dataesr/scientific-paragraphs-categorization huggingface dataset:
55  Nationwide data of air pollution from particulate matter and daily hospitalizations for respiratory diseases in Italy have been recently published. 56  A total of 4,154,887 respiratory admissions were registered during 2006-2015, of which 29% for lower respiratory tract infections, 12% for COPD, 6% for upper respiratory tract infections, and 3% for asthma. Daily mean PM 10 and PM 2. 5 concentrations over the study period were 23. 3 and 17 mg/m 3 , respectively. Excess risks of total respiratory diseases, stronger for asthma and COPD, in association with PM 10 mg/m 3 increases were found. Higher effects were reported in the elderly and in less urbanized areas. In addition, the compliance with a theoretical daily PM 10 standard of 25 mg/m 3 , a value suggested by 2005 WHO annual guideline, would have avoided about 4,900 respiratory admissions.
Similarly, a total of 8,917 admissions were attributable to PM 2. 5 daily concentrations exceeding 10 mg/m 3 in 2013-2015. Using data from the longitudinal analytical epidemiological study in Pisa, individual risk factors recorded during the 1991 survey were considered, and new cases of respiratory diseases were ascertained until 2011. Average PM 10 and PM 2. 5 exposures (mg/m 3 , year 2011) were estimated at the residential address (1-km 2 resolution) on the subsample of subjects living at the same address from 1991 to 2011. Significantly increased risks of incidences of rhinitis and chronic phlegm were associated with increasing PM 2. 5 and an increased risk of incidence of COPD was associated with increasing PM 10. 57  In another recently published study in Pisa, exposure effects were estimated using the case-crossover design and conditional logistic regression (OR 95% CI for 10 mg/ m 3 increase; lag 0-6).
DNA sequencing and analysis All raw FASTQ files have been deposited at the SRA public archive under accession number PRJNA842420. Following fastqc (v0. 11. 9)  [28]  quality checking of the 4a-3A and 4a-3B fastq files, Trimmomatic (v0. 38)  [29]  was used to remove low-quality bases (SLIDINGWIN-DOW:4:20). After reviewing the read length histograms generated with bbmap (v38. 82) [30] , only reads with a minimum length of ≥ 50 bp were kept [4a-3A: 49,480,787 paired end reads kept (66. 87%); 4a-3B: reads kept 48,149,543 (67. 01%)]. Trimmed reads were mapped to the An. gambiae PEST genome version AgamP4 (Vectorbase; https:// vecto rbase. org) using BWA mem (v0. 7. 17)  [31]. Samtools (v1. 11)  [32, 33]  was used to fix mate pair issues, remove reads that mapped singly or not with their mate (4a-3A: 95.
SNP and INDEL variant analysis Prior to GATK variant analysis  [34] , the following read groups were added using PICARD AddOrReplaceRead-Groups  [35] : 4a-3A: RGID = 4a-3A, RGLB = TruSeq_Nano, RGPL = ILLUMINA, RGPU = S1, RGSM = 4a-3A_DNA; 4a-3B: RGID = 4a-3B, RGLB = TruSeq_Nano, RGPL = ILLU-MINA, RGPU = S2, RGSM = 4a-3B_DNA. Both the 4a-3A and 4a-3B bam files were variant called using the GATK HaplotypeCaller program with heterozygosity, heterozygosity-stdev and indel-heterozygosity values set based on the GATK protocol used by the Ag1000G project  [36]. The resulting GVCF files were combined into a database with GenomicsDBimport and then joint genotyped with Geno-typeGVCFs using the same parameters specified for Hap-lotypeCaller above. In total 2,344,505 SNPs and 516,815 INDELs were identified using the GATK variant calling protocol.
Mode mean depth for both SNPs and INDELs was calculated using vcftools and the Tidyverse R package, and a maximum mean depth per variant site was set at 2 × mode mean depth for both variant types  [37, 38]. After all filtering steps, 1,808,124 SNPs (77. 12%) and 410,644 INDELs (79. 46%) remained. SNPs and indels fixed relative to the AgamP4 genome assembly were identified using GATK SelectVariants  [34]. Venn diagrams of fixed INDELs and SNPs were made using the Vennerable R package  [39]. Analysis of the distribution of SNPs and indels across the AgamP4 genome assembly was performed using χ 2 statistical tests in Graphpad Prism version 9. 3. 1. A sliding window analysis (window size 10,000 bp, step 2500 bp) was performed for each chromosome arm using the windowscanr R package  [40]. Further analysis of the 2R chromosome arm was also performed, examining the distribution of SNPs and INDELs across the known inversion regions using Graphpad Prism.
