dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  C. Uggenti ,  P. Heyn ,  A. P. Jackson ,  Y. Crow ,  S. Janssen ,  M. Lorincz  and members of the  Sproul lab  for useful discussions. We thank  Edinburgh Clinical Research Facility Genetics Core, MRC IGC FACs and imaging core  facilities for technical support. This work has made use of the resources provided by the  University of Edinburgh  digital research services and the MRC IGMM compute cluster. D. S. is a  Cancer Research UK Career Development  fellow (reference  C47648/A20837 ), and work in his laboratory is also supported by an  MRC  university grant to the  MRC Human Genetics  Unit. M. D. W. 's work is supported by the  Wellcome Trust  ( 210493 ),  Medical Research Council  ( T029471/1 ), and the  University of Edinburgh. This work was supported by the  Edinburgh Protein Production Facility (EPPF) , which receives funding from a core grant ( 203149 ) to the  Wellcome Centre for Cell Biology  at the.
Illustrating the benefits of sampling through the prediction of Xanthinuria type I biomarkers Xanthinuria type I is a rare genetic disease caused by a mutation in the XDH gene  [20] , and is characterised by kidney stones (urolithiasis), urinary tract infections, and rarely kidney failure  [21]. In patients with this disease, a decrease in urate and an increase in hypoxanthine has been observed (from OMIM  [22] ). Here, we applied both FVA and sampling in order to compare the information which can be drawn from both techniques. Both the FVA bounds and the sampling distributions are displayed on the same plot for both of the expected biomarker metabolites (Figure  4 ). Expected biomarkers are defined as metabolites with observed significant changes in patients of the disease according to the original dataset.
3%) compared to the total feasible range. Xanthinuria type I is one of many IEMs from an entire IEM -biomarker dataset which was curated in Sahoo et al. [23]. We used a subset of this dataset, used in Thiele et al. [14] , to run our analyses by knocking out each gene responsible for each disease: we ran both FVA and sampling on the 49 IEMs for 54 metabolites on Recon 2. Heatmap figures containing the entire set of predictions for both FVA and sampling are included in supplementary data Figure  S1 , and overlaps are shown in Figure  S2. Overall, sampling not only complements FVA by providing new correct predictions, but also attributes more meaning to the scores of the predictions for each metabolite (see Figure  S1 ). Sampling z-scores, as opposed to the binary increase/decrease indicators of FVA, can be used to rank, filter and gain insight on the intensity of changes.
Indeed, once generated, distributions can be used in multiple ways, one of which is calculating z-scores and ranks as shown in this paper; in contrast FVA interval boundaries represent only the flux extremes instead of the general flux behaviour. Generating coherent recommendations and identifying novel metabolites of interest associated with SNPs using SAMBA For this study, we chose a case example for the comparison of mGWAS data and SAMBA ranking system. In general, GWAS datasets are composed of traits associated with Single Nucleotide Polymorphisms (SNP), which are germline genetic substitutions of one nucleotide, present at a specific DNA position in at least 1% of the population. Specifically, mGWAS data consists of SNP-to-metabolic trait associations, such as single metabolite fold changes between non-SNP and SNP individuals, and ratios of two different metabolite levels, again compared between non-SNP and SNP individuals.
The directions of change predicted by FVA are correct except for myristoleate, which was predicted to be increased instead of decreased using the FVA bounds. Their ranks are shown in the column SAMBARank and these ranks are to be compared with the total number of exchange metabolites present in Human 1, i. e. 1497. These four metabolites are in the top 13%, two of which are in the top 1%. The significant metabolite ratios linked to SCD include many different combinations of pairs of metabolites. The assumption here is that at least one of the two metabolites involved in each ratio must change for the ratio to be significantly changed. Figure  6  shows the metabolites present in at least one ratio significantly associated with SCD and their associated predicted SAMBARanks. The second most differentially abundant metabolite predicted by SAMBA for this condition is 10-heptadecenoate, which is present in at least one significant ratio in the mGWAS SCD dataset.
Since BiNChE provides a view of the ChEBI ontology on a per-metabolite scale, using too many metabolites as input results in a large and difficult to read figure. Other methods can integrate more of the predicted metabolic profile (for example 50 metabolites). As a step closer to using chemical structures as opposed to class annotations as well as using more of the metabolic profile, we ran a ChemRich  [30]  analysis using the top 50 metabolites predicted to be differentially abundant. It uses the chemical structure via SMILES, and the MeSH terms associated with PubChem IDs to highlight enriched chemical classes. Figure  8  represents the most enriched clusters from the top 50 metabolite set. The higher the -log(pvalue) (y axis), the more the group is enriched. The ChemRich plot also shows that both saturated and unsaturated fatty acids are significantly enriched by this dataset.
