dataesr/scientific-paragraphs-categorization huggingface dataset:
The output of Augustus and the CLIB918 gff was provided to Funannotate v1. 8. 9 (ref DOI:10. 5281/zenodo. 4054262) for functional annotation. InterProscan was used under Funannotate pipeline locally  (Blum et al. , 2021). Funannotate then searched in the Pfam database v34. 0 and dbCAN database version 10. 0 with Hmmer v3. 3. 2  (Eddy, 2011; Huang et al. , 2018; Mistry et al. , 2021) , in database UniProt version 2021_03 and database MEROPS version 12. 0 with diamond blastp v2. 0. 11 (Rawlings et al. , 2018; The UniProt Consortium et al. , 2021) , eggNOG-mapper v2 on the database eggNOG 5. 0  (Cantalapiedra et al. , n. d. ; Huerta-Cepas et al. , 2019). Cleaned reads were mapped on the reference genomes CLIB 918 and LMA-244 using Bowtie2 v2. 4. 2  (Langmead and Salzberg, 2012). Maximum fragment length was set to 1000 and the preset "verysensitive-local" was used.
SAMtools v1. 7  (Li et al. , 2009)  was used to lter out duplicate reads and reads with a mapping quality score above ten for SNP calling and above one for CNV analyses. In total, we have a dataset of 98 genomes, 88 being sequenced, eight from the University of Laval (LMA strains: Bioproject PRJNA482576, PRJNA482605, PRJNA482610, PRJNA482613, PRJNA482616, PRJNA482619, PRJNA490507, PRJNA490528), one strain CLIB 918 from the Collection de Levures d'Intérêt Biotechnologique (Bioproject PRJEB5752), and one of the strain Phaff72-186 from the 1000 Fungal Genomes project (Bioproject PRJNA334358 NCBI).
SNP calling Single nucleotide polymorphisms (SNPs) were called using GATK v4. 1. 2. 0 HaplotypeCaller, which provides one gVCF per strain (option -ERC GVCF). GVCFs were combined using GATK CombineGVCFs, genotypes with GATK GenotypeGVCFs, SNPs were selected using GATK SelectVariants (option -selecttype SNP). SNPs were ltered 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 (script available at https://github. com/BastienBennetot/Article_Geotrichum_2022).
Phylogenetic analysis We inferred phylogenetic relationships among the 98 isolates using the dataset of 699,755 SNPs in a maximum likelihood framework using IQ-Tree2 v2. 1. 1  (Minh et al. , 2020). The tree has been midpoint rooted. The best-t model chosen according to Bayesian information criterion (BIC) was TVMe + R2. Branch supports are ultrafast bootstrap support (1000 bootstrap replicates,  Minh et al. , 2013).
Genetic structure We used the dataset of 699,755 SNPs to infer population structure based on the mapping on the CLIB 918 reference genome. We used Splitstree v4. 16. 2  (Huson and Bryant, 2006)  for the neighbor-net analysis. We used the R package Ade4  (Bougeard and Dray, 2018; Chessel et al. , 2004; Dray et al. , 2007; Dray and Dufour, 2007; Thioulouse et al. , 2018)  for principal component analyses (PCA, centered 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 le was rst 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".
, 2014). Fixed, private and shared sites were counted using custom scripts available at https://github. com/BastienBennetot/ xed_shared_private_count, with bcftools version 1. 11 (using htslib 1. 13 + ds). F3 tests were computed using the admixr package v0. 9. 1. The pairwise homology index (PHI) test was performed using PhiPack v1. 1 and CLIB 918 genome as reference. Linkage disequilibrium was calculated using vcftools v0. 1. 17 with the --hap-r2 parameter and a minimum distance between SNPs of 15,000 bp. Values were averaged when SNPs had the same distance. Pairwise identity between an admixed strain and each non-admixed strain was calculated using overlapping sliding windows of 30kb span and 5kb step. Admixed clusters are indicated in Table  S1. The custom script is available on https://github.
For each InterPro term present in these regions, we performed enrichment tests, i. e. , a sher exact test comparing the number of a particular InterPro term found in these regions and the whole genome (Table  S9 ). We used snpeff  (Cingolani et al. , 2012)  to assess how each SNP affected the coding sequence of predicted proteins, in the vcf le containing all SNPs and all genomes of our dataset. We detected premature stop codons in the 7,150 CDS of the CLIB 918 genome and the 5,576 CDS of the LMA-244 genome using a custom script and bcftools v1. 11.
