dataesr/scientific-paragraphs-categorization huggingface dataset:
[16]. Creton and Mougin validated their model for several crude oils covering broad ranges of reservoir characteristics, and studied impact of pressure, temperature, and gas composition on predicted live oil EACNs  [14]. These latter crude oils are part of our database (Tab. 1), and we propose hereafter to feed the model proposed by Creton and Mougin with EACNdo values obtained using equation  (5). Table  6  presents for crude oils in common between reference  [14]  and this work, some oil properties and their original reservoir characteristics. Mougin  [14]. Therefore, the proposed combining of the two models appears as a relevant tool to estimate EACN of live crude oils.
Conclusions and perspectives In the context of EOR, some chemical EOR techniques involve surfactant formulations to mobilize oil trapped by capillary forces. In order to assist and speed up experiments necessary for the formulation design, we recently proposed a model based on thermodynamics to predict EACN of live crude oil. This model consists in a linear mixing rule based on volumetric fractions of the EACN of the dead crude oil and the EACN of the representative gas. The objective of the present work was to use data mining based approaches to investigate and develop relations between the EACN and the composition of dead crude oils. We collected 29 crude oil samples originating from around the world and performed analysis to obtained compositional information. Each sample has been described in terms of EACNdo, API gravity, and fractions of C 20 À , saturates, aromatics, resins, and asphaltenes.
The database covers a broad range of API gravity with values ranging from 11 to 50 denoting heavy and light crude oils, respectively. Machine learning methods based on EA have been applied to our database in order to generate QPPR to predict EACNdo. In the case of MGGP, we proposed an approach to parameterize GPTIPS. Obtained QPPR models were compared to each other in terms of capacity to generalizing the database. Note that this work could be done using any SARA analysis but fractions of C 20 À , saturates, aromatics, resins, and asphaltenes within the database must be consistent. The best QPPR model was then used to feed a thermodynamics based model to predict EACNlo for crude oils. Comparisons carried out demonstrate that the proposed combining of the two models appears as a relevant tool for fast and accurate estimates of live crude oil EACNs.
To the best of our knowledge, this work represents the first attempt to predict EACN of crude oils using data mining. When new samples of crude oils will be available, API gravity, and fractions of C 20 À , saturates, aromatics, resins, and asphaltenes will be experimentally determined. The new samples will be used to supplement our database. The QPPR model (Eq. (  5 )) developed in this work will be applied to predict EACNdo values for the new crude oils. According to the accuracy of the property predictions and as the QPPR model is more statistical than a physical law, the QPPR model may necessitate an update using MGGP. Acknowledgments. Authors wish to thank the EOR Alliance team for their technical assistance and all relevant discussions. Table  6. Summary of live crude oil properties.
, 2015) , including gas chromatography-mass spectrometry (GC-MS) analysis when available. The size of our dataset does not allow us to retrieve the volatility distribution for single precursors, which would entail the determination of more than 86 free parameters. This is especially the case as the time series of precursors, decaying with oxidation, are typically strongly correlated, which prevents resolving systematic differences between the yields of the different single precursors. Therefore, lumping is needed to decrease the model degree of freedom. Accordingly, precursors are grouped in six chemical classes: furans, single-ring aromatic hydrocarbons (SAHs), polycyclic aromatic hydrocarbons (PAHs), oxygenated aromatics (OxyAH) and organic compounds containing six and more or fewer than six carbon atoms (OVOC c ≥ 6 and OVOC c < 6 , respectively) (Table  S1 ).
The selected precursors in each class are the same for each experiment and each dataset. This lumping approach is based on the two main objectives of the study: 1. Compare the SOA yields of specific precursors determined in complex emission experiments with those determined in single-compound systems. 2. Identify the main SOA precursors in flaming and smoldering wood emissions, at different temperatures. To be able to compare to literature yields, we lumped species that have similar yields in the same chemical class: e. g. at an organic aerosol concentration of 10 µg m -3 the yield of PAHs is ∼ 20 % (objective 1). In addition, we classified the precursors based on the pathway by which they are emitted, which will allow us to determine which compounds dominate SOA formation in flaming and smoldering emissions (objective 2).