For instance, experimentally-measured in vivo fluxomics data  [36]  could be matched to simulated import/export rates. A database of every unique KO could be simulated and compiled as a repository for comparison with real data to determine which metabolic perturbations are most likely to cause the condition tested by the experiment. This ranking system bypasses the issues that come with using flux values directly, and especially helps in choosing which metabolites to focus on first. The comparison of metabolic profile recommendations between different scenario simulations can be achieved by considering the top most changed metabolites and their ranks, as opposed to the raw flux values. Z-scores prove to be useful in that they reflect an intensity of change similar to fold changes, and are weighted by the standard deviation of the distributions, which helps the z-scores to remain flexible given the variable nature of these distributions.
Acknowledgements This work was supported by the  French National Facility in Metabolomics & Fluxomics, MetaboHUB  ( 11-INBS-0010 ). We would like to thank  Jake Bundy  for his participation in our discussions involving this work. The authors are grateful to the  GenoToul bioinformatics platform (Toulouse, France)  for hosting and providing their computer cluster, used for running all sampling runs.
The measurement of pure standards of metabolites is essential to obtain the highest level of confidence in metabolite identification (level 1 according to Metabolomics Standard Initiative  [6] ). Selecting which standards to measure is by itself a challenge, since samples can contain thousands of metabolites. Hence, SAMBA can be used by laboratories to select which standards to acquire in the context of the disease under study. More broadly, the top ranked list can also be used to identify families of metabolites to study as a whole, such as by extending the panel of measurable metabolites during a metabolomics experiment. Although SAMBA is a predictive method, evaluating the predictions using traditional contingency tables, recall and precision is difficult due to the nature of metabolomics measurements and the available "truth" datasets.
Metabolic models In this study, Human 1 v1. 14  [40] , containing 13 024 reactions, was used to carry out mGWAS analyses. https://github. com/SysBioChalmers/Human-GEM Recon 2  [14] , containing 7 440 reactions, was used to carry out IEM analyses. https://github. com/opencobra/COBRA. papers/tree/master/2013_Recon2.
Sampling method Random sampling is done using Python code written for SAMBA, based on the cobrapy  [41]  Python package. The code uses the CPLEX 12. 10 solver by default and uses the Using the mGWAS SNP dataset, genes were mapped to the Human 1 network using the annotated ENSG IDs. Then, using the model's GPR relationships, reactions were automatically knocked out using SAMBA. In the case of SCD, the GPRs were manually checked. The SCD SNP only affects the SCD1 gene (known as SCD in the metabolic model), as SCD1 and SCD5 are two separate genes. SCD5 codes for the same enzymatic function as SCD1 but they are both expressed in different tissues, fat tissue for SCD1 and brain and pancreas for SCD5. However, Human1 is not tissue-specific and the reactions are not necessarily associated with the genes according to this tissue specificity, so in order to block the enzymatic function completely, both SCD1 and SCD5 were blocked.
Using publicly available bulk RNAseq data, we could evaluate whether the expressed TE we measured in the whole gonads were originating from the somatic cells or the germline compartments. We re-analyzed the datasets obtained at E11. 5 from purified somatic precursor cells, and showed that at this stage, half of the self-expressed TEs expressed in the whole gonads were commonly expressed in the purified somatic cells. Moreover, previous analysis at E13. 5  (Liu, et al. 2014)  suggests that some endogenous retroviruses are much more expressed in somatic than the germ cells of the fetal testes. Ultimately, high quality RNA-seq data of purified cell types composing the developing gonads of both sexes, similarly to what has been done using microarray technology  (Jameson, et al. 2012) , would greatly contribute to the field to study yet unexplored aspects of the gonadal transcriptome with a better sensitivity than currently existing single-cell data.
