dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful for the contributions by  Chris Hartgerink ,  Veronika Cheplygina , and  Johanna Havemann  to the previous version of this article shared as a preprint (https://doi. org/10. 31222/osf. io/et8ak). Samantha MacFarlane helped to improve the style and clarity of this paper beyond recognition, and we are deeply grateful for her editorial skill. We wish to thank  Tony Ross-Hellauer  (aka Master of  Google Docs ) for coming up with the term MOOP and everyone who has been part of one of these collaborations in the past. The authors also wish to extend their thanks to  Barbara Rivera-Lopez  for useful discussion on this topic and to the anonymous reviewers at  KULA  for providing valuable input that helped to shape this paper for the better. This article also went through a round of peer review at  PLOS Computational Biology , and we are thankful to reviews from  Giovanni M.
In silico analysis In silico analysis was implemented with CADD (combined annotation dependent depletion) tool and with Alamut v. 2. 4 (Interactive Biosoftware) (https://www. interactivebiosoftware. com/alamut) to predict splicing effects with SpliceSite Finder-like, MaxEntScan, NNSplice, GeneSplicer, and Human Splicing Finder. We also used two other splice site prediction softwares, SpliceRover (http://bioit2. irc. ugent. be/splicerover/) and SPiCE (https://sourceforge. net/p/spicev2-1/wiki/SPICE%20wiki/).
Moreover, two other recent splice prediction softwares (SpliceRover and SPiCE) also suggested that this variant alters splicing. This variant was also predicted to be deleterious with a CADD score of 22. 3. Finally, this variant was not described in the gnomAD database (https://gnomad. broadinstitute. org/). Only 20 other synonymous variants were described in the database in control individuals (table  S4 ), but none was predicted to alter splicing using these prediction softwares.
Electronic Questionnaire After the roundtable discussion, an electronic questionnaire with closed-and open-ended questions was given to each participant (Multimedia Appendix 4). The questionnaire allowed the collection of in-depth individual data on the implementation and adaptation of a CDSS for antibiotic prescribing. The questionnaire was codeveloped by a multidisciplinary team including 6 members of the Antibioclic study team: infectious diseases clinicians (AP and FL), a microbiologist (AO), public health specialists (JB and GS) with experience in the West African context, and an implementation science and knowledge mobilization lecturer (RA). The first part collected demographic characteristics and information regarding the use of electronic tools for antibiotic prescribing. The second part displayed open-ended questions about potential outcomes and users of a CDSS for antibiotic prescribing in West Africa and the challenges of its implementation and adaptation.
Data Analysis Data from the closed-ended questions of the questionnaire were imported into R software version 3. 2. 4 (R Foundation for Statistical Computing). Numerical data are presented as absolute numbers, proportions, and median (IQR). The entire roundtable discussion was audio recorded and transcribed verbatim by the AMK France professional company. Data from the roundtable discussion and open-ended questions were analyzed using thematic analysis and were coded to identify key categories, which were developed into themes using NVivo 12 software (QSR International). The analysis of questionnaires and the roundtable discussion supported cross-validation and triangulation of the findings.
Acknowledgments The authors thank all the participants in the workshop and the team of the university course Antibiologie et Antibiothérapie en  Afrique Subsaharienne  organized by the  University Nazi Boni of Bobo-Dioulasso  and the  University of Montpellier  and supported by the  WHO, West African Health Organization , and SPILF. RA acknowledges the support of the  National Institute for Health Research  Fellowship in knowledge mobilization at the  National Institute of Health Research Health Protection Research Unit in Healthcare-Associated Infection and Antimicrobial Resistance at Imperial College  and of the  Economic and Social Research Council  as part of the  Antimicrobial Cross Council initiative  supported by the 7 UK research councils, and the authors also acknowledge the support of the  Global Challenges Research Fund.
In silico implementation The ABM was implemented for the M = 2 and M = 16 examples described in the main text using code written in the C++ programming language. Code, configuration files, and analysis scripts for these models can be found on https://github. com/Peyara/Evolution-Counterdiabatic-Driving. The code directly implements the model of the previous section, and is summarized in the flowchart of Fig. 4. The M = 2 selection coefficient s 1 (λ(t)) and other model parameters are as described in the main text. For M = 16, the simulations were run for 4. 5 × 10 4 time steps with a death rate d = 0. 05, a baseline birth rate b 0 = 2, and a carrying capacity K = 5 × 10 6. The mutation probability mji , j = i is zero unless the Hamming distance between the binary string representation of i and j is 1.
