dataesr/scientific-paragraphs-categorization huggingface dataset:
While the numbers of identified miRNAs were similar for all sequenced aneuploid cell lines, the extent of miRNA deregulation differed. We analyzed the differential miRNA expression in aneuploid cell lines in comparison to their diploid counterparts by applying DESeq2 for normalization and statistical testing  [33]. Since miRNAs with very low expression levels are characterized by low read counts and an inherent large variability on the logarithmic scale, these miRNAs were pre-filtered  [33]. This filtering for miRNAs with a read count greater than 10 reduced the number of identified miRNAs to 231-293 (Table  2 ). Using this dataset, we then determined the significantly deregulated miRNAs as miRNAs with the aneuploidy/diploid log2 fold change +/-0. 6 at adjusted p-value < 0. 05. We identified from 23 (in RPE1* 21/3) to 74 (in RPE1 5/3 12/3 and HCT116 5/4) significantly deregulated miRNAs (Table  2 ).
3a ). We employed IPA to infer an activation state of the biological functions as an activation z-score. The IPA activation z-score is a quantitative measure of the predicted effect of the deregulated miRNAome on a biological function  [38]. It is calculated based on the direction of miRNA deregulation and the literature-derived positive or negative effects of these miRNAs on a biological function. The available miRNA dataset allowed to retrieve the z-score for the sub-functional annotation terms defined by IPA within the categories "Cell Death and Survival", "Cellular Development, Cellular Growth and Proliferation", "Cellular Growth and Proliferation" and "Cellular Movement" (Fig. 3b ). The effect of the deregulated miRNAome was mostly negative, as the sub-functional annotation terms within the four parent categories were predicted to be inhibited or deactivated in HCT116 18/3, HCT116 5/4 and RPE1* 7/3.
5b ). The reporter additionally contains a Firefly luciferase gene that allows normalization for transfection efficiency. Indeed, the normalized luciferase signal was significantly lower in four out of five tested aneuploid cell lines (Fig. 5c ). No changes in luciferase signal were detected in the HCT116* 8/3 c7 cell line; this is likely due to a relatively minor increase in the hsa-miR-10a-5p expression in this cell line (see Fig. 5a ). The target database lists 298 unique target genes for hsa-miR-10a-5p with only nine previously validated targets. Only six of the targets were downregulated on both transcriptome and proteome level in HCT116 5/4 (Additional file 1: Figure  S5A ). Only two of the 270 hsa-miR-10a targets were downregulated on transcriptome and proteome in both RPE1 5/3 12/3 and RPE1* 21/3 (Additional file 1: Figure  S5B, C ).
RNA sequencing and data processing Following aneuploid cell lines and parental diploid cell lines were subjected to RNA sequencing: HCT116, HCT116 H2B-GFP (cell lines containing H2B-GFP will be indicated by * in the cell line name), HCT116 5/4, HCT116* 8/3 clone7, RPE1, RPE*, RPE1 5/3 12/3, RPE1* 21/3. Total RNA was extracted using RNeasy Mini Kit (QIAGEN). TruSeq RNA library preparation and Illumina HiSeq2500 sequencing with 25 million 100 bp single reads per library were performed by the Max Planck-Genome-Center Cologne, Germany. Subsequently, sequencing adapters were removed from the raw sequences with cutadapt and sequencing reads were mapped to the human genome (hg19) using TopHat (v2. 0. 10) with the following parameters: "tophat2 -g1 -G". RefSeq information in the GTF file was downloaded from the UCSC genome browser.
featureCounts (v1. 4. 3) was used to generate the count matrix with the same GTF file as for the alignment with the following parameters: "-t exon -g gene_id". Normalization and differential expression analysis was performed using the R/Bioconductor package DESeq2  [33]. For differential expression analysis, trisomic and tetrasomic cell lines were compared to the parental diploid cell line. The NGS data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE102855 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE102855). " Small RNA sequencing and data processing Following aneuploid cell lines and parental diploid cell lines were subjected to small RNA sequencing: HCT116, HCT116*, HCT116 5/4, HCT116 18/3 clone2, HCT116* The asterisk in the cell line name marks the presence of H2B-GFP 8/3 clone7, HCT116* 3/3, RPE1, RPE*, RPE1 5/3 12/3, RPE1* 21/3 and RPE1* 7/3.
fa human_hairpin_rmspace. fa -d -t Human". The three biological replicates of aneuploid and the corresponding parental cell line were analyzed within the same analysis run. For subsequent normalization and differential expression analysis miRNA_expressed_all_samples. csv was used as an input for DESeq2. Differential expression analysis was performed between aneuploid cell lines and corresponding parental diploid cell line. To account for sequencing batch effects, paired replicate information in addition to condition information was input to the DESeq2 analysis for HCT116 5/4, RPE1 5/3 12/3 and RPE1* 21/3 and corresponding parental cell lines. The genome sequencing data of all cell lines are available in the public repository of NCBI under the accession number GSE102855.