Comparison of primary emissions across experiments A larger amount of primary OG was emitted into the chamber in Set 1, with concentrations ranging from 950 to 7860 µg m -3 , while in Set 2 the primary OG concentrations ranged from 300 to 1360 µg m -3. In the same way, the measured OA at the beginning of each test (POA) ranged from 10 to 180 µg m -3 and from 9 to 22 µg m -3 for Set 1 and Set 2, respectively (Table  1 ). The OG composition for Set 1 and Set 2 is summarized in Fig. 2  showing the mean PTR-ToF-MS mass spectra (Fig. 2a, b ), the relative contributions of the different compounds for different datasets (Fig. 2c ) (d) Spearman correlation matrix for the primary OG mass spectra between all experiments highlighting the variability in the composition of the primary emissions. Each experiment is identified by an index (see Table  1 ) and the experiments from Set 2 were reordered according to similarity with Set 1.
and the variability in composition among all experiments (Fig. 2d ). Set 1 shows higher relative contributions of furans and OVOC c < 6 (Fig. 2a ), while the contributions of PAH, SAH and OxyAH are higher in Set 2 (Fig. 2b ). The OxyAH compounds, mainly methyl-and methoxyphenols, are produced by lignin pyrolysis  (Fine et al. , 2001)  while furans are formed from cellulose pyrolysis  (Mettler et al. , 2012). The chemical class referred to as "Others" comprises compounds that do not show a clear decay upon oxidation and are therefore not considered SOA precursors in the following analysis. The category others is dominated by acetic acid, previously reported as a major species in residential wood burning emissions  (Bhattu et al. , 2019). The majority of the compounds differ among datasets and the most significant difference estimated through the p value (probability associated with a Student's t test) occurs for the OVOC c < 6 , which is about a factor of 6 higher than the significance threshold (p = 0.
3b ). Moreover, the calculated averaged OM p / OGs ratios are around 0. 05 and 0. 03 for Set 2 and Set 1, respectively. OGs undergo oxidation during atmospheric ageing to form a complex mixture of products, some of which remain in the gas phase while others have sufficiently low volatility to partition to the particle phase. The consumption of the different OG classes over time is shown in Fig. 4a, b  for Set 1 and Set 2, respectively. The general trends manifest that PAH and OxyAH are the most reactive classes, exhibiting an average consumption of up to 80 % at the end of the experiments (after ∼ 4 h of ageing) while for both datasets SAH appears to be the least reactive class (with an average consumption between 10 % and 20 % at the end of the experiments). Relevant compounds in the latter class are benzene (C 6 H 6 ), toluene (C 7 H 8 ) and xylene (C 8 H 10 ); their slow reactivity is consistent with literature reaction rate constants toward OH, from the NIST database (NIST chemistry WebBook, Linstrom and Mallard, 2018), of 1.
22 × 10 -12 , 6. 13 × 10 -12 and 7. 51 × 10 -12 (cm 3 molec. -  s -1 ), respectively. The two datasets differ for the OH dose; we observe in Set 2 (Expt. 8-14) an overall higher consumption of all precursor classes due to the higher OH dose (representing a longer ageing time in an ambient atmosphere). PAH shows the highest reactivity followed by OxyAH while for Set 1 (Expt. 1-7) the fastest class of compounds to react is the OxyAH followed by PAH and OVOC c < 6. Moreover, despite the lower OH exposure reached for Set 1, the consumption of OxyAH and furans is substantially higher at comparable exposure levels. In the same way, we also observe a higher SOA production for Set 1 compared to Set 2 at comparable OH exposure. The SOA-to-POA ratio ranges between 2 and 6, similar to ratios observed in previous studies  (Heringa et al.
6. The additional reactivity appears to contribute to the total precursor consumption for most of the classes, with particular relevance for the OxyAH and PAH classes. This is in contrast with the calculated loss for Set 2 (Fig. S3 ) where the dominant consumption is due to OH. One possible hypothesis is that the remaining loss process might be due to reaction with the nitrate radical (NO 3 ), which absorbs in the visible region (∼ 500-650 nm) and thus is not efficiently photolysed by the black lights used here  (Reed et al. , 2016). Figure  S4  shows that for Set 1 the NO 3 reaction rate constants (k NO 3 ) for compounds found in the NIST database (Linstrom and Mallard, 2018) (see Table  S1 ) are well correlated with the amount reacted, making nitrate chemistry a likely loss process  (Schwantes et al.
