dataesr/scientific-paragraphs-categorization huggingface dataset:
Code and data availability. Codes related to this study are available on Github (https://github. com/RezaErshadi/ApRES_InverseApproach. git) under the GNU GPLv3 license. Radar data at EDML  (Christmann et al. , 2020)  can be found on Pangaea (https://www. gnu. org/licenses/gpl-3. 0. en. html). Radar data at Dome C will be provided on request to the authors.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) monoclonal antibodies (mAbs) are among the treatments recommended for high-risk ambulatory persons with coronavirus 2019 (COVID-19). Here, we study viral culture dynamics post-treatment in a subset of participants receiving the mAb bamlanivimab in the ACTIV-2 trial (ClinicalTrials. gov: NCT04518410). Viral load by qPCR and viral culture are performed from anterior nasal swabs collected on study days 0 (day of treatment), 1, 2, 3, and 7. Treatment with mAbs results in rapid clearance of culturable virus. One day after treatment, 0 of 28 (0%) participants receiving mAbs and 16 of 39 (41%) receiving placebo still have culturable virus (p < 0. 0001). Recrudescence of culturable virus is detected in three participants with emerging mAb resistance and viral RNA rebound.
A full understanding of the potential benefits and limitations of mAbs and other treatments would help determine their optimal use for preventing and treating SARS-CoV-2 infection. Bamlanivimab is a neutralizing mAb that received EUA as a treatment for individuals 12 years of age and older with mild to moderate COVID-19 in November 2020. 9 We performed viral culture analysis of participants enrolled in the ACTIV-2 randomized placebo-controlled trial of bamlanivimab monotherapy for non-hospitalized adults with mild to moderate COVID-19  10  (Clin-icalTrials. gov: NCT04518410). In that study, bamlanivimab treatment reduced respiratory tract (nasopharyngeal) viral RNA levels by 3 days post-treatment. In this work, we sought to understand how mAb treatment impacts the dynamics of shedding culturable SARS-CoV-2.
All bamlanivimab-treated participants remained culture negative from day 1 onward. 3] [14] [15]  The POSITIVES study is a longitudinal cohort of patients newly diagnosed with COVID-19; following enrollment, nasal swabs are collected thrice weekly for 2 weeks. Each sample is simultaneously analyzed by wholegenome sequencing, spike sequencing, viral load determination, and viral culture. As of April 20, 2022, 109 immunocompetent patients had been enrolled in the cohort; analysis of the dataset revealed that six individuals had received mAb therapy after a positive baseline culture (4 Delta variant treated with casirivimab/imdevimab, 1 Delta variant treated with bamlanivimab/etesevimab, and 1 Omicron variant infection treated with sotrovimab). Analyzing viral load (VL) and TCID 50 for those patients revealed that all six had durable viral culture conversion at the sample taken subsequent to mAb treatment (Figure  S3 ).
ACKNOWLEDGMENTS The authors would like to thank the participants, site staff, site investigators, and the entire ACTIV-2/A5401 study team. The authors would additionally like to thank  Amanda K. Marsh  for her help as well as the Ragon BSL3 core staff. Biorender. comwas used to generate the graphical abstract. This work was supported by the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health  under award numbers  3UM1AI068636-14S2 ,  UM1 AI068634 ,  UM1 AI068636 , and  UM1 AI106701. The viral culture work was performed in the  Ragon Institute BSL3 core , which is supported in part by the  NIH-funded Harvard University Center for AIDS Research  ( P30 AI060354 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Une étude de grande ampleur : premiers résultats Ce travail, mené par un ensemble d'équipes européennes dans lesquelles dominent les laboratoires des Pays-Bas et d'Israël, a porté sur deux cohortes, l'une hollandaise, l'autre estonienne. Après élimination des personnes apparentées et de celles dont l'ascendance n'est pas majoritairement européenne, leur effectif respectif est de 4 120 et 2 327 individus. Les exomes de toutes ces personnes ont été séquencés à haute redondance (couverture 75X) 1 puis analysés pour détecter tous les variants par rapport au génome humain de référence (la séquence humaine « consensus » réguliè- 1 Dans ce cas, la séquence d'ADN étudiée est lue 75 fois, afin de limiter les erreurs ou « trous » de lecture. rement actualisée 2  ). L'étude a ensuite été ciblée sur les 1 929 gènes répertoriés dans la base de données OMIM (On-line Mendelian Inheritance in Man) 3  comme impliqués dans des affections héréditaires de type autosomique récessif.
