dataesr/scientific-paragraphs-categorization huggingface dataset:
manihotis in virulence  (Mutka et al. , 2016). High-throughput imagebased plant phenotyping proved valuable in crop breeding and for research purposes  (Araus and Cairns, 2014; Fahlgren et al. , 2015; Coppens et al. , 2017). There is therefore a need to improve our ability to generate large datasets of quantitative plant disease measurements at low cost, low footprint, and reduced human intervention  (Czedik-Eysenberg et al. , 2018). Because it is generally non-destructive, image-based disease measurement also gives access to the dynamics of disease progression. This enabled for instance to study the spatial and temporal distribution of pathogens in plants tissues  (Mutka et al. , 2016)  and distinguish between infected and non-infected plants long before qualitative symptoms are visible  (Czedik-Eysenberg et al.
, 2018). In spite of remarkable progress in methods and technology in recent years, fundamental advances in plant pathology enabled by automated plant phenotyping are still relatively limited. A method for the image-based phenotyping of plant disease symptoms combining high throughput, high resolution, low footprint, and low cost is currently lacking. This design is crucial to identify novel QDR genes and advance our conceptual understanding of this complex trait. Here, we present the Navautron system, an integrated hardware and software solution capable of automatically calculating disease lesion area over time on detached leaves inoculated by pathogens causing necrotic lesions. As a proof of concept, we compared the susceptibility of sunflower genotypes inoculated by the fungal pathogen S.
We designed a bioinformatics pipeline to extract automatically the size of disease lesions and parameters describing disease kinetics. The first step of the analysis pipeline consists in a python script called INFEST (kINematic oF lESion development) which calculates lesion areas for each leaf in a series of images (Figure  1c  and Boxes 1 and 2). The script requires: (i) a time series of images taken by a Navautron box, all located in the same folder, and (ii) a layout text file providing an identifier and bounding rectangle coordinates for each leaf. For a typical experiment with A. thaliana and S. sclerotiorum (120 leaves imaged for 36 h), a single Navautron will yield c. 26 000 lesion measurements with almost no human intervention. We distinguished three major phases along the kinetics of lesions development (Figure  1d ): (i) a latency phase, during which no necrotic lesion was detected, (ii) a spreading phase, during which the area of necrotic lesions grew exponentially to reach (iii) an asymptotic phase, when lesions have spread over the whole leaf surface.
3 accumulation enhances P. infestans infection. We conclude that our plant phenotyping setup is suitable to track necrotic lesions Box 2. User guide for disease lesion analysis with the INFEST script 1. Determine bounding rectangles coordinates for each leaf (in pixels). To this end, we recommend opening one image file in ImageJ software, tracing a rectangular 'Region of Interest' around each leaf and retrieving the corresponding coordinates. For accurate lesion size measurement, leaves position must remain identical through the whole duration of the experiment (usually the case when images are acquired through the Navautron system) and that possible distortion due to camera lens are corrected. 2. Create a grid_layout/ subdirectory in the image directory 3. Create a grid_layout. layout file within the grid_layout/ directory.
This should be text file containing one line per leaf to be analyzed, and on each line five items separated by tabs: (i) a leaf identifier, (ii) the minimum coordinate of the bounding rectangle on y-axis, (iii) the minimum coordinate of the bounding rectangle on x-axis, (iv) the maximum coordinate of the bounding rectangle on y-axis, and (v) the maximum coordinate of the bounding rectangle on x-axis. For an example of the layout file, see the Github page of the INFEST script here: https://github. com/A02l01/tuto/tree/master/ data_tuto/pictures/grid_layout. Note that placing leaves at the same position when setting up experiments greatly accelerates this step. 4. Run the INFEST script in conda environment with command $ conda activate INFEST $ python infest. py path_to_picture -f first_picture -l last_picture 5.
