dataesr/scientific-paragraphs-categorization huggingface dataset:
Problem definition The finite element software Ansys was used to perform the reliability evaluation of Terminal 2E Roissy at Charles de Gaulle Airport. The precast concrete shell in the collapse area consisted of six full and perforated arches. In this study, we chose to model the first nine rings from the isthmus including the six collapsed rings (Fig. 4 ). The finite element model was developed considering structure complexity, holes in the shell, connection between arches (fragile corners iron), shortened arches, asymmetry of the structure and loads applied. Moreover, MCS were performed exploiting the calculated response surface in order to obtain statistical results such as sensitivity analysis, histograms and cumulative function distributions.
As the structure safety is highly affected by the creep of concrete  [13] , several models were proposed to evaluate it such as the ACI 209R-92  [14] , the Bažant-Baweja B3  [15, 16] , the CEB Model Code 1990-99 (CEB MC90-99)  [17] [18] [19] [20] , the GL2000  [21, 22] , and the Eurocode 2 (EC2)  [12]. The concrete creep is affected by relative humidity, dimension of the element, composition of the concrete, type of cement, maturity of the concrete when the load was first applied (t 0 ) and duration and magnitude of the sustained load. In this study, Ansys calculates the total deflection taking into account the creep of concrete which was estimated using the creep coefficient w(t,t 0 ) according to EC2  [12].
Response surface method using Ansys The finite element simulations were performed using the finite element software Ansys, which includes the Probabilistic Design System (PDS) tool that allows the user to perform a reliability analysis. The finite element model used in this study was parametrically built, and the statistical distribution functions were defined for each variable as shown in Table  2. Ansys performed parametric analyses in which simulation software automatically solves entire ranges of specified variables. The aim of RSM is to approximate the input-output relationship by the mathematical function ŷ which was considered in this study as a quadratic polynomial in the form: ŷ ¼ c 0 þ X n i¼1 c i :x i þ X n i¼1 X n j¼1 c ij :x i :x j ð5Þ where c 0 , c i and c ij with i,j = 1,. ,n are the regression coefficients, and x i and xj are the n input variables.
Results and analysis As mentioned previously, the aim of this study is to calculate the reinforcement ratio required in the shell and to conduct a reliability assessment of Terminal 2E Roissy at Charles de Gaulle Airport by performing a nonlinear finite element analysis to know if the collapse could be predicted during the design phase by studying the parameters affecting the long-term deflection and by considering the permanent as well as the thermal and wind loads. The model used was parametrically built in Ansys. Fig. 7  shows the flexural moment in the shell at the ultimate limit state without taking into account the nonlinearity of materials' properties. The maximum moment in the shell is equal to 0. 111 MN. m which requires a reinforcement ratio of 0. 22 according to Eurocode 2. The existing reinforcement ratio was 0.
, 2018). Hence, the protein composition of our dataset largely resembles that of previously established RBPomes. Only 17 proteins displayed differential interaction with RNA at 4 hpi (Figures  2A, 2B , and S2A; Table  S1 ). Fifteen of these were detected exclusively by the semiquantitative method due to the lack of intensity value in one condition, reflecting possible. By contrast, 236 RBPs displayed altered RNA-binding activities at 18 hpi  (Figures 2C, 2D, and S2B; Table S1). A total of 247 RBPs displayed (E) Silver staining analysis of the ''inputs'' (i. e. , total proteome, left) and eluates (i. e. , RBPome, right) of a representative RIC experiment in SINV-infected cells. (F) qRT-PCR analysis of the eluates of a representative RIC experiment using specific primers against SINV RNAs, actb and gapdh (for normalization) mRNAs.
We included a positive control (the viral RBP SINV capsid [C]), two ''non-altered'' RBPs (MOV10 and EPRS), and a negative control (b-actin [ACTB]). Strikingly, the RNA-binding behavior of each protein fully matched the proteomic outcome, including those classified with 10% FDR (Figure  2E ). Changes in RNA binding increased progressively throughout the infection. The proteomic data assigned a nonsignificant downregulation to HNRNPR (Table  S1 ); however, the reduced activity of this protein was apparent by western blotting (Figure  2E ), suggesting that our dataset may contain false negatives. Nonetheless, the excellent agreement between the proteomic and western blotting data supports the high quality of our results. Determination of the RBP Networks Altered by SINV Infection Among the 247 altered RBPs, 133 presented reduced and 114 increased association with RNA, and they are here referred to as ''inhibited'' and ''stimulated'' RBPs, respectively.
