dataesr/scientific-paragraphs-categorization huggingface dataset:
A C K N O W L E D G E M E N T S C. elegans strains were provided by the CGC, which is funded by the NIH Office of Research Infrastructure Programs (P40 OD010440). We thank Martin Seeuws, Dr Koen Verhofstadt and all the staff of VZW Domino for their daily care of older people and for their collaboration on this study. We thank the Ghent Germ-free and Gnotobiotic Mouse Facility for assistance during germ-free and gnotobiotic mouse experiments (Prof. Lars Vereecke, Dr Vanessa Andries, and Korneel Barbry), and the MyoLine platform from the Institute of Myology in Paris for the human myoblast cell lines. ADS is supported by the Fund Marie-Therese De Lava and Robert Schneider, King Baudouin Foundation, Belgium, as well as by a grant of Research Foundation -Flanders (FWO) (grant number 1158818N).
It was during the studies evaluating anifrolumab in SLE that the use of IFN signature monitoring in patients was developed. By pooling data from the TULIP trials into a post hoc study, it was shown that anifrolumab was more effective in the subgroup of SLE patients with high IFN signature. 30 Of note, the presence of serological biomarkers (anti-DNA, low C3, low C4) also allowed to discriminate patients who could benefit from anifrolumab treatment. In practice, it will therefore be interesting to compare the interest of monitoring the IFN signature (transcriptomic signature including 4 or 5 genes or inducible proteins) or the dosage of serum IFN itself by sensitive techniques (SIMOA or meso scale diffusion) versus the use of classical biomarkers more easily available in clinical practice. In addition, it will be important to assess the reproducibility of calculation of the IFN score.
(2017)  and  Cook and Sgrò (2018)  showed a low level of integration of key evolutionary concepts in biodiversity management, in New Zealand and Australia, respectively. Pierson et al. (2016)  used another approach by evaluating how genetic issues are incorporated into recovery plans. France was included in their analysis but only through the national action plans (PNA) which are clearly not representative of conservation actions in France. Our study aims to assess within-population genetic diversity of threatened Angiosperm species in France and the use of such information in conservation actions. First, we evaluated the data available for neutral genetic diversity of plants species considered as threatened in France using the IUCN list and the legal protection status in France. Although it would be best to make congeneric comparisons to control for confounding effects of phylogeny and life-history traits on genetic diversity  (Gitzendanner and Soltis 2000; Spielman et al.
2004; Hamabata et al. 2019; Hamrick and Godt 1996; Leimu et al. 2006; Duminil et al. 2007) , sometimes genetic data were not available for a non-threatened congener, in which case we instead compared the threatened species with its closest relative for which appropriate genetic data were available. Next, in order to assess the degree of consideration of genetic diversity in conservation actions in metropolitan France, we analysed available recovery plans and performed a survey addressed to managers.
Genetic diversity of threatened plant species in France The IUCN Red List threatened categorization system (CR, EN, VU) was used to identify 452 threatened plant species and subspecies occurring in metropolitan France (https://uicn. fr/liste-rouge-flore/, November 2019). In addition, the list of the 387 legally protected Angiosperms species in France was extracted from the institutional website collecting data on biodiversity in France (https://inpn. mnhn. fr/, November 2019). The two lists were merged to define threatened Angiosperm species and subspecies in metropolitan France. We also added three species (Arenaria grandiflora, Convolvulus lanuginosus, Pinguicula grandiflora) with a local status of protection that are known to have been studied for genetic diversity in the area were there are protected.
The final list included 749 plant taxa. We did not check for synonymy at this stage. It is important to note that genetic diversity is not considered neither in the IUCN criteria, nor for a legal protection status in France. Using this list, automatic data mining was carried out on NCBI (https://www. ncbi. nlm. nih. gov/), on Google Scholar and on the French registry of Ph-D theses (http://theses. fr/) to find any study on these species. A search on the Web of Science database was also performed using the list of species (eg TITLE = "SPECIES1" OR "SPECIES2"…). We obtained 6779 results (20 April 2022), and we refined the results using the keyword "genetic". Among the 625 results, we retained publication with at least one author from France (Countries/Regions = "France"). For two species (Agropyron cristatum and Vitis vinifera) related to largely cultivated plants, a dedicated search was done.
