dataesr/scientific-paragraphs-categorization huggingface dataset:
Identifiability diagnostics Identifiability can be defined as the fact that M(θ 1 ) = M(θ 2 ) ⇒ θ 1 = θ 2  (Rothenberg et al. , 1971; Cole et al. , 2010; Cole and McCrea, 2016; Auger-Méthé et al. , 2020; Cole, 2020)  where M is a model fit descriptor (in a frequentist setting, M will be the likelihood L or some sufficient statistic). We classically distinguish intrinsic or structural identifiability  (Eisenberg and Hayashi, 2014; Cole, 2020) , that is not dependent upon a particular dataset, from extrinsic or practical identifiability, that is pertains to a given dataset. In both cases, identifiability refers to our ability to find a unique maximum of the likelihood. Unfortunately, the literature on identifiability is fraught with ambiguities; for instance  Raue et al. (2009)  referred to practical identifiability as being able to find finite confidence intervals, our more classic definition focuses on the identifiability of point estimates rather than intervals.
Identifiability was inspected in different ways depending on the statistical framework chosen. In a frequentist setting, in order to ascertain structural identifiability, we computed the Fisher Information Matrix (FIM) I T (θ true ) from the likelihood, defined as I T (θ true ) ij = E ∂ ln L(θ; Y T ) ∂θ i ∂ ln L(θ; Y T ) ∂θ j = -E ∂ 2 ln L(θ; Y T ) ∂θ i ∂θ j (10) for a dataset of length T , Y T = (X T , A T ). The FIM is critical to evaluating structural identifiability  (Rothenberg et al. , 1971; Eisenberg and Hayashi, 2014; Cole, 2020) , as it does not depend on a particular dataset. It is obtained here by taking expectations over many time series of length T. In practice, we use T = 100 and k r = 100 simulations; we previously computed the Hessian matrix for very large T , with similar results (the stochastic process is likely ergodic).
Moreover, as classical statistical theory dictates, asymptotically (as the time series length T gets large or as we average of over many datasets), the vector of parameters θ → N (θ true , I T (θ true ) -1 ). We therefore also computed the expected pairwise correlation between the parameters: the expected variance-covariance matrix of the parameters is defined by Σ = I -1 so that we get easily the expected pairwise parameter correlation matrix ρ = (ρ ij ), with each element defined as ρ ij = Σij √ ΣiiΣjj. This analysis was done here for the FIM, or expected Fisher Information, but as a check we have also performed similar inspections of the variance-covariance matrix derived from the Hessian at the maximum likelihood estimate (which is the observed FIM for one simulation,  Viallefont et al.
, 1998)  with similar results. Still in a frequentist setting, we computed extrinsic/practical identifiability by looking at parameter estimator distributions for 100 datasets of length T = 100; these results are presented in Appendix A2. In a Bayesian setting, we examined whether the prior and posterior distributions overlapped  (Gimenez et al. , 2009; Cole, 2020) , which is a classical Bayesian way to check that the model is identifiable in practice, in the sense that the likelihood brings enough information to the posteriors. We also inspected the correlations in the Markov chains for pairs of parameters, which translates into pair posterior distributions of parameters. Parameters whose chains were too positively or negatively correlated were considered to be unidentifiable separately, even when the means of the posterior distributions were close to the true values.
Discussion We have simulated data from a stochastic predator-prey model with a stochastic functional response, for parameters where the deterministic skeleton of the model would predict either convergence towards a stable fixed point or a limit cycle. These dynamic models, when environmental and interaction stochasticity were included, exhibited considerable variation in both population densities and kill rates, as could be expected from theory  (Nisbet and Gurney, 1982; Greenman and Benton, 2005). We then studied the identifiability of parameters in theory (structural identifiability, using the Fisher Information Matrix) and in practice (for a given dataset of finite time series length). We examined the relative contributions to identifiability of density time series and kill rate data. We have found that kill rate data is essential to identifiability in the case of a fixed point equilibrium perturbed by stochasticity, and that it greatly reduces bias and improves precision of functional response parameters in the case of a noisy limit cycle (formally, a noisy invariant loop).
