dataesr/scientific-paragraphs-categorization huggingface dataset:
Fragment length was 34 bases. All data are available in the Gene Expression Omnibus database under the accession number GSE49127. Mapping. The selection of 39UTRs prior to sequencing allowed us to significantly enhance the depth of quantification. Low quality sorting and adapter trimming was done using seqclean software (options: -v -l 32 -y 7 -x 90). Mapping was performed by GATC Biotech using ELAND software supplied by Illumina, using 32 kmer and allowing up to two mismatches (6. 25% error rate). We chose to map the sequences first to the D. melanogaster genome, and only secondly, for those of the sequences that did not map at the first step, to the D. simulans genome. This double mapping strategy was chosen since the genome of D. simulans is not as well annotated and assembled as the genome of D.
Acknowledgments We would like to thank  David Ogereau  for his precious and unique technical skills. Thanks to  Arnaud Le Rouzic  for helpful discussions. We are grateful to the  INRA of Gotheron  for welcoming us on their fields. We also thank GATC, especially  Benjamin Moingeon  and  Dr Yadhu Kumar  for the sequencing and helpful discussions. We are grateful to  Sharylinn Wardrop  and  Joan Sobota  for language edition.
The per minute ventilation/carbon dioxide output slope was calculated using a linear regression analysis based on all points of the incremental exercise  [20]. Before and during the exercise tests, cardiac output and stroke volume were estimated non-invasively by impedance cardiography (PhysioFlow PF07 Enduro, Manatec Biomedical, Paris, France) as previously described  [21]. Arterial oxygen saturation was measured by finger oximetry (Takaoka Oxicap, São Paulo, Brazil). The results of the cardiopulmonary exercise test, including the percentage of the predicted values for girls  [22] , are presented in Table  1. The test was stopped due to fatigue, with the peak heart rate and the peak respiratory exchange ratio compatible with maximal effort. Compared to the reference values, the patient presented peak power output, peak oxygen uptake, and anaerobic threshold consistent with preserved exercise capacity (85 to 101% of predicted).
Genetic analyses Genomic DNA was obtained from (a) peripheral lymphocytes and fibroblasts using the Ilustra™ blood genomic Prep Mini spin Kit (GE Healthcare, Madison, WI, USA) as described by the manufacturer and from (b) formalin-fixed paraffin-embedded tumour tissues using the QIAmp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany). DNA from lymphocytes was screened for the TP53 p. R337H mutation with allele-specific TaqMan W probes (Applied Biosystems, Foster City, CA, USA) using a MX 3000P™qPCR System -Stratagene (Agilent Technologies Inc. , Santa Clara, CA, USA) and analysed using the Stratagene MxPro qPCR Software. Presence of the mutation was confirmed by PCR-RFLP using the restriction enzyme HhaI  [14]  and by bidirectional gene sequencing of exon 10 using an ABI PRISM 3130XL Genetic Analyzer (Applied Biosystems, Foster City, CA, USA) following the sequencing protocol described in the IARC TP53 database  [24].
Acknowledgements The authors wish to thank  Algemir Lunardi Brunetto  for institutional support;  Bárbara Alemar ,  Patrícia Santos Silva  and  Filippo Pinto e Vairo  for clinical, laboratory and logistic support;  Diego Paskulin  and  Ghyslaine Martel-Planche  for their help with laboratory analyses;  José Roberto Goldim ,  Maria Cátira Bortolini ,  Maria Luiza Saraiva-Pereira  and  Hugo Bock  for stimulating discussions on this case; and  David Malkin  for discussions about cancer screening and management in children diagnosed with  Li-Fraumeni  syndrome and its variants.
Financial support The work of JG was supported by fellowships from  CAPES  and  CNPQ (Brazil). The study was supported in part by grants from  GlaxoSmithKline Oncology  ( Ethnic Research Initiative Grant Award   2009 ), U. K. ; CNPq to PA-P (Grant  307779 2009-2 ), Brazil;  FAPERGS-PPSUS  (grant #  09/0103-0 ),  FAPERGS PRONEX  (Grant # 10/0051-9 ) and  Fundo de Incentivo a Pesquisa e Eventos, Hospital de Clínicas de Porto Alegre  (GPPG #  08080 ), Brazil.
