dataesr/scientific-paragraphs-categorization huggingface dataset:
The ground truth bales were extracted by comparing the post-CT scan data with captured in situ images. The labeled dataset was then used to train supervised ML approaches such as support vector machine (SVM) and neural network (NN). Imani et al. [86]  used ML techniques like SVM, K-NN, and NN to find the process parameters such as hatch distance, laser power, and scanning speed that likely to produce more porous parts. The link between process parameters and the location, size, and frequency of the pores was studied. Aminzadeh et al. [87]  used an 8. 8 megapixel USB Digital Camera with high focus lenses to capture the post-printed images of every layer in L-PBF. They used Bayesian inference to detect the porosity and quality of the final part. The training dataset was labeled as "defective" and "non-defective" based on visual inspection.
Unsupervised Learning Approach The use of unsupervised machine learning is limited in the field of the L-PBF process. However, unsupervised can be useful when obtaining the labeled training dataset is not possible. Another advantage of using unsupervised learning compared to supervised learning is that it does not require human interaction to the label training dataset. Grasso et al. [10]  demonstrated the use of the T-mode Principal Component Analysis (PCA) for image data obtained by an Olympus TM I-speed 3 camera mounted outside the build chamber at an angle of 40 • w. r. t build plate to define a spatial statistical descriptor and to detect local over-heating phenomena along the scan path by analyzing the intensity profile of each pixel. These local hotspots correspond to the regions where slow cooling occurred due to variation in heat flux; it can be observed at sharp corners and overhang structures.
K-means clustering learning was applied to the spatially distributed PCA descriptors to detect a defect in an automated fashion. Although an iterative updating of the k-means clustering based on the new dataset allowed to detect defects in complex shapes, the approach is dependent on the comparisons between data collected at different layers or on the signal from successful builds of the same geometry, which limits its applicability to other situations. Grasso et al. [95]  used an off-axis IR camera to monitor the plume of zinc material to design a data-driven and automated approach for process monitoring. The region of interest (ROI), which is the plume, was extracted from the IR images. A multivariate control-charting method was proposed to monitor the ROI features, i. e. , mean intensity and area of the plume, to detect the deviation from the stable plume patterns.
Similarly, Melt Pool Monitoring (MPM) provided by SLM solutions captures the melt pool emissions in the near-infrared region by using two photodiodes with different spectral ranges. Then, captured thermal emissions can be studied in the software module supplied by SLM solutions in a layer by layer fashion. The Layer Control System (LCS), also supplied by SLM solutions, uses the visible range camera to monitor the quality of the powder bed spread. After the powder bed spreading, an automatic algorithm processes the captured image in real-time and automatically activates the recoating operation in case of inhomogeneous powder spread. Renishaw supplied the InfiAM module composed of MeltView, which captures the in situ data in a wide spectral range and LaserView to monitor the part build and laser power, respectively.
The restriction to a single pixel and its neighborhood allows us to carry out the MSD analysis of trajectories locally, in contrast to an ensemble MSD in previous studies  [18] , revealing only average information over many trajectories. The choice of a 3 × 3 window is reasonable with regard to the equivalently chosen filter size in the optical flow estimation. The flow field in this region is therefore assumed to be sufficiently smooth. All calculations, except for the general mixture model analysis, were carried out using MATLAB (MATLAB Release 2017a, The MathWorks, Inc. , Natick, Massachusetts, USA) on a 64-bit Intel Xeon CPUE5-2609 1. 90 GHz workstation with 64 GB RAM and running Microsoft Windows 10 Professional.
Acknowledgements We thank  Maxime Dahan  ( Curie Institute ) for generously providing the U2OS cell line expressing  RPB1 ,  Amy Strom  ( Gary Karpen Lab ,  UC Berkeley, USA ) for providing images of HP1 in NIH3T3 cells,  Silvia Kocanova  ( CBI Toulouse ) for assistance with image acquisition,  Raphael Mourad  ( CBI Toulouse ) and  Genevieve Fourel  ( ENS Lyon ) for fruitful discussions,  Alain Kamgoué  ( CBI Toulouse ) for assistance with computer cluster set-up, and  Fatima-Ezzahra L'Faqihi  ( CPTP-INSERM U1043 ,  TRI ) for technical assistance with the flow cytometry experiments. We acknowledge support from the  LITC imaging platform, CBI Toulouse.
