dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Guillaume Seith  and  Julien Seiler  from the  Institut de G én étique et de Biologie Mol éculaire et Cellulaire  informatics platform for their technical support, in particular the setup and maintenance of the required computational devices. These studies were supported by funds to our team from the Plan Cancer 2009-2013,  AVIESAN-ITMO Cancer , the  Ligue National Contre le Cancer (Equipe Labellisée) , the  Fondation Recherche Medicale ,  Conectus Alsace , and the  Institut National du Cancer (INCa). Currently , the qcGenomics project is supported by the " Genopole Thematic Incentive Actions " ( ATIGE-2017 ) and the institutional bodies  Commissariat à l'Energie Atomique ,  Centre National de la Recherche Scientifique , and  University Evry-Val d'Essonne.
FluidDyn is a project to foster open-science and open-source in the fluid dynamics community. It is thought of as a research project to channel open-source dynamics, methods and tools to do science. We propose a set of Python packages forming a framework to study fluid dynamics with different methods, in particular laboratory experiments (package fluidlab), simulations (packages fluidfft, fluidsim and fluidfoam) and data processing (package fluidimage). In the present article, we give an overview of the specialized packages of the project and then focus on the base package called fluiddyn, which contains common code used in the specialized packages. Packages fluidfft and fluidsim are described with greater detail in two companion papers, Mohanan et al. (2018a,b). With the project FluidDyn, we demonstrate that specialized scientific code can be written with methods and good practices of the open-source community.
The focus was on the theory and the mathematical demonstration, which had to be elegant as it gets included in the articles. In contrast, it was normal to write crude code and to just show the results. Nowadays, codes tend to be at the heart of research. "Open-science" is a new trend taking advantage of these new facts. Pioneering attempts are being made to do better science, improving reproducibility and collective efficiency, by using the open-source methods and tools for science and sharing and collaborating via the world wide web. • Good coding practice with readable and easy to comprehend Python code (PEP 8). • Source control management (Mercurial) and forge (Bitbucket) simple for the new comers. • Packaging and installation procedure. All packages are available from PyPI and can be install simply with pip, the standard Python installation tool.
Organization of the code in packages FluidDyn was originally intended to be a single package to perform experiments and simulations. Since a typical user may not be involved in both experiments and simulations and also, due to increasing complexity as a virtue of rapid development cycle made the need to decentralize FluidDyn evident. Now, FluidDyn project hosts a number of specialized packages, namely: • fluiddyn 4  : The base package which contains pure-python code that can be reused in scripts or in specialized FluidDyn packages. It also contains codes for miscellaneous command-line utilities useful for a typical fluid dynamics user. The code of this package is presented in further detail in its documentation (https://fluiddyn. readthedocs. io) and some prominent features are presented in the following subsection.
2016 ). • fluidfoam: Small package to load OpenFoam data and plot them. A detailed presentation on the above packages can be found in their respective documentations on the web and for fluidfft and fluidsim in the two companion papers  (Mohanan et al. 2018a,b). The code base was designed to follow Python 2. 7 syntax during its genesis. Now, it has been made forward compatible with Python 3 through the use of external package future. This article will now focus on the base package fluiddyn.
API of the Python library fluiddyn All functions and classes defined in fluiddyn are pure Python elements, meaning that no extensions are implemented in fluiddyn. Thus the package fluiddyn is extremely easy to install with just a pip install command and no compilation. The package fluiddyn is organized into five sub-packages: • fluiddyn. io: This subpackage provides utilities for input/output to different file formats. • fluiddyn. util: Miscellaneous utilities. • fluiddyn. calcul: Modules for numerical computations. • fluiddyn. clusters: Classes to launch jobs on HPC clusters. • fluiddyn. output: Utilities to produce scientific outputs (figures and videos). Sub-packages io, util and calcul are the largest in terms of lines of code and provides the Application Programming Interfaces (API) to support fluidsim, fluidlab and fluidimage.
