dataesr/scientific-paragraphs-categorization huggingface dataset:
Biological Dataset To show the usefulness of our approach in a biological setting, we reanalyze the dataset from  Eduati et al. (2017) , in which the authors measured 14 phosphoproteins under 43 different perturbed conditions (combinations of 5 stimuli and 7 inhibitors) in 14 colorectal cancer cell lines. Using CellNetOpt  (Terfve et al. , 2012) , they contextualized independent logical ODE models  (Wittmann et al. , 2009)  for each cell line, and proceed to train a statistical model using the cell-specific parameters to predict the responsiveness of the cell lines to a panel of drugs. This study provides an example of the use of system-level analyses to gain understanding of functional properties that cannot be inferred by genomic features alone. We normalized the data (log 2 difference compared to control) linearly to the [0-1] range across cell lines.
We optimized 49 models, varying the hyperparameter λ from 2 -20 to 2 5 , and we recovered the optimal parametrization for each cell line in the form of regularization paths. We used the value of 0. 01 as threshold for deciding if two parameters should be merged into a single one. For each value of the regularization strength λ, we computed the mean squared error (MSE) and the number of different parameters P in the regularized model, and from these calculate the Bayesian Information Criterion (BIC), which we calculate as Nlog(MSE) + log(N)P, with N the number of individual points in the dataset. Lower BIC values indicate models with favorable balance between goodness-of-fit and model complexity  (Schwarz, 1978; Burnham and Anderson, 2004). We selected the model with the lowest BIC for further analyses.
We grouped cell line-specific parameters together using the above-mentioned threshold, and re-optimized the model using the obtained topology without the regularization term, in order to obtain unbiased parameter estimates. We performed hierarchical clustering with 1000 bootstrap resamplings on the parameter values using WPGMA and euclidian distance. Furthermore, we investigated whether the recovered parameter values are associated with drug sensitivity. We downloaded the IC50 values for the 14 cell lines and 83 drugs directly targeting either one of the network's nodes or a target used in clinical practice to treat colorectal cancer from the Genomics of Drug Sensitivity in Cancer database (www. cancerrxgene. org). We computed the linear regression models between each drug and each of the 31 parameters which showed high variability between cell lines (CV ≥ 10%).
Biological Dataset In order to assess the applicability of our new method of regularization to uncover context-specificity in a realistic modeling setting, we reanalyzed the data from  Eduati et al. (2017)  using a Dynamic Bayesian Network adapted from the topology of the ODE model. The dataset comprised 8428 datapoints (14 phosphoproteins for 14 cell lines under 43 experimental conditions). We screened 49 values for the hyperparameter λ. The computation time was 1,761 h, or 42 h when parallelized among 49 computing cores. The results are presented in Figure  6. Minimum BIC was reached when λ = 0. 5, which corresponds to a model in which 26 of the 79 network parameters can be parametrized identically for all cell lines, and the remaining ones can be organized in 2-9 groups. Overall, the most variable parameter across cell lines is the ERK-EGFR negative feedback (Figures  6A, B ).
Indeed, functional relationships between molecules in cells, like enzymatic rates and binding strengths, usually exist in a small number of versions for a specific interaction. Because we do not expect these properties to change along a continuum but in a discrete way, it is natural to assume that model parameters of a regulatory network display the same type of structure. Our method efficiently reduces the complexity of network models. In our toy model example, we decrease the number of parameters from 32 to 11, and correctly recover the fact that two groups of cell lines exist and should be parametrized differentially. In our biological example, we decrease the number of parameters from 1,106 to 272, without increasing the error disproportionately. We show that this method is applicable to biological studies by re-analyzing the dataset from  Eduati et al.
Furthermore, our regularization scheme is easily adaptable to stronger or weaker priors. Equation 8 can be modified as follows: U = 1 N N i=1 U i w i ( 10 ) with w being the set of relative weights for the different parameters. When w i = 1∀i, all parameters are regularized with the same strength. This weighted average allows the specification of additional prior information, namely that the structural assumptions might not be true everywhere, or that our confidence in these assumptions might be stronger in some cases than others. It is likely that in the near future, single-cell proteomic studies will provide ever-larger datasets, therefore challenging modeling formalisms and requiring them to adapt to larger number of features  (Spitzer and Nolan, 2016). While statistical analyses have largely benefited from regularized parametrizations in the form of more predictive models, the current regularization objectives are not well adapted to the study of signaling networks.
