dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank  Prof Lucie Karayan-Tapon  for providing frozen samples, the staff of the  Department of Pathology (Poitiers University Hospital ) and especially  Céline Marquant  for technical assistance, and " La ligue contre le cancer " ( Comités de Charente, Charente Maritime, Deux-Sèvres, Morbihan, Vienne ) and " Cancéropôle Grand Ouest"  for their financial supports.
Using the extensive sequence data in the 1001 Genomes Project (http://1001genomes. org/) (1001 Genome Consortium, 2016), we accessed the genomic inversion sequence across all accessions. Clustering distance based on sequences across the 1. 2 Mb inverted region was used to construct maximum likelihood (ML) trees by a modified approach with introgression browser (iBrowser;  Aflitos et al. , 2015) , where a total of 72 906 polymorphic positions was identified (from a total of 12 898 225). The ML tree clearly shows a monophyletic clade of 132 inversion accessions (marked in red) from 41 individual locations and low levels of polymorphism between inversion accessions, strongly supporting a single origin of the inversion (Figure  4a ). Similar results were obtained in an analysis of the 2653 SNPs from the 1307 accession panel (Figure  S7 ).
Methodology of SNP analysis 250K SNP chip data from RegMap accessions. RegMap genotype data were downloaded from the Bergelson lab website (http://bergelson. uchicago. edu/wp-content/uploads/2015/04/call_ method_75. tar. gz). Genotype calls specific to the inversion region or a 'control' region were extracted from the full dataset. The proportion of SNPs in each region that differed between each pair of accessions was computed, and arranged in a dissimilarity matrix. These matrices were used as input to generate unweighted pair group method with arithmetic mean trees via the hierarchical clustering function in R [hclust((as. dist(matrix)),method="average")]. Custom Perl scripts were written to describe the patterns of SNP polymorphism within the inversion and non-inversion classes of accessions.
One-thousand, one-hundred and thirty-five VCF files available at the Arabidopsis 1001 Genomes Project (http://1001genomes. org/) were analyzed using iBrowser  (Aflitos et al. , 2015; v. d228c22 ) with 50 kbp window size. Genomic clustering using concatenated homozygous SNPs was created using FastTree2 (v2. 17) for regions in chromosome 4 named: upstream (0 bp to 1. 6 Mbp), short-arm (0 bp to 3 Mbp), inversion (1. 6-3 Mbp), centromere (3-5. 5 Mbp), downstream (> 3 Mbp) and long arm (> 5. 5 Mbp). Trees were plotted using FigTree v1. 4. 2 (http://tree. bio. ed. ac. uk/software/figtree/). , 2016). We used sites with less than 10% missing data, and an accession was inferred to have the inversion if its genetic distance to the reference genome (Col-0) is low. As shown in Figure  S13 , the genetic distance to Col showed a clear bimodal pattern, allowing us to unambiguously assign which accession has the inversion and which has not.
LD between FRI and inversion We used bi-allelic SNPs from beginning of chromosome 4 up to 3 Mb to investigate the pattern of LD within this region. Only accessions from Eurasia were used, and sites with more than 10% missing data were excluded. Accessions were first coded according to their binary inversion/non-inversion status, and the LDs of all SNPs to the inversion were estimated in r2.
ACKNOWLEDGEMENTS The authors greatly acknowledge  A. V. Gendrel  and  Z. Lippman  for their help in chromatin profile analysis,  T. Peterson ,  S. Peters  and  E. Wijnker  for stimulating discussions,  D. Weigel  and  M. Exposito-Alonso  for helpful comments to the manuscript, and  RijkZwaan Breeding , Fijnaart, the  Netherlands , for financial support of X. J. G. L. was supported by the  Marie Curie Individual Fellowship  ( OTKA K 108555 ). C. R. L. was supported by the  EMBO Long-Term Fellowship. T. E. J. was supported by the  National Science Foundation  (grant  DEB-0618347 ). Contribution no. 16-302-J  from the  Kansas Agricultural Experiment Station  (C. T. ).