Global Analysis The relaxation dynamics were accessed using a global analysis method based on singular value decomposition (SVD) to follow the evolution in time of the complex TA signal with several overlapping spectral components  [16, 23, 24]. The open-source software Glotaran  [25]  was used to perform a global analysis of the TA data. The sequential kinetic model, with spectral components known as Evolution Associated Spectra (EAS i ) and related by successively decreasing rate constants (k i = 1/τ i ), is given by the following equation: ∆A(λ, t) = n i=1 (M i EAS i (λ) -M i-1 EAS i (λ)) (2) with the associated time-dependent amplitude M i (t): M 0 = 0 M i (t) = 1 2 exp(-k i t)exp       k i       t 0 + k i ∆ 2 2                     1 + er f         t -t 0 + k i ∆ 2 √ 2 ∆                 f or i > 0 (3) assuming a Gaussian instrument response function (IRF), with full width at half maximum ∆ and centered at t 0 (time zero of the pump probe overlap)  [26].
Acknowledgments :  E. C. thanks  O. Gobert  and  F. Lepetit  for their assistance in the optical and electronic development of the TA apparatus. C. V. F. and E. C. thank  S. Guizard  and  D. Markovitsi  for scientific discussion. C. V. F. thanks the  University Paris-Saclay  for the ED 2MIB Scholarship. B. M. thanks the  PALSE program  of  Claude Bernard Lyon I University  under the framework " Investissements d'avenir"  ( ANR-11-IDEX-0007 ).
The other plantations (Fig. 3a  and Fig. 3c ) were less affected but provide more information each year. The data collected in the artificial inoculation tests and in the plantations enabled IRSTEA to select eight clones of field elm, including six French and two others, which were made available to forest nurseries in 2017 (Tab. 2). However, uptake of this material by nurseries was very limited because it was not resistant enough to DED. Unlike resistant hybrid cultivars, the selected clones are elms with typical characteristics of the field elm (small leaves, corky twigs). These clones show only moderate and unstable resistance to DED, with some individuals of a clone succumbing to the disease while others of the same clone recover or experience remission. "Christine Buisman", of Spanish origin, is the most DED-resistant of the selected clones.
In practical terms, these plots can also constitute sources of plant material, and not only by vegetative propagation. Seeds can probably be harvested in a few years in the plantations of Saint-Herblain (Loire-Atlantique) and Banneville-sur-Ajon (Calvados), and possibly also in the grafted conservation orchard in Nantes. The emergence of new projects and partnerships requires a durable and easily accessible archiving of data (work in progress at CRGF), but also that the existence of plots and datasets is widely known to potential partners. We hope this publication will help. Indicators compiled regularly at the French level (Indicateurs de Gestion Durable des Forêts, Observatoire National de la Biodiversité, etc. ) provide information on the development of national collections and in situ conservation networks.
Acknowledgments The programme could not have developed without the financial support of the  French Ministry of Agriculture , the  Agri-GenRes 009  programme of the  European Commission  ( GenRes  project  CT96-78 ), and  Plan Loire Grandeur Nature  (Conservation of European white elm populations). The authors thank  C. Joly  (Crepan),  G. Philippe  ( Irstea ) and the anonymous referees for the improvements made to the French and English manuscripts;  A. Brookes  and  T. Fenning  for their kind linguistic assistance, as well as many colleagues for their assistance with the elms. The CU selection and the Saint-Herblain planting were carried out with the support of the  ONF , the  LPO, Nature-Midi-Pyrénées and J. Rieffel Agriculture School. The historical work in the animation of the elm programme by  D.
