dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical data Age at diagnosis (in years), sex (male or female), smoking status (nonsmoker, ex smoker or smoker), asbestos exposure (exposed or nonexposed), previous treatment with chemotherapy drugs (yes or no), treatment information (surgery, chemotherapy, radiotherapy, immunotherapy or cancer history) and survival data (calculated in months from surgery to the last day of follow-up or death) were collected for all 123 patients. The median age at diagnosis was 67. 5 years and 73. 3% of patients were male.
RNA extraction Included samples were extracted using the AllPrep DNA/RNA extraction kit (Qiagen) following the manufacturer's instructions. All RNA samples were treated with DNAse I for 15 min at 30 °C. The RNA integrity of the frozen samples was checked with a TapeStation system (Agilent Biotechnologies) using RNA ScreenTape (Agilent Biotechnologies). Because of unsuccessful extraction (impacting either the quality or the quantity), we obtained different numbers of MPM samples for which WGS, DNA methylation or RNA sequencing (RNA-seq) data are available (Supplementary Tables  1 2 3 ).
DNA sequencing Sequencing. WGS was performed by the Centre National de Recherche en Génomique Humaine (Institut de Biologie François Jacob, CEA) on 130 fresh frozen MPMs, 54 of which with matched normal tissue or blood samples. We used an Illumina TruSeq DNA PCR-Free Library Preparation Kit (20015963; Illumina) according to the manufacturer's instructions and sequenced them on a HiSeq X Five platform (Illumina) as paired-end 150-base pair reads. Samples paired with matched normal tissue or blood had a target sequencing depth of 60× and other samples had a target depth of 30×. Data processing. WGS reads were mapped to the reference genome GRCh38 (with ALT and decoy contigs) using our in-house workflow (https://github. com/IARCbioinfo/alignment-nf; release version 1. 0)  66. In summary, this workflow relies on the Nextflow domain-specific language  67  version 20.
10. 0. 5430 and consists of four steps: read mapping (software BWA  68  ; version 0. 7. 15), duplicate marking (software samblaster 69 ; version 0. 1. 24), read sorting (software sambamba  70  ; version 0. 6. 6) and base quality score recalibration using GATK  71  (version 4. 0. 12). https://doi. org/10. 1038/s41588-023-01321-1 Variant calling and filtering on DNA. We performed somatic variant calling using the software Mutect2 (ref. 72 ) from GATK version 4. 1. 5. 0, as implemented in our Nextflow workflow (https://github. com/IARCbioinfo/ mutect-nf; release version 2. 2b). Multiregion samples were processed jointly using the multisample calling mode of Mutect2. We called germline variants using Strelka2 (ref. 73 ) version 2. 9. 10-0 using our Nextflow workflow (https://github. com/IARCbioinfo/mutect2-nf; release version 1.
2a). Annotation was performed with ANNOVAR  74   (16 April 2018) using the GENCODE version 33 annotation, COSMIC version 90 and REVEL databases. To call somatic variants on tumor-only samples (72/115), a similar procedure was performed (Mutect2 tumor-only mode) but including further germline-filtering steps using a random forest classifier. CNV calling. Somatic CNVs were called using the PURPLE software  75  version 2. 52, as implemented in our Nextflow workflow (https://github. com/IARCbioinfo/purple-nf; version 1. 0). We used a total of 57 matched WGS samples of MPM (including multiregion samples) for benchmarking the tumor-only mode of PURPLE. We ran PURPLE twice for each matched sample: first using the matched WGS normal/tumor pair as input and second using only the tumor WGS sample as input.
RNA-seq Sequencing. RNA-seq was performed on 126 fresh frozen MPM samples in the Cologne Center for Genomics, of which 109 MPM samples belonged to the discovery cohort (Supplementary Tables  1 2 3 ). Libraries were prepared using the Illumina TruSeq Stranded mRNA Sample Preparation Kit (20020595; Illumina) and the pool was sequenced using an Illumina NovaSeq 6000 sequencing device and a paired-end 100-nucleotide protocol. Data processing. The 126 raw read files from the MESOMICS cohort and the 21 files from the Iorio and colleagues  18  mesothelioma cohort (downloaded from the European Genome-phenome Archive (EGA) and Sequence Read Archive websites; datasets EGAS00001000828 and PRJNA523380, respectively) were processed in three steps using the RNA-seq processing workflow based on the Nextflow language and accessible at https://github.
