dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We are grateful to  Joanna Barstow  for her constructive and helpful comments. HP has received support from the  Leverhulme Research Project  grant  RPG-2012-661. FM acknowledges the support of the  French Agence Nationale de la Recherche (ANR) , under the program  ANR-12-BS05-0012  Exo-atmos. The work has been supported by the  Spanish MINECO  grants  ESP2013-48391-C4-2-R  and  ESP2014-57495-C2-1-R. Based on observations made with the  Gran Telescopio Canarias (GTC) , installed in the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias , in the island of  La Palma.
Background: Cell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods. Results: Here, we propose a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput.
Method The proposed approach is composed of four steps described in this section. The first step consists in localizing the fibronectin patterns and cropping the whole video at those locations to obtain individual cluster sequences, the second step consists in fitting 2-and 3-components Gaussian Mixture Model (GMM) onto each frame of each video sequence and the third step consists in the identification of the first frame containing three cells (the transition from 2 cells to 3 cells) using the fitting error difference and other features computed from the GMM parameters. The final step consists in the computation of the angle of division in the identified frame. The whole proposed approach is illustrated in Fig. 4  (and the code is freely available at https://github. com/biocompibens/livespin).
Results To our knowledge, no available software could provide a full solution dedicated to the type of assay we propose (i. e. an automated tracking of overlapping cells on thousands of individual movies). Therefore, it was not possible to strictly compare our approach to another possibly existing method. However, a freely available software program that could have matched our need was Cellprofiler  [25]  because in principle, it enables the tracking of cells over time in a large set of image sequences, using the Hungarian algorithm. However, cell detection in Cellprofiler is based on a maxima detection followed by a seeded Watershed segmentation so we expected it not to perform well in detecting overlapping and dividing cells. In accordance, the results we obtained were dramatically poor.
Experimental data The dataset we created to validate the method is made of several videos of hundreds of cell divisions under three biological conditions. Those conditions are as follows: as a negative control, we used an siRNA targeting Cyclophilin, which is proposed as one of several standard negative controls by GE-Dharmacon in their ON-target+ human siRNA libraries. LGN (Leucine-Glycine-Asparagine repeat protein) was used as a positive control: LGN is an adaptor molecule involved in the localized recruitment of dynein motor complexes at the cell membrane, which direct forces exerted on astral microtubules. LGN is a central regulator of spindle orientation in many animal cell types (reviewed in  [1] ). Our paired-cell assay (di Pietro et al, in preparation) is designed to specifically depend on the "LGN-complex" molecular cascade.
Precision of the event detection The dataset proposed was subject to a fully manual analysis on one hand and a fully automated analysis on the other hand. In both cases, the goal was to retrieve the sequences containing a transition from two to three cells and the exact time frame of this transition in order to measure the division angle. Figure  8  shows a few examples of those transition events automatically detected. Table  1  describes in detail the pattern and event count along the process. In summary, about 40 % of the pattern contained no cells, 10 % contained obviously too many cells at the beginning of the sequence to be processed further and 50 % were processed further using the proposed analysis to search for a possible transition from two to three cells. Eventually, the manual analysis identified that 15 % of the sequence contained a transition from two to three cells, while the automated analysis only found 10 %.
Acknowledgements We thank  ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02 PSL* Research University ,  ANR-12-LIVESPIN  and  ARC  for funding,  Felipe Delestro Matos  for helping with the figure formating and  Mary Ann Letellier  for helpful comments on the manuscript. We acknowledge  Léo Valon , for helpful discussions and his initial work at testing various methods to perform an automated analysis.
Availability of data and materials All code and data necessary to reproduce the results of this paper is freely available on GitHub • Project name: livespin • Project home page: https://github. com/ biocompibens/livespin • Archived version: https://github. com/ biocompibens/livespin. git • Operating system(s): Platform independent • Programming language: Python • Other requirements: Python 2. 7 • License: GNU GPL 3. 0.
Pore space characterization by bulk techniques In addition to imaging techniques, pore space characterizations were performed on bulk freeze-dried IDP bulk samples based on classical nitrogen adsorption/desorption isotherms at 77 K coupled with the BJH method and the mercury intrusion porosimetry (MIP) technique. It is well known that the PSD stem from these methods are based on the interpretations of the raw data in particular the assumptions about pore morphology  [33, 34]  but they remain extensively used to characterize the mesopore distribution down to 2 nm. On the same samples, nitrogen adsorption/desorption isotherms were measured using a Micromeritics ASAP 2020 volumetric setup and MIP measurements using a Micromeritics Autopore IV 9500. Data obtained from the nitrogen adsorption isotherms was interpreted on the basis of the Kelvin equation linking the partial pressure of gas to the pore radius, assuming pores as cylinders  [35] : log P P 0 ¼ À2sV N RT $ 1 r p where s is the surface tension, V N the molar volume of liquid nitrogen, R the gas constant and T the absolute temperature in K.
