dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments PC, APJ, JE, TJN, FL, NS, MG, SL, PP, OB, GF, JR, AG, JB, ST, CE, JCG, and AdB are supported by the  Ministry of Research (France) ,  University Paris Diderot, Sorbonne Paris Cité (France) ,  National Institute of Blood Transfusion  ( INTS, France ),  National Institute of Health and Medical Research (INSERM, France) , and labex  GR-Ex (France). The labex  GR-Ex , reference  ANR-11-LABX-0051  is funded by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. This work was supported by the  Czech-France collaboration Partenariat Hubert Curien Barrande  ( MEB021032 ) for JCG, JC, BS, and ADB. This study was also supported by  BIOCEV CZ. 1. 05/1. 1. 00/02. 0109from the ERDF and RVO: 86652036 for BS and JC, by  University of Toronto  ( Canada ) for LSS, by  Max Planck (Germany)  for BMR, by  INTS (France)  for VJ, by  CNRS (France)  and  Univ Lyon (France)  for JB, by  CNRS (France) ,  INSERM (France)  and  Univ Strasbourg (France)  for JE, by  CNRS (France)  and  Univ Nantes (France)  for ST, by  HFSP  to APJ; by  NIH (USA)  for GF, by  Department of Biotechnology (India)  to NS, and by  CNRS (France) ,  Univ Pierre & Marie Curie (France)  and  ANR NaturaDyRe (France ,  ANR-2010-CD2I-014-04 ) to JR.
This Data in Brief report describes the experimental and bioinformatic procedures that we used to analyze and interpret E4F1 ChIP-seq experiments published in Rodier et al. (2015) [10]. Raw and processed data are available at the GEO DataSet repository under the subseries # GSE57228. E4F1 is a ubiquitously expressed zinc-finger protein of the GLI-Kruppel family that was first identified in the late eighties as a cellular transcription factor targeted by the adenoviral oncoprotein E1A13S (Ad type V) and required for the transcription of adenoviral genes (Raychaudhuri et al. , 1987) [8]. It is a multifunctional factor that also acts as an atypical E3 ubiquitin ligase for p53 (Le Cam et al. , 2006) [2]. Using KO mouse models we then demonstrated that E4F1 is essential for early embryonic development  (Le Cam et al.
, 2004) , for proliferation of mouse embryonic cell  (Rodier et al. , 2015) , for the maintenance of epidermal stem cells  (Lacroix et al. , 2010)  [6], and strikingly, for the survival of cancer cells (Hatchi et al. , 2007) [4];  (Rodier et al. , 2015)  [10]. The latter survival phenotype was p53-independent and suggested that E4F1 was controlling a transcriptional program driving essential functions in cancer cells. To identify this program, we performed E4F1 ChIP-seq analyses in primary Mouse Embryonic Fibroblasts (MEF) and in p53 -/-, H-Ras V12 -transformed MEFs. The program directly controlled by E4F1 was obtained by intersecting the lists of E4F1 genomic targets with the lists of genes differentially expressed in E4F1 KO and  E4F1 WT cells (Rodier et al. , 2015). We describe hereby how we improved our ChIP-seq analyses workflow by applying prefilters on raw data and by using a combination of two publicly available programs, Cisgenome and QESEQ.
Mapping ChIP-seq raw and processed files are fully available at GEO DataSet repository under the subseries # GSE57228. FASTQ files can be recovered from the NCBI SRA database under the reference #SRP041609. FASTQ files were aligned over mouse genome (mm9) with a maximum of two mismatches in the first 32 nucleotides with CASAVA 1. 8 and fulfil the quality control standards performed by the MGX facility. Multiple mapping reads were discarded from further analyses. Raw files were delivered as BED files.
This can also be achieved in R  [9]  with the command line #1 (sup data). Our raw files undergo a second filtering by the removal of Chromosomes M and Y mapped reads which display unusual density that could bias peak calling. A manual survey of read density over these two chromosomes did not reveal any significant enrichment that could have been missed because of this filter. Removal is done with the function "filter" (filter and sort tools directory) and "not c1 =='chrY' and not c1 =='chrM'" arguments. This could also be done in batch under a Linux console with the command lines #2 (sup data). The third step of filtering is applied on our raw data. Read tag density background is not random, neither equally distributed over mouse genome and samples. Nevertheless these fluctuations are found both on input and ChIP samples on different chromatins that we analyzed, and nearly at the same position on the genome indicating that they are not relevant for our experiments.
