dataesr/scientific-paragraphs-categorization huggingface dataset:
With DAS1, most clinicians would intend to prescribe the active ingredient 'Metoprolol' rather than a specific drug product name manufactured by a specific company like 'Apo-Metoprolol' because all pharmacies do not have in inventory every possible brand. Whereas DDS1 does refer to such a specific pharmaceutical product. Moreover, even strings that are identical in their composition and order of characters may have different meanings. For example, 'Metoprolol' in DAS1 would usually refer to any drug product containing metoprolol or to the active ingredient metoprolol itself, although it might refer to the generic drug product branded with the name "Metoprolol" in some cases. Moreover, the information on the prescribed drug may differ from the dispensed drug, and more often, databases do not distinguish between them (between the DAS and the DDS).
• The transformation of ontological annotations into SQL comments and views provides documentation and multiple access points within the data model. • The configurable transformation of OWL types into SQL types allows a uniform usage of OWL types. • The generation of a mapping dictionary enables structure reversibility and data extraction in various output formats. • The implementation, OntoRelα, can be used with various OWL ontologies and PostgreSQL databases. However, several limitations will require further work. Firstly, the relatively large size of the resulting relational data model is a challenge, especially when trying to interact manually with the model. As an example, to get the all the data related to a drug prescription we need to build a query with at least 16 joins (see Fig. 11 ).
Conclusion Many conversion methods from an ontology to a relational data model have already been proposed. However, these propositions suffer from limitations regarding the coverage of ontological constructs, the handling of complex axioms, and the accessibility of tools. This paper presented conversion rules and a freely available prototype named OntoRelα that covers more ontological constructs and handles complex axioms, and that can be used and extended by other researchers. This work is a first step towards building a tool to generate a sharable and interoperable database using ontologies. More development is underway to refine the derived relational data model and provide complementary data access tools.
normalised relational schema to minimise data redundancy and potential contradictions. The following illustrates a set of conversion rules with examples presented by ontology constructs. A complete example can be found in Appendix C. 3. 1 Conversion rulesClass [C] is a set of individuals (a. k. a. instances). A class is converted to a relation that includes an Individual Identifier attribute (classIri_iid:iid_type) used as a primary key (see Fig. 1). Each value of iid uniquely identifies an individual. The class Thing (the superclass of all classes) may be converted to a relation with one attribute, the iid. Thus, the relation Thing contains all individual identifiers of the database. This conversion makes it possible to define an independent artificial key to index individuals.
For example,  (Wang et al. , 2018)  performed tensile test simulations focusing on plastic behavior to identify yield and hardening parameters, while excluding deformation twinning and damage behavior taking place at higher strains. (Bronkhorst et al. , 2019)  focused on detailed analysis in defining crystal plasticity parameters from experimental datasets and to investigated the deformation response of realistic image based microstructures. To address the hierarchical microstructure,  (van Nulund et al. , 2021)  employed a subgraining technique with crystal plasticity to capture intra-grain variations in 316L microstructure with no specific focus on the incorporation any special effects from microsegregation or voids. For the assessment of the as-printed state of solidified material, residual stress analysis has been performed with crystal plasticity to explain experimental measurements in AM 316L  (Chen et al.
Computational approach One-way (weak) coupling of phase field solution of rapid solidification with crystal plasticity analysis of mechanics is constructed. The model framework is implemented in Zset software (www. zset-software. com) making use of the previous general efforts of coupled phase field and mechanical analysis of  (Ammar et al. , 2009; de Rancourt et al. , 2016). The phase field simulations inform the crystal plasticity model where solidification shrinkage occurs, which generates microstructural residual stresses in the solid, leading to plastic deformation and generation of dislocations. Heterogeneous solid solution strengthening effects are incorporated in the crystal plasticity model formulation based on the microsegregation obtained from the phase field model. Hence the used approach is sequential and both methods utilize the same time stepping.