The Washburn equation is valid for pores having a circular cross section, which is the most commonly used pore shape. A more general expression for pores of other shapes such as elliptical or slit can used by introducing a shape factor into the Washburn equation  [38] : d p ¼ f gcosa P where d p is the pore diameter and f the shape factor. The shape factor varies from 2 for slit (parallel walls) pores to 4 for cylindrical pores having a circular cross section  [39]. This interpretation is classically considered to be limited since mercury progressively invades the pore network from the external surface of the sample to its core. In order to improve the interpretation MIP tests, a numerical program called XDQ  [40, 41]  was used. The XDQ program is based on a multi-scale percolation system for which isotropic constitutive pore networks' structure with cubic lattice and bond percolation has been adopted.
For a non-periodic texture, semi-variogram g(h) evolves to an asymptotic maximum indicating the loss of correlation between pixels separated by distance h. The distance to reach the asymptote is related to the size of objects composing the image. Moreover when the semi-variograms are computed for several directions, the anisotropy properties of these objects can be analyzed. Preprocessing step for the FIB/SEM raw data Raw FIB/SEM data requires pre-processing treatments to remove artifacts and analyze them in terms of a 3D volume  [13]. The following set of operations was considered and applied using the public-domain software ImageJ (http://rsb. info. nih. gov/ij/) and associated open-source plugins: i. Due to the specific geometry of the FIB/SEM column (tilting angle between the imaged surface and the electron beam) a non-uniform illumination induces a shadowing effect, corrected with a rolling ball algorithm  [44].
By removing the pore volume related to this artefact, the cumulative PSD of the undamaged compacted illite can be recalculated upon intrusion and extrusion (Fig. 4 ). The resulting pore volume probed at the maximum intrusion pressure represents 80% of the theoretical porosity value; this is in a better agreement with limit of resolution of MIP. The cumulative MIP PSD of the connected pore was modeled by simulating the intrusion and extrusion curves using XDQ software  [40, 41]. The best match between the simulated and experimental curves was reached when assuming a shape factor corresponding to slit-shaped pores, so perfect simulation of the extrusion curve was not possible. This is because the idealized slit or cylindrical shape of pores and also the isotropic pore network model implemented in xdq do not match the actual complex pore morphology in clay materials.
Conclusion The originality of this work lies in combining several imaging techniques (FIB-nt and TEM), image analysis methods, and bulk measurements to quantify the pore space geometry of a compacted illite clay material. The consistency of the data provided for each approach was analyzed based on specific work dedicated to the characterization of pore size distribution (PSD). The main result of this work is a 3D pore distribution extracted from FIB-nt data and a dedicated image analysis procedure encompassing the full PSD of a compacted illite material. The morphological and geometrical analysis calculated from TEM and FIB-nt images also shows the anisotropy structure of the compacted IDP sample. Our other main achievement was that we tackled the limitations in FIB-nt resolution described by many authors using an optimized procedure combining image acquisition and image segmentation methods based on morphological techniques.
Acknowledgments This research received funding from the  European Atomic Energy Community Seventh Framework Programme  [ FP7 e Fission e 2009]  under grant agreement no. 249624 ,  Collaborative Project Catclay and from the French ANR SIMISOL  ( ANR-009-SYSC-012 ). We thank  Fr ed eric Charlot  and  Fabienne Warmont  for the acquisition of FIB-nt data and TEM images, respectively.
Overview of the LXCat Website The LXCat website is based on a standard LAMP system (Linux, Apache, MySQL, PHP/Python) with an automatic backup. LXCat was designed to be continuously upgradable and users are encouraged to check back regularly for information regarding new features on the site and updates in the data. Access to previous versions of data as they existed at any time after November 2013 is available and earlier versions of the data can be obtained upon request. An informal forum to share opinions, post doubts and view discussions regarding the content of LXCat is hosted on Google Groups at http://groups. google. com/forum/#! forum/lxcat. A guest or visitor can view all the past discussions, as well as use the search facility using relevant keywords. All posts are moderated. LXCat is structured into individual databases, provided by different contributors, and on-line tools for manipulating the data, as illustrated in the graphical abstract above.
