dataesr/scientific-paragraphs-categorization huggingface dataset:
The integrity of the steam generator must be well managed for safety. Currently, according to the Steam Generator Management Program (SGMP), the integrity assessment for steam generators is divided into three steps with respect to the preventive maintenance time  [2]. The first step is degradation assessment (DA), which is a preliminary assessment before preventive maintenance. The second step is condition monitoring assessment (CM), which assesses the current state at preventive maintenance. The last step is operation assessment (OA), which predicts the future state until the next preventive maintenance. For OA as a prediction step, probabilistic integrity assessment can be applied. This is performed with generic correlations and input data for the specific steam generator. Because of inaccessibility to the steam generator tube failure data from the operating power plants, we generated simulation data from a virtual steam generator by using the PASTA (Probabilistic Algorithm for Steam generator Tube Assessment) program  [13].
PASTA performs assessment of the integrity of steam generator tubes. It is a probabilistic assessment program that accounts for the uncertainty of various variables. PASTA calculates burst pressure as one criterion of tube integrity. Burst pressure is the pressure that equipment can handle before rupturing or bursting. Assessment is possible for axial, radial, and wear cracks; for axial cracks, the burst pressure model is given in Equation (5)  [13] : P B ¼ 0:58 À s y þ s u Á t R i 1:104 À L L þ 2t h ! ; (5) Where The model is derived from data of burst experiments with various crack sizes. For probabilistic modeling, PASTA considers the uncertainty of yield, tensile strength, and crack inspection. MCS is used to obtain the distribution of burst pressure. Then, the burst probability is obtained as a ratio of the number of simulations that are lower than the burst criterion to the total number of simulations launched.
Due to the limitation of failure data availability, we assumed the data from PASTA is a field dataset and used it for prognostics. In other words, some of the data were used to calibrate the generic model, while the rest were regarded as measurement data acquired from the operating plant. We generated 276 data sets of burst probability over time from PASTA. Burst probability is obtained at every EFPY (effective full   power year, 1 EFPY ¼ 18 months). We regarded the tube as being ruptured when the burst probability exceeded 40%. In practice, when the burst probability exceeds 40%, plugging or sleeving is performed  [11, 14]. 209 sets were assumed to be historical or generic failure data and were used to determine the model parameters, while the remaining sets were assumed to be measurement data and were used for testing.
The development of the method was exemplified by a case study regarding steam generator tube degradation considering Paris' raw. Thus, the effect of measurement uncertainty was evaluated by a sensitivity study. The sensitivity study demonstrated how to update the model while balancing between generic and specific information according to their uncertainty. Nevertheless, there are some limitations. In practice, it is not easy to define the uncertainty for existing and new information because of various error factors such as malfunctioning of measuring instrument, human error and so on. Furthermore, it is not clear whether a plant-specific data can modify a degradation evaluation model at a decision-making situation due to managerial or regulatory characteristics, which needs inter-disciplinary study along with field tests.
Acknowledgment This work was supported by " Human Resources Program in Energy Technology " of the  Korea Institute of Energy Technology Evaluation and Planning (KETEP) , granted financial resource from the  Ministry of Trade, Industry & Energy, Republic of Korea  (No. 20164030200990 ) and  National Nuclear R&D Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science and ICT  ( 2017M2B2B1072806 ).
Introduction Bioinformatics usually deals with biological macromolecules, the smallest entities of life, therefore, visualization plays an important role in this field. Stereoscopic rendering has been used for a long time for visualizing biological structures, dating back to the first hype of virtual reality technologies 25 years ago when the CAVE was invented and Head-Mounted Displays (HMD) became affordable. Triggered by the success of new, modern HMDs such as Oculus Rift and HTC Vive, Virtual Reality has recently regained a lot of attention. On the other hand, a new generation of large-scale "computational microscopes" is being established, such as the CAVE2, representing large, nearly 360 degree-spanning visualization facilities. At the same time, advanced "Visual Analytics" solutions have become an important tool in all areas of scientific data analysis, combining visualization, data mining and analysis methods with appropriate user interaction.