com/IARCbioinfo/RNAseq-nf (release version 2. 3)  66. Then, reads were realigned locally using ABRA2 (ref. 80 ); (workflow https://github. com/IARCbioinfo/abra-nf; release version 3. 0) and base quality scores were recalibrated using GATK (workflow https://github. com/IARCbioinfo/BQSR-nf; release version 1. 1). Once processed, expression was quantified using StringTie software (version 2. 1. 2; Nextflow pipeline accessible at https://github. com/IARCbioinfo/ RNAseq-transcript-nf; release version 2. 2). The raw read counts of the 59,607 genes in the expression data matrix, from the MESOMICS, TCGA and Bueno cohorts  3, 4  , from which we removed non-chimionaif samples, were normalized using the variance-stabilizing transform (vst function from R package DESeq2 version 1. 14. 1); this transformation enables comparisons between samples with different library sizes and different variances in expression across genes.
DNA methylation EPIC 850K methylation array. Epigenome analysis was performed on 119 MPMs (Extended Data Fig. 1  and Supplementary Tables  1 2 3 ), two technical replicates and three adjacent normal tissues. Epigenomic studies were performed at the IARC with the Infinium EPIC DNA methylation beadchip platform (Illumina) used for the interrogation of over 850,000 CpG sites (dinucleotides that are the main target for methylation). Data processing. The resulting IDAT raw data files were preprocessed using the R packages minfi (version 1. 34. 0) and ENmix (version 1. 25. 1). Raw data were then normalized using functional normalization (function preprocessFunnorm; minfi), to reduce technical variation within the data, and probe removal steps were performed to ensure reliability and accuracy of the final dataset.
This resulted in a normalized, filtered dataset of 781,245 probes for 139 samples. Finally, beta and M values were extracted (functions getBeta and getM; minfi). Nine probes recorded M values of -∞ for at least one sample, and these values were replaced with the next lowest M value in the dataset. The three normal tissues and one remaining technical replicate were then removed from the beta and M matrices for the subsequent analyses. This resulted in 135 samples: 122 for discovery and an additional 13 for ITH analyses. CIMP index. A CIMP index value was calculated for all samples as follows. The mean beta value across all probes located within CpG islands was calculated per sample, resulting in beta values for 24,891 and 24,924 CpG islands, MESOMICS (EPIC array), TCGA  4  and Iorio and colleagues  18  cell lines (HM450K array), respectively.
7  datasets (for which expression arrays and drug responses are available (38 samples; three drugs)). In addition, some single-omic analyses are also described in this section. Preprocessing of expression data. We used normalized read count matrices (see the section 'RNA-seq') for subsets (1)-(  4 ), encompassing 59,607 genes. Among these genes, those having less than one fragment per kilobase of exon per million mapped fragments (FPKM) difference across the samples were excluded from the unsupervised analyses. Also, to mitigate sex influence on the expression profiles, we removed genes from the sex chromosomes. For each analysis, the top 5,000 most variable genes were selected. Similarly, the 5,000 most variable genes from the normalized array expression of cell lines (see the section 'Processing of publicly available expression array processing' in Supplementary Methods) were selected.
Whenever several probes were available for the same gene, the one with the highest intensity was selected. Preprocessing of methylation data. DNA methylation was available for both the MESOMICS and TCGA cohorts. First, we extracted the M values of the CpGs from the MESOMICS, TCGA  4  , combined MESOM-ICS/TCGA and Iorio  18  cell line cohorts, respectively  81. We excluded sex chromosome CpGs, CpGs that did not pass quality control (see the https://doi. org/10. 1038/s41588-023-01321-1 derived from humans are at risk of re-identification when combined with information from other public sources, access must be requested from the MESOMICS data access committee, as detailed at https:// ega-archive. org/studies/EGAS00001004812. Minimum datasets of processed somatic alterations for genomic, transcriptomic and epigenomic data, sufficient to reproduce, interpret and extend our main results, are publicly available at https://github.
com/IARCbioinfo/ MESOMICS_data/tree/main/phenotypic_map/MESOMICS. A data note manuscript detailing all of the quality controls of the dataset is available at https://www. biorxiv. org/content/10. 1101/2022. 07. 0 6. 499003v1 (ref. 82 ). TCGA whole-exome sequencing, RNA-seq and methylation array data are available from the Genomic Data Commons portal (TCGA-MESO cohort  4  ). Whole-exome sequencing and RNA-seq data from the Bueno and colleagues cohort 3 are available from the EGA under accession number EGAS00001001563. Small variant lists, RNA-seq, expression array and methylation data for the Iorio and colleagues cohort  18  are available from the Gene Expression Omnibus (accession number GSE29354), EGA (accession number EGAS00001000828) and Sequence Read Archive (accession number PRJNA523380).
