dataesr/scientific-paragraphs-categorization huggingface dataset:
For fungi, we amplified the ITS-1 region using the primers ITS1F (5 0 -CTTGGTCATTTAGAGGAAG-TAA-3 0 ) (16) and ITS2 (5 0 -GCTGCGTTCTTCATCGATGC-3 0 )  (45). The sequencing was performed using 11 MiSeq 500 cycle V2 kits following ref. 46. Primer design  (47) , PCR conditions  (48) , and sequencing methods  (49, 50)  are presented in more detail in SI Appendix, PCR and Sequencing. Sequence Processing and Clustering. The demultiplexed fastq files generated by MiSeq reporter for the first read of each run were quality filtered and truncated to remove potential primer sequences and low-quality base calls using the program cutadapt  (51). The reads were then further filtered and converted to fasta files using the FASTX-Toolkit (-q 30 -p 90 -Q33). The fasta files for each run were then dereplicated using AWK code provided in the swarm git repository (https:// github.
com/torognes/swarm) (17). The resulting dereplicated fasta files were filtered for PCR chimeras using the vsearch uchime_denovo command (https:// github. com/torognes/vsearch). The dereplicated fasta files for each run were then combined and further dereplicated at the study level. The fasta files were then used as input for OTU clustering using swarm (-t 4 -c 20000). The clustering identified 150,412 and 251,065 OTUs for the fungal and bacterial communities, respectively. The output files were combined into two separate community matrices using a custom python script (available at GitLab, https://forgemia. inra. fr/ bbrachi/microbiota_paper)  (52). The taxonomy of each OTU was determined using the quiime2 2019. 1 v8 feature classifier trained on the UNITE V8 and SILVA 1. 32 database for bacteria and fungi, respectively  (53, 54).
Differentiation of the Microbial Communities among Sites and Years. This analysis was performed for the fungal and bacterial communities independently, including all samples and only OTUs with read counts above 0. 01% of total read counts (after the filtering described above) across sites and years. To investigate how the microbial communities differed among sites and years, we performed a constrained ordination on log-transformed read counts using the capscale function in the R-package Vegan (55) and following ref. 56. The log transformation offers the advantage of removing large differences in scale among variables. The capscale function performs canonical analysis of PC, an analysis similar to redundancy analysis (rda), but based on the decomposition of a Bray-Curtis dissimilarity matrix among samples (instead of Euclidean distance in the case of rda).
Heritability (H 2 ) was estimated as the percentage of variance explained by the random accession intercept, H 2 ¼ σ 2 a σ 2 a þ σ 2 e : [2] Mixed models were fitted using the function lmer in the lme4 R package  (57). We computed 95% confidence intervals (CIs) using 1,000 bootstraps, and components were considered to have significant H 2 when their CIs did not overlap zero (lower bound of the CI ≥ 0. 01). Heritability of Individual OTUs. This analysis was also performed per site, year, and community, as in the microbiota H 2 estimation analysis. In this analysis, counts were transformed to centered log-ratios (CLR; after adding one to all counts to handle zeros) using a dedicated function in the R package mixOmics  (58, 59). Individual transformed OTU counts were modeled with a model following Eq.
This means that covariance among OTUs induced by microenvironmental and host genetic variation is controlled. SPIEC-EASI was run using the neighborhood selection framework, and model selection was regularized with parameters set to a minimum lambda ratio of 1e À2 and a sequence of 50 lambda values (see documentation for SPIEC-EASI and the huge R package, which provides regularization functions)  (60). Network Statistics. The inferences of microbe-microbe ecological interactions inferred using SPIEC-EASI were passed to the igraph package  (61) , which was used for enforcing simplicity of graphs (no edges that connect vertices to themselves or duplicated edges), computing degree and betweenness centrality of vertices, computing distances between vertices, and plotting. With each of the eight networks thus computed, hubs were defined as OTUs with degree and betweenness centrality both in the 5% tail of their respective distributions.
