dataesr/scientific-paragraphs-categorization huggingface dataset:
Even distinct species, if too closely related, share transcripts which, even if not identical, are likely to have similar numbers of matching reads. CroCo would place such transcripts in the 'dubious' category. If a true transcript in sample ' A' is expressed at a lower level than a very similar transcript in sample 'B' , then it could theoretically be incorrectly classified as a cross contaminant. This outcome is most likely in cases when the sample size is small (less reliable expression quantification), when analysing two very closely related organisms (which is not recommended), and/or using an inaccurate mapping strategy. The higher amount of cross contamination observed in dataset C (Fig. 1 ) comprising closely related species might be a sign of over-estimation of cross contamination.
Conclusions Our results suggest cross contamination is a common issue in comparative molecular biology. Besides being detrimental for phylogenetic inference, it is clear that cross contamination can be massive enough to generate genome-scale impacts in comparative analysis of molecular data. It can also potentially adversely affect species delimitation, bias population genomic metrics, mimic other sources of incongruence between gene phylogenies (e. g. gene duplication, horizontal gene transfer, incomplete lineage sorting) or impact estimates of gene content. CroCo is a database-independent multiplatform tool that has been designed to be as easy to use as possible and that efficiently and accurately detects and removes cross contamination in assembled transcriptomes.
Funding PS was supported by the  Agence Nationale de la Recherche (ANR)  ( TED ,  ANR-13-BSV2-0011-01 ). MT was supported by a  Biotechnology and Biological Sciences Research Council  grant ( BB/H006966/1 , dataset E) and the  European Research Council  ( ERC-2012-AdG 322790 ). HP was supported by the  French Laboratory of Excellence  project entitled  TULIP  ( ANR-10-LABX-41 ,  ANR-11-IDEX-0002-02 ). MM was supported by the  Institut Universitaire de France  ( Junior Membership   2009-2014 ).
Availability and requirements Project name: CroCo Project home page: http://gitlab. mbb. univ-montp2. fr/mbb/ CroCo Operating system(s): Platform independent Programming language: Bash Other requirements: R (optional); Docker (optional) License: GNU GPL Any restrictions to use by non-academics: none Availability of data and materials The datasets generated and/or analysed during the current study are available in the NCBI Sequence Read Archive (SRA) depository at https:// www. ncbi. nlm. nih. gov/sra. CroCo is freely available and can be downloaded with the following command (without <>): <http://gitlab. mbb. univ-montp2. fr/ mbb/CroCo>. It can be used as a command line application on a personal computer or on the Galaxy platform. Our tool is compatible with Linux, MacOSX and Windows.
Reconstruction and analysis of the GRN underlying early neural commitment informed by 3D chromosomal architecture To identify key TFs and CREs driving the transcriptional changes during RA-induced neural commitment, we reconstructed a GRN based on the following information: 1. Data on differentially expressed genes (DEGs) in the course of RAinduced neural commitment in P19 cells, as well as the temporal profiles of RNA Pol II binding to identify actively transcribed genes (M. -A. Mendoza-Parra et al. 2016). 2. Promoter interaction data for the differentially expressed genes (DEG interactions) from the Hi-C analysis described above; 4. The genome-wide binding sites of >600 TFs that we detected in >8,000 mouse ChIP-seq datasets deposited in the GEO database (that we made publicly available at http://ngs-qc.
In turn, the edges in the GRN reflected various types of spatiotemporal regulatory relationships between TFs, CREs and their target genes. To track signal propagation through the reconstructed GRN, we took advantage of our recently developed software tool, TETRAMER  (Cholley et al. 2018; M. -A. Mendoza-Parra et al. 2016). In TETRAMER, the regulatory activity of the nodes (in our case, CREs and TFs) at each timepoint is discretized (-1, 0, 1), and the ability of every node to trigger the observed downstream changes in gene expression ("up", "down", "neutral") is evaluated, taking into account signal propagation in both time and space (TF-CRE, TF-gene, CRE-gene). Nodes whose activity is inconsistent with the observed transcriptional changes are pruned from the network. This analysis produced a refined GRN underlying neural commitment comprised of 2,548 nodes (1,199 DEGs and 1,349 CREs) and 10,858 edges (Fig.