Because these fluctuations do not present the same amplitude on each sample, the peak calling algorithms that we tested, like MACS  [11] , Cisgenome  [5]  or QESEQ  [7]  can call these regions as significantly enriched regions and so contaminate the final lists with false positives. Removal of these potentially false positive regions on input and ChIP-seq samples before peak calling greatly reduces false positive peak calling and greatly increases functional interpretation of the ChIP-seq experiments. We first run "hts_windowsummaryv2" with a 100 bp exploration window to get the metrics of our samples and defined a cutoff for calling these artifactual peaks. We performed a peak calling on inputs alone with the "one sample analysis" module of Cisgenome (hts_peakdetectorv2 -i C:\. \XX_. bar -d C:\.
These two lists are transformed as BED files and are intersected to retain only high confidence peaks. The intersection files have been generated with Galaxy (intersect) and are supplied as "_intersection. bed" files. All processed files (BAR, BED, COD and TXT files) are fully available in GEO DataSet (GSM1377538 and GSM1377540) and as supplementary data in  [10]. Tag densities and significantly enriched regions are visualized with IGB to ensure that our filters and cut-off give a good positive signal/background ratio. These lists have been then used for further functional annotation. ChIP-QPCR validations had been performed on a set of representative peaks from the top to the bottom of the list to confirm or not the accuracy of our analysis pipeline.
Acknowledgments This work was supported by grants from the  French Ligue Nationale Contre le Cancer  ( LNCC, C. S. Equipe Labellisée 2011 ), from the  Agence Nationale pour la Recherche  ( ANR SVSE2-YinE4F1Yang2  and  MetaboCycle 2  to C. S. and L. L. C. ), and from the  Association pour la Recherche contre le Cancer  (to G. R. ). Institutional support was provided by the  Institut National de la Santé et de la Recherche Médicale  (L. L. C. ) and the  Centre National de la Recherche Scientifique  (C. S. ), and technical support by the  Montpellier Genomics (MGX)  and animal facilities. T. H. was supported by fellowships from the  LNCC , and O. K. by fellowships financed on the  ANR  grants  JCJC-0014-01  and  SVSE2-YinE4F1Yang2.
Simple thresholding methods form the basis of many typical segmentation methodologies  [9]. Popular freeware image analysis platforms such as Cell Profiler  [10]  and ImageJ/Fiji  [11]  provide practical implementations of such segmentation methodologies specifically designed for cellular microscopy data and allow for analysis beyond segmentation in specialised pipelines for particular data sets. However such analysis is typically focused on high resolution microscopy data at single cell or even sub-cellular levels and is also mainly focussed on two dimensional (2D) monolayer cell cultures, although 3D volume viewing and rendering is possible in ImageJ. Commercial software products that focus on 3D cellular microscopy image analysis such as Imaris (Bitplane) and Volocity (PerkinElmer) are designed for very detailed analysis of high resolution images at the single cell level where there may be as little as 0.
Cellular microscopy image data We utilise 3D stack confocal microscopy image data from three different but closely related organotypic 3D cell culture models, which we refer to as 'IF image data', the 'LIVE image data' and the 'FAK image data'. Full details of the experimental and imaging protocols have been previously published  [29]  and are briefly outlined below. All 3D stacks of images from both the IF and LIVE image data were manually segmented by the biologists who performed the experiments. The manual segmentation was performed in Adobe Photoshop (Adobe Systems) and resulted in each pixel being given a ground truth label of either 'in focus tumour cells', 'in focus CAFs' or 'out of focus/background'. These ground truth labels are used in the validation of our segmentation method. Commercially available extracellular matrix preparations (growth factor-reduced Matrigel with a stock concentration of 8 mg/ml and collagen type-I with a stock 3 mg/ml) were purchased from BD Invitrogen and used for all 3D co-culture experiments.
