dataesr/scientific-paragraphs-categorization huggingface dataset:
Preprocessing on MZmine 2 software was performed by setting the parameters as follows: range time of interest was set at 2-20 min with this part of the chromatograms being the richest in the focused compounds. Mass detection was performed by fixing the noise level at 2000 for MS1 and 50 for MS2. ADAP Chromatogram Builder was employed to build chromatograms by setting the parameters as follows: minimum group size of three scans, group intensity = 50 and minimum intensity = 50. The m/z tolerance was fixed at 0 or 50 ppm. Wavelet (ADAP) method was used for deconvolution by fixing a minimum feature height at 200 and a peak duration range at 0-5 min. The deconvolution was performed by fixing a value of 0. 1 Da as m/z range for MS2 scan pairing and 0. 5 min as RT range for MS2 scan pairing. Isotopic peak grouper was then performed with an m/z tolerance of 50 ppm, an RT tolerance of 0.
Caffeine, Mangiferin and CQAs Concentration Mass Hunter Quantitative Analysis software version B. 10 (Agilent Technologies) was used to determine the concentration of caffeine, mangiferin and CQA isomers in the extracts. Caffeine concentration was determined by interpolation of caffeine calibration curve (y = -0. 01141x 2 + 606. 5x + 826739. 8; R 2 = 0. 9983) drawn from 0. 001 to 200 µg/mL using a caffeine standard. Mangiferin concentration was determined by referring to a calibration curve (y = -0. 001053x 2 + 217. 1x + 1014728. 6; R 2 = 0. 9930) drawn from 0. 01 to 100 µg/mL. CQAs concentration was determined by referring to a calibration curve (y = -0. 000469257x 2 + 127. 9x + 9847. 5; R 2 = 0. 9901) drawn from 0. 001 to 300 µg/mL. The quantitation limit corresponded to the lowest concentration of each calibration curve.
0389 (M-C 3 H 6 O 3 -CH 2 O-CH 2 O) can be progressively formed from the ion with m/z 303. 0497 after the loss of a CH 2 O fragment [29]. The node with m/z 437. 1068 (RT = 11. 10 min; δ = 2. 38 ppm) was linked to the node of mangiferin with a CS of 0. 97, suggesting that this M + H + ion could be attributed to homomangiferin; moreover, its m/z value was associated to that of the standard compound of the MetGem database with a CS = 0. 74. The fragments with m/z = 317. 0631 and 287. 0525 corresponding to the fragments at m/z = 303. 0497 and 273. 0389, previously found for mangiferin, were detected. The only difference was the presence of a methoxy on C5 of the xanthone unit to the previous fragments, which is missing in the chemical structure of mangiferin conferring a delta mass of 14 m/z (+CH 2 ).
In this study, a three-dimensional individual-based model (IbM) of biofilm was developed by coupling the computational fluid dynamics approach (CFD) with the discrete element method (DEM). This model was implemented on NUFEB (https://github. com/nufeb) which is an opensource tool for individual-based modelling of microbial communities  (Li et al. , 2019). NUFEB integrates CFD-DEM solver SediFoam (https://github. com/xiaoh/sediFoam) which provides a flexible interface between LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator)  (Plimpton, 1993)  and OpenFOAM (Open-source Field Operation and Manipulation)  (Greenshields, 2017). The framework enables us to describe the fluid induced biofilm deformation and detachment subjected to different flow velocities. In this work, we model a bacterial mutant that can produce the same type of EPS at different levels.
Temporal values In addition to the available dataset described above, social and biophysical values were reconstructed for each social and biophysical indicator at different times steps for each country based on the same sources  (Krausmann et al. 2013 , Lenzen et al. 2013 , Helliwell et al. 2018 , World Bank 2015). Because not all required data were available together for the same time period, with some years missing, even in the last two decades, the accessible information was averaged to obtain social and biophysical scores at three time steps  (2005, 2010, 2015)   Because of data limitations, this could not be done for all 151 countries but for 135 countries. This step allows tracking temporal changes in development pathways for the different countries.
The genuine progress indicator (GPI) was not used because it was only available for 17 countries and not for all time periods considered  (Kubiszewski et al. 2013). Finally, the relationship between SEI and population density was analyzed. UN temporal data GDP per capita are based on purchasing power parity (PPP) in constant 2017 international dollars (Int$; UN 2020a). World GDP was taken from the World Bank (2015), population density (from population data and country surface [World Bank 2015, UN 2020b]) and SDI (available at https://www. sustainabledevelopmentindex. org/) were used to calculate the averaged value for each time step  (2005, 2010, and 2015)  and for each country as described for social and biophysical scores. Two separate analyses were conducted. First, the spatial pattern, i.
