dataesr/scientific-paragraphs-categorization huggingface dataset:
Magnetic Measurements Magnetic susceptibility measurements were carried out in the temperature range 2-400 K for 1 and in the range 2-500 K for 2 with an applied magnetic field of 0. 1 T, on polycrystalline samples of both compounds (with masses of 11. 634 mg for 1 and 4. 065 mg for 2) with a Quantum Design MPMS-XL-5 SQUID susceptometer (San Diego, CA, USA) using a sample space oven for compound 2 (in the range 300-500 K). Consecutive cooling and warming scans at a scan rate of 2 K min -1 showed identical behaviours without any noticeable hysteresis. The susceptibility data were corrected for the sample holders previously measured using the same conditions and for the diamagnetic contributions of the salt as deduced by using Pascal's constant tables  [45]. The photomagnetic studies were performed by cooling the samples down to 10 K at a rate of 1 K min -1 and then irradiating them with a green Diode Pumped Solid State Laser DPSS-532-20 from Chylas (λ = 532 nm, power = 20 mW) coupled via an optical fibre to the cavity of the SQUID magnetometer.
Target, imprecision and assessment of laboratories performance Since the number of responses for each seminal biomarker was comprised between 3 and 7, the standard statistical plan of CTCB based on the NF ISO 13528 was not applicable. Thus, the consensus target value for each biomarker was defined as the all-laboratory median. The imprecision was calculated as the coefficient of dispersion (CD): CD ¼ MAD median Â 100 ð1Þ Where MAD is the median absolute deviation calculated as follow: MAD ¼ median x i j -median x i ð Þj ð Þ ð 2Þ The CD is a robust measure of the variability of a univariate sample of quantitative data and can be used as an alternative of the coefficient of variation in case of nonnormal distribution in biological applications  [10]. The use of z-score for estimating assays bias was not possible because of the low number of participants.
Thus, we set the analytical performance specifications according to the model 3 of the Consensus Statement from the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine (or Milan Consensus) which is based on the state-ofthe-art  [11]. Since no data were available in the literature, we analyzed the pilot scheme of 2014 in order to discuss and to set the first allowable limits of performance (ALP) for biochemical markers of SP. Laboratories results were then reviewed and scored according to a procedure described in supplementary data file (Supplementary data S1).
Results Patients and sample characteristics. Among the 37 patients initially screened, samples of patients 2, 7, 28 and 35 were excluded because their corresponding rarefaction curves and/or numbers of sequences were inadequate to allow biodiversity comparison. Samples from 33 patients, corresponding to 246,228 and 304,914 bacterial and fungal reads respectively, were used to evaluate microbial composition, diversity, and perform network analysis. Patients' characteristics, spirometry data, presence of conventionally cultured pathogens in sputum, and therapeutic management at sampling time are recorded in Table  1. Seventeen patients were classified as having CFPE while 16 were not. According to body mass index (BMI), Shwachman-Kulczycki score (S-K score), and spirometry data (in particular Forced Expiratory Volume in 1 s (FEV1) expressed as percent of predicted value), patients were in a medium to advanced stage of CF disease, with several co-morbidities (exocrine pancreatic dysfunction in 90% of cases, and diabetes mellitus or impaired glucose tolerance in 33% of cases).
Dataset from each sputum sample contained an average of 7,909 bacterial reads (ranging from 5,104 to 14,749), and an average of 9,811 fungal reads (ranging from 2,933 to 15,599). Alpha diversity indexes of the bacterial microbiome (a) but not of mycobiome (a′) were positively correlated with FEV1 values. When patients were divided into 3 groups according to FEV1 values  59, 71  , bi-dimensional PCoA representations (b,b′) based on Bray-Curtis similarity matrix did not show clustering between groups. However, we observed a high proportion (62% and 66%) of Pseudomonadaceae (mainly composed of Pseudomonas species) among patients exhibiting a moderate and severe disease, while it represents only 32% of the bacterial composition among patients with a mild ventilatory deficit (c). The fungal composition exhibited also some shifts, especially a decrease in OTUs belonging to Malasseziales in samples from patients with severe lung decline (c′).