However, it is unclear how this phenomenon is triggered and whether it benefits or hampers viral infection. Changes in RNA levels can globally be a consequence of reduced transcription and/or increased RNA degradation. To explore which of these pathways contribute the most to RNA loss in SINV-infected cells, we compared the fold change of each mRNA in our dataset to the rate of synthesis, processing, and degradation of each individual transcript  (Mukherjee et al. , 2017). Transcription could explain most of the differences at 4 hpi, whereas RNA degradation accounted for more than 50% of the explained variance at 18 hpi (Figures  5A  and S5A ). We reasoned that this phenomenon can be a combined effect of the activation of the 5 0 to 3 0 RNA degradation machinery, as the exonuclease XRN1 and its interactor, PATL1, are stimulated at 18 hpi (Table  S1 ), and a reduced transcriptional activity  (Gorchakov et al.
As with TRIM25, overexpression of TRIM56-EGFP reduced SINV fitness (Figure  7B ), confirming its capacity to restrict the infection of the RNA virus, SINV. Importantly, 160 out of the 247 altered RBPs lack previous connections to virus infection  (Garcia-Moreno et al. , 2018). Hence, our dataset likely contains numerous pro-and antiviral RBPs yet to be uncovered. GEMIN5 Binds to the 5 0 UTR of SINV RNAs and Regulates Viral Protein Expression GEMIN5 is a member of the survival motor neuron (SMN) complex, which mediates the assembly of the small nuclear RNPs (snRNPs)  (Gubitz et al. , 2002). It is strongly stimulated by SINV infection and redistributed to the viral factories co-localizing with SINV RNA (Figures  2E, 4C , and S4C). To our surprise, none of the known molecular partners of GEMIN5 (i.
Conventional protein analyses Samples were resolved on SDS-PAGE and analyzed by i) western blotting using specific antibodies, the Li-Cor Odyssey system for visualization and the Image Studio Lite software  (Li-Cor)  for quantification, ii) Coomassie blue staining with the InstantBlue Protein Stain reagent (Expedeon, #ISB1L) or iii) silver staining using SilverQuest kit (Invitrogen, #LC6070). Data shown in the manuscript are representative gels from at least three independent replicates. Details on antibodies can be found in the key resource table. Radioactive labeling of newly synthesized proteins was performed by replacing the growth media for 1 h with DMEM lacking methionine and cysteine and supplemented with Easytag EXPRESS 35 S Protein Labeling Mix [ 35 S]Met-Cys (Perkin Elmer, #NEG772002MC).
Samples were then analyzed by SDS-polyacrylamide gels (15%) followed by autoradiography. Reverse-transcription and quantitative PCR Total RNA was isolated using TRIzol (Invitrogen, #15596026). Reverse transcription was performed using Superscript III reverse transcriptase (Invitrogen, #18080044) with random hexamers priming (Invitrogen, #N8080127), following manufacturer's instructions. RT-qPCR analysis was performed with 2x qPCR SyGreen Mix Lo-ROX (PCRBiosystems, #PB20. 11-01) and gene specific primers (Table  S6 ) in a BioRad CFX96 Real-Time system, and analyzed with REST software  (Pfaffl, 2001).
Maisch GmbH, #r13. 9g) in solvent A using a constant pressure of 500 bar. Peptides were then separated on a 45 C heated EASY-Spray column (50 cm x 75 mm ID, PepMap RSLC C18, 2 mm, Thermo Fisher Scientific #164540) using a 3 h linear 8%-30% [vol/vol] acetonitrile gradient and constant 200 nl/min flow rate. Peptides were introduced via an EASY-Spray nano-electrospray ion source into an Orbitrap Elite mass spectrometer (Thermo Fisher Scientific). Spectra were acquired with resolution 30000, m/z range 350-1500, AGC target 1x10 6 , maximum injection time 250 ms. The 20 most abundant peaks were fragmented using CID (AGC target 5x10 3 , maximum injection time 100 ms, normalized collision energy 35%) in a data dependent decision tree method. Peptide identification and quantitation of all proteomics experiments was then performed using MaxQuant (v1.
5. 0. 35)  (Cox and Mann, 2008). Data were searched against the Human Uniprot database (version, January 2016) alongside a custom database including all the known SINV polypeptides and a list of common contaminants provided by the software. eGFP protein sequence was included in the analysis of GEMIN5-eGFP and eGFP IPs (Uniprot ID C5MKY7). The search parameters for the Andromeda search engine were: full tryptic specificity, allowing two missed cleavage sites, fixed modification was set to carbamidomethyl (C) and the variable modification to acetylation (protein N terminus), oxidation (M). Match between runs was applied. All other settings were set to default, leading to a 1% FDR for protein identification. Raw and processed proteomic data have been deposited to the ProteomeXchange Consortium  (Deutsch et al.