Capillary anisotropy strength ϵc 0. 018  (Bragard et al. , 2002)  Kinetic anisotropy strength ϵ k 0. 13  (Bragard et al. , 2002)  Solute trapping velocity V PF D 1. 0 m/s  (Smith and Aziz, 1994)  Computational interface width W0 50 nm Trapping parameter A 0. 06 Element size dx 0. 7W0 a Thermo-Calc TCFE9 database  (Andersson et al. , 2002)  b Thermo-Calc MOBFE4 database  (Andersson et al. , 2002)  where V PF D = D L AW 0 (16) is a characteristic solute trapping velocity, W 0 is the magnitude of anisotropic interface width W(n). Eq. 15 can be solved numerically, and V PF D can be tuned to represent a specific amount of solute trapping based on experimental k(V) data. Once an appropriate value for V PF D is chosen, the trapping parameter A in Eq. 6 is determined through Eq. 16. As we are not aware of direct measurements of solute trapping for iron alloys similar to ours, we assume that the solute trapping correlates with the material's equilibrium partition coefficient as proposed by  (Smith and Aziz, 1994).
Modern and future surveys effectively provide a panchromatic view for large numbers of extragalactic objects. Consistently modeling these multiwavelength survey data is a critical but challenging task for extragalactic studies. The Code Investigating GALaxy Emission (CIGALE) is an efficient PYTHON code for spectral energy distribution (SED) fitting of galaxies and active galactic nuclei (AGNs). Recently, a major extension of CIGALE (named X-CIGALE) has been developed to account for AGN/galaxy X-ray emission and improve AGN modeling at UV-to-IR wavelengths. Here, we apply X-CIGALE to different samples, including Cosmological Evolution Survey (COSMOS) spectroscopic type 2 AGNs, Chandra Deep Field-South X-ray detected normal galaxies, Sloan Digital Sky Survey quasars, and COSMOS radio objects.
Interpreting these large volumes of multiwavelength data coherently and efficiently is a challenging task for extragalactic studies. Many codes have been developed to fit AGN/galactic spectral energy distributions (SEDs; see Figure  1  of  Thorne et al. 2021  for a summary of different codes). The Code Investigating GALaxy Emission (CIGALE) is an open-source SED-fitting code written in PYTHON   (Burgarella et al. 2005; Boquien et al. 2019). It employs a parallel algorithm, able to build thousands of SED models per second and fit them to data. The SED models are built through a series of "modules" defined by the user. This architecture is designed to allow for easy updates or even the addition of branches in the code that carry scientific investigations. For example, the dust-attenuation module applies a specific attenuation recipe to the starlight and line emission, and the dust-emission module is responsible for the IR dust radiation.
Code Improvement Considering the evidence for X-ray anisotropy in Sections 2. 1 and 2. 2, we modify the code so that the user can model L X as a second-order polynomial function of the cosine of the viewing angle (e. g. , Netzer 1987): ( ) ( ) ( ) q q q = + + -- L L a a a a 0 cos cos 1 , 2 X X 1 2 2 1 2 where the coefficients (a 1 and a 2 ) are free parameters set by the user, and θ is the viewing angle (face-on = 0, edge-on = 90°). The constant term in Equation (2), 1a 1a 2 , guarantees that the right-hand side equals the left-hand side when θ = 0. (a 1 , a 2 ) = (0, 0) means isotropic L X. In CIGALE V2022. 0, α ox is still calculated using Equation (1).
e. , type 2 AGNs are obscured by the torus. However, some recent observations suggest that type 2 AGNs might also have small viewing angles and be obscured by polar dust (e. g. ,  Mountrichas et al. 2021a; Ramos Padilla et al. 2022 ). To consider this possibility, we test new CIGALE runs allowing all available viewing-angle values in SKIRTOR (0-90°with a step of 10°). The result still favors the (a 1 , a 2 ) = (0. 5, 0) anisotropic model, consistent with our original result. Based on the tests above, we consider our main conclusion not to be critically dependent on the adopted parameters of the photon index and viewing angle in Table  1. In the code of CIGALE V2022. 0, we set the default (a 1 , a 2 ) to (0. 5, 0) based on our results above. For general purposes of AGN modeling, the user does not need to change these default values.
Code Improvement X-CIGALE assumes that galaxy X-ray emission from HMXBs and LMXBs can be calculated from the scaling relations of L X HMXB -SFR and L X LMXB -M å  (Fragos et al. 2013a ). However, this is an oversimplified assumption, because these relations are just an approximation for the overall galactic population, and scatters around them exist. For example, the content of globular clusters at a given M å , which is not modeled in X-CIGALE, can significantly affect L X LMXB (e. g. ,  Lehmer et al. 2020 ). Also, since the HMXB and LMXB emissions are from discrete point sources, L X HMXB and L X LMXB inevitably suffer from statistical fluctuations that are especially strong in low-SFR and/or low-M å galaxies (e. g. ,  Lehmer et al. 2019 Lehmer et al. , 2021)). To model the L X HMXB and L X LMXB dispersions of individual galaxies in greater detail, we introduce two new free parameters, δ HMXB and δ LMXB , to account for the scatters of the L X HMXB -SFR and L X LMXB -M å scaling relations, i.
