dataesr/scientific-paragraphs-categorization huggingface dataset:
PCR products were purified on Purelink PCR micro kit columns (Invitrogen). Final libraries were quantified with bioanalyzer (Agilent) and Qubit (Invitrogen), and subjected to sequencing by Solid sequencer (Life Technologies). Sequenced fragments were mapped with the LifeScope 2. 5. 1 pipeline (Life Technologies) and annotated according to the Ensembl non-coding RNA database. Bioinformatics analysis: First, we checked the quality of the replicates using the PCA, then we used the webserver omiRas  62  to map the reads on the mouse genome, to normalize the data, and to compare each RNA-IP sample to the input. To avoid detecting differences at the level of transcriptional noise, we required a minimum of 20 reads for each small RNA identified. In order to specifically select small RNAs enriched in RNA-IP samples respect to the input, we employed Gaussian mixture modeling of the small RNA population distribution (Supplementary Fig.
RNA-sequencing experiments and analysis. To check whether Sfpq may control miRNA targeting by alternative splicing or polyadenylation sites, we performed a RNA-sequencing analysis from siSfpq-transfected P19 cells and control using the Truseq Stranded mRNA kit (Illumina) from 1 µg total RNA and performing 15 PCR cycles. The final library size was centered on 290 bp. Paired-end sequencing was performed on a NextSeq 500 sequencer (Illumina) on a Mid 150 flowchip. The quality of replicates was assessed using the R package "htSeqTools". Normalization of RNA-seq data was performed with the DEseq package available from Bioconductor (http://www. bioconductor. org). To identify differential splicing events between samples, we used the software SpliceTrap 63 (Supplementary Data 8). To assess whether Sfpq interferes with alternative polyadenylation sites and the length of the 3′UTRs we used the software DaPars 64.
Our analyses did not find any differential splicing or shortening-lengthening events regulated by Sfpq in those 3′ UTRs on which Sfpq promotes miRNA targeting (Supplementary Fig. 12a ). Additional bioinformatics and statistical analyses. GO-term analysis was done with the webtool "GoTermFinder" 50 and the pictures were generated using the tool REVIGO  65  and custom R script. miRNA seed searching in peak sequences was performed with a custom python script that takes as input the peak sequences and the miRNA sequences, then calculates the reverse complement of the seed region using the library "Biopython" 66. Venn diagrams were calculated with the webtool "Venny" (http://bioinfogp. cnb. csic. es/tools/venny/index. html). All statistical analyses were performed with the statistical software R, all other analyses were performed with custom python scripts where not specified otherwise.
Microarray analysis. For mRNA profiling analysis three replicates of P19 cells were transfected with siSfpq, mimic let-7a, siSfpq + mimic let-7a, or control (Supplementary Data 6). 48 h after transfection, total RNA was isolated by using RNAeasy kit (Qiagen). RNA samples were labeled with Cy3 dye using the low RNA input QuickAmp kit (Agilent) as recommended by the supplier. 400 ng of labeled cRNA sample was hybridized on 8 × 60 K high-density SurePrint G3 gene mouse GE 8 × 60 K Agilent microarrays. Assessment of the quality of replicates was performed using the Bioconductor package "ArrayQualityMetrics". Normalization of microarray data was performed with the Limma package available from Bioconductor (http://www. bioconductor. org) using the quantile methods. Cellular fractionation. Cellular fractionation of cytoplasm, nucleoplasm, and chromatin was performed as previously described  67  , with a modification for the chromatin solubilization according to a previous protocol.
Acknowledgements We are indebted to  Drs D. Baltimore ,  J. G. Belasco ,  K. W. Lynch ,  C. S. Bond , and  S. Saccani  for reagents and to  Mr. G. Bottini  for the user-friendly interface website. We thank  Drs D. Alcor  for imaging facility and  D. Byrne  for protein expression facility. This work has been supported by the  AVENIR program of INSERM and Marie Curie CIG  to M. T. ,.