Exploratory continent-wide phylogenetic analyses with ORF supercontigs (>515,000 parsimony informative sites) resulted in a fully resolved phylogeny, the backbone of which was also retrieved with UCEs (>11,000 informative sites). Variant calling on ORFs and UCEs of Coelaturini from the Malawi Basin produced ~2000 SNPs per population pair. Estimates of nucleotide diversity and population differentiation were similar for ORFs and UCEs. They were low compared to previous estimates in molluscs, but comparable to those in recently diversifying Malawi cichlids and other taxa at an early stage of speciation. Skimming off-target sequence data from the same enriched libraries of Coelaturini from the Malawi Basin, we reconstructed the maternally-inherited mitogenome, which displays the gene order inferred for the most recent common ancestor of Unionidae.
, 2017; Pfeiffer et al. , 2019; Sun et al. , 2019; Teasdale et al. , 2016). Such ancient divergences imply that orthologue assessments for the reference may differ substantially from those for ingroup taxa. Here, we relax the need of well-assembled reference genomes by assessing orthology from existing genomic databases and representative ingroup transcriptomes. Additionally, by focusing on entire ORFs as functional biological units, instead of individual exons, we do not require to establish intron-exon boundaries prior to target enrichment. Whereas our proposed strategy enhances versatility, various issues could complicate target enrichment of entire ORFs, notably their subdivision in multiple exons. If exons are regularly shorter than the probe length, many probes will be tiled over exon boundaries within ORFs, which could drastically reduce the enrichment efficiency in genomic libraries.
We present a new approach to select orthologous single-copy genes from ingroup transcriptome assemblies, partly based on manual data curation (see  Teasdale et al. , 2016)  and a strategy to successfully enrich their entire ORFs in genomic libraries. Additionally, we developed (to our knowledge) the first set of UCEs for both bivalves and gastropods (but see Moles & Giribet, 2021 for a gastropod-specific UCE set). We evaluate the performance of target enrichment for these heterogenous targets, and analyse the obtained data sets to illustrate their value for phylogenetics and population genetics. Finally, we built a custom, versatile pipeline to skim the raw sequencing data and to evaluate the possibility of recuperating off-target mitochondrial sequences, on which previous Sanger-sequencing studies of Unionidae (Lopes-Lima,  Froufe, et al.
| Transcriptome assembly After transcriptome sequencing we removed adaptor sequences and low-quality reads in multiple steps using TrimGalore! version 0. 6. 6  (Krueger, 2019) , while iteratively examining the quantity and quality of reads with FasTQC version 0. 11. 8  (Andrews, 2010). The cleaned reads were subjected to de novo transcriptome assembly with aGalma version 2. 0. 0  (Dunn et al. , 2013) , which includes several filtering procedures and ribosomal RNA removal followed by assembly with TriniTy version 2. 8. 5  (Grabherr et al. , 2011). We screened against the NCBI UniVeC database to identify vector contaminants and rRNA transcripts. Subsequently, we performed F I G U R E 1 Schematic representation of our workflow indicating the main steps of the analytical pipeline. Our four main phases are represented in different colours; that is, transcriptomic steps for the selection of open reading frames (ORFs, blue), comparative genomics for the selection of ultraconserved elements (UCEs, dark purple), probe design and the sequencing of genomic libraries (light purple), analyses after the processing of sequencing data (red).
We evaluated orthology in two steps. First, we mapped our ORF clusters against the BUSCO meTazoa_oDb9 database  (Waterhouse et al. , 2017)  and against the UnioVerse probe set  (Pfeiffer et al. , 2019)  to identify targets that have high chances of being single-copy and orthologous (=candidate genes). This approach does not allow to detect unexpressed homologues or weakly divergent paralogues, so additional follow-up verifications are performed during probe design (see below). We extracted all complete BUSCO hits, which consist of single-and multicopy hits. For ORFs with single-copy BUSCO hits the likelihood of multiple gene copies is small, given the gene is included in the single-copy orthologous database of BUSCO, and given the lack of expression variants with <95% similarity after clustering 12 ingroup transcriptomes.