Acknowledgements MH would like to thank the  U. S. National Science Foundation  for support through the  CAREER grant  ( BIO/MCB 1651560 ). JGS would like to thank the  NIH Loan Repayment Program  for their generous support and the  Paul Calabresi Career Development Award for Clinical Oncology  ( NIH K12CA076917 ). SD acknowledges support from the  U. S. National Science Foundation  under Grant No. CHE-1648973.
Aims. We present a detailed visible and near-infrared spectro-interferometric analysis of the Be-shell star o Aquarii from quasicontemporaneous CHARA/VEGA and VLTI/AMBER observations. Methods. We analyzed spectro-interferometric data in the Hα (VEGA) and Brγ (AMBER) lines using models of increasing complexity: simple geometric models, kinematic models, and radiative transfer models computed with the 3-D non-LTE code HDUST. Results. We measured the stellar radius of o Aquarii in the visible with a precision of 8%: 4. 0 ± 0. 3 R. We constrained the circumstellar disk geometry and kinematics using a kinematic model and a MCMC fitting procedure. The emitting disk sizes in the Hα and Brγ lines were found to be similar, at ∼10-12 stellar diameters, which is uncommon since most results for Be stars indicate a larger extension in Hα than in Brγ.
g. ,  Chesneau et al. 2012; Stee & Meilland 2012). The bright, late-type Be star (type B7IVe) o Aquarii (HD 209409) is known to have a fairly stable disk  (Sigut et al. 2015). The stability of the circumstellar disk is evidenced by the quasiconstant equivalent width in the Hα line, double-peak separation, and the absence of long-term violet-to-red (V/R) peak variations (e. g. ,  Rivinius et al. 2006; Sigut et al. 2015). This star shows a high value of v sin i ∼ 282 km s -1  (Frémat et al. 2005 ) and a shell absorption in Hα, thus indicating a high stellar inclination angle of about 70°, as discussed below. Meilland et al. (2012)  presented the first spectrointerferometric analysis of o Aquarii with the VLTI/AMBER instrument as part of their AMBER survey of eight bright Be stars. Despite the low data quality and very limited number of observations (just one measurement), they were able to significantly constrain the disk geometry and kinematics.
2011). This multi-wavelength and multi-line approach allows us to draw a more complete picture of the stellar surface and circumstellar environment of the Be star o Aquarii. This paper is organized as follows. In Sect. 2, we present the observations and the data reduction process. Our analysis using geometric models of the VEGA calibrated (absolute) visibility is shown in Sect. 3. In Sect. 4, we fit the VEGA and AMBER differential visibility and phase with a kinematic model using a Markov Chain Monte Carlo (MCMC) model fitting method. In Sect. 5, all the interferometric data are analyzed in terms of 3-D non-LTE radiative transfer models. Our kinematic and radiative transfer models are discussed in Sect. 6. In Sect. 7, our bestfit models are compared to non-interferometric observables: the spectral energy distribution and line profiles (Hα and Brγ).
CHARA/VEGA The VEGA instrument  (Mourard et al. 2009 ) is one of the two visible beam combiners on the CHARA Array  (ten Brummelaar et al. 2005). It can simultaneously combine up to four beams, operating at different wavelengths from 450 to 850 nm. VEGA is equipped with two cameras (blue and red detectors) that can observe in two different spectral domains simultaneously (around the Hβ and the Hα lines). Currently, it is the only instrument at the CHARA Array with a spectral resolution high enough to resolve narrow spectral features such as atomic and molecular lines. It offers 3 spectral modes: R = 1000 (LR), R = 6000 (MR), and R = 30000 (HR). o Aquarii was observed 50 times with VEGA between 2012 and 2016 in MR mode centered on the Hα emission line at 0. 656 µm. The 2012 and 2016 observations were focused on the disk geometry and kinematics and data were taken with small baselines (up to 105 m) and without stellar calibrators.