Monitoring of herpetic infection After infection by HSV-1, corneas were monitored daily for 3 to 6 days (depending on the rate of lesion development) by imaging the epithelium under a macro zoom microscope with retro illumination (MVX-10 macroscope run by the CellSens Dimension imaging software, Olympus, Tokyo, Japan) (Fig 1C ). A x1 objective allowed single-field observation of the whole human cornea. To compensate for the dome shape of the cornea, a z-stack of six images spaced 600 μm apart, from the limbus to the corneal center, was acquired in bright field. Special interest areas were also observed with a x6. 3 objective, using a z-stack of images spaced 85 μm apart. The in-focus images of the epithelium were then reconstructed using the 3-D Extended Depth of Field plugin (http://bigwww.
Robustness of the Classification We test the robustness of the classification by repeating it using higher resolution MODIS (1 km) channel 31, and GOES-16 (2 km) channel 13 brightness temperatures (Tables  S1  and S2 , Figures  S2  and S3 ). MODIS provides twice daily data for the 2000-2019 period. From GOES-16, we use 3-hourly data for the last two winter seasons. The higher-resolution data changes the number of cloud objects, the mean cloud object size, and the absolute value of I org (Figure  S4 ). However, the day-to-day variability of the I org , A and S metrics, correlates well among the different data sets (Table  S3 ). Despite their very different spatial resolution (8 km vs. 2 km), GridSat and GOES-16 classifications correlate best. For the case of I org , the geostationary data (GOES and GridSat) correlate less well with MODIS, suggesting that I org may vary more with temporal sampling than it does with resolution.
Stevens et al. (2019)  showed, based on a visual and thus subjective classification, that the tropical western Atlantic during boreal winter is associated with four prominent mesoscale patterns of shallow convection. The present study shows that these patterns can be objectively identified based on the size and degree of clustering of segmented cloud objects as identified from infrared brightness temperatures. The classification is largely insensitive to the spatial resolution of the brightness temperature data: GridSat data with a resolution of 8 km and MODIS data with a resolution of 1 km lead to very similar classifications.
, 2017). With its large complement of air and sea-going vessels in the same study region as examined here, EUREC 4 A will not only quantify the relationship between cloud patterns and large-scale variables but also the circulation systems that connect the two. These measurements should thus also help determine how much the mesoscale organization of shallow convection has to be considered if one wants to understand and predict the response of shallow clouds to changes in environmental conditions. A closely related question is whether the mesoscale organization of shallow convection matters for cloud-radiative effects. To shed light on this issue, we used daily estimates of top-of-atmosphere radiative fluxes and cloud products from the CERES (Clouds and the Earth's Radiant Energy System) geostationary enhanced temporally interpolated data set  (Wielicki et al.
Background: Transcriptomic analyses are broadly used in biomedical research calling for tools allowing biologists to be directly involved in data mining and interpretation; Results: We present here GIANT, a Galaxy-based tool for Interactive ANalysis of Transcriptomic data, which consists of biologist-friendly tools dedicated to analyses of transcriptomic data from microarray or RNA-seq analyses. GIANT is organized into modules allowing researchers to tailor their analyses by choosing the specific set of tool(s) to analyse any type of preprocessed transcriptomic data. It also includes a series of tools dedicated to the handling of raw Affymetrix microarray data; Conclusion: GIANT brings easy-to-use solutions to biologists for transcriptomic data mining and interpretation.
Introduction Transcriptomic analyses have become a standard procedure to characterize biological systems and to monitor the molecular consequences of tested experimental conditions. Those analyses can be handled on the one hand by bioinformaticians using tools available essentially as R packages. More user-friendly solutions for biologists consist, on the other hand, of licensed softwares. In this context, we present here a series of freely available Galaxy-based tools dedicated to the analysis of transcriptomic data, which we have called Galaxy-based tool for Interactive ANalysis of Transcriptomic data (GIANT). Galaxy is a web-based platform offering access to tools enabling researchers without informatics expertise to perform computational analyses of large biomedical datasets  1. The open source and collaborative characteristics of the Galaxy project supported by an active users and developers community constitute an attractive framework for GIANT.