Materials and methods The standard state adopted in this study is that of unit activity of pure minerals and H 2 O at any temperature and pressure. For aqueous species other than H 2 O, the standard state is unit activity of the species in a hypothetical 1 mol/kg solution referenced to infinite dilution at any temperature and pressure. All thermodynamic calculations reported in this study were performed using the PHREEQC 2. 17 computer code  (Parkhurst and Appelo, 1999)  together with its llnl. dat database. The diopside crystals used in this study were obtained from the Transbaikal region of Russia and are identical to those studied by  Golubev et al. (2005)  and  Golubev and Pokrovsky (2006). Golubev et al. (2005)  determined the chemical composition of the diopside to be: Ca 0. 99 Mg 0.
Exp. T (°C b The steady-state dissolution rates listed here were calculated from the final measured reactive fluids compositions of these experiments. diopside saturation state is illustrated in Fig 5. The distribution of data points in this figure suggests that there is no significant effect of these parameters on the steady-state diopside dissolution rates generated in this study at 25 °C. Within experimental uncertainty, all measured rates at pH 6-8 are equal to ~10 -15. 1 mol/cm 2 /s, as was also shown in Fig. 3b.
Acknowledgments This study is part of the  CarbFix  project (www. carbfix. com) in Iceland, and we would like to thank all colleagues and co-workers within this project. We are grateful for technical support provided by  Jón Matthíasson  at the  Innovation Center Iceland ,  Sophie Gouy  at  GET-CNRS in Toulouse, France , and  David Cornell  at  Geovetarcentrum, University of Gothenburg in Sweden. We are also indebted to  Caroline Piper Hem  at the  University of Copenhagen  for assistance with XRD interpretations. Finally, we would sincerely like to thank  Erik Sturkell  for graphical assistance and continued support. The  Environmental and Energy   Fund of Reykjavík Energy , the  Research Fund of the University of Iceland , the  Nordic Council of Ministers  through  NORDVULK , and the  European Community  through the  MIN-GRO Research and Training Network  ( MRTN-CT-2006-035488 ), the  CarbFix  project (Collaborative project -FP7-ENERGY-2011-1-283148 ) and the  ERASMUS student mobility program  are gratefully acknowledged for their financial support.
g. , to investigate the effects of different drugs or toxicants for single or multiple dilutions on wildtype fish, mutants, or transgenic lines), and N: number of fish for each treatment or combination of treatments. As a first approximation, such an experiment generates a dataset with a total size of P acquired pixels: The possibility to perform automated image analysis relies on a number of a priori conditions. Images should be acquired in a standard image format that can be directly processed by many image processing tools or should be easily converted into other standard formats. An experienced observer should be able to manually extract the targeted biological information from the acquired images. The methodology of analysis developed by this experienced observer should be transferable into an automatic algorithmic strategy.
2 ). Complex data processing on the data acquisition computer is often not feasible, as the amount of data streams limits the overall throughput. Special data-intensive computing environments that are able to process Terabytes of data in minutes are required but often not available locally. Thus, data processing and analysis workflows should run on the infrastructure of available data centers. Modern data centers can be accessed from distributed distant sites, allowing workflows to start automatically after a dataset is integrated into the system. 13 Transferring the ''preprocessed'' data, which are now fitting into well-accepted standard data and metadata for-mats, to such data centers requires data lines and software systems that are capable of reaching the required data rates. Easy and fast access to the raw data itself, to the processed data, and to the metadata is essential for further scientific evaluation of what has been produced.
''Dimension'' is related to the captured images in the dataset described by Equation (1). Most methods and applications can be categorized by 2D: two dimensional with X and Y >> 0, 2D + t: two dimensional with an additional time recording in form of video images with X, Y, and T >> 0 (where T is the total number of time points, and t indicates the temporal dimension of the data), FIG. 2. Data workflow in a large high-throughput screen.