On the other hand, the 2013 and 2014 campaigns were aimed at constraining not only the Hα emission, but also the R-band continuum geometry. Consequently, observations were carried out with longer baselines (up to 330 m) with a standard calibration plan alternating observations of the science target and few calibrator stars chosen using the SearchCal  (Bonneau et al. 2006)  tool developed by the Jean-Marie Mariotti Center (JMMC) 1. Table  A. 1 (Appendix A) shows useful information about the stars used as interferometric calibrators during these campaigns. The complete log of observations is presented in Table  A. 2 and the corresponding uv plane coverage for the VEGA observations is plotted in Fig 1. Data were reduced using the standard VEGA data reduction software 2 described in  Mourard et al.
VLTI/AMBER The AMBER instrument  (Petrov et al. 2007)   2012 ) and the 2014 data in  Cochetti et al. (2019). During this second survey, seven measurements were acquired for o Aquarii with three different triplets. The log of AMBER observations is also presented in Table  A. 2 and the corresponding uv plane coverage is plotted in Fig. 1. Calibration was performed using similar methods as the one described for VEGA. However, AMBER measurements were often affected by a highly variable transfer function mainly due to the variable quality of the fringe tracking performed by the FINITO fringe tracker, during the long exposure time needed to perform HR mode observations. As it was the case during our o Aquarii observations, we present in this paper only the analysis of differential measurements obtained using the standard AM-BER data reduction software amdlib  (Tatulli et al.
2004 ). Moreover, this value of 0. 22 mas represents the polar radius. o Aquarii is a fast rotator likely to be significantly flattened, and our measurements are spread over different orientations, so that we end up measuring a mean radius of the star projected on the sky. Assuming a distance of 144 pc (derived from the Gaia DR2 parallaxes, Gaia  Collaboration et al. 2018) , θ 1 = 0. 26 ± 0. 02 mas corresponds to a stellar radius R = 4. 0 ± 0. 3 R. Finally, to try to detect any possible stellar or circumstellar disk flattening from the squared visibility measurements, we also computed individual uniform disk equivalent diameter for each V 2 measurement. This analysis of the uniform disk diameter for o Aquarii, as a function of the VEGA baseline orientation, is shown in Fig. 3. As expected from our analysis (considering uniform elliptical models), we do not find any evidences of flattening from modeling our V 2 dataset since no clear trends are found in the model residual as varying the baseline position angle.
In Fig 5 , we show examples of VEGA and AMBER data in comparison to our best-fit kinematic models. For later discussion in Sect. 6, the visibility and phase from our best-fit HDUST model is also presented here. Our best-fit kinematic models are able to reproduce both the VEGA and AMBER differential data well. We found a reduced χ 2 of ∼4. 0 and 1. 6 from fitting, in a separate way, respectively, the VEGA and AMBER datasets. We derived compatible values for the disk PA (∼110°) from fitting the VEGA and AMBER data with an uncertainty up to ∼2°. This result agrees well with previous studies (e. g. ,  Meilland et al. 2012; Touhami et al. 2013; Sigut et al. 2015; Cochetti et al. 2019 ). On the other hand, the inclination angle determined from the fit to the VEGA data is significantly smaller (i = 61.
BeAtlas grid Since a few hours are needed to compute a single HDUST model, it is not possible to perform an iterative model fitting procedure similar to the one described in Sect. 4. To overcome this issue, we used a pre-computed grid of HDUST models called BeAtlas  (Faes 2015; Correia Mota 2019). The BeAtlas grid is presented and described in detail by these references. It consists of ∼14000 models with images (specific intensity maps), SEDs, and spectra calculated in natural and polarized spectra, over several spectral regions, including the Hα and Brγ lines that are of interest for the analysis of our VEGA and AMBER dataset. In Table  3 , we show the parameter space covered by BeAtlas. Five physical parameters are varying in the grid. The stellar mass M , the inclination angle i, and the stellar oblateness R eq /R p , fully describe the star.
In Table  4 , we show the statistics from these parameters calculated from the HDUST models within a certain threshold of χ 2 r , which, in each case, is chosen to match a similar number of models (∼15-20 best-models). In Table  4 , the parameters for the models with the lowest value of χ 2 r are also shown. In Table  5 , we show the parameters for the best BeAtlas model to explain simultaneously all our different interferometric datasets. Since our HDUST analysis is limited to the pre-computed BeAtlas grid (limited parameter space and selected parameter values), we stress that the results presented here do not correspond to the real χ 2 minimum to explain our datasets in the framework of HDUST. Furthermore, the values for the standard deviation are shown in parenthesis in Table 4 since these are not determinations for the error bars on the parameters.
