dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of respiratory mechanics. The enclosure was instrumented thanks to Biopac ® system (Biopac, Goleta, CA, USA). Airflow at the ENT replica was followed with a pneumotachograph (TSD 117) while pressures in the enclosure were determined with a differential pressure transducer (TSD160D). Each component was connected to an amplifier (DA100C), connected to the acquisition hardware (MP160). Data were recorded for 4 minutes with AcqKnowledge ® 5. 0 software. Then, eight experimental values were calculated from airflow and pressure: tidal volume (TV), minute ventilation (MV), respiratory rate (RR), inspiratory time (IT), expiratory time (ET), total time (TT), resistances (R) and compliance (C). Mean inspiratory flow (MIF) was manually calculated using Microsoft Office Excel 365 (Microsoft, Redmond, USA).
Deposition experiments were performed 99m technetium complexed with diethylene triamine pentaacetic acid ( 99m Tc-DTPA) as scintigraphic tracer. A 100MBq solution (3 mL) was placed in the Venticis II ® jet nebulizer (Curium, Paris, France). Generated aerosol (MMAD of 0. 86) reached the adult replica of upper airways through the mean of a naso-buccal mask (Ambu ® , Bordeaux, France). Here, we aimed to quantitatively assess the aer- osol regional deposition in the different components of the ex vivo model of pulmonary fibrosis (naso-buccal mask, head replica and lungs) to compare our results to: i) the previously developed healthy ex vivo respiratory model  41, 42  (to determine the impact of the alteration of lung compliance on the aerosol regional deposition) and ii) to in vivo human data from IPF-suffering patients  20, 31, 44  using similar nuclear imaging technique and administration procedure (to determine the reliability of the developed ex vivo fibrotic lung model compared to IPF patients in terms of aerosol regional deposition).
Data were compared to those from the previously developed healthy ex vivo model 41 with an unpaired t-test. The tested hypothesis was the existence of significant difference of regional ventilation repartition between healthy and IPF ex vivo models. All tests were two-sided and p < 0. 05 was considered statistically significant. Statistical analyses were performed using GraphPad Prism ® 7 (GraphPad Software, La Jolla, CA, USA).
, 2019; Berhault, 2017; Spraul et al. , 2017; Berhault et al. , 2016). We focus on the mechanical behaviors of two mooring components (i. e. chain and nylon), the material deterioration during operation and the marine environmental factors that influence such behaviors. Once the previously mentioned information is fully understood and accounted for, the reliability following the service life of mooring lines can be better predicted. The corresponding level of reliability is then used to decide whether maintenance or replacement of lines is necessary. (Berhault, 2017). The hydrodynamic database is calculated by HydroStar  (Bureau Veritas, 2016). However, ones should be aware that the floater and mooring characteristics used in this study are selected only for the study purpose and are not representative of the IDEOL's real floating structure and the FLOATGEN's mooring system.
The considered measured tension time histories are then transformed to power spectral density (PSD) in frequency domain by using fast Fourier transformation (FFT) in Matlab as illustrated in Fig. 4. Since the so-called measured tension time histories used in the present work are obtained from numerical simulations as mention above, they can be considered as non-noisy signals. However, it should be noted that measurement noise is a critical issue to be considered, which may add extra power to the PSD curve. API-RP-2SK (2005) and DNV-OS-301 (2011) recommend T-N and S-N curves to calculate the fatigue damage of different mooring materials. In this study, the fatigue damage is calculated following the S-N curve approach since the uncertainties of the DNV's S-N curve can be known from the publications of  Lardier et al.
