dataesr/scientific-paragraphs-categorization huggingface dataset:
Long & Padir, 2018)  or pygradientpolytope  (Sagar, 2023) , are often very specific to their applications, they lack the documentation and flexibility to be extended to new metrics and integrated with other libraries. Therefore, this paper presents a Python pycapacity package in an effort to provide a set of tools specifically tailored for evaluating task-space physical ability metrics for humans and robots, based on polytopes and ellipsoids. This package groups a set of efficient algorithms for their evaluation in an easy to use framework that can be easily integrated with standard robotics and biomechanics libraries. Furthermore, the package implements several state of the art algorithms for polytope evaluation that bring many of the polytope metrics to the few milliseconds evaluation time, making it possible to use them in online and interactive applications.
pycapacity has been used in several scientific papers, for real-time control of collaborative carrying using two Franka Emika Panda robots  (Skuric et al. , 2021) , for developing an assist-asneeded control strategy for collaborative carrying task of the human operator and the Franka robot  (Skuric et al. , 2022). The package has also been used to calculate the approximation of the robot's reachable space using convex polytope  (Skuric et al. , 2023). On the other hand, the package has been used for the biomechanical calibration of the human musculoskeletal models  (Laisné et al. , 2023).
Robotic manipulators In case of robotic manipulators the methods used are given in the following The average execution time is calculated for 1000 random configuration of a 7 DOF Franka Emika panda robot, the model was used with pinocchio software. All the experiments are run on a computer equipped with a 1. 90GHz Intel i7-8650U processor. The results are obtained using the benchmarking script provided in the by the repository in the examples folder.
Musculoskeletal models In case of human musculoskeletal models the methods used are given in the The average execution time is calculated for 1000 random configuration of a 50 muscle 7 DOF musculoskeletal model introduced by  (Holzbaur et al. , 2005) , the model was used with biorbd biomechanics software. The experiments are run on a computer equipped with a 1. 90GHz Intel i7-8650U processor. The results are obtained using the benchmarking script provided in the by the repository in the examples folder.
Conclusion This paper introduces the pycapacity Python package, a toolkit designed to evaluate taskspace physical ability metrics for both humans and robots based on polytopes and ellipsoids. The aim of this package is to provide efficient tools for evaluating these metrics within an easily accessible framework, which can seamlessly integrate with standard robotics and biomechanics libraries. By implementing state-of-the-art algorithms for polytope evaluation, pycapacity enables the evaluation of these metrics in an efficient manner, making them applicable for interactive online applications.
ddRADseq data processing Raw reads were visually explored with FastQC  (Andrews, 2010)  to ensure data quality and absence of adapters. Then, reads for each individual were demultiplexed and processed with ipyrad v. 0. 9. 84  (Eaton & Overcast, 2020) , discarding sites with Phred score < 33, and reads with more than three missing sites. Consensus sequences with less than six reads, excessive heterozygous sites (more than three), or more than two alleles were also discarded. We created three different datasets: one to explore population structure analyses and gene flow containing all individuals of Cerastes together with E. omanensis (as outgroup), a second to explore more in-depth population structure analyses at the genus level containing only the genus Cerastes samples and a third dataset to explore intraspecific relationships with only C.
cerastes samples. Then, for each dataset three independent ipyrad runs were performed using different clustering parameters for both filtered and consensus sequences (step 3 and 6 in ipyrad, respectively): 0. 85, 0. 89 and 0. 92, selecting the clustering that maximized the number of samples without significantly reducing the number of loci retained (0. 85 with the outgroup and 0. 89 for both datasets without the outgroup). Then, following recommendations from O'  Leary et al. , (2018) , we used Radiator  (Gosselin et al. , 2017) , Plink2  (Chang et al. , 2015)  and VCFr  (Knaus & Grünwald, 2017)  implemented in a custom script (https://github. com/BernatBurriel/Post_processing_filtering) from  Burriel-Carranza et al. , (2023a)  to filter iteratively and alternatively low-quality samples and loci.
For all datasets, values of missing data allowance ranged from 98% to 60% of missing genotype call rate and missing data per individual, decreasing 2% between iterations. Subsequently, we applied a hard threshold of missing genotype call rate of 60%. We removed non-biallelic SNPs, applied a minor allele frequency filter (maf < 0. 05) and removed monomorphic sites. For the different analyses, we retrieved loci, haplotype and SNP data (with or without an outgroup, depending on the analyses' requirements): loci and haplotype datasets were generated with a second round of ipyrad after removing all individuals that did not pass the previously explained iterative filters and retaining only loci that were at least in 60% of all individuals. The SNP dataset (with the outgroup) was used for the introgression analysis, where all previously filtered SNPs were kept.