The photographs were segmented [using custom scripts in R based on the EBimage package  (62) ] to isolate plants from the image background and estimate the total surface of the image they occupied. We validated this method with mature plants harvested from a previous experiment that was planted in NM in fall 2010, and that included the 200 accessions used in this study. We counted siliques and estimated the average silique size for 1,607 mature stems that were also photographed. The total silique length produced per plant (number * average size) was highly correlated with our size estimates based on image analysis (Spearman's rho = 0. 84) and displayed a clear linear relationship. Relationship between Host Effects on Microbial Hubs and Seed-Set. To investigate the relationship between host genotype effects on heritable hubs and seed-set in each experiment, we computed estimates of accession effects (best unbiased linear predictors [BLUPs]) for both log-ratio transformed heritable hubs and Box-Cox transformed seed-set estimates.
We collected two leaves from 10 plants at five locations in Sweden (SI Appendix, Table  S5 ) which we stored in 20% glycerol at À20 °C. Wild A. thaliana microbial isolates were collected using modified methods that were previously described  (25) , using six distinct media selected to capture a diverse set of bacterial isolates  (63). After isolating and cultivating colonies, we performed DNA extraction and identified over 3,900 isolates using 16S and gyraseB sequencing [SI Appendix, Bacteria Sampling from Wild A. thaliana Plants  (64) ]. Matches to our experimental OTUs are indicated in Dataset S2. Of the isolates identified, we focused on the heritable hub, B38, which appears to contribute to seed-set in the field. B38 Whole Genome Assembly. We used a low-input method for Illumina library preparation (65).
Briefly, ∼2 ng of extracted DNA was used in a reduced volume (5 μL) tagmentation reaction with TDE1 (incubate 55 °C for 10 mins, room temperature for 5 mins). The tagmentation reaction was added to a 15-μL PCR, adding the Illumina adapters (Kapa HiFi Hotstart PCR kit KK502, standard Illumina adapters and cycling). The library was cleaned with 0. 8× volume SPRI (solid-phase reversible immobilization) beads, quantified on the Bioanalyzer, and run on the MIseq2500 using paired end 300 chemistry. Reads were trimmed for adapters (BBDuk, ktrim = r, k = 23, mink = 11, hdist = 1 tbo) and quality across a sliding window (k = 4, trimq = 20)  (66). Reads were assembled using SPAdes (using the settings -isolate -k 21,33,55,77) and annotated with the software Prokka designed for rapid prokaryotic genome annotation  (67, 68).
Pathway enrichment analysis. To investigate biological functions associated with seed-set of accessions or their influence over microbial hubs, we searched for enrichment in annotated pathways (in the BIOCYC database) and GO categories (biological processes only) in A. thaliana. Gene-set enrichment methods are designed for assays that directly assign P values or effects to individual genes (i. e. , RNA sequencing experiments). Here, for each trait, each gene was attributed the largest absolute SNP effect within a distance of 5 kb on each side and followed the setRank procedure that accounts for overlapping categories and multiple testing. We set the parameter "setPCutoff" to 0. 01 and set the "fdrCutoff" to 0. 05  (30). To account for specificities of gene-set enrichment in the context of association mapping, we also tested the enrichment of the gene groups identified by setRank using a weighted Kolmogorov-Smirnov score  (31)  and a permutation scheme accounting for the nonindependence of marker effects due to linkage disequilibrium along the genome, as well as the potential clustering of genes with similar function  (32, 33).
Mass accuracy was within 2 ppm to 5 ppm. Samples were eluted with 0. 1% formic acid in water (A) and 100% acetonitrile (B) using the following separation gradient: 95% A injection followed by a gradient to 90% A at 1 min, 45% A at 6 min, and 100% B at 6. 5 min with 4-min hold and 3-min equilibration. An external standard (sinigrin, 1 mM) was run four times before each plate and one time every 20 samples to monitor and maintain run quality. Compounds were characterized using retention times and fragmentation patterns of chromatograms with automatic agile integration in Agilent Mass Hunter Software (Qualitative Analysis B6 2012), and fragments were compared to online databases, massbank (massbank. jp) and plantCyc (plantcyc. org). The XCMS package for peak detection in R (cran. r-project. org) was used to align chromatograms, adjust retention times, and group the peaks.