In parallel, the previously mentioned HMDs, such as Oculs Rift, were developed, and alternative devices like the Microsoft HoloLens ® support Augmented Reality applications, enabling the projection of 3D scenes into the natural real world environment  [11]. In Bioinformatics, many software applications were developed to support VR-related technologies. Obviously, the visualization of the smallest entities of life, not visible to the bare human eye, are one of the application cases where the use of VR-related technologies is reasonable and promising. Educational software like Meta!Blast, enabling the stereoscopic visualization of cell environments, goes a small step towards this direction  [12]. Bryson  [13]  provides a good overview of scientific applications of this 1990s era. Frederick Brooks's paper  [14]  provides an inspiring general overview of this field from a personal perspective.
Furthermore, Immersive Analytics extends these approaches to multiple senses, haptic as well as olfactory feedback are potential elements of these approaches. Although the term is quite new, related approaches were already developed many years ago. Software applications like MetNetVR integrated complex metabolic pathways into abstract cell environments and visualized them in a CAVE for analytical purposes around 2006  [19]. For navigation purposes, a tablet was used. Using the CELLmicrocosmos Pathway Integration, metabolic pathways can be spatially embedded into virtual cell environments and explored in different virtual environments, such as zSpace ® and/or CAVE2 ®  [18] ,  [20]. The zSpace ® is a semi-immersive stereoscopic display device which can be used for head-tracked stereoscopic visualization, enabling the display perspective to be adjusted as the user moves in front of the display.
3D interaction is possible with a special stylus pen. This setup enables hybrid-dimensional visualization and interaction, visualizing spatial structures with abstract data side-by-side  [21]. Aquaria, a molecular graphics and information system which can be used via a web browser to explore protein structures of the Protein Data Bank on different semantic layers, was extended by a Leap motion-based control kit which can be used to navigate the 3D models by hand  [22] ,  [23]. In this way, it unites 3D navigation with hybrid-dimensional visualization, combining 3D protein structures with 2D representations of their primary structure. These are only a few examples and a full review of Bioinformatics-related approaches in the field of Immersive Analytics is not within the scope of this publication.
Heuristic Modeling and 3D Stereoscopic Visualization of a Chlamydomonas reinhardtii Cell Chlamydomonas reinhardtii is an unicellular organism which plays an important role in the field of metabolic engineering as it can be used as an alternative energy source. In this work a spatial mesoscopic model of this cell was created using an interpretative cell visualization approach. The idea is to combine the knowledge of different information sources -such as publications, microscopic images, database information -with the purpose to create a well-evaluated cell model which can be used for visualization and exploration purposes. The so-created model was used as a basis for a stereoscopic 3D cell animation, discussing the underlying metabolic processes to extract energy from a biological cell. The publication discusses the nature of the different information sources, the design choices involved in the process (e.
Molecular Dynamics Visualization (MDV): Stereoscopic 3D Display of Biomolecular Structure and Interactions Using the Unity Game Engine An important field in Bioinformatics as well as Chemoinformatics research is the simulation of molecular structures. One of the most established techniques is the molecular dynamics (MD) simulation approach. Such simulations were performed in the context of this work with the Gromacs  [26]  software. The visualization of molecular dynamics has a long tradition, using standard tools such as VMD or PyMol. However, these visualization packages are not optimized to be used in the context of multiscale virtual 3D stereoscopic environments. The molecular dynamics visualization (MDV) software has been developed on top of the Unity development platform and can be used with a range of large-scale immersive displays.
MinOmics, an Integrative and Immersive Tool for Multi-omics Analysis MinOmics is an integrated analysis pipeline and visualization framework for multi-omics analysis. In this work, a stereoscopic 25 MPixel display wall was used to display proteomic data on 1417 proteins of Chlamydomonas reinhardtii. For this purpose, UnityMol was used, a Unity 3D ® -based software application which visualizes proteins in 3D  [29]. A number of hardware setups are discussed which could be used with MinOmics: a UnityMol-based WebGL and WebVR application using a regular monitor, a split-screen approach combined to a stereoscopic monitor, and a full-immersive environment using head-mounted displays. A number of scientific application cases for these setups are discussed. For example, the MinOmics framework can be used to explore multiple omics datasets.