Integrative mRNA, miRNA and target analysis Data analysis, such as integration and data visualization was performed using the computing environment R. Mapping of identifiers as well as genome locations was performed with BioMart using biomaRt package in R  [54, 55]. miRNA (miRNA) identifiers were retrieved from miRBase (v21). miRNA target information was retrieved from miRTarBase (v6. 1)  [36, 56].
Functional annotation analysis miRNA differential expression datasets were analyzed using the Ingenuity Pathway Analysis Software  [37]. Core analysis was performed with the differential expressed miRNAs (log2 fold change +/-0. 6, adjusted p-value < 0. 05) against the Ingenuity knowledge base considering direct experimentally observed relationships in human species. Functional annotation analysis results were exported and visualized in R. Functional annotation analysis of target genes was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID v6. 8) by applying functional annotation clustering.
Neural Similarity Classifier All data is preprocessed with OpenNMT 3 , performing minimal tokenisation, basically splittingoff punctuation. After tokenisation, the 50, 000 3 http://opennmt. net most frequent words of each language are used as vocabulary. Each out-of-vocabulary word is mapped to a special UNK token. Word embeddings (LT s and LT t ) are initialised using fastText 4  , further aligned by means of MUSE 5  following the unsupervised method detailed in  (Lample et al. , 2018). Size of embeddings is E s = E t = 256 cells. Both Bi-LSTM use 256-dimensional hidden representations (E = 512). We use r = 1. 0. Optimisation of the parameters is done using the stochastic gradient descent method along with gradient clipping (rescaling gradients whose norm exceeds a threshold) to avoid the exploding gradients problem  (Pascanu et al.
Twelve spectra were thus obtained, imported into MALDI BioTyper software (version 2. 0; Bruker) and analysed by standard pattern matching (with default parameter settings) against the main spectra of 7567 bacteria (Bruker database completed with the La Timone database, including species isolated by culturomics and in our routine laboratory). The comparison with the BioTyper database spectra enabled the identification and discrimination of the analysed species from those in the database as a result of the obtained score: a score of >2 with a validated species enabled identification at the species level, and a score of <1. 7 did not enable any identification. If the colony was not identified, despite a clean spectrum, a sequencing of 16S rDNA was performed as previously described  [15]  to define taxonomic criteria.
Two samples were prepared with approximately 60 mg of bacterial biomass per tube collected from several culture plates. FAMEs were prepared as described by Sasser  [18]. GC/MS analyses were carried out as previously described  [19]. Briefly, FAMEs were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ 8 S; Perkin Elmer, Courtaboeuf, France). A spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (National Institute of Standards and Technology, Gaithersburg, MD, USA) and the FAME mass spectral database (Wiley, Chichester, UK).
Genome sequencing and assembly Genomic DNA of C. timonensis was extracted as previously described  [9]  and sequenced on the MiSeq sequencer (Illumina, San Diego, CA, USA) using the mate-pair strategy. The genomic DNA was barcoded in order to be mixed with 11 other projects using the Nextera Mate Pair sample prep kit (Illumina). All genome sequencing steps were performed following the process previously reported by Lagier et al. [9]. Genome assembly was performed using a pipeline that enabled the creation of an assembly with different pieces of software (Velvet  [20] , Spades  [21]  and Soap Denovo  [22] ) on trimmed (MiSeq and Trimmomatic)  [23]  or untrimmed (only MiSeq software) data. For each of the six assemblies performed, GapCloser  [22]  was used to reduce gaps. Contamination with Phage Phix was then identified (BLASTn against Phage Phix174 DNA sequence) and eliminated.