Les phénotypes associés à chacun de ces gènes (lorsqu'ils sont mutés) ont été examinés manuellement pour définir ceux qui sont associés à des phénotypes « sévères » 4  : cela concerne 1 119 gènes sur les 1 929 étudiés. Après divers contrôles de qualité, environ 91 000 variants sont trouvés dans les séquences des exons concernés de la cohorte hollandaise et 46 000 dans l'estonienne. Vient alors une étape essentielle, à savoir définir lesquels de ces variants sont pathogènes. On sait en effet que beaucoup de mutations affectant les exons n'ont pas d'effet majeur sur la protéine correspondante, soit qu'ils correspondent à des mutations silencieuses (un codon différent pour le même acide aminé), soit qu'ils concernent une région non critique de la protéine. Les auteurs ont utilisé une série d'approches pour sélectionner les seuls variants pathogènes : prise en compte de la nature des variants (mutations non-sens ou décalant le cadre de lecture), exploitation de bases de données spécialisées comme ClinVar 5  ou VKGL (Vereniging Klinisch Genetische Laboratoriumdiagnostiek)  6  , qui répertorient les mutations pathogènes connues, classification de l'ACMG (American College of Medical Genetics).
The contribution of the core pressure gradient ∂pB ∂r with the prefactors n B the boron density, its charge state Z B = 5 and the elementary charge e is also taken into account. The core CXRS data are shown in figure  4  as green dots. The poloidal velocity v pol,B has not been measured in this radial region, but an analysis of several L-mode scenarios in the past showed that it stays within -1. 5 to +2 km s -1  [37] , which is in quantitative agreement to findings at other tokamaks  [38] [39] [40] [41]. The error bars combine measurement uncertainties of the toroidal velocity and the standard deviation of the data's scatter during the NBI blip. The edge CXRS data are shown as green line with error bars at three representative locations. They include all three terms of equation (  2 ), whereas the larger uncertainties stem from the scatter of the poloidal velocity measurements.
Acknowledgments We express special thanks to  J Friesen  for his excellent hardware support and acknowledge  G D Conway  for useful discussions. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 and  2019-2020  under Grant Agreement No. 633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
The SNe Ia having a large lsSFR, hence a large fraction of young stars in their vicinity, are fainter than those from passive local environments by 0. 163 ± 0. 029 mag. Since high-mass galaxies favor older stellar populations, massive hosts favor SNe from old environments, and so are, on average, brighter after light-curve standardization; resulting in the mass-step. Surprisingly, while most SN samples now observe significant correlation between host properties and standardized SN magnitudes, the magnitude step amplitudes differ and seem incompatible. For instance, the SNLS-5 years and SDSS data from  Roman et al. (2018) , updating the JLA catalog from  Betoule et al. (2014) , find a local U-V step of 0. 091 ± 0. 013 mag, seemingly incompatible with  Rigault et al. (2020). Jones et al. (2018) , using the low-redshift bin of the Pantheon dataset  (Scolnic et al.
2018) , reported that locally massive environments, i. e. having a large surface density of stars, are fainter by 0. 067 ± 0. 017 mag. While in agreement with the local mass-step reported in  Rigault et al. (2020, 0. 059 ± 0. 024 mag), this effect is significantly weaker than that observed using the lsSFR indicator. Finally, using the Pantheon dataset  Pruzhinskaya et al. (2020)  found that SNe Ia from elliptical and lenticular galaxies are brighter (0. 058 ± 0. 019 mag) in agreement with  Kim et al. (2019, using JLA, 0. 018 ± 0. 052 mag), and  Henne et al. (2017, 0. 04 ± 0. 05 mag). As already pointed out by  Jones et al. (2018) , this variety of results, made using different host tracers, local or global, brings confusion about how to best account for astrophysical biases in SN cosmology, and notably on the derivation of H 0.
e. c ref a = c ref b = 0, Eq. 7 becomes: P(t i , t ref i | c a , c b ) ∝ p ref a × (1 -f ref i ) × (1 -c a ) × (1 -f i ) + c a × f i + (1 -p ref a ) × f ref i × c b × (1 -f i ) + (1 -c b ) × f i. (10) Finally, the estimation of the tracer's parameters c a and c b with respect to the reference tracer is made by minimizing: L = -2 i ln P(t i , t ref i | c a , c b ). ( 11 ) and the total contamination c = c a + c b for a comparison tracer can be computed from: c a = N b N a c b = (1 -p ref a )N (1 -f i ) c b c b = N a N b c a = p ref a N f i c a. ( 12 ) We have tested and validated our model and our code using simulations. We generated mock dataset of various contaminations and sizes, which have then been fitted with our implementation of the likelihood described in this section. The results confirm that our implementation of the algorithm is correct.
