dataesr/scientific-paragraphs-categorization huggingface dataset:
EM and AD developed and maintained the PET code. AD provided new routines and methods, and generated the META3. 1exp datasets and the intermediate atlases. AD and CP developed and analyzed the similarity coefficient, interpreted the results, and wrote the online documentation. CP wrote the manuscript, with inputs from AD. RM aided in interpreting the results and writing the manuscript. GD and YF supervised the project. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
innocua. Surprisingly, the two isolates do not possess such virulence determinants. Instead, both strains carried a set of genes that encode for other virulence factors of the genus Listeria detected using the Virulence Factor Database (VFDB): iap (division and invasion of host cells), lpeA (entry into non-professional phagocytes cells), fbpA (multifunctional virulence factor, including adherence to host cells), lspA (surface protein anchoring), lap (adhesion to enterocytes and trans epithelial translocation), pdgA (resistance to lysozyme), oatA (resistance to different antimicrobial compounds and also required for growth inside macrophages), lplA1 (use of host-metabolites for in vivo growth), gtcA (catalyses teichoic acid of bacterial wall), prsA2 (cell invasion, vacuole lysis and intracellular growth), clpC, clpE and clpP (survival under several stress conditions).
The reads where trimmed using the software Trimmomatic (version 0. 39)  (Bolger et al. , 2014)   S2 ). Whole genome phylogeny was performed from the assemblies using the Enterobase Tool Kit (EToKi) pipeline  (Zhou et al. , 2020). First, the align module was run using the genome sequence of strain Clip11262 as the reference with the following options: -a-and -c 1. Then, the alignment output file obtained in the previous step was used to perform the phylogeny using the RAxML-NG software (version 0. 9. 0)  (Kozlov et al. , 2019)  with the following options: -model GTR+G, -seed 3 and -bs-metric fbp. Using this setup the bootstrapping converged after 550 replicates. The tree support values were drawn on the best-scoring tree (best value for estimated likelihood). To complement this information, a comparative test was performed to count the difference in single nucleotide polymorphisms (SNPs) between the two isolates.
To obtain specific SNPs between pairs of genomes, the nucmer module of the MUMmer software  (Marçais et al. , 2018 ) (version 4. 0. 0) was used with the settings -maxmatch and -l 12, followed by the show-snps module with the -CHlrT setting. Finally, genome sequence analysis was focused on the presence of genes encoding putative virulence factors. The software ABRicate version 1. 0. 1 (https://github. com/tseemann/abricate) was run with default parameters (≥80% sequence identity and ≥80% sequence coverage) using the core dataset from the Virulence Factor Database 2. 0 (VFDB) (last update in April 2020). The core dataset of VFDB only contains genes from virulence factors experimentally verified from the same genus as the query genome  (Liu et al. , 2019). For all the genes found using this approach, the presence of premature stop codons and partial sequencing coverage was checked by manual curation.
To search for orthologs of these putative virulence genes, the genomes were first annotated using Prokka  (Seeman, 2014) , to then retrieve target genes' protein sequences from the. faa files, together with their genome feature metadata (. gff files). Automatic annotations were confirmed by finding orthologous sequences on the UniProtKB database with two rounds of PSI-BLASTp (blast. ncbi. nlm. nih. gov) followed by multiple sequence alignments of hits (using Cobalt at the NCBI server), spanning different Listeria species and other bacterial genera including pathogenic species. These results also allowed us to manually A more extensive virulence profile of all the L. innocua genomes used in this work was also performed using the BIGSdb-Lm server from the Institut Pasteur  (Moura et al. , 2016) ; however, these results were not manually curated.