Libraries with an average length of 320 nt were pooled and sequenced with an Illumina  Molecular Cell 74, April 4, 2019 e8  NextSeq instrument, using 78 nt paired-end sequencing mode with a NextSeq 500/550 High Output v2 kit  (150 cycles, Illumina #FC-404-2002). Raw and processed RNA-seq are available at GEO: GSE125182.
Proteomic quantitative analysis To compare the cRIC inputs and eluates under different conditions, peptide intensity ratios between two samples were computed and summarized. The log2-intensity ratio of each protein was tested to be different from zero in the three biological replicates using moderated t test, which is implemented in the R/Bioconductor package limma  (Smyth, 2004). p values were corrected for multiple testing by controlling the false discovery rate with the method of Benjamini-Hochberg. For proteins for which the protein intensity was 'zero' in one of the two conditions, we applied a semiquantitative approach that assumes that proteins without quantitative information are below the detection limit  (Sysoev et al. , 2016). The approach compiles the number of replicates in each condition in which a given protein has an intensity value.
When comparing 2 conditions and three biological replicates, this leads to a matrix with 16 different groups (detected 0, 1, 2 or 3 times in condition 1 versus detected 0, 1, 2 or 3 times in condition 2). A protein is classified as 'altered RBP' by the semiquantitative method if an intensity value is assigned to it in 3 or 2 of the replicates in one of the two conditions, while only 1 or 0 intensity values are detected in the other condition. The fraction of RNA-bound RBPs was determined by computing the ratio between the protein intensity of each individual RBP in the cRIC eluates and that in the whole cell lysate (Figure  S3D ). Hence, this calculation reflects amount of protein crosslinked to RNA (cRIC eluates) divided by the total amount of protein (cRIC inputs). Results were visualized using the R package ggplot2  (Wickham, 2009).
To assess the scope of previously known RBPs within the RBPome of uninfected and SINV-infected HEK293 cells, proteins identified by cRIC here were compared to those compressing the superset of human RBPs reported in  (Hentze et al. , 2018). GO annotations were obtained from the R package mRNAinteractomeHeLa (http://www. hentze. embl. de/public/RBDmap/) (Castello et al. , 2012)  (Key Resources Table ), and gene set enrichment analysis was performed by applying Fisher's exact test to categories of GO annotations with at least three annotated proteins. We compared the repertoire of RBPs with differential RNA-binding activity at 18 hpi (Table  S1 ) with the mouse ribo-interactome (Table  S2 )  (Simsek et al. , 2017). Specifically, we considered proteins in the Table  S3  of  (Simsek et al. , 2017)  with negative predictive values (NPV) R 0.
99 in puromycin and RNase samples as 'ribosome-associated proteins', as described in that study. To find mouse orthologs for RBPs responding to SINV infection, we used the R package biomaRt to identify ENSEMBL peptide IDs for our RBPome dataset and hom. Hs. inp. db (Carlson and Pages, 2015)  to provide mapping between human and mouse proteins using these IDs (Key Resources Table ). If a mouse ortholog of an altered RBP identified at 18 hpi was found in the 'ribo-interactome'  (Simsek et al. , 2017)  or if the gene symbols between human and mouse matched directly, the human RBP was considered as 'ribosome-associated'. Results of this analysis are provided in Table  S2. For GEMIN5 protein-protein interaction analysis, protein quantification was performed by label free quantification using MaxQuant.
Ratios were compiled and normalized to eGFP protein intensity in each sample, which is expected to be the same across samples. Significance of the fold changes was estimated by t test using the software Perseus  (Tyanova et al. , 2016). We performed three main comparisons with the data from the IPs: i) GEMIN5-eGFP versus eGFP both in uninfected cells; ii) GEMIN5-eGFP versus eGFP both in SINV-infected cells; and iii) GEMIN5-eGFP in uninfected cells versus GEMIN5-eGFP in SINV-infected cells (Figure  7C , left, middle and right, respectively). Resulting data are summarized in Table  S5. Raw and processed proteomic data from GEMIN5-eGFP IPs have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD009789. The R package ggplot2 was utilized to visualize GEMIN5-eGFP proteomics data in volcano plots (Figures 7C).
