dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, Wang et al. , using the cyclic peptide cyclotraxin B, a noncompetitive highly potent and selective TrkB inhibitor (IC 50 TrkB = 0. 3 nM)  [10] , demonstrated the implication of BDNF/TrkB in the pain signaling pathway  [11]. The availability of several structures of TrkA in the protein data bank enabled the use of chemoinformatic approaches for the development of new Trk inhibitors. For example, a structure-based drug design approach led to the discovery of 3-pyrazolyl-indazole 1 (Figure  1 ) as a potent pan-Trk inhibitor with low brain penetration ability and in vivo activity in a thermal hypersensitivity inflammatory pain model induced by Complete Freund's Adjuvant (CFA)  [12]. A structure-based virtual screening with homology-modeled protein structure has been used to identify new TrkA inhibitors such as quinazolines 2 and 3 (Figure  1 )  [13].
The use of a structure-based drug design process enabled the development by Pfizer group of clinical candidate PF-06273340 (Figure  2 ), a selective pan-Trk inhibitor with a limited brain availability to reduce side effects due to Trk inhibition in the central nervous system. The resolution of the X-ray cocrystal structure of PF-06273340 with TrkA revealed a DFG-out binding mode (PDB ID: 5JFX)  [14]. This cocrystal structure was used in further chemoinformatic approaches to predict the binding affinity of a set of Trk inhibitors (General Formula A, Figure  2 ) using enhanced sampling of molecular dynamics with approximation of continuum solvent (ESMACS) and thermodynamic integration with enhanced sampling (TIES). The accuracy of calculated binding affinity of studied TrkA ligands suggested that this computational method could be successfully used in prospective ligand design  [15].
These structural features were the starting point of a work dedicated to the development of allosteric Trk inhibitors. The most potent compound was pyrazolecarboxamide 7 (Figure  3 ), targeting an allosteric pocket located behind the ATP-binding site. Compound 7, exhibiting a selectivity in favor of TrkA versus TrkB and TrkC, is orally bioavailable and demonstrated an interesting profile in preclinical pain models  [17]. A design and structure-activity relationship (SAR) process was based on the cocrystal structure of indole 8 with TrkA (PDB ID: 5KMJ). This structure underlined the role of the residues 440-497 from the juxtamembrane region in the selectivity between the three isoforms  [18]  and led to the synthesis of analog 9 with a better TrkA inhibitory potency and an improved selectivity vs.
FMS-Like Tyrosine Kinase 3 Receptor (FLT3) FLT3 tyrosine kinase is expressed in most hematopoietic cells. Recently, it was demonstrated that in dorsal root ganglia, FLT3 is a critical actor in peripheral neuropathic pain initiation and maintenance in mice. Using X-ray crystal structure of FLT3 bound to cytokine FMS-like tyrosine kinase 3 ligand (FL) (PDB ID: 3QS7), as well as in silico screening, hits were identified that effectively prevented extracellular FL binding to FLT3  [41]. This work led to the synthesis and further biological evaluation of BDT001, a noncompetitive FL-binding inhibitor that disrupted the positive cooperativity of FL binding (Figure  11 ). BDT001 inhibited FL-induced FLT3 phosphorylation in leukemia-derived RS4-11 cells with an IC 50 of 18-24 µM. It was suggested that BDT001 is an FLT3 negative allosteric modulator.
However, the triazole derivative with lower PKMζ inhibitory potency led to reduced expression of proinflammatory mediators in cells  [95]. The use of inhibitors such as SH-6  [96]  (Figure  22 ) demonstrated the involvement of PKB/Akt in mechanical hypersensitivity  [97]. More recently, Cheng et al. studied the role of phosphatidylinositol 3-kinase (PI3K)/PKB/mTOR signaling pathway in neuropathic pain caused by nucleoside reverse transcriptase inhibitors in HIV treatment  [98]. Activation of PKG Iα is involved in long-term hyperexcitability leading to chronic pain. PKG Iα C-domain and PKA Cα are highly homologous with 45% sequence identity. However, N46 is a selective PKG Iα nanomolar inhibitor exhibiting good selectivity over PKA (Figure  23 ). Comparing the crystal structure of N46 bound to PKG Iα C-domain (PDB ID: 6C0T) and PKA Cα (PDB ID: 6C0U), the selectivity of N46 in favor of PKG was explained by steric hindrance  [99].
