dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work has been done with the scientific and technical support of the  SNES (National Seed Testing Station; Beaucouzé, France) , and we are particularly grateful to  Joël Léchappé  who made the partnership with GEVES (the  French Variety and Seed Study  and Control Group; Beaucouzé, France) possible. We are thankful to  Lénaïc Le Moal  and  Jean-Louis Queyreix  for their support to set up ScreenSeed robots in the GEVES facilities. This research was conducted in the framework of the regional program "Objectif Végétal, Research,  Education and Innovation in Pays de la Loire ", supported by the  French Région Pays de la Loire ,  Angers Loire Métropole  and the  European Régional Development Fund. Thanks are also directed to the GreenLab team directed by  Christophe Bailly  ( Sorbonne University, France ) for providing laboratory facilities.
Insecticide resistance, control failure and other concerns Pest population genetic resistance is a common consequence of unbalanced insecticide use, which largely drives the pest control dynamics observed for T. absoluta worldwide  (Guedes and Siqueira 2012; Biondi et al. 2018). This outcome was first recognized in South America, and then elsewhere  (Biondi et al. 2018; Guedes et al. 2019) , reaching nearly 60 instances of resistance to 24 insecticides worldwide as recorded in the Arthropod Pesticide Resistance Database (https://www. pesticideresistance. org) and a recent compilation  (Guedes et al. 2019). Initial detection of insecticide resistance in T. absoluta populations from South America involved organophosphate and pyrethroid insecticides  (Salazar and Araya 1997; Siqueira et al. 2000b; Salazar and Araya 2001; Lietti et al.
Larval parasitoids Among known larval parasitoids of T. absoluta, only Dolichogenidea (= Apanteles) gelechiidivoris (Marsh), Pseudapanteles dignus (Muesebeck) (Hymenoptera: Braconidae), Dineulophus phthorimaeae (de Santis) and Necremnus tutae (Ribes and Bernardo) (Hymenoptera: Eulophidae) have been considered as part of IPM programs in South America and Europe  (Salas Gervassio et al. 2019a , 2019b). Currently, the utility of these parasitoids is restricted to conservation biological control programs. One species, D. gelechiidivoris sourced from Peru, is being studied to develop a classical biological control program for T. absoluta in sub-Saharan Africa (Aigbedion-Atalor et al. 2020). In Chile, the importance of naturally occurring parasitism levels was recognized by  Larrain (1987)  who recommended avoiding early insecticide sprays to conserve existing field populations of D.
phthorimaeae and D. gelechiidivoris. In the 1980s, in Colombia,  Agudelo and Kaimowitz (1997)  reported levels of larval parasitism up to 70% and the implementation of an IPM technology based on the conservation of Apanteles spp. , releases of the Trichogramma spp. and treatments with Bt. The level of adoption and pest control success of this program is uncertain; however, its use decreased with time and ended up being minimal. More recently, Morales et al. (  2014 ) evaluated the effectiveness of the parasitoid D. gelechiidivoris alone or together with sex pheromone traps and obtained better results with the combination of both strategies. Despite all these efforts, Herrera  Rocha et al. (2018)  reported that chemical control is widely used in Colombia and indicates that biological control will only be possible if combined with selective pesticides.
absoluta after pre-planting application of the predatory bug N. tenuis. However, the role of parasitoids in conservation biological control is recognized in Tunisia  (Abbes et al. , 2014)  and in Spain  (Arno et al. 2018b). In fact, an IPM program based on the conservation of N. tutae together with releases of N. tenuis and T. achaeae and the use of mating disruption is currently being recommended in Southeast Spain  (Crisol and van der Blom 2018).