Gas chromatograph (brand Agilent 7890B) was used with a polyethylene glycol (PEG) type polar phase column  (HP-Innowax, ref. Agilent 19091N-116I, 60mx0. 32mm, 0. 25µm lm thickness). Helium ow was set at 1. 6mL/min. Samples were injected in splitless mode with a holding time of 1 minute. To optimize separation of compounds, a speci c program of the gas chromatography oven was used, with initial temperature at 40°C for 5 minutes, rising temperature from 40°C to 155°C with a slope of 4°C/min, rising temperature from 155°C to 250°C with a slope of 20°C/min and then temperature was kept at 250°C for 5 minutes. A single quadrupole mass spectrometer was used difference (HSD) tests. The data normality of residuals was checked; when residues deviated from normality (only for the salt content experiment), we also ran non-parametric tests (Wilcoxon ranking tests) using R.
Acknowledgments We thank everyone who sent cheese crusts and  Riwanon Lemee  for providing 15 strains from  Laboratoires STANDA, Caen, France. We thank  Laura Prugneau ,  Jérémy Raynaud ,  Thomas Mari ,  Véronique Bougie  and  Jules Larouche  for their help in the laboratory experiments. This work was funded by the  Arti ce   ANR-19-CE20-0006-01 ANR grant  to J. R. In addition, a  NSERC-Discovery Grant  supported the scholarship of V. P and data generation, and is held by  S. Labrie. ( RGPIN-2017-06388 ).
In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging.
2021; Philip et al. 2020 ) and a report was made available  (Kreienkamp et al. 2021) , which is consolidated in this paper. Using observational datasets, this study analyses two severely impacted regions. As the robust estimation of return values and the detection and attribution of trends is, however, challenging at the scale of the most severely impacted catchments, the study also uses observational data and model simulations to address a more general question of how much climate change has altered the probability and intensity of similar events occurring in an area of similar size within Western/Central Europe, a region characterised by precipitation climatologies similar to those of the affected areas. Based on the specific characteristics of the event, Regional Climate Model (RCM) simulations which are available at a resolution equal to or higher than 12.
The hydrological and meteorological analysis of the event closes with a discussion on the suitability of hydrological and meteorological datasets for this attribution study (Sections 2. 5 and 2. 6). Then, the event definition (Section 3) and the statistical method (Section 4) are presented. The data are presented in Section 5, followed by the results in Section 6. The paper is finished by discussing vulnerability and exposure with respect to the event, including the early warning systems applied in Germany (Section 7) followed by a summary and outlook (Section 8).
Event definition While the extreme precipitation in Germany and Belgium was embedded into the same synoptic system, the meteorological and hydrological characteristics that led to the impacts differed. Therefore, depending on the region, different respective impact-relevant meteorological indices were analysed. Flooding in the Ahr/Erft catchments was caused by intensive precipitation on the sub-daily to daily timescale (see Fig. 2 ). Mohr et al. (2022)  suggest an accumulation time of approximately 15 h, however, as the data available for attribution studies have a daily resolution, the yearly-maximum of daily precipitation accumulations (RX1day) is analysed for the combined Ahr/Erft catchment (black polygons in Fig. 5 ). For the Belgian Meuse catchment (red polygon in Fig. 5 ) the precipitation leading to impacts accumulated over 2 days, motivating the analysis of yearly-maximum two-day precipitation accumulations (RX2day) for this region.
This decision on the years included in the analyses is a compromise between the standard WWA procedure, which uses data up to the present GMST for the past-to-present analysis, and having sufficient data for a good fit. Confidence intervals (CI) for the individual model or observation datasets express the statistical-model uncertainty, obtained using a non-parametric bootstrap procedure. As required due to the spatial pooling, the bootstrap takes spatial correlation into account by drawing a spatial moving block including all tiles with a correlation > 1/e. The underlying code uses a simple way to handle correlation across series in the bootstrap, i. e. when a random value is drawn corresponding to any of the tiles, the simultaneous value in all tiles which have a serial correlation > 1/e are automatically drawn in the same bootstrap sample (the code is available within subroutine sample_bootstrap at https://gitlab.
com/KNMI-OSS/climexp/ climexp_numerical/-/blob/master/src/attribute_dist. f90. One complication for using CPRMS data is their comparably short simulation timeframe. Several CPRCMs have only about 10 years of data available for the historical climate period (here defined as pre-2030), which is insufficient for a reliable GEV fit. For these models, sufficient data are only available by including the model data of future years, i. e. the entire simulation timeframe of the given CPRCM, including the future period, is used to evaluate the seasonal cycle and to fit a GEV distribution that is used to specify the event's magnitude from the return period. As a result, these models can only produce present-to-future results, but no past-to-present comparison. Models for which this applies are marked with a star (*) in the evaluation (Table  2 ) and in the attribution result, see Tables  3, 4  and Fig.