Genome annotation Open reading frames (ORFs) were predicted using Prodigal  [24]  with default parameters, but the predicted ORFs were excluded if they spanned a sequencing gap region (containing N). The predicted bacterial protein sequences were searched against the Clusters of Orthologous Groups (COGs) database using BLASTP (E value of 1e-03, coverage 0. 7 and identity 30%). If no hit was found, sequences were searched against the NR database using BLASTP (E value of 1e-03, coverage 0. 7 and identity 30%). If the length of the sequence was <80 aa, we used an E value of 1e-05. The tRNAScanSE  [25]  tool was used to find transfer RNA genes, whereas ribosomal RNA genes were found using RNAmmer  [26]. Lipoprotein signal peptides and the number of transmembrane helices were predicted using Phobius  [27].
16S rRNA phylogenetic tree Sequences were recovered after a nucleotide BLAST against the 16S rRNA database of the All-Species Living Tree SILVA project (LTPs119). First, a filter to eliminate sequences smaller than 1450 bp was applied. (Sometimes this filter is decreased to retrieve more sequences. ) Pass filter sequences were aligned using Muscle  [30] , and phylogenetic inferences were obtained using the approximate maximum-likelihood method within FastTree software  [31]. Numbers at the nodes corresponding to local values were computed by the Shimodaira-Hasegawa test. A filter using PhyloPattern  [32]  was applied to the tree to remove duplicate species and bad taxonomic reference species. This pipeline was performed in DAGOBAH  [28] , which include the Figenix  [29]  libraries.
Results Strain identification and phylogenetic analyses Strain sn7 T was isolated in May 2015 by cultivation on 5% sheep's blood-enriched agar under microaerophilic conditions. This strain was not identified by MALDI-TOF MS, and its spectrum was added to our database (Fig. 1 ). Sequencing of the 16S rRNA gene demonstrated that this strain sn7 T exhibited a nucleotide sequence similarity of 98. 4% with Cellulomonas cellasea (GenBank accession no. KR922256), the phylogenetically closest bacterial species with a validly published name (Fig. 2 ). Its 16S rRNA sequence was deposited in the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) database under accession number LN870311. This value was lower than the 98. 7% 16S rRNA gene sequence threshold recommended by Stackebrandt and Ebers  [4]  to delineate a new species without carrying out DDH.
C. timonensis shows negative reactions for oxidase, cystine arylamidase, trypsin, α-galactosidase, β-galactosidase, β-glucuronidase, α-glucosidase, α-mannosidase and α-fucosidase. Positive reactions were observed for catalase, esterase (C4), acid phosphatase, nitrate reduction, indole formation, glucose fermentation and esculin hydrolysis. Strain sn7 T showed resistance to colistin, fosfomycin, oxacillin and trimethoprim/sulfamethoxazole. The fatty acids are mainly composed of 12methyl-tetradecanoic acid (74%). This strain exhibited a G + C content of 72. 42% and a genome length of 4 057 828 bp. The 16S rRNA sequence and the whole genome shotgun sequence have been deposited in EMBL-EBI under accession numbers LN870311 and FCOT00000000, respectively. C. timonensis strain sn7 T (=CSUR P2058 = DSM 100699) was isolated from a stool sample of an obese Frenchman.
2541585 Energy production and conversion G 343 9. 353695 Carbohydrate transport and metabolism E 281 7. 66294 Amino acid transport and metabolism F 96 2. 6179438 Nucleotide transport and metabolism H 142 3. 8723752 Coenzyme transport and metabolism I 111 3. 0269976 Lipid transport and metabolism P 170 4. 6359425 Inorganic ion transport and metabolism Q 86 2. 3452413 Secondary metabolites biosynthesis, transport and catabolism R 248 6. 7630215 General function prediction only S 121 3. 2997 Function unknown - 1262 34. 415054 Not in COGs COGs, Clusters of Orthologous Groups database.
These condition numbers, and thus convergence, could be improved through the deflated PCG (DPCG) method, which is a two-level PCG method for ill-conditioned linear systems. Therefore, the first aim of this study was to compare the properties of the preconditioned coefficient matrices of ssGBLUP and ssSNPBLUP, and to document convergence patterns that are obtained with the PCG method. The second aim was to implement and test the efficiency of a DPCG method for solving ssSNPBLUP and ssPCBLUP. Results: For two dairy cattle datasets, the smallest eigenvalues obtained for ssSNPBLUP (ssPCBLUP) and ssGBLUP, both solved with the PCG method, were similar. However, the largest eigenvalues obtained for ssSNPBLUP and ssPCB-LUP were larger than those for ssGBLUP, which resulted in larger condition numbers and in slow convergence for both systems solved by the PCG method.