Transcriptome and Epigenome assays We have previously described the data on transcriptome dynamics and chromatin immunoprecipitation assays (M. -A. Mendoza-Parra et al. 2016) ; the datasets are available from the Gene Expression Omnibus (GEO) database (GSE68291). ChIPseq profiles for MEIS1, ASCL1 and MAFB are accessible from GEO (GSM2188919, GSM2188920, GSM2188924, GSM1187228 and GSM1964739).
Chromatin structure, epigenome and transcriptome integration We have associated open-chromatin regions -defined by FAIRE-seq assay -to the promoter-associated distal GAPs. FAIRE localization sites were then compared with a comprehensive collection of TF ChIP-seq assays retrieved from the public domain (M. -A. Mendoza-Parra et al. 2013). Note that the TF collection in use in this study includes a large number of datasets in addition to those provided by the ENCODE consortium; thus, our analysis is a comprehensive comparative study not only because of the large number of datasets used but also with respect to the diversity of the cellular systems considered. Transcriptome, RXR binding sites from ChIP-seq, TF annotations from public datasets and Hi-C long-range chromatin interactions were integrated and visualized using the Cytoscape platform (version 3.
Acknowledgments We thank  Peter Fraser  and  William Orchard  for the critical review of the manuscript and helpful comments. We thank the  IGBMC Microarray and Sequencing   platform (France Génomique  consortium- ANR-10-INBS-0009 ). This study was supported by  AVIESAN-ITMO Cancer , the  Ligue National Contre le Cancer , the  Institut National du Cancer (INCa)  and the  Agence Nationale de la Recherche  ( ANRT-07-PCVI-0031-01 ,  ANR-10-LABX-0030-INRT  and  ANR-10-IDEX-0002-02 ).
g. HORIZON2020 Secure Societies, Disaster Management and Emergency Response (UN-SPIDER), UNITAR's Operational Satellite Applications Programme (UNOSAT), Eye on Disaster Management)] to promote geospatial research and innovation as an instrument to aid the postdisaster operations  (Altan et al. 2013). The present research is the outcome of an international cooperation between Europe and Japan within the RAPIDMAP (RAPID MAPping) project funded under the Capacities Programme of the EU FP7 "Resilience against Disasters"  (Cho et al. , 2014). This publication is focused on the processing of high-resolution airborne optical imagery and shows how to exploit such datasets for automatically deriving contextual information about collapsed buildings in urban areas. Besides the image photometry, the algorithms strongly rely on the elevations derived from photogrammetric processing (see "Experiments" section).
Related works Optical remotely sensed datasets are more straightforward for visual interpretation, and therefore are suitable for the crowd-sourcing campaigns and prevailing in rapid mapping  (Kerle, 2010). Satellite imagery is preferred in change detection applications when a large area should be surveyed. Automated satellite-based approaches perform mostly by comparing the multiepoch data in a supervised or unsupervised way. The supervised scenarios classified on the base of image-toimage analyses  (Rosu, Pierrot-Deseilligny, Delorme, Binet, & Klinger, 2015; Voigt et al. , 2007) , detect changes by incorporating prior knowledge, e. g. in the form of cadastral vector layers, or subtract respective nDSMs to extract objects  (Guerin, Binet, & Pierrot-Deseilligny, 2015; Poli & Caravaggi, 2013; Zhou, Parsons, Elliott, Barisin, & Walker, 2015).
Samadzadegan and Rastiveisi (2008)  experimented high-resolution satellite images from QuickBird satellite over the city of Bam, Iran. The data were available in two time epochs: before and after the earthquake occurrence. The building locations were known from a vector map and indicated where the texture features shall be extracted. The building condition was finally assessed comparing the 2D features in preand post-event imagery. When no previous cases are available, unsupervised change detection based on preand post-event data is possible as well  (Dalla Mura, Benediktsson, Bovolo, & Bruzzone, 2008; Pesaresi, Gerhardinger, & Haag, 2007). Guerin et al. (2015)  worked with Worldview-I and IKONOS imagery collected over the city of Phoenix, USA, and Sendai, Japan, respectively. In either case, two epoch datasets were simultaneously bundle adjusted, and two separate DSM were obtained.