RNA-seq mapping and quantification RNA-seq FastQ files from the Trim28 knockout mouse ovaries  (Rossitto, et al. 2022 ) (GSE166385), the Dmrt1 ovarian reprogramming  (Lindeman, et al. 2015 ) (GSE64960), the whole embryonic gonad splicing event  (Zhao, et al. 2018 ) (SRP076584), and Nr5a1-GFP+ purified somatic cells at E11. 5  (Miyawaki, et al. 2020 ) (GSE151474) studies were downloaded from Gene Expression Omnibus using the nf-core/fetchngs pipeline v1. 10. 0  (Ewels et al. 2020 , Patel 2023a ). Gene and locus specific TE expressions were measured using SQuIRE v0. 9. 9. 92  (Yang, et al. 2019). Briefly, FastQ files were mapped with STAR v2. 5. 3a on the mm10 mouse reference genome from UCSC. Gene and TE quantification were performed using the mm10 gene annotation as well as RepeatMasker TE annotation from UCSC.
TE differential expression analysis Differential TE expression analysis by embryonic stage and sex were performed using DESeq2 using both TE and gene quantification for a correct normalization of the data. First, a sample sheet was built with the 'stage_sex' sample annotation (e. g. 'E10. 5_XX') for each sample. A DESeq2 object was created using 'DESeqDataSetFromMatrix' with '~stage_sex" as design. Sexually dimorphic TEs were obtained with the 'DESeq' function with default parameters (Wald test). XX and XY overexpressed genes per stage were recovered using the 'results' function with "stage_XX' and 'stage_XY' contrast for each stage. Dynamically expressed TEs were computed sex independently. For each sex, dynamic TE were obtained using the 'DESeq' function with 'test="LRT", reduced = ~1' options (Likelihood ratio test).
ATAC-seq mapping and analysis FastQ files for the ATAC-seq data from E10. 5 and E13. 5 early gonadal progenitors and supporting cells respectively  (Garcia-Moreno, Futtner, et al. 2019)  were downloaded from Gene Expression Omnibus (GSE118755) using the nf-core/fetchngs pipeline v1. 10. 0. Data were mapped on the mm10 reference genome and analysed using the nf-core/atacseq v2. 1. 2  (Patel 2023b). Sequencing data quality was assessed using FastQC, sequencing adapters were removed with Cutadapt, and reads mapped using BWA. Reads were filtered with Picard to remove unmapped and duplicated reads. Peaks were called with MACS2 using the "narrow peak" parameter. For the subsequent analysis, we selected the peaks found in at least two replicates. Differential accessibility analysis between XX and XY for each stage as well as E10.
ChIP-seq mapping and analysis FastQ files for the ChIP-seq data (H3K4me3, H3K27ac, H3K27me3) from E10. 5 and E13. 5 early gonadal progenitors and supporting cells, respectively  (Garcia-Moreno, Futtner, et al. 2019)  were downloaded from Gene Expression Omnibus (GSE118755, GSE130749) using the nf-core/fetchngs pipeline v1. 10. 0. Data were mapped on the mm10 reference genome and analysed using the nf-core/chipseq v2. 0. 0  (Ewels 2022). Similarly to the ATAC-seq analysis, sequencing data quality was assessed using FastQC, sequencing adapters were removed with Cutadapt, and reads mapped using BWA. Reads were filtered with Picard to remove unmapped and duplicated reads. Peaks were called with MACS2 with the respective input controls using "narrow peak" parameter for H3K4me3, and "broad peaks" for H3K27ac and H3K27me3.
TF motif enrichment analysis Binding sites for transcription factors involved in mammalian sex determination / differentiation were counted using the Matrix scan (full options) algorithm from the RSAT suite (http://www. rsat. eu)  (Santana-Garcia et al. 2022). The DNA-binding preferences for the transcription factor analyzed were modeled as matrices and analyzed with default settings and p-value set to 10 -4. Matrices for transcription factors were obtained from JASPAR database (https://jaspar. genereg. net): SRY (MA0084. 1); SOX9 (MA0077. 1); SOX8 (MA0868. 2); SOX10 (MA0442. 2); SF1, also known as NR5A1 (MA1540. 1); GATA4 (MA0482. 1); GATA6 (MA1104. 1); DMRT1 (MA1603. 1); FOXL2 (MA1607. 1); RUNX1 (MA0002. 2) and WT1 (MA1627. 1). GATA4 and GATA6 are redundant in gonadal differentiation  (Padua, et al.