We demonstrated previously the effectiveness of REST2 at reproducing thermal stability trends for small and midsize proteins in dilute-  [28, 33]  and crowded conditions  [26, [34] [35] [36]. In this paper, all-atom REST2 simulations  [27, 28]  were performed using the GRO-MACS 2019. 4 software  [37]  patched with the Plumed 2. 5. 3 package  [38]. Two distinct sets of force field parameters were used to describe the protein: Amber a99SB-disp  [29]  and CHARMM36m  [30]. The a99SB-disp protein force field was coupled with the a99SB-disp water model, whereas CHARMM36m was combined with the TIP3P water model  [39]. In both cases, K + and Cl -ions were described with the default parameters for the respective force field. A 1. 2 nm cutoff was applied to short-range non-bonded interactions. In addition, van der Waals forces were smoothly switched to zero between 1.
0 and 1. 2 nm in the CHARMM36m simulations. Long-range electrostatic interactions were evaluated using the particle mesh Ewald method  [40]. To improve the computational efficiency of the simulation by removing the fastest degrees of freedom, the LINCS algorithm  [41]  was used to constrain the lengths of all protein bonds involving hydrogen, and the SETTLE algorithm  [42]  was employed to keep water molecules rigid. The temperature of the system was maintained at 300 K by the velocity rescaling thermostat with a stochastic term  [43] , which was coupled separately to the proteins and to the rest of the system with a time constant of 1 ps. The initial SOD1 bar structures were prepared using the following procedure. The crystal structure (PDB ID 4BCZ  [13]  for SOD1 WT  bar and PDB ID 4XCR  [9]  for SOD1 I35A bar ) was processed with the GROMACS pdb2gmx tool  [37] , using the default choices for the protonation states of the amino acid residues.
Alchemical Calculations To estimate the change in the free energy of unfolding due to the I35A mutation, we performed alchemical calculations utilizing the free-energy code of GROMACS with the same force fields and simulation parameters as above (except the fact that all bonds were constrained) and with the soft-core α and σ parameters set to 0. 3 and 0. 25, respectively. From the respective REST2 simulation of SOD1 WT bar , we extracted 10 folded and 10 unfolded geometries, which were sampled in 50 ns time intervals at T 0 and T 20 , respectively (see Table  S2 ). For CHARMM36m, only protein geometries were extracted and were subsequently rehydrated using the same equilibration protocol, as described above. Hybrid I35A geometries were then prepared using the mutate. py script from the pmx package (version 2.
0)  [47, 48]. For each hybrid geometry, a 100 ps transition from the non-mutated (A) to the mutated (B) state was simulated, and a 10 ns equilibration simulation was performed for each hybrid geometry in state A and a 20 ns equilibration simulation in state B. From the last 10 ns of each equilibration trajectory, we extracted 50 snapshots in uniform intervals. For each of these snapshots, we performed a 100 ps transition to the opposite state (B and A, respectively). The derivatives, recorded during the transitions, of the Hamiltonian with respect to the coupling parameter λ were used by the analyze_dhdl. py script from the pmx package to calculate the free energy of mutation using the Crooks Gaussian Intersection method for each of the 10 folded and 10 unfolded geometries.
A. SETUP Each laboratory was equipped with either ''GTE Kolrobot 300N'' or ''GTE CBSF-75 Basic'' force measuring sensors, and with ''TekScan I-Scan'' or ''FUJI foils'' for pressure measurements, each instrumentation included an analysis software. All the labs used the same robot ''family'' (UR10(e) from Universal Robots). The experiments were performed considering body location 8 (sternum), body location 10 (abdomen) and body location 25 (back of the hand), labelled according to the schematization illustrated in Figure  1 , which were considered as the most probable locations possibly subject to an impact during a collaborative task. The used sensors approximate these body regions as a mass-spring-damper system  [31] , as illustrated in Figure  2. Spring stiffness was set to respectively to 10N/mm, 25N/mm and 75N/mm for reproducing the abdominal region, sternum and hand  [7].
B. METHODOLOGY For contact evaluation, apart from the force and pressure values over time, the robot states were also saved in order to monitor the robot controller variables during the collisions. In particular, referring to the Universal Robots' data exchange interface, robot states are expressed as: joint positions, velocities and currents; forces in the Tool Center Point (TCP); Cartesian coordinates (position and orientation vectors (x, y, z, r x , r y , r z )) of the tool; and speed of the tool (also in Cartesian coordinates). The impact force and pressure were recorded in four joint configurations, corresponding to four different positions of the TCP inside the operational volume where an impact between human and robot is more likely to occur. These points of measure are represented in Figure  5  as M 1 , M 2 , M 3 and M 4.