The automated analysis of 74-104 plants per genotype thanks to the Navautron setup allowed assessing this quantitative variation robustly. In agreement with our previous measurements (Figure  3 ), the Lip-0 accession showed an average Log 10 (LDT) of 5. 7, significantly higher than the laz5 mutants (P-value = 3. 0e À07 and 1. 6e À10 ). In the dataset from  (Atwell et al. , 2010) , five non-synonymous SNPs were found in the TIR domain, the LRR domain and the C-terminal extension of LAZ5 (Figure  5a ). Data from http://signal. salk. edu/atg1001/ revealed seven additional non-synonymous SNPs in the NB and LRR domains of the Lip-0 allele of LAZ5 and indicated that several accessions, including Rub and Edi-0, harboured large deletions within LAZ5 coding sequence. To document natural diversity at the LAZ5 locus, we analyzed phylogenetic relationships between A.
thaliana Col-0 LAZ5 and its 42 closest homologues retrieved from the Phytozome 12. 1 database  (Goodstein et al. , 2012)  (Figure  5c  and Data S2). We included the closest homologue retrieved outside the Brassicaceae family (Theobroma cacao 1EG027131) as outgroup, as well as alleles from A. thaliana Edi-0, Rub, and Tsu-0 natural accessions to represent infraspecific diversity. LAZ5 belonged to a monophyletic clade clearly separated from its closest A. thaliana relatives CSA1, RPS4, At4G19530, At4G36150, and At5G45230. LAZ5 clade was only represented in A. thaliana and A. lyrata, while its sister clade is represented in most Brassicaceae species but not A. thaliana. Together with long tree branches in LAZ5 clade, this indicates strong sequence divergence among Arabidopsis accessions at the LAZ5 locus.
(2017b) , using PDA plugs of 5 mm diameter (A. thaliana) and 8 mm diameter (sunflower) colonized by the fungus placed upside down on the adaxial surface of leaves (mycelium in contact with the leaf). The first true leaves were used for experiments with sunflower. The experiment was repeated three times independently and results from the three replicates were combined for analysis. Statistical analyses were performed using R software and the 'car' library for type II ANOVA and pairwise t-test with Benjamini-Hochberg P-value correction  (Benjamini and Hochberg, 1995; R Core Team, 2014). Distributions of LDT were normalized by natural logarithm transformation. Plots were made using the 'ggplot2' library  (Wickham, 2010).
Design of the Navautron phenotyping cabinets Five-mm-wide PMMA plates were cut on a Trotec Speedy 500 Laser cutting machine according to plans provided as Data S1. Holes were drilled at the centre of the upper panel to place full HD 1080p USB cameras with 2. 8-12 mm focal length (model ELP-USBFHD05MT-FV-F1 manufactured by ELP, China). The cameras were plugged to Raspberry Pi 3 Model B motherboards equipped with Waveshare 3. 2 inch thin-film transistor touchscreens. Autofocus and automatic white balance were disabled. For each experiment, pictures were taken every 10 min during 4 days and stored on secure digital cards. To obtain uniform light exposure during image acquisition, a LED flash light is turned on for 5 sec during both day and night time. Day and night images could then be processed identically.
Image analysis pipeline The area of necrotic disease lesions was computed by the INFEST python script available at https://github. com/A02l01/INFEST. Briefly, the script relies on a thresholding method based on colour analysis using the scikit-image python image analysis toolbox  (van der Walt et al. , 2014). The method splits images of an infected leaf into three layers corresponding to the background, the lesion, and the leaf. The 'background' corresponded to pixels with a saturation value superior or equal to 0. 3 in HSV colour space. The lesion corresponded to pixels with red component higher than green value in RGB colour space. The area of disease lesions was computed as the sum of pixels in the 'lesion' layer. The remaining pixels were attributed to the 'leaf' layer. Image analysis allowed the collection of lesion area values over time.
Every kinetics was fitted by a 4-degree polynomial regression to limit the effect of noise on the computation of the LDT. Fit of the kinematics of lesions development was computed by the numpy polytfit library. The latency time and LDT were extracted directly from the fit. The latency time was defined as the time needed to reach a lesion area of 300 pixels. The LDT was computed as the difference between the time when lesion area reached 600 pixels and the time when lesion area reached 300 pixels. Note that these values have been set for the present data analysis but can be changed easily in python script provided at https://github. com/ A02l01/INFEST. Kinetics that exhibited excessive signal to noise ratio were automatically excluded from the analysis as follows: we excluded leaves of size <300 pixels and leaves with a maximum lesion size less than the variance of lesion size during the latency period.