2R inversion typing of 4a-3A and 4a-3B cells In addition to using whole genome sequences to detect chromosomal inversions, molecular and in silico karyotyping assays were used to confirm inversion status. Molecular diagnostics indicated that both 4a-3A and 4a-3B cells (Fig. 2D ) were homozygous for the 2R + j form of the 2Rj inversion. While 4a-3A cells were homozygous for the 2R + b form of the 2Rb inversion, 4a-3B cells were homozygous for the 2Rb form  [45, 47]. Both cell lines were homozygous for the 2L + a form of the 2La inversion (Additional file 1: Fig. S1 )  [46]. In silico karyotyping yields prediction for 2Rb and 2Rc inversion alleles in both cell types  [48]. Compkaryo identified 290 diagnostic SNPs (83. 1% of total diagnostic 2Rb SNPs utilized by Compkaryo) in our dataset for 2Rb with 4a-3A identified as 2R + b (b + : 283; b: 7) and 4a-3B as 2Rb (b + : 1; b: 289), consistent with both the molecular karyotyping results and the whole genome sequence analysis (Fig.
Funding This work received financial support to KDV from the  European Commission ,  Horizon 2020 Infrastructures # 731060 Infravec2 ;  European Research Council , Support for frontier research, Advanced Grant # 323173  AnoPath;  Agence Nationale de la Recherche , # ANR-19-CE35-0004  ArboVec;  National Institutes of Health ,  NIAID  # AI145999 ; and  French Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID , to RJN from  National Science Foundation , IIS# 194727 , and to MMR from  National Institutes of Health ,  NIAID  # AI145999. Funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Patients and samples Patients with Ph-negative B-ALL treated in the GRAALL-2005,  5  GRAALL-2014, and EWALL-INO trials (clinicaltrials. gov numbers NCT00327678, NCT02617004, and NCT03249870, respectively) were included. Screening for recurrent cytogenetic aberrations was performed by standard cytogenetic procedures in local laboratories. Targeted molecular analyses were performed centrally, including screening for recurrent fusion transcripts by reverse transcription multiplex ligation-dependent probe amplification,  6  screening for ERG and IKZF1 intragenic deletions by breakpoint-specific PCR,  7, 8  and quantitative reverse transcription PCR (qRT-PCR) for CRLF2 and EPOR transcripts. All cases negative for the major classifying chromosomal abnormalities (ie, KMT2A rearrangements, TCF3::PBX1, high hyperdiploidy, low hypodiploidy, and ETV6::RUNX1) or for BCR::ABL1-like alteration or ERG intragenic deletion were subjected to RNA sequencing (RNA-seq).
High-throughput sequencing RNA-seq libraries were prepared using the Truseq stranded mRNA library preparation kit (Illumina, San Diego, CA), and 2x150bp paired-end sequencing was performed using a Next-Seq 500 (Illumina). Raw, unfiltered RNA-seq reads were aligned to human reference genome hg19 using STAR 2. 5. 2b. 9  Fusions transcripts were detected using FusionCatcher 0. 99. 7c b. 10  For UBTF::ATXN7L3 fusion, visual inspection of each BAM file was done because it could be occasionally overlooked by bioinformatic algorithms. Details of gene expression analysis and mutation detection are provided in supplemental Methods. Whole-genome long-read sequencing was performed using the PacBio platform. High-molecular-weight DNA was extracted and purified using the MagAttract HMW DNA kit (Qiagen).
DNA shearing was performed using g-TUBE (Covaris) with a targeted 20 kb fragment size, and fragments below 5 kb were removed using AMPure PB beads at 3. 1X. Library preparation was conducted according to the manufacturer protocol (SMRTbell Express Template Prep Kit 2. 0; Pacific Biosciences), and samples were processed using sequencing primer v2 and Sequel II Binding Kit 2. 0. Samples were sequenced on a Sequel II instrument in consensus circular sequencing (CCS) mode using 2 SMRTcells per sample. CCS sequencing reads were generated using SMRTlink software and aligned against the human genome (hg38) using pbmm2 of PacBio SMRTtools v. 8. 0. Structural variant calling was performed using SMRT Analysis. Structural variants of a minimum of 10 bp were retained if covered by at least 2 CCS reads.
