dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION This article demonstrates that a multichannel earthquake information system such as LastQuake aiming to satisfying eyewitnesses' information needs can efficiently engage with them and collect, at least in some cases, essential information about the damage caused by an earthquake with 60-70 min of its occurrence and then reduce uncertainties of the damage scenario and improve rapid situational awareness. It also identifies some weaknesses in the LastQuake system that will need to be addressed. More fundamentally, it shows that earthquake predictions, which tend to flourish after a damaging earthquake, could be better debunked by having a clear and concise consensual statement available in various languages about what science can and cannot offer today in terms of time evolution of seismicity.
We report the discovery of TOI 837b and its validation as a transiting planet. We characterize the system using data from the NASA TESS mission, the ESA Gaia mission, ground-based photometry from El Sauce and ASTEP400, and spectroscopy from CHIRON, FEROS, and Veloce. We find that TOI 837 is a T = 9. 9 mag G0/F9 dwarf in the southern open cluster IC 2602. The star and planet are therefore 35 +11 -5 million years old. Combining the transit photometry with a prior on the stellar parameters derived from the cluster colormagnitude diagram, we find that the planet has an orbital period of 8. 3 d and is slightly smaller than Jupiter (R p = 0. 77 +0. 09 -0. 07 R Jup ). From radial velocity monitoring, we limit M p sin i to less than 1. 20 M Jup (3-σ). The transits either graze or nearly graze the stellar limb.
Gaia Astrometry and Imaging Between 25 July 2014 and 23 May 2016, the ESA Gaia satellite measured about 300 billion centroid positions of 1. 6 billion stars. The positions, proper motions, and parallaxes of the brightest 1. 3 billion were calculated for the second data release (DR2)  (Gaia Collaboration et al. 2016; Lindegren et al. 2018; Gaia Collaboration et al. 2018). TOI 837 was assigned the Gaia DR2 identifier 5251470948229949568, and had 276 "good" astrometric observations. Its brightness was   measured in the G, Rp, and Bp bands of the Radial Velocity Spectrometer  (Cropper et al. 2018; Evans et al. 2018 ). The Gaia imaging, reduced to its point-source catalog, provides the initial context for analyzing the TESS data. Stars brighter than T = 16, as queried from the Gaia DR2 source catalog, are shown with white circles in Figure  2 , overlaid on the TESS image.
Given its galactic latitude of b = -6 • , it is not surprising that the field of TOI 837 is crowded. The resolved stars that were of immediate concern for our false positive analysis were as follows. • TOI 837 ≡ TIC 460205581 (T = 9. 9). The target star. • Star A ≡ TIC 847769574 (T = 14. 6), 2. 3 West. The proper motions and parallax of this star imply it is comoving with TOI 837 and that the two stars are separated by 6. 6 ± 0. 1 pc. Star A is therefore likely an IC 2602 member, but unlikely to be a bound binary companion. • Star B ≡ TIC 460205587 (T = 13. 1), 5. 4 North. The Gaia parallax implies this is a background giant star. An additional source, TIC 847769581, is 4. 9 from the target, but too faint (T = 18. 8) to be the source of the observed transit signal. The Gaia DR2 data for Star A seems poorly behaved.
Ground-based Time-Series Photometric Follow-up We obtained ground-based seeing-limited time series photometric observations of TOI 837 bracketed around the times of transit. These observations confirmed that the transits occurred on-target to within ≈ 2 , and that they were achromatic. Both features are essential for our ability to eliminate false-positive scenarios. We scheduled our transit observations using the TESS Transit Finder, which is a customized version of the Tapir software package  (Jensen 2013). The photometric data were calibrated and extracted using AstroImageJ  (Collins et al. 2017). Comparison stars of similar brightness were used to produce the final light curves, each of which showed a roughly 4 ppt dip near the expected transit time. The data are reported in Table  1  and plotted in Figure  4.