ACKNOWLEDGMENT Author contributions:  A. Scibilia ,  S. Herbster ,  A. Magisson ,  C. Bidard  and  M. Kühnrich  designed research and conducted experimental activities;  M. Valori ,  R. Behrens ,  I. Fassi ,  N. Pedrocchi ,  J. Saenz ,  A. B. Lassen  and  K. Nielsen  conducted review and editing. The reproduction of excerpts from ISO/TS 15066:2016 has been authorized by  UNI Ente Nazionale di Normazione also on behalf of ISO International Organization for Standardization. The only authentic version is that available in full version at  UNI  and ISO website www. uni. com and www. iso. org.
The effective reproduction number R eff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate R eff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland.
The time-variation of R eff is computed indirectly by simply fitting the model to different time periods (before or after the lockdown in the simplest cases) or by using exponential decay models  [21] , but see Lemaitre et al  [20]. To overcome all these numerous weaknesses in the data available for estimating R eff , we propose using a framework that has been already implemented to tackle non-stationarity in epidemiology  [25] [26] [27]. It uses diffusion models driven by Brownian motion to model timevarying key epidemiological parameters embedded in a stochastic state-space framework coupled with Bayesian inference methods. The advantages of this approach compared to the existing ones consist in (i) the description of the mechanisms underlying pathogen transmission and hence its particularities (asymptomatic phase), (ii) the joint use of multiple datasets (incidence and hospital data), (iii) the explicit taking into account of the uncertainty associated with the data used and especially (iv) the monitoring of the temporal evolution of some of the model parameters without inclusion of external variables.
The effect of vaccination (already implemented in studied regions) is introduced in our model simply by considering its effect on the depletion of susceptibles. The number of "effectively protected vaccinated people", proportional to the number of people vaccinated with one and/or two doses, are removed from the susceptible compartment (see S3 equation in S1 Text). For parameter estimation we used PMCMC  [28]  suitable for partially observed stochastic non-linear systems (see S1 Text). The implementation is done with the SSM software  [26]. Our estimations of R eff were compared, based on data from Ireland, with those obtained with two other methods. The first method, proposed by Cori et al  [14]  and frequently used in recent studies analyzing COVID-19 data, relies on the number of new infections generated during a given time period and the serial interval distribution and is implemented in the EpiEstim package  [29].
Data used During the first wave of the COVID-19 epidemic the number of cases reported was very low and associated with large uncertainties  [10, 12]. This was due, on the one hand, to the testing capacity (RT-PCR laboratory capacity) that was limited and varied greatly during this epidemic  [12]  and, on the other hand, to the features of this new virus, such as transmission before symptom onset and substantial asymptomatic transmission, which resulted in a low fraction of infected people attending health facilities for testing. This suggests that hospitalization data were likely to be the most accurate COVID-19 related data  [9, 17]. Thus, we focused on hospital multiple datasets in France and in Ireland. We used incidence data to avoid all the shortcomings associated with the use of cumulative data (see  [31] ), i.
R eff reduction between the We also found other interesting results such as a significant high correlation between the trend of mobility and our estimation of the transmission between the epidemic waves (see S22 Fig and  [41] ), highlighting the importance of following the evolution of mobility when relaxing mitigation measures to anticipate the future evolution of the spread of the SARS-CoV-2. We have also compared the trend of our estimations of R eff with those of two other methods from the literature  [14, 30]  on data from Ireland (Fig  5 ). One of the differences between our estimations and the two others is a greater variability of our R eff estimations due to an underlying complex mechanistic model used and to uncertainties in the transmission rate, asymptomatic transmission and in the different delays needed for describing hospital multiple datasets.