RESULTS Histopathology analyses of both CNS samples showed a moderate suppurative meningoencephalitis with presence of multifocal microabscess lesions, compatible with listeriosis. In both CNS cultures grew suspicious colonies of Listeria spp. The colonies were non-haemolytic, made up of Gram-positive rods, catalase-positive, capable of hydrolysing esculin, sucrose and glycerol fermenters, and unable to produce acid from D-xylose, D-mannitol or D-mannose. The API Listeria yielded the bionumber 7510 corresponding to L. innocua with a 99. 6% probability. They were PCR-negative for the gene encoding internalin A. According to these results, the isolates were identified as L. innocua, and named L. innocua 1074 (cattle isolate) and L. innocua 1174 (sheep isolate), respectively. Whole genomes from both isolates were sequenced (reads are available in the SRA database from NCBI; see supplementary Table  S2 ), and their ANIb analysis confirmed both as L.
innocua species (98. 8% identity with the reference genome). A whole genome phylogenetic analysis was performed, including the two isolates reported here and 12 additional genomes that span L. innocua diversity. The obtained tree showed that the local isolates obtained from both animals were closely related to each other (Figure  1 ). This was further studied by a SNPs comparative analysis, which resulted in a difference of 86 SNPs using the cattle isolate as reference, and of 115 SNPs if the reference was the sheep strain. Virulence factor profiling using the VFDB core dataset identified the presence of 13 genes conserved in both isolates as shown in Figure  1. Manual curation of these results showed that none of these genes had premature stop codons or failed to be covered by the sequencing process.
, 2020). The second gene present is prsA2, which encodes a peptidyl prolyl cis-trans isomerase that assists in correct protein folding. As such, PrsA2 regulates the maturation and secretion of some proprotein virulence factors (such as phospholipase C PC-PLC) of L. monocytogenes  (Forster et al. , 2011). Finally, three genes that encode proteases, clpC, clpE and clpP, were also identified; these are proposed to act as stress response mediators and to assist with intracellular replication  (Vázquez-Boland et al. , 2001). This genetic background might be consistent with virulence retention in both L. innocua isolates from clinical cases of listeriosis. The genes identified using VFDB and BIGSdb-Lm databases, code for virulence factors called minor or accessory but it has been proven that many are capable of promote cell invasion and/or intracellular replication  (Burkholder & Bhunia, 2010; Forster et al.
, 2011; Keeney et al. , 2007; Meireles et al. , 2020; Osanai et al. , 2013; Rae et al. , 2011; Réglier-Poupet et al. , 2003a,b; Vázquez-Boland et al. , 2001). It is noteworthy that genes identified in both isolates are present in most of the reference strains of L. innocua from the NCBI genome database. However, none of them were obtained from diseased ruminants. As we mentioned above, there are only two cases of animal listeriosis due to L. innocua reported previously, but their genome sequences are not available to compare with those described here. Regarding that just one of several animals per farm was affected, other issues to be considered in order to attempt to explain the situation are (i) the previous animal health condition and (ii) bacterial exposure load to which these animals were subjected.
I G U R E 1 Whole genome maximum likelihood phylogenetic tree including the two reported L. innocua strains (L innocua 1074 and L innocua 1174) and another 12 representative strains of the L. innocua diversity, selected from the NCBI Genome Database. The tree is rooted to the LIN67 leaf and the reference genome is Clip11262. Coloured boxes represent the presence of the different genetic traits obtained with the VFDB core dataset. Red boxes indicate the presence of a complete gene. Red boxes with stars indicate truncated genes due to the presence of internal stop codons. Pale orange boxes indicate genes partially covered by the assembly. FBP bootstrap values are shown confirm conservation of key residues of target proteins in some cases.
F I G U R E 1 Whole genome maximum likelihood phylogenetic tree including the two reported L. innocua strains (L innocua 1074 and L innocua 1174) and another 12 representative strains of the L. innocua diversity, selected from the NCBI Genome Database. The tree is rooted to the LIN67 leaf and the reference genome is Clip11262. Coloured boxes represent the presence of the different genetic traits obtained with the VFDB core dataset. Red boxes indicate the presence of a complete gene. Red boxes with stars indicate truncated genes due to the presence of internal stop codons. Pale orange boxes indicate genes partially covered by the assembly. FBP bootstrap values are shown confirm conservation of key residues of target proteins in some cases.