Hence, the total volatility distribution was used to calculate the mass yield instead of a specific one for each dataset (Fig. 9 ). Mass yields calculated for the specific datasets Set 1 and Set 2 are reported in Fig. S6. Figure  7  shows the modelled and measured OA mass for all 14 experiments, where Set 1 accounts for both OH and NO 3 chemistry while Set 2 includes OH chemistry only. The modelled OA is divided into POA and six SOA classes attributed to the respective precursor classes. Overall, the model performance is satisfactory, although in general the final OA concentration is slightly overpredicted while the initial production rate is underpredicted. Most of the SOA is attributed to furans (30. 8 %), OxyAH (19 %) and OVOC c < 6 (12. 5 %) for Set 1 while for Set 2 there is a generally lower contribution from OVOC c ≥ 6 (7.
The reported published yields were discriminated according to experimental NO x regimes, as low-NO x conditions generally lead to higher OA yields. Our study is more likely representative of a high-NO x regime and thus assesses the SOA forming potential for this atmospherically relevant condition. For the model and measurement conditions (C OA ∼ 20-600 µg m -3 ) the following median mass yields ranges were found: 7 %-20 % for furans, 10 %-25 % for SAH, 14 %-32 % for PAH, 9 %-24 % for OxyAH, 23 %-46 % for OVOC c ≥ 6 and 6 %-18 % for OVOC c < 6. Mass yields were also calculated for the two separate datasets and reported in Fig. S6  in order to assess differences driven by specific combustion regimes. Considering ambient-relevant conditions of C OA ∼ 50 µg m -3 , we note a generally good agreement between the two datasets except for OxyAH, which exhibit higher median yields (representing smoldering phase) of ∼ 25 % in Set 1 and only ∼ 10 % in Set 2 (representing flaming phase).
Acknowledgements. We appreciate the availability of the VBS framework in CAMx and support of  RAMBOLL. Financial support. This research was supported by the  Swiss National Science Foundation  (project  WOOSHI  ( 200021L_140590 )) and  SNSF   Starting Grant IPR-SHOP  ( BSSGI0_155846 ), the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 290605  ( PSI-FELLOW ), the  European Union 's  Horizon 2020 Research and Innovation programme  through the  EUROCHAMP-2020 Infrastructure Activity  under grant agreement no. 730997 , the  Competence Centers Environment and Sustainability (CCES)  and  Energy and Mobility (CCEM)  (project  OPTIWARES ), and the  French Environment and Energy Management Agency (ADEME)  under the grant  1562C0019  ( VUL-CAIN  project) and the  Provence-Alpes-Côte d'Azur (PACA) region.
2011 ). Indeed, language, cognitive ability, social engagement and motor skills have emerged as the most robust predictors of ASD among toddlers  (Landa et al. 2013; Ozonoff et al. 2018; Iverson et al. 2019 ) and during childhood and adolescence  (Stahmer et al. 2011 ). Thus, an early dysfunction in IS could have cascading consequences and even participate in explaining the heterogeneity in ASDs. This observation reflects the difficulty of assessments by reliable and scalable markers of IS over several ages and the need for a personalized analysis. In this work, we first want to validate in children with neurotypical development how the HDC can measure different behavioural mechanisms involved in social dynamic interactions, and then to evaluate how the HDC can assess the alteration of IS in ASDs.
Human Dynamic Clamp (HDC) paradigm The HDC system  (Kostrubiec et al. 2015; Dumas et al. 2014 ) is a human-machine interface. It consists of three parts : a sensor measuring the movement of the participant's index which is positioned in the moving sensor a mathematical model integrating the position and speed of the human participant to simulate in real time the behaviour of an avatar; and a screen facing the participant where the actions of the Virtual Partner (VP) appear as an index hand. The HDC software computes in real time the corresponding position of the VP (Figure  1 ). Three main positions for the indexes are possible: extension, in the axis of the wrist, flexion. At the beginning of each trial, an instruction was given to the participant to synchronize its movement in-phase i. e.
