dataesr/scientific-paragraphs-categorization huggingface dataset:
Species delimitation We used Bayes Factor Delimitation (BFD) in SNAPP v1. 5. 2  (Grummer et al. , 2014; Leaché et al. , 2014) , a module within the BEAST v2. 6. 6 software  (Bouckaert et al. , 2019) , to determine the most plausible assignment of individuals to species using a multispecies coalescent model with no gene flow. BFD requires a priori species models, which are competing assignment of individuals to species. These models are further analyzed separately using SNAPP and ranked by comparing Bayes factor (BF). In our analysis, we considered four species delimitation models. In the first model, which we considered as a full model, individuals were assigned to species based on the original morphological determination, including the putative new species. In the subsequent models (two to four), we merged individuals representing the following species: G.
occiduus and Gerbillus sp1 in the second model, G. campestris and G. rupicola in the third model, and G. amoenus and G. nanus in the fourth model. Since consistent phylogenetic trees were obtained from data generated under various parameters in ipyrad (see Results section), we simply opted for the one with the lowest amount of missing data as a source of SNPs for SNAPP analyses. To ensure the independence of sampled SNPs, we converted the unlinked SNPs file from ipyrad (. usnps) to a three-state numeric (0, 1 and 2) nexus file suitable for SNAPP. For this purpose, we utilized the python script unlinked_snps_to_nexus. py, available at https ://github. com/brunoasm/usnps_to_nexus (accessed 13 April 2022). After conversion, we faced a large number of loci (over 12 000), making it impractical to analyze them by the software in a reasonable time frame.
html, accessed 17 June 2022). Estimation of divergence times was performed using BEAST v. 2. 6. 6  (Bouckaert et al. , 2019)  with a GTR + Γ substitution model and the uncorrelated lognormal (UCLN) model for substitution rates across lineages. We used the optimized relaxed clock algorithm, implemented in the ORC package for BEAST 2, which more efficiently explores relaxed clock parameters compared with previous setups  (Douglas et al. , 2021). The rate of cladogenesis was assumed to follow a Yule process for which we specified gamma priors with shape parameter set to 0. 001 and scale set to 1 000. The priors for all other parameters were left at their default values. Two independent Markov chains were run, each for 10 000 000 generations and sampled every 1 000 generations. Prior to data analysis, initial runs were performed without data to generate so-called effective priors on calibration points used by the software.
Barcoding Consistent with problems regularly encountered with traditional molecular procedure to obtain the cyt-b gene from Gerbillus  (Bryja A. and Bryja J. , personal communication) , we successfully obtained only 14 sequences, representing 10 species of Gerbillus (including a putative new species) and one of Psammomys obesus (Table  1 ). We were unable to obtain the barcode for G. nancillus, as a pseudogene was amplified instead for each studied individual. After merging these sequences with those obtained from GenBank and filtering out short (<900 bp), poor quality and identical sequences, the final alignment contained 387 sequences representing 19 species of Gerbillus and two outgroups. Five analyzed species-G. nanus, G. henleyi, G. pyramidum, G. occiduus, G. gerbillus-clustered together with their own conspecifics in well-supported clades (99-100%) (Fig.
B. 1). Two individuals representing G. rupicola were also placed in a clade containing only conspecifics, albeit with lower bootstrap support (76%). The putative new species (Gerbillus sp1) did not cluster with any of the analyzed species. Instead, it grouped with other undetermined specimens collected from the same area  (Ndiaye et al. , 2012) , in a highly supported group (100%). Two individuals of G. amoenus were placed with other G. amoenus individuals from GenBank where they were intermingled with certain individuals of G. nanus. Because G. nanus formed a separate clade, the intermingled clade appears to be a historical artifact from the time when African sequences representing G. amoenus were described under the name of G. nanus. Currently, this name is restricted only to Asian individuals.
Assembly of RAD-seq data A total of 433 411 452 reads (R1 + R2) were generated by Illumina sequencing of the RAD-seq library. The percentage of bases with quality scores Q20 and Q30 in the dataset was 95. 85 and 92. 18, respectively. After filtering out low quality reads and those potentially containing Illumina adapters with Trimmomatic, 191 544 678 paired reads were retained, along with 13 107 415 and 5 324 807 unpaired for R1 and R2, respectively. For further analyses, we used only R1 paired and unpaired reads. After demultiplexing R1 reads with ipyrad, the number of raw reads for studied specimens ranged from 247 542 for Gerbillus nanus 759 to 8 409 730 for Psammomys obesus ZBSC0655 when no mismatch in the barcode site was allowed. When one mismatch was permitted, the range changed from 257 798 to 8 583 974.
