenotypes. The MANOCCA approach three main limitations. First, we used principal components analysis to address situations where the number of covariance terms is larger than the sample size. We defined guidelines that constrain the maximum number of PCs that can be used to ensure the validity of the test when analysing binary or categorical predictors. Regarding power, both simulation and empirical data analyses show that the optimal number of PCs to be included to maximize power can vary substantially conditional on the true covariance association pattern.
Omics data from other sources (e. g. , RNAseq) might carry additional complexity that would have to be investigated by simulations before conducting real data applications. Given the increasing number of high dimensional omics data available in existing human cohorts, our approach provides opportunities to investigate multivariate outcomes from a new perspective. Because MANOCCA is built on a standard linear framework, the approach can be extended in many including the derivation of the individual contribution of outcomes and the development of predictive models. Altogether, we expect the application of our method to produce novel insights on the complex structure linking highly intertwined omics data. The Milieu Intérieur Consortium ¶ is composed of the following team leaders: Laurent Abel (Hôpital Necker), Andres Alcover, Hugues Aschard, Philippe Bousso, Nollaig Bourke (Trinity College Dublin), Petter Brodin (Karolinska Institutet), Pierre Bruhns, Nadine Cerf-Bensussan (INSERM UMR 1163 -Institut Imagine), Ana Cumano, Christophe D'Enfert, Ludovic Deriano, Marie-Agnès Dillies, James Di Santo, Gérard Eberl, Jost Enninga, Jacques Fellay (EPFL, Lausanne), Ivo Gomperts-Boneca, Milena Hasan, Gunilla Karlsson Hedestam (Karolinska Institutet.
, which was approved by the Comité de Protection des Personnes -Ouest 6 (Committee for the protection of persons) on June 13th, 2012 and by French Agence nationale de sécurité du médicament (ANSM) on June 22nd, 2012. The study is sponsored by Institut Pasteur (Pasteur ID-RCB Number: 2012-A00238-35), and was conducted as a single centre interventional study without an investigational product. The original protocol was registered under ClinicalTrials. gov (study# NCT01699893). The samples and data used in this study were formally established as the Milieu Interieur biocollection (NCT03905993), with approvals by the Comité de Protection des Personnes -Sud Méditerranée and the Commission nationale de l'informatique et des libertés (CNIL) on April 11, 2018. All donors gave written informed consent.
Simulations of AUG with the plasma boundary code SOLPS-ITER  [6]  show that the high Ne leakage may be explained by its elevated First Ionization Potential (FIP). A large enough fraction of Ne atoms is ionized sufficiently far away from the divertor target that they can be dragged upstream by the thermal force and by friction with the main ion flows  [7]. However, simulations of JET and ITER with SOLPS-ITER  [8] , supported by JET experiments  [9] , show that for high power discharges in these machines both N and Ne can provide semi-detached conditions without strong impact on the pedestal region. There is, however, a strong preference to avoid N in ITER due to the formation of tritiated ammonia when DT fuel is used  [10]. An alternative to the low Z radiators, which has only been previously considered for ITER in the years before the design of the machine now under construction was finalized  [11] , is argon (Ar).
Modeling setup In this paper AUG and ITER H-mode discharges with N, Ne and Ar seed impurity are simulated, 6 cases in total, with 3 ones for each device. The modeling was performed with the SOLPS-ITER code, which is designed to simulate processes at the plasma periphery  [6] , with full account of drifts and currents in the plasma solver B2. 5 and neutral-neutral collisions in the Monte-Carlo neutral kinetic code Eirene. For AUG the modelled cases are based on the geometry, input power and outer midplane (OMP) profiles of shot N • 28903, which was studied in detail in  [7, 14]. The detailed description of the ITER modelling setup can be found in  [15]  and the cases with Ne and N were chosen from  [8]. Note that for ITER cases with Ne seeding a STRAHL radiation rate database is used to keep consistency with previous publications  [8, 15] , while for other gases this is replaced by the ADAS database.