As we consider here an annual reduction of diameter of the chain links, the stress response is considered unchanged within one year for the same sea states, so as the fatigue damage Dannual. With a more frequent measurement, the cumulative fatigue damage can be determined by summing the damage calculated from the measured data for every month or couple of months. The annual probability of failure of the chain links can be calculated as ( )  (  ) FLS P _FLS=P G t <0 f (13) where ( ) ( ) FLS FLS_1 annual G t = G t = Δ -t. D for the cases S1 and S2 ( ) ( ) t FLS FLS_3 annual k=1 G t = G t = Δ -D ∑ for the case S3 ( ) ( ) t FLS FLS_4 annual ULS k=1 G t = G t = Δ - D +D       ∑ for the case S4 Δ is the model uncertainty in the Palmgren-Miner rule, presented in Table  3. Table  3. The uncertainties of the Palmgren-Miner rule and the DNV's S-N curve  (Lardier et al.
Acknowledgement This work would not have been possible without the funding from the  Vietnamese  scholarship program 911. The authors would also like to acknowledge the financial support from  France Energies Marines  and the State, managed by the  National Research Agency  under the " Investissements d'avenir" program  with the reference  ANR-10-IEED-0006-19  and the  FP7-EU   FLOATGEN  project with the support of the  European Union.
Electrophoresis migration was done in 1% agarose (Sigma) for 1 h at 100 V. Then, gel was stained in ethidium bromide with a concentration of 0. 5 μg/ mL for 20 min, rinsed, and visualized using «G Box» system (Applied Biosystems, USA). The obtained PCR products were purified by QIAquick PCR purification kit (ExoSAP-IT Affymetrix, USA), and sequenced bi-directionally using Big Dye Terminator Kit version 3. 1 (Applied Biosystems) to prepare sequencing reactions. The sequencing procedure was conducted by 3130 XL Genetic Analyzer according to the manufacturer's instructions (Applied Biosystems, USA). Then, the collected data were analyzed by data collection software version 3. 0 and sequencing analysis software version 5. 3. 1 (Applied Biosystems, USA). The sequences were manually trimmed and analyzed with the Basic Local Alignment Search Tool (BLAST).
Phylogenetic Analysis. The sequences were aligned with ClustalW, the evolutionary history was inferred using the neighbor-joining method, and the evolutionary distances  a F = forward; R = reverse. were computed using the maximum composite likelihood method (Mega7 software) with 1000 bootstrap replications. Salmonella enterica str. 08-00436, Salmonella Mbandaka str. SA20026234, Salmonella Agona str. 460004 2-1, and Salmonella Typhi str. CT18 were used as in-group strains, and Escherichia coli ATCC 8739 was used as an out-group strain to root the phylogenetic tree. Ethics. Ethical approval was not required in this study since no live animals or humans samples were used in the experiments.
The results showed that all isolated strains contain the 6 virulence genes (Table  4 ). Amplification and Sequencing of the 16S rRNA Genes. Amplification of 16S rRNA genes was performed on extracted genomic DNA of isolated Salmonella strains. The results showed that all examined strains generate an amplicon of 1500 bp (Figure  3 ). Then, PCR products were sequenced and analyzed by the online software "BLAST" (https://blast. ncbi. nlm. nih. gov/Blast. cgi?PAGE_TYPE=BlastSearch), and the obtained sequences were deposited in GenBank under the corresponding accession numbers (Table  4 ). Phylogenetic Analysis and Identification Criteria. The phylogenetic analysis of 16S rRNA genes organized all the strains into 3 clusters (I, II, and III), and cluster I is divided into 3 sub-clusters (Ia, Ib, and Ic) (Figure  4 ).
Specimens and strains. A total of 51 species HAdV-C strains were obtained between the years 2000 and 2017 from clinical specimens originating from the upper respiratory tract (URT, n = 9), lower respiratory tract (LRT, n = 2), feces (n = 27), blood (n = 12) and urine (n = 1). For 11 of these specimens adenoviruses were isolated on A549 cell cultures prior to sequencing, direct sequencing was applied to the other specimens (Table  1 ). Specimens originated from 40 patients; strains 6C1, 7C1 and 30C1 originated from a single patient as did strains 35C2, 36 C2, 37C2, 38 C2 and 39C2 from another patient. Strains 1C2 and 2C2 also originated from a single patient, as did strains 4C1 and 5C1, 14C2 and 15 C2, 33 C2 and 34 C2, 45C6 and 46C6. Specimens originated from different regions of Germany. Table  1  shows an overview of specimens sequenced, year of isolation and Genbank Accession Number.