Population structure analyses We performed a Principal Component Analysis (PCA) with the unlinked SNP dataset (a total of 2,856 uSNPS) containing only Cerastes samples using Plink v1. 9  (Chang et al. , 2015). Then, we used ADMIXTURE  (Alexander et al. , 2009)  to analyze the same dataset to detect population ancestry from K=2 to K=5. A total of 20 replicates for each K were calculated, selecting the best K value after 20 cross validations. We also used fineRADstructure  (Malinsky et al. , 2018)  on the haplotype dataset to explore population structure within the three species, plus the outgroup, setting the minimum number of samples per loci to four. This analysis is optimal to detect different levels of structure within and between populations and to explore co-ancestry patterns for non-model organisms  (Malinsky et al.
, 2018). We also performed a Mantel test with the Cerastes cerastes samples (SNP dataset containing only C. cerastes samples). Lastly, we calculated genetic diversity and pairwise Fst values with GenoDive v. 3. 0  (Meirmans, 2020)  with 10,000 permutations at the species and subspecies level, using the SNP dataset. Visualization of results from these analyses was performed with R v. 3. 6. 3 (R Core  Team, 2021)  and the R package ggplot2  (Wickham, 2016).
COI sequencing and phylogenetic inference We sequenced a COI fragment for a total of 24 individuals for the three species within the Cerastes genus (information regarding the specimens used can be found in Table  S1 ). Primers and PCR amplification conditions were implemented according to  Nagy et al. , (2012). PCR products were purified and Sanger sequenced by Genewiz (UK). Then, we performed a multiple sequence alignment with MAFFT  (Katoh & Standley, 2013)  for the 24 samples together with two previously sequenced samples  (13693 and 12953, accession number ON943580 and ON943571, respectively, from Velo-Antón et al. , 2022)  as well as two outgroups (two individuals of Echis carinatus, accession numbers MG699965 and MG699966, respectively). We used BEAST2 v. 2. 6. 4  (Bouckaert et al. , 2019)  to reconstruct a Bayesian Inference (BI) time-calibrated tree.
Phylogenomic reconstructions We reconstructed the phylogenomic history of the three species of Cerastes using three different phylogenetic methods. First, with the concatenated loci dataset (531,260 bp, including the outgroup) we reconstructed a Maximum Likelihood (ML) tree. For that, we used RaxML-NG  (Kozlov et al. , 2019) , with the GTR+I+G model with a total of 100 starting trees (50 random and 50 parsimony) and performing 1,000 bootstraps for branch support. Later, we filtered the loci dataset to maintain only loci without missing data, obtaining a total of 627 loci and 40,831 bp (including the outgroup). With those, we used BEAST2 v. 2. 6. 4  (Bouckaert et al. , 2019)  to obtain a time-calibrated tree. We used the same calibration point as described for the mitochondrial phylogeny. We selected a GTR model with four gamma categories (base frequencies and proportion of invariant sites were estimated), and a relaxed clock LogNormal was used with a coalescent exponential population tree prior.
Niche overlap analyses In order to investigate the potential role of environmental adaptation in the diversification of the genus, we performed pairwise niche overlap tests between the major clades within the genus, resulting from our ddRADseq inferences, considering two geographic scales and two types of variables. The global scale analyses included a total of 990 records at a spatial resolution of 5 arc minutes (~10x10km; 561 C. cerastes, 213 C. gasperettii and 216 C. vipera) spanning across the whole distribution of the three species (Fig. S2 ), and two distinct sets of low correlated variables (R < 0. 72; Table  S2 ): (1) a climatic dataset, which included seven precipitation-and temperature-related variables downloaded from WorldClim v. 2. 1 (www. worldclim. org), and (2) a landcover dataset, which comprised three percentages of landcover variables, which resulted from calculating the number of 10 arc seconds resolution pixels of three landcover categories representative of the Saharan and Arabian deserts (from the ESA GlobCover ver 2.