The corresponding Ar separatrix impurity concentration and the core effective charge (averaged over the poloidal separatrix contour above the X-point and over the inner core boundary of the computational domain respectively) may be considered as acceptable by comparing to the N and Ne seeded cases from ITER modeling scenario database  [14]. Peak heat loads, q peak inn , q peak out at both inner and outer targets appear to be similar for all three gases, however, in AUG the target in-out asymmetry is more pronounced than in ITER. The compression is defined here as the ratio of the impurity density averaged over the outer and inner divertor volumes in the near SOL (corresponding to the first ~3λ q from the separatrix traced back to the OMP) to its value averaged over the poloidal separatrix contour above the X-point.
They were proposed as a theoretical foundation for parametrised automata used in verification tools and called pNets. An OA  [17]  is similar to a classical automaton but with variables and holes. Variables make automata symbolic and allow them to encode infinite-state systems. Holes enable the composition of automata: an automaton with a hole is an operator that takes another automaton as parameter and reacts to the actions it emits; the composed automaton is an automaton where the behaviour of one "process parameter" of the main automaton has been provided. Due to their generic nature, the notion of OA model is quite abstract but we already illustrated previously how to derive OAs for process algebra operators  [17]  or for component systems  [6, 5]. In previous works  [6, 24]  a bisimulation relation was defined for OA and open parameterised hierarchical LTSs.
This apparent contradiction has been discussed by Cao et al. [5]. Others have suggested to use Bayesian inference -where probabilities are assigned to parameter values -to go beyond point-estimates  [7]. Here, we present a computational workflow that starts from defining microscopic transition rates -describing ecological and evolutionary events -and works with macroscopic statistical moments of the microbiome composition. Our Bayesian inference workflow, which naturally bypasses known limitations of point-estimate inference  [5] , is sufficiently flexible to test models as diverse as microbiome samples while quantifying the parameter uncertainty stemming from data limitations (Fig. 1 C ) -including its extrinsic noise. Two classical ecological models are used to illustrate its application on datasets describing absolute or relative abundances of microbes -namely, logistic growth and Lotka-Volterra models.
Properties of microbiome data Given a microbiome dataset, all statistical moments ḡk can be estimated from raw data. Concretely, it can be done by averaging the variable of interest g k , over all replicates in each specific time point (Fig. 1 C ). Microbiome data is nowadays typically produced by metagenome sequencing. Conventionally, for technical reasons metagenomics only quantifies the relative abundance of each microbial type in a sample (Fig. 1 C )  [9]. More recently, some studies have measured absolute numbers of culturable  [10]  and non-culturable microbes in samples  [11]. We call these counts absolute abundances. Our former equations only track moments of absolute abundance, g k. As Gloor et al. [9]  show, inferring parameters from relative abundance (x k ) data using them would lead to spurious correlations (Fig.
2  A-B). To find equivalent expressions for the relative abundance, first, we propose n Σ ≡ j n j to be defined as a scaling factor and an equation for its first moment, n Σ. Then, a transformation to moments of relative abundances, γ k , is given by, d γ k dt = f ( γ , n Σ , θ, t). (5) Because relative abundance datasets lack information about the scaling factor, its initial condition, nΣ (0), must be inferred as a free parameter, one parameter more than for absolute abundance data. Note that because k x k = 1, the number of equations for the types decreases but the number of parameters per type remains. A detailed derivation of transformations for a logistic growth and the Lotka-Volterra models is shown in the Sup. Methods A. Apart from tracking the microbiome of the same hosts over time, as in animal gut studies, commonly, various hosts are pulled together to produce a single time series (Fig.