Statistical analysis and Normative Modelling All statistical data analyses were performed using the Python 3. 7 (Millman et Aivazis 2011) (numpy 1. 17. 2 (Oliphant 2006) and scipy 1. 3. 1  (Virtanen et al. 2019 )): differences between groups, multiple linear regression models, and normative modelling. The normative modelling (NM) provides a metric similar to a Z-score, but accounts for the underlying structure of the population across multiple covariates. NM uses Gaussian Processes (GP) to model the distribution of control group measures while estimating separately the overall trajectory in the covariate space, the heterogeneity in the population, and the uncertainty of the fit  (Zabihi et al. 2019 ). The Python code is available at https://github. com/GHFC/SoNeTAA/.
FUNDING This work was supported by the funding of the  Institut Pasteur , the  INSERM , the  Fondation FondaMental , the  APHP , the  DHU Protect , the  Fondation Bettencourt-Schueller , the  Fondation Cognacq Jay , the  Fondation Conny-Maeva , the  Fondation de France , and the  Labex BioPsy. FB was funded by the  Fondation FondaMental  and the  Congrès Français de Psychiatrie.
Results Do the E5 ORFs Present in the Genomes of PVs Belonging to Different Crown Groups Have a Common Ancestor? We collected 354 full length PV genomes from the PaVE and GenBank databases (supplementary table S1, Supplementary Material online). After removing 11 recombinant sequences, we constructed a maximum likelihood phylogenetic tree of the concatenated E1E2L2L1 sequences at the nucleotide and amino acid levels. Out of the 354 PV genomes, we identified 339 with an intergenic region (of at least one nucleotide) between the E2 and L2 genes. Of these, 83 contain an E5 ORF in the inter-E2-L2 region (fig. 1  and supplementary fig. S1 , Supplementary Material online). The E5 ORFs have a median size of 144 nucleotides (min: 126, max: 306). Based on the size of inter-E2-L2 region in which E5s are present (min: 289, median: 517, max: 938), we identified four PV clades containing an intergenic region selecting for a minimum size of 250 (min: 262, median: 512, max: 1,579).
1 )  (Rector et al. 2008; Gottschling et al. 2011; Robles-Sikisaka et al. 2012). Finally, the most cogent examples of recombination between distant viral sequences are two viruses isolated from bandicoots and displaying the early genes related to Polyomaviruses and the late genes related to PVs  (Woolford et al. 2007; Bennett et al. 2008). The inter-E2-L2 sequences may occasionally be very long and span >1 kb, a considerable size for an average genome length of $8 kb. Additionally, for many viral genomes, the sequences in the inter-E2-L2 region do not resemble other sequences in the databases, and do not seem to contain any functional elements, neither ORFs nor transcription factor binding sites or conserved regulatory regions  (Rector et al. 2007; Schulz et al. 2009; Garc ıa-P erez et al.
Acknowledgments We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) for providing computing and storage resources. The authors acknowledge the  IRD itrop HPC (South Green Platform) at IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this article. We also thank the  Peer Community In Evolutionary Biology  for reviewing and recommending the preprint version of this article. This work was supported by the  European Research Council   Consolidator Grant CODOVIREVOL  (contract number  647916 ) to I. G. B. and by the  European Union   Horizon 2020 Marie Sklodowska-Curie research and innovation program  grant ONCOGENEVOL (contract number  750180 ) to A. W.
To address this problem, we and others recently introduced the Brain Imaging Data Standard (BIDS;  (Gorgolewski et al. , 2016) ), a specification meant to standardize the process of representing brain imaging data. BIDS is deliberately designed with adoption in mind; it adheres to a user-focused philosophy that prioritizes common use cases and discourages complexity. By successfully encouraging a large and ever-growing subset of the community to adopt a common standard for naming and organizing files, BIDS has made it much easier for researchers to share, re-use, and process their data  (Gorgolewski et al. , 2017). The ability to efficiently develop high-quality spec-compliant applications itself depends to a large extent on the availability of good tooling. Because many operations recur widely across diverse contexts-for example, almost every tool designed to work with BIDS datasets involves regular file-filtering operations-there is a strong incentive to develop utility libraries that provide common functionality via a standardized, simple API.