The change detection was defined as a labelling problem on a differentiated DSM, and solved with dynamic programming, considering the contextual information in pixel neighbourhood. As far as aerial platforms and UAVs are concerned, the benefit is the more flexible data acquisition, both in terms of time and flight pattern. The on-board sensors have a much higher spatial resolution than satellite sensors, making them more suitable for detailed inventory. Rezaeian and Gruen (2011)  proposed a methodology to detect man-made damages using two-epoch analogue aerial stereo pairs. Image intensity values and 3D information from the generated DSM served directly and indirectly as the damage assessment evidence. Partial and total collapses were recognized. Guo, Lu, Ma, Pesaresi, and Yuan (2009)  identified collapsed building after the 2009 earthquake in Wenchuan China, with only post-event data captured by an aerial pushbroom camera.
Having delineated building candidates, the gaps in the 3D data were interpreted on the basis of a set of photometric features. Airborne and UAV imagery after the Mirabello (Italy) earthquake in 2012 was incorporated in the study. Rapid mapping conducted from lower altitudes with UAVs was also deployed for reconnaissance purposes, for instance after the Katrina hurricane in 2005, L'Aquila earthquake in 2009, Morakot typhoon in 2009, Haiti earthquake in 2011, Sendai earthquake in 2011 and Hurricane Sandy in 2012  (Adams & Friedland, 2011; Baltsavias, Cho, Remondino, Soergel, & Wakabayashi, 2013; Shanley, Burns, Bastian, & Robson, 2013). Refer to  Kerle, Heuel, and Pfeifer (2008)  for a review of different airborne sensors used in mapping hazards and natural disasters, and  Joyce, Belliss, Samsonov, McNeill, and Glassey (2009)  for available space-borne solution.
All adopted algorithms' implementations were available from open-source codes: Random Forests (RFs) and K-means were part of the Open source computer vision library (OpenCV)  (Bradski & Kaehler, 2008) , αexpansion for robust PN potentials as described in  Kohli, Ladicky, and Torr (2008)  and the maxflow code from  Boykov and Kolmogorov (2004).
Experiments The experiments evaluate the proposed methodology on two datasets: high-resolution (San Felice dataset, Table  2 ) and medium-resolution (Aquila dataset, Table  2 ) aerial imagery. In each case, three strategies are adopted, i. e. when building candidates (i) are furnished by the cadastre, (ii) originate from sampling the class likelihood generated by RFs, or (iii) from the class likelihood generated by the sheer mean height parameter (H MEAN ). To give a sense of the sensibility to the threshold parameters isolating the building candidates, two distinct height values (σ h ¼ 10 m and σ h ¼ 15 m) are applied and assessed. In both cases, the buildings' candidates are ousted from the likelihoods using a 20% threshold value. Areas below 20 m 2 were regarded small (nS). Table  1  outlines the processing strategies and the set of features adopted.
In the RF approach, 300 trees are built and trained using 1000 samples. The following high-order potential parameter were selected: γ l ¼ 1, γ max 1 ¼ 10, γ max 2 ¼ 1000 Q = 100. Two different γ max values were tested to verify their influence on the outcome. In order to evaluate the performance of the developed methodology, damaged areas are manually identified on the cadastral layer by visual inspection, thus producing a ground truth (GT) map for the datasets. The adopted assessment approach is a compromise between purely pixel-based and object-based strategies, i. e. both corresponding pixels and objects are considered as entities to be compared. First, building objects are automatically extracted from the GT map. Then, for each building, corresponding pixels are compared and three quality measures are derived, corresponding to the proportion of the damaged area of a building that is correctly classified (overlap), missed (underlap), or incorrectly detected as damaged (extralap).
Results The first dataset (San Felice, Italy) includes 21 images that were collected with a Vexcel Ultracam XP system after an earthquake in 2012. The images feature an average ground sample distance (GSD) of 5 cm and an overlap of about 80/50 (along-/across-track). The region comprises historical and residential buildings, together with warehouses and factories. Buildings are mainly low-storey and detached, surrounded by trees and bare ground. The second dataset (L'Aquila, Italy) was acquired by a Pictometry imaging system, a fivelens camera system that incorporates vertical and slanted views. For the present work, only the nadirlooking images were used, consisting of 103 images with a mean GSD of 11 cm and a 70/40 overlap (along-/across-track). The flight surveyed the city of Aquila in 2009, after the massive seismic events that severely damaged most of the buildings.