Available Software and Computation Tools Computer scientists involved in image processing research often rely on scientific computing environments (e. g. , MAT-LAB, SciPy, or OpenCV) for prototyping and evaluation of their algorithms. However, the challenges of bioimage analysis and the need for better software applicability motivated the development of novel tools that could be subdivided into generic tools (not specialized to zebrafish) and specific zebrafish tools. A complete list of these tools, including WWW links and short descriptions, can be found in the Excel table zip_ tools. xls in the additional material. An updated version of this list will be later available at (http://sourceforge. net/projects/ zebrafishimage/).
Generic tools We provide next a nonexhaustive list (in alphabetical order) of generic bioimage analysis tools (for more detailed descriptions, see the survey paper of Eliceiri et al. 86  ). Although not primarily developed for zebrafish imaging data, these tools might be re-used or tailored to answer specific zebrafish biological questions; see, for example: BioImageXD 87 : a 3D visualization and analysis tool using processing pipelines, Bisque 88 : a database for the exchange and exploration of biological image data, CellProfiler 89 : a software to measure phenotypes in cell and noncell images, CYTOMINE  90  : a rich Internet application that allows data visualization remotely. It enables collaborative annotations, data sharing, and analyses of large ( > Gigabyte) biomedical images on the web.
It is based on generic design principles and machine learning methods that could be used for various tasks (including assessing pathologies in silico, and zebrafish developmental and toxicological studies), Definiens (Definiens AG): a commercial image processing package for a rule-based design of pipelines, EthoVision (Noldus): a commercial package for the tracking of animals with many applications to zebrafish, Gait-CAD 91 : a MATLAB-based toolbox for image processing, tracking, and integrated data mining, Icy  92  : a platform for image analysis, ImageJ: the pioneer in bioimage analysis. Newer variants: ImageJ2/Fiji,  93  ITK/VTK: an open-source segmentation and registration toolkit for multidimensional imaging data, ilastik  94  : an interactive image classification and segmentation toolkit using machine learning, Knime 95 : a workflow system for data analysis and image processing, Labview (National Instruments Corp.
Applications Based on Automated Image Processing The previous sections show that many methods and tools have been developed for the automated image processing with applications to zebrafish data. However, most of the publications in the field are method-oriented papers that are using zebrafish datasets for proof of concepts to evaluate new methods and to highlight future applications, rather than focusing on new biological insights. Many examples for such proof-of-concept papers are mentioned in the previous section. However, a few method-oriented papers also give some additional biological insights, for example by enabling a sort of ''quantitative phenomenology'' of complex biological processes such as gastrulation movements  7  or the geometry and timing of the egg cleavages. 58 ost application papers are based on phenotypic analysis (with a focus on behavioral parameters, discussed in Phenotypic analysis: behavioral parameters section, or on simple morphometric parameters or patterns of reporter gene expression, discussed in Phenotypic analysis: simple morphometric parameters or patterns of reporter gene expression section) or on the construction of 3D atlases of gene and protein expression (Constructing 3D atlases of gene and protein expression section).
Phenotypic analysis: simple morphometric parameters or patterns of reporter gene expression In addition to the image processing approaches that were customized for the zebrafish data previously discussed, many standard approaches using commercial devices such as plate readers or cell counters have been proposed. Here, simple features are detected (e. g. , mean signal intensity in the image, number of fluorescent cells in a region), followed by further analysis and determination of statistically relevant differences. The main advantage of these approaches is their availability in the market, but the level of complexity that can be investigated is usually limited. Large-scale phenotypic screens can use simple readouts. Automated Reporter Quantification in vivo (ARQiv) provides a quantitative data acquisition approach that is amenable to reporter-based assays in zebrafish embryos, larvae, or juveniles.