In the first set of ipyrad de novo analyses (min_samples_locus = 4, max_Indels_locus = 8 and max_SNPs_locus = 0. 1), data generated under CT = 0. 87 and 0. 96 yielded the highest mean bootstrap support (avgBS = 95. 35%) and the largest number of SNPs (SNP = 1 160 209), respectively, when no mismatches were allowed in the barcode sequence. With one mismatch allowed, CT = 0. 95 and 0. 96 provided the highest scores for our criteria (avgBS = 95. 16% and SNP = 1 160 352; Table  3  and Fig. C. 1). In the second set of analyses under different min_samples_locus values (12, 20 and 32), we obtained identical threshold values for both criteria only in the case of min_samples_locus = 20, which was 0. 95 in both datasets (with and without mismatches in barcode sequences). For the remaining analyses, the best threshold.
The avgBS decreased along with the number of missing data comparably in both sets of analyses, reaching approximately 85% for min_samples_locus = 32. Similarly, number of SNPs dropped below 100 000 for analyses with the lowest number of missing data, irrespective of the applied settings to the barcode sequence. The median bootstrap values were in all cases close to or equal to 100%, indicating that a few poorly supported nodes strongly affect avgBS (Table  3 ). The ipyrad reference analyses, under max_SNPs_locus = 0. 1, resulted in 544 450 and 541 406 SNPs for datasets with and without mismatches in barcode sequences, respectively, and the resulted alignment matrices consisting of 67. 60% and 67. 42% missing data. Increasing the max_SNPs_locus parameter to 0. 6 led to a much higher number of SNPs, 1 874 648 and 1 864 829, with less missing data in final alignments, 52.
Phylogenetic analyses When considering only interspecific relationships, the concatenation analyses with RAxML and ExaBayes consistently resulted in one topology, regardless of the number of mismatches allowed in barcode sequence, the amount of missing data in the final alignments, and the type of assembly (de novo or reference-based) (Fig. 1 ). In the case of RAxML, all branches except for two were strongly supported (BS = 96-100%), while Exabayes assigned maximal posterior probability values (PP = 1. 0) to all branches. The two exceptional branches in ML analyses showed varying bootstrap support values, ranging from 1% to 100% for the first branch and from 80% to 100% for the second branch (Fig. 1 ). In the majority of cases (30/40 analyses), SVDquartets produced identical topology to the concatenation analyses, although some branches exhibited more variable bootstrap support values (Fig.
The QS scores provided strong support for focal branches and demonstrated a strong phylogenetic signal in all branches, except for two (Table  4  and Fig. E. 1). These two branches corresponded to the ones with varying bootstrap support in ML analyses. For the first branch (referred to as branch '1' in Figs. 1 2 ), the high QI values, ranging from 0. 90 to 1. 00, indicated a sufficient amount of information for all sampled quartets. However, despite the dependency of QC and QD scores on analyzed datasets, they showed some degree of support or a lack of support for branch '1' (QC = -0. 06-0. 83) and strong to moderate support for alternative evolutionary histories (QD = 0. 00-0. 59) (Table  4 ). The interpretation of QS scores for the second branch (referred to as branch '2' in Figs. 1 2 ) was more complex.
The QI score ranged from 0. 72 to 1. 00, indicating moderate information for sampled quartets in some cases. Furthermore, QC and QD scores showed mutually exclusive interpretations, with varying degrees of support for the focal and alternative branches (Table  4 ). For example, in some datasets, QS scores provided full support for branch '2' (QC = 1. 0, QD = -, QI = 1. 00), while in others, strong support was observed for alternative evolutionary histories (e. g. , QC = 0. 28, QD = 0. 00, QI = 0. 94) Likely, the low number of reads acquired for G. nanus 759 is responsible for these discordant results for branch '2', leading to the differing interpretations.
Concatenation and coalescence methods confirm the division of the genus Gerbillus into four subgenera but show alternative evolutionary histories for inter-subgeneric relationships Previous phylogenetic analyses of the genus Gerbillus did not account for the potential gene tree-species tree incongruence. This incongruence can arise from various factors, either biological (such as incomplete lineage sorting (ILS), introgression, and recombination) or technical Fig. 1. An exemplary maximum likelihood tree inferred from the analysis of sequences generated from RAD-seq data for Gerbillus. This topology, omitting intraspecific variation, was obtained for all ML and BI analyses of each dataset produced by ipyrad under various parameter settings. All branches, except two (branch '1' and '2' in the figure), were strongly supported (96-100%) by ML analyses and marked by stars.