Funding: This research was funded by brazilian agencies  FAPEMIG ,  FAPESP ,  FINEP ,  CNPq  and  CAPES  and  Agence Nationale de la Recherche (ANR)  ( ANR-10-IDEX-03-02 ,  ANR-17-CE08-0042-01 ), the  Idex Bordeaux  and the  New Aquitaine Region.
Acknowledgements We acknowledge  Alison Forrester  for revision of the manuscript,  Marine Ghazarian ,  Pascal Kessler ,  Gilles Mourier , and  Denis Servent  from  SIMoS, CEA, for Cys-TAT synthesis, Raphal Rodriguezs team from Institut Curie  for the use of various instruments, and  U830   INSERM  ( Institut Curie ) for the use of the Bioruptor Pico sonicator. We acknowledge the Cell and  Tissue Imaging  ( PICT-IBiSA ) and  Nikon Imaging Centre, Institut Curie , member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ) for microscopy use, and the  Antibody (Table -IP ) and Cytometry platforms from  Institut Curie  for their assistance. This work received grant support from the  Institut National du Cancer  ( n8 2019-1-PLBIO-05-1 ),  Agence National de la Recherche  ( ANR-16-CE23-0005-02 ,  ANR-19-CE13-0001-01 ),  Fondation pour la Recherche MØdicale  ( EQU202103012926 ),  Mizutani Foundation  for Glycosciences (reference n8 200014),  Institut Carnot  ( Synthetic STxB program ).
A. B. was funded by the  French Ministry of Higher Education and Research (AMX funding)  and  La Ligue Nationale Contre le Cancer , and received support from " Frontires de lInnovation en Recherche et Éducation" (FIRE) Doctoral School-Bettencourt Program. M. L. was funded by the  ITN ProteinConjugates European Union  program  H2020-MSCA-ITN-2015  and by  La Ligue Nationale Contre le Cancer. J. H. was funded by the  Fondation pour la Recherche MØdicale (FRM). The Johannes team is member of  Labex Cell(n)Scale  ( ANR-11-LABX-0038 ) and  Idex Paris Sciences et Lettres  ( ANR-10-IDEX-0001-02 ).
SDG4 is specifically aimed at education-"Good education for all-Ensuring an inclusive and equal education of good quality and promoting lifelong learning for all". However, extensive involvement of research and teaching is required for the fulfilment of each and all the SDGs, which is why the United Nations have underlined the key role of universities. In 2017, Peter Thomson, the former President of the United Nations General Assembly, used the contact information collected in the vast IAU World Higher Education Database (whed. net) to reach out to all higher education institutions (HEIs) in the world: "It goes without saying that young people are the most capable of the transformation required, having the most to gain or lose, from the success or failure, of Agenda 2030. I therefore make this sincere request to you to make these goals an integral part of research, teaching and study at your institutions".
The search for Dark Matter (DM) has great potential to reveal physics beyond the Standard Model. As such, searches for evidence of DM particles are being carried out using a wide range of techniques, such as direct searches for DM particles, searches for DM produced with colliders, and indirect searches for the Standard Model annihilation products of DM. Dwarf spheroidal galaxies (dSphs) are excellent targets for indirect Dark Matter searches due to their relatively high DM content and negligible expected astrophysical background. A collaboration was formed to maximise the sensitivity of DM searches towards dSphs by combining for the first time dSph data from three imaging air Cherenkov telescope (IACT) arrays: HESS, MAGIC, and VERITAS; the Fermi-LAT satellite, and the water Cherenkov detector HAWC.
Due to the diverse nature of the instruments involved, each experiment will analyse their individual datasets from multiple targets and then the results will be combined at the likelihood level. For consistency of the likelihoods across the five experiments, a common approach is used to treat the astrophysical factor (J-Factor) for each target and an agreed set of annihilation channels are considered. We also agree on a common statistical approach and treatment of instrumental systematic uncertainties. The results are presented in terms of constraints on the velocity-weighted cross section for DM self-annihilation as a function of the DM particle mass. 36th International Cosmic Ray Conference -ICRC2019-July 24th -August 1st, 2019 Madison, WI, U. S. A.
Three of the experiments included are imaging atmospheric Cherenkov telescope (IACT) arrays: HESS, MAGIC, and VERITAS. The others are HAWC, a water Cherenkov array, and Fermi-LAT, a satellite telescope. A collaboration was formed in 2018 to combine results from these five experiments for the first time, to optimise the sensitivity of DM searches by maximising the available statistics. MAGIC and Fermi-LAT published together a joint paper  [13]  combining their dSph likelihoods to improve sensitivity to DM, which inspired this project to combine data from a wider range of experiments. From each of the five experiments, data from observations of several sources are included. Each experiment performed the analysis of their own data using agreed DM models, to avoid the need for sharing low-level experimental data and instrument response functions (IRFs) outside of the collaborations, and to allow for the appropriate handling of the different energy and angular resolutions and sensitive energy ranges of the various detector types.