Only proteins that were identified as high-confidence interactors of GEMIN5-eGFP (i. e. , p value < 0. 01 and positive log2 fold change) in the left panel of Figure  7C  were displayed in the comparison between infected and uninfected cells in the right panel. Proteins with names starting with 'RPS' or 'RPL', were classified as 'ribosomal' and displayed in the volcano plots as pink dots. STRING  (Szklarczyk et al. , 2017)  was used to display the connectivity between altered RBPs in SINV-infected cells (Figures  S2C  and S2D ) and between the proteins comprising the GEMIN5 interactome (Figure  S7D ). Protein networks were generated using the following parameters: display -confidence; Interaction sources -experiments and databases; interaction score -high-confidence (0. 700). Disconnected nodes were hidden from display and nodes colored based on functional enrichment within the network as determined by STRING.
899, 1 and 0. 473 for Mock, 4 hpi and 18 hpi respectively). We confirmed by RT-qPCR that rRNA does not change in abundance in course of infection. The R package DESeq2  (Love et al. , 2014)  was used for differential gene expression analysis based on rRNA normalized read counts. As DESeq2 requires the reads counts to be un-normalized and in the form of integer values, rRNA normalized read counts were rounded to the closest integer to make the ''DESeqDataSet'' to start the differential analysis. We estimated the size factor of each sample separately in DESeq2, instead of pooling all the samples prior to estimating this parameter. Differential RNA expression between infected (4 and 18 hpi) and uninfected cells was visualized in MA plots (Figures  3F  and 3G ) using DESeq2. To visualize the overall effect of experimental covariates and potential batch effects, a principal component plot of the samples was generated using the plotPCA function in DESeq2, based on the principal component analysis (PCA) of the variance stabilized expression of the top 500 genes with the highest expression variance among samples.
As shown in Figure  S3F , the variance explained by the first and second PC (on X and Y-axes) combined accounts for a high percentage (96%) of the total variance, and samples within the same condition clustered better between them than with the other two conditions. It is interesting to note that the first PC along accounts for 94% of the total variance, and it distinctly separates 18 hpi to the other samples (i. e. , uninfected and 4 hpi), indicating that the cellular transcriptome is dramatically altered at 18 hpi. Genes related to GO terms 'Response to virus' (GO:0009615) and 'Defense response to virus' (GO:0051607) were extracted from ''hsapiens_gene_ensembl'' dataset (GRCh37) from Bioconductor package biomaRt  (Durinck et al. , 2009)  and plotted as a heatmap using the R package pheatmap  (Kolde, 2015)  (Figure  S1E ).
This package was also used to make a heatmap for differentially expressed cellular RNAs, including those transcripts passing the following thresholds: i) log2 fold change > 3 or < À3 and ii) adjusted p value < 0. 01 (Figure  S3E ). Reads mapping to positive and negative strands of viral RNAs were separated using SAMtools view utility  (Li et al. , 2009). In Illumina reverse paired end sequencing, paired reads came from opposite strands. Therefore, reads with the second pair mapping to the positive strand, or with the first pair mapping to the negative strand, were both counted as mapping to the positive strand and vice versa. The total read counts mapping to each strand were compiled and counted using SAMtools merge and SAMtools depth, respectively.
iCLIP-seq data processing To identify GEMIN5 binding sites on SINV RNAs, reads in the fastq files from sequencing were demultiplexed to separate the samples according to the sample barcodes. Molecular and sample barcodes as well as trailing adaptor sequences were trimmed off. Molecular barcode information was stored in the read name. Reads were then mapped to a combined human (GRCh38) and SINV genome (pT7-SVwt) sequence using STAR. Uniquely aligned reads were then extracted using SAMtools. Binding sites were determined as the 5 0 -most base of each uniquely mapped read. PCR duplicates were identified as reads with the same mapping position and molecular barcode and each unique fragment counted just once. The 5 0 -most base in sequenced reads corresponds to the base directly 3 0 of the crosslinked base.
This process was done for the GEMIN5 replicates separately as well as for the library size normalized average of the five replicates. Figure  7D  additionally shows a heatmap that indicates how many replicates support a genomic position as binding site when determined individually per replicate. ggplot2 was used to facilitate plotting the heatmaps. To look at base composition around the start of the SINV sgRNA, the 5 0 -most base of unique fragments was extracted from aligned reads taking softclipping into account. Count per base relative to total count in the sgRNA region is show in Figure  S7H  to indicate relative binding site frequency and whether the sequenced base matches the genome. Raw and processed iCLIP-seq data are available at GEO: GSE125182. The number of counts (binding events) is highest at the region corresponding to the beginning of the sgRNA.