FEROS data was processed with the ceres pipeline  (Brahm et al. 2017) , which delivers precision radial velocities and bisector span measurements through cross-correlation of the extracted spectra with a binary mask resembling the properties of a G2V star. The radial velocities are given in Table  2 , and shown in Figure  5. To check for the presence of secondary lines, we performed a similar injection-recovery exercise as with the CHIRON data. We achieved slightly worse limits, likely due to the lower spectral resolution of FEROS, and therefore adopted the CHIRON limits.
Veloce We acquired 34 spectra over 10 visits of TOI 837 using the Veloce spectrograph, mounted on the 3. 9 m Anglo-Australian Telescope at Siding Spring Observatory near Coonabarabran, Australia  (Gilbert et al. 2018). The currently operational "Veloce-Rosso" channel provides coverage from 600-950 nm at a spectral resolution of ≈ 80,000. Many of the exposures were taken in average or poor seeing conditions, when the SNR is lowest and the fiber-to-fiber crosscontamination on the IFU-style fiber feed is strongest. To reduce the spectra to velocities, we cross-correlated against a template of δ Pavonis, because with spectral type G8 IV it was the closest high-SNR TOI 837 analog available in the Veloce spectral database. The velocity RMS seen across each visit was hundreds of meters per second, likely due to uncorrected fiber-to-fiber cross-contamination.
RVs The radial velocities from FEROS, CHIRON, and Veloce can be used to detect massive bound companions orbiting TOI 837. We searched for planetary and stellar-mass companions in two different regimes: first, at the orbital period of the transiting object, and second, at longer orbital periods to constrain the presence of a massive bound companion. For the first fit we set a prior on the period and time of conjunction using the known ephemeris from the transit. We then fitted for the semi-amplitude, instrument offsets, and jitter parameters using radvel  (Fulton et al. 2018) , and assuming circular orbits. This yielded a non-detection of the planet's orbit. The corresponding 3-σ (99. 7 th percentile) upper limit on M p sin i is 1. 20M Jup. The data and corresponding model are shown in Figure  5.
We defined the reference neighborhood as the group of at most 10 4 randomly selected non-member stars within 5 standard deviations of the mean IC 2602 right ascension, declination, and parallax. In other words, the neighborhood members are chosen based on the observed spread in the cluster's parameters. We queried Gaia DR2 for these stars using astroquery  (Ginsburg et al. 2018). Many low-mass stars appear above the main sequence, even though they were not identified as 5-dimensional kinematic members through the unsupervised Cantat-Gaudin et al. (  2018 ) membership assignment process. Figure  7  also compares the data to the MIST isochrones  (Choi et al. 2016). We used the web interface 7  to interpolate isochrones at 10, 20, 30, and 40 million years. We assumed solar metallicity, and a fixed extinction value of A V = 0.
217  (Randich et al. 2018)  with the data for stars with masses ranging from roughly 0. 7-7 M. The pre-main-sequence (PMS) K and M dwarf models are bluer than observed in the Gaia photometry. This discrepancy was noted and discussed at length by  Choi et al. (2016). One suggested explanation was that strong magnetic fields in low-mass pre-main-sequence stars inhibit convection and produce a high filling factor of starspots (e. g. ,  Stauffer et al. 2003; Feiden & Chaboyer 2013). This explanation however fails to explain poor isochrone fits in both old open clusters (e. g. , M 67) and the field, particularly in blue bandpasses. An alternative explanation is that the molecular line lists for M dwarf atmospheres are incomplete in these wavelength ranges  (Rajpurohit et al. 2013; Mann et al. 2013).
In brief, we fitted for the shared stellar parameters {log g, R , u 0 , u 1 }, and the shared planetary parameters {t 0 , P, b, log(R p /R )}. There were also three free trend parameters for each transit window to account for the local rotational variability. In the TESS-only model this yielded 23 free parameters, of which 8 were physically relevant and 15 were nuisance parameters. In the combined TESS and ground-based model, there were an additional 7 transits, and therefore an additional 21 nuisance parameters for a total of 44 free parameters. We fitted the models using PyMC3  (Salvatier et al. 2016; Theano Development Team 2016). For the exoplanet transit, we used the exoplanet code  (Foreman-Mackey et al. 2020). After initializing each model with the parameters of the maximum a posteriori model, we assumed a Gaussian likelihood, and sampled using PyMC3's gradient-based No-U-Turn Sampler  (Hoffman & Gelman 2014).