HESS The HESS observatory is an array of 5 IACTs situated at an altitude of 1800 m in the Khomas Highland of Namibia. The HESS phase I array consisted of four 12 m diameter telescopes. In 2012, phase II was implemented by the addition of a 28 m diameter telescope at the centre of the existing array. With the full phase II array HESS is can detect gamma rays with energies from 30 GeV to around 100 TeV. The data used in this analysis were collected with the phase I array between 2004 and 2012. Previous HESS combined results of DM searches in dSph data can be found in  [4]  and  [5].
Conclusions Preliminary results from the combination of data on 20 dSphs from Fermi-LAT, HAWC, HESS, MAGIC and VERITAS have been shown. Data from individual instruments were provided in the form of likelihood functions in a common format by the individual instruments, then combined. No significant gamma-ray excess was observed after combination, so upper limits derived from the combined data have been presented. The sensitivity of this combined analysis improves over the previously published results from the individual instruments, acting as a proof of principle for the full combination, which is in progress and will be published in the near future.
Acknowledgements-2019. html     • MAGIC full acknowledgements: https://magic. mpp. mpg. de/acknowledgments_ICRC2019TM acknowledges support from the  former Spanish Ministry of Economy, Industry, and Competitiveness / European Regional Development Fund  grant  FPA2015-73913-JIN. • VERITAS: This research is supported by grants from the  U. S. Department of Energy Office of Science , the  U. S. National Science Foundation  and the  Smithsonian Institution , and by  NSERC in Canada. This research used resources provided by the  Open Science Grid , which is supported by the  National Science Foundation  and the  U. S. Department of Energy's Office of Science , and resources of the  National Energy Research Scientific Computing Center (NERSC) , a  U. S. Department of Energy Office of Science User Facility  operated under Contract No.
They directed that the analysis of heat convection modes in a typical subsystem of the electronic equipment is oriented not only toward applied development in microelectronics but also it can be considered as a test database at the creation of numerical codes of convective heat transfer simulation in complicated energy systems. In another case, the heat source in an enclosure has been numerically examined by  Afolabi et al. (2019). They maintained the cavity with insulated horizontal walls and vertical cold constant temperature walls while the heat source at hot and constant temperature is placed on the center. The results show that there is a significant influence of Ra number on the heat transfer thus the temperature distribution in the cavity. The heat transfer by forced convection from two equal circular cylinders located at the mid-section of cavity with a square shape was examined.
Results. The census results in 29 LAE-LGRBs detected to date. Among them, we present five new Lyα emission detections in hostgalaxy spectra and the corresponding unpublished VLT/X-shooter data (GRBs: 060926, 070110, 081121, 081222 and 100424A). From the comparison of the statistics and properties of LAE-LGRBs to those of LAE samples in the literature, we find evidences of Lyα suppression in dusty systems, and a fraction of LAE-LGRBs among the overall LGRB hosts lower than that found for Lymanbreak galaxy (LBG) samples at similar redshift range. This result can be explained by the different selection criteria of the parent samples and by the shallower spectral observations of LGRB samples compared to LBG ones. However, we find that LAE-LGRBs are representative of Lyα emission from the bulk of UV-selected galaxies at z ∼ 2.
Data reduction To perform the census, we reduced archival data of several Xshooter spectra of GRB host galaxies. We describe here the method applied for the data reduction. All observations of the host galaxies, the telluric stars, and the spectrophotometric standards were reduced in the same way using the version 2. 8. 5 of the X-shooter data reduction pipeline  (Modigliani et al. 2010). Before processing the spectra through the pipeline, the cosmicray hits and bad-pixels were removed following the method of  van Dokkum (2001). Then, we subtracted the bias from all raw frames and divided them by the master flat field. We traced the echelle orders and calibrated the data in spatial and wavelength units using arc-line lamps. The flux calibration was done using spectrophotometric standards  (Vernet et al.