Corresponding drug responses are available from the cancerrxgene. org website (https://www. cancerrxgene. org/downloads/ drug_data?tissue=MESO; accessed July 2021). Expression array data for the de Reyniès and colleagues cohort 5 are available from the ArrayExpress platform (E-MTAB-1719) and corresponding drug response data are available from the supplementary material of Blum et al. 7. All of the other data supporting the findings of this study are available within the article and its Supplementary Information files. Further information and requests for resources should be directed to and will be fulfilled by M. F. (follm@iarc. who. int). Source data are provided with this paper. https://doi. org/10. 1038/s41588-023-01321-1 Extended Data Fig. 9  | Multiomics intra-tumor heterogeneity (ITH) of 13 multiregion samples.
Overview of the MPM datasets for multiomic integration with MOFA. Overview of the omic data sets integrated into multiomics factor analyses (MOFAs), for (a) a MOFA of the MESOMICS cohort (n = 120), (b) MOFA of the TCGA cohort (n = 73), (c) MOFA of the Bueno cohort (n = 181), and (d) MOFA of the 3 cohorts (n = 120 + 73 + 181). D is the number of integrated omic features from genomic (rearrangements and mutations within DNA Alt; allele-specific copy number (CN) in Total CN and Minor CN), transcriptomic (RNA), and epigenomic data at promoter (MethPro), gene body (MethBod), and enhancer regions (MethEnh). https://doi. org/10. 1038/s41588-023-01321-Proportion of interpatient variance explained by MOFA latent factors. a) Example feature for which a latent factor explains 0% of interpatient variance (here factor 2 explains no variance at all in the expression of gene NCR3-R 2 = 0).
Acknowledgements The  MESOMICS  project is part of the  Rare Cancers Genomics initiative  (www. rarecancersgenomics. com) led by the  Rare Cancers Genomics  team at the IARC (https://www. iarc. who. int/teams-gem-rcg/). We thank the patients for donating tumor specimens. The human biological samples and associated data were obtained from the  French MESOBANK. We also thank  R. Argelaguet  for advice on using MOFA,  H. Begueret ,  N. Rousseau ,  D. Bozonnet ,  E. Wasielewski ,  G. Clapisson ,  C. Bonnetaud ,  K. Washetine ,  A. Lupo Mansuet ,  C. Cuenin  and  E. Clermont  for their contribution to the biorepository. We acknowledge the  American Association for Cancer Research  and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing.
Interpretations are the responsibility of the study authors. The results published here are in part based on data generated by the the  TCGA Research Network  (https://www. cancer. gov/tcga). We also thank the  French National Mesothelioma Surveillance Program and Santé Publique France. This work has been funded by the  French National Cancer Institute  ( PRT-K 2016-039  to L. F. -C. and M. F. ) and the  Ligue Nationale Contre le Cancer  ( LNCC 2017  and  2020  to L. F. -C. and M. F. ). L. M. has a fellowship from the  Ligue Nationale Contre le Cancer. This work also benefited from support from the  France Génomique national infrastructure , funded as part of the  Investissements d'Avenir program  managed by the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ). Other funding was provided by the  Spanish Ministry of Science and Innovation  ( PID2019-105201RB-I00  to J.
P. C. ), the  Instituto de Salud Carlos III , co-funded by the  European Union  ( ERDF/ESF; Investing in Your Future ), a  Sara Borrell postdoctoral grant  ( CD19/00255  to A. I. -C. ), the  Spanish Ministry of Universities  (predoctoral contract  FPU18/02275  to R. B. -E. ), the  Junta de Andaluciá  ( BIO-0139 ) and the  Universidad de Córdoba-FEDER  ( UCO-202099901918904 ) (to J. P. C. and A. I. -C. ), a  GETNE2019 Research grant  to J. P. C. and the  CIBER Fisiopatología de la.
Extended Data Fig. 1 | 1 Extended Data 2 | 112 Overview of the MPM datasets for multiomic integration with MOFA. Overview of the omic data sets integrated into multiomics factor analyses (MOFAs), for (a) a MOFA of the MESOMICS cohort (n = 120), (b) MOFA of the TCGA cohort (n = 73), (c) MOFA of the Bueno cohort (n = 181), and (d) MOFA of the 3 cohorts (n = 120 + 73 + 181). D is the number of integrated omic features from genomic (rearrangements and mutations within DNA Alt; allele-specific copy number (CN) in Total CN and Minor CN), transcriptomic (RNA), and epigenomic data at promoter (MethPro), gene body (MethBod), and enhancer regions (MethEnh). https://doi. org/10. 1038/s41588-023-01321-Proportion of interpatient variance explained by MOFA latent factors. a) Example feature for which a latent factor explains 0% of interpatient variance (here factor 2 explains no variance at all in the expression of gene NCR3-R 2 = 0).