The samples were analyzed on an Agilent 1200 RRLC coupled to a Bruker micrOTOF ESI-hybrid quadrupole-time of flight mass spectrometer (Wissembourg, France), both devices driven by the Compass 1. 3 SR 1 for micrO-TOF/maXis software (Bruker Daltonics). The LC conditions were: injection volume, 5 lL; autosampler temperature, 4 °C; column type, EC 100/2 Nucleodur C18 pyramid (Macherey-Nagel, Les Ulis, France); particle size, 1. 8 lm; column length, 100 mm; column internal diameter, 2 mm; column temperature, 40 °C; solvent flow, 0. 4 mL min -1 (solvent A: 95 % water, 5 % acetonitrile, 0. 1 % formic acid, and solvent B: 95 % acetonitrile, 5 % water, 0. 1 % formic acid); and gradient, 3 % B for 1 min, 3-30 % B for 7 min, 30-95 % B for 1 min, 95 % B for 1 min, 95-3 % B for 1 min, and 3 % B for 4 min (running time: 15 min).
Data preprocessing and filtering LC-MS raw data were exported to ''. cdf'' file format with the manufacturer's DataAnalysis software (Bruker, Wissembourg, France) and preprocessed with the freely available XCMS software, including the CAMERA script  (Smith et al. 2006). Peak picking was performed with the 'centWave' method ('peakwidth' parameter reduced to 3-15 s to fit UPLC performances, and 'snthresh' to 5 to detect more peaks), retention time correction with the obiwarp method ('profStep' reduced to 0. 1 m/z as recommended for QTOF mass spectrometers), peak grouping with the 'bw' and 'mzwidth' parameters reduced respectively to 5 and 0. 025, and gap filling with the default parameters. Raw data quality was checked by principal component analysis (PCA), with SIMCA-P ? 12. 0 software (Umetrics, Umea ˚, Sweden), both for each single batch and for all batches together.
The presence of any individual outlier was ruled out. Signal drift over time was quite weak within batches and unsurprisingly higher between them. Signals were corrected for both drifts with the Van der Kloet algorithm (a linear model) (van der  Kloet et al. 2009)  embedded into an R script (generous gift from Jean-Francois Martin, INRA AXIOM METATOUL, Toulouse, France). Data normalization was followed by a filtering step based on the coefficient of variation of variable intensity in the pooled sample (cutoff set at 20 %), which reduced the number of variables from 2583 to 1736 (67 % remaining). The removal of variables detected before 24 s (mostly from the calibration solution) left the dataset with 1718 variables. Finally, the data were log10-transformed and Paretoscaled before the statistical analysis  (Martin et al.
Statistical analyses Multivariate statistical analyses were performed with either SIMCA-P ? 12. 0 software (Umetrics, Umea ˚, Sweden) or R packages (base, pRoc, HDMD). Partial least squares discriminant analysis (PLS-DA) models were validated by CV-ANOVA (threshold for significance set at 0. 05) and by permutation tests (200 permutations, test passed for R2Y Panel A Mean ± SEM of (i) whole-body weight, kidney weight and ratio of kidney to whole-body weight in each group, and (ii) uranium concentration in kidneys after 9 months of chronic radionuclide ingestion through drinking water (0. 015, 0. 15, 1. 5 and 40 mg L -1.
) The number of rats for each measurement is indicated in parentheses Results are significantly different for: * P and Q2Y value decreased, below zero for Q2Y one). Three ''blocs'' of statistical analyses and features selection were applied to the preprocessed and filtered matrix (Supplemental Fig. 2 ), aiming at (1) compare the present cohort to a previous one, (2) distinguish the time point effect from the dose effect, and (3) investigate finely the dose effect. 2. 4. 3. 1 Discrimination between controls and rats contaminated at the E-dose (40 mg L -1 ) after 9 months The aim was to compare the results obtained in the present cohort with those obtained in our previous cohort in 2013  (Grison et al. 2013) , to check its reproducibility. PLS-DA was performed on a subset of the filtered dataset including the control rats (n = 20) and the E-dose contaminated rats (n = 19; 1 sample lost) at 9 months.
Separate analyses of time-point and dose effects At this second step of our statistical analysis we performed ANOVA-PCA (APCA) on the overall dataset. Indeed, we investigated two contamination factors simultaneously: the duration (time-point effect) and the level (dose effect). As for the multivariate data, depending on several factors, we used APCA, which is a powerful tool to evaluate significant factors of an experimental design and select the principal features associated with each factor  (Harrington et al. 2005; Zwanenburg et al. 2011; Climaco Pinto et al. 2008). Briefly, it decomposed the original matrix into effect matrices (time-point effect, dose effect, dose 9time-point interaction effect) and a matrix containing the residual error. Each effect matrix was calculated as the average of the variables at each level of the corresponding factor, and the residual matrix as the difference between the original matrix and the sum of the effect matrices.