If different splicing or minor difficulties in assembly were suspected, the longest ORF was retained. A similar approach was used to evaluate the 811 loci from the UnioVerse probe set, which was developed for anchored hybrid enrichment using the distant Bathymodiolus platifrons genome  (Pfeiffer et al. , 2019). This probe set contains 173,707 nucleotides, on average 214 per locus. To avoid overlap with targets that were already retained from the abovementioned BUSCO comparisons, we first screened the UnioVerse loci against the BUSCO meTazoa_oDb9 database and against our already retained ORFs with yass version 1. 15  (Noé & Kucherov, 2005). In total 109 UnioVerse loci were accounted for by these verifications, leaving 702 loci to be examined. Our remaining ORF clusters produced hits on all 702 remaining UnioVerse loci, and all hits for the same UnioVerse locus were compiled and aligned.
| Enrichment balance between ORFs and UCEs To verify the enrichment balance between ORFs and UCEs, we examined the proportion of reads that mapped on either type of target. This task has been performed starting from the reads that were identified with Hybpiper and pHylUCe as mapping to ORF or UCE targets, respectively, which also provides information on the specificity of our enrichment strategy. More detailed information is obtained by extracting kmers of 18 to 21 bases from various. fasta files with JellyFisH version 2. 2. 10 (  Marcais & Kingsford, 2011) , that is, first from. fasta files that contain the ORF and UCE target sequences, and then for a sample of specimens in our data set. Each kmer that occurs in both the specimen and ORF dictionary is considered an ORF hit, whereas each kmer that occurs in the specimen and UCE dictionary is considered an UCE hit.
05%, indicating a substantial underrepresentation of UCEs compared to ORFs. On average over 1102 of the 1114 ORFs were consistently enriched and mapped for all 95 unionids (857 are consistently recovered for over 50% of their length in all specimens), with the exception of the distant iridinid specimen (dna0240; see Figure  2 ). Hybpiper detected hidden paralogy in at most 2 ORFs per specimen. As the number of reads obtained for a sample decreases, we see a gradual decrease in the recovery of ORFs, which becomes more marked for samples with <500,000 reads (n = 12). As to UCEs, we recovered data for up to 1905 out of the 4104 UCEs (46. 5%), and the coverage per sample was proportional with the number of reads (linear model: r 2 =. 557, p <. 001), as was observed for the ORFs (Figure  2 ). The combination of 55% and 60% thresholds on sequence coverage and identity, respectively, maximized the total yield over all specimens, but it decreased the number of retrieved UCEs slightly to 1895.
| Macroevolutionary analyses All 95 Coelaturini specimens were included in our phylogenetic analyses. Selection of loci with >50% taxon-completeness resulted in a data set that contained 1109 ORF supercontigs with a mean and total alignment length of 2118 and 2,348,614 bp, respectively. For UCEs, the cleaned and trimmed data set contained 276 loci with a mean and total alignment length of 432 and 119,105 bp, respectively. For these, 515,219 (21. 9%) and 11,001 (9. 24%) sites, respectively, were phylogenetically informative. Additional data on specimen representation, alignment length, and the proportion of informative and missing sites for each ORF and UCE locus are given in  Ortiz-Sepulveda et al. (2022). The partition analysis resulted in a 402-partition scheme with 45 unique substitution models for ORFs, and a 155-partition scheme and 51 unique substitution models for UCEs  (Ortiz-Sepulveda et al.
| Microevolutionary analyses Estimates of nucleotide diversity, pairwise population sequence divergence and F ST -values were similar for ORF and UCE data sets, suggesting general robustness. Synonymous nucleotide diversity was overall low for the "Malawi" clade (average π S = 0. 00271) compared to species-level estimates from transcriptomic data across molluscs (π S = 0. 02878), or Metazoa altogether (π S = 0. 01912; see  Romiguier et al. , 2014). Our estimates of π N /π S were high compared to those of other Metazoa but similar to those of certain Darwin finches and Echinodermata (see Figure  S6 ;  Leroy et al. , 2021; Romiguier et al. , 2014). This low diversity may relate to the brooding ability and parasitic life-cycle of Coelatura species, but also to Late Pleistocene ecological crises in the Malawi Basin, which may have caused population bottlenecks in the aquatic fauna  (Cohen et al.
| Advantages and disadvantages of our workflow The here presented workflow provides good perspectives to enrich entire ORFs and intronic/intergenic flanking regions without prior knowledge of exon-intron boundaries, thus in the absence of a proximate well-assembled and annotated reference genome. Combining the selection of candidate ORFs using existing databases with orthology assessment from ingroup transcriptomes provides a useful approach for non-model organisms, that moreover allows leveraging museum specimens as long as some fresh samples across the ingroup are available. Our verification includes target alignment and manual verification, as recommended previously  (Teasdale et al. , 2016) , which provides empirical scientists a trackable connection to their high-throughput sequencing data.