Analysis of diversity and identification of candidate genes We used a dataset of 213 624 SNPs on 1196 A. thaliana accessions  (Atwell et al. , 2010; Horton et al. , 2012)  to search for non-synonymous mutations present in Lip-0 and Rubezhnoe accessions but not in Col-0, Nok-1, Shahdara, and Rld-2. The functional consequences of SNPs were predicted automatically using a script derived from the BioPython library  (Cock et al. , 2009). For this, gene models from TAIR version 9 were used. Predictions were verified manually for the top candidates using EXPASY translate tool and http://signal. salk. edu/atg1001/ genome browser. LAZ5 closest homologues were identified using a BlastP search against PHYTO-ZOME 12. 1 database. A protein sequence alignment of 350 positions was used as input for phylogenetic tree reconstruction by maximum likelihood method in phylogeny.
fr (Dereeper et al. , 2008). Branch support was obtained by approximate likelihood-ratio test using the WAG substitution model, four substitution rate categories, gamma = 1. 794, proportion of invariants = 0. 041, and gaps automatically removed from alignment. The input sequences, alignment, and resulting tree in newick format are provided in Data S2. Total RNA extraction was performed with Macherey-Nagel Nucleospin RNA extraction kit following the manufacturer's instructions. One µg of total RNA was used for cDNA synthesis in a 20-µl reaction according to Roche Transcriptor Reverse Transcriptase protocol, using 0. 5 µl of SuperScript II reverse transcriptase. Quantitative RT-PCR reactions were performed on a LightCycler 480 apparatus (Roche Diagnostics, Basel, Switzerland, https://diagnostics.
(c) Phylogenetic relationship of LAZ5 and its 42 closest homologues in the PHYTOZOME 12. 1 database. LAZ5 closest homologue outside of the Brassicaceae family (Theobroma cacao 1EG027131) was included as outgroup, and alleles from A. thaliana Edi-0, Rub, and Tsu-0 natural accessions to represent infraspecific diversity. The tree obtained by a maximum likelihood analysis is shown, with the number of substitution per site used as branch length, and branch support determined by an approximate likelihood-ratio test (grey labels). Terminal nodes are colour coded according to plant species. (d) Relative expression of the LAZ5 gene determined by quantitative RT-PCR in healthy plants. Values shown correspond to three independent biological samples analyzed through four technical repeats each. Statistical difference from expression in Col-0 plants was assessed with Student's t-tests.
ACKNOWLEDGEMENTS We are grateful to past and present members of the  QIP laboratory  for stimulating discussions and suggestions. We thank the two anonymous reviewers for their comments that helped significantly improve our manuscript. This work was supported by a starting grant of the  European Research Council  ( ERC-StG 336808  project  VariWhim ) to SR and the  French Laboratory of Excellence  project  TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ).
Introduction Time-series gene or protein expression data can give invaluable insight into the temporal dynamics of biological processes. It informs about the changes in activity of molecular pathways and key players upon a cellular stimulus or helps characterize molecular activity in cyclic processes, such as the cell cycle or the circadian rhythm. Methods and protocols exist to analyze time-series expression data and extract the dynamically expressed genes from a temporal dataset, some of which have been reviewed and compared in  1. Results from such tools however do not provide insights into the activity of key molecules or pathways at a given time point. Clustering temporal expression of genes is another possibility to analyze time-series data  2. Here, especially the clustering of expression profiles over time points is useful to follow the trajectory of expression dynamics of genes over time and to identify coregulated gene groups  [3] [4] [5].
We also applied TimeNexus to our own data from an injury induced pain assay in mouse, following mechanosensitivity and associated transcriptional changes over 30 days. We predicted pathways relevant for this process, including immune response, stress response, apoptosis regulation and axonal growth. Although TimeNexus has been optimized for temporal and multiplex networks, it is also applicable to all other forms of multilayer networks. TimeNexus is freely available from the Cytoscape App store (https://apps. cytoscape. org/apps/TimeNexus). The source code is also available on GitLab (https://gitlab. com/habermann_lab/temporal-network-project).
