dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, energy requirements for maintenance or activity can increase depending on space allowance or temperature. This can imply lower availability for protein deposition. Moreover, this can affect the level of observed performances without deviating The InraPorc profiles obtained in the present study reflect the genetic differences among the breeds concerning NE intake, growth capacity, and precocity of growth. For breeds where several profiles have been calibrated, profiles of the same breeds are more or less comparable. For instance, both of the Alentejana profiles have quite comparable parameters, except for B Gompertz. The differences among Iberian profiles are more pronounced on some parameters, such as B Gompertz between data source, or even for the same source, depending on the diet composition parameters (e.
Conclusions Despite some methodological limitations due to the availability of data fitting with the needs of the calibration process, the present study provides initial insights into the modelled growth characteristics of some local breeds. Indeed, to our knowledge, this methodology has never been previously applied for these breeds. Parameters such as precocity (reflecting the shape of growth kinetic), PDm, or intake curve characteristics were obtained and have been used to determine nutrient requirements (here for SID lysine). These elements are currently scarce for most of the local breeds, even experimentally. Such breeds are often studied in field conditions that impair the definition of growth and intake potential or nutrient use. Important differences between local breeds were determined regarding growth potential, reflecting genetic differences but likely also differences in management during the lactation and post-weaning periods.
Funding: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 634476 (Project acronym: TREASURE). The contents of this manuscript reflects only the views of the authors and the European Union Agency is not responsible for any use that may be made of the information it contains. Core financing by the Slovenian research agency (grant P4-0133 for M. C. -P. ) is also acknowledged.
Polarized neutron diffraction allows to determine the local susceptibility tensor on the magnetic site both in single crystals and powders. It is widely used in the studies of single crystals, but it is still hardly applicable to a number of highly interesting powder materials, like molecular magnets or nanoscale systems because of the low luminosity of existing instruments and the absence of an appropriate data analysis software. We show that these difficulties can be overcome by using a large area detector in combination with the two-dimensional Rietveld method and powder samples with magnetically induced preferred crystallite orientation. This is demonstrated by revisiting two test powder compounds, namely, low anisotropy (soft) ferrimagnetic compound Fe3O4 and spin-ice compound Ho2Ti2O7 with high local anisotropy.
As a proof of concept the local susceptibility parameters of Tb were found by a two steps procedure. First, the integrated intensities of the spin-up and spin-down components I + (hkl) and I -(hkl) were obtained by pro-file matching from the corresponding powder patterns. Then, the program CHILSQ of the Cambridge Crystallography Subroutine Library 14 was used to fit the integrated intensities. It is clear that such a procedure of data treatment can be applied only to highly symmetric crystal structures with small unit cell. For more complex structures a Rietveld method needs to be developed. Rietveld analysis has become mandatory in powder diffraction for nuclear and magnetic structure refinement  [15] [16] [17]. It refines various metrics, including lattice parameters, structure and magnetic parameters, a preferred orientation to derive a calculated diffraction pattern.
Once the calculated pattern becomes nearly identical to an experimental one, various properties pertaining to that sample can be obtained. However, the Rietveld method for polarized neutron powder diffraction (PNPD) has not been implemented yet. In the above-mentioned polarized powder experiments special softwares (model dependent) were developed for the data treatment. We note that at first PNPD measurements were performed on conventional powder diffractometers equipped by one-dimensional (1D) detectors while modern unpolarized neutron powder diffractometers (Super-D2B, D20, SPODI) at reactor sources are equipped with twodimensional (2D) detectors. Area detectors increase the efficiency of the instrument by an order of magnitude, but the common approach at these instruments consists in reducing the accumulated 2D data from area detector into 1D diffraction pattern by "unbending" measured Debye cones.