Data Observational data are introduced here and an overview of the analysed model data is provided. The observational analysis uses the German gridded REGNIE dataset for the Ahr/Erft region, a gridded Belgian dataset for the Meuse catchment, and E-OBS data for the Pooling Region. Four-year low-pass filtered GMST anomalies (based on NASA's GISTEMP) are used as a measure of anthropogenic climate change within the observational analysis. The anomalies are expressed relative to 1951-1980.
Observational data E-OBS E-OBS is a 0. 25 • × 0. 25 • gridded temperature and precipitation dataset covering Europe, based on interpolation of precipitation observations from rain gauges  (Cornes et al. 2018). The daily values refer to the 24-h period over which the measurements are made (as it is also the case for the other gridded datasets). E-OBS v23. 1e, extended in time to cover the event of July 2021 using near real-time synoptic messages as described in  Cornes et al. (2018) , was used within the analysis of the Pooling Region to produce the climatology, to analyse trends, to estimate return periods, and to validate models. An enhanced, non-public E-OBS dataset that includes more observational data in the years since 2004 and additional observations of the July 2021 event from regional and federal authorities, was produced specifically for Fig.
REGNIE The Regionalised Precipitation Heights (REGNIE) dataset of DWD is used for the observational analysis of the Ahr/Erft region. REGNIE provides daily gridded precipitation data with a resolution of 1 km 2 for Germany derived from station data. The methodology is described in  Rauthe et al. (2013). Belgian gridded dataset For the Belgian Meuse catchment, the observations are interpolated on a grid of 5 km × 5 km. The data used are collected from different observation networks, i. e. RMI's climatological rain gauge network, the network of Service Publique de Wallonie -Mobilité et Infrastructures (SPW-MI) in Wallonia, and the network of Hydrologisch Informatie Centrum (HIC) of Waterbouwkundig Laboratorium in Flanders. The data were quality controlled by RMI prior to the interpolation. The interpolation has been done using as weight the inverse of the distance to the stations.
Time series of 24 stations over Belgium have also been incorporated into the enhanced E-OBS dataset. GISTEMP As a measure of anthropogenic climate change the 4-year low-pass filtered Global Mean Surface Temperature (GMST) from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Science (GISS) surface temperature analysis (GISTEMP;  Hansen et al. 2010; Lenssen et al. 2019 ) is used.
In  Kreienkamp et al. (2021)  a conservative estimate of 700 years was selected to analyse model simulations; however, it was found that the selected region is too small for robust detection and attribution. The analysis of the two-day precipitation, spatially averaged over the Belgian part of the Meuse catchment upstream of Eijsden using the Belgian gridded dataset, indicates a RX2day of 53 mm day -1 for 2021 (see Fig. 6(b) ), with a GEV-estimated return period of about 1000 years (95 % CI 200 to 120,000 years, see Fig. 6(d) , where the GEV fit intersects the horizontal magenta line giving the observed magnitude of the event). Note that the GEV fit to these observational data does not include the values of the analysed events, as the areas were specifically chosen because they exhibited the extremes.
e. including only the highest value from a 5-year block per tile in the Pooling Region, are used to calculate the GEV fit. This extension of the block size is supported by, e. g. , Ben  Alaya et al. (2020)  who found that, for extreme precipitation, and given a sufficient sample size, increasing the block size generally leads to a better fit. Data from the event are included in the fit, as we selected a wider region rather than a small area that has been most affected by the event. Figure  7  (b) shows the GEV fit to the 5-year block maxima. The estimated return period of the analysed event under present climate conditions is about 400 years (95 % CI 170 to 2500 years, determined as vertical position of the intercept between the "GEV scale fit 2021" and the line that provides the "observed 2021" magnitude).