2,  Bicheron et al. , 2008)  in 5 arc minutes resolution pixels, using the function aggregate from ArcGIS v. 10. 5  (ESRI, 2016). Occurrence data for the global scale analysis were gathered from distinct sources, including fieldwork conducted by the authors for the period 2001-2021 (n = 241), bibliographic references (n = 180; e. g. ,  Werner et al. , 1999; Brito et al. , 2011; García-Cardenete et al. , 2017) , museum collections (n = 106; e. g. , NHM-London, MNHN-Paris) and GBIF  (n = 463; www. gbif. org, 2021). Due to the underrepresentation of the landcover heterogeneity for the Saharan and Arabian deserts as available in the ESA GlobCover dataset, we also approached niche overlap for the Saharan species at a regional scale of the Atlantic Sahara (i. e. , Mauritania and Western Sahara), where a detailed landcover map was recently derived  (Campos et al.
, 2018). Consequently, for this regional scale analysis, we considered a subset of 170 records (118 for C. cerastes and 52 for C. vipera), available at a higher accuracy (i. e. , 30 arc seconds, ~1x1km), since 69% were gathered during fieldwork campaigns (the remaining from bibliography, 19% and GBIF, 12%). In addition, we used seven percentages of landcover variables (see Table  S2 ), which resulted from using the function aggregate from ArcGIS over seven landcover categories or groups of categories with 1 arc second resolution pixel (see Table  S2 ) to new rasters with 30 arc seconds resolution pixel. For the three approaches (global-climate, global-landcover and regional-landcover), occurrence data were used to extract the corresponding variable values and then run three distinct PCA. Following a three-dimensional hypervolume approach, the first three components of each PCA were used to describe the environmental niche of each major clade (i.
e. , total volume and unique part, intersection and union with the other species' volume) as obtained from ddRAD data analyses and perform pairwise niche-overlap tests between them (see Martínez-Freiría et al. , 2020 for a similar procedure). Analyses were performed in R with the 'hypervolume' package  (Blonder et al. , 2014) , using a Silverman bandwidth estimator and a set of 999 random points to sample the kernel density to build hypervolumes. To quantify environmental niche overlap, the Sørenson index (K) and the Overlap Index (OI) were used (e. g. ,  Martínez-Freiría et al. , 2020). These statistics quantify predicted niche similarity, and they both range from 0 (no overlap) to 1 (identical niche). The Overlap Index (OI) relates the observed and maximum values of K, and is preferred when niches present different sizes  (Simó-Riudalbas et al.
Introgression analyses To study ancestral introgression, we ran the ABBA-BABA test implemented in Dsuite  (Malinsky et al. , 2021)  with the SNP dataset (32,254 SNPs), including the outgroup. To do so, we split our samples per species (and subspecies when possible) and calculated the D-statistics using the new phylogenetic topology. Patterson's D-statistic uses asymmetry in gene tree topologies to quantify introgression between either of two lineages which share a common ancestor (P1 and P2) and a third lineage (P3) that diverged from the common ancestor of P1 and P2. A fourth individual (P4) is needed to inform the ancestral state for each comparison  (Malinsky et al. , 2021). In here, we set E. omanensis as P4 to set the ancestral state. C. gasperetti or C. vipera were used as P3 while C.
Population structure analyses Population genomic results of the unlinked SNP dataset (2,856 uSNPs) showed an unexpected result (Fig. 2 ). PC1 split C. gasperettii from both C. cerastes and C. vipera individuals whilst PC2 separated C. cerastes from C. vipera (Fig. 2A ). The C. c. hoofieni individuals clustered together with the rest of African C. cerastes (Fig. 2A ). PC3 sorted C. cerastes following their geographical locations (northwestern Africa, northeastern Africa and Arabia). The best supported scenario in Admixture recovered the three different species for K=3 (Fig. 2B. For K=4, the samples of C. c. hoofieni formed a unique cluster, with the two geographically closer African individuals (92 and 12953; Table  S1 ) sharing a limited amount of coancestry (Fig. 2B ). FineRADstructure analyses showed five different clusters (including the outgroup) representing the different species and with C.
49 < K < 0. 81; 0. 70 < OI < 0. 84; Table  S5 ). Statistical tests were all significant (p < 0. 05), with the exception of the test of K index for C. cerastes vs C. vipera in the global-landcover dataset (Table  S5 ). Overall, niche overlap analyses show that the three species exhibit non-equivalent but highly overlapping niches.