GIANT consists of a series of Galaxy-based tools working as interrelated but independent modules. This allows a customized utilization through which users can choose to perform all or only a subset of the available data processing and analysis steps. GIANT puts together tools by encapsulating independently freely available R packages and programs, offering an easy access to both statistical analyses and interactive visualizations of data. Nowadays, RNA sequencing (RNA-seq) has become the preferred technology for transcriptomic studies and numerous tools are already available in Galaxy to analyse RNA-seq datasets, especially for quality controls (QC)  2, 3  and differential analyses  4, 5. In this context, microarrays, which have been the reference technology for decades, have been neglected in recent Galaxy tool developments even through microarrays are still commonly used in laboratories and contribute to a great extent to available datasets in public databases.
Indeed, surveying the Gene Expression Omnibus (GEO) database indicated that 60,000 transcriptomic studies based on microarrays were available, with 4,270 new datasets (24% of transcriptomic studies) submitted between January 1st, 2019 and June 1st, 2020. Despite various tools developed to analyse transcriptomic data in Galaxy, none of them allows deep exploration of preprocessed data through interactive and highly customizable visualisations. GIANT offers the possibility to mine any type of preprocessed transcriptomic data such as RNA-seq normalized counts, microarray normalized expressions and most of differential analysis result files. In addition, to fill the lack of existing Galaxy tools for thorough microarray analyses, we added dedicated tools to handle Affymetrix microarrays raw data and to perform differential analyses with complex contrasts.
Most GIANT tools have both graphical and non-graphical outputs. Non-graphical outputs are provided as tabular files containing various statistics directly used as input in other GIANT tools allowing to build personalized workflows. The tabulated content of these outputs can also be inspected by hand if needed or exported for further mining as GO term enrichment analyses. Graphical outputs play a major role in data analyses, facilitating results interpretation for users. Several R packages are employed to display clear and interactive plots. The ggplot2 R package  6  is the most used graphical package allowing to generate various kinds of plots such as bar plots, volcano plots and histograms. Thanks to its association with the plotly R package  7  , generated plots are easily converted to interactive plots.
Available svg format for snapshot also facilitates the integration of generated graphics in publications, since it offers high definition and the possibility to modify each svg element through free software as Inkscape(https://inkscape. org). Furthermore, as numerical information is displayed dynamically when the mouse hovers over the graph, only the requested information is displayed ensuring figure clarity. In addition, each tool generates a text file (log file) where important information is recorded such as the version of the R packages used and warning messages. In case of error during tool execution, this log file may contain additional information to those displayed by the main Galaxy interface to help in error identification and correction.
Specific RNA-seq analysis steps Unlike for microarrays, numerous tools and workflows to process RNA-seq data have been proposed in Galaxy, especially for alignment to the genome  [9] [10] [11]  , assembling/counting  12, 13  and differential analyses  4, 5. However the lack of flexible and configurable tools allowing to fully exploit the generated results is limiting and forces users to export their results out of Galaxy into graphical and statistical analysis software such as PRISM (graphpad : https://www. graphpad. com). GIANT offers the possibility to mine these results (normalized counts, differential statistics) in Galaxy and to compare information from various 2/16 studies in a simple way. Amongst proposed data mining tools, the Quality check tool can generate 3D Principal Component Analysis (PCA) plots based on count data and the Volcano plot tool can be directly applied to output files of common RNA-seq differential Galaxy tools such as Limma-voom  5  and SARTools 4 that includes DESeq2 14 and edgeR  15  , without the need for tedious data formatting steps.
Specific microarray analysis steps Despite available tools to analyse transcriptomic data in Galaxy, the limited number of microarray dedicated tools prevent from defining complete workflow for such data especially for unsupervised analysis. GIANT allows to build an end-to-end workflow with dedicated tools to normalize Affymetrix microarray raw data with various normalization options, to check data quality before and after normalization and to define complex contrasts for differential analyses with Limma. The resulting files can then be processed through the generic data mining tools (Figure  1 ). Quality check tool It allows to check the quality of transcriptomic data. Input data can be a collection of. CEL files to assess integrity of Affymetrix raw data (in a microarray workflow), or more broadly, any common tabular file containing expression data with samples in columns and transcripts/probes in lines.