Module fluiddyn. util. paramcontainer Sub-package paramcontainer defines class ParamContainer which is a hierarchical container for any type of parameters. As shown in this tutorial, various strengths of an object of this class include: • Support containing and printing documentation on the parameters. • Support printing to console as XML and saving as XML, HDF5 and NetCDF Files. • Evaluate data types automatically while loading from saved files. • Easy exploration in an interactive console. • Allows modification of default parameters through simple Python script files. An error is raised if the user attempts to use an invalid parameter. • Graphical User Interface (GUI) frontend with PyQt. Thus, it makes it a much more robust implementation for saving key parameters, compared to conventional methods which rely on text or CSV files.
Module fluiddyn. util. mpi This simple module makes simultaneous sequential and MPI programming a breeze by providing number of processes, nb proc = 1 and rank = 0, when used in sequential mode, otherwise providing the appropriate values provided by mpi4py package. Use of this module thwarts coding several if-else clauses. If the program was using MPI, also defines the variable comm as an alias for the MPI. COMM WORLD communicator.
Quality control The package fluiddyn currently supplies unit tests covering around 70% of its code. These unit tests are run regularly through continuous integration on Travis CI with the most recent releases of fluiddyn's dependencies and on Bitbucket Pipelines inside a static Docker container. The tests are run using standard Python interpreter with all supported versions. For fluiddyn, the code coverage results are displayed at Codecov. Using third-party packages coverage and tox, it is straightforward to bootstrap the installation with dependencies, test with multiple Python versions and combine the code coverage report, ready for upload. It is also possible to run similar isolated tests using tox or coverage analysis using coverage in a local machine. Up-to-date build status and coverage status are displayed on the landing page of the Bitbucket repository.
Programming language Python 2. 7, 3. 4 or above. (3) Reuse potential As a library, fluiddyn has been used in the project's specialized packages. The common code base for packages with such varied applications is a proof of fluiddyn's versatility and generality. It can be used in other packages outside the FluidDyn project easily depending on the need. The command-lines tools can be useful to all scientists working with Python. Other use cases could be: • ParamContainer as a generic parameter storage standard. • Modules within fluiddyn. util for miniature tasks, printing in terminal with colours, getting memory usage information, detecting if the current Python session is inside IPython, creating a string with time and date, etc. • Modules within fluiddyn. io to read and save images of various formats including TIFF files with multiple images; easily ask user with yes/no queries; handle different file formats using classes, including CSV, HDF5, Digiflow, Dantec formats.
• Modules inside fluiddyn. clusters subpackage to make job submission scriptable in HPC clusters with OAR or SLURM job schedulers. It could be even used for non-Python jobs. There is no formal support mechanism. However, bug reports can be submitted at the Issues page on Bitbucket. Discussions and questions can be aired on instant messaging channels in Riot (or equivalent with Matrix protocol) 5  or via IRC protocol on Freenode at #fluiddyn-users. Discussions can also be exchanged via the official mailing list 6.
Acknowledgements We thank the  CNRS  to finance the work of  Pierre Augier  while giving freedom in terms of scientific project. Similarly,  Ashwin Vishnu Mohanan  could not have been as involved in this project without the kindness of Erik Lindborg. We thank  Antoine Campagne ,  Miguel Calpe Linares  and  Julien Salort  for their implications in the development of some of the FluidDyn packages. We are grateful to  Bitbucket  for providing us with a high quality forge compatible with Mercurial, free of cost. Finally, we thank  Gabriel Moreau  and  Joël Sommeria  for their constant will to discuss and share their knowledge.
Microtubules are polar, dynamic filaments fundamental to many cellular processes. In vitro reconstitution approaches with purified tubulin are essential to elucidate different aspects of microtubule behavior. To date, deriving data from fluorescence microscopy images by manually creating and analyzing kymographs is still commonplace. Here, we present Mtrack, implemented as a plug-in for the open-source platform Fiji, which automatically identifies and tracks dynamic microtubules with sub-pixel resolution using advanced objection recognition. Mtrack provides automatic data interpretation yielding relevant parameters of microtubule dynamic instability together with population statistics. the application of our software produces unbiased and comparable quantitative datasets in a fully automated fashion.