A natural extension of this regularization scheme is to consider subsets of M parameters, corresponding to coherent parts of the model, like known signaling pathways. In that case, regularization will act simultaneously on the different constituent parameters of the pathway, and will allow the determination on cell line-specific pathway activity, a high-level information which is usually recovered by ontology-based pathway analysis. However, in such twostep analysis, the confidence for the different parameters is lost. In addition, ontology-based analyses use pathway knowledge from databases, thus suffer from their incompleteness and inaccuracy. Finally, although we have demonstrated the applicability of this novel method to the study of regulatory networks with logical models, it would be straightforward to extend its use to other modeling environments.
Shoot Apical Meristem Imaging To access the inflorescence meristem, flowers and floral buds were dissected out and imaged on a Leica SP8 up-right confocal microscope (Leica, Wetzlar, Germany). Fluorescence emission was detected at 525-600 nm in sequential line scanning mode with a line average of 4 and stacks of serial optical sections were generated. Projections of the signal in the L1 layer were obtained using MorphoGraphX software (http://www. mpipz. mpg. de/MorphoGraphX), according to parameter describe in MorphoGraphX User manual (http://www. mpipz. mpg. de/4085950/MGXUserManual. pdf). FM4-64, BFA, WM, PAO, R59022, R59949 and Cold Treatments The plasma membrane and endosomes of 5 to 7-day old transgenic lines expressing mCITRINE-C2 LACT were stained by incubating roots with 1 mM FM4-64 (thermofisher scientific, www.
Dissociation Index The effects of PAO, R59022, R59949 and PS depletion (lyso-PS add-back in the pss1-3 and lyso-PS/lyso-PA add-back in R59949 treated plants) on the localization of our charge biosensors were analyzed by calculating the ''dissociation index''. First, we calculated ''indexMock'': the ratio between the fluorescence intensity (Mean Grey Value function of Fiji software) measured in two elliptical region of interest (ROIs) from the plasma membrane region (one at the apical/basal PM region and one in the lateral PM region) and two elliptical ROIs in the cytosol in the mock condition. ''IndexMock'' was quantified in 150 cells over three independent replicates (50 cells per replicate). Next, we measured a similar ratio in perturbed conditions (''indexExp''). ''indexExp'' was also quantified in 150 cells over three independent replicates (50 cells per replicate).
Colocalization Analyses For quantitative co-localisation, we used an object-based analysis method (OBA). OBA is used to determine the centroid of each spot (intracellular compartment) and to compare their respective localization. Co-localization between the two structures is validated if the distance between the two centroids is below the optical resolution  (Bolte and Cordelieres, 2006). The OBA was performed as followed; first the intracellular compartments were automatically detected in each channel applying a ''DoG'' filter with a sigma of 3, and then a ''Triangle'' thresholding was applied  (Bayle et al. , 2017). Next, with the binary images obtained we used JACoP plugin on Fiji  (Bolte and Cordelieres, 2006)  to acquire quantitative data of the co-localization with the following parameters: minimum size of 3 mm 2 and maximum size of 20 mm 2.
The collision gas was also nitrogen; collision energy varied from -48 to -62 eV and +47 eV on a compound-dependent basis. Reverse-phase separations were performed at 50 C on a Luna C8 150x1 mm column with 100-A ˚pore size and 5-mm particles (Phenomenex). The gradient elution program was as follows: 0min, 30%B (isopropanol + 0. 2%formic acid + 0. 028%NH 3 ); 5 min, 50% B; 30 min, 80% B; 31 to 41 min, 95% B. The flow rate was set at 40 mL/min, and 3 mL sample volumes were injected. The areas of LC peaks were determined using MultiQuant software (version 2. 1; ABSciex) for relative phospholipid quantification. Quantification of molecular phospholipids species were performed on five independent samples for pss1-3 and pss1-4 and ten independent samples for Col0. Lipid Quantification for R59949 and R59022 Treatment (HPTLC) Treatments R59949 and R59022 8 day old seedlings (0.