Acknowledgments The authors thank the patients and all the GRAALL investigators and biologists who contributed samples and data for this study. The authors thank the  Saint-Louis Molecular Hematology laboratory  for technical support, especially Agn es P erus, H el ene Boyer, and  Emilie Gaudas , and  Loïc Maillard  from the research team. The authors thank  Pierre Lemaire  and  Cl ementine Chauvel  for expert advice on immunophenotypic data. The authors thank  Delphine Ndiaye-Lobry  and  Alexandra da Silva Babinet  for the help with A-DNA FISH experiments. They thank the  IMAG'IC facility  (member of the  National Infrastructure France BioImaging  [ ANR-10-INBS-04 ]) of  Institut Cochin. The authors thank the  Platform of Biological Resources from Robert Debr e hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.
Long-read WGS was performed on the Meary third-generation sequencing platform of  Universit e de Paris. Sequencing of 4C-seq was performed by the GenomEast platform, a member of the " France G enomique" consortium  ( ANR-10-INBS-0009 ). UMR-944 ,  EA-3518 ,  UMR-1274 , Meary platform, and GRAALL are members of the  Institut Carnot OPALE. This study was supported by grants from  Association Laurette Fugain, La Ligue r egionale du Haut-Rhin Contre le Cancer  ( 2019-20 ), and  Institut National du Cancer  ( PLBIO 19-289 ). M. P. was a recipient of a grant from  ITMO Cancer of Aviesan Cancer Control Strategy , on funds administered by  INSERM. This work was supported by  THEMA , the national center for precision medicine in leukemia.
Statistical analysis Comparisons between categorical variables were performed using Fisher's exact test while comparisons between continuous variables were performed using Mann-Whitney U test. All tests were two-sided and p-values<0. 05 were considered significant. The p-values generated are descriptive and were not adjusted for multiple testing. All statistical analyses and plots (including plotting of the trees) were done in R v4. 0. 2. Heatmaps were plotted with R package ComplexHeatmap. 21.
38À40  Confirming these results in larger cohorts, where potential associations with clinical features and outcomes could be better explored, is necessary and could potentially influence the clinical management of ILC. This work was supported by Les Amis de l'Institut Bordet, MEDIC and the Breast Cancer Research Foundation (BCRF). D. F. , M. R. and C. S. are funded by the Belgian Fonds National de la Recherche Scientifique (F. R. S-FNRS). D. V. is partly supported by a grant of the R egion Wallonne.
They overlap in parts of their scientific scope (Figure  1 ), but joint research is rare. Sometimes, publications find their way into the other domain. This has become much easier since the dawn of the digital era with large, easily searchable literature databases, open access and scientific networks expanding online (e. g. ,  Dengler et al. , 2011). Nevertheless, only a few research groups bridge both disciplines. Another development facilitated in the digital era is the collection of formerly disparate data sets in large databases and networks like GBIF for species occurrence data (GBIF: The Global Biodiversity Information Facility, 2020), TRY for plant-trait data  (Kattge et al. , 2020) , the CESTES database for combined species, trait and environment data  (Jeliazkov et al. , 2020) , or EVA and sPlot for vegetation-plot  Bürger) , Czech Science Foundation (grant number 19-28491X for Milan Chytrý and Zdeňka Lososová), and by Slovenian Research Agency (research core funding No.
Methods: We compared 13 311 phytosociological relevés and 13 328 records from weed science, concerning both data collection properties and the recorded species richness. To deal with bias in the data, we also analysed different subsets (i. e. , crops, geographical regions, organic vs conventional fields, center vs edge plots). Results: Records from vegetation science have an average species number of 19. 0 ± 10. 4. Metadata on survey methodology or agronomic practices are rare in this collection. Records from weed science have an average species number of 8. 5 ± 6. 4. They are accompanied by extensive methodological information. Vegetation science records and the weed science records taken at field edges or from organic fields have similar species numbers. The collections cover different parts of Europe but the results are consistent in six geographical subsets and the overall data set.