Robust Detection of Microtubule Seeds using MSER and Sum of 2D Gaussians. A common way to reconstitute and analyze microtubule dynamics is by the use of TIRF microscopy and fluorescently labeled tubulin  14, 16. Stabilized (non-dynamic) fluorescent microtubule seeds are immobilized onto glass surfaces. These microtubule seeds serve as nucleation points from which dynamic fluorescent microtubules will grow (Fig. 1 ). Previous microtubule tracking software relies on manually clicking each individual microtubule to be analyzed  23, 27. Therefore, our aim was to develop an approach that robustly detects microtubule seeds in the image in a fully automated fashion. It is essential to precisely determine the exact end point of each seed, as these are the sites from which microtubules will subsequently grow and shrink.
Typically such data is displayed as a length versus time graph (kymograph). Microtubules stochastically switch between growth and shrinkage. Growth velocities observed in vitro range from 0. 6 μm/min 32 to 40 μm/min  33. Moreover, depolymerization velocity is typically an order of magnitude greater than the corresponding growth velocity  21. Thus, the frame-to-frame difference in microtubule length can be significant. In addition, brightness changes, growth heterogeneity, microtubules bending, crossing, and moving out of focus present further challenges to accurate tracking. We will show that fitting polynomial functions enables us to robustly track microtubules, even when bending or crossing. In more detail, MSER first detects an image region for each dynamic microtubule within each frame of the fluorescent time-lapse movie (as described for seed detection).
Automatic Derivation of Microtubule Dynamic parameters using Iterative Robust outlier Removals. The aim of this module is to extract relevant microtubule dynamic parameters from length versus time data produced by the tracking (Fig. 4a ). Each microtubule track has an unknown number of growth and shrinkage events. To automatically derive these dynamic parameters, we developed an iterative, model-based robust outlier removal algorithm based on RANSAC (Random Sample Consensus)  35. RANSAC is a non-deterministic algorithm that fits a function to data points by maximizing the number of data points that support the function fit (inliers), given an error ε (Fig. 4b ). First, all growth events are detected. Microtubule polymerization velocity (vg [nm/s]) was shown to be a linear function of tubulin concentration and is usually approximated by a single constant growth rate  13.
We therefore assume that periods of microtubule growth follow an increasing, near-linear polynomial function, which is implemented as a 2 nd or 3 rd order polynomial regularized with linear function. To do so, RANSAC first identifies the largest subset of consecutive time points that follow near-linear growth (Fig. 4b ). To identify all growth events in one track, the software iteratively removes time points belonging to an identified growth event from the sampling set. It then repeats the RANSAC sampling until no further growth events can be found. In the example shown in Fig. 4 , RANSAC identifies four growth events in order of their length. To achieve compatibility with the general accepted assumption of linear microtubule growth, the software finally fits only a linear function to all inlier points of each growth event (Fig.
Discussion In the last decade, advances in high-resolution techniques have shed new light on how the dynamic behavior of microtubules is modulated (as reviewed in  37  ). Here, we present new software, MTrack, which automatically identifies and tracks dynamic microtubules with subpixel resolution and provides automatic data interpretation and statistics. MTrack will be valuable to any experimentalist (1) who studies different microtubule populations  38, 39  , (2) who aims to characterize the effect of a MAP or motor protein on microtubule dynamics  [8] [9] [10] [11] [12] [13] 15, 17  or (3) who is interested in characterizing the molecular mechanism of microtubule binding drugs. We be ieve that MTrack is a powerful tool, which together with high-resolution imaging will provide unbiased, high-accuracy data with sufficient statistics to help elucidate new mechanisms of microtubule dynamics control.
