dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotyping For genome-wide analyses on the 1,240,000 SNP set, we genotyped our. bam files using pileupCaller (https://github. com/stschiff/sequence-Tools) by randomly calling one allele per position considering the human genome to be a pseudo-haploid genome. We called the SNP genotypes according to the Affymetrix Human Origin (HO) panel (around 600,000 SNPs)  55, 56  and the 1,240,000 panel  42. The numbers of SNPs covered at least once are provided in Supplementary Table  1.
Imputation and screening for IBD sharing The samples were imputed using the software GLIMPSE  57  according to the standard processing steps (https://odelaneau. github. io/GLIMPSE/ docs/tutorials). Each sample was imputed separately to avoid batch effects. As a reference panel for imputation, we used the phased haplotypes from the 1000 Genomes dataset (http://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/release/20130502/). The imputed and phased data were then used as the input into the software ancIBD  17  (v. 0. 2a; https:// pypi. org/project/ancIBD/). As recommended, we downsampled to 1,240,000 SNPs, for which ancIBD is optimized, and then screened Article all pairs of all Gurgy individuals (n = 72) with at least 500,000 SNPs covered for long IBD segments, using the recommended default settings of ancIBD.
In the case of uncertain first-degree relationships being either siblings or parent-child, we investigated the spatial distribution of pairwise mismatch rate along the chromosome using a new tool under development, BREADR  61  (v. 1. 0. 1; https://github. com/jonotuke/BREADR; Supplementary Note 2). In cases of pairs of individuals in which several relationships of different degrees were possible, we developed a method using the binomial distribution for the pairwise mismatch rate to assign posterior probabilities for relatedness classification (Supplementary Note 2; https://doi. org/10. 5281/ zenodo. 7224898).
Acknowledgements This study benefitted from excavation grant support from the  region of Bourgogne, France  and the  Service Régional de l'Archéologie de Bourgogne  (site number  89 198 008  issued by SRA authority). This research was funded by a ministerial grant from the  French Research Foundation  as a program of prospects investments  ANR-10-LABX-52  (to S. R. , project DHP,  LaScArBx- ANR ,  2012-14 );  Fyssen Foundation Postdoctoral Stipend  (to M. R. ,  2017-18 ); the  Max Planck Society , by the  French Research Foundation  and  German Research Foundation  (to M. R. , W. H. and M. -F. D. , project  INTERACT ,  ANR-17-FRAL-0010  and  DFG-HA-5407/4-1 ,  2018-21 ; to A. S. , project  ImMiGeNe ,  DFG-433115696 ); and the  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Programme  ( PALEoRIDER ,  771234 ,  2018-23 , to W.
Data availability New genomic sequencing data (BAM format) are available at the European Nucleotide Archive (ENA; PRJEB61818). Previously published genomic sequencing data (BAM format) are available at the ENA (PRJEB36208 and PRJEB45741). The Genome Reference Consortium Human Build 37 (GRCh37) is available at the National Center for Biotechnology Information under accession number PRJNA31257. The revised Cambridge reference sequence for the mitochondrial genome is available at the National Center for Biotechnology Information under NCBI Reference Sequence NC012920. Clusters of different first-degree related individuals emerge when plotting these measures of relatedness against each other. Colours are given a posteriori according to the reconstructed trees. One inconsistency can be observed with the red point (parent-offspring relationship) plotting in the sibling cluster in blue, representing the pair GLN285A and GLN285B (Supplementary Note 2 and 3, Supplementary Table  9 ).
The resulting features were aligned using the same settings as above. This resulted in 11 217 features identifiable by their mass and retention time. After excluding the features present in every blank sample, unless 5-fold greater in intensity in the samples, and removing the compounds that were not detected in at least 40% of the samples (a threshold we have used in previous metabolomic-wide association studies  21  ), 4294 features remained. Annotation. The features were searched against a database of metabolites known to be detectable with the assay used in this study. This database was constructed by combining the elemental composition and retention time of the metabolites identified to MSI levels 1 or 2 in previous studies, where the same laboratory assay was used for the analysis of human plasma or serum (see Table  S1  in the Supporting Information for more details).