Turchin and Ellner (2000) , using nonlinear forecasting to fit a similar stochastic predator-prey model to ours (whose likelihood had no closed form, because of continuous time), also reported a lesser identification of parameters for stable fixed points. We are therefore confident that our main result has some degree of generality. However, we concede that quasi-cycles  (Nisbet and Gurney, 1982)  with more discernible cyclic fluctuations (e. g. , closer to the discrete Hopf bifurcation,  Wiesenfeld, 1985; Neiman et al. , 1997)  should logically be easier to identify without kill rate data. In fact, quasi-cycles might, in many cases, be difficult to distinguish from limit cycles (but see  Louca and Doebeli, 2014). Surprisingly, our investigations of the RMA model in Supplement B1 do not quite appear to support that theory, since the functional response was poorly estimated without kill rate data for the QC parameter set.
Acknowledgements FB was supported by  Labex   COTE  ( ANR-10-LABX-45 ) and OG by the  French National Research Agency  (grant  ANR-16-CE02-0007 ). We thank  Bret Elderd  for insightful suggestions that helped improve the paper, as well as two referees and the associate editor for constructive comments. FB also thanks  Wojciech Uszko  for exchanges regarding functional responses.
Analysis of the cellular genome We applied the over-representation tool from the PAN-THER classification system to the list of DE genes. 1703 (90%) DE genes were annotated in the GO-database  [14]. Over-represented categories, which are more highly represented in the DE gene list than would be expected by chance  [15] , were determined for biological process, molecular function and cellular component. This resulted in a total of 38 over-represented GO-categories (Fig. 2ac ). 11 GO categories corresponding to cellular component were judged as too general and non-informative (Additional file 3: Fig  S2). Interestingly, 8 GO-categories (21%) corresponding to general transcription, and 8 GO-categories (21%) corresponding to rRNA synthesis were over-represented in the DE genes list. The mitochondrial part was also overrepresented with 4 GO-categories (11%).
Mitochondrial respiration was also assessed by SeaHorse analysis but did not show any difference between LCL-WT and LCL-AT (data not shown). In order to further analyze the DE gene pathways, we performed the Ensemble of Gene Set Enrichment Analyses (EGSEA)  [16]. Given the large amount of data generated (Additional file 6: Table  S3 ), we kept only Gene-Sets (GS) with adjusted p value < 0. 05 and absolute average log2FC with genes regulated in the same direction as the GS (|avg. logfc. dir|) > 2 (Fig. 3 ). GS related to cancer were particularly enriched. DE genes were enriched in the signature of the KRASdependent Prostate Cancer in both directions. This trend was also corroborated by the GS "Neoplasm" and "Breast Cancer". However, GS related to telomere maintenance was found to be significantly downregulated in LCL-AT.
Analysis of the viral gene expression The normalized viral counts were viewed using the IGV software (Fig. 4a ). A zoom on an intergenic region showed no reads, supporting the absence of contaminating reads from cellular DNA (Additional file 7: Fig. S4 ). The AT2, AT4 and AT6 lines express more viral transcripts than the other lines (Fig. 4b ). This could be explained by a difference in lytic cycle induction between all lines at the time of RNA extraction. Indeed, there is always 0. 1-4% of cells undergoing lytic cycle  [17]. EBVinfected cells in the latent phase express 9 latent genes and 2 non-coding EBERs, whereas during the lytic cycle the cells express around a hundred lytic genes under the control of BZLF1, the lytic cycle master-gene regulator  [1]. Knowing that cells in latent cycle express little viral RNA, a small difference in the proportion of cells undergoing lytic cycle would be enough to drastically change the total number of viral transcripts expressed in each line.
This allows us to analyze latent genes, regardless of lytic genes expression. Differential analysis of latent genes did not show any DE genes between LCL-AT and LCL-WT (Fig. 4d, e ) and all latent genes were expressed in LCL-WT and LCL-AT. We compared the cellular DE genes list with the non-redundant viral latent genes interactome available on the Polygenic pathways database. From this analysis we found 71 interactions representing 54 DE genes, of which 42 have an |log2FC|> 1. EBNA-3A appears to interact with 39 DE genes (Fig. 5a ), EBNA-3C with 11 DE genes (Fig. 5b ) and LMP1 with 11 DE genes (Fig. 5c ). These data suggest that although the EBNA-3A, EBNA-3C and LMP1 transcripts were not DE, the activity of these genes may be disrupted in LCL-AT. Interestingly, most of the DE genes described as downregulated or upregulated by EBNA-3A were also downregulated or upregulated in LCL-AT respectively, indicating that EBNA-3A may be more active in LCL-AT than in LCL-WT.