General information about the project, detailed list of contributors, quick-start visual instructions, FAQ section, and a link to scientific publications, and unpublished notes are available on the site. Users are encouraged to make use of these. The workflow to access data is unified for all types of data. The user is sequentially asked to specify the global data category (electrons and/or ions), choose from the list of active databases, and select species and data types. As a result of the filtering, the list of available data records is shown. At this step, users can choose those records which will be shown more in detail, including the option to download the data in several text (txt, xml) and image formats. It is always possible to return to any previous step and refine search conditions.
Electron Scattering Cross Sections Both ''partial'' and ''complete'' sets of cross sections for electron scattering from individual target species are available on LXCat. A ''partial'' cross section set for a given target species includes information about only one or a subset of the important scattering processes for that species. The complete sets include a momentum transfer cross section for elastic scattering and total (angle integrated) cross sections for excitation, ionization, attachment processes for a given target species. If the cross section for a particular process is part of a ''complete'' set, it is so indicated in the comment line for each process. The complete sets in the BSR database (see below) are from ab initio calculations. Most of the other complete sets available in the LXCat databases were compiled by individual contributors using partial cross section data from the literature, either measured or calculated, as available.
Missing data are estimated and partial cross sections are adjusted within error bars, in order to achieve good agreement between measured swarm parameters and those calculated using the complete set as input to a Boltzmann equation solver. This procedure for determining complete sets of cross sections was pioneered in the 1960's by Phelps  [12]  and by Crompton  [13]  and their colleagues and is described in the review paper by Petrovi c et al. [10]  The determination of a complete set of cross sections consistent with measured swarm parameters and available measurements or calculations of partial cross sections is typical of inverse problems found in many branches of physics. As such, uniqueness is an issue. For this reason, the complete sets reported for the same target species in the LXCat databases often differ in the details.
These cross sections can be downloaded for use in Boltzmann solvers or in Monte Carlo simulations. Or, they can be used in directly on the LXCat site for on-line calculations using an abridged version of the two-term Boltzmann solver, BOLSIGþ. [14] Thus, electron transport and rate coefficients in pure gases or in arbitrary gas mixtures can be obtained directly from the LXCat site when complete sets of cross sections for each of the components in the mixture are available on LXCat. Note that only those datasets flagged as ''complete'' can be used in the on-line Boltzmann calculations. Note, too, that the common ''twoterm'' approximation used in many Boltzmann solvers does have an influence, slight in most cases, on the calculated transport and rate coefficients. Thus, the type of solver (twoterm, multiterm, or Monte Carlo) used in the compilations of swarm-derived cross section sets is stated in the comments for each dataset.
The elastic cross sections in the databases on LXCat are described with a comment line to indicate exactly what is tabulated. These can be total (angle integrated), momentum transfer, viscosity, or effective momentum transfer. The first three are angle-integrated cross sections weighted by the factors, 1, (1-cosu), or (1-cos 2 u), where u is the scattering angle. The effective values include the effects of inelastic collisions as is appropriate for use in the two-term spherical harmonic expansion. See, for example, Baraff and Buchsbaum,  [15]  and Sec. IIB of Pitchford and Phelps. [16] here data are available, the effective momentum transfer cross section is set equal to the sum of the inelastic cross sections plus the elastic momentum transfer cross section. This is an approximate relation.
Electron Differential Scattering Cross Sections (DCS) It is usually assumed that elastic momentum transfer and total cross sections for inelastic processes and for ionization provide sufficient information about the angular scattering of electrons for use in homogeneous Boltzmann solvers or Monte Carlo simulations. The accuracy of this assumption has been evaluated in certain gases for calculation of swarm parameters, but because of the lack of full sets of DCS data, very few tests have been made in discharge conditions (e. g. , with gradients of fields or densities). [17] A set of DCS data for argon elastic and inelastic scattering is now available in the BSR database on LXCat. Some limited DCS data from measurements are available for elastic scattering of electrons in SF 6 and CF 3 I in the Christophorou database.
Oscillator Strengths Also available on LXCat are optical oscillator strengths measured using a method known as Dipole (e,e) Spectroscopy  [18]  as briefly described below in the description of the UBC database. Oscillator strength is a quantity that expresses the probability of absorption or emission of electromagnetic radiation in transitions between energy levels of an atom or molecule. These data, combined with a suitable model such as BEf scaling (binary-encounter; oscillator strength, f)  [19]  can be used to infer the energy dependence and magnitude of electron-neutral scattering cross sections for specific processes for energies greater than several times the threshold energy. These data are included on LXCat because they provide a trove of information about electron-neutral scattering, and in some cases for gases where other scattering data are not available.