Materials and Methods Patients and sputum samples. Among CF patients enrolled into a 3-year multicenter national prospective study (PHRC number 06/1902, acronym: MucoFong), 37 sputa consecutively collected at hospital centers of Lille (n = 19), Dunkerque (n = 3), and Grenoble (n = 15) were sequenced using NGS. They were alternatively selected from the first 64 sample collection  21  according to CFPE presence or absence, if spirometry, therapeutic, radiological and biological data were collected, and if residual sputum sample was sufficient to perform DNA extraction. The clinical status (pulmonary exacerbation or stable period) of each patient was defined by the physician as follow. Recent changes in clinical parameters and/or modifications of the pulmonary function provide criteria of exacerbation according to European Respiratory Society statement  10.
Targeted metagenomic library preparation, sequencing and taxonomic assignation. V3-V4 and ITS2 regions of bacterial and fungal rDNA were amplified and sequenced as previously described  37, 44. All reactions were prepared in a sterile PCR hood, and negative control reactions were performed and examined by electrophoresis on 1. 5% agarose gel. We obtained 429,565 and 443,270 pyrosequences with prokaryotic and eukaryotic loci, respectively. Sequencing data processing was performed using the Metabiote© pipeline where potential chimeras, read with poor quality (≤20 quality scores), short read (≤150 nucleotides for 16S rRNA gene, ≤100 nucleotides for ITS2 rRNA gene), homopolymers, singletons, and doubletons were removed before the Figure  6. Adaptation of Climax/Attack model (CAM) in CF as inferred from previous studies and our study  4, 7, 13, 14, 31, 32.
Genera in black refer to our bootstrap-enhanced Phy-Lasso analysis, and genera in grey to previous CAM studies. creation of Operational Taxonomic Unit (OTU). OTUs were created applying complete-linkage clustering with 97% similarity criteria (UClust v1. 2. 22q); OTUs' annotation was performed using Ribosomal Database Project classifier against Greengenes (v13. 8 containing 203,452 16 S rRNA sequences) and UNITE (v12. 11 containing 97,868 ITS1-ITS2 sequences) databases (Supplementary Table  1 ). Rarefaction curves were calculated to determine whether deep sequencing was sufficient to accurately characterize the bacterial and fungal community diversity, as previously described  44. Microbial community composition analysis. Bacterial and fungal data were analyzed at phylum to species levels. Clinical status was assessed using CFPE (patient acutely exacerbated or stable), S-K score, BMI, and the respiratory capacity measured by FEV1 value.
Bacterial and fungal community diversities were analyzed through the vegan and fossil R packages (v 3. 4. 3). Their structures were analyzed using QIIME (v 1. 9. 1) software and vegan package. To assess inter-kingdom equilibrium between the microbial communities, we computed a fungus-to-bacteria diversity ratio and a Basidiomycota-to-Ascomycota relative abundance ratio  35  that were compared between patient groups using Wilcoxon signed-rank test. Relative abundances of OTUs were compared between groups as follow: for each feature (be OTU, genus, etc. ) present in at least 10% of the patients, we first tested the distributional adequacy of the Poisson, the negative binomial and the Gaussian to the normalized non-zero data. Secondly, we considered the model presenting the best fitted distribution and we tested the differences between groups.
When none of the three distributions fitted, Wilcoxon signed-rank test was used. Goodness-of-fit tests for discrete distributions were implemented through the vcd package (v 1. 4. 4). The Shapiro-Wilk test was used to test for normality as well as graphical check. Bioconductor packages DESeq. 2 (v 1. 22. 1) and edgeR (v 3. 16. 1) implemented differential analysis for the model with discrete distributions, while metagenomeSeq package (v 1. 18. 0) implemented differential analysis for the zero inflated Gaussian model  72. Finally, each sample was divided into majority and minority populations (abundant population referring to an OTU relative abundance greater than or equal to 1%, and minority population as having a relative abundance of less than 1%), since their contribution may differ  12, 34.
According to Carmody et al. 15  , we defined a dominant OTU as the most abundant OTU having a relative abundance higher or equal than twice the relative abundance of the second most abundant OTU for each sputum sample. We compared both abundant and minority populations as well as dominant OTUs between clinical status groups. Inter-kingdom network analysis. To assess the relationships among microorganisms and to determine their underlying physio-pathological senses, joint analysis of fungal and bacterial relative abundance data was performed by estimating their correlation matrix. According to Gevers et al. 73  , to test the correlation while accounting for the compositional nature of data (i. e. proportions that sum to 1), the permutation-renormalization bootstrap method (ReBoot) was used.