To understand the dynamic nature of the genome in real time, the localization and rearrangement of DNA and DNA-binding proteins must be analyzed across the entire nucleus of single living cells. Recently, we developed a new computational light microscopy technique, called high-resolution diffusion mapping (Hi-D) 1 , that can accurately detect, classify, and map the types of diffusion and biophysical parameters at a high spatial resolution over the entire genome in living cells. Hi-D combines dense Optical Flow to detect and track local chromatin and protein motion, and Bayesian inference to characterize this local movement at nanoscale resolution. The initial implementation requires some experience using MATLAB (MathWorks) and computational resources, for instance in form of access to a computer cluster, to perform the Hi-D analysis.
In addition, this implementation takes ∼18-24 hours to analyze a typical image series. To avoid these limitations and emphasize high-performance implementation, we present a customized version called Hi-D-Py. The new open-source implementation is written in the open source Python programming language, and options for parallelizing the calculations have been added to the code pipeline. We introduce user-friendly python notebooks and automatically generated reports that summarize and tabulate the results of the analysis. Our efficient implementation reduces the analysis time to less than one hour using a multi-core CPU with a single compute node. We also present different applications of Hi-D for live-imaging of DNA, H2B, and RNA Pol II sequences acquired with spinning disk confocal and super-resolution Structured Illumination Microscopy.
Therefore, we recently developed the highresolution diffusion (Hi-D) method 1. The Hi-D method combines computer vision (dense Optical Flow ) to quantify the local motion of bulk structures with sub-pixel resolution. A Bayesian inference is used to precisely classify types of motion throughout and to assign biophysical parameters to the motion modes throughout the nucleus. Using Hi-D, we have investigated the dynamic roles of chromatin (both DNA and histones), heterochromatin protein 1, RNA polymerase II, and transcription factors in genome organization. Here we present a framework written in Python as an accessible and user-friendly tool, with an improvement in processing time. Using this framework, we present a step-by-step Hi-D analysis of DNA and nuclear proteins imaged with spinning disks confocal and super-resolution microscopy.
Limitations The principal limitations of the existing protocol of the Hi-D technique 1 can be summarized in the following points: 1. The protocol was implemented mainly in MATLAB, a commercial software application that requires specific and hefty licensing. The GMM analysis module is implemented using the pomegranate package in Python. The interaction between MATLAB and Python required the user to establish the connection between MATLAB and python manually, which may limit unexperienced researchers to run the protocol in their research environments. 2. The execution of the current protocol is time-consuming; processing a single video sequence might take around 24 hours or more on mid-range commodity hardware. This issue limits the analysis of lengthy time lapses with specific experimental conditions on personal computers and might require high-end computing clusters for large-scale processing.
2. Reducing the execution time of the entire workflow by using more efficient yet accurate implementations of the Optical Flow algorithm and making use of parallel execution environments supported by Python including the multiprocessing and joblib packages by default. Even with a singlethreaded execution, the Python implementation is faster than the MATLAB-based one. 3. Making the software more user-friendly and easier to run, either via Python notebooks which guide the user in a step-by-step fashion to analyze the results on the fly, or via a simple python command line interface (CLI) that can be launched relying on configuration files or command line arguments. The results are generated in HTML reports that can be viewed in any browser.
Overview of the procedure The full protocol of Hi-D is divided into two complementary procedures. The first procedure is performed in the laboratory to acquire video sequences of living cells showing the dynamics of chromatin and nuclear proteins. Further details on this procedure are available 1,5,9,18. The second procedure (Stages 1 and 2) analyzes these sequences relying on Hi-D-Py in a matter of minutes. The presented protocol is focused on the Python workflow implemented for analyzing the video sequences. Nevertheless, we briefly describe a few details of the first procedure for users aiming to replicate the conditions and setups that were used to generate the presented sequences (refer to the video sequences provided in the Supplementary Material). The Python workflow is composed of two stages.