The resulting pattern is then treated using standard 1D Rietveld refinement  [15] [16] [17]. Most recent powder diffractometers at advanced neutron spallation sources (WISH, POWGEN) use very large area detectors and operate in TOF mode. This generates rather com-arXiv:1910. 11822v1 [cond-mat. mtrl-sci] 25 Oct 2019 plex three-dimensional (3D) angular-and wavelengthdispersive data which are eventually transformed into one-dimensional diffraction pattern I(2θ) (or I(λ))  18  to allow standard Rietveld refinement. It has been noted that two-dimensional extension of Rietveld method for neutron TOF powder diffraction taking into account the variation of diffraction angle 2θ and wavelength λ decreases the number of data-reduction steps and avoids the loss of high-resolution information  19  , but full scale multidimensional Rietveld software for neutron TOF powder diffraction still need to be developed.
Since area detectors increase considerably the efficiency of the instruments, we performed our PNPD measurements on diffractometers equipped with large 2D position sensitive detectors. When using area detectors, the polarized neutron scattering is a function of 2θ and ϕ but also of the angle between the magnetic field and the scattering vector. Moreover, neutrons are sensitive only to the magnetic moment perpendicular to the scattering vector. Therefore, the variation of intensity along the Debye cones can be used for the separation of nuclear and magnetic scattering contributions. For these reasons the transformation of angular-dispersive polarized neutron data from area detectors into one-dimensional 2θ pattern is not applicable. We note as well that the equation for powder averaging derived in the paper  13  is valid only for the vertical field and scattering in the horizontal plane.
B. Experiment and data treatment Neutron diffraction studies were performed at the Orphée 14MW reactor of the Laboratory Léon Brillouin, CEA Saclay. The diffraction patterns were collected on the diffractometer 5C1, equipped by position sensitive detector with cylindrical geometry covering 80 • and 25 • in horizontal and vertical directions, using neutrons of wavelength λ = 0. 84 Å obtained with a Heusler alloy monochromator. The incident beam polarization P is 0. 91. PNPD data were collected on powder sample of Fe 3 O 4 in an external field of 0 T and 6 T below the Verwey transition (at 10 K) and above it (at 150 K). The experiments with sintered powder sample of Ho 2 Ti 2 O 7 were performed in the temperature range from 5 K up to 50 K in the magnetic field of 1 T. Measurements of the Co(II) complex with singlemolecule magnet behavior have been carried out on the thermal polarized neutron lifting counter diffractometer 6T2 (LLB-Orphée, Saclay).
The Cherenkov Telescope Array The Cherenkov Telescope Array (CTA) is the next generation of Imaging Atmospheric Cherenkov Telescopes (IACT) used for gamma-ray astronomy. CTA aims at studying the very-high-energy universe, such as cosmic ray origins, astrophysical phenomena, fundamental physics and cosmology  [1]. CTA will be composed of two arrays of IACTs in two different observing locations (one in each hemisphere) in order to be able to observe the full sky and achieve different science goals. Today in pre-production phase, CTA is planned to acquire its first observational data in the coming years.
GammaLearner components As shown on Fig. 4 , GammaLearner relies on PyTorch  [10]  for the Deep Learning fundamental components (tensor objects, automatic differentiation) augmented by the indexed convolution, described in 2. 1, to handle unconventional images. It uses Ignite  [11]  for the organization of the training process itself, Tensorboard  [12]  (via TensorboardX  [13] ) and Gammaboard as visualization tools. GammaLearner consists of an experiment runner tool, the engine of the framework, and a collection of functions and classes needed for the Deep Learning process to: • load datasets, • pre-process data (filter, augment, transform), • train, validate and test networks, • monitor the training process, • visualize training results (Gammaboard).
Experiment work-flow To launch an experiment, i. e. train and test a neural network, one fills an experiment settings file (a python script) describing the conditions of the experiment : location of the data and data pre-processing, loss function, optimizer, network parameters (related to the network) etc. If specific functions or classes are needed for this experiment and are not yet in the collection of files provided in the framework (i. e. criterions. py, optimizers. py, steps. py and plots. py), one can simply write them in these files. One also defines the network to train, as a Python class inheriting from PyTorch nn. Module class to benefit from automatic differentiation. Then one starts the experiment runner, the settings and the network are saved for reproducibility, and the process runs to the end by its own.