Data We work with the Nearby Supernova Factory (SNfactory,  Aldering et al. 2002 ) SNe Ia dataset published in Rigault et al. (2020, see also the  Aldering et al. 2020). This dataset has two benefits for this analysis: (1) it is at low-redshifts (0. 02 < z < 0. 08), so the local environment is measurable, and (2) it contains spectrophotometric IFU environmental data, necessary to accurately estimate the local specific Star Formation Rate (lsSFR). We use the publicly available catalog and images from SDSS and PS1 for the photometric measuments or their derived quantities. This section briefly summarizes the methodology to extract the different tracers considered in this analysis, which follows those developed in the literature, namely: the spectroscopicallyderived lsSFR (Section 3. 1), photometrically-derived lsSFR (Section 3.
SED fitting and k-correction From each ugriz photometric dataset, we use LePhare 4   (Arnouts et al. 1999; Ilbert et al. 2006; Arnouts & Ilbert 2011)  to fit for the associated Spectral Energy Distribution (SED) using  Bruzual & Charlot (2003)  templates (hereafter BC03) as did, e. g. ,  Jones et al. (2018). For the reproducibility, our configuration file is available online 5. It contains the following assumptions: dust: we use 41 bins of E(B -V) extinction values ranging from 0 to 1 (per 0. 01 step from 0 to 0. 2, 0. 03 from 0. 2 to 0. 5 and then a 0. 05 step up to 1) and we use the  Fitzpatrick (1999)  extinction curve (extracted from the hyper-z program with R V = 3. 1;  Bolzonella et al. 2000) ; redshift range: we use a redshift range from 0 to 0. 1 with a bin size of ∆z = 0. 002; emission lines: this contribution is included  (Kennicutt 1998) ; cosmology: we use H 0 = 70 km s -1 Mpc -1 , Ω m = 0.
Results The derived tracer contaminations with respect to the spectroscopic lsSFR and their associated magnitude steps γ, using the SNfactory dataset, are summarized in Table  1  and shown in Fig. 4. The step amplitudes as a function of the tracer contaminations follow the expected trend given by Eq. 3, shown by the straight black line in the figure, remarkably well. As detailed in Section 2, this diagonal -going from γ 0 at c = 0% (the reference tracer step value) to 0 at c = 100% (the black diamond in the figure) -is expected if: (1) two SN Ia populations exist with different mean magnitudes, and (2) we are using tracers that are not perfectly able to discriminate the two populations (c > 0) to measure their magnitude offset. We emphasize that c = 100% corresponds to randomly distributed SNe Ia between the two classes, the expected magnitude difference between the two resulting groups thus is 0 by definition (as seen in Fig.
7 % 8. 0 +5. 9 -4. 4 % 9. 4 +9. 5 -5. 7 % 43. 6 +4. 6 -5. 0 % 22. 6 +7. 0 -6. 4 % 15. 5 +8. 2 -6. 9 % Table  1. Table  comparing  the standardisation coefficients α, β and γ (aka. the stretch and color coefficients and the magnitude step value) and the tracer intrinsic contamination (c = c a + c b ) with respect to the spectroscopic lsSFR used as a reference tracer; γ* is the magnitude step value when fixing α and β to those obtained by the reference tracer standardisation (see Section 6. 1); t cut is tracer's cut-off. SNe Ia magnitude steps, γ, as a function of the tracer contamination (c = c a +c b ), using the spectroscopic lsSFR as reference (c spec-lsSFR ≡ 0). Large markers are the steps derived using the SNfactory dataset (this work) while small hexagons are literature results (see Section 5).