Purified DNA of the other nine specimens (6C1, 19C2, 20C1, 23C2, 24C2, 35C2, 47C2, 48C2 and 50C1) was sheared by sonication, and sequencing libraries were prepared directly (KAPA library preparation kit, KAPA Biosystems, Wilmington, MA, USA) as previously described  49  , with a few modifications. DNA fragments were end-repaired, A-tailed and ligated to NEBnext Illumina adapter (New England Biolabs, Ipswich, MA, USA). After PCR pre-amplification (6-14 cycles) with adapter specific primers, up to 750 ng of DNA was target enriched for HAdV fragments with RNA baits. These 120-mer RNA baits were designed to span the length of the positive strand of 24 Genbank HAdV-C1, -C2 and -C5 reference genomes (SureSelectXT kit, Agilent Technologies, CA, USA). Next generation sequencing and bioinformatics.
After quality control, libraries were sequenced on an Illumina MiSeq platform (2*300 bp paired-ends run, v3 sequencing chemistry). The obtained sequence data was quality controlled and de novo assembled using CLC Genomics Workbench v9 (Qiagen, https://www. qiagenbioinformatics. com/products/clc-genomics-workbench/ and SPAdes v3. 6  50  with minimum average coverage of 22 and maximum average coverage 78111 per nucleotide. Genome annotations were transferred from the most closely related type and cross-checked by using an ORF finder followed by BLAST analysis using Geneious v10 (https://www. geneious. com/).
Phylogenetic and recombination analysis. A multiple alignment of whole genomic sequences was constructed using fast Fourier transforms (MAFFT)  51. This software (https://www. ebi. ac. uk/Tools/msa/mafft/) was applied using the default gap parameters. Pairwise alignment, comparisons and visualization of genomes were performed on BioEdit version 7. 2. 0 (http://www. mbio. ncsu. edu/BioEdit/page2. html). Bootstrapped, neighbor-joining (Kimura 2-parameter model) phylogenetic trees with 1000 replicates were constructed using MEGA v7 software. Similarity plots and recombination detection (bootscan approach) was performed using Simplot software (version 3. 5. 1) with a window size of 1000 bp and step size of 200bp  52. The following complete genomic nucleotide sequences representing all prototypes of the HAdV-C species were used in the analysis (Genbank accession numbers in parentheses): HAdV-C1 (AC000017), HAdV-C2 (AC000007), HAdV-C5 (AC000008), HAdV-C6 (FJ349096) and HAdV-C57 (HQ003817).
Acknowledgements The authors thank  Gabriele Harste ,  Jenny Witthuhn  and  Sandra Flucht  for excellent technical assistance and  Eric Delwart  for providing virological analysis in-kind. This work was supported by the  SFB 900  and  A. Dhingra  was supported by the  graduate program  " Infection Biology " of the  Hannover Biomedical Research School (HBRS).
Specimen (strain #/ species/type) Year Imputed serology (partial hexon sequencing) Source Derived from immunosuppressed patient (+/-) Clustering of complete genomic sequence with prototype Relabelled as novel type Accession Number 1C2 2000 2 Feces + 2 - MH121072 2C2 2000 2 Blood + 2 - MH121070 3C2 2002 2 Blood + 2 - MH121071 4C1 2002 1 Urine + 1 - MH121073 5C1 2002 1 Blood + 1 - MH121074 6C1 2008 1 Blood + 1 - MH121075 7C1 2008 1 Feces.
