dataesr/scientific-paragraphs-categorization huggingface dataset:
The small eliminated contigs show a higher coverage compared with the retained contigs, suggesting they correspond to highly repeated regions of the See also Figures  S2  and S3. genome, which have been collapsed as one region in the current genome assembly. For this reason, we suspect that an important target of PDE in M. belari is the repeated regions of the genome, as previously found in ascarids. 17  We found that besides satellite repeats, transposable elements (which we have not further analyzed in this study; see discussion) and also 113 genes were eliminated (90 in the E cluster and 23 in the ME cluster). Another 46 genes were found in the PE cluster (Data S1A). To confirm these results, we performed soma-specific PCR on eight genes chosen randomly in the E and ME clusters (Table  S2 ; Figure  S5 ).
We next added 0. 6 volume of isopropanol to the supernatant in order to precipitate the DNA. The DNA pellet was next washed two times with EtOH 70%. For RNA preparation in M. belari, we used the SmartSeq2 protocol. 54  We manually selected single L1 larvae or dissected single gonads from male and female adults. We sequenced 5 individuals per condition. RNA-seq from mixed stages had been previously obtained. 22  Sequencing, data cleaning and trimming DNA sequencing of M. belari adult males or females had previously been obtained. 22  New libraries were prepared as described before. 22  We sequenced a new sample of M. belari males and females in order to obtain more coverage of the germline genome on an Illumina NovaSeq 6000 Sequencer (fragment length of 500 nt and paired-end reads of 250 nt).
Sequencing of DNA from M. belari L1s or M. belari cDNA from L1 and male and female gonads was performed as previously described in Grosmaire et al. 22  on an Illumina HiSeq 4000 Sequencer with fragment length of 500 bp and paired end reads of 100 bp. We used fastp (v0. 23. 2)  30  to clean data for bad quality reads (phred score< 30). We also identified reads coming from E. coli OP50 contamination using minimap2  33  to discarding them for the following analysis. De novo Genome Assembly of M. spiculigera Paired-end genomic libraries were prepared from L1, population, females and males samples using TruSeqNano with gel-free size selection (insert size 500 bp). The library was sequenced on an Illumina HiSeq 4000 with 2 X100-nt paired-end read length. Long reads were also generated to improve the genome assembly.
DNA was prepared for Nanopore sequencing mainly as described in Grosmaire et al. ,  22  except we used the SQK-LSK109 protocol instead of SQK-LSK108. We obtained a total of 970K reads (5. 6 Gb) with a N50 length of 8. 67 kb using 2 runs on R9. 4 Flow cells after base calling with Guppy (Fast base calling, version 4. 0. 11+f1071ce) corresponding to an expected 35X coverage of the genome. An additional RNA library was prepared from total RNA to generate a paired-end library following the TruSeq RNA Sample Preparation v2 Guide (mean size=600 bp). Sequencing was performed on a Hiseq 4000 (100-nt reads). Illumina short reads were trimmed by quality using UrQt (d62c1f8, -t 20  31  ). We analysed the reads quality before and after quality trimming using FastQC (v0. 11. 5) and MultiQC (v1. 7). 32  We used Porechop (109e437, https://github.
com/rrwick/Porechop) on Nanopore reads to find and remove adapters. To remove E. coli related reads, we mapped trimmed long and short reads on the genome sequence of E. coli OP50 (NCBI BioProject PRJNA41499) using minimap2 (ccb0f7b  33  ). with the default parameters (-x sr for short reads and -x map-ont for long reads) and the option -secondary=no). We filtered non-E. coli reads by extracting unmapped reads from the minimap2 output (sam files) using samtools  34  with the command (samtools view -bh -f 4 ) and converting them in fastq using ''samtool fastq'' command to end with cleaned fastq files. To assemble the genome, we compared different strategies based on Jaworski et al. 55  and benchmark the different assemblies using Quast  35  in order to optimize the N50 of the assembly and its completeness via Busco score.
7. 3a, default options  42  ). Finally, we ran BRAKER (2. 1. 5-2, ETP mode  43  ) to create the gene annotation of the assembled genome using the result of STAR mapping, the soft masked genome and homologous sequence from close relative species (metazoans preoteins from orthoDB (v10  44  ) and nematoda sequences from Busco database (v4. lineage)). 45  Overall, we obtained 2,807 contigs with a N50=557,898 bp. The estimated genome size was 198 Mb and the largest contig was 6. 8 Mb. Accession number is PRJEB59059 on https://ebi. ac. uk/ena.