The demand for software, which automatically detects and tracks microtubules is mirrored by the recent development of several software packages that allow microtubule detection and/or tracking  [22] [23] [24] [25] [26] [27]. The most recent approach by Bohner and colleagues is a further development of the previously published tracking software FIESTA  27  by optimizing it for low SNRs. In other words, the former software  27  is ideal to track non-dynamic microtubule seeds, for example in single-motor stepping assays  40  , while the latter  23  is well-suited for tracking microtubules in dynamic growth assays with free fluorescent tubulin. In addition, in order to validate the tracking performance across a broad range of conditions, Bohner and colleagues tracked simulated dynamic microtubules with varying experimental conditions.
While they found the fluorophore labeling density, the pixel size of the image as well as the exposure time to be important parameters, they found the SNR a good single predictor of tracking precision  23. Motivated by this observation, we systematically varied the SNR ratio and determined the detection and tracking accuracy of our software. In Module 1, endpoints of non-dynamic microtubule seeds can be accurately localized with subpixel resolution even at low SNRs. Moreover, the detection error is normally distributed showing no bias towards microtubule direction. From this we concluded that the detection precision was robust even at experimentally relevant SNRs and did not depend on the filament orientation. We then showed that allowing the final fit to follow a 3 rd order polynomial function enabled us to track straight, bending, and crossing microtubules.
The tracking accuracy achieved pixel resolution, providing a close to molecular precision. While the above mentioned programs reliably track microtubules with nanoscale precision, both approaches require the manual selection of each individual microtubule to be tracked, neither of the program offers automated data analysis nor do the authors comment on how either software performs on bending or crossing microtubules. MTrack is a fully automated workflow that provides detection and tracking of microtubules and is -to our knowledge -the first software that offers automated data interpretation using iterative robust outlier removal. Directly tracking microtubules and analyzing their dynamics is different to tracking and analyzing end-binding proteins (EBs)  41, 42. Microtubule end-binding proteins such as EB1 accumulate exclusively at growing microtubule ends  11, 43  but get lost once microtubules shrink.
In such datasets, microtubule behavior has to be interpolated during phases of microtubule shortening and pausing. In contrast, MTrack can make use of the full length over time information giving by the tracking module to extract information about MT dynamics. In sum, MTrack is the first software that reliably detects, tracks, and analyzes the behavior of dynamic microtubules. Each module is automated yet highly adaptable and can be used (1) to robustly detect the end points of any linear, fluorescent, filamentous structure, (2) to reliably track fluorescent structures or (3) to analyze length over time plots in an automated fashion.
simulations. Microtubules are simulated using linear models and 3 rd order polynomials, where the parameters of each function are determined by a random number generator. Images are created by convolution with the PSF, adding a background, and rendering the final pixel intensities using a Poisson process. The simulation code is available on Github https://github. com/PreibischLab/MTrack/tree/master/src/main/java/dummyMT. Documentation and Installation. Detailed documentation and installation instructions are available on the ImageJ wiki http://imagej. net/MicrotubuleTracker.
Acknowledgements We thank  Tobias Pietzsch  ( Tomancak lab, MPI-CBG, Dresden ) for implementing MSER in ImgLib2,  Curtis Rueden  ( LOCI, UW Madison ) for Fiji maintenance and support,  Hadrien Mary  ( Brouhard lab, McGill University, Montreal ) and  Mohammed Mahamdeh  ( Howard lab, Yale University ) for constructive criticism and comments on the manuscript. We thank the  AMBIO imaging facility (Charité, Berlin ), where the experimental data were acquired. We thank all former and current members of the  Preibisch and Reber labs  for discussion and helpful advice, and  Helena Jambor  for constructive comments on figure design. V. K. thanks  David Hörl  ( LMU München/ MDC Berlin ) for stimulating discussions. V. K. was supported by the  IRI Life Sciences postdoc fellowship  in the labs of S. R.
and S. P. C. H. and S. R. acknowledge funding by the  IRI Life Sciences (Humboldt-Universität zu Berlin ,  Excellence Initiative/DFG ). W. H. was supported by the  Alliance Berlin Canberra  " Crossing Boundaries: Molecular Interactions in Malaria ", which is co-funded by a grant from the  Deutsche Forschungsgemeinschaft (DFG)  for the  International Research Training Group (IRTG) 2290  and the  Australian National University. S. P. was supported by the  MDC Berlin.