Inference from simulated and empirical data We tested our inference workflow in two ways. Firstly, we recovered the correct parameters from simulated data (Fig. 2 ). Our approach proved successful in cases with and without inter-specific interactions, namely, data simulated from logistic growth and Lotka-Volterra models. In both cases, our approach identified the correct model while in addition estimating their parameters and certainty. Secondly, we inferred parameters of an empirical time series of absolute abundance in a reduced mice microbiome -OMM 12 [12] (Fig. 3 ). The posteriors suggested the growth rates of Bacteroides caecimuris, Muribaculum intestinale, Flavonifactor plautii, Akkermansia muciniphila, Turicimonas muris to be most certain, with average doubling times ranging from hours to days.
This allows our workflow to handle very diverse models, where model comparison  [15]  in a Bayesian sense is possible. The workflow is not limited by the properties of microbiome data  [9]. As we have shown, analyzing datasets describing the relative abundance of microbial types -even if the total absolute abundance is dynamic  [14]  -is possible. Such is the nature of metagenomic sequencing data -the most common method to characterize microbiomes  [5]. In addition, by tracking statistical moments of the microbiome, our approach naturally accounts for the diverse types of experimental samplings, such as those where ensembles of hosts are used to obtain a single time series. Concretely, compared to other methods, we track the demographic variation between hosts explicitly, and assign the remaining variation to external noise.
By tracking numerous statistical moments readily solved numerically, we believe our approach combined with data of sufficiently high quality can overcome this to some extent -exploring the parameter space thoroughly in a reasonable time. We implemented an Approximate Bayesian Computation with Sequential Monte Carlo in our workflow using tools from the Python package pyABC  [16]  (Sup. Methods C), on which further optimizations could greatly improve its wider application  [7]. As proof-of-principle, we applied our workflow to a simulated dataset and recovered the true parameter values. We also applied it to a reduced microbiome in mice  [12] , where we estimated values and certainties of parameters describing logistic growth. In summary, we presented a Bayesian inference workflow bridging microbiome data to theoretical modelling.
These were wide to test the ability of the inference workflow to find the true parameters in simulations (Table  1 ). U ) maximum number of cells in data  [12]  Table  5 : Priors for logistic growth with immigration and death in empirical mouse data (Fig. 3 ). Available Table  6 : Settings for Approximate Bayesian Computation -Sequential Monte Carlo (ABC-SMC) code. These settings were chosen to decrease the computing time, but still robustly minimize the distance between data and model. We used tools from the Python package pyABC  [16] , mainly ABCSMC. * The maximum number of generations, mismatch threshold (ε) minimum, and minimum ε change between generations, are all stopping criteria. LSODA is a numerical solver capable of selectively adapting to the stiffness of a system of differential equations.
Patients and tumour samples The first part of the cohort is composed of 60 patients diagnosed with H3-K27M or EZHIP-overexpressing tumours harbouring a coding mutation in BRAF or FGFR1 genes, from the Necker Enfants-Malades/GHU-Sainte Anne Hospital/ Gustave Roussy center and Biomede 1 Trial (NCT02233049) (n = 29), or from other published cohorts (n = 31)  [3, 18, 23, 25, 30, 31, 37]. The control cohort includes patients with wild-type FGFR1/BRAF, in the above-mentioned cohorts of H3K27M or EZHIP-overexpressing DMG (flowchart in Supplementary Fig. 1 , online resource). Tumour tissue and clinical data were collected under informed consent obtained from the parents or guardian according to the IRB approved protocol (CNIL 1176643).
DNA Methylation array processing Genome-wide DNA methylation analysis was performed using either the Illumina HumanMethylation450 BeadChip (450 k) or EPIC arrays as previously published  [2, 3]. Data were obtained from different platforms (DKFZ Heidelberg; Integragen; Diagenode; published data) and were analysed with R (v4. 0. 4). For t-Distributed Stochastic Neighbour Embedding (t-SNE) analysis, the minfi package was used to load idat files and preprocessed with the function preprocess. illumina for dye bias and background correction. Probes located on sex chromosomes or not uniquely mapped to the human reference genome were removed. Probes containing single-nucleotide polymorphisms or that were not present in both EPIC and 450 k methylation array were also eliminated. A batch effect correction was done with removebatchEffect function from limma package, to remove difference between formalin-fixed paraffin-embedded and frozen samples.