Code Improvement To allow for deviations from the default  Schartmann et al. (2005)  optical spectral slope as suggested by the observed quasars (Section 4. 2), we introduce a free parameter, δ AGN , i. e. , In CIGALE V2022. 0, we also allow the user to choose the disk continuum from the SKIRTOR model  (Stalevski et al. 2012 (Stalevski et al. , 2016)) , i. e. ,  The median and standard deviation values are labeled on each panel. The observed uz color can be bluer than 0. 5 mag. This cannot be reproduced with X-CIGALE, where the AGN disk SED shape is fixed, but CIGALE V2022. 0 can account for these blue SEDs with uz < 0. 5 mag. [ ] [ ] [ ] [ ] ( )      l l l m l l m l l m l l m µ < < < [ ] [ ] [ ] [ ] ( )      l l l m l l m l l m l l m µ < < <.
Motivation X-CIGALE can only account for radio emission from SF  (Boquien et al. 2019). This SF radio emission has two components: one is a thermal component contributed by the "nebular" module; the other is a synchrotron component contributed by the "radio" module. The latter is often dominant, and is calculated in X-CIGALE using the radio-IR correlation parameter q IR (e. g. ,  Helou et al. 1985) , i. e. , ( ) = ´ń q L L H z log 3. 75 10 7 IR SF, IR ,21cm 12 ⎜ ⎟ ⎛ ⎝ ⎞ ⎠ where L SF, IR is the total star-forming IR luminosity (mostly in FIR) and L ν,21cm is the corresponding radio synchrotron luminosity at 21 cm (1. 4 GHz). The default value of q IR is 2. 58 in X-CIGALE. Other than q IR , which sets the normalization at 21 cm, there is another free parameter (α SF ) that controls the power-law slope of the SF synchrotron emission, i.
Miscellaneous Updates In addition to the major changes of the code detailed in previous sections, we also implement several minor updates as below. 1. In X-CIGALE, the α ox parameter (X-ray module) is internally set to -1. 9, -1. 8, -1. 7,. , -1. 1, and the user cannot control it. In CIGALE V2022. 0, we change α ox to an explicit parameter that is set by the user, although the default values are still -1. 9, -1. 8,. , -1. 1. This change allows the user to run X-CIGALE more effectively when using the X-ray module. For example, if the sample consists of luminous quasars that typically have more negative values of α ox (e. g. ,  Just et al. 2007 ), then the user can set α ox as, e. g. , -1. 9, -1. 8, -1. 7, and -1. 6. This setting will reduce the number of models by a factor of 2. 25, significantly boosting the efficiency.
If l l = min max , then the code will use the monochromatic luminosity at this wavelength. If lambda_fracAGN is set to "0/0" (the default values), then the code will still use the definition of frac AGN in X-CIGALE. This change allows the users to model AGN versus galactic relative strength in their interested wavelengths. 3. X-CIGALE allows three extinction laws for AGN polar dust (i. e. ,  Calzetti et al. 2000 , nearby star-forming galaxies;  Gaskell et al. 2004, large dust grains; Prevot et al. 1984, Small Magellanic Cloud, SMC). These extinction laws extend to ≈100 nm, below which X- CIGALE adopts analytical extrapolations. These extrapolations lead to large, nonphysical extinctions below 100 nm. This has a direct impact on the models: the dust reprocessing has too much radiation that is re-emitted in the infrared while it dramatically steepens the slope of the accretion-disk emission.
Summary and Future Prospects In this work, we test X-CIGALE on different AGN/galaxy samples and improve the code accordingly. We publicly release the new code as CIGALE V2022. 0 on https://cigale. lam. fr. Our main results are summarized below. 1. The X-CIGALE fits of COSMOS type~2 AGNs produce systematically negative Δα ox , indicating that the observed X-ray fluxes are below the expectations from the isotropic AGN X-ray model. In CIGALE V2022. 0, we allow the user to model AGN L X (intrinsic X-ray luminosity) as a second-order polynomial of q cos. We test three different sets of polynomial coefficients, i. e. , (a 1 , a 2 ) = (0. 5, 0), (1, 0), and (0. 33, 0. 67), and compare the results with those of the isotropic model. We find that the fits from (a 1 , a 2 ) = (0. 5, 0) have the best quality in terms of both Δα ox and ΔAIC.