We built both the null distribution of correlations from renormalized permuted data which represents the correlation structure arising purely from the compositionality, and the distribution of correlations from bootstrap sampled data referring to the confidence interval of the observed correlation. The compositional null distribution and the bootstrap distribution were compared by Z-test with the variance pooled from both distributions. The ccrepe Bioconductor package (v 1. 10. 0) implemented this ReBoot method, and was used here; it also allows applying network analysis up from 20 samples. The significance of association between features was based on the Spearman correlation as similarity measure (at the 0. 05 level). We generated 1,000 bootstrap replicates. In vitro co-cultures. A. fumigatus strain CBS144.
The experiment was done in triplicate. Identification of taxonomic traits independently associated with the clinical outcomes. Among a high number of OTUs (greater than the number of patient that we assimilated to a high-dimensional dataset), we determined which ones were significantly associated with clinical status by applying Least Absolute Shrinkage and Selection Operator (Lasso) regression  78  , after adjusting for the contribution of the other OTUs. Lasso linear or logistic regression models were used when the clinical status was measured, respectively, as a continuous (by FEV1) or binary (by CFPE) variable. In Lasso regression, a penalty on absolute coefficient size is added to the usual loss functions used for regression problems (mean squared error in the linear model and negative logistic log likelihood in the logistic model).
In Bolasso, only OTUs frequently chosen by Phy-Lasso over bootstrap samples are selected, which improve results stability. We generated 200 bootstrap replicates. The frequency thresh-old of Bolasso was specified to be conservative (70%), to not miss any OTU associated with clinical status. PhyLasso associated function as well as all the codes used in this study are available at: https://github. com/psBiostat/MucoFong-study. git, and summarized in the Supplementary Table  2.
Figure1. Diversity and taxonomy summaries of sputa collected from CF patients with a mild ( ), moderate ( ) and severe ( ) alteration of the lung function measured by FEV1. Dataset from each sputum sample contained an average of 7,909 bacterial reads (ranging from 5,104 to 14,749), and an average of 9,811 fungal reads (ranging from 2,933 to 15,599). Alpha diversity indexes of the bacterial microbiome (a) but not of mycobiome (a′) were positively correlated with FEV1 values. When patients were divided into 3 groups according to FEV1 values 59, 71  , bi-dimensional PCoA representations (b,b′) based on Bray-Curtis similarity matrix did not show clustering between groups. However, we observed a high proportion (62% and 66%) of Pseudomonadaceae (mainly composed of Pseudomonas species) among patients exhibiting a moderate and severe disease, while it represents only 32% of the bacterial composition among patients with a mild ventilatory deficit (c).
Acknowledgements Laurence Delhaes has received a research grant from " Vaincre La Mucoviscidose " ( RF20160501626 ); she is member of ESGHAMI and NGS-MycA working groups of  ESCMID  and ECCM respectively. Genoscreen Company  provided support in the form of  Louise-Eva Vandenborght's salary  ( AST CT 2016-394 ). All the funders had no role in article design, decision to publish, or preparation of the manuscript. The authors would like to thank  Katarzyna Hooks  (postdoctoral researcher at  University of Bordeaux ) for having edited the English language of the text, and  Binbin Xu  (postdoctoral researcher at  Inserm   BPH U1219 ) for his help on testing the R and Fortran codes.
Proteomic Data Analysis The acquired raw mass spectrometry data were processed using the PD (Proteome Discoverer 1. 3; Thermo Fisher Scientific, San Jose, CA, United States) software with the following parameters: mass range of parent ion, 350-6,000 Da; minimum number of peaks in MS/MS spectrum, 10; signal-to-noise ratio (S/N) threshold, 1. 5. The spectra extracted using the PD software were searched against the Basidiomycota_UniProt database using Mascot 2. 3. 0 (Matrix Science, London, United Kingdom) with the following identification parameters: fixed modification, carbamidomethyl (C); variable modification, oxidation (M), Gln→Pyro-Glu (N-term Q), iTRAQ 8 plex (K), iTRAQ 8 plex (Y), iTRAQ 8 plex (N-term); peptide tolerance, 15 ppm; MS/MS tolerance, 20 mmu; max missed cleavages, 1; enzyme, trypsin.
