dataesr/scientific-paragraphs-categorization huggingface dataset:
IBM Corporation, United States). We tested the data distribution using the Kolmogorov-Smirnov test. Qualitative variables were summarized in numbers (percentage), and quantitative variables by median (interquartile range [IQR]) for non-normally or mean ± standard deviation for normally distributed data. Outliers were assessed for all variables and analyzed in their national and geographical context. The differences in qualitative variables between two European regions and according to gender distribution of the head of the laboratories were tested with the chi-squared test. Differences in quantitative variables were tested with the parametric independent samples t-test or non-parametric Mann-Whitney test, depending on the data distribution. Two-tailed p values <0. 05 were taken to indicate statistical significance.
14681331, 2022, 12, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/ene. 15538by Area Sistemi Dipart & Document, Wiley Online Library on [24/01/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons LicenseIn conclusion, our data provide a good basis for understanding the challenges of clinical autonomic practice in Europe and for planning concerted actions for better disease management and collaborative research. It also highlights significant differences in the availability of healthcare services for people with ANS disorders across European countries, stressing the need for improved access to diagnostic and treatment facilities across Europe, and thereby investment in health service improvement and spending by national governments.
( IBM Corporation, United States). We tested the data distribution using the Kolmogorov-Smirnov test. Qualitative variables were summarized in numbers (percentage), and quantitative variables by median (interquartile range [IQR]) for non-normally or mean ± standard deviation for normally distributed data. Outliers were assessed for all variables and analyzed in their national and geographical context. The differences in qualitative variables between two European regions and according to gender distribution of the head of the laboratories were tested with the chi-squared test. Differences in quantitative variables were tested with the parametric independent samples t-test or non-parametric Mann-Whitney test, depending on the data distribution. Two-tailed p values <0. 05 were taken to indicate statistical significance.
60-70 species (World Checklist of Dalbergia, B. Klitgaard pers. comm. 2021; POWO 2021) , although it can be assumed that species diversity is underestimated in these areas as well. The taxonomic inadequacies and uncertainties regarding Malagasy Dalbergia can in part be attributed to the previous lack of phylogenetic information, but they can also be explained by a shortage of high-quality, fertile collections documenting the full range of morphological and eco-geographic diversity found in the genus. A total of ca. 1400 databased collections were available prior to 2006, of which ca. 1000 were examined and identified by Bosser and Rabevohitra, while ca. 400 collections were either left undetermined because they were sterile or in poor condition, or had not been seen by them (Madagascar Catalogue 2021).
Measurements of continuous characters were made on digital images of leaves or dissected flower organs using ImageJ  (Schneider et al. 2012 ) version 1. 53a. Ecological Characteristics-We downloaded specimen records from the Catalogue of the Plants of Madagascar (Madagascar Catalogue 2021) including all 424 currently known collections belonging to the entities for which morphological measurements were made (Supplementary Material 1, deposited in Dryad,  Crameri et al. 2022). From these, we selected 330 records for collections made since 2000 that contain precise geo-coordinates obtained using a GPS device or derived post facto from precise locality data. This was done to minimize the risk of inaccurate associations between occurrences and vegetation class due to imprecise georeferencing or as a result of deforestation that has occurred in the past decades (see  Vieilledent et al.
2018a). We assessed 17 potentially relevant ecological characteristics from available spatial raster or vector data for Madagascar (Table  2 ). We obtained land surface boundaries from the GADM database available through the raster package (Hijmans and van Etten 2012) version 3. 4. 5, surface lithology classes from the SERVIR database available at http:// geoportal. rcmrd. org/data/africa_surface_lithology. zip, vegetation classes from  Moat and Smith (2007) , and bioclimate raster data from the CHELSA version 1. 2 Bioclim database  (Karger et al. 2017a (Karger et al. , 2017b)). We used R (R Core Team 2020) version 4. 0. 2 and the elevatr package (Hollister et al. 2020) version 0. 3. 1, the terra package (Hijmans 2021) version 1. 0. 10, and the fasterRaster package (Smith 2020) version 0. 6.