ACKNOWLEDGMENTS We dedicate this work to our colleagues and friends  Bernd Fischer  and  Katrin Eichelbaum , who sadly passed away during the development of this work. We thank  Matthias W. Hentze ,  Encarnacion Martinez-Salas ,  Javier Martinez ,  Kui Li ,  Quentin Sattentau ,  Ilan Davis ,  Richard Parton , and  Jan Rehwinkel  for reagents and advice. We thank the  Oxford Micron Advanced Bioimaging Unit  for support in microscopy experiments. A. C. is funded by  MRC Career Development Award   MR/L019434/1 ,  MRC  grant  MR/R021562/1 , and  John Fell Funds  from the  University of Oxford. M. G. M. is funded by the  European Union 's  Horizon 2020 research and innovation programme  under  Marie-Sklodowska-Curie  grant agreement  700184. I. D. is funded by  Wellcome Trust   Investigator Award   209412/Z/17/Z.
V. P. is funded by a  SciLifeLab Fellowship , the  Swedish Research Council  ( VR 2016-01842 ), a  Wallenberg Academy Fellowship  ( KAW 2016. 0123 ), and the  Ragnar So ¨derberg Foundation. L. C. is funded by grant  DGICYT SAF2015-66170-R (MINECO/FEDER ). E. G. A. was awarded with a short-term  EMBO  fellowship ( ASTF 358-2015 ) and an  FPU  fellowship  FPU15/05709. G. M. was supported by the  Wellcome Seed Award in Science  ( 210144/Z/18/Z ) and the  Wellcome Trust   Centre Core Grant  ( 092076 ). G. H. was a recipient of a  Wellcome Trust   PhD studentship  ( 105246/Z/14/Z ).
We include the following files, in Mathematicareadable format: • Analytic expressions for the basis set of one-loop five-parton amplitudes expressed in terms of one-loop master integrals, valid to all orders in ; • Expressions for all one-loop master integrals written in terms of pentagon functions through order 2 ; • The basis set of two-loop five-parton finite remainders, which are the main result of this work. For each remainder and at each power of N f , we give a list of independent coefficient functions r i , a matrix M ij and a list of pentagon functions h j , consistent with the decomposition of eq. (4. 11); • Scripts that assemble two-loop amplitudes from the remainders. In particular, the Mathematica script TwoLoopAmplitudesNumerical. m uses the analytic expressions to reproduce the numeric values of refs.
Acknowledgments We thank  L. J. Dixon ,  D. A. Kosower  and  M. Zeng  for many useful discussions. We thank  C. Duhr  for the use of his Mathematica package PolyLogTools. The work of S. A. is supported by the  Fonds de la Recherche Scientifique-FNRS ,  Belgium. The work of J. D. , F. F. C. and V. S. is supported by the  Alexander von Humboldt Foundation , in the framework of the Sofja Kovalevskaja Award 2014, endowed by the  German Federal Ministry of Education and Research. The work of B. P. is supported by the  French Agence Nationale pour la Recherche , under grant  ANR-17-CE31-0001-01. The authors acknowledge support by the state of  Baden-Württemberg  through bwHPC and the  German Research Foundation (DFG)  through grant no  INST   39/963-1 FUGG.
To elaborate the proposed approach, a real-world data set about an industrial semi-conductor manufacturing process is used to demonstrate the whole process. The remainder of the paper is structured as follows. In Section 2, the related works about using ontology-based approaches for predictive maintenance are introduced. In Section 3, the proposed ontology-based approach is demonstrated, including the description of the MFPO ontology, and the fuzzy SWRL rules for classifying failures. Section 4 elaborates the proposed approach according to a real-world industrial data set. Section 5 concludes the paper and outlines future perspectives.
Predictive maintenance data collection and analysis In smart factories, predictive maintenance tasks are performed by learning from historical maintenance data and predicting future events  [2]. Within manufacturing systems, system operation data are collected from sensors, actuators, and mobile devices that are located at machine components and also the manufacturing environment. Normally, data collected for predictive maintenance are represented as sets of sequences with time stamps  [20]. To deal with this type of data format, SPM techniques have been used to mine frequently occurring sequential patterns. In our approach, the aim of applying SPM to industrial data sets is to derive the temporal information of failurs. This enables the implementation of fuzzy clustering techniques on SPM results, for the identification of failure criticality based on their time of occurrences.