Compliance With Ethical Statement This paper received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 649186 -Project ISIGrowth. The authors declare that they have no conflicts of interest. The vacancy rate for firm k then is the ratio between vacancies and the overall labour demand: v n,k (t) = V k (t) L k (t) +V k (t) (28) The vacancy rate for the whole economy is computed as the average of firms' vacancy rates weighted by their contribution to total employment (L(t)): L(t) = N ∑ n=1 F(t) ∑ f =1 Λ f (t) ∑ i=1 L i, f (t -1) + G ∑ g=1 (L g (t) + L 0,g (t)) v(t) = N ∑ n=1 F ∑ f =1 L n, f (t) L(t) v n, f (t) + G ∑ g=1 L g (t) L(t) v g (t) (29) A. 1. 2 Aggregate Consumption The selection procedure described in Section 2. 2. 2 is replicated H times per consumer class, representing a distribution of H random draws of perceived price and quality.
, 2006) , which uses the change of the characteristic path length under removal of individual nodes. They have been shown useful to identify key residues involved in the function of proteins, folding, allostery or long range interactions. Cytoscape is a tool for the visualization and analysis of networks  (Shannon et al. , 2003). Recently, the structureViz Cytoscape app  (Morris et al. , 2007)  integrated the possibility of generating RINs from a PDB structure through a connection made with the Chimera structure visualization and analysis program  (Pettersen et al. , 2004). This simplified the creation and analysis of such networks which can now be done with Cytoscape NetworkAnalyzer tools  (Assenov et al. , 2008)  or i. e. with its RINalyzer app, designed specifically for the purpose of RIN analyses and visualization  (Doncheva et al.
Example and validation of the approach Figure  1  illustrates the use of RINspector on the FimH structure (PDB: 3JWN) of the commensal Escherichia coli F18 strain. FimH is the tip adhesin of the E. coli type 1 fimbriae, which are responsible for adhesion of the bacterium to target cells. FimH consists of two relatively rigid domains that show inter-domain flexibility. We generated the RIN using structureViz (default parameters, hydrogens added) and performed a residue centrality analysis. Seven central residues (V155, R166, N33, T55, L225, T264, V154) are identified on the protein (Z-score 2). Indeed, R166 has a very high Z-score and is known to be associated to AIEC (Adherent-Invasive Escherichia coli) bacteria when mutated into cysteine, histidine or serine  (Dreux et al. , 2013). Moreover, mutating Y55 into alanine was shown to have an impact on adhesion  (Feenstra et al.
Discussion and perspectives In addition to investigating single structures, our approach can in principle also be used for an ensemble of conformations like those provided in PDB files from NMR experiments. Calculating centralities on the different conformations in the ensemble identifies those residues that are central in the ensemble. The console dialog provided by Cytoscape makes it possible to run script files but is resource intensive for these calculations. We are currently working on a command line workflow for batch generation of RINs and calculation of centralities. We also foresee, in a future release of RINspector, the integration of additional analyses like the calculation of Z-scores on eigenvector centralities. This would permit to identify clusters of residues, which could highlight zones of interest as opposed to individual residues.
Acknowledgments This project was cofunded by the  EU's Erasmus+ program  " Smoking Cession Advice: Healthcare Professional Training"  under grant agreement No. 2016-1-UK01-KA202-024266. The  European Commission  support for the production of this publication does not constitute an endorsement of the content, which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.
The development of a 3D or cube analysis to simultaneously fit a spectral and morphological model to the data is a challenge in Cherenkov γ-ray astronomy. The strong variation of the instrument response functions and of the residual cosmic rays background with the observation conditions makes it very difficult to build a coherent background model across the whole energy range. Nevertheless, with improving sensitivities and angular resolution of current Cherenkov telescopes and next generation instruments such as the Cherenkov Telescope Array, the complex morphology of the regions with diffuse emission or multiple sources requires the development of this technique. We developed a prototype for cube analysis along with corresponding background models for varying observations conditions. The prototype as well as the production of background model was implemented in the software Gammapy, an open source Python package for γ-ray Astronomy that provides tools to simulate and analyse the γ-ray sky for imaging atmospheric Cherenkov telescopes.
In the section 2, we will present the general principle of the 3D analysis we have implemented in the software Gammapy  [1] , an open-source Python package for γ-ray astronomy that provides tools to simulate and analyse the γ-ray sky for imaging atmospheric Cherenkov telescopes. In particular, we will briefly explain the method we adopt to construct a background model from H. E. S. S. data. In the section 3, we will present the Monte Carlo (MC) tool we developed to test the 3D analysis. From MC simulation realized by the H. E. S. S. collaboration, a systematic comparison between the 3D analysis and the classical 1D spectral fitting on point sources will be presented.