During the training process, various metrics can be monitored via a log file and Tensorboard: • loss, • accuracy (in case of a classification task), • features and convolution kernels, • weights and gradients of the network, • hardware usage. Once the network is trained, the raw data of the test phase and the network are saved (network checkpoints can also be saved during the training). The test results can then be analyzed with the Gammaboard tool, that plots end result curves (energy and angular resolution, migration matrices) and allows an easy comparison between experiments and CTA requirements/performances.
Simplification of operating procedures with specific tools and interfaces that meet users' needs is a way to reconcile users with such systems  (Rector et al. , 1998). Since 12 years, two authors (CB and M-CJ) are conducting a pharmacovigilance research program that aims to demonstrate the benefits of adding formal definitions to terminologies used to code adverse drug reaction (ADR)  (Bousquet et al. , 2005; Henegar et al. , 2006). Indeed, reconciliation between MedDRA (Medical Dictionary for Regulatory Activities) and SNOMED CT (SNOMED -Clinical Terms) is desirable for standard harmonization  (Richesson et al. , 2008). For this purpose, we have developed OntoADR, a semantic resource of ADRs  (Souvignet et al. , 2016b). OntoADR expresses the meaning of the terms of MedDRA terminology, using formal definitions and is mainly used for pharmacovigilance purposes  (Souvignet et al.
, 2016a). Searching into the MedDRA terminology is limited to two search methods: a hierarchical search, or a string search (see Figure  1 , left), as recommended in the MedDRA introductory guide and implemented in softwares such as MedDRA Browser  (MSSO, 2018). In previous work, we presented results of querying MedDRA using a third method: semantic querying through the OntoADR resource  (Declerck et al. , 2012). Using the Protégé ontology editing software  (Musen and Protégé Team, 2015) , we aimed to search terms, and were able to build queries and perform semantic reasoning with encouraging results. However, such an approach to query MedDRA (through OntoADR) turned out to be tedious. As we proved our ability to improve search in MedDRA using knowledge engineering techniques, we decided to take a more pragmatic approach: analyzing the practices of users and their expectations when querying MedDRA.
When using a terminology such as MedDRA for coding or searching for cases in ADR databases, the most important identified users' needs are: (1) improving term searches by making it easier and faster, and (2) searching for similar terms corresponding to the same medical condition using defining criteria. Adding formal definitions to MedDRA terms would address these problems by adding a new search method: semantic reasoning (see Figure  1 , right;  Bousquet et al. , 2014). The domain lacks a dedicated interface for potential users who have no training in computer science and would be uncomfortable using a tool such as Protégé. Interface simplification and userfriendliness of a dedicated query form, in response to end-users needs, will make this method for searching in MedDRA accessible to new users, complement existing methods, demonstrate the relative advantage of a formal system, and especially, facilitate MedDRA search.
The ability for non-experts to query ontologies (or any knowledge resource) without having to learn a particular query language is a step toward scientific popularization of formal systems. To date, four approaches for providing access to concepts in an ontology are available (see Table  1 ). We assume that forms-based interfaces can result in an even more user-friendly interface in pharmacovigilance. This paper investigates the hypothesis that such interface would lower the complexity of querying and corresponds to the user's expectations for searching MedDRA terms. We implemented semantic queries in a prototype named OntoADR Query Tools (OQT), a dedicated forms-based interface to query MedDRA terminology through semantic criteria. Our objective was to demonstrate that such interface improves search in MedDRA compared to classical approaches.
SNOMED CT SNOMED -Clinical Terms is a large-scale medical resource terminology with a great depth and coverage of healthcare data. SNOMED CT is a formal system representing its semantics with DL EL++  (Baader et al. , 2005). Thanks to our UMLS (Unified Medical Language System) license we accessed to SNOMED CT Release Files, and we used a SNOMED CT 20130131 version (January 2013) in this study.