Data Availability The code is open-source, mostly implemented in ImgLib2  49  and is provided as Fiji  28  plugin. The iterative RANSAC for functions is based on the mpicbg package written by Stephan Saalfeld  50. The source code is available on GitHub https://github. com/PreibischLab/MTrack. It is released under GPLv3. An example dataset is available for download on the ImageJ wiki including the MTrack parameters to successfully run and analyze the demo movie: http:// imagej. net/MicrotubuleTracker#Example The expected runtime is 10-20 min on standard hardware.
Data intensity and quality was evaluated by plotting the I(0) and R G as a function of frames. Curves from consecutive images showing high intensity I(0) and similar R G were averaged. ARNO FL data were collected at BM29 beamline, ESRF, France. Images were recorded throughout the HPLC elution process using the Bio Sec300 column. 600 mg in 60 mL volume was injected. Data reduction to absolute unit, subtraction and averaging was done with the EDNA pipeline implemented in the ISPyB software (ESRF BM29 beamline).
For the triglycine variants of ARNO, SAXS data analyses were performed with the ATSAS 2. 8. 3 package  (Franke et al. , 2017). Radii of gyration (R G ) were evaluated by Guinier Wizard using the data within the range of Guinier approximation sR G <1. 3 and by Distance Distribution Wizard, both of which are modules of the PRIMUS program. The maximum distance D max was estimated with PRIMUS and refined by trial and error with GNOM. The distance distribution functions P(r) were calculated with GNOM. The dimensionless Kratky plot was calculated by plotting (qR G ) 2 I(q)/I(0) against qR G. Molecular weights were estimated by Primus Molecular Weight Wizard using different algorithms. The fit between scattering amplitude calculated for the crystal structure of autoinhibited Grp1D Nt (residues 63-399) and the SAXS curve of ARNOD Nt was calculated with CRYSOL.
Rigid Body and Ensemble Modeling Models for the autoinhibited and active cores were derived from the crystal structure of Grp1 63-399 (PDB: 2R09) or the most frequent model in the minimal three state MultiFoXS ensemble for Grp1 63-390 , respectively. CC models were generated with CCBuilder  (Wood et al. , 2014). The CC-Sec7 domain linker and missing terminal residues were built with MODELLER  (Webb and Sali, 2014)  in CHIMERA  (Pettersen et al. , 2004). Rigid body modeling was performed with CORAL  (Petoukhov et al. , 2012). For ensemble and MEM analyses, pools of 10,000 models for each chain/topology were generated with RRT_SAMPLE  (Raveh et al. , 2009). The head group was represented as atoms in glycine residues that retained the chemical information in the last column of the PDB file required to specify the correct scattering form factors.
py and volume projections compared with 2D class averages at 10 increments using e2classsvsproj. py. Two python scripts (e2pdbs2mrcs. py and e2classesvsprojs_ best_scores. py) were previously developed to automate these steps and rank order models based on best scoring projections for each class  (Malaby et al. , 2018). For comparison with 3D volumes, automated rigid body docking of models at a resolution of 40 A ˚was performed with ADP_EM  (Garzon et al. , 2007). Models and volumes were visualized in Chimera  (Pettersen et al. , 2004).
6 kV. Spectra were collected in positive ion mode. Peptide identification was performed by MSe  (Silva et al. , 2006)  using an identical gradient of increasing acetonitrile in 0. 1% v/v formic acid over 11 min. The resulting MSe data were analyzed using Protein Lynx Global Server software (Waters, UK) with an MS tolerance of 5 ppm. Mass analysis of the peptide centroids was performed using the DynamX HDX data analysis software 3. 0 (Waters, UK). Only peptides with a score > 6. 4 were considered. The first round of analysis and identification was performed automatically by the DynamX software, however, all peptides (deuterated and non-deuterated) were manually verified at every time point for the correct charge state, presence of overlapping peptides, and correct retention time. Deuterium incorporation was not corrected for backexchange and represents relative, rather than absolute changes in deuterium levels.