Differential gene expression analysis RNA-seq reads were cleaned using fastp  30  for bad quality reads (phred score< 30). Then, we used kallisto  49  to quantify the abundance of coding sequence in M. belari (NCBI BioProject PRJEB30104) where we used the ''-bias'' parameter to perfom sequence bias correction. We used the package DEseq2 50 to perform differential expression analyses between L1s samples and adult gonad samples (padj < 0. 05 and default parameters) after importing data in R using the package tximport. 51.
Homology detection To infer homology between M. spiculigera, M. belari (NCBI BioProject PRJEB30104) and C. elegans (NCBI BioProject PRJNA13758) genes, we obtained protein sequences from the genome annotation of M. spiculigera and we downloaded the protein sequences from https://parasite. wormbase. org/ for the 2 other species, we then identified reciprocal Best BlastP Hit 52 between pair of species proteomes. We also downloaded C. elegans genes common names and description from https://parasite. wormbase. org/ using bio-maRt  53  and interproscan protein domains prediction of M. belari genes. Antibody and DNA stainings Embryos were fixed with methanol using the freeze-cracking method, as described in Launay et al. 20  The following antibodies were used: mouse anti-tubulin (1/200, Sigma DM1A), secondary donkey anti-mouse Alexa 488 (1/1000, Jackson Immunoresearch, ref 115-545-166), secondary donkey anti-rabbit Cy3 (1/1000, Jackson Immunoresearch, ref 711-165-152).
ACKNOWLEDGMENTS This work has been supported by a grant from the  ENS Lyon  and from  ANR-22-CE12-0027. We thank the  French Institute of Bioinformatics (IFB CNRS   UMS 3601 ) (funded as part of  Investissement d'avenir program  managed by  ANR-11-INBS-0013 ) for providing life science data and tools, storage, and computing resources on the IFB national service infrastructure in bioinformatics. We acknowledge the contribution of the imaging platform PLATIM from  SFR Biosciences Lyon , as well as the sequencing platform  Genomeast_ Strasbourg , the  IGFL sequencing platform in Lyon , and MGX in Montpellier. We thank  Clara Bourgeay ,  Ingrid Dourlens ,  R emy Mimbre , and  Caroline Blanc  for their technical help and  Michael Ailion  and  Brice Letcher  for their critical reading of the manuscript.
Data and code availability d All sequencing data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB59059 and PRJEB30104 (https://ebi. ac. uk/ena/browser/view/PRJEB59059 or PRJEB30104) d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
It was showed that the FDA framework surpasses all its counterparts such as the landmark-based approach or even the set theory approach with principal component analysis (PCA), using a well-known database of bone outlines  14. Bochi el al. (2021) applied FDA techniques to highlight significant patterns of COVID-19 mortality across 20 Italian regions and their association with mobility, positivity, and socio-demographic, infrastructural and environmental covariates  15. The landmarks obtained from the craniodental shapes of three species of shrews are represented in the form of functional data. This data is used to perform multivariate functional principal component analysis (MFPCA) to observe variation among the three shrew species and compared with the classical PCA. The principal component scores obtained from MFPCA (MFPC scores), which capture the major sources of shape variation among the shrew species.
Landmark data acquisition After acquiring the images, TPSUtil32 is used to obtain the TPS files for all three views which will be used in TPSDig2 for landmarking. Based on Figure  2 , each view has different numbers of landmarks and semi-landmarks, i. e. , dorsal (25 landmarks), jaw (50 landmarks) and lateral (47 landmarks). The statistical analysis of three views was performed in R version 4. 2. 1. To use the geometric morphometric data, the raw coordinates obtained from the landmarks of all three craniodental views were processed using generalised Procrustes analysis (GPA) for optimal registration using translation, rotation, and scaling using the gpagen function in the geomorph package  17. Outline methods can produce useful and valid results when suitably constrained by landmarks  18. This leads to the main idea of this work to incorporate the FDGM approach to observe the separation among the three shrew species.