QUANTIFICATION AND STATISTICAL ANALYSIS Quantitative co-localization results were statistically compared using the Kruskal-Wallis bilateral test (p-value=0. 05) using XLstat software (http://www. xlstat. com/). Pairwise comparisons between groups were performed according to Steel-Dwass-Critchlow-Fligner procedure (different letters indicate statistical difference between samples). For quantitative co-localization results of Figure  5E  we used the bilateral test Kruskal-Wallis test (p-value=0. 15) using XLstat software (http://www. xlstat. com/). Pairwise comparisons between groups was performed according to Dunn procedure (different letters indicate statistical difference between samples). Statistical analyses between two samples were performed using the non-parametric Wilcoxon-Mann-Whitney test (p-value=0.
ACKNOWLEDGMENTS We thank  A. Martinie  `re-Delaunay,  O. Hamant ,  M. Dreux , and the  SiCE group  for discussions and comments;  G. Du ,  T. Taguchi ,  S. Grinstein ,  B. Kost ,  T. Graham , and addgene for providing plasmids;  N. Geldner ,  K. Schumacher ,  I. Moore ,  D. Ehrhardt , the  GABI-KAT , and the  NASC collection  for providing transgenic Arabidopsis lines;  A. Lacroix ,  J. Berger , and  P. Bolland  for plant care;  J. C. Mulatier  for help in preparing lipids. We acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL)  facilities:  C. Lionet  and  C. Chamot  at the  LBI-PLATIM-MICROSCOPY  for assistance with imaging. Y. J. is funded by  ERC  no. 3363360-APPL under FP/2007-2013. L. C. N. , M. D. , and M. S. are funded by PhD fellowships from the  French Ministry of Higher Education.
Capture by hybridisation was performed using either Agilent Sure Select All Exon V5 [Agilent, Les Ulis, France] for WES or biotinylated complementary 120-bp RNA baits designed with SureSelect SureDesign software [H. sapiens, hg19, GRCh37, February 2009] that cover the 1130 regions of interest [ROI] encompassing all exons of the 66 selected genes. Targeted exons or ROI were pulled out with magnetic streptavidin beads, polymerase chain reaction [PCR]-amplified using indexing primers and sequenced on an Illumina HiSeq2500 HT system [Illumina, San Diego, USA]. Data analysis was performed with Paris Descartes University/Imagine Institute's Bioinformatics core facilities. Paired-end sequences were mapped on the human genome reference [NCBI build37/hg19 version] using the Burrows-Wheeler Aligner.
Downstream processing was carried out with the Genome Analysis Toolkit [GATK], SAMtools,  23  and Picard, according to documented best practices [http://www. broadinstitute. org/gatk/guide/ topic?name = best-practices]. Variant calls were made with the GATK Unified Genotyper based on the 72nd version of ENSEMBL database. Genome variations were defined using the in-house software PolyQuery for WES or PolyDiag for TNGS, which filters out irrelevant and common polymorphisms based on frequencies extracted from public databases: US National Center for Biotechnology Information database of single nucleotide polymorphisms [SNP] [dbSNP  24  ], 1000 genomes,  25  Exome Variant Server [EVS, http:// evs. gs. washington. edu/EVS/], and Exome Aggregation Consortium [ExAC, http://exac. broadinstitute. org].
For extracellular staining, cells were incubated with anti-CD3-Bv510, anti-CD14-Pe/Cy7, anti-CD11c-APC, anti-CD86-Bv421 [Sony Biotechnologies], and anti-CD19-Bv711 [BD Biosciences]. LRBA and NCF1 mutations were confirmed by absent protein expression on western blot performed on cell lysates according to standard protocols. Proteins were detected with monoclonal antibodies against human NCF1 [sc-14015 phox-p47, Santa Cruz Biotechnology, Dallas, USA], LRBA [clone HPA023597, Sigma], horseradish peroxidase-linked secondary antibodies or horseradish peroxidase-conjugated GAPDH rabbit antibody [Ozyme, Montignyle-Bretonneux, France], and ECL Prime Western Blotting Detection reagent [GE Healthcare].
Funding This work was supported by  ERC - 2013-AdG-339407-IMMUNOBIOTA, Investissement d'Avenir   ANR-10-IAHU-01 ,  Fondation des Maladies Rares , and  Association François Aupetit. FC-H was supported by a fellowship from  INSERM. JN received a fellowship from the  Polish National Science Centre  [ 2015/16/T/NZ5/00168 ].