It has been suggested that the dimension of SNP-based models can be considerably reduced by applying random regression to principal components (PC) of the SNP genotypes, and that the remaining noise of genomic information can be ignored  [14]. To our knowledge, a linear system of equations of single-step principal component BLUP (ssPCB-LUP) has never been solved with the PCG method for large datasets. The ssGBLUP, ssSNPBLUP and ssPCBLUP models have linear systems of equations with sparse and symmetric positive (semi-)definite (SPSD) coefficient matrices. Thus, the preconditioned conjugate gradient (PCG) method is the primary choice as iterative solver for solving linear systems of ssGBLUP, ssSNPBLUP  [11, [15] [16] [17] , and of ssPCBLUP. The PCG method belongs to the family of conjugate gradient (CG) methods that are a realization of an orthogonal projection technique onto the Krylov subspace, which is generated by the initial residual and the system matrix (e.
Methods The first part of this section describes the ssSNPBLUP model that was proposed by Mantysaari and Stranden  [13]  and its re-parametrization into a ssPCBLUP model. The second part describes the CG, PCG, and DPCG methods. The last part describes the datasets used for comparing the properties of the system matrices of the different models, and for testing the DPCG method.
Data and models Two datasets, a reduced dataset and a field dataset, were provided by CRV BV (The Netherlands). To achieve the first aim of this study, the reduced dataset was used to compare properties of the system matrices ( M -1 C or M -1 PC ) of ssSNPBLUP and of ssGBLUP, and to relate this to observed convergence patterns. To achieve the second aim of this study, the field dataset was used to test the feasibility of the DPCG method for solving the linear system of equations associated with ssSNPBLUP and ssP-CBLUP applied to large multi-trait datasets. The reduced dataset and associated variance components were extracted from the Dutch single-step genomic evaluation from August 2017 for ovum pick-up (OPU) and embryo transfer of Holstein dairy cattle. After extraction of the OPU sessions, the data file included 61,592 OPU sessions from 4109 animals, and the pedigree included 37,021 animals.
The genotypes of 6169 animals without phenotype were available. Bulls were genotyped using the Illumina 50 K SNP chip. Cows were genotyped using the Illumina 3 K chip and were imputed to 50 K density using a combination of the Phasebook software  [30]  and Beagle  [31]. Because some currently used (own) libraries cannot handle sparse matrices with more than 2 31 -1 elements, and also to keep the system of equations at a reasonable size for subsequent analyses (e. g. , for the computation of all eigenvalues), genotypes included 9994 segregating SNPs with a minor allele frequency higher than or equal to 0. 01 and randomly sampled from (imputed) 50 K SNP genotypes. The univariate mixed model included random effects (additive genetic, permanent environmental, and residual), fixed co-variables (heterosis and recombination), and fixed crossclassified effects (herd-year, year-month, parity, age (in months), technician, assistant, interval, gestation, session, and protocol)  [32].
The field dataset and associated variance components were from the 4-trait routine genetic evaluation of August 2017 for temperament and milking speed of dairy cattle for the Netherlands and the Flemish region in Belgium  [33, 34]. Performance in both countries were considered as different traits, with genetic correlations between Flemish and Dutch traits higher than 0. 85. The data file included 3882,772 records with a single record per animal. The pedigree included 6130,519 animals. The genotypes of 15,205 animals without phenotype and of 75,758 animals with phenotype were available. Animals were genotyped in the same manner as described above. After removing non-segregating SNPs and SNPs with a minor allele frequency lower than 0. 01, genotypes included 37,995 segregating SNPs. The four-trait mixed model included random effects (additive genetic and residual), fixed co-variables (heterosis and recombination), and fixed cross-classified (herd × year × season at classification, age at classification, lactation stage at classification, milk yield and month of calving).
More details on this genetic evaluation can be found in  [33, 34]. For both datasets, genetic groups were removed from the pedigrees (for simplicity), the proportion w for resid- ual polygenic effects was assumed to be equal to 0. 05, and the centered genotype matrices Z for ssSNPBLUP and the matrices G -1 -A -1 gg for ssGBLUP were computed with the software calc_grm  [35]. For the field dataset only, a matrix T that contained PC kept for ssPCBLUP was also computed using the software calc_grm. The number of PC kept was equal to the number of the largest eigenvalues that together explain 99% of the genomic variation.