Multivariate functional principal component analysis (MFPCA) for craniodental views of shrew specimens The two univariate functional datasets composed a multivariate functional data with 𝑛 outlines, each yielding a vector of 𝑛 observations defined as a 𝑑 -dimensional functional domain. The MFPCA package is used to compute the MFPCA estimates on the multivariate functional data based on their univariate counterparts  19. MFPCA uses the observations that are independently and identically distributed (multivariate functional data obtained from the landmarks). The PCA basis functions are estimated from the multivariate functional data, denoted by 𝐗. These basis functions were then applied on 𝑛 observations of 𝐗 based on the PACE (PCA through the conditional expectation) approach  20. Let 𝐗 = (𝑋 1 , 𝑋 2 ) 𝐓 be a vector-valued stochastic process which correspond to the functional random variables related to the standardised landmark, 𝑥 and 𝑦coordinates respectively.
The proposed GAN model (which can be accessed at either https://github. com/taborzbislaw/PhSpGAN or https://doi. org/10. 5281/zenodo. 7986115 for the exact version used in this work), together with our previously developed machine-learning-based method of tuning the primary electron beam of the MC simulator by exploiting measurements of depth and lateral dose distribution profiles in a water phantom (https://github. com/taborzbislaw/DeepBeam), delivers a complete solution to the problem of tuning the MC simulator against any specific physical linear accelerator.
Linac Simulator All computer models described in this paper exploited the Monte Carlo simulation PRIMO software  [13] , version 0. 3. 1. 1772, of the Clinac 2300C/D linear accelerator (Varian Medical Systems Inc. , Palo Alto, CA, USA) in 6 MV operation mode. The PRIMO software has several variance reduction options, which enhance the MC-based generation of particles in the phase space. In our case, only interaction forcing for electrons in the target was activated. As for other variance reduction options, an additional discrete parameter (a statistical weight) is assigned to each particle in the beam of ionizing radiation. For example, when using splitting roulette  [14]  variance reduction, the statistical weight can acquire four values, two of which are very rare. Because GANs are not designed to learn discrete distributions (the differentiability of the generator of a GAN implies continuous outputs of the generator), we used MC data generated without such variance reduction methods.
At the photon generation stage, the CGAN receives as input an 8D Gaussian noise variable with zero mean and an identity covariance matrix concatenated with a 3D vector of conditional parameters. When generating new photons, RoCGAN receives as its first input the same data as the CGAN. The data presented at the prediction stage at the second input of a RoCGAN can be arbitrary, as the second output is ignored when generating photons. CGAN and RoCGAN also differ in the loss function used during training. In the case of CGAN, it is simply the Wasserstein loss, while, in the case of RoCGAN, it is the sum of the Wasserstein loss, the mean square error between the second input and the second output (autoencoder loss), and the mean square error between the latent vectors of its two branches. The details of implementation can be found directly in the code, which is available at https: //github.
Training and Testing Data To learn the conditional six-dimensional probability distribution of parameters characterizing the particles of ionizing radiation, the values of the three conditional parameters for the training set were taken on a regular grid with mean energy between 5. 6 MV and 6. 4 MV in 0. 1 MV steps, the focal spot size ranging between 0 mm and 4 mm in 0. 5 mm steps, and beam divergence ranging between 0°and 3°in 0. 5°steps. For each of these 567 grid nodes, 10 7 photons were generated by MC simulations and used for training. To obtain a smooth energy spectrum, photons originating from electron-positron annihilation (about 1% of all photons) were removed from the training data. Testing of the trained models was performed on 10 selected sets of electron beam parameters at points between nodes of the training grid.
Values are given as percentage of the maximum dose in the profile calculated from PRIMO-generated phase space. The 'mean' and 'std' rows contain, respectively, the mean and standard deviation of values in their respective columns. The last row contains the p-value of the Wilcoxon signed-rank test for equality of mean values of RMS for CGAN and RoCGAN models. The universality of the conditional GANs was achieved at the cost of handling a large volume of training data-almost 570 MC phase spaces, each containing some 10 7 particles, were generated. This resulted in some 0. 5 TB of data collected. Such a massive simulation campaign could not be completed without the grid infrastructure of our University. As mentioned in the Introduction, we decided not to use variance reduction techniques when preparing the training data for conditional GANs.