The table presents the variation in bootstrap values (BS-1 and BS-2) for the two marked branches in reference and de novo analyses obtained under different values of min_sam-ples_locus (msl). Bootstrap values are shown for the best datasets, in terms of average bootstrap support and number of SNPs (separated by colon), generated in ipyrad under a range of clustering threshold values allowing max_barcode_mismatch to be 0 or 1 (separated by slash). For msl = 20 (marked by stars), the dataset obtained for the same clustering threshold value was considered the best by both bootstrap and SNP criteria. The major clades corresponding to the traditionally recognized subgenera are bracketed. The representatives of Psammomys obesus were treated as outgroup. The alternative history supported by QC score for branch '1' is also shown.
1  and 2 ). Similarly, the potential usage of paralogs does not pose a problem for the analyses for two reasons. First, considering that species from the genus have large genome sizes (haploid size ≈ 4 Gb according to the c-value database https://www. ge nomesize. com/ accessed 27. 10. 2022), which is associated with a large number of potentially paralogous regions, we tested the whole range of clustering thresholds values in ipyrad to find a balance between undersplitting (clustering of non-homologous regions) and oversplitting (splitting of homologous regions into several loci)  (McCartney-Melstad et al. , 2019). In all cases, the optimal clustering values chosen according to two criteria -the largest mean bootstrap value and the highest number of SNPs-produced concordant results (Figs. 1  and 2 ).
, 2003; Nylander et al. , 2004). Given the size and complexity of concatenated genomic sequences used in this study, this scenario is highly plausible. The above examples show that BI and PP scores can be misleading in certain situations. The QS method used in this study for concatenated datasets overcomes some of these issues. Despite variation in the QS scores across analyses (Table  4 ), we can infer the following: (1) branch '1' is poorly supported (QC values never reach 1. 00) or even countersupported in some cases (QC < 0), indicating weak consensus for a dominant relationship (Figs. 1-2), (2) there is moderate to high support for an alternative relationship for branch '1', as shown by QD score values (ranging from 0. 00 to 0. 59), and (3) the low support for branch '1' is not due to low phylogenetic information (QI score close to 1.
Acknowledgments This work was supported by the  National Science Center  grant no. 2015/18/E/NZ8/00716  to MP and the  Portuguese Foundation for Science and Technology  grant no. PTDC/BIA-ECO/28158/2017  and  National Geographic Society  grant no. NGS-53336R-19  projects to ZB. Voucher specimens (and their samples) from Mali, Morocco and Senegal collected by  L. Granjon  and his colleagues are deposited at the Small mammal Collection of the " Centre de Biologie pour la Gestion des Populations"  (https://doi. org/10. 15454/WWNUPO).
3″ N 2 • 11′16. 4″ W, 7 May 2014, leg. Boratyński et al. ZBSC0592 Gerbillus tarabuli (Thomas, 1902) Morocco, 32 • 15′26. 5″ N 2 • 14′26. 6″ W, 7 May 2014, leg. Boratyński et al. ZBSC0593, GenBank no: OQ230812 Psammomys obesus Cretzschmar, Morocco, 27 • 54′03. 5″ N 11 • 36′18. 4″ W, 15 1828 February 2015, leg. Boratyński et al. ZBSC0655 Psammomys obesus Cretzschmar, Morocco, 26 1828 • 40′27. 8″ N 12 • 56′52. 3″ W, 21 February 2015, leg. Boratyński et al. ZBSC0671 Psammomys obesus Cretzschmar, 1828 Morocco, 28 • 00′02. 7″N 12 • 13′09. 6″W, 13 August 2015, leg. Boratyński et al. ZBSC0711, GenBank no: OQ230813.
ast-of-bacteria/warnings). Among tested isolates, 774 were CPE, including 243 NDM producers. The MIC 50 (MIC to inhibit growth of 50% of isolates) of cefiderocol was higher (2 mg/L) for NDM producers among isolates tested compared with other carbapenem-resistant E. coli (0. 12 mg/L) (Figure  1 , panel B), as previously reported  (12). To genomically characterize E. coli ST361, we added all (n = 51) ST361 isolates sent to F-NRC during 2015-2021 to the 29 isolates collected during the study period. We conducted short-read sequencing on those 80 isolates by using the NextSeq500 system (Illumina, https://www. illumina. com). We assembled sequences by using Shovill 1. 1. 0 (https://github. com/tseemann/shovill) and SPAdes 3. 14. 0 (https:// github. com/ablab/spades) under GenBank BioProject no.