Introgression analyses We used the filtered SNP dataset containing 32,265 SNPs that were present in at least 60% of the samples to study introgression between the different clades. Both D-statistics and f4-ratio analyses showed a significant excess of shared alleles between C. vipera and C. c. cerastes individuals (Table  1  and Fig. 5 ). Moreover, f-branch analyses supported D-statistics and f4-ratio and suggested an introgression of around 10% of the sampled sites between C. vipera and C. c. cerastes (Fig. 5 ). Ancestral introgression was not found between C. gasperettii and C. c. hoofieni, both occurring in Arabia (Table  1  and Fig. 5 ).
Insights into the systematics of Cerastes cerastes This study provides novel genetic and genomic information for the subspecies C. c. hoofieni. Moreover, our genome-wide data does not recognize C. c. mutila as an independent genetic unit, as it did not exhibit any genetic differences compared to other individuals of C. c. cerastes (Fig. 2 -3 and Fig. S4 ). This finding further refutes the previous discussion on the validity of C. c. mutila as a subspecies  (Martínez del Mármol et al. , 2019). Conversely, our relatively small dataset seems to support the recognition of C. c. hoofieni as a subspecies within C. cerastes (Fig. 2 -3 and Fig. S4 ), with C. c. cerastes being paraphyletic. Nevertheless, in spite of the existence of morphological differences  (Werner et al. , 1991) , our genomic data so far does not support an elevation to a species status of C.
Acknowledgements This work was supported by grants  PGC2018-098290-B-I00  ( MCIU /AEI/ FEDER, UE) ,  Spain ,  PID2021-128901NB-I00  ( MCIN/AEI/10. 13039/501100011033  and by  ERDF ,  A way   of making Europe) ,  Spain , and grant  2021-SGR-00751  from the  Departament de Recerca i Universitats  from the  Generalitat de Catalunya, Spain  to SC. GM-R is supported by an  FPI  grant from the  Ministerio de Ciencia, Innovación y Universidades, Spain  ( PRE2019-088729 ), BB-C is supported by  FPU  grant from  Ministerio de Ciencia, Innovación y Universidades, Spain  ( FPU18/04742 ), AT is supported by  "la Caixa" doctoral fellowship   program  ( LCF/BQ/DR20/11790007 ), HT-C is supported by a " Juan de la Cierva   -Formación" postdoctoral fellowship  ( FJC2021-046832-I ) funded by  MCIN/AEI/10. 13039/501100011033  and by the  European Union NextGenerationEU/PRTR , GV-A was supported by the  FCT  ( CEECIND/00937/2018 ) and recently by a  Ramón y Cajal research  grant ( Ref.
RYC-2019-026959-I/AEI/10. 13039/501100011033 ), JŠ was supported by the  Czech Science Foundation (GAČR)  under grant number  22-12757S  and by the  Charles University Research Centre  under grant number  204069  and FM-F and JCB are supported by  FCT -Fundação para a Ciencia e Tecnologia de Portugal  ( DL57/2016/CP1440/CT0010 ,  CEECINST/00014/2018/CP1512/CT0001 , respectively) and.
DNA extraction and whole-genome bisulfite sequencing (WGBS) analysis Genomic DNA from strawberry plants was extracted with the DNA isolation, plant DNA mini kit, peqGOLD (VWR Life Science, cat# 13-3486-01). For DNA library preparation and bisulfite sequencing, samples were sent to Novogene (Hongkong, China). Paired-end reads were obtained on an Illumina (150 bps) NovaSeq6000 instrument. The DNA methylation datasets were analyzed using the Epidiverse/wgbs pipeline  (Nunn et al. , 2021b)  with the parameters previously reported in  López et al. , 2022. Lambda DNA reads were used to evaluate the bisulfite conversion rate. An average of  61,257,191 reads (~ 30X coverage)  were produced per sample, of which 81% mapped properly to the F. vesca genome of the line that we previously sequenced  (López et al.
, 2022). The average non-conversion rate among the samples was 0. 28% (See Supplemental Table  S1  for more details). To calculate global methylation ratios, we use the parameters previously described in  (López et al. , 2022). Global DNA methylation levels were computed by combing all bedGraph files into a unionbedg file pre-filtered for a minimum coverage of 5 reads per cytosine position and per sample. Only cytosine positions in common in all samples were kept. R-package ggplot2 v. 3. 3. 5 was used for the visualization plots and unpaired Student's t-test for statistical analysis. p<0. 05 was selected as the point of minimal statistical significance in all the analyses.