Based on the Mascot search results and the extracted spectra, protein quantitative analysis was performed using the PD software with the following parameters: protein ratio type, median; minimum peptides, 1; normalization method, median; P-value, < 0. 05; ratio, ≥ 1. 2. In this study, proteins with at least two unique peptides, scores more than 70, P-values less than 0. 05, and fold changes more than 1. 2 or less than 0. 83 were identified as DEPs between the different Pst strains. The fold changes of the up-regulated DEPs were more than 1. 2, and those of the down-regulated DEPs were less than 0. 83. For the convenience of expression, CYR32-5 vs. CYR32, CYR32-61 vs. CYR32, or CYR32-61 vs. CYR32-5 was used to represent the combination in which the proteome of the former was compared with that of the latter to screen DEPs.
Functional annotations of the proteins were carried out using the Blast2Go program 1   (Conesa et al. , 2005). The COG annotations of proteins were conducted using WebMGA 2   (Wu S. T. et al. , 2011). The annotations of the identified DEPs at the biological pathway level were performed using the KEGG database 3. The KOBAS 2. 0 software was used to annotate pathways by comparing similar protein sequences  (Xie et al. , 2011). Signal peptides were predicted and analyzed using the SignalP 5. 0 program 4   (Armenteros et al. , 2019). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium 5  via the iProX partner repository  (Ma et al. , 2019)  with the dataset identifier PXD018136.
Relative Quantification of mRNA by qRT-PCR Using the method described above, 20 mg of the germinated urediospores and germ tubes of each of the three strains CYR32, CYR32-5, and CYR32-61 were collected and treated as a sample. Total RNA from each sample was extracted using the RNeasy kit (Omega) according to the manufacturer's instructions, and then genomic DNA contaminants were removed by DNase I (RNase-free) treatment. The synthesis of the first-strand cDNA was performed by reverse transcription using the PrimeScript TM RT Master Mix kit (Takara, Dalian, China). Specific primers were designed for the selected DEPs according to their corresponding coding sequences using NCBI Primer-BLAST 6  (Table  1 ). Pst β-tubulin gene TUBB (GenBank accession No. EG374306) was used as the reference gene, and the corresponding primer reported by  Huang et al.
Overview of the Proteomic Data of the Three Pst Strains A total of 337,731 spectra were acquired from the eight samples of the three Pst strains, and 52,290 spectra were matched by searching against the Basidiomycota_UniProt database using Mascot 2. 3. 0 (false discovery rate < 1%). In total, 8,882 peptides and 2,271 proteins were obtained (Supplementary Table  2 ).
KEGG Pathway Enrichment Analysis of DEPs The biological pathways that the DEPs were involved in were investigated using the KEGG database, and 5, 7, and 7 significant enriched KEGG pathways (P < 0. 05) were acquired for the DEPs of CYR32-5 vs. CYR32, CYR32-61 vs. CYR32, and CYR32-61 vs. CYR32-5, respectively (Figure  3  and Supplementary Table  3 ). The DEPs of CYR32-5 vs. CYR32 were involved in pathways including metabolic pathways, biosynthesis of secondary metabolites, carbon metabolism, ribosome, and pyruvate metabolism. The DEPs of CYR32-61 vs. CYR32 were mainly involved in the carbon metabolism pathway, calcium signaling pathway, and melanogenesis pathway. The DEPs of CYR32-61 vs. CYR32-5 were mainly involved in the ribosome pathway, melanogenesis pathway, and calcium signaling pathway.
Each OTU was taxonomically assigned at 80% confidence score against the Ribosomal Database Project database (version 16) using mothur (version 1. 39) software (Michigan University, Ann Arbor, MI, USA)  [17]. To improve taxonomic assignment, the main OTUs unidentified at the genus level were blasted against the nucleotide collection database using Megablast in the Nucleotide BLAST (version 2. 8. 1) tool from NCBI (National Center for Biotechnology Information/National Institutes of Health, Rockville Pike, Bethesda, MD, USA). Based on the ZymoBIOMICS standard analysis, only OTUs whose abundance exceeded a cut-off threshold of 0. 016% were considered, to minimize false-positives rate (Table  S5 ). Alpha diversity was calculated using mothur. For sample comparison and mean value calculation, sequencing results were subsampled at the second lowest read count (28,977 reads) using mothur.