yond our current scope. First, we aligned reads on the mitochondrial genome of Pygandon grandis (NCBI Genbank: NC_013661; Breton et al. , 2009)  with bowTie2 version 2. 3. 5 (Langmead & Salzberg, 2012)  to produce a. bam file, which was sorted and indexed with functions of samTools. Subsequently, the files for all individuals were merged with samTools and visualized with IGV. This merged. bam file was converted into. fastq files after which the reads were assembled with spaDes. The resulting assembly was aligned to the Pyganodon grandis mitogenome and visually inspected to retain unambiguous contigs. The retained contigs were used to recover reads for 48 Coelaturini specimens from the Malawi Basin using again bowTie2, samTools and spaDes, after which individual assemblies were reassembled with TriniTy.
The total assembly was evaluated and the procedure was iterated each time with laterally extended starting contigs. Annotations were performed with MITOS (Bernt et al. , 2013). 2. 10 | Macroevolutionary analyses2. 10. 1 | Alignment of UCE and ORF dataA single transcriptome was available in NCBI Genbank for the subfamily Parreysiinae at the start of these analyses, namely that of Scabies phaselus (Lea, 1856) (SRX5281799; Pfeiffer et al. , 2019) , which was hence used as outgroup for phylogenetics. We used Hybpiper as described above to recover information on our ORF targets for this outgroup. Phylogenetic analysis of the ORFs was performed using the supercontigs reconstructed for ingroup taxa with Hybpiper. These supercontigs contain exons and intronic/ intergenic flanking regions; alignments were produced with MAFFT.
We used 1000 bootstrap replicates; branch support values were calculated with a Shimodaira-Hasegawa approximate likelihood ratio test (SH-aLRT; Guindon et al. , 2010)  and ultrafast Bootstrap (UFBoot2; Hoang et al. , 2018). 2. 11 | Microevolutionary analyses2. 11. 1 | Variant callingAssessments of population genetic diversity and population structure are based on ORF targets without intronic/intergenic flanking regions, and UCEs with flanking regions. Variant calling was performed by alignment of the raw reads against custom-built consensus sequences for ORFs and UCEs with their respective flanking regions using bowTie2 on a sample-by-sample basis, after which. sam files were converted to. bam using samTools, modified with piCarD version 2. 21. 4 (available at http://broad insti tute. github.
Interestingly, the Shire River cluster, although being geographically in the far south, clusters with the north in the K = 3 scenario. Target selection and capture Our screening illustrates the robustness of identifying ORFs that are probably single-copy and orthologous from existing databases followed by paralogue detection based on ingroup transcriptomes. Our enrichment strategy performed well and was not affected much by the unknown intron-exon boundaries upon probe design. This result opens opportunities to use ORF predictions from transcriptome assemblies in other Metazoa as direct targets for probe design, provided orthology is verified. Therewith, our strategy simplifies the development of genomic data sets significantly, especially for non-model organisms. If many short exons are expected, the use of shorter probes, for example, of 80 nt, or covering targets more densely with probes, for example, at 3× or 4×, may further enhance the capture efficiency.
Our study was substantially improved following constructive criticism by  R. Nicolas Lou ,  John Pfeiffer , three anonymous reviewers and the subject editor,  Andrew DeWoody. This study was funded by  ANR-JCJC-EVOLINK  (to BVB),  FEDER-ERC-EVORAD  (to BVB) and it contributes to the CPER research project CLIMIBIO (XV). We thank the  French Ministère de l'Enseignement Supérieur et de la Recherche , the.
Funding information Grant/ Award Number :  ANR-JCJC-EVOLINK ,  ANR-17-CE02-0015 ,  ANR-10-EQ PX-07-01  and  ANR-10-LABX-46 ;  FEDER-ERC-EVORAD ;  French Ministère de l'Enseignement Supérieur et de la Recherche , Grant/Award Number: CPER_ CLIMIBIO;  European Fund for Regional Development (FEDER) , Grant/Award Number:  FEDER-ERC-EVORAD and CPER_CLIMIBIO; Hauts-de-France (HdF).