Among all the studies collected, only those with data on within-population genetic diversity were retained. We used genetic diversity at population scale since it is the pertinent scale to detect effects of drift, absence of migration and because selection needs within-population diversity to act. For each species, parameters estimating genetic diversity within populations were extracted from the studies. We also documented population size and sampling size, and several life-history traits known to influence genetic diversity such as mating system, longevity, vegetative reproduction, dispersion vector and pollination vector (see Appendix 1). As our goal was to compare genetic diversity of threatened species to genetic diversity of non-threatened species, we searched for studies performed on related nonthreatened species.
The collection of data for non-threatened species was performed in several phases. Ideally, each pair of species should include a threatened and nonthreatened congeneric species (see  Lavergne et al. 2004 ) occurring in the same habitat and sharing common life-history traits. As we aimed to compare genetic diversity, we must also have genetic parameters estimated on the same type of genetic markers although the best comparison would use the same loci for those same markers. We made a first search on Web of Science using the name of the genus and the types of genetic markers (e. g. TOPIC= Anchusa AND TOPIC= AFLP). In absence of results, we relaxed the congeneric criteria and searched using the family name (e. g. TOPIC= Caryophyllaceae AND TOPIC= SSR). Species were not considered as a valid control if they were said to be "rare" or "threatened" by the authors of a study, if they were cultivated species, or if they were invasive species.
Data analyses The raw data were the expected heterozygosity within populations, also named Nei's gene diversity, extracted from published and unpublished papers (see Appendix 1). We computed the mean expected heterozygosity per population for each species. Expected heterozygosity was chosen to estimate genetic diversity because this measure is only slightly affected by sample size, in contrast to the number of alleles per locus. To test for the difference in expected heterozygosity between threatened and non-threatened controls, a non-parametric pairwise comparison test was performed. Because of the small sample size (see results), we adapted the classical Wilcoxon test for pairwise comparisons following  Manly (1991). First, the V-value was computed using the Wilcox. test function in R (version 4.
0. 3). Next, the same statistic was computed for permuted dataset. Species status (threatened vs non threatened) was permuted 1,000 times within pairs, and the original V-value was compared to the distribution of the permuted values. Finally, the percentage difference in heterozygosity within pairs was estimated as the signed difference in expected heterozygosity between the threatened and the non-threatened members of the pair divided by the largest value of expected heterozygosity in the pair.
Genetic diversity of threatened plant species in France From the initial list containing more than 700 plant threatened species, withinpopulation expected heterozygosity at neutral nuclear loci was gathered only for 25 threatened species (Appendix 1). Only 12 species are considered as threatened species under IUCN criteria on the French redlist, and two species (Aster pyrenaeus and Biscutella neustriaca) are considered as threatened at a global scale. All the 25 threatened species have a French legal protection status either at the national scale (17 species) or at the local scale (8 species). Twenty-two non-threatened species filled the require conditions to build the pairs, 13 in same genus and 9 in the same family than a threatened species. For the 3 other pairs, we used data from Nybom (2004, see Materials and Methods section, Appendix 1).