β3 s are site-specific regression coefficients (interactions between the site and molecule effects). ε i eN ð0, σ 2 e Þ captures the residual variances. We fitted 380 models (19 hubs and 20 molecules) and used F tests to estimate term significance. All P values corresponding to the molecule effect β2 were corrected for FDR (n = 380). Repeatability of Analysis and Data Availability. All scripts used to perform the analyses presented in this paper, as well as nonessential but complementary figures, are available in the GitLab repository https://forgemia. inra. fr/ bbrachi/microbiota_paper  (52). Data tables for OTU counts, seed-set estimates, and plant growth data for the B38 experiment are also available in a Zenodo repository  (73). Metabarcoding Illumina sequences (ITS and 16S amplicons) and the B38 sequence data have been deposited in National Center for Biotechnology Information under BioProject PRJNA707473  (74).
ACKNOWLEDGMENTS. Thanks go to Mia Holm for her hospitality and wonderful dinners after hard work in the field as well as help during harvesting; to Einar Holm for helping with field work and taking photos of harvested plants; to Torbj€ orn S€ all for assistance with sampling and providing greenhouse space in Lund; and, finally, to the Kleen family, the € Ohman family, Nils J€ onsson, and the Rathckegården farm for allowing us.
to install our experiments on their land. Thanks go to Timoth ee Flutre and  Talia Karasov  for helpful discussions on previous versions of the manuscript. Thanks go to  Man Yu  from the C. D. lab, who helped generate stem images used for seed-set estimates and manual seed-set estimates. This work was funded by a grant from the  National Health Institute  (Grant  R01 GM 083068 ) to J. B. , M. N. , and C. D. ; by a  Dropkin Foundation Fellowship  to B. B. ; and with support from the  University of Chicago and New York University  (to J. B. ). B. B. has received the support of the  European Union  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills/AgreenSkills+ fellowship (under Grant Agreement   267196 ). P. D. , M. L. M. , and P. L. G. are students in the  Magist ere de G en etique Graduate Program  at  Universit e de Paris.
Computing resources and storage were provided by the  Center for Research Informatics , funded by the  Biological Sciences Division at the University of Chicago  with additional funding provided by the  Institute for Translational Medicine; CTSA  Grant  UL1 TR000430  from the  NIH ; the  genotoul bioinformatics platform Toulouse Occitanie, France  ( Bioinfo Genotoul , http://bioinfo. genotoul. fr);and  Bordeaux Bioinformatics Center at the University of Bordeaux, France.
In the last decade, a large number of clinical trials have been deployed using Cardiac Magnetic Resonance (CMR) to evaluate cardioprotective strategies aiming at reducing the irreversible myocardial damage at the time of reperfusion. In these studies, segmentation and quantification of myocardial infarct lesion are often performed with a commercial software or an in-house closed-source code development thus creating a barrier for reproducible research. This paper introduces CMRSegTools: an open-source application software designed for the segmentation and quantification of myocardial infarct lesion enabling full access to state-of-the-art segmentation methods and parameters, easy integration of new algorithms and standardised results sharing. This post-processing tool has been implemented as a plug-in for the OsiriX/Horos DICOM viewer leveraging its database management functionalities and user interaction features to provide a bespoke tool for the analysis of cardiac MR images on large clinical cohorts.
CMRSegTools includes, among others, user-assisted segmentation of the left-ventricle, semi-and automatic lesion segmentation methods, advanced statistical analysis and visualisation based on the American Heart Association 17-segment model. New segmentation methods can be integrated into the plug-in by developing components based on image processing and visualisation libraries such as ITK and VTK in C++ programming language. CMRSegTools allows the creation of training and testing data sets (labeled features such as lesion, microvascular obstruction and remote ROI) for supervised Machine Learning methods, and enables the comparative assessment of lesion segmentation methods via a single and integrated platform. The plugin has been successfully used by several CMR imaging studies.
