dataesr/scientific-paragraphs-categorization huggingface dataset:
We do not work on a newspaper archive but on native digital data directly produced by the media: RSS feeds. RSS feed data continuously produce elements that can be collected in real time from the feed. We use the Media Cloud database, which is a collection of news RSS feeds. It is possible to obtain not only the title of news (available freely on the Media Cloud website) but also the description part of the RSS, which is generally the first sentence of the full text of the news (available only through specific extraction). This data source is of interest for two reasons: the quality of the collection allowed by technical means, and the standardized collection of news published on the web by each media outlet. As raw material, an RSS item includes everything needed for a spatiotemporal analysis: 1) textual content: the title of the original article published on the website of the newspaper and a description (between one and four sentences); and 2) several metadata describing the context of the publication (date and hour of publication, and the media outlet).
• When: The idea of news is necessarily related to the question of time, and each news item is associated with a precise day or even hour. However, daily newspapers are characterized by a weekly news production cycle: during the weekend (i. e. , Saturday or Sunday), these media outlets publish 10%-30% fewer items than during the week. Empirical studies also reveal qualitative changes between the day of the week and weekends regarding the topics of interest. Two solutions exist to reduce this disruption of the weekly cycles: 1) work on data aggregated by week; 2) as in the present study, use of a more refined solution with a rolling mean of 7 days (combined with the exclusion of news published on Saturday or Sunday, according to  Vasterman (2018) ). Even if weeks are the most important factor of variation, other cycles play a role in the quantitative production of news: for instance, in Western Europe, the end of the year (the Christmas and New Year period) is characterized by a decrease in the number of publications and a specific kind of production (retrospectives, e.
, 2019; Grasland, 2020). However, in the present study, we opt to use the global production of each newspaper on all channels, thanks to the Media Cloud database, which provides a global collection of all RSS channels of each media of interest. The qualification of international news, therefore, is realized a posteriori through the identification of textual units where foreign countries are mentioned. The identification of the country of location of the media outlets (guest country) is important, because it opens up the possibility of aggregation of isolated media toward a sample of national press. The allocation of a country to the newspapers is generally obvious because we select newspapers characterized by their national audience, that is, because the readers of the print version are distributed throughout the country where the media is located.
e. , the quantity of news that are observed when crossing the previous dimensions. The number of news in the hypercube dataset is as follows: each news item (title or sentence from the description) receives a set of four attributes: 1) a unique news outlet (e. g. , The Guardian; 2) a unique day of publication (e. g. , April 1, 2014); 3) a nonempty list of foreign countries mentioned (e. g. , Russia and Ukraine); 4) a Boolean test of the existence of the topic (e. g. , Border TRUE). When a single foreign country is mentioned, the news receives a weight of 1. However, when several foreign countries are mentioned, the item is divided into a fraction of news to keep the total sum of the hypercube equal to the sum of news (see Supplementary Material for details).
Exploration and Statistical Modeling The exploration of the data is undertaken with TELEMAC, an interactive dashboard elaborated by the authors as part of the H2020 ODYCCEUS project. The technical aspects of the tool and the numerous possibilities of analysis it proposes, especially concerning the exploration spatial dimension of the geopolitical agenda, are not presented in this paper; however, some elements are available in the Supplementary Material https://claudegrasland. github. io/telemac/ and GitHub https://github. com/ClaudeGrasland/telemac. Validation of the corpus and checking the tagging procedure of topics and guest countries is the first interest of this interface, which makes it possible to project a complex object like the hypercube over different facets, which is easier to analyze than the whole object.
Choice of the relevant scale of time and media aggregation is the second and probably the major function of TELEMAC. In the next section, we aim to test H2 by computing the correlation coefficient between the levels of salience of media over time. Salience is defined as the ratio between the observed and expected number of news items, using the average probability of each media outlet over the full period and is shown in panel When. Who. What in Figure  2 (middle). At the week level, we notice that data are missing for some newspapers, such as the Spanish newspaper La Vanguardia until April 2017, and this lack of coverage during the major crisis of 2015-2016 explains why the average level of coverage was low in the previous analysis. Therefore, this newspaper should be removed from the correlation analysis.