The authors discuss the exploration of redox proteomic datasets in the context of Chlamydomonas reinhardtii for glutathionylation, nitrosylation and disulphide bonds. MinOmics can be used to explore protein specificity of multiple redox post translational modifications (PTM), the cysteine specificity of multiple redox PTMs, and to explore structural determinants of redox-modified cysteines  [30].
Conclusions and Outlook Here, six new applications along the path from Virtual Reality to Immersive Analytics in the field of Bioinformatics are presented. The introduced work discusses: • the evaluation of the performance of Augmented Reality-related technology in the field of molecular visualizations, • aspects of content creation for stereoscopic cell visualization environments, • interactive modeling of extracellular matrix structures in a virtual environment, • a new molecular dynamics simulation visualization tool, enabling the collaborative evaluation of underlying structures, • a new system to immersively explore multiple redox proteomic datasets, and finally • semantics to describe semantically linked multi-dimensional molecular data and its interactive visualization. With this overview it is our hope to encourage more Bioinformatics-related research in the area of Immersive Analytics in the future.
A package first A software package is self-contained software including all the dependency libraries and packages necessary to execute the software. Some of the most popular and well-known package management systems are operating system-level, such as apt or yum, are language-specific resources, such as pip/PyPI, CPAN, or CRAN, or are third-party package managers such as Zero Install, Homebrew, and Conda. When choosing a package manager, it is important to select one that is crossplatform (e. g. works on various Linux distributions and MacOS), allows multiple versions of each package, does not require administrative nor elevated privileges to use and, ideally, is not restricted to a single programming language. Being cross-platform enhances reusability, providing for multiple versions enables reproducibility, and allowing user-based installation and multiple development languages simplifies usability.
Amendments from Version 1 The current version of the manuscript contains minor changes regarding the structure and minor comments suggested by the reviewer. This new version contains the correct Dockerfile recipe and a link to the example in GitHub. We have added a couple of paragraphs to discuss the relation between BioContainers and work-flow environments. In the 'Grant information' section we list NIH grants that supported Felipe da Veiga Leprevost, as this was missed out on version 1.
Keep data outside of the container Data can dramatically increase the size of the container (Recommendation 5), thereby reducing the capability to share, deploy and deposit it in public registries. In order to implement tests during the building and deployment steps, we recommend downloading or cloning the data from public data repositories and deleting it after the testing is finished. This mechanism is similar to the one stated in Recommendation 5 for retrieving source and binaries. Many bioinformatics tools require access to large reference datasets to perform meaningful analysis. These reference datasets should also not be included within the container but should be stored in a user-configurable location and retrieved either on-demand during runtime, or as part of a setup process. Not only does storing reference datasets outside of the container reduce the size of the container, but other tools that require access to the same reference data will be able to directly access the data without additional overhead.
Version 1 Author Response 20 Nov 2018 , University of Rennes 1, France olivier sallou Hi, regarding test data, if small, we ask user to include them along container in git repository. However, data persistence is indeed a global issue in science. For larger data, they should be available via a data management plan linked to the upstream tool/databases. About quality, biocontainers recommendations do not improve the quality of the tool itself, but their usage by other users forcing some tags (version, no latest tag) and information (license,. ). With this, user execute a known and fixed version of a tool, and will refer to this exact same version in his communications/research. It also enforces resulting quality because a tagged container will run the same way on any computer and using the same libraries version (software one but also system ones and dependencies).
Bioconda recipe for "deepTools", a set of user-friendly tools for normalisation and visualisation of deep--3. 0. 2. tar. gz url: https://files. pythonhosted. org/packages/21/63/ 095615a9338c824dcc1496a302d04267c674175f0081e1ee2f897f33539f/ deepTools-3. 0. 2. tar. gz md5://github. com/fidelram/deepTools license: GPL3 summary: A set of user-friendly tools for normalisation and visualisation of deep-sequencing data extra: identifiers: -biotools:deeptools -doi:10. 1093/nar/gkw257.