3D analysis Principle The 3D analysis is the simultaneous fit of a spectral and morphological model on a data cube. It is based on the comparison between a cube of measured events (ON cube) and a cube of predicted events by a combined model (figure  1 ). The 3D cube is defined for a given dataset using a 2D spatial binning (e. g. longitude, latitude) and a 1D energy binning. The ON cube is composed of the residual background as well as the γ-ray signal from sources. Therefore, in order to be compared to the data, the predicted data cube have also to be composed of a background (predicted by a background model) and from a γ-ray signal (predicted by the source model). The adjustment of the spectral and morphological parameters assumed for the source is obtained by a comparison between the number of predicted events by the model and the number of measured events in each energy bin and pixel of the cube.
PoS(ICRC2017)765 3D analysis in Gammapy • the mean exposure cube (EXPOSURE). In particular for the study of diffuse emission, it is really important to take into account the gradients of the exposure. This is why the spatial model is multiply by the exposure cube. The exposure is the product of the effective area by the time of each observation. The total exposure cube is the sum of the exposure cube for each observation of the given dataset. For a given dataset composed of N obs observations, it is evaluated at each true energy E true at the center of the energy bin of the cube and at each pixel with an offset θ i coord from the center of the FOV of the run i: Exposure(θ i coord , E true ) = N obs ∑ i=1 A i (θ i coord , E true ) × T i obs (2. 1) with A i the effective area and T i obs the livetime associated to the run i.
g. gene i encodes a transcription factor of gene j). The edges may also have signs, representing the modes of the gene regulation: positive for activation and negative for repression. In comparison to the other two classes of algorithms, there have been lesser algorithmic developments on the inference of such GRNs from single cell transcriptional profiles, possibly because of the extreme difficulty in this task  (Bacher et al. , 2016; Liu and Trapnell, 2016; Stegle et al. , 2015). One of the challenges in using single cell expression data for GRN inference is the zero-inflated characteristic of the dataset, resulting from both technical dropouts (mainly in RNA-seq data) and the stochastic bursty dynamics of the gene expression process  (Bacher et al. , 2016; Coulon et al. , 2010; Liu and Trapnell, 2016; Stegle et al.
, 2012). For these reasons, here we consider timestamped cross-sectional single cell transcriptional profiles, i. e. the expression profiles of single cells taken at multiple time points after cell stimulation. Such type of dataset is commonly generated in studies of cell differentiation process, where cells are induced to differentiate at the beginning of the experiment and are then collected at multiple time points for single cell analysis  (Chu et al. , 2016; Kouno et al. , 2013; Richard et al. , 2016). In this work, we created a network inference algorithm, called SINCERITIES (SINgle CEll Regularized Inference using TImestamped Expression profileS). The GRN inference was formulated as regularized linear regressions based on temporal changes of the gene expression distributions. The modes of the gene regulations, i.
Gene regulatory network inference using SINCERITIES Figure  1  illustrates the main steps of the gene regulatory network inference in SINCERITIES. In the following, let m be the number of genes, n be the number of measurement time points, and s k be the number of cells in the kth time point sample (k ¼ 1, 2,. , n). The time-stamped cross-sectional dataset (see Fig. 1A ) comprises n data matrices E s k Âm , where the matrix element E i k ;j is the transcriptional expression value of gene j, i. e. the amount of mRNA molecules of gene j in the ith cell at the kth time point. SINCERITIES is based on the assumption that changes in the expression of a transcription factor (TF) will alter the expression of the target genes. Thus, in the first step of SINCERITIES (see Fig. 1B ), we quantify the temporal changes in the expression of each individual gene by computing the distance of the marginal gene expression distributions between two subsequent time points.
While the most obvious distributional distance (DD) metric is the mean difference, the transcriptional regulation of a gene could alter the gene expression distribution beyond its first moment  (Altschuler and Wu, 2010; Vallejos et al. , 2016). In SINCERITIES, we make use of the information contained in the single cell gene expression dataset, particularly changes in the gene expression distributions, for the purpose of GRN inference. In the current implementation of SINCERITIES, we have chosen the Kolmogorov-Smirnov (KS) distance, i. e. the maximum absolute difference between two cumulative density functions, as the DD metric  (Massey, 1951). However, if desired and whenever appropriate, other DD metrics, such as the mean difference, Anderson-Darling (AD) statistics  (Anderson and Darling, 1952)  and the Cramer-von Mises (CM) criterion  (Anderson, 1962) , could also be used in place of the KS distance (see also Section 2.