Treatments evaluation The survival curves were established using six rats for control, ten for ConvRT, ten for MRT/ConvRT/ ConvRT, nine for ConvRT/MRT/ConvRT and ten for ConvRT/ConvRT/MRT groups. The time between the first fraction irradiation and death was recorded as the survival time; one day was added in cases of euthanasia. Kaplan Meier survival data and median survival time (MST) were compared using a log rank test in GraphPad Prism version 9. 0. 1 (GraphPad Software, La Jolla, California, USA; https:// www. graphpad. com/). The average increase in life span (% ILS) was calculated using the following formula: % ILS = (T/C Â 100) -100, where T and C are the mean survival days of treated and control groups of rats, respectively.
Acknowledgements This work is dedicated to 'notre collaboratrice preferee', our colleague and friend  Elke Bra ¨uer-Krisch  who left us much too soon in 2018. Elke contributed to this study. We miss her. The authors thank  Helene Bernard, Andrea Latoure, Eric Collomb and Loı ¨c De Saint Jean  for their technical help and logistic support.
Furthermore, the presence of HSO4 -shows that the solution pH is an essential parameter of speciation. Trivalent cations tend to precipitate as hydroxides at intermediate and high pHs  (Brown and Ekberg, 2016). Therefore, aggregation of PSL-NF(-) in these solutions is more likely caused by heteroaggregation rather than homoaggregation. Mao et al. (2020)  performed experiments at pH 7. 5 with Al concentrations ranging from 0. 25 to 10 mmol L -1 and Fe(III) concentrations from 0. 1 to 10 mmol L -1. We performed geochemical modeling of these experimental conditions using PHREEQC software and sit database, and without considering the potential Al and Fe complexation onto PSL. This predicted that >95% and 100% of Al(III) and Fe(III) are present in solid form due to precipitation. Similarly, Fe(III) is present in a solid form in 0.
J o u r n a l P r e -p r o o f Journal Pre-proof Geochemical speciation models provide information about solids or complexes that can form in different environments if they contain the equilibrium constants of all complexes and solids considered. For example,  Singh et al. (2019)  calculated that Hg(II) occurred mainly in the Hg(0) form at pH 6, whereas using an updated database  (Lian et al. , 2020) , we calculated that Hg(II) is mainly in the HgCl2 and HgOHCl forms. Also, Ramirez Arenas et al. (  2020 ) calculated that Fe(III) mostly occurred as Fe(OH)2 + at pH 8. 2. However, by including precipitation as a possible process in the models, we calculated that Fe(III) is mainly present in solid form.
Acknowledgements This work was supported by the  French Ministry of Higher Education and Research , the  French ANR  through the program  PLASTI-SCARE  and the  French Brittany Council  through  MONANO (SAD-Attractivité ). Thank you to  Mandy Ducy  from  Agence IMPAKT Scientifik Inc. for the illustrations. Thank you to  Christa Bedwin  for editing our work. J o u r n a l P r e -p r o o f.
The tuning process of the ADRC units and the conventional techniques was carried out using the Genetic Algorithm (GA) in the MATLAB ® environment to minimize the OPI of  (28). The sample data of the tuned parameters of the ADRC units are given in Table  2. The performance indices obtained from the numerical simulations are listed in Table  3. It can be seen that the proposed observer outperformed its counterparts, where the minimum value of the ITAE of the proposed NHOESO indicates that its time-domain performance was the best among the other conventional observers found in the literature, while the minimum delivered energy of the proposed NHOESO is very clear from the value of the ISU in Table  3.
Acknowledgments: The authors would like to acknowledge the support of the  Norwegian University of Science and Technology for paying the Article Processing Charges (APC)  of this publication. The authors would like to thank  Prince Sultan University, Riyadh ,  Saudi Arabia  for their support. Special acknowledgement to Automated Systems & Soft Computing Lab (ASSCL),  Prince Sultan University, Riyadh ,  Saudi Arabia. In addition, the authors wish to acknowledge the editor and anonymous reviewers for their insightful comments, which have improved the quality of this publication.