d and e Non-redundant genes in over-represented GO categories were regrouped into functional classes and displayed as heatmaps: d GO categories related to transcription (GO:0010467; GO:0006396; GO:0016070; GO:0051252; GO:0003723; GO:0090304; GO:0019219; GO:0034654). e GO categories related to ribosome (GO:0022613; GO:0042254; GO:0016072; GO:0006364; GO:0030684; GO:0005730; GO:0030686; GO:1990904). f All mitochondrial differentially expressed genes, as determined by the BioMart R annotation package are displayed as a heatmap. g Expression of rRNA in LCL-AT and LCL-WT shown as a percentage of total RNA expression for 28S and 18S + 28S. h Analyze of nascent RNA transcription in LCL-AT and LCL-WT 1 h after 10 µM 5-ethynyl uridine (EU) incorporation. i Analyze of nascent protein synthesis in LCL-AT and LCL-WT 30 min after 50 µM O-Propargyl-puromycin (OPP) incorporation.
Further studies are needed to address this hypothesis. Transcription of many mitochondrial genes were decreased in LCL-AT including several genes involved in the respiratory chain and in ribosomal protein synthesis. Inhibition of ATM leads to mitochondrial dysfunction and ROS production  [23]. The latter could be involved in the increased incidence of cancers in patients with ataxia-telangiectasia by increasing genotoxic stress. Fig. 3  EGSEA analysis of differentially expressed genes. The EGSEA algorithm was used to identify significantly induced or inhibited Gene-Sets from the MSigDB, GSetDB and KEGG databases. Significantly induced (green) or inhibited (red) Gene-Sets of interest are displayed, as given by EGSEA results with |avg. logfc. dir|> 2 and P. adj < 0. 05. Interesting Gene Sets were reordered in functionnal categories: Oncogenesis, Growth Factors receptors, Tumor suppression, Cytokines, B-cell phenotype and Immunity.
Reads quantification and differential analysis Salmon v0. 8. 2  [39]  was used to pseudo-align raw RNAseq reads to both human and viral genomes and get quantification estimates at the transcript level. The human reference genome GRCh38 was downloaded from the ENSEMBL website  [40]  (http:// ftp. ensem bl. org/ pub/ relea se-92/ fasta/ homo_ sapie ns/ cdna), and the EBV genome B95-8/Raji from Flemington Lab (http:// www. flemi ngton lab. com/ rnaseq. html). Differential analysis was performed using R software, version 3. 5. 0 [41]  and the negative binomial generalized linear modelling implemented in DESeq2 package version 1. 20. 0 [42]. A Wald test was applied on viral transcripts to perform comparisons among conditions. p Values were corrected for multiple testing using Benjamini-Hochberg correction  [43].
Gene ontology and gene sets analysis Differentially expressed genes were subjected to GO-Analysis, using the over-representation test within the PANTHER classification system  [50]  (version 13. 1). Statistically over-represented GO-categories (FDR < 0. 05) containing less than 25% of the input were selected for visualization. Functional analysis was carried out using the Ensemble of Gene Set Enrichment Analyses (EGSEA) R package (version 1. 8. 0). EGSEA uses 12 algorithms and combine the results by calculating a Wilkinson adjusted p-value. The EGSEA database consists of approximately 25. 000 genes-sets classified into 16 collections from the MSigDB  [51, 52] , GeneSetDB  [53]  and KEGG  [54]  databases.
(1) OVERVIEW An idiolect is the language of an individual and, like language in general, it is subject to change over time  (Dittmar, 1996; Kuhl, 2003). However, the notion of idiolect remains an understudied topic, especially in quantitative linguistics, due to the lack of relevant large corpora  (Barlow, 2010 (Barlow, , 2013;; Mollin, 2009). We thus developed the CIDRE corpus, the first corpus for the diachronic and quantitative study of idiolect in French (logo in Figure  1 ). Together with the EMMA corpus on 17 th -century English  (Petré et al. , 2019) , it is one of the rare quantitative resources suited to stylochronometry (see  Klaussner and Vogel, 2018 and Stamou, 2008  for examples of stylochronometric studies). With the purpose of collecting as much data per person as possible within one genre (to enable comparison), we decided to use the fiction works of prolific 19 th -century writers.