Yeast cell cycle dataset The yeast dataset from Kelliher et al. 34  was retrieved from the NCBI GEO database (GSE80474). We reprocessed the raw fastq files corresponding to the 36 first samples of the wild-type S. cerevisiae cultures from 0 to 175 minutes by mapping the reads to the S. cerevisiae S288C genome R64-1-1 with STAR aligner 36 with default parameters. Raw read counts were determined using featureCounts  37. For all following steps, we selected 16 time points representing the first complete cell division cycle. These start at time point 25 minutes and last until time point 100 minutes as described by Kelliher according to the expression profiles of key cell cycle regulators. We renumbered these time points in our dataset to start at 0 min of the first full cycle (corresponding to 25 minutes in the original dataset) until 75 min (corresponding to 100 min in the original dataset).
Using edgeR  38  , lowly expressed genes were removed by the automatic function filterByExpr and the read counts were normalized by the Trimmed Mean of M-values (TMM normalization), resulting in normalized log-counts per million (logCPM). Then, we calculated the log2FC for each gene at time point i (ti) versus its mean over the entire first cycle as follows: log2FCnode(ti) = logCPMnode(ti) -<logCPMnode> where logCPM is the log-counts per million given by edgeR and <logCPM> is the average logCPM over time for a given gene. Genes with a |log2FC| higher than or equal to 0. 25 were considered differentially expressed and defined as query nodes at the respective layer where this cut-off criterion was met. As no replicates were available, we did not consider statistical significance for this dataset.
Mouse sequencing data were quality controlled using FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). We used cutadapt (https://cutadapt. readthedocs. io/) to trim adapter sequences. Resulting trimmed reads were mapped to the M. musculus genome version 10 (mm10) using STAR aligner with default parameters. Mapped data were re-analyzed with MultiQC 40. Raw read counts were determined and filtered as described above for the yeast dataset. Differential expression analysis was done using edgeR, comparing the time points 1day post injection (PI), 3 days PI and 30 days PI always against the 0 day control prior to injection. Finally, we showed the evolution of gene expression for the significantly differentially expressed genes of the mouse dataset by first computing the z-score of log counts per million and then splitting the significantly differentially expressed genes according to the time of their significant differential expression.
The final edge lists gave the undirected intra-layer edge tables. The nodes of the tMLN represent both, the genes and the proteins as the same entities, in case a node is both, a protein in a PPI or a regulated gene in a PDI. Nodes of genes that were not detected in the RNA-seq datasets were removed. Consequently, edges where one partner was removed were also filtered out. The weight for each individual layer-node was computed as follows: wnode = -log10(p_adj) * |log2FC| where p_adj is the adjusted p-value and log2FC the log2-fold change for the time point represented by that layer. As no replicates were available for the yeast cell cycle data, the p_adj term was ignored for this dataset. The node weight was then standardized between 0. 01 to 1 (0. 01 was chosen to avoid rejection of nodes with weight 0 by extracting apps).
Moreover, a layer-node was tagged as a query for a layer if this layer-node had a |log2FC| ³ 0. 25 for the yeast cell cycle dataset; or if it had been defined as significantly differentially expressed with an adjusted p-value < 0. 05 for the mouse dataset. Node names, node weights, as well as the information whether a node is a query node were contained in the node tables, enabling TimeNexus to set the correct attributes to the layer-nodes.
Extraction of active subnetworks Active subnetworks were extracted with TimeNexus in combination with either ANAT or PathLinker from the yeast cell cycle time-series dataset. The algorithm "anchored network" with the sub-algorithm "approximation" was applied for ANAT. The network was set as "undirected" for PathLinker. For performance tests with PathLinker, we selected the optimal parameter K=750 by testing PathLinker with Kvalues from K50 to K=2000 and optimizing for the F1-score (Supplementary Table  S4 ; see below for calculating performance measures). PathLinker with a K=50 was used to extract an active subnetwork for the mouse dataset, as the network size and the number of queries were both smaller. All other parameters were chosen by default.