ACKNOWLEDGMENTS The authors thank  K. Anderson  for fruitful discussions, and are also grateful to the anonymous referee for their constructive comments and suggestions. L. G. B. and J. H. acknowledge support by the  TESS GI Program , program G011103, through  NASA  grant  80NSSC19K0386. This study was based in part on observations at  Cerro Tololo Inter-American Observatory at NSF's NOIRLab (NOIRLab Prop. ID  2020A-0146 ; PI:  L. Bouma ), which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. This paper includes data collected by the TESS mission, which are publicly available from the  Mikulski Archive for Space Telescopes (MAST). Funding for the TESS mission is provided by  NASA's Science Mission directorate.
TOI 837 was included on the TESS 2-minute target list in part thanks to the  Guest Investigator program  of  G. Sacco  ( G011265 ). The  ASTEP  project acknowledges support from the  French and Italian Polar Agencies, IPEV  and  PNRA , and from  Université Côte d'Azur  under  Idex UCAJEDI  ( ANR-15-IDEX-01 ). We thank the dedicated staff at Concordia for their continuous presence and support throughout the Austral winter. This research received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant n  • 803193/BE-BOP ), and from the  Science and Technology Facilities Council (STFC ; grant n •  ST/S00193X/1 ). This research was based in part on observations obtained at the  Southern Astrophysical Research (SOAR) telescope , which is a joint project of the  Ministério da Ciência, Tecnologia e Inovações (MCTI/LNA) do Brasil , the  US National Science Foundation's NOIRLab , the  University of North Carolina at Chapel Hill (UNC) , and  Michigan State University (MSU).
Data A total of 35 German and Dutch populations were used in this project and studied either for cyst size or molecular genotyping using microsatellite and/or AS-PCR tools. The origin and details of the use of each population during the project is indicated in the table below. Virulence status: nearly full (Vir) or severely reduced (Avir) multiplication on potato harbouring the Grp1 locus. The present document has been produced and adopted by the bodies identified above as authors. In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the authors in the context of a grant agreement between the European Food Safety Authority and the authors. The present document is published complying with the transparency principle to which the Authority is subject.
e. FAM), Gp109 (labelled in yellow, i. e. NED), Gp111 (FAM), Gp118 (labelled in red, i. e. PET), Gp135 (labelled in green, i. e. VIC) and Gp145 (VIC). The panel 2 include Gp108 (FAM), Gp112 (NED), Gp116 (NED), Gp117 (PET), Gp122 (FAM), Gp126 (PET) and Gr67 (VIC). For each panel, PCR was performed using a 96-well reaction module in a 10 µL volume containing Type-it Microsatellite PCR kit (QIAGEN), the primer mix and the template DNA. PCR products were then diluted in sterile water and mixed with GeneScan 500 LIZ Size Standard and formamide. All the PCR products were run on an ABI Prism® 3130xl sequencer (Applied Biosystems) at INRAE facilities. Allele sizes were determined by the automatic calling and binning module of GeneMapper v4. 1 with manual examination of irregular results. To minimize the rate of genotyping errors, a second round of PCR and electrophoresis was performed for 10% of the global number of individuals.
First repetitive DNA sequences were identified. After checking these sequences did not show a resemblance to known genes or non-coding RNA, these repeats were masked. Next, non-coding RNA was identified, annotated and masked. Subsequently a structural gene annotation was performed using two different annotation programs in parallel: Maker 2. 31. 10 and Braker 2. 1. 4. As part of this process, all publicly available RNAseq data for G. pallida was mapped against the genome assembly. The annotation programs used a combination of comparison with known genes from closely related species, mapped RNAseq data and de novo gene prediction to come to a gene annotation. The resulting gene annotations were compared with each other and overlapping loci were identified. A combined structural gene annotation was created by selecting the annotation that best matched the mapped RNAseq data for each locus.