0 to download high-resolution elevation data (3 arc seconds, ca. 90 m resolution) and to perform raster calculations. All rasters were projected to Universal Transverse Mercator (UTM) zone 38S and re-sampled to the resolution of the highest-resolved raster (30 m) when needed. We then extracted the ecological characteristics of the 330 selected collections, resulting in an ecological dataset for multivariate analysis. Commented R code documenting the download and extraction of ecological characteristics from occurrence data is available in Supplementary Material 2, deposited in Dryad  (Crameri et al. 2022 ) and on GitHub (https://github. com/ scrameri/DalbergiaTaxonomy). We produced a combined distribution map based on 414 distinct occurrence records, supplemented by nine records of the undescribed SAVA material (Supplementary Material 1).
We drew alpha convex hulls around the occurrence points of each entity using the EOO. computing function in  SYSTEMATIC BOTANY 402  [Volume 47 the ConR package  (Dauby et al. 2017 ) version 1. 3. 0, with an entity-specific alpha parameter (1 for isolated occurrences of D. chapelieri s. l. , and 10 for the other entities) and buffer (ca. 3 km for the southeastern paniculate material and ca. 9 km for the other entities). The map was displayed using the tmap package (Tennekes 2018) version 3. 2, with the estimated forest cover for the year 2017  (Vieilledent et al. 2018b ) drawn at lower resolution (ca. 700 3 1400 pixels), and including the officially recognized terrestrial protected areas in Madagascar  (Goodman et al. 2018). Because these Dalbergia species are under threat from illegal exploitation, we have systematically refrained from making detailed distribution maps and precise geo-coordinates publicly available.
Specimen records are provided in the Catalogue of the Plants of Madagascar (Madagascar Catalogue 2021), but with restricted public access to precise geo-locations (delivered on demand to bona fide researchers). Multivariate Analyses-Analyses of morphological data were carried out separately for the leaf/leaflet and inflorescence/flower datasets, while ten collections with flowers and mature leaves were represented in both datasets. Two missing values in the leaf/leaflet dataset were assigned using class means. For each dataset we calculated a morphological distance matrix using the daisy function in the cluster package  (Maechler et al. 2019 ) version 2. 1. 0 with variable standardization, Gower's distance  (Gower 1971)  as the distance metric, a specification of ordinal variables (indument and leaflet margin) as ratio-scaled variables, and binary nominal variables (leaflet texture and inflorescence type) as asymmetric binary We subjected the scaled and centered matrix of ecological data to principal component analysis (PCA) and visualized the first two axes and the rotation matrix as a biplot as above.
Commented R code documenting the complete workflow for multivariate analyses and the distribution map are available in Supplementary Material 2, deposited in Dryad  (Crameri et al. 2022 ) and on GitHub (https://github. com/scrameri/DalbergiaTaxonomy). Species Concept and Delimitation-We follow a unified species concept  (De Queiroz 2005 , 2007)  and its generalized view of species as "separately evolving metapopulation lineages (or, more properly, segments thereof)," where lineage separation is "the only necessary property of species" (De Queiroz 2007). We regarded evidence for genetic (population) structure, as indicated by phylogenomic data and analyses  (Crameri 2020) , as a starting point for species discovery sensu  Carstens et al. (2013). We further considered the discovered units as putative species pending validation, and employed monophyly, genetic differentiation, morphological distinction, and eco-geographical separation or co-occurrence, along with a lack of evident admixture, as operational species criteria to assess lineage separation.
Using this interpretation, taxonomic species are hypotheses of evolutionary species, which do not necessarily have to: (i) be monophyletic, (ii) exceed a particular genetic differentiation threshold, (iii) form distinct morphological clusters, (iv) occupy different adaptive niches, or (v) be intrinsically reproductively isolated, although each of these properties provides a line of evidence for lineage separation  (De Queiroz 2007). The phylogenomic data collected for Malagasy Dalbergia provide a highly relevant line of evidence for lineage separation, because the number of assessed characters contained in this dataset (925,216 polymorphic loci distributed across 2396 nuclear regions; Crameri 2020) is much larger than the 24 morphological characters (Table  1 ) and the 17 eco-geographic characteristics (Table  2 ) available.
We further assumed that the number of statistically independent characters is largest in the phylogenomic dataset because the morphological and eco-geographic datasets likely include a higher fraction of potentially inter-dependent characters, such as variables coding indument on various surfaces, or elevation and mean annual temperature. Morphological divergence, notably in leaf, inflorescence, flower, and fruit characters, is another important line of evidence for lineage separation, as variation in these characters was attributed to species-level diversity in the 49 taxonomic species delimited by  Bosser and Rabevohitra (1996 , 2002 , 2005). Of these, at least 46 (94%) were confirmed as separately evolving lineages after incorporating phylogenomic data, even though these analyses suggested that the species diversity was underestimated and that many of the delimitations adopted by  Bosser and Rabevohitra (1996 , 2002 , 2005)  require revision  (Crameri 2020).