4. After estimating the best parameter set for a model, the model, and its chosen parameters are selected to maximize their probability to describe the data in which 𝜃 ̂𝑘,MLE = arg 𝑚𝑎𝑥 𝜃 𝑘 𝑃(𝑌|𝜃 𝑘 , 𝑀 𝑘 ). MSD analysis is performed locally 1 , only the 3×3 neighborhood of a pixel is chosen to determine the MSD covariance matrix used for the Bayesian inference 23. The choice of a window size of 3×3 is based on the selected filter size in the Optical Flow estimation. All the calculations are implemented in Python. Scipy 24 is used for numerical fitting. The type of diffusion characterizing each trajectory motion was chosen in an unbiased manner using the Bayesian inference described previously from a set of five common models to fit each trajectory's MSD. These methods were detailed earlier 1 , and a summary of them is presented in Table  1.
Evaluation of the protocol implementation in Python Dense Optical Flow fields are reconstructed with the Gunnar Farneback's algorithm 20 using an existing and fast implementation with the OpenCV library 19. A manual screen across the input parameters for the Gunnar Farneback algorithm was performed, and the performance of the Gunnar Farneback algorithm was evaluated against the previously used MATLAB implementation 1,27. The evaluation was performed on a range of synthetically generated images which we used previously to assess to compatibility of the Optical Flow algorithm for fluorescently labeled nuclear proteins 1. Here, the diffusion constant of diffusing emitters varied between 0. 01 -1 px 2 /s. These values correspond to a range of 1. 8 nm 2 /s (for a pixel size of 13. 5 nm, such as for super-resolution imaging 16 ) and 12 000 nm 2 /s for confocal imaging.
All emitters were subject to normal diffusion with the respective diffusion constants. To account for the observed spatial correlation of nuclear proteins 18,28-31 , we introduced 1 -100 coherently moving domains. Emitters falling within a domain experience an additional displacement which was randomly chosen according to a free diffusion with the same diffusion constant as single emitters. The fixed parameters to generate synthetic images were as follows: 100 frames of 150 × 150 pixels were generated and the interval between frames was chosen as 200 ms. Emitters were randomly seeded within the images at a density of 50 emitters/µm 2. The ground-truth trajectories of simulated emitters were recorded and compared to MATLAB and all trials of the Gunnar Farneback implementation. An objective evaluation is performed by computing the angular error (AE), endpoint error (EE), and relative error (EErel) (Supplementary Equations 6, 7, and 8).
We concluded the parameter screen of the Gunnar Farneback after 14 trial runs. The trial run parameters are summarized in Supplementary Table  S1. Several parameter combinations yielded similar results and were thus ranked together. Exemplary heatmaps showing the AE, EE, and EErel of trials 2, 3, 7, 8, and 13 are shown in Figure  3. From the Gunnar Farnback implementation parameters, the most influential parameters according to first-and second-order correlations with the manually assigned ranking of trial runs (refer to Supplementary Table  S1 ) are the number of levels, the window size (winsize), and the number of iterations, as displayed in Supplementary Table  S2. Figure  4  shows a comparison between the results of the Bayesian analysis 1 using MATLAB and that resulting from our Python implementation.
Installation • TIMING a few minutes The code is installed in a few easy steps as follows: 1. Go to the open-source GitHub repository at https://github. com/marwan-abdellah/hidpy. 2. If you have a GitHub account, clone the repository to download its contents into the file system. CRITICAL STEP If you wish to contribute to the code, you can fork the repository before cloning it. 3. If you do not have a GitHub account, you can download the project as a. Zip file.
Code execution • TIMING less than 1 hour Hi-D-Py can be executed as Python notebooks located in the notebooks directory or from a command line interface (CLI) either using individual command line options or relying on preset configuration files (refer to Figure  1 ). Stages 1 (hidpy_stage1. py) and 2 (hidpy_stage2. py) are executed with the same configuration file. Setting the parameters of the configuration file demo. config, the workflow is executed by running the scripts using the following commands: hidpy_stage1. py --config demo. config and hidpy_stage2. py --config demo. config. Depending on the output results of Stage 1, the configuration file is updated to run the python script of Stage 2 (hidpy_stage2. py). The CLI and configuration parameters are detailed in the Procedure. The results of each stage are stored in an HTML report that can be opened with any web browser.