Introduction Myocardial infarct size is a crucial parameter serving as a surrogate endpoint in clinical studies of new therapeutic interventions after Acute Myocardial Infarction (AMI). Therefore, considerable research engagement has been directed toward the automatic quantification of the infarct size as well as the size of the post-reperfusion no-reflow lesion based on CMR Late Gadolinium Enhanced (LGE) images  [1] [2] [3] [4] [5]. These studies require reliable post-processing tools for the segmentation and quantification of the myocardial infarct lesion; in general, these tools are reported to be an in-house code development in a numeric computing environment (i. e. MATLAB) or a commercial application software. Not only do these tools restrict reproducibility of the studies but they can also prevent a full understanding of the use of computational methods.
In general, a commercial software is a product designed to be marketed under a utilisation licence. The user must pay for a binary version which must be used as in accordance with the licence directives. There is no access to the source code of the commercial product (closed-source). Freeware software is free of charge but closed-source. In open-source software, the user has access to the source code and the different licences (i. e. BSD, GNU GPL, etc. ) provide the rights to copy, modify, redistribute the code, as well as build and release binary versions for commercial purposes  [11]. Open-source software promotes open collaboration and research communities have been attracted to this innovation methodology  [12]. Overall, open-source image processing software is not closely linked to the CMR concepts and practice.
This paper presents CMRSegTools, a novel application software that allows users to benchmark new lesion segmentation methods as compared to state-of the-art methods in the field. CMRSegTools, includes customised tools for user-assisted myocardial segmentation; manual, semi-and automatic lesion segmentation which can be used in the generation of training data sets for Machine Learning (ML) strategies. This computer-based tool can be easily deployed on clinical infrastructures, closing the gap between analysis methods and their direct utilisation in clinical trials, as demonstrated in the studies  [22] [23] [24] [25].
CMRSegTools plug-in CMRSegTools is an application software specifically designed to ease myocardial segmentation, quantification and tissue characterisation on CMR images. CMRSegTools has been implemented as a plug-in for the widely available commercial OsiriX and open-source Horos DICOM viewers  [26, 27]  in order to take advantage of usability features and advanced visualisation tools (ROI outline tools, colour look-up tables, pixel interpolation algorithms for display, etc. ) as well as the effortless deployment on a clinical or research infrastructure. Fig  1  shows the main components of the plug-in and its integration within the OsiriX/Horos environment. The user-friendly Graphical User Interface (GUI) empowers the user to: segment the Left-Ventricle (LV); calculate infarct size, Microvascular Obstruction (MVO) lesion size, Endocardial Surface Length (ESL), and Endocardial Surface Area (ESA) amongst others.
The real-time feedback updates the GUI with statistical information (number of pixels, minimum and maximum pixel value, mean value, standard deviation, etc. ) of the pixels selected by the segmentation along with an interactive histogram. The image of interest is also dynamically updated (Epicardium and Endocardium contours, LV/RV junction landmark, region segments statistics in the 17-segment model, AHA  [10] ) according to the user interaction (Fig 2 ). Furthermore, CMRSegTools saves the workspace state, which means that it restores the GUI and image viewer to the state they were in when the plug-in was last used. The source code of the plug-in is available at https://github. com/OpenCardiacMRISoftware/ CMRSegTools.
Interoperability and extension The CMRSegTools plug-in is independent of scanner vendor, sequence type and protocol. The OsiriX/Horos framework  [26, 27]  eliminates common requirements for data access (access to PACS), database management, transformation from the DICOM standard to final postprocessing format, and data provenance; requirements which are critical in the development of clinical trials. In addition, the plug-in includes a functionality to import ROI from cvi42 (Circle Cardiovascular Imaging Inc. , Calgary, Canada) file format. This application software has been designed with the CMR lexicon in mind. All graphical elements generated by the plug-in (i. e. segmentation contours, set of pixels corresponding to the lesion, etc. ) are tagged with keywords for identification within the OsiriX/Horos runtime environment.
This information can be exported to an Excel file or as a delimited text file (comma-separated values) for a more comprehensive examination over different studies or cohorts of interest using application software for statistical analysis (e. g. pandas  [28] , R scripts  [29]  or any statistical package). The main computational methods are written in C++ using the Insight Segmentation and Registration ToolKit (ITK)  [30]  and the Visualisation ToolKit (VTK)  [31] , which grant extensibility and interoperability in terms of new functionalities required, that is new image processing methods.