The area is characterized by a dense development of historic buildings having rather complex shapes, narrow roads and trees. For both datasets, images were photogrammetrically processed using Apero/MicMac (Pierrot-  Deseilligny & Clery, 2011, March; Pierrot-Deseilligny & Paparoditis, 2006). After the image block triangulation, 2. 5D DSMs and true orthophotos were extracted at full resolution. The developed methodology was then tested on a subset of the surveyed areas, corresponding to the most densely-built region (770 m by 560 m in San Felice; 616 m by 330 m in L'Aquila). Both subsets include different types of damages, ranging from completely collapsed buildings to partially damaged ones. The datasets were processed on a PC with i7 (1st generation) 1. 87 GHz, 8 GB RAM. The features (spectral and geometric) calculation times for a tile of 8MPixel took approximately an hour, while the classification part took a few minutes.
The algorithms were implemented in C++, without parallelization. Using parallel computing and GPU, the estimated computation time could drop to few minutes which is compatible with the end-user needs. It shall be underlined that the set of algorithms was conceived in a proof of concept framework. High-resolution imagery -San Felice dataset Some of the classification results are shown in Figure  7 , whereas Table  2  presents the numerical results of the evaluation. Various combinations of features were adopted throughout the tests, and the reported figures correspond to the feature sets discussed in the paper. The appearance of rooftops in the aerial images is detailed and very clear due to the high-resolution data. For that reason, the red and blue band layers were not taken into account as independent features (see Table  1 ).
It indicates that our training did not represent  holistically all the possible damages. The average building height in the dataset is closer to 10 m than 15 m because the first assumption (H MEAN,10 ) reports all four quality measures at higher levels. The influence of higher order term proves significant. For both H MEAN,10 and H MEAN,15 , giving more importance to the higher order term (γ max 2 ) improves the accuracy/ precision/completeness. The detected regions are usually better delineated as the segmentation gains its importance. On the other hand, small and just partially detected often disappear: this usually reflects on a lower percentage of detected damages. Medium-resolution imagery -L'Aquila dataset Figure  8  and Table  3  present the classification results. The lower resolution images render the geometric features less significant, as opposed to the San Felice images.
Some parameters can be tuned in the whole workflow in order to reduce the dependency of results from the initial set up. The algorithm has been tested on two different datasets in order to assess its reliability in different urban context and with different data resolutions. The high-resolution and high image overlap of the first dataset represent the standard of nowadays image acquisitions. On the other hand, the second test represents a sub-optimal data due to the poor image resolution and reduced image overlap. In both cases, the results have been encouraging, showing quite good performances in the detection of all the damage typologies. As expected, the results are strongly conditioned by the image resolution and the quality of the DSM and the orthophoto: results are satisfying only when features are meaningful and not affected by poor input data quality.
The size of the damage together with the resolution of the data plays an important role in the detection process. Delineation of the damages is often imprecise. This is particularly evident in the second dataset, where most of the damages are only partially detected as stated by the variability of completeness according to the used threshold. However, the underestimation of damages often does not affect the practical usability of the results (i. e. percentage of detected damages), as rescuers often need a first map of the damages location without caring of their actual extension. Field inspections or simply higher resolution data  (Vetrivel et al. , 2015)  could provide better results. The use of higher γ max improves the accuracy and the precision of the results, showing that the classification process greatly benefits from the higher order terms (i.
is performed on each of these segmented areas, yielding various information: area of the crack as well as its principal orientation (angle of the crack) and the shape anisotropy (derived from the eigen values and vectors calculated in the principal component analysis of the crack shape). These data can then be compared for multiple samples and sample series. In principle, other staining method may be used on any types of tissues or materials, as long as the contrast is strong enough for the script to detect the cracks.