QUALITY CONTROL The first Python script, step1-getEBooks. py, is responsible for the correct naming of all e-books. The second, step2-convertToTei. py, removes prefaces, image descriptions and license declarations by first converting the epub file into a TEI file, using the software, then parsing through the TEI structure and selecting only the text that has been written by the author in the year of writing of the novel. Finally, a manual cleaning phase removed dedications and prefaces that remained undetected. (3) DATASET DESCRIPTION.
FORMAT NAMES AND VERSIONS Fiction works are distributed in repositories named after the authors' last name in. txt format. The filenames of the works always start by the year of writing, followed by _ and the title of the novel, with words separated by underscores. For example, 1886_Un_mysterieux_amour. epub. txt in the repository 'lesueur'. The metadata of the corpus is stored in a CSV file. The scripts to gather corpora from online libraries (e. g. Wikisource, Project Gutenberg, etc. ) must be executed using Python 3. Beforehand, one needs to install the Python packages selenium, Geckodriver (from https://github. com/mozilla/ geckodriver/releases), and pandoc (from https://pandoc. org/installing. html). 2 We chose the plain text format to facilitate import into different NLP tools. However, our programming scripts allow users to produce a TEI-format file from the epub downloads.
ACKNOWLEDGEMENTS We thank all contributors who worked on the open source projects we collected data and information from:  Wikisource ,  Project Gutenberg ,  La Bibliothèque  électronique du Québec, the website of  Les Amis de Daniel-Lesueur , the eBalzac Project and the  Gallica platform of the Bibliothèque nationale de France. We also thank the anonymous reviewers for their helpful suggestions.
3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of 3D EM. The pipeline employs convolutional nets to compute the nonsmooth transformations required to align images of serial sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as axon, dendrite, soma, and other semantic categories, and detect synapses and assign them to presynaptic and postsynaptic segments. The output of neuronal boundary detection is segmented by mean affinity agglomeration with semantic and size constraints. Pipeline operations are implemented by leveraging distributed and cloud computing.
Introduction Progress in 3D electron microscopy (EM) can be seen in the increasing size of imaged volumes from the Drosophila brain. A reconstruction of seven medulla columns was based on 0. 13 TB of raw image data  (Takemura et al. 2017). A "hemibrain" reconstruction was based on 20 TB of raw image data  (Scheffer et al. 2020). A 100 TB dataset of an entire Drosophila brain  (Zheng et al. 2018 ) has been automatically reconstructed and is currently being proofread (P. H. Li et al. 2019; S. Dorkenwald et al. 2020). Scaling up to even larger EM volumes is of clear interest for tackling mammalian brains. As a step in this direction, we have released a new automated reconstruction of a near cubic millimeter of visual cortex from a P87 male mouse  (MICrONS Consortium et al. 2021 ). The reconstruction is based on roughly 1.
5 petapixels of raw images acquired by serial section EM  (Yin et al. 2020) , a popular method of 3D EM. This is comparable in size to a recently released dataset acquired from human temporal cortex (A. Shapson-Coe et al. 2021). Both petascale datasets are much larger than recent cortical reconstructions, which were based on 0. 15 TB  (Motta et al. 2019 ) and 4. 9 TB (N. L. Turner et al. 2020; S. Dorkenwald et al. 2019; C. M. Schneider-Mizell et al. 2020)  datasets. The present dataset is accompanied by the visual responses of 75,000 cells in the volume recorded in vivo by calcium imaging prior to electron microscopy. In the plane tangential to the cortical sheet, our mouse cortical volume spans roughly 1. 3 mm (mediolateral) by 0. 88 mm (anterior-posterior), including portions of primary visual cortex (V1) and higher visual areas LM, RL, and AL  (Wang and Burkhalter 2007).
We will also refer to these cortical areas as VISp, VISlm, VISrl, and VISal  (Swanson 2018 ). In the radial direction, normal to the cortical sheet, the volume spans roughly 0. 8 mm. These dimensions were estimated by registering the EM image to a light microscopic image acquired from the same volume in vivo. The volume includes all cortical layers, though extremes of L1 and L6 were omitted, where the alignment of the serial section EM images could not handle the increased frequency of imaging artifacts. The goal of this paper is to describe the computational pipeline that was used to process the petascale dataset. Serial section images were aligned into a 3D image stack using a new approach based on convolutional nets that were trained to generate the nonsmooth transformations required to align images of sections that contain cracks and folds  (Mitchell et al.