63 Mapping expression data of analyzed specimen into an anatomical reference model enables the establishment of 3D atlas databases with a unified spatial representation for many different expression patterns. To reach this goal, several preconditions and algorithmic steps are required: (1) Imaging: 3D datasets need to be of standardized high quality and resolution (see Preprocessing methods section above). (2) Registration: A common anatomical reference staining needs to be recorded for each specimen to enable registration to the anatomical reference model; landmarks have to be recognized in an automated fashion; and registration strategies need to be developed, which usually start with a coarse landmark-based registration and are subsequently optimized by fine elastic registration that relies on local information provided by the anatomical reference stain (see description of the ViBE-Z system above, Phenotype assessment by detection of cells and of tissue patterns section and Fig.
5 ). (3) Last but not least, algorithms for threshold determination of signal versus background need to be determined. Except for the very early stereotypic cell cleavages of the early embryo, identification of individual positive cells will not be possible due to stochastic aspects of cell positioning within anatomical elements of zebrafish embryos. However, using high-quality data and precise registration, ViBE-Z has achieved cellular resolution with regard to mapping individual nuclei to even small anatomical brain domains at one to five micrometer resolution. 63 An initial database (atlas) with 15 aligned gene expression patterns (each averaged over approximately three individuals) along with a manual segmentation of morphological structures is provided for virtual colocalization studies (Fig.
9 ). In addition, the development of dedicated interactive visualization tools is an issue to fully exploit the atlas resources. The future of these atlases will certainly depend on the effort that is made to help them grow through the addition of new data by the community. The design of the ViBE-Z system  63  is geared into this direction: ViBE-Z accepts 3D confocal stacks generated by the community, and the web interface performs landmark detection, fine elastic registration, and provides the 3D registration to the anatomical reference for download in a format that can be further processed by investigators with ImageJ or other programs. It will be interesting to see whether this or similar systems will be accepted by the community as data standards and for upload into databases. It is necessary that 3D expression atlases will be tightly integrated into the extensive and standardized annotation system provided in ZFIN.
Integration of anatomical information, gene expression patterns, and functional brain data The recent development of optogenetic tools has enabled the functional interrogation of brain function in zebrafish larvae. Defined neuronal groups can be activated by depolarization or inhibited by hyperpolarization using light-switchable ion channels and ion pumps in whole zebrafish larvae. This can be combined with genetically encoded fluorescent calcium sensors to visualize circuit activity in normal behaving or optogenetically manipulated larvae. The transparent nature of the larval brain enables ''holistic'' imaging of calcium activity throughout the whole brain, opening unique opportunities for new insights into circuit function. 103 New challenges for image processing emerge: Circuit activity data should be merged with anatomical information to provide information on the neurons that show activity.
Furthermore, data from multiple larval brains should be integrated to identify contributions to defined circuits. Ahrens et al. 103  have provided algorithms that enable this type of analysis. Ideally, the spatial coordinates of each individual experimental brain could be recorded before calcium imaging, and registration to anatomical reference models could be used to predict the location of regions that may be subjected to optogenetic manipulation. Such advances in optical imaging and automated image processing will significantly enhance our understanding of neural circuits. Discussion: Open Questions and Future Trends Automated processing is changing the standards of quantitative analysis of imaging data in the zebrafish model. Automated image analysis is of particular interest for high-content screening.
However, method and software integration into standardized, modular, and open processing pipelines, including data storage, image processing, data analysis, visualization, and web-based publication of results, still needs to be developed. We identify a number of open questions for a newly born interdisciplinary community: How Can Existing Zebrafish-Specific Image Processing Algorithms, Tools, and Data Be Made Available for the Community? As a first step, the publication of existing zebrafish-specific modules and a clear description of necessary interfaces for the corresponding input and output data would help the community get a better overview of existing methods and tools. In addition, this list should be enriched with links to papers describing successful applications, including user rating and comments.