X-CIGALE only accounts for galactic radio emission. It's fits of COSMOS radio sources fail to account for the observed radio ∼3 GHz fluxes in many cases, e. g. , 28% of the sources have model fluxes more than 0. 5 dex below the observed ones. Therefore, in CIGALE V2022. 0, we add an AGN radio power-law component, parameterized by AGN loudness (R AGN ) and power-law slope (α AGN ). With this AGN component, the model agrees with the observed ∼3 GHz (and 1. 4 GHz when available) data point within ∼0. 5 dex for most sources. From the fits of CIGALE V2022. 0, we find that about half of the radio sources have a significant radio AGN component (as defined by P AGN,1. 4GHz /δP AGN,1. 4GHz > 2), and we classify the rest as radio SF galaxies. This result suggests that AGN activity is common among sources selected by deep radio surveys.
5. We also implement several miscellaneous updates in CIGALE V2022. 0. We allow the user to set the AGN α ox grid instead of fixing it. We introduce a new free parameter "lambda_fracAGN," which sets the wavelength range for frac AGN definition. We improve the AGN polar-dust extinction curves at λ  100 nm based on realistic dust mixtures and optical properties taken from the literature. These updates make CIGALE V2022. 0 more flexible and physical (see Section 6). Multiwavelength deep and/or wide surveys have become increasingly popular in extragalactic research. CIGALE V2022. 0 serves as a reliable and efficient tool to physically interpret the multiwavelength survey data from radio to X-ray wavelengths. Its open-source nature and module-based structure  (Boquien et al. 2019 ) will also benefit the community.
In our experience, Figure  19. Top: the extinction curves in CIGALE V2022. 0 (solid) and X-CIGALE (dashed). Different colors indicate different extinction laws as labeled. Below 100 nm, the CIGALE V2022. 0 curves have been calculated based on the optical properties of the realistic dust mixtures, while the X-CIGALE curves are analytical extrapolation. Bottom: the type 1 AGN SED models with different polar-dust E(B -V ). The models are normalized at 10 μm. The solid and dashed curves are based on the CIGALE V2022. 0 and X-CIGALE SMC extinction curves, respectively. The polar-dust re-emission (∼30 μ m) is lower (bottom panel) when using the CIGALE V2022. 0 extinction, because the CIGALE V2022. 0 extinction is weaker than the X-CIGALE one at <10 nm and both codes follow energy conservation. most user-specific needs can be satisfied by the original code or with slight/straightforward modifications.
Materials and Methods A detailed description of all mathematical derivations and formulations, biophysical models, and algorithms are included in the Extended Data 1. In brief, this work uses numerical and analytical mathematical methods to theoretically investigate and characterize the dopaminergic innervation of the human striatum, and the networks that break down following denervation. In the first part of the paper, we use mean-field theory to derive a differential equation describing dopamine (DA) signaling in a mesoscopic region of the striatum. By inducing three distinct periods of dopaminergic neuron firing, we solve this numerically. Following this, we introduce individual axonal arbors in the striatum, using the random generator applied in MATLAB. To characterize the organization and spatial coverage of arbors we analyze whether innervation was coherent within the striatum.
Inspired by the mathematical analysis of communication classes, we quantify the number of what we termed contiguous arbor classes (CACs) and estimate the unoccupied space by placing 10,000 random points in the striatum. Next, we repeat these measures for the three denervation mechanisms: random denervation (RD), prionlike denervation (PLD), and stress-induced denervation (SID; described in further detail below). We then introduce a differential equation describing cAMP following DA stimulation, which we solve numerically. With this, we record the maximal cAMP value for both D1-and D2-SPNs. We next implement a Hodgkin-Huxley-inspired neuronal model and simulated this in Python using the Numba package. Using this, we measure the number of elicited action potentials in a short temporal window, in which the D1and D2-SPNs were stimulated with the corresponding maximal cAMP level.