42 additional lipid targets were included based on matching of the accurate mass and MS/MS spectra by using Agilent Lipid Annotator 1. 0 software as described earlier. 22  The database was created using Agilent MassHunter PCDL Manager B. 08. 00 software, and searching was performed with Agilent IDBrowser B. 08. 00 identification module of the Mass Profiler Professional 14. 9. 1 software. The software uses isotope patterns associated with the feature for the determination of charge state, allowing more specificity than searching for matching accurate mass alone. Matching tolerance was ±10 ppm and ±0. 15 min for the mass and retention times, respectively. Only singly charged [M + H] + ions were allowed with up to 10 matches per target, ranked by score consisting of the closeness of mass, retention time, and isotope spacing and abundance when detected.
Terms had to have at least seven members to be considered. Information on biological pathways a chemical compound was active in was retrieved from the KEGG database. 24  Statistical Methods. We used a linear mixed effects model with censored responses (multilevel tobit model) to estimate the variance components of the features. We defined a threelevel nested random-intercept model that takes the nesting of subjects into centers into account: y u u log( ) ijk jk k ijk (2) (3) (1) = + + + (1) for intensity measurements i = 1,•••, n jk and level-2 groups (subjects ) j = 1,•••,M 1k nested within level-3 groups (centers) k = 1,•••, M 2. Here, u jk  (2)  is a level-2 random intercept, u k  (3)  is a level-3 random intercept, and ϵ ijk  (1)  is a level-1 error term (within-subject error). We assumed the error term and level-2 and level-3 random intercepts to be normally distributed with a mean of 0 and variances σ 1 2 , and σ 2 2 and σ 3 2 , respectively.
All error terms and random intercepts were assumed to be independent of each other. We assumed the feature intensity y to be left-censored at the limit of detection (LOD), which we defined as the lowest detected value for a compound. The models were implemented in R (version 4. 2. 1) using the brms package (version 2. 17) which provides an interface to fit Bayesian models using the full Bayesian inference tool Stan. [25] [26] [27]  Coding scripts to reproduce the statistical analysis is available at https://doi. org/ 10. 5281/zenodo. 8247461. From this model, we calculated the following intraclass correlation coefficient y y Corr(log( ), log( )) ijk i jk 2 2 3 2 1 2 2 2 3 2 = = + + + (2) This coefficient relates measurements from the same subject and center to measurements of different subjects and different centers.
64 (IQR 0. 23)). Sensitivity Analyses. ICCs for the compounds calculated using the two-level model were comparable to those calculated using our main model (median ICC: 0. 60 on the natural logarithm scale and 0. 57 on the data scale for the identified compounds; Figure  S6 ). ICCs were similar across the four centers (Figure  S7 ). Model diagnostics were considered sufficient for all models (see the Model Diagnostics section in the Supporting Information). Comparison with Targeted Assays. For nine of the 184 compounds with confirmed identities from our study, we were able to retrieve ICCs for measurements in peripheral blood using the BIOCRATES kit from the literature. 14, 15  For six compounds, our ICCs were lower than reported in studies with comparable time frames (citrulline, methionine, proline, tryptophan, tyrosine, valine), and in three cases, our ICCs were higher (isoleucine, leucine, phenylalanine), but in general, the confidence intervals overlapped, and averages were in the same range (median ICC Floegel 0.
54, Yin 0. 52, our results 0. 44). The repeatability was greatest in the study over the shortest time span (Breier 0. 67). Detailed results can be found in Figure  S8. The ICCs and the 95% credibility interval (main and adjusted model) of the identified compounds are available in Table  S4. The full results for each compound, all variance components, convergence statistics, and resulting ICC per compound are available in the online repository https://doi. org/10. 5281/zenodo. 8247461.
The  EXPANSE  project is funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 874627. The  EXPOSOME-NL  project is funded through the  Gravitation program  of the  Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research  ( NWO  grant number  024. 004. 017 ). The research leading to this data has received funding from the  European Community 's  Seventh Framework Program  ( FP7/2007e2011 ) under grant agreement number:  308610  ( EXPOsOMICS ). The study center in Basel was additionally funded by Grants from the  Swiss National Science Foundation   33CS30-148470  and  33CS30-177506. The authors greatly acknowledge all of those who are responsible for data collection and management in the EXPOsOMICS study.
2 , sixth row). We observed a saturation of the secretion levels for induction above 50% of the maximum but no marked decrease. Yet stress levels continue to increase, showing that high induction levels cause unnecessary stress to the cells. In summary, this dataset shows markedly different secretion behaviors for the different POIs, confirming that secretory constraints and bottlenecks are protein specific. In particular, we observed induction sweet spots above which cellular secretory stress increases and protein production plateaus or even decreases. Such situations are associated with surprising non-monotonic dynamics in iPOI and stress levels.