AFLPs (N=20, He=0. 20 SD 0. 08) and microsatellites (N=16, He=0. 43 SD 0. 21) were the most frequently used marker types. ISSR (N=10, He= 0. 14 SD 0. 07) and allozymes (N=4, He=0. 08 SD 0. 05) are the least frequent and the least diverse markers. All the threatened species in the dataset are perennials except Ranunculus nodiflorus and R. lateriflorus which are annuals (Appendix 1, Table  S1 ). Threatened species are mainly polycarpic (76%) and their pollination is mostly entomophilous (76%). Agents of seed dispersal varied: anemochory (24%), barochory (28%), hydrochory (8%), and zoochory (8%) (missing data for 8 species). Mating system is generally considered as allogamous (36%) or mixed (20%). There are 44% of threatened species able to reproduce asexually. For most pairs, threatened species share the same life-history traits as their non threatened relatives (Appendix 1, Table  S1 ).
Considerations of genetic diversity in management actions Sixty-two recovery plans concerning 59 protected species were found on-line. Two species (Centranthus trinervis and Liparis loeselii) were studied in more than one plan. From the on-line survey, we collected data for 21 species, six species were mentioned both in recovery plans and in the survey. Overall, we collected information for 74 species. Among them, 20 are considered as threatened on the IUCN national list, 26 species are protected at the national level and the others are locally protected either at the department or regional level which justifies management actions. Habitat management, with 46 citations out of 74, is the most frequent action recommended for in situ management. Legal protection for habitat was recommended for 45 species.
Manipulation of population size was recommended for 21 species with introduction for 11 species, reinforcement for 15 species, and reintroduction for 5 species. Among species in this dataset, six species (Anchusa crispa, Aster pyrenaeus, Biscutella neustriaca, Eryngium viviparum, Liparis loeselii, Ranunculus nodiflorus) were in the previous analysis on genetic diversity. Life-history traits are unequally represented in recovery plans. While habitat and species longevity are well characterized (100%, 96. 8% respectively Table  1 ), the ecology of dispersal and pollination are often not considered (29%, 22. 6% respectively Table  1 ). Important information such as the mating system is even more rarely given in recovery plans (13 occurrences, Table  1 ), and tests for self-compatibility have also seldom been carried out (Table  1 ).
A general recommendation concerns the importance of communication between researchers and practitioner  (Taylor et al. 2017; Fabian et al 2019; Holderegger et al. 2019; Taft et al. 2020; Hohenlohe et al. 2021). Regular networking events between practitioners and conservation geneticists, such as Annual Meetings in Conservation Genetics  (Holderegger et al. 2019) , or conservation geneticist hub bringing together genetic researchers to provide one platform of expertise visible to managers  (Taylor et al. 2017 ) should be more widespread. It also would beneficial to increase the number of collaborative partnerships between genetic researchers and conservation managers to support management strategies of specific wild populations  (Taft et al. 2020; Hohenlohe et al. 2021). Table  S1 : Description of 6 life-history traits within pairs of species according to the status of the species, Appendix 1: Dataset on genetic diversity of threatened plant species in France, Appendix 2: Information that has been searched in recovery plans, Appendix 3: Survey addressed to practitioners, Appendix 4: Dataset on considerations of genetic diversity in management actions 415 416 417 Table  S1.
A consequence is that if one wants to fix a minimum threshold for a robustness statistical criterion (for instance the minimum logarithm of odds (LOD) between the best order found and any other order), the number of markers in the map will be limited for a given population type and size: the higher the threshold, the lower the number of markers which can be included in the map. For usual levels of threshold (e. g. LOD=3) and large data sets, the maximum number of markers in the map will most often represent only a fraction of the SNPs available. The problem then translates into chosing the largest subset of markers which allows the order to be statistically robust at a given threshold. Here we propose the SeSAM (Seriation-based Suite for Automatic Mapping) package as a way to address the genetic mapping problem from this perspective.