This tool is also used to ensure efficiency of the normalization step by checking uniformity of normalized data, before performing differential gene expression analyses. Affymetrix microarray normalization tool It encapsulates the apt-probeset-summarize program from the Affymetrix Power Tools package (www. thermofisher. com/fr/fr/home/life-science/microarray-analysis/microarray-analysis-partners-programs/affymetrixdevelopers-network/affymetrix-power-tools. html) and requires. CEL files as input and array-specific configuration files (. pgf,. clf,. cdf,. mps,. bgp). Normalized data are saved in a tabular output file. Additionally, users can select annotation files to annotate probe IDs contained in the output tabular file. Several strategies are proposed for probes that share the same annotation: average expression, duplicate probes, keep probe with the highest/lowest variance.
Available normalization methods for Affymetrix microarrays are the standard RMA  16  algorithm with or without additional GC correction and scaling, as recommended by Affymetrix (www. affymetrix. com/support/developer/powertools/changelog/VIGNETTEapt-probeset-summarize-GCCN-SST. html). For more recent arrays (for example : Human/Mouse Transcriptome Arrays, Clariom D arrays), 2 normalization levels are available (probeset/core genes). To improve the user's experience, the array configuration files necessary for apt-probeset-summarize execution can be durably hosted and referenced by the Galaxy instance through Galaxy configuration files, thus avoiding redundant file uploads. Differential expression tool encapsulates functions from the Limma R package 5 dedicated to differential gene expression analyses in a microarray analysis workflow.
The first input required is a generic tabular file containing microarray normalized expression data with sample names as first row and probe IDs / gene names as first column (as provided by Affymetrix microarray normalization tool). A tabular file containing the study design information is required as second input (e. g. generated by Factor table generation tool). Next, users define the Limma linear model according to the study design by selecting factors from which contrasts will be specified  17. The list of available factors and possible combinations to define contrasts are automatically generated from the factor file provided as input. Users can specify as many contrasts as needed, each contrast being defined through the Galaxy interface without requiring equations, thus lowering the complexity for users.
A circular heatmap can also be plotted thanks to the circlize R package  21. Additionally, a scree plot showing within-clusters variance as a function of cluster number, is generated to help users to choose the best number of probe/gene clusters. Among numerous available options, a gene filtering function has been implemented. Filtering can be based on a gene list or from a differential statistic file with adjustable FC and FDR thresholds. As hierarchical clustering is applied independently on rows (probes/genes) and columns (samples/contrasts), users can select the specific number of clusters for each dimension. Various distance measures and agglomeration strategies are also available. GSEA formatting tool It helps users to generate properly formatted files for Gene Set Enrichment Analysis (GSEA) designed by the Broad Institute  22.
Microarray data analysis -case study Microarray data from an in vivo mouse experiment (GEO:GSE46495) designed to study the transcriptomic response of white adipose tissue, liver, and skeletal muscle to fasting  23  were analysed. This dataset is composed of 30 samples with fasted and fed conditions. For each condition, 3 different tissues and 5 biological replicates are available. First, the Factor table generation tool was used to generate the factor file in accordance with the experimental design, including diet, tissue and replicate ID information for each sample (Figure  2 ). Then, the Quality Check tool was run on the 30 raw files (Affymetrix. CEL files) to detect potential technical issues during data collection. Graphical outputs (expression densities, boxplots, MA plot and chip image) are shown in Figure  5a-c   Supp.
Again, influence of the "Tissue" factor was confirmed by the sample clustering in which the 3 principal clusters corresponded to this factor. Scree plot was also generated to assist users in choosing the optimal number of clusters by looking for the elbow in the curve, which was located at 4 clusters for this clustering (Figure  7c ). Finally, in order to perform a GSEA for identified DEGs, the GSEA formatting tool was run using the differential statistics file as an input. A formatted file was generated containing a list of ranked genes according to differential statistics for a selected contrast, ready to be used as an input for the pre-ranked mode analysis of the GSEA software.