Cytometry data analysis To account for minor day-to-day variability and settings adjustments for the cytometer (Guava EasyCyte 14HT, Luminex), we computed a correction coefficient for all the channels used in each experiment. Such coefficient was computed by using the Guava® easyCheck™ Kit, in which fluorescent beads are measured in every channel, providing the changes in mean fluorescence intensities from one experiment to another. These coefficients are then used to normalize the fluorescence obtained from the different channels in each experiment. The channels used for each fluorescent reporter were mNeonGreen: GRN-B channel, mCerulean: BLU-V channel and mScarlet-I: ORG-G channel. Gating is used on the forward light scatter (FSC) channel to discard cell doublets and cell debris (only events having values between 1000 and 2000 in FSC were kept).
Mathematical model We used an ordinary differential equation model having three state variables and six parameters. dPrd dt = K prd Á Ind À K prd Á Prd ð1Þ dTrf dt = K trf Á Prd À K sec + K deg + K dil Á Trf ð2Þ dSec dt = K sec Á K units Á Trf À K dil Á Sec, ð3Þ Article https://doi. org/10. 1038/s41467-023-38807-9 Nature Communications | (2023) 14:3028 where Prd, Trf, and Sec correspond to production intermediates, proteins in traffic, and secreted proteins, respectively. Two parameters (Kprd and Kdil) were fitted to data from cells expressing a nonsecreted mNeonGreen protein and then kept constant. The same parameter was used for cell growth and media renewal (Kdil) because the model captures only the behavior of non-accumulator cells that grow normally and because the bioreactors are operated in turbidostat mode.
The Kunits parameter is a scaling factor between internal and secreted fluorescence values. It was set based on secreted mNeon-Green data. The remaining three parameters were fitted on individual time series data (Trf levels) and on secretion data (Sec levels) for each induction level and each protein based on the non-accumulator cell population data. The non-accumulator and accumulator cell populations were distinguished using a Gaussian Mixture Model (Supplementary Note 5, Python package sklearn. mixture. GaussianMixture  71  ). The ODE model was integrated using the numerical solver scipy. integrate. solve_ivp 70 , and parameter fitting was performed thanks to the CMA-ES algorithm using the pycma package from Hansen and colleagues  72.
Data availability All the raw experimental data generated in this study have been deposited on Zenodo (https://doi. org/10. 5281/zenodo. 7418639) and Pride PXD041650. Sequences of plasmids used to construct all yeast strains are available in the GenBank format in the YeastCyberSecretion Inria Git repository (https://gitlab. inria. fr/InBio/Public/ yeastcybersecretion). Source data are provided with this paper.
Code availability To grow cells, control gene expression, take samples and measure cell fluorescence by cytometry, we used an automated platform driven by the ReacSight software available at https://gitlab. inria. fr/InBio/Public/ reacsight. ReacSight operations are described in a Jupyter notebook associated with each experiment. ReacSight notebooks are available online at https://doi. org/10. 5281/zenodo. 7418639. In addition to ReacSight (v1), we also used the OT-2 Python API (v2), and GuavaSoft (v3. 3). The Python code to process and analyze the raw data and generate figures for the manuscript can be found at https://gitlab. inria. fr/InBio/Public/yeastcybersecretion. It uses pandas, numpy, scipy, sklearn, cma and matplotlib packages.
The Nanopore library was prepared with the SQK-LSK109 ligation sequencing kit, and sequencing was performed in-house. We assessed the run quality using Porechop version 0. 2. 3_sequan2. 1. 1  (Wick et al. , 2017). All raw data are available at NCBI under the project IDXX (to be completed upon manuscript acceptance). We also used some available genomes  (Dumas et al. , 2020)  (Suppl. Table  1 ). We trimmed all Illumina reads and cleaned adapters with Trimmomatic v0. 36  (Bolger et al. , 2014). We removed the three bases at the beginning and end of reads using the Leading and Trailing options, dropped reads having a length inferior to 36 with the MINLEN option and performed a sliding window trimming approach using the Slidingwindow option and 4:25 as values. Cleaned reads were mapped on the new high-quality P.