Gene expression analysis Reads were pre-processed using the nf-core RNAseq pipeline (v3. 0), mapped to the reference genome GRC38/hg38 with the STAR tool (v2. 6. 1d), annotated with GENCODE v36 and counting was performed with the Salmon quantification tool (v1. 4. 0). Differential gene expression analysis was performed with the DESeq2 package (v1. 30. 0, minRep-licatesForReplace = 7, betaPrior = TRUE) with a threshold of 0. 01 for Benjamini-Hochberg adjusted p value (adj-p). For gene set enrichment analysis (GSEA), hypergeometric tests were used to identify overrepresented gene sets from the MSigDB v7. 4 database, amongst genes ranked by significance and fold-change in differential expression analysis, with Benjamini-Hochberg multiple testing correction using the package Clusterprofiler. Catalogues considered included Hallmark and C2.
Univariate and multivariate survival analyses Overall survival (OS) was estimated with the Kaplan-Meier method and median overall survival was computed using a log-rank test. OS was obtained from the post-diagnosis until death patient or last known information. The analysis was realised in Prism9 software. Multivariable Cox proportional hazards regression model on OS was performed including histone H3, BRAF, FGFR1, TP53 status, age at diagnosis and tumour location with R software using the function coxph() of the survival package (Version 3. 2-13).
Acknowledgements LA, MAD, JG and DC acknowledge the  Etoile de Martin charity  (annual grant support to LA and  RARE program  with the  Fondation Carrefour "Les Boucles du Coeur") ,  Imagine for Margo, La Marche de l'Ecureuil charity  and programme  GLCE fondation Gustave Roussy  for the financial support of this project,  Necker Imagine Tumor and DNA biobank  ( BB-033-00065 ) for tumor and blood samples and centers participating in the BIOMEDE International Study and national PIs ( Karsten Nysom ,  Klas Blomgren ,  v Hargrave ,  Dannis Van Vuurden ,  Francisco Bautista-Sirvent ,  Geoffrey McCowage ).
INTRODUCTION 2] [3] [4] [5]  This ghost proteome, being a part of the total protein landscape, points to proteins not referenced in conventional databases like UniProt  6  and RefSeq. 7 Such ghost proteins, called alternative proteins (AltProts) or proteins coded by small open reading frames (smORFs),  8  were identified to be translated from regions of mRNA molecules described as non-coding, e. g. 3 0 and 5 0 UTR, reading frame shifts  3  or involve all kinds of non-coding RNA (ncRNA)  9  (Figure  1 ). AltProts have the particularity of having an average size of less than 100 amino acids,  10  likewise their sequences, despite being derived from a mRNA coding for a referenced protein (RefProt), have a completely different amino acid sequence, suggesting a different biological function. AltProts are estimated at 450,000 potential sequences,  3, 11  compared to 79,038 RefProt sequences (Uniprot-01.
21 Such tools allow to redraw the signaling pathways and group together RefProts belonging to the same biological process, molecular function, or cellular localization, increasing the information about the cellular mechanism. Such information can be obtained through databases holding information on protein-protein interactions (PPIs) such as STRING,  22  BioGrid,  23  and IntAct  24  allowing them to be applied to a large-scale protein analysis such as a bottomup approach by chromatography coupled to mass spectrometry analysis (LC-MS/MS) of RefProts. However, similar PPI data are for AltProts are currently largely unknown and AltProts remain largely understudied as baits for identifying PPIs. One interesting approach to obtain PPI data involving AltProts is based on the use of crosslinkers combined with analysis by mass spectrometry (XL-MS).