In practice, such a bin-mapping strategy is usually carried out through an interactive process between an expert user and computer programs. Tools have been developed to automate the placing step  [33] , but to our knowledge, there is today no integrated software able to carry out a complete automated mapping process based on the bin-mapping strategy. So here we propose the SeSAM package, which automatically chains all steps necessary for genetic map construction based on this approach (Figure  1 ), the two main steps being: (1) producing a framework map by selecting an optimal subset of markers from the initial data set, so order robustness can be statistically supported, and (2) producing a total map by placing all remaining polymorphic markers one by one into that framework. In the first step, the determination of linkage groups is done during the elongation of an ordered low-density highrobustness map (scaffold map) through a seriation-based algorithm (Figure  1A )  [35, 36] , after which iterative densification of that scaffold leads to adding as many markers as possible while keeping the order robust at the desired threshold, which produces the framework map (Figure  1B ).
Implementation SeSAM is implemented as a R package, which allows to easily chain map construction to any other input data formatting (or map output exploiting) R script. The package includes C++ functions for its most computation-intensive parts (e. g. likelihood computations, EM algorithms). A detailed description of all algorithms and functions of SeSAM is provided in the user manual available as Additional file 3, and also from ForgeMIA at https://forgemia. inra. fr/gqe-acep/sesam/-/releases. The main functionality of SeSAM lies in the function autoMap(), which is a completely automated pipeline going through different main steps carried out by the following functions: loadData(), which reads and checks segregation data, generateSeeds(), which draws the seed markers used to initiate the seriation process, buildScaffold(), which extends a highly robust sparse map by seriation from the seed markers, assignment(), which assigns all polymorphic markers to a linkage group, buildFramework(), which densifies the scaffolds with the maximum possible number of markers while keeping a given statistical level of order robustness to the framework map, and placement(), which adds all remaining markers to the framework map without ensuring a statistical value for order robustness.
Missing data are imputed in all multipoint calculations via an EM algorithm, and putative genotyping errors are detected and taken into account via two different methods. Finally, the SeSAM package includes a toolbox of functions to perform various types of format conversions on data files, interactive step-by-step custom mapping processes, and assessment of map and data quality through different types of graphs. More detailed information about these different functions is available as Supplementary Text S1 in Additional File 2, and in the reference user manual available as Additional file 3 together with a quick-start guide.
Results and Discussion Behavior with number of markers and population size was assessed by simulating data sets for different population types, numbers of markers, and numbers of individuals. SeSAM was run on a desktop computer using 4 cores Intel(R) Core(TM) i7-4790 CPU @ 3. 60GHz (2 threads per core) under the Debian 11 Linux OS, using SeSAM default parameters. The scripts and data used to produce these benchmarking results are available in Additional file 4. Computation time for total maps construction was more or less linear with the number of markers for F2 and CP populations (Figure  2 ). It was also close to linear with the number of individuals of the F2 population, but close to quadratic with the number of individuals for the CP population (Figure  3 ). Finally, Figure  4  shows that CP data sets necessitate substantially longer computation time than other population types, which is expected due to the extra phasing process required for such data.
The map length ratios were always between 0. 88 and 1. 03 for the framework map except for very small F2 and CP populations (50 individuals) for which the scaffold could not meet the robustness criteria up to the extremities of the chromosomes and thus dropped some terminal regions. Similar behaviours were observed for the total maps. All markers of the data sets could be included into the total maps except in the case of the F2 population of 50 individuals for which the framework did not cover the whole of the chromosomes as seen before. On the other hand, when looking at the framework maps, we see as expected that their inclusion rate increases with population size, and decreases with the number of markers. Sensitivity to data quality was assessed by simulating data sets with increasing rates of genotyping errors up to extremely high rates (20%).
Comparison with other existing softwares. To assess how the level of map quality achieved by SeSAM compares with that of other mapping softwares currently available, we generated simulated data sets with different numbers of individuals and markers, and ran them with SeSAM as well as with four different programs: IciMapping, ASMap, MapDisto, and TSPmap (see Supplementary Table  S4  in Additional File 2). We also tried to use HighMap, but we could not obtain the software from the address mentioned in the paper. All tools tested excepted TSPmap produced high-quality total maps, showing high colinearity and similar lengths when compared to the theoretical map used to simulate the segregation data. However, with increasing numbers of markers (>= 10,000), we couldn't get some of the softwares complete the mapping (see Supplementary Table  S4  in Additional File 2).