This paper is a first step in this direction, and it provides an Excel table hosted at Sourceforge (http://sourceforge. net/projects/ zebrafishimage/) that will regularly be updated with a short description of open source and commercial tools. A second step is to host open source components in online code repositories (e. g. , Sourceforge, GitHub) to avoid the loss of algorithms when developers leave and group websites get outdated. How Can Algorithms and Tools be Modularized, Generalized, and Bundled into Compatible Pipelines? It is unlikely that the variety of different standards, image repositories, algorithms, and tools will vanish in the near future. Strong communities exist for many of these tools (such as ITK, ImageJ/Fiji, and MATLAB), and at the moment, none of them have a clear general advantage or disadvantage.
Previously cited generic tools such as CellProfiler, CYTOMINE, Icy, and ImageJ provide API or plug-in functionalities to extend their capabilities. (2) A condition for interoperability is a clear definition and description of standards and compatibilities between different algorithms and tools. With this effort, output images of one tool can be imported into other tools. This requires import and export routines to translate between different formats. One example is the import routines for more than one hundred bioimage formats in OMERO,  96  but robust bidirectional tools are missing. (3) To avoid manual work, it will also be necessary to establish zebrafish-specific guidelines for the specification of metadata corresponding to all types of assays, imaging setups, image types, algorithms, and tools.
In addition, all relevant metadata should be saved in an open and machine-readable format. These guidelines should be implemented as extensions to data formats of other more general initiatives and integrated into existing zebrafish databases, mainly into the Zebrafish Model Organism Database of the Zebrafish Information Network ZFIN (http://zfin. org). How Should Screens Be Designed to Make Image Processing Easier? Another important question is the planning of screens that can be easily processed. Here, a first requirement is the standardization of all the parameters in the assay (fish lines, age of the specimen, etc. ) and of the image acquisition parameters (e. g. , microscope parameters such as resolution in space and time, number of voxels, etc. ). Image processing experts should be consulted in an early phase of the planning of the experiments to choose all these parameters and assay conditions according to the requirements of image processing routines.
How Can Datasets with Terabytes of Data Be Handled? It is a known and successful paradigm in the analysis of large datasets to ''move the knowledge extraction algorithms to the data rather than the data to the algorithms. ''  105 It means that data should be transferred to repositories, and these repositories should be able to process the data using distributed file systems and computer clusters such as, for example, Hadoop. 106 One option is the ''going public'' of existing large screening centers for customized screens of external partners and the integration of next-generation data repositories into these centers. Steps in this direction can be found, for example, in the connection between the European Zebrafish Resource Center and the Large-Scale Data Facility of the Karlsruhe Institute of Technology.
Acknowledgments The authors acknowledge all participants of the  Satellite Workshop ''Future image processing platforms '' of the 2nd European Zebrafish Principal Investigator Meeting in Karlsruhe, 108 Germany, March 21st-24th, 2012, and the members of the  Zebrafish Image Processing (ZIP) Group  for many fruitful discussions. This work was supported by the  European Union  ( EU COST Action   BM0804 ,  EU IP ZF-Health for WD, NP and US) , the grant  APVV-0184-10  (KM), the  CYTOMINE grant  ( DGO6 WIST3 1017072 , RMa), the grant  ANR-10-INBS-04-05  (NP), the  GIGA  (with financial support from the  Wallonia  and the  European Regional Development fund, RMa) , and the  Helmholtz Association (RMi, TD, RS , and US). PG is a research associate of the  Belgian Science Research Foundation (FRS-FNRS ).
The work of WD and OR was funded by the  Excellence Initiative of the German Federal and State Governments  ( Centre for Biological Signalling Studies EXC 294 ). MJL-C and AS acknowledge partial funding from  Spain's Ministry of Science and Innovation  ( TEC2010-21619-C04-03 & TEC2011-28972-C02-02 ) and  European Regional Development Funds. The work of PP was funded by the  Swiss National Center of Competence in Research (NCCR) ''Nanoscale Science' '. RS was supported by  Helmholtz Portfolio Extension ''Large Scale Data Management and Analysis '' with the contribution of the  Data Life Cycle Lab ''Key Technologies'' and the ''Data Services Integration Team ''.