Discussion of the results This study investigated how climate change has influenced the likelihood and severity of the heavy precipitation event that led to severe flooding in Western Europe in July 2021. Due to data availability an attribution based on hydrological data was not possible and, therefore, the attribution analysis focused on rainfall as a precursor to the flooding. At the scale of the most severely affected catchments, a robust estimation of return values and the detection and attribution of trends is challenging. Based on large climate model ensembles it has been demonstrated that variability dominates over trends in heavy precipitation at the scale of grid points and small catchments, for which the signal can differ significantly between several realisations of a model  (Li et al.
2022)  including the development of standardised tools to select regions in which data can be pooled for attribution analysis. The analysis shows that, at a given location within this region, one such event can be expected every 400 years under the current climate conditions. As a result, such events are expected to occur more frequently than once in 400 years within the entire Pooling Region. Within the Western European Pooling Region a robust signal towards an increased frequency and intensity of one-day rainfall events with a similar return period was found. Climate change made such a rainfall event 1. 2 to 9 times more likely. The magnitude of the rainfall increased by about 3-19 % compared to a global climate 1. 2 • C cooler than today, based on the analysis of rainfall events in the boreal summer half-year.
Acknowledgements We thank  Andrew Ciavarella  ( UK Met Office ) for contributing to the WWA report which build the foundation for this work;  Matthias Voigt  ( Federal State Office for the Environment, Rhineland Palatinate, Germany ) for the preparation of some CPRCM simulations;  Ewelina Walawender  ( DWD ) for preparing Fig. 2;  Sabrina Wehring  ( DWD ) for preparing Figs. S1 and S2 in the Supplementary Information;  Michel Journée  ( RMI ) for the generation of the Belgian Gridded Dataset and the RMI quality-control team for providing the required data on time; Copernicus Climate Change Service (C3S) and  ECMWF  for providing the ERA5-Reanalysis; the  EURO-CORDEX  community for RCM simulations which were partly funded by the  Copernicus Climate Change Service (C3S)  under  D34b Lot2041_201802.
Availability of data and materials Most of the precipitation data analysed here are available via the Climate Explorer at https://climexp. knmi. nl/ardennen_eifel_floods_timeseries. cgi. ERA5 data is available at the C3S Climate Data Store. Hydrological data from the following sources were evaluated: Belgium http:// voies-hydrauliques. wallonie. be/opencms/opencms/fr/hydro/Archive/annuaires/index. html and http://voieshydrauliques. wallonie. be/opencms/opencms/fr/hydro/Actuelle/crue/cruetableau. do?id=38; Netherlands: https://waterinfo. rws. nl/; Germany http://www. dgj. de/ and https://gda-wasser. rlp-umwelt. de/ and https:// luadb. lds. nrw. de/LUA/hygon/pegel. php?interaktiv=P. Observational data of DWD, e. g. Regnie (https:// opendata. dwd. de/climate_environment/CDC/grids_germany/daily/regnie/) are available under an open data policy.
Proofs The aim of this work is threefold: to prove the correctness of the algorithms, to extract the confidence code from the proof of correctness, and to investigate whether the monotonicity constraint of the classifiers can be relaxed. To achieve these objectives, we used a proof assistant that allows code extraction: Coq  5. The Coq proof, the Python codes used to generate and check the models, and the OCaml code used to run the experiments (section 5) are available at this link: https://github. com/hurault/tap23.
Coq Coq is a proof assistant that is built on a programming language called Gallina, which allows expressing mathematical theorems and software specifications. This language combines higher-order logic and functional programming. Coq provides a command language for defining functions and predicates, stating theorems and specifications, and developing formal proofs interactively. The proofs can then be checked by a small certification "kernel". Additionally, Coq allows the extraction of certified programs into languages such as OCaml, Haskell, or Scheme.
The Coq formalization To ensure compatibility with Coq, it was necessary to modify the method used to encode the original algorithms (1, 2, 3, and 4), while ensuring that the changes facilitated the proof of termination and correctness, as well as code generation. In Coq, the data structures are homogeneous, so all features should have the same type T. That is, for all i in F, D i = T. This type T requires a total order relation. This may seem restrictive, but all digital types, for example, meet these constraints. A point in feature space is represented by a list of elements of type T. The output of the classifier has type T k. The only requirement on T k is the existence of a decidable equality (Tk_eq_dec). C and D are not used and are dropped, while P is coded by a list of naturals (indexes of the features).