; n À 2), as the response (dependent) variable, while setting the normalized DDs of all other genes from the previous time window l ( d DD p;l ; p ¼ 1; 2;. ; m) as the regressor (independent) variables. The linear regression is thus given by: d DD j;lþ1 ¼ a 1;j d DD 1;l þ a 2;j d DD 2;l þ Á Á Á þ a m;j d DD m;l (1) where a p, j is the regression coefficient describing the influence of gene p on gene j. The least square solution vector a Ã j is constrained to be non-negative since the normalized DDs take only non-negative values. In formulating the regression problem above, we have followed the standard mathematical statement of the Granger causality, and therefore made a simplification in which the relationship between the DDs of the regulators and those of the target gene is linear. While higher order (nonlinear) relationships could be incorporated into the regression problem above, the applications of SINCERITIES to in silico and actual single cell expression dataset below demonstrated that the linear approximation could provide reasonably accurate predictions of the GRN structure.
4). The sign of an edge is set to the sign of the corresponding partial correlation. In other words, a Presently, SINCERITIES cannot directly handle single cell data from stem cell differentiation process that produces more than one cell type (i. e. branching). In such a scenario, a pre-processing step is needed to group cells into individual cell lineages [for example, using time-variant clustering  (Huang et al. , 2014) ], and SINCERITIES could subsequently be applied to data from each differentiation branch. In the case studies, we tested SINCERITIES performance in inferring moderately sized GRNs. While there exist no technical limitation in applying SINCERITIES to single cell expression data with many more genes, for example using RNA-seq data, we expect that network inferability would become the limiting issue in such an inference  (Szederkenyi et al.
Ridge regression As shown in Figure  1C  and Eq. (  1 ), for each gene j, we solved a linear regression problem of the form: y ¼ Xa, where y denotes the n-2 vector of d DD distances of gene j corresponding to time windows Dt 2 to Dt n-1 , and X denotes the (n-2)Âm matrix of d DD distances corresponding to time windows Dt 1 to Dt n-2 , for all genes. To obtain the solution vector a, we performed a ridge regression penalized least square optimization as follows: min a ky À Xak 2 2 þ 1 2 kkak 2 2 (4) with the constraint that a i ! 0. We used GLMNET algorithm (MATLAB) to generate the regularization path, i. e. the solution a as a function of different k values  (Friedman et al. , 2010). In addition to ridge regression, we also tested SINCERITIES with two other penalty functions: the 'Least Absolute Shrinkage and Selection Operator' (Lasso) L 1 -norm penalty  (Tibshirani, 1996)  and the elastic-net penalty  (Zou and Hastie, 2005).
These alternative penalty functions however led to less accurate GRN predictions than the ridge regression (for further details, see Supplementary Material and Supplementary Table  S2 ). The optimal weight factor k above is typically data dependent. Here, we performed a leave-one-out cross validation  (Kohavi, 1995)  to determine the optimal weight factor k. In LOOCV, we allocated one row of y and X as the test dataset and the remaining as the training dataset. Then, we generated the regularization path for the training dataset using GLMNET, and computed the error of predicting the test dataset as a function of k. We repeated this exercise for every permutation of test and training dataset assignment, and selected the optimal k that minimized the average prediction error. Finally, we ran GLMNET on the full dataset and took the solution a* that corresponded to the optimal k value above.
We set A ij to 1 for gene activation, À1 for gene repression, and 0 otherwise. For the main dataset in the case study, we set the parameters to the following: V ¼ 30, b ¼ 1, h ¼ 0. 2 and r ¼ 0. 1. We simulated the SDE model above using the Euler-Maruyama method  (Higham, 2001)  with an initial condition x j (0) set to 0 for every gene, until the gene expression reached steady state (t ¼ 3 arbitrary time unit). For each GRN structure, we generated 100 stochastic trajectories for each time point (a total of 8 Â 100 ¼ 800 independent trajectories for 8 time points), representing 100 single cells. The simulations above mimicked the scenario where single cells are lysed for gene expression profiling. To test the robustness of SINCERITIES with respect to the intrinsic noise in gene expression and to the number of sampling time points, we further generated two additional datasets from the 10-gene E.