Large-scale variations in replication initiation landscapes To study the evolution of replication initiation landscapes, we generated the first map of replication origins in a bird genome, by sequencing short nascent strands (SNS) in the chicken DT40 cell line (derived from a bursal lymphoma, see Methods). For comparison with mammalian origins, we reanalysed all previously published human and mouse SNS datasets  (Almeida et al. 2018 , Besnard et al. 2012 , Cayrou et al. 2015 , Picard et al. 2014 ) using the same detection pipeline based on scanning windows and family-wise error rate control, with a resolution of 500 bp  (Picard et al. 2014). This method detects genomic segments on which the accumulation of reads is significantly higher than the background, which depends on the global read depth of the experiment (Table  S1 ).
Given that the sensitivity of origin detection depends on the sequencing depth (Fig. S1 ), we sub-sampled SNS reads so that all three species reached the same sequencing depth (see Methods section). In human and mouse, SNS datasets are available for different cell lines (human: IMR90, H9, K562, HeLa and iPS; mouse: mESC10, MEFs). To limit differences due to tissue-specific origin activity, human/mouse comparisons are based on origins detected in H9 and mESC10 cell lines, both of which correspond to embryonic stem cells. As expected given the differences in genome sizes (1. 2 Gb in chicken vs. ∼ 3 Gb in mammals), we identified fewer origins in chicken  (33, 500)  than in human  (155, 395)  or mouse (205, 881) (Table  S1 ). However, variations in the number of origins were not entirely explained by genomes size since we observed a 2.
2 ). In mouse, 5% of the genome concentrates 26. 7% of the replication initiation activity, compared to 49. 7% in human and 65. 3% in chicken. Thus, origin activity is more uniformly distributed in mouse than in human and chicken (Fig. 2 ). It should be noted that for both human and mouse, we obtained consistent results with SNS datasets corresponding to distinct cell lines generated by different laboratories (Fig. S2 ). Thus, the differences in replication initiation landscapes observed between human and mouse were reproducible, and were not explained by the cell type variability. Interestingly, the more uniform replication initiation landscape that we observed in mouse compared to human coincided with a more uni-form GC content distribution  (Waterston et al. 2002). In agreement with previous observations in human  (Besnard et al.
In humans, we used data from the 1000 genomes project (based on 2500 individuals), focusing on single nucleotide polymorphisms (SNPs) for which the nature of the ancestral and derived alleles could be inferred by comparison with great apes genomes (1000 Genomes Project Consortium 2015). To detect signatures of selection, we analysed SNPs with different derived allele frequencies (DAFs). Common variants (DAF > 10%) generally correspond to relatively old mutations that have spread in the population as a result of genetic drift, selection or GC-biased gene conversion (gBGC)  (Glemin et al. 2015). Rare variants (DAF < 1%) predominantly correspond to recent mutations, which are less affected by selection or gBGC and hence are expected to more accurately reflect the prevalence of mutational events  (Kimura and Ohta 1973).
4-(C ), dotted line), it clearly indicated that the sequence of human replication origins was subject to quite strong purifying selection. Hence, the core region of replication origins included genetic elements that are important for their function. In mouse and chicken, polymorphism datasets are based on relatively small population samples, which does not allow the comparison of rare and common variants. However, profiles of SNPs density also revealed a specific signature in the ∼ 40-bp region centred on the SNS peak (Fig. S5 ), which suggested that origins are subject to similar selection patterns in the three species.
Since the compositional heterogeneities of genomes strongly impacted motif detection, the random segments were first clustered based on their kmer composition (5-mers) using k-means, hence providing clusters of homogeneous composition (see Methods, Supp. Table  S2 ), in which the motif search could be performed. We next annotated each core region of origins to a background cluster depending on their 5-mer contents and then searched for motifs cluster-wise to adjust the procedure to a heterogeneous background. Using the DREME software in the MEME suite  (Bailey 2011) , we selected motifs with strong e-values (≤ 1e -200 ) and further inspected motifs that were most enriched in origins compared with random segments. The detected motifs were all very short (≤ 6 bp), and strong enrichments were detected cluster-wise in motifs that were rich in CG dinucleotides for all species (Table  5 ).