The stellar mass is fixed to 4. 2 M and disk PA to 110°. Local regression fits to χ 2 r , as a function of the parameter values, are shown as a red line. The mean parameter values for the sets of best models (Table  4 ) are marked in dashed black line. Our best-fit BeAtlas model to fit all the interferometric data is shown in Table  5. See text for discussion. M (M ) R eq /R p i (deg) PA (deg) Σ 0 (g cm -2 ) m R p (R ) v rot (km s -1 ) v rot /v crit 4 shown, in this case, null standard deviation in Table  4  for these parameters (top 54% best-models). From the separate analysis of the VEGA and AMBER differential datasets, we are able to describe the stellar and disk parameters, in Hα and Brγ, by the same HDUST model with: R eq /R p = 1. 45, Σ 0 = 0. 12 g cm -2 , and m = 3. 0. One clear exception is found for the inclination angle.
From the Hα analysis, χ 2 r is minimized for i = 56. 3°. On the other hand, this is achieved with i = 77. 2°in the Brγ line. Such discrepancy of ∼ 20°is in agreement with the one found from our kinematic modeling. As expected, the joint analysis to all the data provides an intermediate mean value of ∼65°for the inclination angle, showing a larger dispersion (higher standard deviation) in comparison to the results found from the separate analysis for VEGA and AMBER. One sees that the mean value for stellar oblateness is somewhat decreased, when considering all the datasets. However, in this case, the dispersion is significantly increased (±0. 09) when compared to the separate VEGA and AMBER differential fits (±0. 05-0. 07). This happens due to the inclusion of the calibrated VEGA data in the joint analysis that do not allow us to properly infer this parameter (see, again, Fig.
From the kinematic modeling, we found χ 2 r ∼ 4. 0 and 1. 6 to explain these same datasets. For VEGA, in particular, our best-fit HDUST model adjustment for the measured visibility width is worse than with the kinematic model. This particular issue in modeling the VEGA data can be explained; in HDUST, the disk velocity law exponent is fixed by β = -0. 5 (Keplerian disk rotation), while in the kinematic model it is a free parameter. As shown in Sect. 4. 3, we find values for β that are higher than -0. 5, and this is accentuated from the analysis of the VEGA data (β ∼ -0. 3). Apart from this issue regarding the analysis in Hα, we are able to describe well the disk density with the same physical parameters in both the Hα and Brγ lines: Σ 0 = 0. 12 g cm -2 and m = 3. 0. As will be later discussed, this result found using HDUST is consistent with the ones presented in Sect.
The observed profiles in Fig. 12  were binned in wavelength in order to have a spectral resolution equal to one of the synthetic profiles from the kinematic and HDUST models: 1. 3 Å (Hα) and 1. 8 Å (Brγ). The mean EW in Hα from the BeSOS data is 19. 1 Å. This is in agreement with the mean value of 19. 9 Å found in  Sigut et al. (2015) , based on contemporaneous spectra, and adopted in our analysis with the kinematic code (Sect. 4. 3). First, we note that our best-fit kinematic and HDUST models provide a fairly reasonable match to the observed Hα and Brγ line profiles. The kinematic models correspond to our bestfits obtained from modeling the VEGA and AMBER differential data separately (Sect. 4). On the other hand, our best-fit HDUST model shown in Hα and Brγ is derived from the simultaneous fit to all our interferometric data (Table  5 ).
1 ). Moreover,  Stee & Bittar (2001) , using the code SIMECA, found that Be star disks can be larger (up to two times) in Brγ than in Hα. For a quantitative comparison of the disk extension in Hα and Brγ, we fitted simple Gaussian distributions to the intensity map of our best-fit HDUST model for all the values of inclination angle in BeAtlas. In order to remove the contribution from the star and disk continuum, we removed the image from the continuum before performing the fit and we hide the central part of the image which is affected by the stellar contribution. In Fig. 13 , we show the major-axis FWHM from our fit as a function of the inclination angle for the Hα and Brγ lines. First, one sees that the disk size-extension (major-axis FWHM) varies differently in the Hα and Brγ lines as a function of the inclination angle.