Identification of differentially methylated regions (DMRs) The previously pre-filtered bedGraph files obtained were used as input for the EpiDiverse/dmr bioinformatic analysis pipeline to identify DMRs  (Nunn et al. , 2021a)  with default parameters (Supplemental Figure  S1 ). Global DNA methylation and DMR plots were performed with Rpackage ggplot2 and deepTools v. 3. 5. 0. We produced genome browsers tracks with DMRs in our publicly accessible JBrowse instance: https://jbrowse. agroscope. info/jbrowse/?data=fragaria_sub.
RNA-seq analysis and definition of differentially expressed genes (DEGs) Total RNA extractions were performed using NucleoSpin RNA Plus, Mini kit for RNA purification with DNA removal column (cat# 740984. 50). In vitro mother plant samples (CM; MP; n= 6) and daughter plant samples (St1; St2; St3; n=36) were sent for Illumina paired end read sequencing (150 bp) to Novogene (Hongkong, China). RNA-seq analyses were performed as previously described in  (Roquis et al. , 2021; López et al. , 2022). Briefly FastQC v. 0. 11. 9 and trimmomatic v. 0. 39 packages were used for quality control and trimming. Salmon v1. 4. 0 was used as sequence mapper. DESeq2 package was use for the quantitative differential gene transcription analysis at the European Galaxy platform with default parameters (Supplemental Figure  S2 ).
Genes showing a DMR (promoter and gene body located) and DEGs were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation downloaded from the Genome Database for Rosaceae (GDR)  (Jung et al. , 2019). Genes containing hypo-and hypermethylated DMRs were classified to investigate potential functions through GO enrichment by AgriGOv1. 2  (Tian et al. , 2017). Similarly, all DEGs were extracted with p-adj≤0. 05 for the GO functional analysis with Fisher's exact test p-values and Hochberg (FDR) as multi-test adjust method (≤ 0. 05 as the cut off). To identify potential enriched pathways, KEGG analysis was performed with the R-package: clusterProfiler  (Wu et al. , 2021) ; pvalueCutoff = 0. 05; pAdjustMethod = "fdr". Plots were generated with the R-package ggplot2.
Acknowledgements The  European Training Network  " EpiDiverse " received funding from the  EU Horizon 2020 program  under  Marie Skłodowska-Curie  grant agreement No  764965 ; The  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program ; No  725701  " BUNGEE " to E. B. Funding for open access charge:  Agroscope  institutional funding. This study was supported by  Agroscope, Nyon-Switzerland. We would like to thank all the members of the  EpiDiverse consortium  (www. epidiverse. eu) and the  Crop Genome Dynamics research group  for invaluable support,  Eric Remolif  for taking care of the plants at the greenhouse.
Data availability The datasets generated and/or analyzed in this study are available in the Zenodo repository DOI: 10. 5281/zenodo. 7898322. All the sequencing data from this study have been submitted to European Nucleotide Archive (ENA, www. ebi. ac. uk/ena/, ERP136614). The data can be accessed under the project PRJEB51950. The Bisulfite-sequencing raw read fastq accessions under ERS11389778-ERS11389819. The raw reads of the RNA-sequencing under the accessions: ERS15458182-ERS15458217.
The literature review provided in the current article is based on the manual search (through Google Scholar platform as well as through the scientific databases of Scopus and Web of Science) using relative to the topic keywords (such as cartography, map reading, eye tracking, and eye movement analysis) and the extensive study of research works published in the period of the last decade (since 2009). Finally, having demonstrated the updated overview of the field, the prospects for future and challenges are discussed. The present article consists of two main sections including this of the literature review on research studies using eye movement analysis towards the examination of map reading process and this of concluding remarks and future perspectives where current and future trends in the field are summarized and discussed.
The study combined eye tracking with time response recording measures and aimed to reveal critical information about users' performed strategies and the cognitive load processes. The results showed that experts turned out to be more efficient in the performed map tasks related to the identification of specific names while novices needed more time for map image interpretation  (Ooms et al. , 2012a). However,  Ooms et al. (2014)  concluded that both experts and novices map users had difficulties with the execution of map tasks that required the interpretation and memorization of mirrored maps objects. The influence of map users' expertise was also tested in specific types of visualizations. More specifically, Stofer and Che (2014) examined the visual reaction of both experts and novices during the observation of scaffolded (ocean) data visualizations.