In conclusion, according to our explorations with TELEMAC, we exclude from the sample two newspapers with too many missing values (La Vanguardia) or those that do not correspond to broadsheet newspapers, which are likely to produce a geopolitical vision of the world (The Daily Mirror). However, TELEMAC is mainly dedicated to exploration, and specific statistical models have been used to test our overarching question and our hypotheses. To test H1, we use a simple model of variance analysis, whereby we compare the average level of salience of a topic for media located in the same country and media located in different countries. To test H2, we build a correlation matrix between the levels of salience of the 20 media outlets at the month level (using pairwise complete observations when missing data are present).
ACKNOWLEDGMENTS Media Cloud: The authors are particularly grateful to the researchers and engineers of the  Media Cloud  ( Rahul Bhargava ,  Dennis Jen , and  Emily Ndulue ) who prepared a specific extraction of news contained in this database. They are generally grateful to the institutions that have created and supported this project since 2013. Media Cloud is a consortium research project across multiple institutions, including the  University of Massachusetts Amherst ,  Northeastern University , and the  Berkman Klein Center for Internet and Society at Harvard University  (https:// mediacloud. org/). ODYCCEUS: The authors also thank the researchers of the project involved in the  H2020   ODYCCEUS  project with whom they discussed the concepts and tools developed in this paper over a period of 4 years.
Sánchez-Cano et al. [133]  in line with Ulusen et al. [142]  analyses found that high-energy electrons accelerated by the solar wind created a dense and global layer of ions and electrons at ~90 km around the whole planet. This layer attenuated radar signals continuously for 10 days, preventing the radars from receiving any signal from the planetary The main properties of the low ionosphere was estimated using a combination of data analysis from the MAVEN, Mars Express, and MRO orbiters together with numerical simulations of the ionospheric response. This is only an indirect low-limit estimation of the low ionosphere properties because the low ionosphere (in the mesosphere region) has never been explored. Understanding the Martian response to space weather is essential in order to assess how the plasma environment reacts and dissipates energy from the solar storms.
Electric field fluctuations are also associated with terrestrial ionospheric irregularities and can be used to characterize the dominant wave numbers of the observed irregularities (e. g. [44] ). Such measurements are limited in the Martian ionosphere because MAVEN's Langmuir Probe and Waves (LPW) instrument provided one dimensional electric field wave power spectra throughout 2015 only  [47]. Acquiring even one-dimensional time series electric field data during such irregularity events at Mars would greatly aid in conclusively identifying which instabilities are responsible for the generation of the Martian ionospheric irregularities. In the terrestrial ionosphere, currents driven by strong ion-neutral coupling (while electrons remain magnetized) can be important drivers of these irregularities (e.
While Earth's ionosphere is dominated by the dipole magnetic field, Mars' magnetic environment is highly variable in both time and space due to the crustal magnetic fields that rotate with the planet (Fig. 2 ), and the nature of the induced magnetosphere (Fig. 1 ) that is highly responsive to changes in the upstream solar wind. The formation of ionospheric irregularities at Earth show strong seasonal dependencies (e. g. [5] ). The precession of MAVEN's orbit means that currently, the MAVEN dataset does not provide enough coverage in time, planetary longitude, latitude, solar zenith angle or local time, to conclusively determine if seasonal variations exist or not. A dataset spanning several Martian years and simultaneous multi-point observations is required to conclusively determine if seasonal dependencies exist there.
2. 4 Science question 4: Why should we have a permanent in-situ space weather monitor at Mars? Space weather real-time forecasting at Mars is currently very challenging because among other factors, it needs a continuous solar wind monitoring platform to provide timely and accurate space weather information. This is only possible if sufficient observation data are continuously available. At Earth, we have several spacecraft that for a few decades have been monitoring the Sun's activity and the solar wind. In fact, the most accurate measurements of the upstream solar wind at Mars occur when Mars and Earth are in apparent opposition or perfectly aligned in the Parker spiral (once every ~two years) because plasma missions such as Mars Express or MAVEN do not continually sample the solar wind. The Mars Upper Atmosphere Network (MUAN) community  [115]  has been leading coordinated efforts to have several Mars Express campaigns (with as many plasma instruments operating as possible) when both planets were aligned along the Parker spiral to better understand any Martian plasma variability due to external conditions  [116].