Metabolite identification For the most discriminant features, a tentative annotation was performed with MZedDB (http://www. maltese. dbs. aber. ac. uk:8888/hrmet/index. html)  (Draper et al. 2009 ) from the chemical formulas generated from the accurately measured masses (accuracy \10 ppm) and isotopic patterns, calculated with Bruker DataAnalysis software. According to the putative identifications returned, 20 standard molecules were purchased. L-lysine (L5501-1G, CAS 56-87-1), N6-methyl-L-lysine hydrochloride (04685- Panel A Models discriminating the control rats from those contaminated for 9 months at the dose 40 mg L -1 in the present study and in our previous proof-of-principle study Panel B Analyses performed on the ''dose matrix'' after feature selection (126 variables) to investigate the dose effect; models are discriminating the control from the contaminated rats for each dose (dose B: 0.
These results confirm our previous observations for the E-dose (40 mg L -1 ) after 9 months of contamination  (Grison et al. 2013). Our data also confirm that uranium accumulates in the kidney at a rate equivalent to that we reported previously. Furthermore, our measurements of the rate of uranium accumulation in the kidneys were correlated to the uranium concentrations in the drinking water. Thus, for example, at 9 months of age and for the E-dose of 40 mg L -1 , the absorbed dose rate in the kidneys of the contaminated rats was estimated at 5. 4 9 10 -7 Gy day -1 , corresponding to a maximum dose absorbed by the kidneys at sacrifice as low as 0. 15 mGy (assuming a constant intake of NU throughout the entire 9-month period).
N1-methylnicotinamide was detected as [M1?] (m/ z 137. 07) in both standard solution and experimental sample at 29-30 s. Fragmentation spectra were superimposable and showed a major fragment at m/z 94. 06 (Supplemental Fig. 3 ). This metabolite was previously identified in our proof-of-principle study  (Grison et al. 2013). Such a fragmentation spectrum was recorded in the Human Metabolome Database (HMDB00699). N1-Methyl-2-pyridone-5-carboxamide was detected as [M ? H] (m/z 153. 06), along with its sodium adduct (m/ z 175. 05), in both the standard solutions and experimental samples at 50-53 s. Fragmentation spectra were superimposable and showed a major fragment at m/z 108. 04 (Supplemental Fig. 4 ). No spectral data were available in databases. 4-Hydroxyphenylacetylglycine was detected as [M ? H] (m/z 210.
08), along with its sodium and potassium adducts (m/z 232. 06 and 248. 03, respectively), in both the standard solution and experimental sample at 123-126 s. Fragmentation spectra were superimposable and showed a major fragment at m/z 107. 05, as recorded in the Human Metabolome Database (HMDB00735) (Supplemental Fig. 5 ). Both [M ? H] and its fragment were in the top 11 discriminant features. L-alpha-Lysophosphatidylcholine from egg yolk (with its usual fatty acid contents of approximately 66 % palmitic and 33 % stearic acids) was detected as [M ? H] (m/ z 496. 34), along with its sodium adduct (m/z 518. 32), in both the standard solution and experimental sample at 672-676 s. Both peaks had similar split shapes, corresponding to the isomerization of the molecule. Fragmentation spectra were superimposable and showed two major fragments at m/z 104.
10 and 184. 07, corresponding to choline and phosphorylcholine, respectively (Supplemental Fig. 6 ). Such losses are described for phosphatidylcholine (Metlin spectra for MID182) but no spectra was available for LysoPC. Finally, 4-pyridoxic acid was detected as [M ? H] (m/ z 184. 06), along with a small peak corresponding to water loss (m/z 166. 06), in both the standard solution and experimental sample at 60-61 s. Fragmentation spectra were superimposable and showed three major fragments at m/z 166. 05, 148. 04 and 138. 05, as recorded in the Human Metabolome Database (HMDB00017) (Supplemental Fig. 7 ).