This work was partially supported by  ELIXIR-EXCELERATE. ELIXIR-EXCELERATE  is funded by the  European Commission  Grant information: within the  Research Infrastructures programme of Horizon 2020 , grant agreement numbers  676559. The BioContainers workshop (Paris 2017) and the BioContainers community that developed these recommendations are supported by the  ELIXIR Tools platform. FVL is supported by  NIH  grants  R01GM94231  and  U24CA210967. Gruening B. terms of the ,.
Box 1. 1 Bioconda recipe for "deepTools", a set of user-friendly tools for normalisation and visualisation of deep--3. 0. 2. tar. gz url: https://files. pythonhosted. org/packages/21/63/ 095615a9338c824dcc1496a302d04267c674175f0081e1ee2f897f33539f/ deepTools-3. 0. 2. tar. gz md5://github. com/fidelram/deepTools license: GPL3 summary: A set of user-friendly tools for normalisation and visualisation of deep-sequencing data extra: identifiers: -biotools:deeptools -doi:10. 1093/nar/gkw257.
Box 2. BioContainers recipe (Dockerfile) for Comet software The public Dockerfile is here: https://github. com/BioContainers/containers/blob/ 5bec8724bf61d696523216244264db11eba137d6/ comet/2015025/Dockerfile FROM biocontainers/biocontainers:v1. 0. 0_cv4 LABEL base_image="biocontainers:v1. 0. 0_cv4" LABEL version="3" LABEL software="Comet" LABEL software. version="2016012" LABEL about. summary="an open source tandem mass spectrometry sequence database search tool" LABEL about. home="http://comet-ms. sourceforge. net" LABEL about. documentation="http://comet-ms. sourceforge. net/parameters/parameters_2016010" LABEL about. license_file="http://comet-ms. sourceforge. net" LABEL about. license="SPDX:Apache-2. 0" LABEL extra. identifiers. biotools="comet" LABEL about. tags="Proteomics" LABEL maintainer="Felipe da Veiga Leprevost <felipe@ leprevost.
com. br>" USER biodocker RUN ZIP=comet_binaries_2016012. zip && wget https://github. com/BioDocker/software-archive/releases/download/Comet/ $ZIP-O/tmp/$ZIP&&unzip/tmp/$ZIP-d/home/biodocker/bin/ Comet/&&chmod-R 755/home/biodocker/bin/Comet/*&&rm/ tmp/$ZIP RUN mv/home/biodocker/bin/Comet/comet_binaries_2016012/ comet. 2016012. linux. exe/home/biodocker/bin/Comet/comet ENV PATH /home/biodocker/bin/Comet:$PATH WORKDIR /data/.
Objectives: Assessment factors (AFs) are commonly used for deriving reference concentrations for chemicals. These factors take into account variabilities as well as uncertainties in the dataset, such as inter-species and intra-species variabilities or exposure duration extrapolation or extrapolation from the lowest-observed-adverse-effect level (LOAEL) to the noobserved-adverse-effect level (NOAEL). In a deterministic approach, the value of an AF is the result of a debate among experts and, often a conservative value is used as a default choice. A probabilistic framework to better take into account uncertainties and/or variability when setting occupational exposure limits (OELs) is presented and discussed in this paper. Material and Methods: Each AF is considered as a random variable with a probabilistic distribution.
These concentration levels are established considering a population of workers, thus excluding both the children and the elderly. Occupational exposure limits are determined based on information available from epidemiological, clinical and experimental studies. The establishment of OELs as for other reference values such as toxicity reference values (TRVs) or indoor air quality guidelines (IAQGs) usually relies on a deterministic approach [2]. This approach produces a single point estimate of the OEL by dividing the point of departure (POD), which may be the no-observed-adverse-effect level (NOAEL), the lowestobserved-adverse-effect level (LOAEL) or a benchmark dose (BMD) by the product of assessment factors (AFs). Assessment factors applied to the POD are expected to offset variability and uncertainties related to the data taken into account and their interpretation in the adjustment process.