This hypothesis-free strategy, when applied to a complex mixture such as a cell extract, fixes actual PPIs present and allows us to identify new interactions of a bait protein. When processing XL-MS data, one may search for AltProts by using a database holding AltProt sequences. XL-MS has been applied for the structural analysis of purified proteins and to identify interactions in purified protein complexes. However, XL-MS holds some limitations when applied to the large-scale exploration of cellular PPIs, the main ones being the low number of crosslinked peptides that get identified compared to non-crosslinked peptides and the identification of cross-linked peptides because of their complex spectra. To increase the identification of the former, enrichment workflows can be implemented. Such enrichment depletes non-crosslinked (or free) peptides upon sample fractionation, which is generally carried out by size exclusion chromatography (SEC) or cation exchange chromatography (SCX).
Overview of the workflow used As the function of the vast majority of AltProts predicted from OpenProt Database 3 remains unknown, as mentioned earlier, we used crosslinking mass spectrometry to characterize AltProts in a non-targeted way. To obtain more information on AltProts on a large scale and to optimize the identification of crosslinked peptides, we set up a workflow combining in cellulo crosslinking with subcellular fractionation and analysis by nLC-MS/MS. Additionally, to confirm the presence of crosslinked proteins SDS-PAGE was used and Western blotting to confirm the efficiency of subcellular fractionation (Figure  2A ). Finally, the generated data were integrated to identify AltProts, their partners, and the signaling pathways they are involved in. For crosslinking, we used in cellulo DSSO treatment on replicates of 3E6 immortalized human ovarian cells (T1074-ABM).
Identification of RefProts The MS/MS data from the crosslinked samples were analyzed by Proteome Discoverer V2. 5 using Sequest HT. 31  We initially focused on the RefProts (databased Uniprot 02-2022) and could identify 4,753 unique RefProts, of which 2,557 were identified in the cytoplasmic fraction, 2,731 in the membrane, 2,808 in the nucleus, 1,794 in the chromatin fraction and 2,781 in the cytoskeleton, with a high number of proteins shared by different fractions (Figure  3A ). The fraction in which more compartment-specific identifications were found was the membrane fraction (538), followed by the cytoskeleton (375), cytoplasm (369), nucleus (344), and chromatin fraction (127). A gene ontology (GO) cellular component enrichment analysis was performed using the STRING app 32 at ClueGO 33 Figure  3B.
Identification and characterization of AltProts Following a similar approach as for the RefProts, AltProts were identified, now using the OpenProt database. A total of 112 AltProts were identified in at least two replicates in the same subcellular compartment (Figure  3C ). The highest number of AltProts (44) was found in the membrane-bound fraction, followed by cytoplasmic AltProts (41), 30 in the nucleus, 25 in the chromatin fraction, and eight in the cytoskeletal fraction. Of note, 24 AltProts were identified in two or more cellular compartments. With the ability to separate subcellular proteins we can propose information about localization for the AltProts identified. Such information is important as the function of a protein depends, amongst others, on the cellular compartment or organelle where it is localized, as this provides the necessary physiological context, aiding the functional characterization of AltProts.
Further analyses showed that 88. 3% of the identified AltProts originate from non-coding RNAs (ncRNA), 5% from miscellaneous RNAs (misc_RNA), 3. 3% from a frameshift in the mRNA CDS, and 1. 7% from each of the 3 0 and 5 0 UTR mRNA regions (Figure  4A ). Considering the distribution of the molecular weights of the identified AltProts, more than 5% of the AltProts have molecular weights below-30 kDa (Figure  4B ). In Table  S1 , the complete description of the AltProts, protein Blast results, and the unique peptide identified in MS/MS corroborate by NextProt Peptide uniqueness checker. 34 he OpenProt database holds information on the prediction of protein domains in AltProts, made possible by comparisons with RefProt sequences and domain annotations made with algorithms like InterProScan. 35 omains describe a structural or functional entity that is typically evolutionary conserved among orthologs.