This reinforces the relevance of our modeling and inference framework to present a more coherent picture of the evolution of this epidemic. The main characteristic of our approach is the dependence of the R eff (t) estimations on the relevance of the underlying mechanistic model and the accuracy and completeness of the available data. In our case, inference was based on hospital data that are clearly of a higher quality and accuracy compared to the observed number of infected cases. Moreover, despite its relative simplicity, the model incorporating time varying transmission rate is able to accurately describe the hospital multiple datasets that included daily hospitalized admission for COVID-19, daily ICU admission, daily deaths at hospital and also the number of beds used each day both in hospital and ICU.
It is indeed interesting to note that our model estimates for asymptomatic cases in Ireland lead to similar ratios but with a large 95% CI (see S7 Fig). Our study is not without limitations. The model used here is, like all complex SEIR models developed for COVID-19, non-identifiable. This means that it is likely that several solutions, i. e. several sets of parameter values, allow to reproduce observations and we only present one of the most likely ones. This point is overlooked very often but see  [45]. One limitation is the use of the classical homogeneous mixing assumption in which all individuals are assumed to interact uniformly and ignores heterogeneity between groups by sex, age, geographical region. However, this kind of data is not readily available. When mixing patterns among age groups are available at the individual level in contact tracing databases, they are only accessible following extensive ethical reviews.
As demonstrated previously, modeling with time varying parameter is an interesting framework for modeling the temporal evolution of an epidemic even if the knowledge about disease transmission is either incomplete or uncertain  [27]. Indeed a large part of all the unknowns can be put in the time-varying parameters described by a diffusion process but driven by the observed data. This is exactly what we have been confronted during the COVID-19 pandemic, as the data are uncertain, as are the transmission mechanisms of SARS-CoV-2. We therefore proposed to model the spread of this disease using a stochastic model with a time-varying transmission rate inferred using well-documented hospital multiple datasets. The knowledge of the transmission rate makes it possible to easily calculate the R eff (t), which is a key parameter of the epidemic, in order to monitor the potential effects of public health policies on the course the COVID-19 epidemic.
Acknowledgements We acknowledge support from  Academies of Excellence on Complex Systems and Space, Environment, Risk and Resilience of the Initiative d'EXcellence"Joint, Excellent, and Dynamic Initiative" (IDEX JEDI)  of the  Université Côte d'Azur and the Center for Planetary Origin  ( C4PO ) A Material approach (https://www-n. oca. eu/morby/C4PO/C4PO-home. html). We also acknowledge support from The  DIGIMU ANR  industrial chair (https://chaire-digimu. cemef. mines-paristech. fr/)as well as support from the  French space agency CNES.
In Sec. 4, we present preliminary results of velocity distributions as a function of mass using a modified version of the RAMSES 3D simulation code incorporating the gravitational behavior of the D-M universe, as described in  Manfredi et al. (2018)  and  Manfredi et al. (2020). In Sec. 5, we show that D-M follows a Faber-Jackson relation with a very small scatter and an exponent ≈ 3 over a range of more than three orders of magnitude in mass. In Sec. 6, we discuss in more detail the MOND-like behavior of D-M and in particular the value of the predicted acceleration parameter a 0. In the final section, we summarize our findings, and provide some perspectives and possible lines of development for future work.
General properties of matter, antimatter and depletion zone In order to introduce the properties of rotation curves and virial velocity distributions in the D-M cosmology, let us first consider the following example of cluster configuration, represented on Fig. 1. This configuration was obtained in a 3D simulation using a modified version of the RAMSES code  (Teyssier 2002 ), that we discuss more precisely in the next section. This figure represents a small cluster configuration in the D-M universe at redshift z ≈ 20 for self-gravitating particles, at this stage without dissipation. The full tomography of the matter, antimatter, and matter+antimatter configurations can be found as supplementary material at the following link 2. An animation of the formation of structures centered on a massive cluster of the simulation can be found at the following link 3.