Three-dimensional (3D) culture models are critical tools for understanding tissue morphogenesis. A key requirement for their analysis is the ability to reconstruct the tissue into computational models that allow quantitative evaluation of the formed structures. Here, we present Software for Automated Morphological Analysis (SAMA), a method by which epithelial structures grown in 3D cultures can be imaged, reconstructed and analyzed with minimum human intervention. SAMA allows quantitative analysis of key features of epithelial morphogenesis such as ductal elongation, branching and lumen formation that distinguish different hormonal treatments. SAMA is a user-friendly set of customized macros operated via FIJI (http://fiji. sc/Fiji), an open-source image analysis platform in combination with a set of functions in R (http://www.
In this article, we describe a software tool that can provide a rapid and biologically relevant analysis of the 3D culture models of the breast. Here we introduce our open-source SAMA (Software for Automated Morphological Analysis) that takes into account various parameters that define a normal breast while using 3D simulations of breast morphogenesis. For the sake of simplicity, we have described in this paper the analysis of only one 3D model, specifically a hormone-sensitive 3D culture model of the human breast. In the normal breast, during early development as well as during puberty and pregnancy, the gland responds to hormones, including estradiol, progesterone and prolactin. These hormones regulate mammary morphogenesis by increasing ductal elongation, lateral branching of the ducts, budding, i.
The variety of epithelial structures makes this model a suitable choice for testing the power of SAMA in measuring several morphological parameters. While we have used SAMA to specifically analyze in vitro 3D models of the breast, we believe that developmental biologists in general as well as scientists keen on understanding tissue morphogenesis can use this technique effectively. SAMA is particularly useful for multidimensional and multi-parametric analyses and is able to measure the size, shape, lumen formation and quantitative branching of a variety of structures in a high throughput manner. In the following sections, we will describe the technical features of SAMA, including a description of how images are processed to obtain the final output. SAMA meets the following demands of 3D image analysis: 1) it uses novel algorithms for image analysis that are able to identify, count and analyze crowded 3D structures, 2) it has a versatile design allowing analysis of epithelial phenotypes in several types of matrices, 3) it is open-source and written mostly in R and ImageJ macro language which are simple languages, so the underlying methodology is known and can be modified or improved by users with relative ease, 4) it is fully operated from ImageJ through a user-friendly interface and finally, 5) it is an automated, high-throughput analysis that eliminates the monotony that is typically involved in image analyses (for instance, image formatting, looping several image analysis steps and repeating the analysis without human bias).
Description of SAMA SAMA is a novel method by which epithelial structures grown in 3D cultures can be imaged, reconstructed and analyzed with minimum human intervention and bias. It is a powerful and flexible tool for high-throughput morphological and statistical analysis of 3D images. Additionally, we have included an analysis to evaluate the reproducibility of results within and among experimental groups, a critical necessity in biological experiments. SAMA is a user-friendly set of customized macros with an interactive and user-friendly interface that uses many tools of FIJI (http://fiji. sc/Fiji) [18] , an open-source image analysis platform, in combination with a set of functions in R (http://www. r-project. org/), an opensource software tool for statistical analysis. The website for downloading SAMA is http:// montevil.
theobio. org/en/content/sama. Herein, it is accessible as a FIJI plugin, compatible with Windows, Mac, and Unix along with downloadable source code. In addition, the website includes detailed technical description of the software with sample images for the user to download and learn how to navigate SAMA. Several plugins used in SAMA are available when FIJI is downloaded, while others have to be installed by the user, the details of which are available on the website.
This step ensures an accurate count and morphological measurement of individual structures. However, in images where crowded structures are not an issue, these steps can be left out of the algorithm. Image processing steps described here are incorporated at several stages in the SAMA-images section of the code depending on the end-point needed. A drawback of most commercial software is the inability to adapt new parameters that result from either including additional steps for image processing or calculating new features of epithelial structures. In contrast, SAMA's modular algorithm and open-source code allows users the choice of including additional image processing steps or novel phenotype measurements as needed. As an example of giving users the option to choose steps during image processing, we used images in which most of the background noise was removed by using wavelet techniques  [20] , a de-noising plugin.