Ion Scattering Cross Sections The data presently available on LXCat for ion-neutral scattering are for atomic ions scattering from atoms. The data in the Phelps database were obtained by supposing that the DCS can be approximated as the sum of an isotropic and a backscatter component. These two components were derived for consistency with known information about the DCS and charge exchange cross sections. ''Transport'' cross sections are provided in the Viehland database, which is presently being updated. These cross sections are calculated from the interaction potentials and are equivalent to the angle averaged DCS's, weighted by the factor (1-cos k u), where k identifies the order of the transport cross section.
Biagi-v7. 1 and Biagi Databases These databases contain the complete sets of electron scattering cross transcribed from those available in SF Biagi's Monte Carlo code, Magboltz. [22] The data in Magboltz in its various versions have been continuously updated and improved over the years. The Biagi database contains detailed sets of cross sections for noble gases and for selected molecular gases (H 2 , D 2 , N 2 , O 2 , and SF 6 ) and transcriptions for additional species are planned. This database was previously named Biagi-v8. 9 but since the intent of the contributor is to maintain the most recent sets of cross sections in this database, the name has recently been changed to ''Biagi. '' The Biagi-v7. 1 database is retained because it includes convenient, simplified representations of data for noble gases as transcribed from version 7.
The recent Magboltz database cross sections are mainly taken from modern accurate experimental electron scattering measurements and only normalized within the allowed experimental error to give best fits to the measured radiation and transport parameters. An example is the vibrational and rotational cross section in molecules where drift velocity measurements in mixtures of the molecular gas with a noble gas allow the accurate normalization of the overall magnitude of the inelastic scattering in the molecule. The accuracy of modern drift velocity data with mixtures of argon, neon and helium allows a normalization of the experimental rotational or vibrational cross sections to typically better than 5%. The ionization data have been improved in recent years due to the measurements of the Rice University Group  [24]  (see also http://www.
ruf. rice. edu/~atmol/electron_data. html) of accurate dissociative ionization in many molecular gases and the charge states in noble gases. The Rice data are consistent with the total ionization measured by Rapp and Englander Golden. [25] The consistency between these measurements is within typically 4% and a weighted average is used in the Magboltz datasets. A useful cross check and also some missing ionization data can be supplied by the accurate measurements of Tian and Vidal  [26]  and also Nishimura et al. [27]  The effect of fixing the ionization leaves only the excitation cross section as a variable to fit the measured Townsend coefficients. The noble gases have well known level structures and have recently been calculated with good accuracy at threshold by Zatsarinny and Bartschat.
[28] he threshold cross sections are used in Magboltz and smoothly joined to experimental values at higher energy. The oscillator strengths of many levels have been measured by the British Columbia group  [29]  and use is made in the database of the BEf scaling  [19]  to obtain cross sections at high energy. The calculated radiation parameters using the BEf scaling are consistent within 2% of the measured radiation parameters. The cross sections of the non-dipole levels in the noble gases are normalized by an overall scaling factor to give a good fit to the measured Townsend coefficients. The molecular gases contain additional channels to neutral dissociation that do not exist in the noble gases. The database derives these cross sections from the known oscillator strength distributions from Olney et al.
BOLSIGþ Database This is a temporary database that is created when requested by the user following execution of the on-line Boltzmann solver. The contents of this database include swarm/ transport parameters versus E/N resulting from the on-line Boltzmann calculations. The data existing in this temporary database can be plotted along with measured quantities available on the other databases or compared with results from other on-line calculations in different gas mixtures. This temporary database is deleted when the user exits the LXCat site.
Bordage Database Experimental swarm data in pure gases and in gas mixtures (with argon) were used in an unfolding procedure to derive the complete sets of cross sections for electron scattering in CF 4 , CHF 3 , and Si(CH 3 ) 4 provided in this database. Cross sections for the dominant processes are tabulated up to electron energies of 500 eV. All the cross sections were derived by using a multiterm solution of the Boltzmann equation in the approximation of the hydrodynamic regime. [30, 31] The use of these cross sections in the multiterm Boltzmann solver yields drift velocity, ionization and attachment coefficients, diffusion coefficients Â N (longitudinal and transverse), and characteristic energies, within a few percent of measured values for all gases included in this database. The more common two-term solvers can lead to increasingly large error with increasing E/N and in cases where cross sections for inelastic processes (vibrational excitation in particular) are large in the region of the Ramsauer minimum.