PyBIDS is a Python package that makes it easier to work with BIDS datasets. In principle, its scope includes virtually any functionality that is likely to be of general use when working with BIDS datasets (i. e. , that is not specific to one narrow context). At present, its core and most widely used module supports simple and flexible querying and manipulation of BIDS datasets. PyBIDS makes it easy for researchers and developers working in Python to search for BIDS files by keywords and/or metadata; to consolidate and retrieve file-associated metadata spread out across multiple levels of a BIDS hierarchy; to construct BIDS-valid path names for new files; and to validate projects against the BIDS specification, among other applications. In addition to this core functionality, PyBIDS also contains an ever-growing set of modules that support additional capabilities meant to keep up with the evolution and expansion of the BIDS specification itself.
Currently, PyBIDS includes tools for (1) reading and manipulating data contained in various BIDS-defined files (e. g. , physiological recordings, event files, or participant-level variables); (2) constructing design matrices and contrasts that support the new BIDS-StatsModel specification (for machine-readable representation of fMRI statistical models); and (3) automated generation of partial Methods sections for inclusion in publications. PyBIDS can be easily installed on all platforms via pip (pip install pybids), though currently it is not officially supported on Windows. The package has few dependencies outside of standard Python numerical and image analysis libraries (i. e. , numpy, scipy, pandas, and NiBabel). The core API is deliberately kept minimalistic: nearly all interactions with PyBIDS functionality occur through a core BIDSLayout object initialized by passing in a path to a BIDS dataset.
For most applications, no custom configuration should be required. Although technically still in alpha release, PyBIDS is already being used both as a dependency in dozens of other open-source brain imaging packages -e. g. , fMRIPrep  (Esteban et al. , 2019) , MRIQC  (Esteban et al. , 2017) , datalad-neuroimaging (https://github. com/datalad/ datalad-neuroimaging), and fitlins (https://github. com/poldracklab/fitlins) -and directly in many researchers' custom Python workflows. Development is extremely active, with bug fixes and new features continually being added (https://github. com/bids-standard/pybids), and major releases occurring approximately every 6 months. As of this writing, 29 people have contributed code to PyBIDS, and many more have provided feedback and testing. The API is relatively stable, and documentation and testing standards follow established norms for opensource scientific software.
Nature of the Task The participants were asked to seat on the haptic chair in front of a panoramic wall of screens. The setup was comparable to the one we can found in an operational RT facilities room. The participants could use radars to avoid being out of their elements. Another screen was used to have a minimalist view on the secondary airport, in the case of Multiple RT context. Two pseudo-pilots took place in another room to pilot the aircraft which were visible on the scene and to speak with the participant in aeronautical language, following the scenario scripts. These scripts had to be both plausible for the ATCos and comparable to each other to further be able to conduct statistical analyses. They were designed to get as close as possible to the actual working conditions in a real airport.
Experimental Conditions There were 4 different configurations: Single RT context without any augmentation, Single RT context with augmentations, Multiple RT context without augmentation, and Multiple RT context with augmentations. In order to decrease the learning effect, 4 different scripts were designed for a total of 8 scenarios. Two experimental conditions were tested: Context [Single, Multiple] and Augmented  [No, yes]. Single and Multiple scripts were made similar using comparable traffic complexity and events to raise. Fog was added to the visuals to make poor visibility conditions all along the experiment (Figure  5 ). The scenarios order has been randomized across the experimental conditions for all the participants. Hence, each participant had to come through 4 distinct scenarios in a different order.
The 4 scripts were followed by the pseudo-pilots during the core experimental phase to create the different situations in which they could raise the events we wanted to test. During augmented scenarios, each event was linked to the appropriate augmentation modality. For the scenarios without augmentations, these events were raised in the same way than for augmented scenarios, to make the comparison between these two types of scenario feasible. The only difference was that for scenario without augmentations, the augmentation modalities were not activated.
Objectives and Hypothesis The experimental platform was composed of hardware and software, which, in conjunction, provided realistic environment that was as much suitable as possible to immerse the ATCos in the context of their work as if they were in a real tower. The overall objective of the experiment was to promote user immersion and to increase performance while reducing workload. In this perspective, the working hypothesis can be formulated as following: "User performances, in terms of reaction times and perceived workload, are improved when all the augmentation modalities are activated (i. e. Audio Focus interaction, Spatial Sound Alert, Runway Incursion alert, and Call from Secondary Airport feedback".