Computation times varied a lot between programs, with Lep-map performing much faster than all others, and SeSAM being in the average of the remaining ones. Overall, SeSAM produces maps with at least similar quality as the other softwares tested. Using SeSAM thus allows to have a fully automatic tool to produce total maps with a level of quality similar to most other software currently available, but contrary to those other programs, in addition to producing a total map with all polymorphic markers, SeSAM also automatically selects an optimally large subset of markers to produce a framework map statistically robust at any desired LOD threshold. Examples with biological data. To illustrate how SeSAM can perform with real biological data, mapping results obtained from five anonymized experimental data sets from agricultural plant species are presented in Additional File 2 as Supplementary Table  S5  and Supplementary Figures  S1 to S5.
The corresponding anonymized data sets are available as Additional File 5. The number of markers included in the framework map was always lower than the total number of polymorphic markers, because no more markers could be included without losing the order robustness at the given default LOD threshold (3. 0). In BC_ano, RIL1_ano, and CP_ano data sets, which have small population sizes, the frameworks include less markers than in F2_ano and RIL2_ano, which have larger populations (see Supplementary Table  S5  in Additional File 2). This is expected because there are more informative crossovers contributing to the order information in the latter. Moreover, the backcross-derived BC_ano data contain less crossovers (only one effective meiosis) than the other populations, which contributes to the fact that relatively few markers could be incorporated to the BC_ano framework map.
Finally, with similar population sizes, the CP_ano map included less markers in its framework map than RIL1_ano. This is partly due to the fact that not all 2-point marker configurations are informative in CP populations (e. g. there is no linkage information between one male pseudo-backcross marker and one female pseudo-backcross marker). As expected, the number of markers in the framework map is thus commensurate with population size and population type since this ensures statistically supported marker orders. Considering now the total maps obtained after placement, they include almost all polymorphic markers for all data sets, the few non-mapped markers being unlinked to any linkage group, or linked to several linkage groups with similar LODs. To visualy assess the quality of maps produced, SeSAM generates heat maps of pairwise 2-  2), the Marey map is nonmonotonic.
Since the 2-point linkage matrix indicates a high-quality genetic mapping, the quality of the physical map may be questionable here. Elsewhere, the case of the RIL1_ano data set illustrates the possibilty of using a previously existing genetic map to guide the choice of the seed markers to initiate the seriation process, when no physical map is available. In such cases, the 'phyMap. txt' file actually contains a genetic map, so the 'Marey map' obtained has an almost constant slope, but it may also be used to compare recombination landscapes between different crosses. Finally, using the same general algorithm as SeSAM, but with earlier generations of codes, we already produced genetic maps used in several published studies on Maize  [6, 34, [37] [38] [39] [40] [41] [42] , Pea  [43] [44] [45] , and Faba bean  [46].
Conclusions Compared to existing mapping software, SeSAM is to our knowledge the only one to carry out a completely automatic bin-mapping procedure producing first a mid-density framework map from an optimized subset of markers which allow the order to be statistically supported at the desired statistical threshold, and then a high-density total map including nearly all polymorphic markers, but preserving the global structure and length of the framework map. SeSAM is freely available to all users, including the source code, and is compatible with Linux, macOS, or Windows platforms.
Funding This work has received funding from the  French Government  managed by the  Research National Agency (ANR)  under the " Investment for the Future" program   Amaizing  (project  ANR-10-BTBR-03 ), from a  French State grant  ( LabEx Saclay Plant Sciences-SPS ,  ANR-10-LABX-0040-SPS ), managed by the  French National Research Agency  under an " Investments for the Future" program  ( ANR-11-IDEX-0003-02 ), and from  MARS-WRIGLEY  which co-funded the.