In hUCB-MSCs, higher levels of CDH2 expression correspond to a higher expression of vascular endothelial growth factor (VEGF) and greater therapeutic efficacy; thus, it is important to ensure the presence of high levels of CDH2 and VEGF in hUCB-MSCs to be used for cell therapy. 2  The ideal strategy to prime MSCs for elevating levels of CDH2 and VEGF would be small molecule, such as a peptide, which would be useful as a biomaterial component rather than artificial means, such as genetic manipulation. In this study, we used the cDNA array data (M01 versus M02) from our previous report to identify a priming factor. From among the genes with greater than three-fold high expression in the effective batch (M02), endothelin-1 (EDN1) was identified. To examine whether EDN1 could be used as priming agent, we investigated its ability to increase the expression of CDH2 and VEGF in the ineffective batch, M01.
3 database, and putative GATA2 and MZF1 binding regions were identified. To evaluate the transcriptional activity of these sites, we prepared point mutants (Figure  4B ). A point mutation in either the GATA2 (DGATA2) or MZF1 (DMZF1) binding site of the fragment significantly blocked the EDN1 effect on promoter activity (Figure  4C ). To determine the biological relevance of the transcriptional activity of GATA2 and MZF1 in this region, we used GATA2 and MZF1 expression vectors (Figure  4D ). The promoter activity of the À462to À335-bp region was increased with co-expression of GATA2 or MZF1, whereas no response was observed in each mutant form, even in the presence of co-expression of GATA2 or MZF1, respectively (Figure  4E ). Interestingly, luciferase activity was not turned on by MZF1 transfection in the DGATA2 mutant (DGATA2-MZF1), even with normal MZF1 site.
MATERIALS AND METHODS cDNA Microarray Total RNAs from hUCB-MSCs (M01 or M02) were isolated using TRIzol (Invitrogen) and the RNeasy Mini kit (QIAGEN). The purity and integrity of the total RNA were checked by Bioanalyzer (Agilent). Probe synthesis for total RNA, hybridization, detection, and scanning were performed according to the manufacturer's protocols (Affymetrix). Briefly, cDNA was synthesized from total RNA using the One-Cycle cDNA Synthesis Kit. After clean up with a Sample Cleanup Module, double-strand cDNA was used for in vitro transcription (IVT). cDNA was transcribed using the Gene-Chip IVT Labeling Kit, in the presence of biotin-labeled CTP and UTP. After clean up with a Sample Cleanup Module, labeled cRNA was fragmented in fragmentation buffer. Fragmented cRNA was hybridized to the Affymetrix Human Genome U133 Plus 2.
0 Array. After hybridization, the arrays were washed in a GeneChip Fluidics Station 450 with wash buffer and were stained with a streptavidinphycoerythrin complex. Staining intensities were determined with a GeneChip scanner 3000 using GCOS Affymetrix software. As a normalization process, RMA (robust multi-array average) normalization was applied to remove systematic variance. By applying RMA, the raw intensity values are background corrected, log2 transformed, and then quantile normalized. We identified genes that were differentially expressed using t test (p < 0. 05) among triplicated experiments. Culture and Treatment of hUCB-MSCs hUCB-MSCs (Medipost) were cultured in a-minimum essential medium (a-MEM) (Gibco-BRL) containing 10% fetal bovine serum (FBS) (Gibco-BRL), 10,000 units/mL penicillin G sodium, and 10,000 mg/mL streptomycin sulfate (InvivoGen, San Diego, CA, USA).