2009 ) and a correction for flexure was applied. Lastly, the sky-subtraction and the rectification and merging of the orders was done to obtain the final two-dimensional spectra. Additionally, the spectra were corrected for the Milky Way (MW) extinction using the extinction curve from Pei (1992). The A V values are obtained from the NASA Extragalactic Database (NED) and correspond to the extinction map of  Schlafly & Finkbeiner (2011). The wavelengths of the extracted 1D-spectra were converted in the vacuum reference and corrected for the Earth's rotation and revolution around the Sun (heliocentric correction). To optimally select the extraction regions we chose the spatial extension of the brightest emission line and applied this 1D extraction throughout the whole spectrum. When the Lyα line is detected, we selected the extraction region according to the spatial extension of this line which can be larger than Balmer lines due to resonant scattering.
Overall detections The results of the three samples presented above are detailed in the tables of Appendix A. By merging them and removing the overlapping cases, we find 14 LAEs out of 84 LGRB host and afterglow spectra. erature. To this purpose, we selected from two GRB databases 5  , maintained by D. Perley and J. Greiner, all  LGRBs detected until December 1, 2020, with a spectroscopic redshift z > 1. 6. This results in 234 LGRBs (see Fig. 4 ) with 193 having a spectrum covering the Lyα line and for which it is possible for us to verify whether the Lyα emission is detected. For those not included in the sample presented in the previous sections, we searched the literature for articles or Gamma-ray Coordinates Network (GCN 6  ) Circulars claiming a detection of Lyα line. If unpublished spectra were available in the ESO archive, we retrieved and reduced them.
Overall we found thirteen additional LAE-LGRBs from the literature, and two LAE-LGRBs from unpublished data: GRB 081121 at z = 2. 5134 and 081222 z = 2. 770. Both objects have X-shooter spectra (Prog. ID: 097. D-0672; PI: S. D. Vergani). We reduced the data following the procedure described in Sect. 2. 2. Their Lyα emission is shown in Fig. 3. The measured Lyα fluxes of GRB 081121 and 081222 corrected for Galactic extinction and slit loss are F Lyα = (4. 9±0. 5)×10 -1  erg s -1 cm -2 and F Lyα = (1. 6 ± 0. 4) × 10 -17 erg s -1 cm -2 , respectively. Additionally to the Lyα line, GRB 081121 shows the presence of the [OII] doublet, [OIII]λ5007 and Hβ lines. Residual sky lines contaminate strongly the [OIII]λ4959 line and partially Hβ. We determine F [OII] = (1. 4 ± 0. 4) × 10 -17 erg s -1 cm -2 , F [OIII]λ5007 = (9.
Acknowledgements. We thanks  A. Verhamme  for very useful discussions. This work is part of the  BEaPro  project (P. I. :  S. D. Vergani ) funded by the  French Agence Nationale de la Recherche  ( ANR-16-CE31-0003 ) and supporting JBV PhD thesis. JBV is now supported by  LabEx UnivEarthS  ( ANR-10-LABX-0023  and  ANR-18-IDEX-0001 ). MG is supported by  NASA  through the  NASA Hubble Fellowship  grant  HST-HF2-51409  and acknowledges support from  HST  grants  HST-GO-15643. 017-A,HST-AR-15039. 003-A , and  XSEDE  grant  TG-AST180036. The Cosmic Dawn Center is funded by the  Danish National Research Foundation  under grant no. 140. BMJ is supported in part by  Independent Research Fund Denmark  grant  DFF -7014-00017. Based on observations collected at the  European Southern Observatory  under ESO programmes:  079.
The empty red diamond and empty blue star correspond to 2 at. % t = 6. 9 nm epitaxial films grown by using same conditions on STO(001) and Si(001), respectively. 23 The remanent polarization of a t = 12 nm epitaxial film on STO(001) reported by Li et al. 22  is indicated by a solid orange square. Data of series of polycrystalline films with varied La concentration are shown with open gray circles (t = 10 nm),  20  open up blue triangles (t = 10 nm),  18  open pink right triangles (t = 45 nm),  17  and open gold left triangles (t = 10 nm). 19 Other reported data of polycrystalline films with fixed La concentration are indicated by open pink pentagon (t = 45 nm)  44  and open violet diamond (t = 50 nm). 45   45 and open down green triangles. 20 Values from ref 20 were taken from Figure  3b , and only the current peaks of greater amplitude in the films showing double peaks were considered.