Those approaches were anchored to the vast database available in the N-N sector, which includes precise determination of scattering cross sections that enable differential studies and partial wave analyses, to measurements of Λ-N cross sections and, to a lesser extent, to Σ-N cross sections. Very recently, the extension of NLO χEFT potentials from S = -2  [14, 15]  to S = -3 and S = -4 systems has been explored  [16]. These potentials are in accordance with the few experimental constraints on the Λ-Λ and Ξ-N interactions and account for effects from SU(3) symmetry breaking in the extension. At the time when most of those potentials were constructed there was little hope for precise data on the interaction between baryon pairs with more than two strange quarks, and the experimental information was limited to the scarce data derived from the detection of hypernuclei, such as binding energies for double-Λ hypernuclei  [17] [18] [19] [20]  and Ξ hypernuclei  [21, 22].
This second measurement was collected after several sample changes and after data collections made on several cultural heritage samples at the same energy; these data illustrate the alignment stability of this MAD filter that uses a single-crystal-comb. This stability is important because it allows the measurement of about 20 diffractograms in parallel with a constant contribution from each analyser crystal: even if they show some deviations in intensity, these constant measurement ratios allow all spectra/samples to be measured under the same conditions and thus to be corrected by an ad hoc data processing, as we had observed in 1995 when the first multiple analyser system was set up at ESRF  (Hodeau et al. , 1996).
(2019). In this last study, the surface information of the archaeological drawings was evaluated in detail by non-invasive in situ methods (digital microscopy and Raman spectroscopy), and some micro-samples were analysed by scanning electron microscopy energy-dispersive X-ray spectroscopy (SEM-EDX) and X-ray diffraction (XRD) methods. We used one of these micro-sampling units to test the sensitivity of our MAD filter by performing an XRD experiment on it, with a quasi-parallel beam of 0. 4 mrad at an energy of 22 keV. The corresponding powder diffractogram (Fig. 6 ), collected using Debye-Scherrer geometry in $ 4 h with a step size/time of 0. 004 /1. 5 sec, illustrates the richness/complexity of such samples that contain the signatures of various materials. The green curve in Fig. 6  corresponds to a direct data collection using a conventional 2D detector, the orange curve corresponds to the data collection using the MAD filtering using the 20 analyser crystals of the single-crystal-comb and the Soller-collimator block, and the blue curve corresponds to the signal received by a single analyser.
Context. The third release of the Gaia catalogue contains radial velocities for 33 812 183 stars with effective temperatures ranging from 3100 K to 14 500 K. The measurements are based on the comparison of the spectra observed with the Radial Velocity Spectrometer (RVS; wavelength coverage: 846-870 nm, median resolving power: 11 500) to synthetic data broadened to the adequate along-scan line spread function. The additional line-broadening, fitted as it would only be due to axial rotation, is also produced by the pipeline and is available in the catalogue (field name vbroad). Aims. We describe the properties of the line-broadening information extracted from the RVS and published in the catalogue, and analyse the limitations imposed by the adopted method, wavelength range, and instrument. Methods.
We used simulations to express the link between the line-broadening measurement provided in Gaia Data Release 3 and V sin i. We then compared the observed values to the measurements published by various catalogues and surveys (GALAH, APOGEE, LAMOST, etc. ). Results. While we recommend caution in the interpretation of the vbroad measurement, we also find a reasonable general agreement of the Gaia Data Release 3 line-broadening values and values in other catalogues. We discuss and establish the validity domain of the published vbroad values. The estimate tends to be overestimated at the lower V sin i end, and at T eff > 7500 K its quality and significance degrade rapidly when G RVS > 10. Despite all the known and reported limitations, the Gaia Data Release 3 line-broadening catalogue contains measurements obtained for 3 524 677 stars with T eff ranging from 3500 to 14 500 K, and G RVS < 12.
Introduction In addition to its high-quality astrometry, the ESA Gaia space mission provides valuable spectroscopic data. The satellite carries a spectrometer with intermediate resolving power that covers the 846 to 870 nm wavelength range, with the initial primary goal of measuring the radial velocity (RV) of the sources transiting one of its four CCD rows  (Sartoretti et al. 2022; Cropper et al. 2018)  down to the magnitude G RVS = 16. 2  (Katz et al. 2023). During one such transit, the instrument acquires three spectra (i. e. one per CCD strip) in ∼4. 4 s each. A spectroscopic pipeline processes the data  (Sartoretti et al. 2018)  to calibrate and extract the transit spectra, then derives the RV and a line-broadening parameter through the single-transit analysis (STA) and multipletransit analysis chains (MTA).