2014 , Padua, et al. 2015)  and the HMG domain of SOXs proteins display related function  (Bergstrom et al. 2000 , Polanco et al. 2010). Therefore, the pooled results of matrices scanning for GATA4 and 6 were expressed as "GATAs", and "SRY/SOXs" for pooled scanning with SRY, SOX8/SOX9/SOX10 matrices. To normalize counts, each motif was expressed in the number of matches per kilobases of the total length of the datasets. Statistical analyses were made using GraphPad PRISM 10 with ordinary one-way Anova and Tukey's multiple comparisons test. Detailed results of the statistical tests are provided as a supplementary table (Sup. Data 6). A) Classification of the TEs as "self-expressed" when they are found in intergenic regions or within a gene that is not expressed; or as "gene-dependent" when they are found within a transcribed gene.
Acknowledgments We are grateful to the genotoul bioinformatics platform  Toulouse Occitanie (Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12) for providing the computing resources necessary for the current study. We thank  Stéphanie Le Gras  from the  GenomEast platform of the IGBMC in Strasbourg  for her preliminary work on the TE expression analysis. We thank all the members of the  Poulat's  and the  Gonen's  teams for the constructive discussions and comments.
Funding This work is co-funded by the  Israel Science Foundation  ( ISF_710_2020 ) and the  European Union  (ERC, EnhanceSex,  101039928 ). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. IS and NG are funded by the  ISF  and  ERC. FP was funded by  ANR (Agence Nationale de la Recherche) :  ANR-21-CE14-0061-01  and  ANR-23-CE14-0012-01.
Force field parameters Classical all-atom molecular dynamics (MD) simulations have been performed using the Amber18 package  35. Cytc monomer structure (108 residues), given the same sequence, was taken from PDB 5LFT (ref. 32  ), and not from the PDB 5NCV (co-crystallized with pmclx 4 , ref. 21  ), because in the latter a small part of one flexible loop (residues -5 to -2) was missing. The structure was then protonated via the H++ server (http://newbiophysics. cs. vt. edu/H++) 36 at the experimental pH of 6. 0 21. The heme C group, extracted from the Cytc structure (PDB ID 5LFT), was connected via direct metal-ligand bonds with the Fe(II) center to H18 and M80 protein residues, while to C14 and C17 via thioether bonds  37, 38  (see Figure  S1 ). The chiral carbon atoms of the porphyrin ring, involved in the bonds, have been maintained with a S configuration.
The new C14/C17 amino acids were defined as a new class of residues to be heme-bound with name "CYB" for AMBER/ff14SB force field. The phosphonated calixarene pmclx 4 ligand was extracted from the PDB ID 5NCV structure in complex with Cytc. At the experimental pH of 6. 0 , the ligand features one deprotonated hydroxyl group, bearing a total charge of -5. Optimized structure was obtained at the DFT-B3LYP/6-31+G(d,p) level of theory with D3BJ dispersion correction  40. The atomic charges for the structure were assessed from the RESP procedure at DFT-B3LYP-D3BJ level of theory with a 6-31+G(d,p) basis set for the oxygen and phosphorus atoms, and 6-31G(d) for carbons and hydrogens. All DFT calculations were performed using the Gaussian 16 revision B. 01 series of programs  41. The force field parameters for the heme-c and pmclx 4 ligand were taken from GAFF2 46 , while the protein was described with the AMBER ff14SB 44 force field.
Long-range electrostatic interactions were computed using Particle Mesh Ewald (PME) algorithm  47, 48. Equilibration and production runs were carried out at T = 300 K. Post-processing analysis (cluster analysis, RMSD, distances, approaching angle and SASA) was performed through the cpptraj program. Heme-C parametrization In order to validate the heme C parametrization, molecular dynamics (MD) simulations were carried out to investigate the dynamic behavior of the whole protein structure. Cytc monomer (PDB ID code 5LFT) system (see Figure  S1  for details) was initially minimized for 10000 steps (5000 of steepest descent and 5000 of conjugate gradient), and then heated up from 0 K to 300 K. Equilibration was carried out for 1 ns. During these phases, position restraints were applied on the protein and heme heavy atoms, with a force constant of 5 kcal/mol• Å2 during the minimization and heating processes and of 2.
95-100%, identical to Illumina sequences [38]. The bioinformatics pipeline described in this manuscript was deliberately based on open-source algorithms that are freely available on dedicated internet platforms. Based on our dataset, we were able to identify AIV HA subtypes, the sequence of the cleavage site and potential clusters of outbreaks. One of the main concerns voiced by government veterinary services is the need to unveil links between outbreaks. These links may suggest direct or indirect farm-to-farm contamination routes. In contrast, when an emerging strain is identified in a region, this may indicate a new introduction, for example from wild birds. The protocol presented in this study can be carried out for a set of 30 samples in less than 24 hours, starting from the reception of the samples to the production of the consensus sequences.