ACKNOWLEDGMENTS This study was supported by a grant of the  Korea Health Technology R&D Project Strategic Center of Cell and Bio Therapy  (grant number:  HI17C2085 ) and  Korea Research-Driven Hospital  (grant number:  HI14C1277 ) through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare (MHW) ,  Republic of Korea. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
However, the field of mutational signature extraction still lacks a unanimous consensus and standardization of analysis, often resulting in discrepancies between results from similar datasets obtained using different methodological approaches  4, 9, 10, 21, 22, [30] [31] [32] [33]. As WGS and WES are becoming common practice, with implications for both basic and translational research, we believe that more should be done to improve the performance and the reproducibility of mutational signature analysis. In this study, we use different publicly available bioinformatics tools to analyze public datasets from multiple myeloma (MM) and chronic lymphocytic leukemia (CLL) samples, and validate our findings in additional published and unpublished sequencing data from acute myeloid leukemia (AML) samples, to summarize the main factors that should be considered in a high-confidence mutational signature analysis.
Here, we first applied NNMF-based, de novo extraction algorithms, i. e. , the framework from Alexandrov et al. 6, 7. (Fig. 1a, b ) and the NNMF approach of the mutationalPatterns R package  37  (Supplementary Sofware 1). Both NNMF approaches extracted five signatures: the clock-like signatures (Signature 1 and 5 merged together), APOBEC (Signature 2), Signature 8, Signature 9, and a new signature named MM1, again highlighting the impact that NNMF approaches can have in new signature discovery (Supplementary Data 1)  6, 9, 16, 23. Then, using the same input data we then ran two fitting approaches (deconstructSigs and the fitting approach of mutationalPatterns) without a priori knowledge of the active mutational processes in MM and therefore including all 30 COSMIC signatures. DeconstructSigs forced the extraction of a large number of signatures, including ones not previously extracted by NNMF, and some of which clearly representing false positives (Fig.
8a-d ). These data are in line with the ability of WES to identify c-AID signature within the IG loci only among M-CLL cases  52  , and strengthen the need for WGS for a comprehensive signature analysis. Scores are ordered from highest to lowest and a classification threshold of 0. 7 is used to classify samples as HRD-positive (see Davies et al. 18  ). Below each score, the contribution of the six features that are used by HRDetect is shown. Each contribution is given by the amount of a feature in a sample, log-transformed and standardized according to mean and standard deviation of the features in Davies et al. 18  and finally multiplied by the corresponding HRDetect logistic regression coefficient. Thus, a positive contribution indicates a feature value higher than the average of the HRDetect original training set, and feature contributions are directly comparable.
11 ). Next, either a second NNMF run or a fitting approach using the NNMF shortlist can be performed on each single subgroup, as explained above  21. This inter-sample bleeding of signatures is of course a universal phenomenon and as such can be also observed in non-B-cell hematological malignancies. To extend the validity of our findings we therefore focused on acute myeloid leukemia (AML), where we (i) performed WGS on two cases of therapyrelated AMLs (t-AML) arisen after platinum-based chemotherapy for ovarian carcinoma and (ii) analyzed publicly available WGS data from the TCGA repository of primary AML cases (n = 50)  59. In this setting, we extracted four main mutational processes: Signature 1, Signature 5 and two signatures currently not included in COSMIC. Of these, one was recently associated with platinum exposure (platinum signature) and the second to the hemopoietic stem cell nature (HSPC Signature) (Fig.
7a, b  and Supplementary Data 1)  31, 38, [60] [61] [62]. The platinum signature contributed for >30% of the mutational burden of t-AMLs, but its activity was also found among primary AML from TCGA (Fig. 7c ). This is inconsistent with the prior knowledge of these samples being treatment-naive. Confirming that platinum signature in primary AML samples represents a further example of inter-sample bleeding, analysis of TCGA primary AMLs without the two t-AML cases led to disappearance of the Platinum Signature (Fig. 7d  and Supplementary Sofware 3). Furthermore, our analysis confirmed the added benefit of performing a de novo signature extraction as a first approach, as two out of four mutational signatures extracted in this cohort of 52 AMLs are not currently included in COSMIC.