, 2020) , which have seen the strongest decrease in diversity and population sizes of individual species compared to other habitats in Europe over the last decades  (Eichenberg et al. , 2021; Leuschner & Ellenberg, 2017). This need has spurred the development of efficient sampling schemes  (Wietzke & Leuschner, 2020) , but also a call for an integrative data culture using data from disparate sources and involving stakeholders  (Kühl et al. , 2020). In this paper, we report on two collections of vegetation-plot data that were assembled to make records of weed vegetation visible and available for further analysis. These collections broadly originate from the two scientific disciplines concerned with weed vegetation mentioned earlier. This is most notable from the bodies that helped to mobilise the data: the International Association for Vegetation Science (http://iavs.
org), through its working group European Vegetation Survey (EVS) and the European Vegetation Archive (EVA) database, on the one hand and the European Weed Research Society (https://ewrs. org), through its working group Weeds and Biodiversity, on the other. Records of arable weed vegetation in EVA date back to the 1930s but have very little associated information regarding the agricultural background of the surveyed fields or metadata on sampling methodology. In contrast, the weed science-based collection holds data starting from the 1990s but is accompanied by extensive management information and detailed metadata of survey methods. In this paper, we aim to check the suitability of the collections for joint analysis and for addressing research questions from the opposing domains. The paper is structured into three parts: (1) description of the collections and comparison on a qualitative level, (2) basic comparative statistical analysis of both collections, and (3) exploration of whether the two collections are complementary and how they can be used for potential joint analyses.
g. , soil pH, slope, altitude or crop species. In this paper, we use the following terminology to distinguish between observation units: a field is a distinct parcel of land and usually the smallest unit of a farmer's management (Figure  2 ). Within a field or field parts, the surveyor places one or several plots, i. e. , distinct small areas on which the actual vegetation is recorded. Each time the vegetation is recorded, this is called a plot observation. When compiling a database, each line or record corresponds to one observation, which can be identified by field ID, possibly field part attribute, plot ID and observation date. Observations may be aggregated to a higher-level observation unit, for example, by summing up all the observations from the same field or field part; the aggregated data are also called records.
| Data collection: weed vegetation subset of the European Vegetation Archive (EVA-W) This data collection was obtained from the EVA  (Chytrý et al. , 2016) , which includes the European Weed Vegetation Database (GIVD ID EU-00-028;  Küzmič et al. , 2020)  and several other databases with plot observations of weed vegetation. The plots from EVA were first selected using the expert system based on species lists characterising F I G U R E 1 Overlap between the objectives of vegetation science and weed science in arable landscapes weed vegetation classes from EuroVegChecklist  (Mucina et al. , 2016). Plots assigned to the classes Papaveretea rhoeadis, Chenopodietea, Sisymbrietea and Digitario sanguinalis-Eragrostietea minoris were retained. The data stored in the EVA come from various sources, e.
g. , scientific papers, technical reports, monographs, doctoral theses and final projects of students such as master theses. The selected data sets either come from studies of several different vegetation types including weed vegetation, or studies focussing on a selected crop type or area with weed vegetation as the main subject. Since the main aim in many of these studies was the floristic and ecological characterisation of plant communities  (Braun-Blanquet, 1964) , usually well developed and species-rich communities were recorded. The metadata (e. g. , crop type, plot position in the field, georeference or record date) are often missing either because such data were not recorded in the original publication or not digitised. Because the records with weed vegetation were selected from EVA according to the occurring species and species communities, they could also originate from non-arable sites with ruderal or disturbed vegetation.
For the comparison to the Arable Weeds and Management in Europe (AWME) data collection, we wanted to restrict the selection to plots that originated from arable sites. We therefore performed a second selection step and identified the plots from arable land either from specific keywords in the location description (i. e. , 'cereals', 'rye field') or when the species list included a crop species with a cover class >2 on the Braun-Blanquet scale. We added a georeference to approximately 400 records which only had a description of their location via geocoding. Afterwards, we limited the data set to georeferenced plots and excluded a small number of plots with longitudes >39°E (Russia) due to the scarcity of data in this region. Finally, we only used records dating from 1980 onwards to make the time periods of both data collections more comparable.