OntoADR OntoADR is a semantic resource we created to describe the meaning of MedDRA terms in a formal language. The semantic properties used to define ADRs have been selected from SNOMED CT. Today about 13,000 MedDRA terms (63%) have been defined in OntoADR using automated methods  (Bousquet et al. , 2014). Moreover, 1,935 terms related to 23 safety topics defined by  Trifirò et al. (2009)  were manually curated by an expert in order to be completely defined in OntoADR  (Souvignet et al. , 2014; Asfari et al. , 2016).
The "web" version of MedDRA browser has the same features as the "desktop" version. To compare OQT with an existing software able to manage semantic knowledge, we chose Protégé (Stanford Center for Biomedical Informatics Research, 2018) because: (1) Protégé is the reference software in terms of semantic knowledge base management; (2) Protégé allows the creation of semantic queries similar to OQT. While MWB is a classical search software, accessible by novices and not requiring any technical skills, Protégé requires expertise about query languages and semantic knowledge. We judged that executing a query in Protégé would have necessitated extensive training and was unachievable for users who had no experience of semantic querying. Moreover, the comparison of results with Protégé and OQT should be limited (or exactly the same), as for a given query, they are using similar semantic inference engine.
User's Profiles In order to address potential user profile range, we selected eight healthcare professionals (see Table  2 ). We decided to select users according to the usual profiles in regional pharmacovigilance centers in France. This is why our sample consists of physicians and pharmacists on one hand, and juniors and seniors on the other hand. Additionally we included two physicians who had no previous knowledge of coding with MedDRA, but had experience of coding patient stays with ICD-10 in the case mix database of a university hospital.
Software Evaluation Comparison With MedDRA Browser Figure  3  presents the main results of the comparison of OQT and MWB. The use of OQT allows improving recall (+37%, p = 0. 01) and precision (+24%, p = 0. 02). Meanwhile, on average, the time spent on OQT (about 4 min 30 s) is significantly lower (-35%, p < 0. 001) than time spent on MWB (about 7 min). Computed "performance" (correct terms found per minute) is more than three times better with OQT than with MWB. On average with MWB, users make 1. 95 string searches and use the hierarchy 82. 5% of the time before having a satisfying result. For OQT, only one query is necessary 80% of the time. We observed that users had no major difficulties identifying the relevant SNOMED CT terms in OQT. The "perceived usefulness" and "perceived ease of use" is better in OQT than in MWB (p < 0.
Software Evaluation We observed a trend of perceived Usefulness and perceived Ease of Use increasing with OQT, when evaluators select MedDRA terms for new safety topics, while it is slightly decreasing with the MWB. Consequently, we assume this phenomenon is related to a training effect with OQT. Additionally, durations were shorter with OQT after two topics, while the duration with MedDRA browser was stable over the time. The users had no experience of OntoADR nor OQT, but the 6 pharmacovigilance specialists are knowledgeable about MedDRA organization, and previously used the MedDRA browser that is associated with the French pharmacovigilance databases. This constitutes a bias favoring MWB over OQT that we were not able to avoid. The peripheral demyelination (#4) requires more specialized knowledge than other topics.
We followed these development principles which could explain why our tool is well received by users. To our knowledge, no development has been realized on forms-based interfaces for knowledge engineering in pharmacovigilance. The only work, so far in the biomedical domain, is from  Joubert et al. (1998)  describing a software application where the user may select pairs of concepts and a relation between them, in order to build a conceptual graph and retrieve records in medical databases. However, it does not meet the needs of pharmacovigilance who aim to select medical terms. The SUS score for OQT suggests that forms-based interfaces show a better usability than existing systems. Rogers  (Rogers, 2010)  described factors that can influence a decision to adopt or reject an innovation. In our case, the development of OQT helps to significantly improve the trialability of OntoADR (i.