Testing data A series of synthetic images emulating myocardial infarct lesions were generated with arbitrary size, transmural extent, percent Signal Enhancement (%SE) and CNR. In order to test the influence of the noise level on the results, several synthetic levels of noise were computed to reproduce what was previously measured in vivo using various CMR sequences in patients  [43]. To achieve a realistic model of the cavity, myocardium, infarct size and shape, and signal intensity in each compartment, the numeric images were built using multi-slice 3D data sets acquired on patients. Synthetic images were generated with a resolution similar to the one used in clinical acquisitions  [41]. In vivo images from the MIMI study (ClinicalTrials. gov Identifier: NCT01360242,  [44] ) were used in this study to validate the plug-in functionalities on real clinical data.
Since manufacturers are under regulatory restrictions, the integration of the latest hardware and software on their platforms typically requires long production time, often resulting in regrettable delays in early testing of new, advanced and potentially valuable algorithms. CMRSegTools is therefore a contribution to enable reproducible research by assembling the most used methods for infarct segmentation in a suitable workflow executed in a single and widely available environment: Osirix/Horos DICOM viewers. This platform was chosen because it provides an Application Programming Interface (API) that grants access to the data management, user interaction and visualisation functionalities. It was also chosen because it has an open-source licence. However, OsiriX/Horos is a macOS-only software, which limits its deployment on platforms based on a different operating system.
At present, there are no automated algorithms that can reliably distinguish the bright LV cavity from the bright endocardial border of the infarct. This challenge is being addressed by new segmentation methods (including non-LGE images) based on ML  [5, 21, 48, 49]. To validate the correspondence between the mathematical model (S1 Appendix) and the code implementation, the correctness of the computational methods has been verified with synthetic data. This also allowed the benchmarking of infarct segmentation methods (Fig  6 ) on synthetic data. The main lesson emerging from the analysis is that STRM (n-SD) methods with n < 5 and GMM are easily influenced by CNR. At lower CNR conditions the outcomes from these methods are unreliable, making them unsuitable for real clinical conditions. When CNR > 5, 2-SD and 3-SD are insensible to CNR.
The HMRF-EM has shown a good performance in most of the different scenarios (CNR > 3). A comparison between expert and automated segmentation methods is presented in Figs  7  and 8  as an example of the output of the current version. CMRSegTools has been tested and used successfully in several studies  [22] [23] [24] [25]. An extended assessment or benchmarking of infarct segmentation methods is beyond the scope of this paper. The goal is to provide a computer-based tool which includes well-known lesion segmentation methods (S1 Appendix) in order to empower research scientists to reproduce results and assess comparatively new methods. In this direction, CMRSegTools source code has been released under the CeCILL licence (a French free software license compatible with GPL) that grants users the right to copy, modify, and distribute new versions of the plug-in.
Machine learning approaches for SI classification would require reference data that can be applied to a learning process  [50]. With CMRSegTools, it is possible to generate labeled features (infarct, MVO and remote ROIs) extracted from the data and the expected quantification based on reference segmentation methods. These specific features are the main input of supervised learning approaches. As shown in Figs  7  and 8 , outcomes from ML methods can be easily compared to manual, semi-or full-automated state-of-the-art methods. Additionally, errors performed by ML methods may be labeled as well in order to improve the accuracy, specially in strategies involving active learning. The analysis scenario presented in this paper (methods benchmarking and expert vs computational methods assessment) can be replicated by research scientists working in CMR on their own data or by downloading the testing data used for this work from the Human Heart Project  [45].
Conclusions CMRSegTools is an application software for the comparative assessment of infarct segmentation methods applied to CMR images via a single and integrated platform (widely available Osirix and Horos DICOM viewers). This helps to improve the reproducibility of post-processing methods in clinical studies. This application software works on native DICOM images; the OsiriX/Horos functionalities enable direct connection to PACS making the plug-in independent of scanner vendor, imaging sequence and protocol. Interoperability functionalities allow importing data from external software such as ROI created in other application software (e. g. cvi42). New segmentation methods can be easily integrated into the plug-in by developing components based on well-known image processing and visualisation libraries such as ITK and VTK in C++ programming language.