roqueforti LCP06133 reference genome assembly (ID Genbank, to be completed upon manuscript acceptance) using Bowtie2 version 2. 3. 4. 1  (Langmead & Salzberg, 2012). This reference genome was built using raw ONT reads with Canu version 1. 8  (Koren et al. , 2017)  with the option genomeSize=28m and polished twice using Illumina reads  (Dumas et al. , 2020)  with Pilon version 1. 24  (Walker et al. , 2014) ), after a mapping using Bowtie2  (Langmead & Salzberg, 2012)  with a maximum length (-X) of 1000bp for both runs. Redundant contigs were removed based on a selfalignment. In Bowtie2, the maximum length (-X) was set to 1000 and the preset "verysensitive-local" was used. SAMtools v1. 7  (Li et al. , 2009)  was used to filter out duplicate reads and reads with a mapping quality score above ten for SNP calling.
Single nucleotide polymorphisms (SNPs) were called using the GATK v4. 1. 2. 0 Haplotype Caller  (McKenna et al. , 2010) , generating a gVCF file per strain. GVCFs were combined using GATK CombineGVCFs, genotypes with GATK GenotypeGVCFs, and SNPs were selected using GATK SelectVariants. SNPs were filtered using GATK VariantFiltration and options QUAL <30, DP < 10, QD < 2. 0, FS > 60. 0, MQ < 40. 0, SOR > 3. 0, QRankSum < -12. 5, ReadPosRankSum < -8. 0. All processes from cleaning to variant calling were performed with Snakemake v5. 3. 0  (Koster & Rahmann, 2012 ) using an internally developed script. We used Splitstree v4. 16. 2  (Huson & Bryant, 2006)  for the neighbour-net analysis. We used the R package Ade4  (Chessel et al. , 2004; Dray & Dufour, 2007; Bougeard & Dray, 2018; Thioulouse et al.
, 2018)  for principal component analyses (PCA, centred and unscaled). We used NGSadmix v. 33  (Jørsboe et al. , 2017)  from the ANGSD  (Korneliussen et al. , 2014)  package (version 0. 933-110-g6921bc6) to infer individual ancestry from genotype likelihoods based on realigned reads, by assuming a given number of populations. A Beagle file was first prepared from bam using ANGSD with the following parameters: "-uniqueOnly 1 -remove_bads 1 -only_proper_pairs 1 -GL 1 -doMajorMinor 1 -doMaf 1 -doGlf 2 -SNP_pval 1e-6". The Beagle file was used to run NGSadmix with 4 as the minimum number of informative individuals. The analysis was run for different K values, ranging from 2 to 6. A hundred independent runs were carried out for each number of clusters (K). The nucleotide diversity π  (Nei's Pi; Nei and Li 1979; Hudson et al.
1992 ), the Watterson's θ  (Watterson, 1975) , the fixation index FST  (Hudson et al. 1992 ) and the absolute divergence dXY  (Nei & Li, 1979)  were calculated using the popgenome package in R  (Pfeifer et al. , 2014). Fixed, private and shared sites were counted using custom scripts available at https://github. com/BastienBennetot/fixed_shared_private_count, with bcftools version 1. 11 (using htslib 1. 13+ds). The strain ESE00421 was not included for these computations. The pairwise homology index (PHI) test was performed using Splitstree v4. 16. 2  (Huson & Bryant, 2006). Mating types in the Termignon population were determined using a mapping approach: the two mating-type alleles previously identified in P. roqueforti  (Ropars et al. , 2012)  were used as references to map the Termignon strain reads.
/ < -< -12 (4) where pHmin and pHmax are the minimum and maximum pH values for which fungal growth is possible, respectively. To model growth parameters as a function of lactic acid concentration, we applied the model developed by Le  Marc et al. (2002) , designed for estimating the minimum inhibitory concentration: ! = 1 -[45]/[45] 12 , [45] < [45] 12 (5) where  [LA]  represents the total concentration in lactic acid and [LA]max the maximum lactic acid concentration for fungal growth. Growth rates and latencies obtained from primary modelling were fitted with these equations by minimising the sum of squared error (nlinfit function, MATLAB R2018b). Five percent confidence intervals were computed using the Jacobian matrix (nlparci function, MATLAB R2018b). We assessed how well our models predicted the data by computing the root mean square error (RMSE), measuring the error of a model in predicting quantitative data, and the determination coefficient (r²), measuring the proportion of variance in the growth parameter explained by the factor variation in the model.