We wanted to take full advantage of the information provided by the temporal multilayer network. We therefore decided to include edge weights for the inter-layer edges of the tMLN that connect the same gene between two layers. These edge weights represent transition weights and describe the change in gene expression of a gene between two consecutive time points. The functionality for active subnetwork extraction was realized in the TimeNexus extractor. We wanted to demonstrate and test the usability of TimeNexus by extracting active subnetworks from two temporal gene expression datasets: from the yeast cell cycle which offers highly resolved temporal information; and from mouse temporal gene expression data following pain response after injury with low temporal resolution. Active subnetwork extraction using TimeNexus and PathLinker identifies relevant processes involved in early and late cell cycle events in S.
These objects are created either globally over the entire tMLN by combining all layers in a singlelayer network with layer-nodes as separate entities and by ignoring the differences between intra-and inter-layer edges; pairwise by creating a single-layer network from two consecutive layers similar to the global method, over the entire tMLN structure; or one-by-one by creating a single-layer network for each individual layer. The global method has the drawback that the network to be analyzed increases drastically, as the initial interactome is multiplied by the number of layers. Active subnetwork extraction is therefore compute-intense. Moreover, only nodes from the first and last layers will be used as source and target nodes and active subnetworks will only be extracted if they span the entire dataset.
each extracted active subnetwork from the yeast cell cycle dataset, we counted the number of nodes in this active subnetwork (# subnetwork nodes) and the number of active subnetwork nodes overlapping with the 130 KEGG cell cycle genes (True Positives (TPs)). We then calculated the percentage of the active subnetwork size, the False Positives (FPs, as subnetwork size minus TPs), as well as the false negatives (FNs, as # KEGG cell cycle genes minus TPs). From these values, we computed a set of scores: the ratio of extracted nodes and the interactome size, as well as Recall, Precision, and F1-score as follows: Recall = TP / (TP + FN) Precision = TP / (TP + FP) F1-score = 2 * ((Recall * Precision) / (Recall + Precision)).
Some cell cycle regulators are tightly controlled at transcriptional level. To test TimeNexus, we used time-resolved expression data from a previous study on the transcriptional dynamics of the cell cycle 34 : in that study, S. cerevisiae cells were synchronized before releasing them to undergo three cell divisions. RNA was extracted each 5 minutes and subjected to RNA-sequencing to monitor the changes in gene expression during the three cell division cycles. We re-processed the raw read counts and used the normalized counts (see Methods) to calculate the log2 foldchange (log2FC) in expression for each gene of a time point versus the mean over one cycle. We created a tMLN of the first full cell cycle, representing time points 25min-100min as described in the original publication 34. For demonstration purposes, we focused on three early time points of the cell cycle, which are characterized by cell growth and DNA replication (time points 0min, 5min and 10min representing time points 25min, 30min and 35min of the original dataset); and three late time points, which fall into the mitotic phase (60min, 65min and 70min, representing the time points 85min, 90min and 95min of the original dataset; see Supplementary Table.
Context. Dust plays a key role during star, disk and planet formation. Yet, its dynamics during the protostellar collapse remains a poorly investigated field. Recent studies seem to indicate that dust may decouple efficiently from the gas during these early stages. Aims. We aim to understand how much and in which regions dust grains concentrate during the early phases of the protostellar collapse, and see how it depends on the properties of the initial cloud and of the solid particles. Methods. We use the multiple species dust dynamics solver multigrain of the grid-based code RAMSES to perform various simulations of dusty collapses. We perform hydrodynamical and magnetohydrodynamical simulations where we vary the maximum size of the dust distribution, the thermal-to-gravitational energy ratio and the magnetic properties of the cloud.
This is the so-called one-fluid or monofluid approach in the diffusion limit  (Laibe & Price 2014a). In a recent study  (Lebreuilly et al. 2019) , we presented a fast, accurate and robust implementation of dust dynamics for strongly coupled gas and dust mixtures that allows an efficient treatment of multiple grain species in the adaptive-mesh-refinement (AMR,  Berger & Oliger 1984)  finite-volume code RAMSES  (Teyssier 2002). In this study, we extend this one-fluid formalism to neutral dust grains in a partially ionized plasma. We present the first simulations of protostellar collapse of gas and dust mixtures with multiple grain species (multigrain hereafter). In particular, we investigate how the maximum grain size of the dust distribution, the ratio between the thermal and gravitational energy, i.