An overview of the results is obtained by principal component analysis (PCA) (Figs  3B  and 4B ), which enables to identify the variables that have a greater influence in the variance of the samples. This technique enables the user to represent a data set with many parameters by defining new dimensions (which are combinations of the original variables) and represents a simple way of understanding the variance of the data set. This is helpful when many parameters are correlated to each other. In the case of SAMA, some data sets, for example, lead to a size related quantity and a shape related quantity. Note however, that the definitions of such quantities are obtained de novo for each dataset. Note also, that parameters that are biologically relevant but are not correlated with other parameters are usually not taken into account by this analysis because it focuses on trends present in many quantities.
Computer generated images In order to validate SAMA, we tested our software on computer generated (CG) stacks (S2 Fig). The stacks were generated with 40 ellipsoids, which mimic an image containing 40 epithelial structures in a z-stack obtained by confocal microscopy of 3D cultures. These CG structures are at random positions, orientation and sizes in the three axes. Lumena are smaller structures constructed with a randomly chosen radius within the larger structures. The reference condition (C1), are ellipsoids that contain a lumen, have similar probability distribution in terms of  size and orientation towards any of the axes. Condition C2 was designed to differ from C1 in that the maximum size of the ellipsoid oriented in one axis of the ellipsoid is twice the size that is oriented towards the other axes.
Imaging parameters A description of the parameters and the formula that were used to give an in-depth morphological analysis of the epithelial structures is described in Table  1. The basic morphometric parameters generated by 3D ROI manager plugin used in SAMA's program are elongation, flatness, sphericity and volume. For lumen analysis, we incorporated two parameters, the presence of a lumen and the relative lumen volume (ratio of the volume of the lumen to the volume of the structure). Quality is a derivative quantity, which takes into account the elongation of the structure and relative lumen volume. Morphologically relevant epithelial structures in 3D gels are ones that are optimally elongated, not very flat and contain a lumen. These features resemble those of a normal breast epithelium.
Validation by computer generated images To ensure that SAMA is functional, we validated the software using computer generated model images. The model images were constructed to test several analytical functions of SAMA. SAMA provides the expected outputs from our designed conditions (S2 Fig). The analysis shows that C2 has more elongated structures than C1 (p = 0. 002). In C3, there are more structures with lumen (p = 0. 016) and overall structures have bigger lumena (p = 0. 002). Condition C4 has structures that are more complex than C1 for all of our measures of complexity (p<0. 026). As a result, SAMA performs as expected in computer generated images.
Biological Validation and Analysis To validate SAMA, we used images from a hormone-sensitive 3D culture of the breast  [11]. We chose to analyze gels that have previously been analyzed using 2D image measurements as well as 3D computational modeling and visualization using the commercial software package, Volocity  [11]. We observed that T47D cells that were not exposed to hormones did not develop any epithelial structures and hence, these gels were excluded from our analysis. In the 3D gels analyzed here by SAMA, the cells responded to E2 by proliferating and forming elongated and spherical 3D structures. Further, treatment of cells with E2+P and E2+Prl caused significantly higher elongation of structures in the gels compared to E2 treatment alone (Figs  3A  and 4A ). These results are consistent with previously published results from 2D analysis  [11].
SAMA versus current software Currently, there are several commercial as well as open-source software packages available that combine image acquisition and analysis (Table  2 ). Among them, Imaris (Bitplane), Volocity (Perkin-Elmer), Image-Pro (MediaCybernetics), Amira and Metamorph (Molecular Devices) are examples of commercially available software. CellProfiler  [24] , V3D  [25] , BioImageXD  [26]  and NEMO  [27]  are examples of open-source software platforms for the analysis of biological images. The advantage of commercial software is that it is tailor-made for the imaging system purchased, with the availability of customer support and warranties from the company. Their drawbacks include the high costs of purchasing hardware-software bundles and most critically, the inability to know and modify the proprietary codes.
This prevents researchers from knowing the details of the computing processes and modifying them to acquire specific and biologically relevant end-points. While these are aspects where open-source software has an edge over commercial software, the presently available open-source software packages have limitations as well. Existing open-source software enables basic morphometrics (structural parameters only), but there are limited options available for an exhaustive, high throughput, biologically relevant multi-parametric image analysis bundled with statistical analysis that takes into account reproducibility among experiments. For example, there are limited software packages that can carry out in-depth analyses (a combination of morphological, statistical and reproducibility analyses of shape, size, lumen formation and branching) of 3D epithelial structures in models of biological systems.