Equipment 1. Spinning disk confocal microscopy system (CSU-X1-M1N, Yokogawa). This system is used to image live-DNA 150-frames sequences with an exposure time of 200 ms per frame. 2. A Nipkow-disk confocal microscopy system (CSU22, Yokogawa). This system is used to image live H2B and RNA Pol II 150-frames sequences with an exposure time of 200 ms per frame. 3. Super-resolution structured illumination microscopy (DeltaVision OMX V3 Blaze). This system is used to image 20 frames of live H2B sequences with a frame duration of 2 seconds. The system is equipped with a PlanApo 60/1. 42 oil immersion objective, a 488 nm laser, and sCMOS cameras. Hi-D-Py can be downloaded and run locally as a stand-alone application on Windows, Linux, and macOSX machines. 2. Python's scientific computing package: Numpy (numpy.
org). 3. Python's scientific computing package: Scipy (scipy. org). 4. Python's plotting package: Matplotlib (matplotlib. org). 5. Python's plotting package: Seaborn (seaborn. pydata. org). 6. Python's probabilistic models' package: Pomegranate (pomegranate. readthedocs. io). 7. Python's pipelining package: joblib (joblib. readthedocs. io). 8. Python's concurrency package: multiprocessing (docs. python. org/3/library/multiprocessing. html). 9. Image processing application: Fiji (https://imagej. net/software/fiji). This is an optional package. 16. GMM evaluation. GMM is evaluated on all types of distributions and for subpopulations (1 to npop). Evaluation is done using the Bayesian information criterion (BIC). The deconvolution results are summarized in a table representing the fraction of cells from the analyzed dataset.
Timing The timing reported in the current protocol is registered based on several video sequences with 150 frames. However, we expect a total running time -on average-between 15 minutes to 40 minutes to run the entire pipeline. This timing is computed based on the hardware described in the Materials sections. The processing time of the Optical Flow and Bayesian inference steps depends on the number of cores in the CPU if the multicore functionality is enabled. This assumes that the running machine has sufficient memory if lengthy video sequences are supplied. The timing is estimated under the assumption that the user has basic coding experience in Python and understands how to run Python notebooks either via Jupyter notebooks or relying on Visual Studio Code (Microsoft). Average timing estimates for the entire protocol based on the exemplar datasets are listed in Table  4.
Acknowledgments H. A. S acknowledges the financial support of the  ISREC  foundation. C. A. V-C acknowledges the funding by  Labex Cell(n) Scale  ( ANR-11-LABX-0038 ) as part of the  Idex PSL  ( ANR-10-IDEX-0001-02 ). M. A. and R. B. contribute to this work independently.
3 Procedure 3. 1 2. 3. 4. 5. 6. 7. 1 Optical Flow fields is computed. 8. 9. 12. 13. 14. 3. 2 Stage 2 : 15. 33123456718912131432215 Stage 1: Pre-processing, Optical Flow calculation, trajectories construction, MSD estimation, and Bayesian inference • TIMING minutes to one hour1. CAUTION We recommend downloading the provided datasets (Supplementary Data) and using the Jupyter notebooks as a step-by-step guide to getting an understanding of the code with a validated example. A full example is shown in Figure5for analysis of H2B in U2OS, data is available in (Supplementary Data). Select and crop single nuclei and convert the input video sequence into an AVI or TIFF file format. TROUBLESHOOTING. If you are using the CLI, fill in the parameters of the configuration file required to run Stage 1 successfully including (i) the absolute path to the input video sequence, (ii) the pixel size, (iii) the time step, (iv) the pixel threshold value required for trajectory estimation and (v) the output directory where the results will be generated.
To more explicitly link tree mortality to exogenous factors such as wind disturbance, we updated the treefall module for this study to simulate treefall more mechanistically (cf. below). Inputs for a typical run of TROLL (v3. 0) include (1) monthly mean climate forcing data for the simulated location, (2) average trajectory of daily climatic variation, (3) species-specific plant traits for the simulated forest, and (4) species-independent general parameters, including those related to the stochastic tree death and treefall. The source code of TROLL (v3. 0) is written in C++ and is available on https://github. com/troll-code/troll. On a computing cluster, each simulation of 200 × 200 m and 500 years uses around 15 min of computer processing unit (CPU) time.