Species abundance was transformed to presence/absence data. Within our study, we use the abbreviation EVA-W (weed subset of EVA) for this data set. Details on the used data sets can be found in Appendix S1. We found a variety of sampling designs. Some studies were done with a narrow focus on one crop or study region, with a number of fields surveyed exactly once, sometimes within a study period of more than one year. Other studies were more complex. Some visited the same field more than once. Other studies took samples in various parts of the same fields, for example, aiming to study differences between field core and field edge or, more rarely, between plots treated with herbicides and control plots.
Field observations that recorded no weed plants (species number = 0) appeared in some cases where the study design included a survey after herbicide treatment, but were not included in the collection. All data sets were formatted to uniform spreadsheets of species data, a catalogue of field metadata and the accompanying manage- files. Records were classified by field part (core/edge/margin) and given a marker whether weed control had taken place before the survey (treated/untreated). Observations made on the same date from the same field, field part and weed control status were aggregated into one record. The plot size of the aggregated record was set to the sum of the original plots. Species occurrence was transformed to presence/absence format. The timing of the weed survey was classified in AWME in relation to the cropping season (early, mid-or late season), which may differ in calendar dates depending on the sowing time of the crop.
(2007). If the cultivated crop was included in the vegetation record, we deleted it. Volunteer crops were retained. We found no records duplicated in both collections. To compare species richness between data collections, we plotted sample-based species accumulation curves, generated by repeated accumulation in random order  (Gotelli & Colwell, 2001; Oksanen et al. , 2020; Ugland et al. , 2003). Then we calculated species number per record for both data collections and used this measure as our unit of analysis for subsequent comparisons. We tested the effect of various variables on species number per record. We used Kruskal-Wallis and Wilcoxon rank tests to assess whether species numbers differed significantly between groups (regarding data collection, crop, weed control treatment or survey date).
| Quantitative description Arable Weeds and Management in Europe contains more than twice as many records as the EVA-W, but the AWME records originate from a lower number of distinct locations. For many fields, the AWME contains more than one record, for example from repeated visits to the same field. The ratio of records to distinct geographical coordinates is 2:1 in EVA-W and 6:1 in AWME. In EVA-W, coordinates are often assigned to records according to the locality description; therefore, different fields of the same settlement can have the same coordinates. In AWME, some studies -as mentioned before -have repeated records from the same fields. The two data collections cover different parts of Europe (Figure  3a ). There are countries for which both collections contribute records, but largely the distribution of plots is largely comple- The EVA-W collection contains nearly twice as many species as the AWME collection (Figure  3b ).
For the EVA-W plots, no information is available on whether they received a weed control treatment. Records in AWME from treated plots have on average 7. 9 species, records from untreated plots 8. 7 species, compared to the 19. 0 species in the EVA-W plots (all significantly different at p < 0. 001, Kruskal-Wallis test). The same pattern is found for the comparison of treated vs untreated plots within a crop (Figure  6 ). The timing of the weed survey was classified for the AWME data in relation to the cropping season. Early surveys, taken after crop sowing and mid-season surveys, mostly reported similar species numbers. Records taken late in the season, near to harvest, usually show higher species numbers except for beet and untreated maize plots in AWME.
| DISCUSS ION In our study, we show that arable vegetation records collected for vegetation science research have consistently higher species numbers than records from weed science surveys. In the unfiltered comparison of species numbers, the EVA-W and AWME collections seem to originate from different vegetation and quite different conclusions on the state of species richness in European arable landscapes would be drawn depending on the data collection used. However, the difference decreases substantially when focussing on subsets from organic management or records taken at field edges. This is in line with many studies on the effect of organic farming on weed species richness  (Hyvönen et al. , 2003; Winqvist et al. , 2011)  and the possible role of field-margin vegetation in preserving diversity in arable landscapes  (Fried et al.
, 2009; Metcalfe et al. , 2019). The difference between species numbers in AWME and EVA-W also depends on crop types: it is higher in autumn-sown cereals and oilseed rape but much less pronounced in maize and other crops sown in late spring. First, we will discuss which methodological factors may have contributed to our results and then give an outlook how the data might be used with respect to the encountered biases.