Additional lemmas The proof of preservation of R requires lemmas on FreeAttr and FixAttr, such as the preservation of list size, bounded properties of features, and modification of only the i-th element. In total, 137 lemmas are necessary to prove the two correctness theorems for the algorithms. Let us recall that the objective of this work is to compute explanations that are guaranteed to be correct. Coq is used to extract OCaml code that is certified as correct, i. e. it validates proven properties. We can provide a certified tool that computes correct explanations. findAXp_aux κ i v v l v u p findCXp_aux κ i v v l v u p List. length v = N R 0 List. length v l = N idem List. length v u = N ∀j ∈ [0, N [, λ(j) ≤ v j ≤ µ(j) R 1 ∀j ∈ [0, N [, λ(j) ≤ v lj ≤ µ(j) idem ∀j ∈ [0, N [, λ(j) ≤ v uj ≤ µ(j) R 2 κ(v l ) = κ(v u ) = κ(v) κ(v l ) ̸ = κ(v u ) R 3 i ≥ 0 idem ∀j ∈ [0, N [, R 4 λ(j) = v lj ∧ µ(j) = v uj not needed ∨ v j = v lj ∧ v j = v uj R 5 ∀j ∈ [0, N [, j ≥ i → j / ∈ p idem R 6 ∀j ∈ [0, N [, j ∈ p → ∀j ∈ [0, N [, j ∈ p → v j = v lj ∧ v j = v uj λ(j) = v lj ∧ µ(j) = v uj R 7 ∀j ∈ [0, N [, j < i ∧ j / ∈ p → ∀j ∈ [0, N [, j ≥ i → λ(j) = v lj ∧ µ(j) = v uj λ(j) = v lj ∧ µ(j) = v uj R 8 is_sorted p idem R 9 ∀j ∈ [0, N [, j ≥ i → ∀j ∈ [0, N [, j < i ∧ j / ∈ p → v j = v lj ∧ v j = v uj v j = v lj ∧ v j = v uj ∀x, x 0 , x 1 , p = x 0 @(x :: x 1 ) → ∀x, x 0 , x 1 , p = x 0 @(x :: x 1 ) → ∃nv l , nv u , (∀j ∈ [0, N [, ∃nv l , nv u , (∀j ∈ [0, N [, ((p ∈ x 1 ∨ j > x)∧ ((p ∈ x 1 ∨ j > x)∧ R 10 v j = nv lj ∧ v j = nv uj ) λ(j) = nv lj ∧ µ(j) = nv uj ) ∨ ∨ (¬(p ∈ x 1 ∨ j > x)∧ (¬(p ∈ x 1 ∨ j > x)∧ λ(j) = nv lj ∧ µ(j) = nv uj )) v j = nv lj ∧ v j = nv uj )) ∧ ∧ κ(nv l ) ̸ = κ(nv u ) κ(nv l ) = κ(nv u ) Fig.
ml file generated by Coq is converted into a functor that conforms with the signature module and is parameterized by a dataset (file OCaml/explain. ml). For each dataset, the axioms (N , T , T k, Tk_eq_dec, λ, and µ) need to be realized (file OCaml/dataset. ml). k is implemented using the pyml library 6  , which allows for an efficient binding between the OCaml generated by Coq and the Python used to generate and run classifiers (file Ocaml/runAXpCXp. ml).
Datasets Four datasets are used from https://www. kaggle. com and can be found in the XGBoost/dataset repository: -Car Evaluation Data Set 7 -Heart Failure Prediction 8 -Placement data full class 9  -The Complete Pokemon Dataset 10 Some modifications, such as feature digitization, removal of non-digital features, removal of incomplete instances, and modification of some output features for certain instances, are done to make the datasets monotonic. A summary of the properties of the datasets is presented in Figure  3.
Models XGBoost 11  [2]  is used to create models that exhibit monotonicity. The Python code can be found in the file XGBoost/build_mono. py. After generating the model, a Python program is used to perform a test to ensure that its predictions on the dataset adhere to both the monotonicity (file XGBoost/verif_mono. py) and stability (file XGBoost/verif_stable. py) constraints. Although XGBoost claims to ensure monotonicity 12 , it may not hold true for certain datasets. However, since the predictions are stable, the models can still be used to benchmark the code generated by Coq. A summary of the properties of the models is presented in Figure  4.