Simulation results In order to validate the analytical approximations studied in Section 2, we modified the Adaptive Mesh Refinement code RAMSES  (Teyssier 2002)  in order to implement the gravitational behavior of matter and antimatter present in D-M, following Eqs. (  2 )-(3). We note that in most cosmological simulations, the average density is first calculated and subtracted from the local density in order to calculate the evolution of the scale factor a(t), so that the Poisson equation is usually written ∇ 2 φ = 4πGa 2 δρ. In D-M, this is directly the case, since ρ = 0 in this matter/antimatter symmetric universe, so that δρ ≡ ρ(t, x) -ρ(t) = ρ(t, x). The new element introduced in this D-M cosmological version of RAMSES is an extra set of particles with negative mass, which in turn introduces the new aspect of a depletion zone.
Acknowledgements. We are indebted to the anonymous referee and to  Benoit Famaey ,  James Rich  and  Yves Sacquin  for their thorough reading of the manuscript and their insightful comments. Needless to say, they are not responsible for the errors and approximations remaining in this paper. This work has made use of the Horizon Cluster hosted by the  Institut d'Astrophysique de Paris. The work of the YT (Turk et al. 2010), IPython (Perez & Granger 2007), Matplotlib (Hunter 2007),  NumPy  (van der Walt et al. 2011) and SciPy (Virtanen et al. 2020) development teams is also gratefully acknowledged. The halo catalogs have been computed using the AdaptaHOP algorithm (Aubert et al. 2004).
We assessed this with a sample of capuchin monkeys and a sample of preschool age children. The data from children were considered important because computerized tasks of other cognitive faculties often are compared in children and monkeys using near-identical software as are manual tasks, and this is true for tasks that require degrees of inhibition such as maze tasks. For example, maze performances have been assessed with human children, chimpanzees, gorillas, orangutans, and gorillas in identical manual formats  (Völter and Call, 2014)  and with children, chimpanzees, rhesus monkeys, and capuchin monkeys using computerized formats  (Beran et al. , 2015). Thus, it is important to know if a computerized detour task provided the same outcome for multiple species as did the more traditional manual version of the problem.
Children Children performed the task on laptop computers with digital joysticks using the same computer program as the nonhuman primates. However, rather than food rewards, children received visual and auditory feedback following correct/incorrect trials (i. e. , happy/sad faces and melodic/buzz tones). Additionally, children received a small toy of their choice at the end of each test session as a reward for participating, regardless of how many trials they completed correctly or incorrectly. The computer program was written in Visual Basic 6. 0.
Monkeys The capuchin monkeys were tested using the LRC's Computerized Test System comprising a personal computer, digital joystick, color monitor, and pellet dispenser  (Evans et al. , 2008). They manipulated the joystick with their hands to produce isomorphic movements of a small cursor on the computer screen. Contacting stimuli with the cursor resulted in the delivery of a food reward (a 45-mg banana-flavored chow pellets; Bio-Serv, Frenchtown, NJ) via a pellet dispenser. The computer program was written in Visual Basic 6. 0.
Acknowledgements The authors thank the parents, teachers and administrators at  Child Development Center of Georgia State University  for allowing us to work with children in those locations. The  Ecole Normale Supérieure Paris-Saclay funded Lilas Gurgand 's internship at the  Language Research Center. We also thank  Maisy Bowden ,  Molly Flessert ,  Kristin French ,  Myah Vogt ,  Will Whitham  and  Brielle James  for help with data collection.
Results To test this hypothesis, we first define a retention index for a given oDNA gene, measuring its propensity to be retained in oDNA. To this end, we acquired data on organelle gene content across eukaryotes, using 10328 whole mtDNA and 5176 whole ptDNA sequences from NCBI. We curated these data with two different approaches, resembling supervised and unsupervised philosophies, to form consistent records of gene presence/absence by species (see Methods). The supervised approach (manual assignment of ambiguous gene records to a chosen gene label) and the unsupervised approach (all-against-all BLAST comparison of every gene record from the organelle genome database) agreed tightly (Supplementary Fig. S1 ). Simply counting observations of each gene across species is prone to large sampling bias, as some taxa (notably bilaterians and angiosperms) are much more densely sampled than others.