The present document is published complying with the transparency principle to which the Authority is subject. It cannot be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. To investigate whether there is a difference in size between other type of virulent and avirulent populations, an infection assay was performed on in vitro grown roots of a susceptible potato genotype Line V with or without the single dominant R gene Gpa2. Our data (Fig. 6 ) indicate that there is no difference in size when comparing adult females from the virulent (Rookmaker) and avirulent (D383) populations. On Gpa2 resistant roots, as expected D383 did not develop properly only resulting in a few underdeveloped females which explains their reduced size.
That kind of functionalization was described in literature  [27]  and involves carboxylation and subsequent derivatization with N'9-(2-aminoethyl) guanine. Preparation methodology of the molecular model of the functionalized nanotube was the same as already described in one of our recent publications  [28]. Shortly, we first prepared a template of the guanine containing residue with only one aromatic ring cut off the CNT structure. That template was subjected to quantum chemical calculations in order to determine the values of point charges. To that purpose, we took advantage of the RESP/ESP charge derive server  [29]  (https://upjv. q4md-forcefieldtools. org/REDServer-Development) and next we build the force field topology according to the generalized amber force field, gaff, using AcPyPE script  [30] [31] [32].
Standard Lorentz-Berthelot mixing rules were applied in the case of mixed interactions between atoms from fCNT and, for example, atoms belonging to doxorubicin molecule. Doxorubicin, DOX, was used in its protonated form (Figure  1C ) because in the considered conditions of pH (5-7) and its pK a = 8. 2 the standard unprotonated form is very unlikely  [35]  The force field components for DOX were determined according to the same scheme like in the case of Gu residue. Thus, first the partial charges were determined using RESP scheme and next the full force field topology was generated using AcPyPE script which calls various routines from AmberTools package. The two other components were nucleic acid sequences in a form of single stranded DNA fragments representative to telomeric region of chromosome.
Precisely, these are only the cytosine-rich strands which are able to make reversible transitions from random coil form (Figure  1D ) to the i-motif form (Figure  1E ) depending on pH or protonation state of cytosines. The structure in Figure  1D  is the representative structure at neutral pH when all cytosines are in their native unprotonated forms. We will call those ssDNA sequences as iMu because they use unprotonated cytosines. The structure from Figure  1E  is the representative one at acidic pH when the sequence folds into noncanonical i-motif structure which is kept by triplets of hydrogen bonds (Figure  1F ). We will call this state as iMp as it contains protonated cytosines. Both iMu and iMp will be sometimes called as iM when their symmetry or protonation state is not important. The iMu atomic structure was build using a script which generates ideal ssDNA strand (see Figure  2A  for illustration).
The force field associated with that structure was build using the tleap program from AmberTools package with the included calls to ff99 force field for nucleic acids including the bsc1 modifications  [31, 36]. The iMp structure was however produced using the pdb file (pdb id 1EL2) published by Phan et al. [1, 37]  with the sequence CCCTAA5mCCCTAAC + C + C + UAAC + C + C + T folded into i-motif. We replaced the 5mC and U residues by standard cytosine and thymine in order to obtain the true human telomeric i-motif molecules. Furthermore, the force field components were generated using the same scheme as in the case of iMu but here we additionally added the libraries for protonated nucleic acids in tleap script. As already mentioned, the components described above were picked up and put into the simulation box in various combinations using self designed scripts.