PRJNA925451 (Appendix 2 Table  1 ). We used Resfinder 4. 1  (13)  to analyze resistome content and PlasmidFinder 2. 1  (14)  to analyze replicon content (Appendix 2 Table  2 ). Among 80 E. coli ST361 isolates, 50 produced NDM carbapenemase, 49 of which were NDM-5; another 20 produced oxacillinase 48-like carbapenemase; 6 coproduced NDM-5 with another carbapenemase; and 4 did not produce carbapenemase (Figure  2 ). Analysis of cefiderocol MIC distribution for ST361 showed that isolates with MICs >2 produced NDM, but that analysis also suggested that mechanisms besides NDM are involved in cefiderocol resistance (Figure  1 , panel C). Thus, we analyzed the bla NDM gene copy number on CLC Genomics Workbench 21. 0 (QIAGEN, https://www. qiagen. com), where we mapped the raw data (fastq reads) on the genome (fasta) of each corresponding E.
coli sequence. Then we normalized the average coverage of bla NDM mapping reads to the average coverage of 10 different chromosomal genes used as references. However, correlation analysis did not reveal an association between bla NDM gene number and the cefiderocol MIC (data not shown). Then we used E. coli K-12 MG1655 (GenBank accession no. NC_000913) as a reference to investigate cirA, fiu, fepA, fepB, fecA, fhuA, tonB, pcnB, exbB, exbD, baeS/baeR, and ompR/ envZ gene mutations involved in siderophore-iron uptake. To eliminate polymorphisms linked to the ST itself, we only considered amino acid substitutions not shared by all ST361 isolates. A total of 14 (18%) isolates displayed a mutation in 1 of those genes (Appendix 2 Table  1 , https://wwwnc. cdc. gov/EID/ article/29/9/23-0390-App2.
coli ST361 and reduced cefiderocol susceptibility, France. Isolates were sent to the French National Reference Center for carbapenem-resistant Enterobacterales testing as part of routine surveillance. The phylogenetic tree was built by using SNIppy version 4. 6. 0 (https://github. com/tseemann/snippy) on whole-genome sequences. Data were visualized using iTOL 6. 5. 2 (https://itol. embl. de). The most ancient isolate, isolate no. 86J1 collected in 2015 (bold text, lower left of tree), was used as reference genome. A total of 4,957,882 nt positions were analyzed in the comparison. Colors indicate various outbreaks involving 13 OXA-244 producers (yellow) and 7 NDM-5 producers (blue). Two specific features are represented with filled circles: a YRIN(K) insertion in PBP3 (green) and chromosomal mutations within genes involved in siderophore-iron uptake (red).
Discovering an efficient new molecule can have a huge impact on the chemical research field. For several problems, the current knowledge is too scarce to train robust deep learning models. An exploratory approach can be a solution. However, when we consider several types of atoms, a phenomenal amount of combinations are possible even for small molecules. Many of these combinations contain very exotic associations. In addition to connectivity feature filtering (based on ECFP4), we introduce and stress the importance of a new filter based on cyclic features. In this article, we show that whitelists including all connectivity and cyclic features of either ChEMBL or ChEMBL and ZINC allow for the definition of large realistic chemical spaces. An enumeration dataset, Evo10, has been built with more than 600 000 molecules having 10 or fewer heavy atoms (C, N, O, F, and S).
Starting only from a methane molecule, we were able to navigate through the chemical space of those realistic molecules and rediscover all molecules passing these same filters from the reference datasets which are here ChEMBL, ZINC, QM9, PC9, GDB11, and GDBChEMBL. Unlike previously published scores, SAscores and CLscores, which are based on similarity averages on the most common chemical environments, the method proposed here excludes any molecule with an ECFP and cyclic feature that is absent from the lists. The visualisation of the proposed top solutions, that pass all the filters, for the optimisation of the QED or HOMO and LUMO energies, convinces us of the relevance of this approach for the systematic de novo generation of realistic solutions.