An antibody specific for pan-keratin was used for the specific staining of tumour organoids, whereas CAFs were stained with an antibody against human vimentin. CAF cells eventually merged into large multicellular structures, which were characteristically surrounding the periphery of tumour organoids. In the LIVE 3D co-culture setting, variations of the same cells as for the IF image data set were used. In this experiment, LNCaP tumour cells expressing DsRed protein were co-cultured with PF179T CAFs expressing GFP. The same experimental conditions, extracellular matrix extracts and other settings as above were used. The formation, growth, differentiation and morphogenesis of tumour organoids, as well as the formation of multicellular structures from single CAFs was monitored over a period of 14 days, after which imaging was performed.
The FAK 3D co-culture setting was the same as the LIVE setting with the addition of focal adhesion kinase (FAK) inhibitors Y11 and PF-573228 purchased from Tocris Bioscience. The three conditions were: DMSO control (0. 01%), 5 μM concentration of Y11 and 5 μM concentration of PF-573228. Multichannel 3D confocal digital images were acquired where the tumour cells and CAFs were imaged separately. Both cell types are presented below in different (false) colour channels: red for tumour cells and green for CAFs. The IF and LIVE image data were acquired with a Zeiss Axiovert-200M microscope, equipped with a Yokogawa CSU22 spinning-disc confocal unit using a Zeiss Plan-Neofluar 20 × objective (numerical aperture 0. 17). The IF image data consist of 8 stacks of images while the LIVE image data consist of 12 stacks of images.
We additionally consider segmentation performance with the 2D phase contrast 'Melanoma' and 'Tscratch' image data (BBBC019), also from the Broad Bioimage Benchmark Collection. The Melanoma image data consist of 20 images each with dimension 1024 × 1280 pixels while the Tscratch image data consist of 24 images each with dimension 1028 × 1384 pixels. Both data sets are from 'wound healing' experiments with ground truth segmentations also provided.
As a pre-processing step, we use a local entropy filter  [9]  to quantify local texture (built-in function entropyfilt in the MATLAB Image Processing Toolbox). The local entropy filter is applied to both the red and green channels separately. The raw greyscale images are downsampled to 8-bit so that each pixel directly corresponds to an intensity value in the set {0, 1,. , 255}. For each pixel i, the local entropy filtered value is Z i ¼ À X j2N i p j log 2 ðp j Þ where p j is the proportion of pixels in the neighbourhood N i that have equal intensity to pixel j. The neighbourhood N i of each pixel i is the 9 × 9 square centred on pixel i inclusive with symmetric padding around the border of the image. Our 3D segmentation method is summarised in the following steps (  4. Find the minimum energy labelling.
Results and Discussion We present a series of results in order to discuss the benefits of our MRF based method for our particularly complex biological application, in particular, multichannel 3D image data with large distances between images in the stack. Using the 3D segmentation output, it is possible to investigate if tumour cells and CAFs are forming cell-cell contacts or are physically separated in our 3D co-culture model, which has important implication for tumour biology. We additionally show the utility of the local entropy filter and that our MRF based approach obtains the most accurate segmentation results. The code for our segmentation method, the image data and the ground truth labels are all provided as supplementary material (S1, S2 and S3 Files).
Validation results Segmentation output for the IF and LIVE image data is given in Figs  8  and 9  respectively. The mean (and median) macro F 1 -score for each segmentation method is shown in Table  1. These scores generally follow the way that the methods 'built up' to our MRF based approach. Box plots of the F 1 -scores are given in  the null hypothesis that there is no difference between the paired F 1 -scores. Our MRF based method generally has significantly higher F 1 -score compared to each of the other methods  (Fig 11). This is clearly shown for the IF image data, but it is not as clear for the LIVE image data. A possible explanation for this difference in performance is that the CAF structures in the LIVE image data are noticeably less complex than in the IF image data (Figs  8  and 9 ).