Industry 4. 0 and green dependencies The advancements in and adoption of digital technologies are the key factors for the economic and social development of a territory. It can radically improve public services and improve the quality of life of individuals, and can cope with multiple-environmental, social, and economic challenges. Indeed, the interest in innovative, creative, and digital territories is growing. A great deal of research is being carried out on these subjects in a wide variety of fields, e. g. Intelligent Transportation Systems (ITS), Smart Cities, and Smart Roads, among others. For the purpose of using real-time and inline data to achieve more efficiency and effectiveness in the logistic process, Industry 4. 0 becomes unavoidable in a transportation management system. Intelligent transportation systems have yielded new and innovative ways to improve the safety, operation, and environmental impact of transportation systems-for instance, Smart roadside servers for driver assistance and traffic safety warning  [37] , planning, and synchronization between different transport modes during the various logistic operations  [38] ; the use of big data analysis for demand forecasting and improving inventory planning, warehousing fulfillment, and distribution  [39] ; connected vehicles to improve operational performance on road links and intersections, leading to increased mobility and improved operations  [40] ; and an intelligent monitoring system of traffic accidents to collect and process sensor data  [41] , among other things.
These indicators are validated by experts and will serve as a basis for decisions because they are based on data and knowledge essential for decision-making. The last two steps in this first phase consist of constructing causality diagrams, which can be interpreted using these variables and relationships, and then having them validated by experts to check that the model meets the assumptions of the mental maps of those linked to the system. The data used in the definition of the indicators were collected through a literature review and interviews with experts (decision-makers, suppliers of management tools, etc. ). To control the indicators defined previously, formulas are developed and help to provide quantified elements to concretely evaluate the impacts of each decision. From these data, causality cycles were identified.
Dynamic hypothesis formulation Each parameter identified in the causality loops was formalized mathematically. In this way, an initial model for roadside management was formalized. The model consists of two types of indicators, exogenous and endogenous. The exogenous indicators or variables are variables external to the model and can be defined by the user of the model. They are also technical parameters that are validated by consulting experts or literature. The endogenous variables are quantifiable by the model and result from calculations integrating the exogenous variables. The objective of this modeling phase is to obtain a conceptual model based on dynamic hypotheses. In this research, the modeling of the roadside management system was carried out on the Stella© software. This makes it possible to simplify the modulation of complex systems, to look at their evolution, and to act on some parameters to test different scenarios.
It enables a similar approach to be used for objects that are a priori very different. Stella© allows you to build "stock and flow" diagrams. Stocks are like reservoirs, accumulators that are filled and emptied. Flows are taps, connections that let the elements of the stocks in or out. Connectors can be added to indicate what the flows depend on. With the help of a few simple equations, this model can be made quantitative; it then calculates the evolution of the system from the initial conditions, and through the disturbances that can be caused to it. Mathematically, Stella© allows us to program the resolution of differential equations  [64].
Design of the model dynamic Indeed, the construction of the model was based on data collected "manually" at first to be able to build the first models and thus determine the data to be collected afterwards. The model obtained is divided into 27 sectors, of which 11 are specific to the circular economy and are presented synthetically in Appendix B. A sector of the model is presented in Figure  7  as an example. Currently, in our research, data are collected manually from experts and specialized literature. Our vision is to digitize the data collection, processing, and division process by taking advantage of Industry 4. 0 concepts. The real-time capability, the interconnection between the Internet and objects, and the data mining process provided by the Industry 4. 0 paradigm can be exploited to interact with the information from Open Data.
We present the open-source image processing software package PySAP (Python Sparse data Analysis Package) developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image reconstruction problems in various research domains. In particular, PySAP offers fast wavelet transforms and a range of integrated optimisation algorithms. In this paper we present the features available in PySAP and provide practical demonstrations on astrophysical and magnetic resonance imaging data.
There is significant overlap in these fields, especially for astrophysical radio imaging that, like MRI, collects data in Fourier space. The primary output of this collaboration has been the development of the Python Sparse data Analysis Package (PySAP). PySAP is an open-source software package written in Python that provides highly optimised sparse image transforms and a library of modular optimisation tools for solving linear inverse problems. While PySAP has been designed with specific applications to the MRI and astrophysics domains in mind, the versatility of the software and the universality of the mathematical techniques mean that it can also be applied to a variety of other imaging domains such as microscopy, tomography and echography. Compared to existing inverse problem solving packages, such as SPAMS  (Mairal et al.