We quantify flood damage by combining our flood maps with data on spatially distributed land and buildings value. Further details on the approach can be found in the "Methods" section. We applied the dynamic approach to obtain flood damage with erosion and the traditional static approach to obtain flood damage without erosion. The scenarios without erosion assume that the coastline and beach morphology are fixed over time and that changes in flood damage are caused by changes in the TWL and subsequent flooding. We define the beach flood protected area as the increase in the flooded area due to erosion. The beach flood protected value is the flood damage that occurs in the flood protected area and can be considered as the benefit we obtain in terms of avoided flood damage if the present shoreline is maintained in the face of storms and SLR.
Replicability The approach presented here (Fig. 1 ) is replicable in any other sandy beach provided that coastal climate and topo-bathymetric data, observations, assets value and type, and vulnerability functions are available. Note that climate information can be obtained from global databases and there are vulnerability functions adjusted to building typologies or representative of regions that can be used in case no empirical data are available. Table  S1  provides a summary of the data required to implement each step of the methodology. While Narrabeen-Collaroy is an embayed beach, the same modelling chain is applicable to pocket, open and inlet-adjacent beaches. In more complex environments or when required, the 1D hydro-morphodynamic model  60  can be replaced by its 2DH version to consider local 2DH hydrodynamics that would be otherwise missed (e.
Climate data We obtained hourly offshore wave conditions for the period 1980-2020 from the global wave hindcast GOW2  63. For the same period of time, we obtained hourly storm surges from the Sydney Port Jackson tide gauge station of the GESLA dataset (https://gloss-sealevel. org) and reconstructed hourly astronomical tides using the harmonic constituents from the TPXO7. 2 global model  64. We used AR6 SLR projections  46  for the SSP2-4. 5 and the SSP5-8. 5 emissions scenarios, for medium and low confidence driving processes (that account for uncertainty in ice-sheet mass loss and its regional impact on SLR), for the 5th, 50th and 95th percentiles, and for 2050 and 2100 (Fig. S1 ). We provide a description of the SLR scenarios and the rationale for their choice in the Supplementary Material.
Topo-bathymetric data and non-erodible layer We used the NSW Marine LiDAR Topo-Bathy 2018 that is available at the Geosciences Australia ELVIS website (https://elevation. fsdf. org. au). This topo-bathymetry is a combined gridded terrestrial (elevation) and subtidal (bathymetry) data at 5 × 5 m (horizontal resolution), Geotifs in GDA 2020 (horizontal datum) to Australian Height Datum (vertical datum) and vertical precision to International Hydrographic Order (IHO) 1B. We used the aerial imagery (World Imagery from Esri) to define a non-erodible barrier to limit the coastline landward displacement according to physical constraints. For deep water, we used the GEBCO Grid, which is an ocean terrain model that provides global coverage of elevation data on a 15 arc-second interval (https://www.
Land and building values and land use data We obtained land values in Narrabeen-Collaroy from the NSW Office of the Valuer General. We used the July 2021 land value, which reflects the real estate market at that date and is based on the analysis of over 67,000 property sales. The land value is the market value of only the land, it does not include the value of buildings or other structures. This information is provided by district in a. csv file at http://valuation. property. nsw. gov. au. Additionally, we consulted real estate websites to obtain the spatial distribution of the built properties prices in the area. We used the NSW Landuse 2017 v1. 2 (https://datasets. seed. nsw. gov. au/dataset/nsw-landuse-2017-v1p2-f0ed) to infer the spatially distributed Manning roughness values required for the flood modelling, as in ref.
Nearshore downscaling We obtained hourly nearshore waves at 200 m-spaced transect ends located throughout the -15 m isobath (Fig. S2 ) during the period 1980-2020 by efficiently propagating a high-fidelity spectral description of offshore wave climate following ref. 66. A representative subset of 500 offshore sea states characterised by their full frequencydirection spectrum was obtained from the GOW2 dataset using the maximum dissimilarity selection algorithm  67. Then, we propagated offshore sea states using the nearshore spectral wave model SWAN  68  to the nearshore points. We designed two SWAN grids for accurately capturing the wave propagation processes: a general grid of 500 m resolution extending 100 km alongshore and forced by the GOW2 points; and a detailed grid of 50 m resolution covering the study site and nested to the general grid (Fig.