Note that these details are not readily captured by the F 1 -score, which was 0. 871 and 0. 868 for the MRF and 'mixtures' segmentations given in Fig  12 respectively. To further validate our MRF based segmentation method, we considered the independent mouse embryo, Melanoma and Tscratch image data. All phase contrast image data was converted to greyscale. For the 'mixtures' and MRF method, rather than constructing bivariate mixtures as for multichannel data, the univariate mixture components themselves were used. The dotted lines correspond to equal F 1 -score and the p-values correspond to Wilcoxon signed-rank tests that there is no difference between the paired F 1 -scores. Note that for the IF image data, the p-values are all equal because the MRF F 1 -score is always higher (all points are below the dotted line).
For the comparison with the 'intOstu' method for the LIVE image data, the MRF F 1 -score is also always higher but the p-value is lower here because there are more data points. Since this is also 2D image data, we considered a 4-neighbour MRF although the energy function Eq (1) still has the same form as in the 3D case. The mouse embryo image data has so far only been used for evaluating cell counting methods  [43] , however ground truth segmentations of the entire multicellular embryo structures are also provided. Example segmentation output is given in S3  'wound healing' assays. However, overall performance is reasonable in comparison and suggests that it may be fruitful to further investigate a MRF based method for such data. Although there are increasingly more bioimage benchmark data sets becoming available to evaluate a range of computer vision tasks  [45] , for the most part, these data sets are not applicable for our purposes as they are benchmarks for segmenting (and counting) either single cells or cell nuclei.
Implementation in MATLAB and computational expense We provide all of the necessary MATLAB code and data to implement our 3D segmentation method (S1 File). The file MRFsegmentation. m provides an implementation of our MRF based method as outlined in Fig 3. By placing all files provided in S1 File on the current path and compiling the code as outlined in README. txt, the user simply needs to be run MRFsegmentation. m to reproduce the segmentation output given in Figs 4 and 12. Extensive comments within the file provide a step by step tutorial of the implementation. All other files are ancillary and are called within MRFsegmentation. m. Our 3D segmentation method was implemented on a mid 2012 MacBook Pro with a 2. 6 GHz Intel Core i7 processor (quad-core) and 16 GB of RAM. Applying the local entropy filter to a 3D stack of images took on average *10 seconds and scales linearly with the number of images in the stack.
Acknowledgments The authors would like to thank  Lionel Herve  for the sparse matrix source code and,  Itebeddine Ghorbel  and  Ville Härmä  for discussion. The authors would also like to thank the editor and two anonymous reviewers for valuable suggestions. SR is the beneficiary of a CEA-Industry thesis contract and a VTT thesis contract. This work was supported by the  Academy of Finland , grant numbers  284619  (MÅ) and 267326 (MN).
g. ,  [12] ) most studies in Mongolia have focused on monumental archaeology. This is coupled with poor resolution of household archaeology (centimeters of deposition equating to centuries of occupation). Consequently, our understanding of the past can be blurred. Simulations, therefore, help us to address these gaps in our knowledge. Notably missing from our study is a goodness-of-fit exercise between the model and the real settlement patterns  [1, 35]. This is due in part to there not being many complete archaeological datasets in the region to do goodness-of-fit tests against yet. Consequently, we must make do and use models as a way to inform our understanding of the limited archaeological information available at this time. This model, while not meant as a reproduction of reality, presents a plausible scenario based on developed theory and hopes to address key questions of how semi-nomadic Mongolians address local weather events, such as drought and heavy winters.
Acknowledgments We would like to thank  Luke Premo ,  Camilla Kelsoe  and  Bryan Hanks  as well as the participants and organizers of the Complex Systems in Prehistoric Research symposium at the European Conference on Complex Systems for feedback on earlier versions of this model and paper. Clark recognizes funding from  NSF   Doctoral Dissertation Improvement Grant  # BCS-1236939. Crabtree recognizes funding from  NSF   Graduate Research Fellowship  # DGE-080667  and the  Chateaubriand Fellowship Program. Crabtree  built the model Ger Grouper so any faults in the model design belong exclusively to her faulty logic.