We then assume a c a and a c b , which define the four N j i with i = {a, b} and j = {a, b}. We randomly shuffle the sample to follow these N j i and we measure γ. This last step is repeated 5000 times to determine the scatter on γ caused by the randomness of which target belongs to the off-diagonal terms or not. If the two-populations model is correct, and if we measure the tracer γ parameters with a single dataset, then this scatter corresponds to the expected variations given tracer c a and c b parameters. The amplitude of this scatter as a function of the tracers contaminations is shown as a grey band along the model's diagonal in Fig. 5. Once we have determined the scatter σ(c) expected given the amount of contamination, c, we can measure the χ 2 associated to the ability of each reference tracer to explain the data, such that: χ 2 ref = t γ * t -γ(c t ) σ(c t ) 2 ( 14 ) where t refers to the comparison tracers, γ * t is the fitted step value fixing α and β to these of the reference (see Section 6.
10 mag intrinsic SNe Ia scatter were fully caused by the existence of two underlying populations, the average standardized magnitude difference between these populations would be 0. 2 mag. This is illustrated in the left panel of Fig. 7 : in this mock example, the observed full distribution of q seems like a flattened distribution with a larger scatter than the underlying individual Gaussian distributions. If we apply this concept to the SNfactory dataset that also has an intrinsic dispersion of ∼ 0. 10 mag  (Rigault et al. 2020) , we can then guess the two underlying population distributions that would cause this effect; this is illustrated in the right panel of Fig. 7. In the case of the SNfactory standardisation SNe Ia distribution, the central part (mag ∼ 0) does not seem to qualitatively follow the expected distribution.
Conclusion We use a sample of 110 SNe Ia from the Nearby Supernovae factory dataset to study the apparent inconsistencies in the literature between the different observed environmental dependencies of the standardized SNe Ia magnitudes. In the last ten years, the SNe Ia luminosity has been shown to significantly depend on host properties, ranging from barely significant variations when    4 , but changing the reference tracer choice by, from left to right, up to down, the spectroscopic lsSFR, the host galaxy morphology, the global host galaxy stellar mass, the local ur color, the photometric lsSFR and the the local stellar mass, respectively. In this figure, the α and β have been fixed to that of the reference tracer used in each subplot (see Section 6. 1). The grey band around the black line is the expected scatter along the diagonal (see Section 6.
Results Leadership patterns We carried out a set of dedicated experiments where a group of participants was asked to oscillate a manipulandum (or their index finger) while synchronizing their motion with each other (see Fig. 1  and "Methods" for further details). Data was analysed to extract the phases (or positions) of the players' motion and compute two metrics to assess their leader/follower roles. Namely, an influence index based on the computation of causation entropy, and a phase leadership index based on the phase analysis of the time series of the players' motion (see "Methods" for further details). As shown in Figs. 2  and 3 , we found that in over 60% of all trials, one out of three different leadership patterns clearly emerged. In Pattern 1-"the fastest leads"-the individual who was the most influential, as assessed by computing the causation entropy between each pair of group members, was also the one leading the group motion in phase (see Fig.
1f , see Tables S2-S3 in Supplementary Information). These coupling configurations are also those that were found in previous work  10, 12  to facilitate the onset of motor coordination in the group [ F(3, 76) = 31. 55 , p < 0. 001 , η 2 = 0. 55]. Leadership patterns persist in the absence of physical interaction We repeated the experiment in the digital world without physical interaction, by means of Chronos, a software platform allowing remote coordination between players  33  , see Fig. 1b  and "Methods" for more details on the experimental set-up. We found again that, even in the absence of direct visual/auditory coupling among the players, one of the three leadership patterns Again, the average order parameter in the trials where these patterns are observed was higher than that observed in the remaining trials [ M 1 = 0.
Discussion We analyzed data from controlled experimental set-ups where a group of individuals was asked to perform an oscillatory task synchronizing its motion with that of others in the group. We found that leadership emerges spontaneously in complex human networks as an organizing mechanism facilitating synchronization, even when coupling among the players is digitally mediated. Three different patterns were detected through which leadership emerged. One, where the most influential player is the one ahead in phase, another, where the lagging one is the most influential, and the third, where a shared leadership arrangement emerges between the players furthest ahead or lagging in phase. Our results show that, as noticed for groups in the animal world, the emergence of leadership roles is a natural phenomenon occurring in human groups even when it is not solicited from external instructions or assignments.