Acknowledgements This study was performed in the framework of the " Chaire de Biosécurité et Santé Aviaires ", hosted by the  National Veterinary College of   Toulouse (ENVT)  and funded by the  Direction Generale de l'Alimentation, Ministère de l'Agriculture et de l'Alimentation, France. We are grateful to the genotoul bioinformatics platform Toulouse Occitanie ( Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12) for providing help and/or computing and/or storage resources We would like to thank farmers for their collaboration with the sampling of their flocks.
We focused our analysis on the period for which we had very highresolution trajectories for all six ibex (i. e. , June) resulting in a total of 3412 GPS relocations (341 locations on average per individual, range: 204-395) from the 10 GPS-collared ibex and 18 070 914 DR relocations (3 011 819 locations on average per individual, range: 3 010 683-3 012 356) from the 6 ibex that were also equipped with biologgers. Variations in the number of relocations between ibex from GPS-enabled dead-reckoning came from a few missing relocation estimates but as this phenomenon was marginal (i. e. , less than 0. 5% of the dataset) and the time difference between two successive relocations never exceeded 4 seconds, we considered missing location to be the same as the location immediately preceding it. We used slope and aspect layers provided by the R package terra to describe terrain characteristics in the ibex range at a resolution of 13x13m.
7 ). Sampling frequency also impacted the detected pattern of connectivity as many identi ed links were actually spurious (Fig. 7 ). For example, 20% of detected links were spurious when relocations were sampled every 20 minutes and it raised to 27% when relocations were collected every hour (Fig. 7 ). Commonly used theoretical networks showed relatively poor capacity to accurately predict landscape connectivity derived from our empirical dataset on Alpine ibex. Indeed, Cohen's Kappa equalled 0. 29, 0. 10, \varvecα α 0. 26 and 0. 00 for the DIST50, DIST95, MPG and random graphs, respectively. Consequently, the DIST50 predicted more accurately inter-patch movements for ibex than the MPG, even though the difference is weak, but it only predicted successfully about one-third of landscape connectivity.
Conclusions Animal behaviour, the level of landscape fragmentation and the frequency of individual relocations were shown to have an effect on the assessment of landscape connectivity using spatial networks. In general, very high-resolution trajectories (e. g. , relocations collected every 1 minute in Alpine ibex) were necessary to accurately identify complex network topologies and avoid the generation of spurious information on landscape connectivity. New technologies providing such high-resolution datasets over long period, as for example dead-reckoning techniques, should thus grow in the movement ecology sphere to help unravelling many ecological questions and conservation challenges. In addition, commonly used theoretical models should be applied with caution to the study of landscape connectivity in real-world systems as they did not perform well as predictive tools.
Introduction Global developmental delay (DD), often leading to intellectual disability (ID), has a prevalence of about 2% and is among the most frequent indications for genetic testing  (Sheridan et al. 2013 ). Despite continuous improvements in sequencing technologies, a genetic cause for DD/ID can only be found in 27 to 42% of routine diagnostic cases  (Gilissen et al. 2014 ;  Aspromonte et al. 2019 ). The high genetic heterogeneity is one reason for the incomplete diagnostic rate. According to the SysNDD  (Kochinke et al. 2016 ) database, pathogenic variants in 1,780 (gene statistics from October 26, 2023) different genes are currently established as causes of diverse neurodevelopmental disorders (NDD). However, as pathogenic variants in novel NDD genes are still being identi ed at a high pace  (Kaplanis et al.
Cell Culture and Transfection Transfection was performed as described previously  (Rahimi et al. 2022). Brie y, Neuro-2A cells (RRID:CVCL_0470) were originally obtained from the DSMZ (German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany) and cultured in T-75 culture asks (Sarstedt AG & Co. ) in DMEM/25 mM glucose, without pyruvate (Thermo Fisher Scienti c), supplemented with 10% fetal bovine serum (FBS superior, Biochrom), 2 mM l-alanyl-l-glutamine and antibiotics (Thermo Fisher Scienti c) at 37°C and 5% CO2 in humidi ed air in an incubator. For transfection of plasmids, Neuro-2A cells were seeded in 12well plates and transfected with 6 µL JetPEI (Polyplus, Illkrich, France) and 2 µg of DNA encoding wild-type or mutated variants of DOCK4-mRFP.