The characterization of the main clinical and genomic features of MM and CLL series is summarized in Supplementary Table  1  and Supplementary Data 2, respectively. Kataegis was defined as a cluster of 6 or more consecutive mutations with an average intermutation distance of less than or equal to 1 Kb  20. The study involved the use of human samples, which were collected after written informed consent was obtained (Wellcome Trust Sanger Institute protocol number 15/046 for the myeloma samples, Fondazione IRCCS Istituto Nazionale dei Tumori code 127/16 for the t-AML samples). Mutational signature workflow. Mutational signatures were investigated using three published and available algorithms: the Alexandrov et al. 6  NNMF framework, deconstructSigs  24  and mutationalPatterns 37 R packages.
The full mutatio-nalPatterns analysis was written in R and the code is provided in Supplementary Sofware Files 1-3 for MM, CLL, and AML respectively. Each of the above methods produces a matrix decomposition C ≈ SE, where C is the catalog matrix, with mutation types as rows and samples as columns, S is the signature matrix, with mutation types as rows and signatures as columns, and E is the exposure matrix, with signatures as rows and samples as columns (Supplementary Fig. 1 ). The reconstruction error indicates how similar the mutational profiles of samples in C are to those in the product SE, and can be computed using different metrics, such as cosine similarity, Kullback-Leibler divergence (KLD) or RMSE. Each of the signatures extracted with either mutationalPatterns or the method from Alexandrov et al.
6, 7, 37  were assigned to one or a combination of two COSMIC signatures. To do so, cosine similarities between the extracted signatures and each COSMIC signature, or a linear combination of two COSMIC signatures (using non-negative least squares R package NNLS), were computed. These results are available in Supplementary Data 1. HRDetect in multiple myeloma. Analysis of homologous recombination deficiency (HRD) from BRCA1/BRCA2 deficiency as a possible source of genomic instability was performed using the recently published HRDetect algorithm  18. The structural variant and indel catalog in MM were generated using BRASS and Pindel, respectively  20, 67. Single-nucleotide variants on IGH. The mutation cancer cell fraction for c-AID SNVs was estimated using the Dirichlet process for both CLLs and MMs  4, 9.
Acknowledgements F. M. is supported by A. I. L. ( Associazione Italiana Contro le Leucemie-Linfomi e Mieloma ONLUS ), by  S. I. E. S. (Società Italiana di Ematologia Sperimentale ) and by the  Memorial Sloan Kettering Cancer Center NCI   Core Grant  ( P30 CA 008748 ). N. B. is funded by the  University of Milan  (project  22597-PSR2017_DIP_032 ) and by the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 817997 ). X. S. P. is supported by thr  Ministerio de Economía y Competitividad  Grant No. SAF2017-87811-R. F. N. is supported by a pre-doctoral fellowship of the  MINECO  ( BES-2016-076372 ). This work was supported by the  Instituto de Salud Carlos III  (project  PMP15/00007 , F. N. , E. C. ), the  "la Caixa" Foundation  Grant No  HR17-00221  ( Health Research 2017 Program , F.
N. , E. C. ), the  Ministerio de Economía y Competitividad (MINECO)   SAF2013-45836-R (E. C. )  from. Plan Nacional de I + D + I ,  Generalitat de Catalunya Suport Grups de Recerca   AGAUR 2017-SGR-1142  (E. C. ) and the  European Regional Development Fund "Una manera de hacer Europa". E. C. is supported by  ICREA  under the  ICREA Academia program. A. D. is funded by a  CRUK Pioneer  Award  C60100/A23433. S. N. Z. is funded by a  CRUK   Advanced Clinician Scientist Award  ( C60100/A23916 ) and a  CRUK Grand Challenge Award  ( C60100/A25274 ). This work was supported by:  Department of Veterans Affairs Merit Review  Award  I01BX001584-01  (N. C. M. ),  NIH  grants  P01-155258  (N. C. M. , H. A. L. , M. F. , P. J. C. , K. C. A. ) and  5P50CA100707-13  (N. C. M. , H. A. L. , K. C. A). We thank  Michael R.