Software 1. Fiji program (http://fiji. sc/) Open-source plugin-based image analysis software based on ImageJ (https://imagej. nih. gov/ij/) 2. Python 2. 7 (Interpreted high-level programming language for general purpose programming) (https://www. python. org/) 3. Python modules: a. Matplotlib (2D visualization and data plotting library) (https://matplotlib. org/) Nose (Python unit test framework) (http://nose. readthedocs. io/) b. Numpy (N-dimensional linear algebra library) (www. numpy. org/) c. Pandas (Data manipulation and analysis library) (https://pandas. pydata. org/) d. Pillow (Image manipulation library) (https://pillow. readthedocs. io/) e. Pycircstat (Circular statistics library) (https://github. com/circstat/pycircstat/) f. Scipy (Scientific computing library) (https://www. scipy.
org/) 4. Open source package management and environment management system a. Miniconda: https://conda. io/docs/ b. LINUX: https://repo. continuum. io/miniconda/Miniconda2-latest-Linux-x86_64. sh c. MAC: https://repo. continuum. io/miniconda/Miniconda2-latest-MacOSX-x86_64. sh d. Windows: https://repo. continuum. io/miniconda/Miniconda2-latest-Windows-x86_64. exe 5. Cell Separation Image Analysis Pipeline (Image analysis script described in this protocol) (https://github. com/sverger/Cell_separation_analysis) Note: See "Installation procedure" in "Procedure" for the installation of Software 2 to 5.
B. Prerequisite: Image quality, preprocessing and threshold for "crack detection" in Fiji In order to determine if your images are suitable for this image analysis pipeline you need to make sure that the cracks will be properly segmented by pixel intensity: 1. Load your 2D image in Fiji (Software 1). 2. Change your image type to 8-bit. Image > Type > 8-bit (Figure  2B ) and/or pick the right channel from a multichannel (e. g. , RGB) image (Image > Color > Split channels). 3. Optionally you may enhance the contrasts of your image using the "Enhance Contrast. " function (Process > Enhance Contrast…> Set "Saturated pixels" to 0. 3%).
Note: This folder contains the script ("Cell_separation_analysis. py"), a dependencies installation file for miniconda ("cell-sep-env. yml") and test sample images ("Test_files"). 5. In a terminal, navigate to the "/cell_separation_analysis-master" folder that you have extracted. cd path/to/Cell_separation_analysis-master/ Note: Input the actual path that leads to the folder "/Cell_separation_analysis-master". This should run the script and generate its output in the "/Test_files" folder of "/Cell_separation_analysis-master".
D. Running the script In order to run the script with your own images, you need to edit the "parameter" section of the script. To do so, open the "Cell_separation_analysis. py" file in a text editor. Scroll down to the section called "parameters" where there are 7 entries that you may modify (see Figure  3 ): 1. Set the directory where you placed the images to analyze. Remember that your folder has to be organized in a specific way: A "main" directory corresponding here to "updir", containing subdirectories (e. g. , different mutants or growth conditions), each containing all the corresponding images (Figure  2F ). You can either enter the full path to the directory (e. g. , /Home/Path/to/updir/), or simply enter. /updir/ (where you replace "updir" by the actual name of your folder). In the latter case, you will have to navigate to the parent folder of your "updir" in Ipython before running the script (as described in Step C8).
4. Set the threshold type. The cracks can be of either a higher or lower pixel intensity than the surrounding region. Thus the threshold can be set as "min" or max". The "max" will detect and segment zones with lower signal intensity (i. e. , the crack is darker than the surrounding region) and the "min" will detect and segment zones with higher signal intensity (i. e. , the crack is lighter than the surrounding region). "Data analysis" section to determine if you should run these analyses. These are by default set to "False" which mean they will not be performed. Replace "False" by "True" if you want them to be performed. 6. Once all the parameters are correctly set, save the script and run it as described in Step C8.
Data analysis 1. As explained in the background section, this script can generate different outputs. From the detected cracks in each image, the script will generate three images: a pixel intensity inverted version of the image, the same image with an overlay of the segmented areas, and the same image with an overlay of the anisotropy and principal angle of the area, directly saved as vectorial PDFs. It will also generate a. csv file containing, for each segmented cracks, the label number, center position, area in pixels and micrometer square, the main orientation (angle) of the crack, the shape anisotropy, the eigen values and vectors. Finally for each image, a polar histogram representing the distribution of crack orientations in the image is created and saved as a vectorial PDF (see output generated in the test of the script in the previous step and Figure  4 ).