2021b ). We therefore applied the Red List criterion B and calculated the extent of occurrence (EOO) and area of occupancy (AOO) using the GeoCAT online tool  (Bachman et al. 2011). Continuing decline was informed by a forest cover time series for Madagascar  (Vieilledent et al. 2018a (Vieilledent et al. , 2018b ) and a dedicated application (current version available at https://github. com/ scrameri/ConservationAssessments), which estimates the reduction in forest cover within the minimum convex polygon encompassing known occurrences, and a user-specified elevational range.
RESULTS Morphological measurements (medians per individual) are presented in Supplementary Materials 3 (leaf/leaflet dataset) and 4 (inflorescence/flower dataset, all deposited in Dryad,  Crameri et al. 2022). The first principal coordinate of foliar (leaf and leaflet) characters (which accounts for 57. 15% of the total variation) was mainly associated with leaf indument (present in collections of D. louvelii s. l. and D. maritima var. pubescens, Table  3 ), as well as with coriaceous leaflets with revolute margins, which are typical of both varieties of east-central D. maritima, but also occur in D. chapelieri s. l. and in D. louvelii s. l. (Fig. 3A ). The second principal coordinate of foliar characters (which explained 25. 65% of the total variation) was mainly associated with leaf, petiole, and petiolule length, as well as with leaflet size, and separated collections with shorter leaves and smaller leaflets (i.
louvelii type, east-central D. maritima, and the southeastern racemose material [Fig. 5] ). The second principal coordinate of inflorescence and flower characters (which explained 23. 11% of the total variation) was mainly associated with flower size (smallest in east-central D. maritima, largest in the D. louvelii type; Table  3 ) and the ratio of flower length to calyx length (small in D. chapelieri s. l. and the southeastern paniculate material, large in east-central D. maritima; Fig. 3B ). The extracted ecological data are presented in Supplementary Material 5, deposited in Dryad  (Crameri et al. 2022). The first principal component of ecological characteristics (which explained 29. 17% of the total variation) separated collections from areas at higher elevation inland and on steeper slopes, where evergreen humid forests are located (D.
ACKNOWLEDGMENTS We are grateful to the botanists of the  Missouri Botanical Garden Madagascar Program  (notably to  Sandratra Andrianarivelo ,  Patrice Antilahimena ,  Roger Bernard ,  Chris Birkinshaw ,  Adolphe Lehavana ,  Jeannie Raharimampionona ,  Fortunat Rakotoarivony ,  Charles Rakotovao ,  Toky Ralainaorina ,  Richard Randrianaivo ,  Richardson Razakamalala , and  Anselme Tilahimena ),  Sonja Hassold ,  Michael Zehnder ,  Ravo Ramanantsialonina , and undergraduate and doctoral students of the  University of Antananarivo  for botanical inventory work, numerous field collections, photographs of living specimens, and fruitful discussions. We also thank the G3D team at the MBG office (notably  Sylvie Andriambololonera ,  Faranirina Lantoarisoa ,  Nadiah Manjato , and  Fano Rajaonary)  for the organization of field work and facilitating export permits and the exportation of herbarium vouchers.
We would also like to thank the cited herbaria and their staffs (notably Odile Poncy at P and  Alessia Guggisberg  at  ZT ) for specimen management and access  SYSTEMATIC BOTANY 414 [Volume 47 to collections, and for allowing minimal invasive sampling. This work was supported by  ETH Zurich  and a grant from the  R€ ubel Foundation  to AW. The participation of PBP, NW, and PPL was supported by a grant from the  Fondation Franklinia  to PPL. Field work and NR's participation were funded by the  D el egation de l'Union Europ eenne a Madagascar (DEUM). The funders had no role in study design, data collection and analysis, or preparation of the manuscript. We further thank  Luciano Paganucci de Queiroz ,  Daniel Potter ,  George Garnett , and an anonymous reviewer for helpful inputs and comments on an earlier version of the manuscript.