Storm definition and surf-zone storm modelling From the 32-year database of nearshore waves and water levels, we defined the storm once in 30 years as a reference. We first calculated an empirical TWL as the sum of storm surges, astronomical tides, wave setup and infragravity swash following ref. 71. We fitted the annual maxima to an empirical distribution and obtained the reference storm as the observed event closer to the 30-year return period TWL. After identifying the peak of the storm through the TWL, the duration was defined by the period of time in which the significant wave height exceeded a threshold  72. We set the threshold based on the monthly running mean of the significant wave height (Fig. S9 ). We used hourly nearshore sea states and water levels during the storm duration to force the hydro-morphodynamic model XBeach 60 in a 1D surfbeat configuration with the calibrated parameters obtained from ref.
Flood protection value assessment We computed the flooded area and the flood damage using ArcGIS. To calculate flood exposure, we digitised each land parcel in the Narrabeen and Collaroy districts and assigned them a unique value per m 2 , computed using the street name, house, area, and land value fields of the NSW Office Value General for each land parcel (Fig. S14a ). We also digitised each building and assigned them a unique value per m 2 , computed considering the floor area, the number of floors (estimated with Google Street View) and the spatial distribution of real estate prices per m 2 (Fig. S14b ). Likewise, we digitised the roads in the area. We removed the buildings from the land parcel dataset to avoid double counting. We combined the three spatially distributed datasets with the flood maps and multiplied the flooded area of each land parcel and building by their values per m 2.
Variance partitioning analysis We performed a four-factor, ANOVA-based variance decomposition where SSPs, SLR percentiles and modelling approaches (dynamic approach with erosion and static approach without erosion) were the uncertainty sources. We used the n-way analysis of variance (anovan) MATLAB function. The relative contribution of a component to the total TWL, flooded area and flood damages uncertainty is expressed by the fraction of each component's variance to the total variance. We set the nesting relationships among the SSPs and SLR percentiles so that their interactions capture the interaction between the modelling approach and the SLR dimensions but not between the SLR dimensions themselves.
Acknowledgements A. T. acknowledges financial support from the  Ministerio de Ciencia e Innovación (MCIN/AEI  and  NextGenerationEU/PRTR)  through the  Ramon y Cajal Programme  ( RYC2021-030873-I ). This research was also funded by the  Ministerio de Ciencia e Innovación  through the grant  COAST-ALfutures  ( PID2021-126506OB-100  with funding from  MCIN/AEI/ 10. 13039/501100011033/FEDER UE ) and the  ThinkInAzul Programme  (with funding from  European Union   NextGenerationEU/PRTR-C17. I1  and the  Comunidad de Cantabria)  to A. T. and I. J. L. ; the  Comunidad de Cantabria  through the  FENIX Project  to A. T. ; and the  European Union  through the  Horizon 2020 CoCliCo Project  (grant agreement No  101003598 ) to A. T. , I. J. L. and G. L.
Climate and Satellite Data The meteorological forcing data in TRENDY v10 ensemble is CRUJRA data set, which is a gridded land surface blend of the CRU and the JRA data sets with a spatial resolution of 0. 5° × 0. 5°. We chose air temperature (TEMP), shortwave radiation (SW), precipitation (PRE), and vapor pressure deficit (VPD) to examine the responses of GPP to climate change. VPD was calculated based on air temperature and actual vapor pressure derived from this data set  (Yuan et al. , 2019). All the annual meteorological variables were calculated as the average from only the growing season data. To compare modeled LAI with observation-based LAI data, we utilized the satellite-based 500-m spatial resolution LAI product Global Land Surface Satellite (GLASS,  Liang et al. , 2021)  based on Moderate-resolution Imaging Spectroradiometer (MODIS) observations.
Eight types of major biomes including evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), mixed forest (MF), savanna (SAV), grassland (GRA), shrub (SHR), and cropland (CRO) were selected to evaluate the IAV in GPP at the sites. All TEMs simulated GPP for each ecosystem type individually within a given 0. 5 × 0. 5° grid, and we compared the simulations of the corresponding ecosystem types against the GPP derived from EC site measurements. Since the TEMs in TRENDY v10 ensemble are driven by the coarse spatial resolution climate data, the meteorological conditions may differ from those at the flux sites and lead to uncertainties in the evaluation of IAV in GPP of the models. Therefore, we compared IAV in the site-measured meteorology data (i.