They were separated on an Acclaim PepMap 100 C18 column using a 60 min gradient of CH 3 CN in 0. 1% formic acid  [20]. The Q Exactive HF instrument was operated with a Top20 strategy with MS/MS on ions potentially charged +2 or +3 and a 10 s dynamic exclusion. MS/MS spectra were interpreted with the National Center for Biotechnology Information non-redundant fasta file (NCBI-nr) database with the Mascot (version 2. 6) search engine (Matrix Science, London, UK). Peptide to MS/MS spectrum assignation was performed with full trypsin specificity, a maximum of one missed cleavage, mass tolerances of 5 ppm on the parent ion and 0. 02 Da on the MS/MS, static modification of carboxyamidomethylated cysteine (+57. 0215), and oxidized methionine (+15. 9949) as dynamic modification. Mascot data files were post-processed to obtain taxonomical proteotyping based on peptide-to-taxa assignments.
By contrast, the other two main genera of the Actinobacteria phylum were uniquely detected by proteotyping. Proteotyping can be applied on any biological material, dead or alive  [23, 24]. The protocol used does not introduce specific bias depending on the Gram staining. Conversely, the metabarcoding approach suffers from non-quantitative representativeness, particularly for Gram-positive bacteria. Most classical DNA extraction protocols considerably underestimate this fraction, likely due to the higher mechanical resistance of the bacterial cell walls  [25]. Proteotyping with shotgun tandem mass spectrometry data probably gives a more accurate assessment of the biomass contributions of the main members of the consortia, despite biases linked to protein extraction, cell size, and a less extensive database  [10].
Acknowledgments: The authors gratefully acknowledge  Michel Delage  and  Claude Dubois  ( CEA-Saclay ,  DEN/DDCC/UADS/SEROS ) for providing access to the facility and for discussion on the reactor functioning,  Frédéric Coulon  and  Eric Latil  ( CEA-Saclay ,  DRF/PSAC/USPS/SPRE/SRP ) for completing the gamma spectrometry measurements and the radiological controls,  Stéphane Echivard  ( CEA-Saclay ,  DEN/DDCC/UADS/SEROS ) for the water sampling,  Jacqueline Martin-Laffon  (CEA,  DRF/BIG/PCV ) for bibliographic research, and  Kenneth McCreath  for English proof-reading of the manuscript.
Training datasets are obtained from a Representative Volume Element (RVE) model of the composite microstructure with a number of randomly-distributed linear-elastic fibers surrounded by a matrix with pressure-dependent plasticity. The approaches are evaluated with a comprehensive set of numerical tests comprising pure stress cases and three different stress combinations relevant in the design of laminated composites. The models are assessed on their ability to accurately reproduce the training cases as well as on how well they are able to predict unseen stress combinations. Gains in execution time are compared by using the trained surrogates in the FE 2 model of an interlaminar shear test.
Comparing the strategies The surrogate modeling strategies have been implemented in an in-house Finite Element code based on the Jem/Jive C++ numerical analysis library  [40]. All models were executed on a single core of a Xeon E5-2630V4 processor on a cluster node with 128 GB RAM running CentOS 7. The micromodel used as a basis for training the reduced-order models is the one shown in Fig. 1. This is the same RVE adopted by Van der Meer in  [7]  and is assumed to be sufficiently representative of the mechanical response of a mesoscopic material point. Material properties for both the micromodel and the calibrated mesomodel of Section 3 are also adopted from  [7]. In order to guarantee constant stress ratios in biaxial scenarios while avoiding large strain steps during the perfect plasticity regime, a special arc-length constraint a is adopted: a = i sign f Γ i u i -u = 0 with ∂a ∂λ = 0 ∂a ∂u i = sign f Γ i (53) with which the load factor λ that scales unit forces applied at the corner nodes of the RVE is controlled so as to guarantee that the unsigned sum of displacements at the same locations is equal to a prescribed value u.
All snapshots used for training come from models loaded monotonically with a constant stress ratio (proportional loading) until the norm of the strain at controlled nodes reaches a value of 0. 1. To test the trained surrogates, a homogeneous mesoscopic 1-element model 3  with a single integration point and the same dimensions as the original micromodel is used, with the fiber direction (1-axis) aligned with the mesoscopic x-axis. Neural networks with a single hidden dense layer are considered. Deeper networks with up to 5 hidden layers have also been investigated, but were found to provide lower accuracy than shallow networks with a similar number of parameters. Results from these deeper networks are therefore not included in the discussion. Unless otherwise specified, training sets are formed by randomly drawing 80 % of the samples of the original dataset without replacement, with the remaining 20 % serving as a validation set.