DATA AVA I L A B I L I T Y S TAT E M E N T The generated genomic data have been made available via the SRA of NCBI GenBank under BioProject PRJNA893605, including 108 BioSamples. BioSamples SAMN31437443-SAMN31437454 contain raw sequencing reads from RNA-seq, whereas raw sequencing reads from target enrichment have been made available under SAMN31439307-SAMN31439402. Additional data and tables have been deposited on Dryad (see Ortiz-Sepulveda et al. (2022); https://datad ryad. org/stash/ datas et/doi:10. 5061/dryad. 4j0zp c8gc). Bioinformatic scripts are available via www. github. com/bertv anboc xlaer/ target_enric hment_ORF_UCE.
14, 15 In the Simons Foundation Autism Research Initiative (SFARI) database, 7/82 RhoGEFs (9%), 7/57 RhoGAPs (12%), and 6/73 downstream effectors (8%) are categorized as autism spectrum disorders (ASD) candidate genes. 14 At least 1. 6% of all 1231 listed ASD candidate genes are therefore directly involved in the signalling pathways of Rho family GTPases. More specifically, the link between the abnormal function of Rho GTPases per se and NDDs has been recently highlighted as follows. As for the Rac subfamily, de novo missense RAC1 variants affecting protein function have been associated with heterogeneous conditions characterized by variable psychomotor delays and brain malformations,  16  while variations in RAC2 have been identified in patients with different forms of primary immunodeficiencies.
Patient enrolment and ascertainment Patients were recruited through international collaboration, also using GeneMatcher,  21  from several clinical and research centres in Canada, France, Germany, Italy, Spain and the USA (further details available in the Supplementary material). Patients were evaluated by paediatricians with expertise in neurological disorders, paediatric neurologists and geneticists with expertise in the field of neurogenetics. Vineland Adaptive Behavior Scale (VABS, third edition) tests were also administered for adaptive behaviour assessment to Patients 4, 5 and 8. Clinical and molecular data of Patient 6, who was partially described in a previous report,  20  were critically reviewed. Brain MRI scans, locally performed for patient care, were reviewed by an expert paediatric neuroradiologist (M.
Previously reported cases assessment All the articles indexed in PubMed (https://pubmed. ncbi. nlm. nih. gov/?term=RAC3&sort=date) between April 2019, when RAC3 variants were first associated with NEDBAF by Costain et al. ,  18  and May 2021  22  were retrieved using the terms 'RAC3', 'Rac3 GTPase' and 'neurodevelopmental syndrome'. All the articles concerning the molecular, clinical, and neuroradiological spectrum associated with NEDBAF were thoroughly reviewed. Inclusion criteria for previously published patients included clinical data availability, unambiguous identification of pathogenic/likely pathogenic RAC3 variants and lack of duplication from other previous reports. Ambiguous clinical presentation not consistent with NEDBAF and inconclusive genetic testing were exclusion criteria.
Next generation sequencing We investigated 10 individuals presenting with syndromic NDD and neuroimaging abnormalities. Genomic DNA was extracted from peripheral blood leucocytes using standard protocols (Supplementary material). Different next generation sequencing (NGS) approaches were employed, including trio-exome sequencing (Trio-ES) (Patients 1-3, 8 and 10), singleton exome sequencing (Patients 4, 6 and 9), trio-genome sequencing and RNA sequencing (Patient 5) and a NGS panel including 480 intellectual disability genes (Patient 7). Sanger sequencing was performed for the validation of the candidate variants and the parental segregation analysis. The presence of copy number variants was investigated in all subjects, either through array comparative genomic hybridization (aCGH) (Patients 1, 2, 4, 5, 6, 7, 8, 9 and 10) or CNV detection on exome sequencing data (Patient 3) (Supplementary material).
Variant identification and analysis The identified variants were filtered according to minor allele frequency ≤0. 001 in Genome Aggregation Database (gnomAD, https:// gnomad. broadinstitute. org),  23 conservation (Genomic Evolutionary Rate Profiling-GERP, http://mendel. stanford. edu/SidowLab/downloads/ gerp/) and predicted impact on protein function. In silico prediction tools were used for the interpretation of candidate variants, including combined annotation dependent depletion (CADD, https://cadd. gs. washington. edu), Mutation Taster (http://www. mutationtaster. org), Sorting Intolerant From Tolerant (SIFT, https://sift. bii. a-star. edu. sg) and Polyphen-2 (http://genetics. bwh. harvard. edu/pph2/). Ultimately, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP) guidelines were used to classify the candidate variants based on their predicted pathogenicity.