Participants. Experiment 1: pendula Twenty-eight right-handed students at the University of Montpellier took part in the experiment. Participants were asked to move their pendula in synchrony with the others, see Fig. 1a. Participants were distributed in four groups of seven individuals, each as follows: • group 1 (G1)-mean age = 25. 6 y ± 3. 1, 5 females, 2 males; • group 2 (G2)-mean age = 22. 4 y ± 2. 4, 6 females, 1 male; • group 3 (G3)-mean age = 20 y ± 2. 6, 3 females, 4 males; • group 4 (G4)-mean age = 21. 9 y ± 2. 27, 4 females, 3 males. These data were collected in the study of Bardy et al. 12  , and have been re-analysed here in terms of leadership emergence. Please, see the reference for further details. Experiment 2: Chronos Two groups of six participants, each recruited from an initial pool of forty volunteers, employees and students at the EuroMov center and at University of Montpellier, took part in the experiment (all right-handed).
Specifically, participants were distributed in 2 groups of 6 individuals each, and were asked to move their index finger on a leap motion controller so as to move a ball on the screen representing their own avatar, oscillating from left to right and vice versa, in a synchronized way with the others. Namely, they were instructed to Synchronize the movement of your finger from left to right with the movement of the others, as naturally as possible, as if you could do it for 30 minutes. A demonstration was performed to make sure the task was understood by each participant. The Chronos software allowed to manipulate the structure of the information shared on the screen of each player so as to implement the 4 different interaction topologies depicted in Fig. 1c-f. Moreover, participants were separated by barriers and wore headphones playing white noise.
In Experiment 1: pendula an acoustic signal notified the change between visual conditions whereas in Experiment 2: Chronos the balls manipulated by the neighboring players were made to appear/disappear on the players' screens as needed. For the purpose of this study, which focused on the emergence of leadership in groups of interacting agents, we only considered data from the Eyes-open condition. Data preprocessing Before performing data analysis, we preprocessed data. Experiment 1: pendula Potentiometers were used to compute the projection of the hand motions on the floor through a voltage-divider formula. The position time-series were smoothed out through a moving average filter with time window equal to 10 samples, that is, with a time-step t = 0. 05 s. Experiment 2: Chronos The Chronos architecture is a computer-based platform consisting of different hardware/software devices.
A central server unit receives position data from the client-players and broadcasts movement information to a subset of the others, according to the desired implemented topology, through a Wi-Fi network. The positions of each agent were captured by a Leap Motion device-a low-cost position sensor-and appear on the screens of each individual personal computer. The position time-series of each player is sampled at 10 Hz. Data were interpolated through a spline to obtain a 100 Hz sampling. Then, the position time-series were filtered through a Butterworth filter with a cutoff frequency equal to twice the typical human natural movement frequency ( ∼ 3 Hz). For both datasets, the Hilbert transform method  52  was employed to reconstruct the phase associated to each agent from its position time series.
Data analysis. For every individual, say i, we reconstructed from data the time-series of the phase of their motion. We then determined the phase leadership index, say H i (t) , of that individual in the group at every time instant. Namely, the value of H i (t) ranges from 1 to 7, with 1 (7) corresponding to player i indicates what group members are being followed (led) in phase by all the other player. We then computed the average phase index of individual i over the entire trial ( H i ) to quantify their tendency to lead in phase. To complement the analysis and uncover who is influencing whom in terms of the information flow between group members, we evaluated next, from the time series of the phases of their motion, the causation entropy between every pair of agents in the group  31. Then, we computed the net predicted information flow, NetCaus i , from agent i to the rest of the group.
Acknowledgements The authors wish to acknowledge support from the  Italian-French University VINCI  Program 2017 that supported the PhD Scholarship of C. C. P. D. was supported by the programme " STAR 2018 " funded by the  University of Naples Federico II  and  Compagnia di San Paolo ,  Istituto Banco di Napoli ,  Fondazione , project  ACROSS. M. d. B wishes to acknowledge support from the  Internationalization Program of the University of Naples Federico II, Italy supporting the International agreement between Naples and University of Montpellier , France. B. B. was supported by grant  H2020-FETPROACT-2018-01  "EnTimeMent". The authors wish to also thank  Alex DeWitt  ( Clarkson University ) for providing help with the code that was used to compute the causation entropy from the data,  Fabio Della Rossa  for the useful suggestions on the statistics and all the participants in the experiment.