Thus, µ meso is a function of α (where α is the mesoscopic state). The vector µ meso provides a tool to define macroscopic state variables. Indeed one can demonstrate with a large database of computations at the mesoscopic scale (using the model presented in section 3) that the total energy only depends on µ meso : E meso (T, α) ≈ L 0 γ S G(T ) G(0) f (µ meso ) (7) Where f (µ meso ) is a deterministic function identified by using the database of computations at the mesoscopic scale, γ S = 1 J. m -2 , and G(T ) is the temperature dependent shear modulus that captures the temperature dependance of the GB energy. From the definition (6), µ 0 /L 0 is the GB length density (length per unit surface). In addition, the mean of misorientations in the RVE denoted by µ 1 , and the square of the relative standard deviation, which is the ratio of the standard deviation over the mean, denoted by µ 2 read: µ 1 = µ 1 µ 0 and µ 2 = µ 2 µ 0 µ 2 1 -1 (8) The evolution law of µ meso is obtained from (  5 ) and a simple derivation formula: μmeso = - m(T ) L 3 0 ∂µ meso ∂w • M meso • ∂E meso ∂w (9) One can demonstrate that there exists a second order mobility tensor Γ meso (α) of size 3 × 3 that depends on the entire mesoscopic state α such as: μmeso = - ( γ S m(T ) L 2 0 G(T ) G(0) ) Γ meso (α) • ∂ f (µ meso ) ∂µ (10) The macroscopic model is obtained by using the large database of computations at the mesoscopic scale to determine a macroscopic second order tensor Γ macro (µ macro ) such as Γ macro (µ macro ) ≈ Γ meso (α), which does not depend on the mesoscopic state α but only on the reduced information µ macro without any knowledge of the detailed mesoscopic state.
The index macro has been used for µ macro instead of meso to highlight the fact that the detailed mesoscopic state is unknown. The macroscopic evolution law reads: μmacro = - ( γ S m(T ) L 2 0 G(T ) G(0) ) Γ macro (µ macro ) • ∂ f (µ macro ) ∂µ (11) The mobility tensor Γ macro (µ macro ) is determined on the basis of the database of computations at the mesoscopic scale providing numerous estimations of Γ meso associated to µ meso. The identification is probabilistic as there is an epistemic uncertainty due to the loss of information in the process of reducing the amount of data by introducing the overall state µ meso. Bayesian inference techniques have been used to estimate the posterior probability density function of Γ macro (µ macro ). For numerical applications, draws of the Bayesian model are used to generate N stat = 500 macroscopic evolutions whose means, medians and point-wise standard deviations are computed.
In the same way, one who aims to simulate laser shock experiments should calculate the corrected intensity value when either calculating or simulating the pressure loading on the target. Furthermore, with a better availability of optical elements, the spatial distribution of intensity may be made smoother by using diffractive optical elements (DOEs), as shown in Figure  8. This improvement in the beam shaping helps to make faster simulations (the laser spot size is homogeneous and with an axis of symmetry) with more accuracy. Finally, regarding the improvements made in the field of materials behavior in the past 20 years ( mainly material parameters and model parameters), simulations based on the finite element method (FEM) code such as Abaqus are more accurate. Indeed, the material properties and their model parameters (for a Johnson-Cook model for example [105]) have been refined, and as described above, the precision on the optical loading has also been improved.
Laser energy deposition is computed using an inverse Bremsstrahlung model, with the restriction that no energy in the beam passes beyond the critical surface. LSPSIM is a one-dimensional model intended for the estimation of the pressure wave applied to the target material in LSP experiments, similar to those developed by Griffin  [116]  and Fabbro  [28]. It provides a direct input interface for the detailed plasma results obtained by HELIOS for the very beginning of the laser interaction, and for the later times, it provides a time-dependent estimate of the pressure buildup and mechanical target compression when the generated plasma has been exhausted and the shocking dynamics are dominated by gas expansion. On the basis of the time-dependent pressure profile calculated by HELIOS or LSPSIM, the module HARDSHOCK solves the shock propagation problem into the solid material, with specific consideration of the material response to thermal and mechanical alterations induced by the propagating wave itself.
ACKNOWLEDGEMENTS LN2 is a joint  International Research Laboratory  ( Unité Mixte de Recherche IRL 3463 ) funded and co-operated by  Université de Sherbrooke (Canada)  and  CNRS (France)  as well as  INSA Lyon , ECL,  CPE ,  Université Grenoble Alpes (UGA)  and the  French national nanofabrication network RENATECH. The authors acknowledge FRQNT and  CNRS  for financial support. We acknowledge funding support from the  Natural Sciences and Engineering Research Council of Canada ,  NSERC CREATE 497981. Finally , the authors warmly acknowledge  C. E. Valdivia  for reviewing this article and  K. Hinzer  for hosting us at SUNLAB, ( University of Ottawa, Canada ) to perform electro-optical measurements.