bioinformatics. babraham. ac. uk/projects/fastqc )  [4]. The trimming and filtering of raw data was performed using fastqp ( https://github. com/OpenGene/ fastp ). All reads containing more than 5% of unknown nucleotides, and low-quality reads were discarded. Short reads ( < 35 bp) were removed from the filtered data  [5]. Repetitive microsatellite sequences raging from dinucleotides to hexanucleotides were identified for E. fetida and E. andrei. Only SSRs with an overall repeat motif size from 2 to 7 bp and a length larger than 12 bp were considered adequate ( Figure  1  ). For E. fetida 1 709 140 million 2 × 250 bp reads were generated whereas for E. andrei 2 094 624 million 2 × 250 bp reads ( Table  1  , Table  2  ). The raw sequences are deposited in NCBI under the following accession numbers: for Eisenia andrei -BioProject PRJNA843202, for Eisenia fetida -BioProject PRJNA842580.
Specific primer pairs were designed from flanking sequences of di to hexanucleotides of E. fetida and E. andrei (supplementary data, tables 1 and 2 ). The final analysis and the design of the primers were carried out with QDD v3 software and Primer 3 software ( https://www. imbe. fr/ ∼ emeglecz/qdd ), ( https://primer3. ut. ee/ ). Overall, 19 758 pairs of primers were designed for E. fetida and 23 255 for E. andrei. After that, 3 777 primer pairs for E. fetida and 4 258 primer pairs.
Data accessibility The nucleotide sequences of raw reads, the designed primer pairs and the products of their amplification are presented in S1 and S2 tables. The primer sets are also deposited in Mendeley Data: https://data. mendeley. com/datasets/hs67kkzkr5/1 The raw sequences are deposited in NCBI under the following accession numbers: for Eisenia andrei -BioProject PRJNA843202, for Eisenia fetida -BioProject PRJNA842580, links to both Bioprojects are provided below: https://www. ncbi. nlm. nih. gov/bioproject/PRJNA843202 https://www. ncbi. nlm. nih. gov/bioproject/PRJNA842580 Related research article Jaskulak, M. , Rorat, A. , Vandenbulcke, F. , Pauwels, M. , Grzmil, P. , Plytycz, B. (2022). Polymorphic microsatellite markers demonstrate hybridization and interspecific gene flow between lumbricid earthworm species, Eisenia andrei and E.
722; CI: 0. 659-0. 784), regardless of the type of acute leukemia. Of importance, high expression levels of LAMP5 estimated the occurrence of all KMT2A-USP2 fusions. Also, we performed drug sensitivity analysis using IC50 data from 345 drugs available in the GDSC database to identify which ones could be used to treat KMT2A-r leukemia. We observed that KMT2A-r cell lines were more sensitive to 5-Fluorouracil (5FU), Gemcitabine (both antimetabolite chemotherapy drugs), WHI-P97 (JAK-3 inhibitor), Foretinib (MET/VEGFR inhibitor), SNX-2112 (Hsp90 inhibitor), AZD6482 (PI3Kβ inhibitor), KU-60019 (ATM kinase inhibitor), and Pevonedistat (NEDD8-activating enzyme (NAE) inhibitor). Moreover, IC50 data from analyses of ex-vivo drug sensitivity to small-molecule inhibitors reveals that Foretinib is a promising drug option for AML patients carrying FLT3 activating mutations.
Patient Cohorts The acute leukemia patients included in this study were selected based on known KMT2A status (KMT2A-r or KMT2A wild-type/ KMT2A-WT) and available gene expression data from six different datasets (Supplementary Table  S1 ). The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset (https://portal. gdc. cancer. gov/ projects; dbGAP accession number phs000218) is composed of four different cohorts with pediatric patients diagnosed with 1) B-cell precursor acute lymphoblastic leukemia (B-ALL), 2) T-cell ALL (T-ALL), 3) acute myeloid leukemia (AML), and 4) acute leukemia of ambiguous lineage (ALAL). The diagnosis of KMT2A-r in TARGET dataset was obtained from the respective clinical data available in the TARGET Data Matrix (https://ocg. cancer. gov/programs/target/data-matrix; see the Common Data Element (CDE) files for more details).