This was done for 100 neurons at several different levels of denervation. Finally, we use the average number of action potentials recorded in this window as input for 10,000 randomly positioned points in the striatum, dependent on the denervation model and levels. Using these numbers, we calculate a spatial average and SD of the maximal firing rates of D1-and D2-SPNs. In this work, we did not employ statistical significance testing to compare conditions since all results were derived from analytical simulations and thus the major source of uncertainty is inherent to the chosen parameter values rather than variance across simulation iterations. All code is made publicly available on a GitHub repository: https://github. com/Mathiasheltberg/ Theoretical_Denervation_ParkinsonsModel.
Method G3Point is a Matlab program which aims at measuring the size, shape, and orientation of a large number of individual grains as detected from any type of 3D point clouds describing the topography of surfaces covered by sediments. The main functions of G3Points are described in the following and summarized by Figure  1. A 3D point cloud represents a topographic surface defined by a set of points associated to a 3D coordinate system. Compared to 2D digital elevation models where elevation 𝑧 is https://doi. org/10. 5194/egusphere-2022-75 Preprint. Discussion started: 5 April 2022 c Author(s) 2022. CC BY 4. 0 License. defined as a function of 2 horizontal coordinates (𝑥, 𝑦), 3D point clouds can include several points located at the same horizontal position (e. g. , the face above and below a grain), allowing a better description of geomorphological features such as grains.
This represents a sufficient condition, but not a necessary one, and ellipsoids can be mathematically defined without respecting 4𝐽 -𝐼 2 > 0. Anyhow, we use an efficient and robust Matlab version  (Hunyadi, 2022)  of a direct least square fitting method  (Li and Griffiths, 2004) , based on the condition that 4𝐽 -𝐼 2 > 0, to describe the geometry of the segmented grains by minimizing the square of the distance between labeled points and the ellipsoidal model. For ellipsoids fitting grains which do not respect this condition, the fitting method might still lead to ellipsoids or to other quadric surfaces. Grains suffering from fitting issues or leading to quadric surfaces other than an ellipsoid are filtered out, leaving 630 correctly fitted ellipsoids over 657 labelled grains. The resulting ellipsoids, fitted to each labelled grain, appear qualitatively consistent with the shape, size and orientation of the labelled grains (Fig.
Acknowledgments. We thank  Thomas Croissant ,  Edwin Baynes ,  Benjamin Bruneau ,  Benjamin Guillaume ,  Lucas Pelascini ,  Romy David  and  Simon Abel  for their help acquiring data. This project has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No  803721 ). We also acknowledge support by  Université Rennes 1  and  CNRS.
Workforce planning is an approach used in many sectors to guide recruitment, identify skills gaps and capacity needs, calculate investment requirements and improve efficiency  21. Fundamental stages in workforce planning include analysing the current and required workforces, determining future workforce needs and identifying workforce gaps against those needs  22. Within the protected areas sector, however, no review of the global workforce has been published since 1999  23. Furthermore, there have been no global surveys of ranger numbers and few reviews of what constitutes adequate personnel numbers and densities, or of the relationships between personnel numbers and enabling factors associated with their performance, such as training, equipment, and institutional and societal support. We conceived this study to provide a basis for systematic workforce planning and resourcing that will enable effective management of both the current protected area system and the expanded 30-by-30 system, with the specific aims of (1) determining the current total global numbers and density of protected area personnel and of rangers within entities responsible for managing terrestrial protected areas listed in the United Nations Environment Programme World Conservation Monitoring Centre's World Database on Protected Areas (WDPA; https://www.
internationalrangers. org/). We conducted the survey between 2017 and 2021, collating data in varying formats from diverse sources, including questionnaires, published surveys and reports, websites and information provided on request by individuals linked to organizations responsible for protected areas. We did not include data from Antarctica or areas identified in the WDPA as 'predominantly or entirely marine' or as 'polar' because such remote, large and sparsely inhabited areas are likely to have quite different staffing needs and management models (see Methods for further details). We encountered considerable challenges in obtaining complete and/or accurate data for many countries for six main reasons: the information was considered sensitive, the data had not been collated or were dispersed among multiple agencies, there were difficulties in differentiating non-protected area and protected area personnel, there were differing definitions of rangers, there were overlaps in staff duties between WDPA-listed and other areas, and there were inconsistencies in the reported number and sizes of protected areas.