Turner et al. 2020; J. Wu et al. 2021; Lu, Zlateski, and Seung 2021). Pipeline operations communicated with each other by using cloud storage to hold intermediate results, which could be viewed effortlessly for debugging purposes. The aligned images and automated reconstruction have been publicly released at the MICrONS Explorer (microns-explorer. org) site  (MICrONS Consortium et al. 2021). Code is also publicly available (see Data and Code Availability). While the automated reconstruction contains errors, these can be interactively corrected by human experts to yield accurate semiautomated reconstructions of neurons. This process is called "proofreading," and is described in a companion paper. After the images were acquired, the displacement field was inferred based on consecutive images using convolutional nets to align the images.
The microscopes were equipped with reel-to-reel mechanisms for handling GridTape  (Yin et al. 2020) , enabling round-the-clock imaging that was automatically controlled by custom software. The EM imaging data were acquired at a 4 nm pixel resolution. The axial resolution was set by the section thickness, which was nominally 40 nm. Unless otherwise noted, we employed a nominal voxel resolution of 4 ✕ 4 ✕ 40 nm 3. Each section image was assembled by stitching together thousands of tiles, each of which contained tens of megapixels. Most stitched montages were about 60 gigapixels, though the exact dimensions varied. A contiguous range of 19,939 sections was selected for alignment into a 3D image stack. During cutting and collection, virtually every section had developed numerous cracks and folds  (Fig.
Some defects were up to 0. 6 mm long. Many tiny folds, typically just a few microns long, were observed near myelin and blood vessels. Such folds generally occurred at roughly the same location in successive sections, making alignment difficult. In a previous terascale pipeline that was built to reconstruct a pilot dataset of the MICrONS project (N. L. Turner et al. 2020; Casey M. Schneider-Mizell et al. , 2020; Sven Dorkenwald et al. 2019) , we used traditional computer vision methods  (Saalfeld et al. 2012)  supplemented by human-in-the-loop exception handling. This left misaligned bands of tissue surrounding cracks and folds. We blacked out the misaligned regions, and our automated segmentation was able to trace neurites through the missing data with fairly high accuracy  (Sven Dorkenwald et al.
2019). However, the old approach was not applicable to the current dataset because it would have blacked out such a large fraction of the data due to the high frequency of image defects. Furthermore, the approach would have required prohibitive human-in-the-loop labor, because our petascale volume was 400✕ larger than the pilot dataset, and also had a much higher defect rate per unit volume. Instead we developed a new method based on four elements: (1) a multiscale convolutional net trained by self-supervised learning for one-shot prediction of a transformation that aligns a source image to a target image (Fig. 1a )  (Mitchell et al. 2019) , (2) a procedure for iteratively refining the transformation by gradient-based optimization of image similarity, (3) a convolutional net that identifies image defects (Fig.
This approach removes the need for a time-consuming global elastic relaxation, facilitating the longest series of sections ever aligned and eclipsing a previous record of 10,000 sections  (Morgan et al. 2016). This dataset contained a series of partial sections that occurred after a long hiatus in sectioning (Fig. 2e , Movie S1), during which the sample was retrimmed and the knife was replaced  (MICrONS Consortium et al. 2021 ). After the hiatus, the initial sections were partial because the new cutting plane was slightly tilted relative to the old cutting plane (Fig. 2e ). During alignment, sections were processed in a sequence that was opposite from the cutting order. In particular, the partial sections were processed from largest to smallest. Each partial section was completed by duplicating pixels from larger partial sections (Fig.
2f ). The smallest partial section was extended to a composite image of all the partial sections (Fig. S2 ). In the final aligned stack, there is a stretch of 38 partial sections that have been completed by compositing. In the "filled-in" region, neural morphologies are unnatural but traceable by human or computer. In a recent human cortex EM dataset, there were two stretches of tens of partial sections, which were treated similarly  (A. Shapson-Coe et al. 2021). It seems likely that robust handling of partial sections will be a standard requirement for petascale EM reconstruction.