Perspectives OntoADR Query Tools interface is still evolving; multiple tools are currently in development such as a "common parenthood" feature that would list common properties from a set of terms, or a "query by example" feature that would suggest closest terms from a given selection. OntoADR Query Tools is currently limited by the coverage of medical conditions by OntoADR and the number of semantic definitions validated by an expert. In this article we showed the applicability of the proposed method on five safety topics covering different pathologies and expect that OQT may be applicable to the whole MedDRA terminology when full coverage will be achieved. In order for MedDRA users to benefit from the semantic queries we plan to present the current results to the MedDRA Maintenance and Services Organization (MSSO) that is in charge of delivering new versions of MedDRA, and to software providers that implement access to spontaneous reports databases.
Re-usability The interface is currently only compatible with MedDRA and SNOMED CT. With minor modifications, it would be possible to query any terminology aligned with SNOMED CT, such as International classification of diseases (ICD) or SNOMED CT itself. In addition, SNOMED CT may be replaced by any terminology using the DL standard EL++ (such as NCI thesaurus, e. g. ). OQT communicates with the database using an internal API as web services. This programming interface is not documented yet, but it would be possible for external applications to launch queries using this web service. We also plan to explore possibilities offered by Common Terminology Services 2 standard (Object Management Group, 2018) for this API.
Magnet positions were tracked using motion tracking software (Tracker). Magnetic potential energy is shown to be decreasing almost monotonically throughout reconfiguration with some areas of near stability. (h-n) Experiment II: Two consecutive 90 °turns to produce a 180 °turn. The process is very similar to (a-g) but M2 and M3 are held within curved paths. Note that two folding motions can occur with some overlap due to their proximity and the chosen length of s. See electronic supplementary material, Video SI for further detail. We observed failure cases where the chain split apart while carrying out experiments with larger numbers of tiles, as depicted in figure  7. This tended to occur if propagation occurred slowly relative to folding speed due to variables such as friction. During folding, the tile sections on each side of the hinge point experience an amount of torque and rotation that is dependent on the section length.
Simulation Simulations were carried out using Blender Game Engine to investigate the scalability of the system. We also developed a MATLAB code which can take a 2D image and produce the tile sequence required to create the shape at a prescribed pixel resolution. Tiles are modelled as having neither mass nor friction against the ground as in an ideal system, while magnets are provided with mass so that its motion dynamics can be reproduced with greater accuracy. Figure  8a -d shows the self-folding of a rectangle composed of 57 tiles and 115 magnets. Figure  8e -h shows the self-folding of another rectangle composed of 54 tiles and 109 magnets, but with lower tile density due to the change in tile sequence. Both simulations are initiated by Catalyst. In both simulations, folds occur uniaxially in order along the chain until all magnets are in their final positions and the final shape is produced.
We further assume the infinite sites mutation model of  Watterson (1975)  with mutation parameter m and known ancestral states for each of the sites. Then, we can encode the data into a binary matrix Y of n rows and s columns with elements y i; j 2 f0; 1g, where 0 indicates the ancestral allele. To calculate the Tajima's conditional likelihood PrðY j g T ; mÞ, we first record each haplotype's frequency and group repeated columns to form mutation groups; a mutation group corresponds to a shared set of mutations in a subset of the sampled individuals. We record the cardinality of each mutation group (i. e. , the number of columns that show each mutation group). In Figure  2A , there are two columns labeled "b," corresponding to two segregating sites that have the exact same pattern of allelic states across the sample.
Inferring human population demography from mtDNA We selected n ¼ 35 samples of mtDNA at random from 107 Yoruban individuals available from the 1000 Genomes Project phase 3 (1000 Genomes Project  Consortium et al. 2015). We retained the coding region, 576 2 16; 024 bp, according to the rCRS reference of human mtDNA  (Anderson et al. 1981; Andrews et al. 1999)  and removed 38 insertions/deletions. Of the 260 polymorphic sites, we retained 240 sites compatible with the infinite sites mutation model. The final file is available in https://github. com/JuliaPalacios/phylodyn. To encode our data as 0s and 1s, we use the inferred root sequence RSRS of  Behar et al. (2012)  to define the ancestral type at each site. To rescale our results in units of years, we assumed a mutation rate per site per year of 1:3 3 10 28 (Rebolledo-Jaramillo et al.