Inclination angle and vertical disk structure From our Hα and Brγ differential data analysis, using the kinematic model, we achieved good precision in the determination of the stellar inclination angle: i ∼ 61. 2 ± 1. 8°(VEGA) and i = 75. 9 ± 0. 4°(AMBER). Nevertheless, there is a clear discrepancy between the inclination angle found from fitting the VEGA and AMBER datasets. The value determined from VEGA is about 15°lower than the one found in the analysis of the AMBER data. We can show that this issue does not stem from an intrinsic limitation of the kinematic code (2-D model) for Be stars seen under high inclination angle (i 60°). Indeed, by using a sophisticated 3-D radiative transfer model (HDUST), not subjected to such a limitation, we verified the same discrepancy on i from the fit to these datasets separately (see, again, in Fig.
Such a high value of opening angle is far beyond what is measured and expected by the VDD model, typically less than ∼10°(cf. Rivinius et al. 2013). This might indicate the necessity of more complex physical assumptions in the physical properties of our disk model. Since the code HDUST provides a pure hydrogen modeling for the photosphere plus disk regions, this disagreement between the VEGA (Hα) and AMBER (Brγ) analyses in the determination of i could be due to an opacity effect. It is well-known that the inclusion of heavy elements can impact the density and temperature stratifications in the circumstellar disk of Be stars by shielding emission from the star. (see, e. g. ,  Sigut & Jones 2007). Furthermore, we stress that our best-fit HDUST model is a parametric model (based on a vertically isothermal structure).
However, the addition of an external constraint, the measured v sin i, removed this degeneracy, allowing us to derive a more accurate value of β in comparison to the other MCMC fitting tests (Appendix B). From our MCMC fit to the AMBER dataset, with a preset v sin i, we derived a β of -0. 426 ± 0. 003. Thus, the disk appears to be rotating in a nearly Keplerian fashion. Despite this very low error on β, note that the error bars on β change with respect to the presented MCMC tests, up to ± 0. 008 (see  Fig. B. 2). On the other hand, the value derived from the fit of the VEGA data is about 0. 1 higher than from AMBER. We stress that this dis-crepancy cannot be explained by a radial dependent rotational law because both lines roughly stem from the same region in the disk (similar disk extensions in these lines).
Spectroscopy The Be star o Aquarii is known to possess a stable Hα line profile for up to several years. For example,  Sigut et al. (2015)  verified that the EW in the Hα line is stable (within about 5%) up to about nine years (from 2005 to 2014). To go further in the analysis of the disk stability, we analyzed 70 Hα line profiles, spanning from 2001 to 2018, from the BeSS database 10 (Neiner et al. 2011). Since these observations are performed with several instruments, the line profiles shown here are interpolated to have spectral resolution of 0. 5 Å (lowest resolution in the dataset). From these observations, we calculated the equivalent width (EW) in the Hα line. In Fig. 15   analyzed Hα spectra together to the temporal evolution of the Hα EW. We found that the disk is fairly stable over this 17-year time span with a mean value of EW = 18.
VEGA measurements span four years and the AMBER ones span three years. The observation date and the baseline length (projected onto the sky) are indicated in the top of each panel. Our best-fit kinematic models derived from the fit, in a separate way, to each dataset (VEGA and AMBER) are shown in dashed red line. Note that our best-fit kinematic models match well to the differential visibilities obtained at different epochs. ent baseline lengths (projected onto the sky), and, thus, covering different levels of spatial resolution. One sees that, regardless of the period of time of the observations, our final kinematic models provide a very reasonable match to both the VEGA and AMBER data. Thus, considering our interferometric data, we are not able to detect any conspicuous variation of the circumstellar disk extension within a period up to five years (from 2011 to 2016).
Conclusions We analyzed VEGA V 2 , as well as VEGA and AMBER differential visibility and phase of the Be-shell star o Aquarii. To date, the spectro-interferometric dataset analyzed in this paper is the largest for a Be star, considering quasi-contemporaneous observations in both the Hα (VEGA) and Brγ (AMBER) lines. For the first time, we measured o Aquarii's stellar radius (R = 4. 0 ± 0. 3 R ) and determined the disk extension in the Hα and Brγ lines as, respectively, 10. 5 ± 0. 3 D and 11. 5 ± 0. 1 D. Using radiative transfer models computed with the code HDUST, we explained the quasi-identical extension of the emission in these lines by an opacity effect found for disks seen under a high inclination angle. We showed that the inclination angle derived from Hα is about 15°lower than the one determined in Brγ, when analysing each line separately with HDUST.