The UBOCC-A-118017 strain appeared intermediate between the Termignon and silage/spoiled food clusters in the PCA but the LCP06059 strain clustered well in the silage group. Because of these assignment uncertainties, we tested differences between clusters with and without these two strains. We present below the statistical test results with the two strains included. Similar results were obtained when removing them from the dataset, unless specified otherwise. The four Termignon strains carried the whole CheesyTer region in their genome, while only two (ESE00424 and ESE00685) harboured the Wallaby region. Only two strains (LCP04180 and LCP03969), both isolated from spoiled foods and belonging to the silage/spoiled food population, carried the SORBUS horizontally transferred region.
Acknowledgments This study was funded by the  ANR-19-CE20-0002-02 Fungadapt  and  ANR-19-CE20-0006-01 Artifice ANR  ( French National Research Agency)  grants. We thank  Alodie Snirc  and  Stephanie Le Prieur  for DNA extractions. We thank the  GenoToul INRAe platform  and the  CNRS I2BC platform  for genome sequencing.
These measurements were made with the ©ZEN software using photos taken with a camera (AxioCam ERc 5s, ZEISS, Munich, Germany) connected to a binocular magnifier. Each sampling started with a visual estimation of the relative abundance of 'dark' (D-morphotype, i. e. , black coloration) and 'light' insects (L-morphotype, i. e. , brown coloration) at a given altitude in order to collect each morphotype in proportions reflecting their relative abundances at each altitude: 12 insects of the 'dark' morphotype and 11 insects of the 'light' morphotype at 0 m asl, 12 'dark' and 12 'light' insects at both 130 m and 300 m, 26 'dark' and 20 'light' insects at 600 m, and finally 13 'dark' and 9 'light' insects at 900 m asl. All collected specimens were preserved in 70% ethanol at -25 °C until they were processed for the analyses described below.
These measurements were made with the ©ZEN software using photos taken with a camera (AxioCam ERc 5s, ZEISS, Munich, Germany) connected to a binocular magnifier. For each individual, we then calculated a body size index corresponding to the coordinates of the individuals on the main axis from a principal component analysis (88. 2% of the explained variance). The body size index was computed from the measurements performed on the above-mentioned morphometric traits (cf. , Figure  S1 ). The body water contents and ethanol residues of the insects were discarded by drying the individuals (Speed Vac Concentrator, MiVac, Genevac Ltd. , Ipswich, UK) for 24 h at 32 °C. Each insect was then weighed (XP2U Mettler Toledo Balance, Mettler Toledo®, Columbus,USA OH; d = 0. 1 μg), and its sex was identified  [49].
Statistical Analysis All statistical analyses were performed in the R software environment (R version 4. 0. 5, R Core Team, 2021). As an exploratory analysis, we first ran a multivariate factor analysis of mixed data (FAMD)  [51]  using the R packages 'FactoMineR'  [52]  and 'FactoExtra'  [53]  to simultaneously explore the relationships among the functional traits (i. e. , the body size index, the body mass, and the amounts of proteins, sugars, and lipids), morphotype, sex, and altitude.
Univariate Approach: Influence of Altitude, Sex, and Morphotype on Functional Traits We built linear mixed-effect models ('lme4' R package  [54] ), using each of the five continuous traits (body size index, body mass, lipids/mass, sugars/mass, and proteins/mass) as response variables in five separate models. We log-transformed the sugars/mass and lipids/mass traits to approximate a normal distribution. In each of these five models, we tested for the main effects of altitude, sex, and morphotype as well as their two-and three-way interactions. We also included a quadratic term for altitude to test for potential non-linear effects and its two-way interactions with the other variables and added "Site" as a random variable to control for non-independence among insects collected from the same altitude.
To check for the simple effects of morphotype and sex without the altitude effect, we also compared hypervolumes by pooling sites for each subset. As a hypervolume size depends on the number of species (or individuals) that were used to calculate it, we bootstrapped the trait values from the subset of individuals of interest and calculated the aforementioned metrics based on 100 resampled hypervolumes  [59, 60]. This enabled us to calculate confidence intervals and identify the cases where the observed values fell outside of these intervals. As it is difficult to obtain a balanced dataset of individuals, we set the minimal number of individuals needed for simulations to the minimum number of collected specimens per group (e. g. , n minimum = 21 random insects per altitude level for pooled data).