2. Thus, we provide here an approach based on whitelist ltering that can be easily adapted to the specic chemistry of certain applications and allow for some explainability. In this paper, we propose to dene realistic chemical spaces of molecules that are constituted of only chemical environments, up to 2 neighbours, of ChEMBL25 and ZINC20. [24] [25] [26]  Prohibiting the creation of new chemical environments at any moment of the optimisation means that the whole areas of chemical combinations are forbidden. Nevertheless, we show that limiting the generator to molecules with only known chemical environments still allows for very large search spaces. We will compare here a whitelist extracted from the whole ChEMBL25 and one including the ChEMBL25 and ZINC20. In addition, we propose a renement method based on a lter on the cyclic features present in those datasets.
This new descriptor lls a lack of information on this subject in the extendedconnectivity ngerprints (ECFP). The importance of the cyclic features is demonstrated in the rst objective focused on the optimisation of the QED. Since it is legitimate to ask whether it is still possible to navigate between the different possible molecules with an atom-centred evolutionary algorithm under constraints, we will then present the results of rediscovery objectives starting only from methane. The chemical diversity generated using an enumeration objective will be assessed and compared to reference datasets. The last objective is associated with a goal-directed generation of molecular electronic properties. The connectivity feature ltering has been implemented in our molecular generator called EvoMol.
21  It is open source and freely available. Furthermore, the lists of connectivity and cycle features used to dene the realistic chemical spaces are also available to be used in any other molecular generators. We believe that it is not only evolutionary algorithms that can benet from this ltering. In addition, a JSON le is provided. It describes the generated dataset named Evo10 that includes 676 875 realistic molecules with up to ten heavy atoms (C, N, O, F, S) with their SMILES and their connectivity lter scores. Similar JSON les of the molecules in reference datasets are also available to ensure the reproducibility of the results presented in the article.
Data To dene the ECFP whitelists, we used two datasets. One corresponds to the substances of ChEMBL25 as downloaded in September 2019 with 1 817 766 unique molecules. It is a large curated database of bioactive molecules. The second one is based on ZINC. ZINC is larger than ChEMBL and is based on  commercially available compounds and is not restricted to bioactive molecules. It encompasses in proportion more inorganic and organometallic compounds than ChEMBL. We have used the already prepared version ZINC20-ML by Artem Cherkasov and Francesco Gentile with all the 1 006 651 037 ZINC20 molecules as of early March 2021. ZINC20-ML is available at https://les. docking. org/zinc20-ML/. In order to compare the various chemical spaces, several other published datasets were also employed. We have built a standardization procedure so that we may compare the chemical spaces with the one produced by the EvoMol program, see specications below.
Aer converting the SMILES to the RDKit molecular graph object, the stereochemical information is removed. Then, only non-radical neutral compounds were retained whose SMILES writing does not contain formal charges. These last criteria remove only 63 498 molecules from the ChEMBL dataset. With this procedure, tautomers should have different SMILES/graphs and correspond to different molecules. Because we needed the cyclic features that are associated with the neutral compounds, the whitelists of cyclic features were built on these subsets of ChEMBL and ZINC. The nal lter will be the list of allowed chemical elements and the heavy atom count (HAC, i. e. Z > 1) limit, depending on the objective. The total number of molecules in these subsets is detailed in Table  1. The selected datasets are QM9 (ref.
27) and PC9 (ref. 28) as dened in the OD9 publication. 29  These two sets contain molecules ranging from HAC 1 up to 9 that can be either carbon, nitrogen, oxygen, or uorine. QM9 is a subset of molecules of the GDB enumerations. 30  QM9 was built from a constrained combinatorial approach while PC9 is a subset taken from the PubChemQC dataset with the same number of heavy atom limits and chemical element constraints. 28, 31  Once ltered the union of these two sets contains 190 300 different molecules. Two other combinatorial reference datasets, GDBChEMBL and GDB11, have been also ltered in the same way. The GDB datasets can be downloaded at https:// gdb. unibe. ch/downloads/. The total number of molecules in these ltered subsets is detailed in Table  1.
Whitelist denitions The denition of whitelist ltering based on connectivity has been inspired by the silly walks program of Patrick Walters. 32  The idea is to be able to highlight the chemical environments that are unknown in a reference dataset. To list the connectivity features for each molecular graph, we have used the GetMor-ganFingerprint function of the RDKit program. 33  Considering a medium size radius, i. e. up to 2 bonds for the extendedconnectivity ngerprints ECFP4 one could expect that if such chemical environments have never been described, it could be associated with a synthetic challenge. 34  The ECFP4 of each molecular graph is composed of all the connectivity features centred on each atom up to 2 bonds. All the connectivity features found in the reference datasets form the allowed whitelist.