Statistical analyses Computation of eigenvalues and effective spectral condition numbers Properties of the system matrices and convergence patterns of ssSNPBLUP and ssPCBLUP were compared to those of ssGBLUP. For the reduced dataset, the spectrum of the preconditioned coefficient matrix, M -1 C , of both ssGBLUP and ssSNPBLUP, and of the preconditioned deflated coefficient matrix, M -1 PC , of ssSNPBLUP with different deflation-subspace matrices, were computed using Intel(R) Math Kernel Library (MKL) 11. 3. 2 subroutines. For the field dataset, due to the large number of equations (> 10 7 ), the smallest and largest positive eigenvalues of the different system matrices of ssSNPB-LUP, and ssPCBLUP were approximated using the Lanczos method based on information obtained from the (D)PCG method  [36, 37].
Solving ssSNPBLUP, ssPCBLUP, and ssGBLUP Linear systems of equations of ssSNPBLUP, of ssPCBLUP, and of ssGBLUP, were solved by using the PCG method. Systems of ssSNPBLUP were also solved by using the DPCG method with sets of 1, 5, 50, and 200 randomly chosen SNP effects per subdomain for the reduced dataset, and with sets of 5, 50, and 200 randomly chosen SNP effects per subdomain for the field dataset. The set of 1 SNP effect per subdomain was not used for the field dataset due to a Galerkin matrix of size 155,980, which was considered as too large for inversion. Similarly, systems of ssPCPBLUP were solved by using the DPCG method with sets of 1, 5, 50, and 200 consecutive PC effects per subdomain for the field dataset. For both the PCG and DPCG methods, the iterative process was run for a maximum of 10,000 iterations, or until termination criteria reached δ = 10 -6.
In addition, for both the PCG and DPCG methods, the preconditioner M was equal to: where the subscripts f and r refer to the equations asso- ciated with fixed and random effects, respectively, and blockdiag(C rr ) is a block-diagonal matrix with blocks corresponding to equations for different traits within a level (e. g. , an animal). For the field dataset, diagonal elements of Q = A gn (A nn ) -1 A ng were approximated using a Monte Carlo method  [40, 41]. Linear systems of equations of ssSNPBLUP, of ssPCB-LUP, and of ssGBLUP were solved by using a Fortran 2003 program exploiting BLAS and sparse BLAS routines and the parallel direct sparse solver PARDISO, all from the multi-threaded Intel Math Kernel Library 11. 3. 2, and OpenMP parallel computing. For the reduced dataset, the coefficient matrix C was held in memory using a compressed sparse row format, and the multiplication of P by a vector v required by the DPCG method, was per- formed as Pv = v -Ŵ E -1 Z ′ d v , where the brackets [•] indicate the order of the matrix-vector operations.
The matrices E -1 and Ŵ = CZ d were readily computed from the coefficient matrix C held in memory. These matrices were computed before starting the iterative process and held in memory. The number of OpenMP threads was limited to 3 for the reduced dataset. For the field dataset, the coefficient matrix C was reconstructed using a matrix-free approach when required for its multiplication by a vector. The matrix E -1 was held in memory. The computation of E was not as straightforward as for the reduced dataset because the coefficient matrix C was not held in memory. Therefore, the matrix E was computed following a suboptimal 4-step approach. The first step consisted of sequentially pre-and post-multiplying the coefficient matrix C by the first t deflation vectors of the deflation-subspace matrix Z d , i.
e. , the deflation vectors corresponding to the subdomains that included all effects associated with a same trait and other than the SNP effects. The second step consisted of computing each vector 1-w G -1 0 computed explicitly beforehand. Furthermore, because the matrix Ŵ = CZ d was too large to be held in memory for the field dataset, the multiplication of P by a vec- tor v required by the DPCG method, was performed as M = M ff 0 0 M rr = diag C ff 0 0 block -diag(C rr ) , Z ′ d,sub M ′ z W ′ g R -1 g W g M z z d, Pv = v -C Z d E -1 Z ′ d v. Due to this latter implementation of the multiplication of P by v , each iteration of the DPCG method requires two matrix ( C)-vector products, instead of one matrix-vector product for the PCG method. The number of OpenMP threads was limited to 5 for the field dataset.
All real vectors and matrices were stored using double precision real numbers, except for the preconditioner, which was stored using single precision real numbers. All computations were performed on a computer with 528 GB and running RedHat 7. 4 (x86_64) with an Intel Xeon E5-2667 (3. 20 GHz) central processing unit processor with 32 cores. Main random access memory (RAM) and time requirements are reported for the field dataset. All reported times are indicative, because they may have been influenced by other jobs running simultaneously on the computer.