Evaluation of SINCERITIES using in silico data To evaluate the efficacy of SINCERITIES, we simulated in silico time-stamped single cell expression datasets using 10-gene and 20gene gold standard GRNs. The gold standard GRNs comprised 40 random subnetworks of E. coli and S. cerevisiae GRNs, i. e. ten networks for each size and from each species (see Supplementary File), generated using GeneNetWeaver  (Schaffter et al. , 2011). For the main dataset, we simulated single cell gene expression data for 100 cells at 8 unevenly spaced time points using a stochastic differential equation model (see Section 2. 5). In order to test the robustness of SINCERITIES with respect to the number of sampling time points and to the degree of stochasticity in the gene expression, we further generated additional datasets using the 10-gene GRNs above, for varying degrees of intrinsic noise (by changing r parameter) and different numbers of sampling time points (see Section 2.
The ROC curve was constructed by plotting the true positive rates (TPR ¼ TP/(TP þ FN)) versus the false positive rates (FPR ¼ FP/(FP þ TN)) for increasing q (q ¼ 1; 2;. ; m 2 ). Similarly, the precision (TP/(TP þ FP)) versus recall (TP/(TP þ FN)) curve was plotted for increasing q. Figure  2  shows the AUROC and AUPR values of SINCERITIES predictions for the main dataset, respecting the signs of the gene regulatory edges. As expected, the larger GRNs (20-gene) were more difficult to infer than the smaller GRNs (10-gene), as indicated by the lower AUROC and AUPR values. GRNs with a larger mean or maximum distance among the genes (nodes) were also more difficult to infer (see Supplementary Fig. S6 ). As we have shown previously (Ud-Dean and Gunawan, 2014), an indirect regulation of a gene by another (i.
e. a network distance of 2 or higher) is often predicted as a direct regulation, leading to a false positive error. Meanwhile, Table  1  gives the mean AUROC and AUPR values of SINCERITIES for the additional single cell dataset. In general, the performance of SINCERITIES decreased slightly with increasing intrinsic stochasticity. On the other hand, decreasing the number of time points did not appreciably change the performance of SINCERITIES. We further compared the performance of SINCERITIES to three other network inference methods, namely TSNI  (Bansal et al. , 2006) , GENIE3 (Huynh-Thu et al. , 2010) and JUMP3 (Huynh-Thu and Sanguinetti, 2015). TSNI (Time Series Network Inference) is a GRN inference algorithm developed for time series gene expression data, relying on a linear ordinary differential equation model of the gene transcriptional process  (Bansal et al.
Inferring GRN driving THP-1 differentiation In the following, we applied SINCERITIES to infer the GRN that drives the differentiation of monocytic THP-1 human myeloid  leukemia cell differentiation into macrophages. The time-stamped cross-sectional single cell data came from qRT-PCR expression profiling of 45 TFs in 960 THP-1 cells that were collected at 8 distinct time points (0, 1, 6, 12, 24, 48, 72, 96 h) after stimulation by 12-myristate 13-acetate (PMA)  (Kouno et al. , 2013). This dataset provided a good benchmark inference problem as the GRN of THP-1 differentiation has previously been constructed using deep sequencing (deepCAGE) and RNA interference (RNAi) experiments  (Tomaru et al. , 2009; Vitezic et al. , 2010). More specifically, we used a previously constructed anti-/pro-differentiation TF network  (Tomaru et al.
Inferring novel regulator(s) of T2EC differentiation In this application, we used SINCERITIES to infer the GRN associated with the differentiation process of T2EC chicken erythrocytic cells. The single cell RT-qPCR dataset comprised 90 genes at 0, 8, 24, 33, 48 and 72 h after induction to differentiate  (Richard et al. , 2016). The 90 genes were selected based on differential expression and clustering analysis of time-series bulk RNA-seq data from induced and uninduced T2EC cells, and included highly significantly upregulated and downregulated genes as well as non-differentially regulated genes. Here, there exists no gold standard network against which we could compare the accuracy of the inferred GRN. The goal of the analysis was to identify candidate novel genes that drive the differentiation process.