2015) , hence indicating the potential existence of different classes of origins driven by distinct combination of DNA binding factors  (Aladjem and Redon 2017, Cayrou et al. 2015). However, the causes of the observed selective pressure remain enigmatic and might reflect constraints on the local structure of the DNA, rather than on sequence motifs  (Comoglio et al. 2015). Intriguingly, we showed that this selective pressure was not due to the presence of G4s, which tended to be located further upstream of the SNS peak (Fig. 3 ). There is evidence that G4s functionally important, but their presence is not sufficient to initiate replication  (Valton et al. 2014). One striking result of the present analysis is that despite this evidence of purifying selection, which was clearly visible in intra-species polymorphism datasets, inter-species comparisons revealed very limited conservation of replication origins on larger evolutionary scales.
All Illumina sequencing runs were performed at the GENOM'IC facility of the Cochin Institute. SNS data meta-analysis. For human and mouse data, we reanalysed SNS replication data generated in previous studies. Human H9, IMR90, HeLa and iPS data were from  (Besnard et al. 2012 ) and K562 data from  (Picard et al. 2014). The analysis of mouse data was performed on data from  (Cayrou et al. 2015)  and  (Almeida et al. 2018). For the  (Cayrou et al. 2015 ) dataset, we kept only 2 of the 3 replicates of the experiment (rep1 and rep2) due to the apparent low specificity of data collected for replicate 3 (as confirmed by personal communication with the authors). Origins detection. We reanalysed all SNS data using the same bioinformatics procedure. Reads were mapped using Bowtie2 (Langmead and Salzberg 2012) on hg19, mm10 and gal5 genomes.
We excluded reads with quality scores lower than 40, as well as those mapped to non-mappable regions of each genome. To detect origins, we applied the methodology from  (Picard et al. 2014 ) with a smaller threshold (500 bp instead of 2 kb in the original method), as we found that this window size better reflected the outcome of the SNS experiment. To study origins at the base pair scale, we defined "SNS peaks" as the position inside each origin for which the read accumulation profile was maximal. We computed this maximum on a smoothed read accumulation profile using a Gaussian kernel transformation with a bandwidth of √ 500 bp. Databases. CGI annotations were downloaded from the UCSC database (http://genome. ucsc. edu/index. html) and TSSs from the Eukaryotic Promoter Database (EPD: http: //epd.
vital-it. ch/) (Dreos et al. 2017)  for human and mouse, and from the Ensembl website (http://www. ensembl. org/index. html) (Aken et al. 2016)  for chicken. Detection of G-quadruplexes. G-quadruplex positions were detected on both strands based on the simple motif G 3 N 1-7 G 3 N 1-7 G 3 N 1-7 G 3 , using a dedicated python script (https://github. com/dariober/bioinformatics-cafe/tree/master/fastaRegexFinder). Resampling procedure. To correct for the higher sequencing depth of the chicken SNS experiment, we pooled together all human H9 replicates on the one hand, and all mouse mESC replicates, on the other hand, to generate one large human and one large mouse sample. These two datasets showed a similar sequencing depth of 28 reads per kb in human and 24 reads per kb in mouse. We randomly sampled ∼ 20% of the chicken SNS data to generate three independent datasets in the chicken genome with a sequencing depth of 28 reads per kb, comparable to the datasets in human and mouse.
To limit the inter-cell line variability, we focused on the human H9 cell line, because it is the closest cell type to mESCs in mouse for which SNS data are available. Cell type specificity. To control our analysis of origin cell type specificity (Fig. 6  (D)), we first pooled and subsampled reads of different replicates of each cell type experiment to generate 5 datasets (one for each cell line) with en equal sequencing depth (32 million mapped reads) and detected origins in these datasets. The sequencing depth of these datasets was much lower than the one previously described such that we in the new H9 set, we retrieved 80% of the H9 origins in the previous set only. Base-specific SNP density. The base-specific SNP density shown in Fig. 4  and S4 were defined as the number of observed x → y variants divided by the observed number of bases x, where x and y are either GC or AT nucleotides.
ACKNOWLEDGEMENTS M. N. P. and F. P. teams are supported by the  Association pour la Recherche sur le Cancer  ( Labellisation PGA120150202272 ). F. M. , M. -N. P. and F. P. are supported by the  Agence Nationale de la Recherche  ( ANR-15-CE12-0004 , OriMolMech). JMF-J and MG are supported by the  Spanish Ministry of Economy and Competitiveness  ( BFU2016-78849-P , co-financed by the  European Union FEDER  funds). This work was performed using the computing facilities of the computing center  LBBE/PRABI.