III. RESULTS A firmware has been designed, that makes efficient usage of the device resources to allow for a future-proof implementation, maintaining a significant performance overhead to allow for AMChip upgrades. Track fitting performance reaches 2GFits/s, and the rest of the architecture (namely the Data Organizer and Combiner modules) can sustain that performance. The total latency of the system (here defined as the time interval between the first pattern matching result coming from the AMChip and the first track candidate having been fully processed, as the first track that actually passes the χ 2 cut can't be predicted) is ∼300 ns. The design has already been implemented on a Xilinx 7-series Kintex FPGA device and is currently being integrated as a full system on a mezzanine PCB, to be seated on a PULSAR board.
Then, forward engineering starts where such models are analyzed and transformed (if necessary) to become the specification of the modernized system. Finally, developers and/or (semi)automated code generation techniques use these models to produce corresponding code for the targeted platform. There are already tools that can help in some of these tasks, e. g. UML model discoverers from Java code or code generators for various platforms. But there is currently no global methodology that can guide engineers through the whole process. Moreover, beyond purely technical aspects, there is no real support either for assessing project risks or final software quality. Based on our past and present experience in conducting (and building tools for) software modernization projects, we elaborate on some important factors to consider in such projects and on a few recommendations to maximize chances of success, notably by following the ARTIST approach 7.
For all the generated models to be interoperable, the modeling platform shares a common meta-metamodel (provided by the Eclipse Modeling Framework in our case). Once in the modeling realm, we benefited from the most appropriate mix of modeling languages: UML, SysML, BPMN, KDM (cf. the Architecture Driven Modernization (ADM) initiative 8  ) as from the OMG, or more domain-specific languages (DSLs). We also relied on corresponding techniques, such as model transformation or text/code generation, to process models in a generic way. Different views for different stakeholders: Modeling frameworks separate the visualization of modeled information (a. k. a. the concrete syntax) from its content (a. k. a. the abstract syntax). This allows processing model contents efficiently and, at the same time, providing several visualizations (possibly using alternative notations) for a same model or model fragment.
A Concrete Approach for Modernization to the Cloud We introduce hereafter (cf. Figure  1 ) the actual realization of the previous principles in a concrete methodology developed within the ARTIST EU project. Following the identified guidelines, the ARTIST methodology is split up into three phases:  A pre-migration phase dealing with the evaluation of the existing software, notably in terms of migration feasibility from both a technical and business perspective (e. g. , checking Cloud compliance based on standards (TOSCA) or drafts (ISO CCRA)).  The migration phase itself, covering both reverse engineering and forward engineering activities (including possible optimizations within the migrated software). The selection of the target Cloud provider is also performed in there.  A post-migration phase, addressing the verification and validation of the migrated software as well as the potential certification for the newly produced (Cloud-based) pieces of software, notably against Cloud standards and current best practices.
We have implemented two versions of the tooling supporting this methodology, one relying on the Eclipse framework and another based on SparxSystem Enterprise Architect (to deal respectively with both the Java/JEE and C#/. NET cases). All along the process, but more particularly during the migration phase and optimization actions, we are extensively using UML models (sometimes extended with some profiles) to represent the different required aspects of the system. This also facilitates model exchange between different tools and the building of several viewpoints. Complementarily, all used and/or produced models can be stored and retrieved from the so-called ARTIST repository at any time during the process. Other modeling languages and techniques are employed in more domain-specific tasks. To mention a few, we are using a Goal Modeling Language (GoalML) as part of the pre-migration phase in order to precise the scope of the intended migration (e.
g. , as far as non-functional properties are concerned) and help in determining whether it is actually feasible or not. In the post-migration phase, fUML behavioral models of the deployed system are automatically checked against the initial goal models to validate if corresponding objectives are satisfied. The target specification and deployment process itself depends on the CloudML language (expressed as a UML profile) to represent required cloud infrastructures. This is made easier by the reuse of open source modeling solutions (thanks to the shared modelware TS), mainly coming from the Eclipse ecosystem. We can notably mention the Eclipse Process Framework (EPF) (in charge of enacting and customizing the ARTIST Methodology Process Tool to follow the various ARTIST phases), the MoDisco model driven reverse engineering framework (for code-tomodel discovery), ATL (for model-to-model transformation) or Acceleo (for model-to-code generation).