This identification was based on karyotype and FISH results for the B-ALL samples, FISH with confirmation by RNA-Seq  (Liu et al. , 2017)  for the T-ALL cohort, RNA-Seq, Whole Genome Sequencing (WGS) and/or karyotype for the AML patients, and karyotype for the ALAL cohort. The Beat AML programme consists of a large dataset of AML pediatric and adult patients (data viewer: http://www. vizome. org/aml/). As previously described, the diagnosis of KMT2A-r in this cohort was obtained by conventional karyotype and RNA-Seq. All clinical data are found in the Supplementary Material of the original paper  (Tyner et al. , 2018). The data from The European Genome-phenome Archive (EGA -https://ega-archive. org) study EGAS00001004212 (here called ALL-Ekert dataset) are from pediatric patients diagnosed with B-ALL and T-ALL.
For this cohort, the KMT2A status was retrieved from the original study, which used conventional karyotype/FISH and RNA-Seq techniques  (Brown et al. , 2020). We also included data from adult patients diagnosed with AML deposited in the EGA database under the study accession number EGAS00001003096 (here called AML-Griffioen dataset). The identification of KMT2A-r was obtained by RNA-Seq analysis and validated by RT-PCR. The results were also previously published in the original paper  (Arindrarto et al. , 2021). Another dataset evaluated in this work was composed of pediatric and adult AML samples from The Cancer Genome Atlas (TCGA) database (http://cancergenome. nih. gov/). To identify the patients with KMT2A-r, we used annotation of the structural variant from the "Acute Myeloid Leukemia" study in cBioPortal database (https://www.
cbioportal. org/), which was based on RNA-Seq analysis  (Gao et al. , 2018). The last cohort included B-ALL, T-ALL and ALAL samples from a dataset of pediatric patients submitted to mRNA sequencing as part of the diagnostic workup at the Robert Debré hospital (Assistance Publique des Hôpitaux de Paris, AP-HP) in France (here called French dataset). The KMT2A status classification was based on both conventional cytogenetics and RNA-seq data.
Gene Expression Data All gene expression data were obtained from paired-end RNA-Seq methodology (Supplementary Table  S1 ). For TARGET cohorts, we downloaded data corresponding to read counts (not normalized), when available, and FPKM (Fragments Per Kilobase Million) values of each gene annotated in the human reference genome GRCh37/hg19 (Ensembl release 59, Refseq and UCSC knownGene) from open-access files of these patients. The description of RNA-Seq data processing is available at the TARGET project portal (https://ocg. cancer. gov/programs/ target/target-methods). In the case of ALAL samples, we used the read counts to calculate FPKM values using the  Bioconductor R package "edgeR" version 3. 30. 3 (Robinson et al. , 2010; McCarthy et al. , 2012). For the Beat AML dataset, we downloaded gene expression log 2 normalized FPKM values for each sample from the Supplementary Table  S8  in the original paper  (Tyner et al.
, 2018). We converted the log 2 FPKM values to normal FPKM values by raising 2 to the power of the logarithmic values so that they would be correctly used in further analysis. Gene assignments were also based on the human reference genome GRCh37 (Ensembl release 75). For more details on processing steps, please see the methods section in the reference  (Tyner et al. , 2018). The gene expression data of the TCGA dataset were downloaded using the  Bioconductor R package "TCGAbiolinks" version 2. 16. 4 (Colaprico et al. , 2016). Through the GDCquery function, we used the following parameters to obtain the FPKM values of data aligned to the human reference genome GRCh38 (UCSC knownGene)  (Gao et al. , 2019) : project = "TCGA-LAML," data. category = "Transcriptome Profiling," data. type = "Gene expression Quantification," workflow.
type = "HTSeq -FPKM," legacy = False. The fastq files from ALL-Ekert and AML-Griffioen datasets were downloaded via the EGA download client, pyEGA3 (https:// github. com/EGA-archive/ega-download-client), after gaining access permission to EGA studies EGAS00001004212 and EGAS00001003096, respectively. The reads were trimmed using the software Trimmomatic version 0. 39  (Bolger et al. , 2014)  and then were mapped to the human reference genome GRCh37 (Ensembl release 82) using STAR algorithm version 2. 6. 0c  (Dobin et al. , 2013). The RSEM software package version 1. 3. 0 (Li and Dewey, 2011)  was used to estimate read counts and FPKM values of each gene annotated. The sequencing and gene expression data generation of all samples included in the French dataset were performed by IntegraGen. Libraries were prepared with the NEBNextUltra II Directional RNA Library Prep Kit for Illumina protocol, according to supplier recommendations, and sequencing was carried out on Illumina NovaSeq (paired-end 100 bp reads).