3 times lower and within the 2. 9-3. 3 range. It was concluded that AFs based on the NOAEL data may have lead to unnecessarily conservative values. Moreover, the 95th percentiles values observed by Bokkers and Slob were all below 10. Studies investigating the subacute to subchronic assessment factor are limited and tend to be based on smaller datasets than subchronic to chronic studies. Unsurprisingly, the variability of the observed distributions tends also to be higher. Vermeire et al. reported the default lognormal distribution with the GM of 2 and the GSD of 4  [6]. Lower values were found by Groeneveld et al. , who obtained the lognormal distribution with the GM of 1. 6 and the GSD of 3. 3  [20]. With the 95th percentile of 11, this distribution is consistent with the default value of 10 for extrapolation from subacute to subchronic.
7; GSD = 5. 6), suggesting that the default assessment factor of 10 could be too low. The large variability of AF S was further highlighted by a thorough review conducted by Vermeire et al. , which reported the GM and GSD ranges of 1-4. 1 and 1. 3-5. 6, respectively (including the results already reported by Pieters)  [6]. Vermeire et al. pointed out that the observed variability might have been due to the heterogeneity of the data (inter-species variation, variable subchronic exposure The first step of the probabilistic approach is to assign the probability distribution (probability density function) to each uncertain AF. These distributions may be derived from chemical-specific data, when available. In such a case, the GM represents the central estimate of the available data base of chemicals; while the GSD (width) is associated with the number of chemicals in the database, the methods to estimate the ratios/quantities that form the quantitative estimate of the distribution as well as the specific characteristics of the chemical.
Quality of data extrapolation factor The assessment factor for quality of data, the AF D, takes into account the amount, quality, completeness and consistency of the available data. There is little argument to support a specific range or distribution for the AF D. Some authors suggested that the AF D could run up to 100  [6]. Although, it could be argued that when confidence in the data is so poor, it will unlikely be used for deriving the OEL. Evans and Baird compared the NOAEL estimates for 35 pesticides derived from censored and uncensored datasets  [21]. A substantial increase in the precision of estimates was observed when the database became more complete. A wide dispersion of the estimated assessment factor, defined as the NOAEL-censored/NOAEL-uncensored ratio, was found. Database limitations, in terms of representativeness and size, however precluded a strict probabilistic interpretation of these results.
ACKNOWLEDGMENTS Authors wish to thank the  French Agency for Food, Environmental and Occupational Health and Safety (ANSES)  for its support, the members of the  Expert Committee  for recommending occupational exposure limits for chemical agents ( OEL Committee ) for their advice, as well as  Billy Amzal ,  Patrick Breton ,  Marie-Laure Cointot ,  Mounia El Yamani  and  Renaud Persoons  for their major contribution to the development of the methodology described in this paper.
) and thus to generate outputs of the intervention. Output or "product": It consists of the product resulting from the intervention, including that which does not come directly from the research if the intervention is not purely a research intervention. It can take the form of scientific or non-scientific knowledge (publication, report, database, method, etc. ), professional or academic training, expertise, technology, network or other forms of products. Outcome or "result": It is the appropriation of a research or intervention output by actors interacting directly or indirectly with the research community, leading to change in practices (agricultural or managerial), changes in organizations or in interactions or new rules. Impacts: Long-term effects, positive or negative, intended or unintended, direct or indirect, induced by an intervention.
Tips & tricks n°1: Undertaking the initial diagnosis Project development officers at CIRAD typically consider addressing the following list of topics during the initial diagnosis: -contexts (research-related, geographical, social, political, etc. ); -actors (who is acting to change the situation?); -target actors (who comprises the target audience of the activities to be implemented by the intervention?); -funding possibilities and opportunities; -existing projects. They also suggest using analytical tools such as: -data collection tools (documentary research, questionnaire, interview, observation); -brainstorming, 5W2H (What, Why, Where, When, Who, How, How much); -the cause-effect diagram or Ishikawa diagram, the force field analysis diagram. An approach for building ex ante impact pathways The six stages of the impress ex ante approach 1.