Since 2014, there have been several coordinated efforts between Mars Express and MAVEN teams to have solar wind observations from one spacecraft while the other one takes upper atmosphere observations. However, the orbital period of MA-VEN changed in 2018 after an aerobraking campaign, resulting in the orbit's apoapsis being reduced. As a consequence, MAVEN is now taking less in-situ solar wind data than before. As largely discussed in this White Paper, continuous in-situ solar wind and space weather observations are extremely important for most of the science questions that still remain unknown at Mars. A continuous in-situ solar wind monitor at Mars, together with atmospheric simultaneous observations is a first need in order to fully understand the 3D dynamics of the plasma system, as well as for having an efficient and continuous thermosphereionospheremagnetosphere -solar wind monitoring service, which is absolutely needed in the eve of the Martian human exploration.
Phobos and Deimos as travel platforms The near equatorial and circular orbits of the Martian moons Phobos and Deimos offer also great opportunities for long-life and low maintenance stations, as recently assessed by Sefton-Nash et al. [138]. Moon stations could be used for different purposes such as for meteorological studies, or data relays, but also, for plasma physics. The two moons, Phobos and Deimos, orbit Mars at ~3 and ~7 Mars radii respectively, and cross the whole horizontal structure of the tail (including bow shock boundary) several times per day, as well as transit the solar wind in each orbit. Both moons offer large possibilities for science, such as evolution of the solar wind between 7 and 3 Mars radii and the bow shock, or one moon being a solar wind monitor and the other one sampling the tail.
Acknowledgements B. S. -C. , and M. L. acknowledge support through  UK-STFC  grant  ST/S000429/1. M. C. and R. O. are funded by the  Italian Space Agency  through contract  ASI-INAF 2019-21-HH. 0. X. F. acknowledge support from  NASA  grant  80NSSC19K0562. C. M. F. was supported by  NASA  funding for the  MAVEN  project through the  Mars Exploration Program  under grant number  NNH10CC04C. K. P. acknowledges support through  DFG  grant  TE664/4 -1  and  PA525/24 -1. M. P. acknowledges support through the  German Space Agency (DLR)  under Grant  50QM1802. F. C. acknowledges support through  NASA  Contract  1560641  with the  Jet Propulsion Laboratory.
Remotely-sensed satellite-data provide globally standardized quantitative environmental measures enabling the tracing of environmental features going back nearly 50 years  10. Population-density is a well-established and quanti able general measure of urbanicity, frequently applied for neighbourhood classi cation and used around the globe  9. Here, we aimed to use population-density as a general measure of urbanicity to investigate if the urban environment is correlated with brain and behavior, and if these correlations are comparable in China and Europe. Speci cally, we developed a satellite-based measure of population-density termed 'UrbanSat', and applied UrbanSat in China and Europe to investigate the relation of population-density, as a proxy of urbanicity, with brain structure, function and behavior in two neuroimaging datasets of young people: as exploration dataset, we used the Chinese CHIMGEN cohort (www.
chimgen. tmu. edu. cn)  11 and as replication dataset, we used the European longitudinal IMAGEN-cohort (www. imagen-europe. com)  12. While we did not have any a priori hypotheses, we were interested in investigating if: (i) UrbanSat is associated with brain structure, function and behavior; (ii) Brain features associated with UrbanSat mediate the association between UrbanSat and behavior; (iii) Correlations of UrbanSat with brain and behavior are similar in Chinese and Europeans; Furthermore, we were interested in (iv) identifying susceptibilityperiods for the effects of UrbanSat during child and adolescent development on brain and behavior. A schematic summary is shown in Fig. 1.
We observed a negative association of UrbanSat with mPFC-GMV from age 4 to 15 (Fig. 3c ) and SA from age 5 to 7 (Fig. 3c ), indicating a susceptibility-period during childhood and adolescence, driven by NL, built-up%, cropland% and NDVI (Supplementary Fig. 13 ). Correlation of UrbanSat with cerebellar GMV was signi cant from age 1 to 10 years, indicating a susceptibility-period during childhood (Fig. 3c ), driven by NL, built-up% and cropland% (Supplementary Fig. 13 ). To investigate the relation between UrbanSat and brain development, we used the longitudinal IMAGEN dataset to calculate volumetric (n=340) and SA/CT change-rate/year (n=325) between baseline (BL) at 14 and FU2 at 19 years (IMAGEN BL-FU2). Consistent with the susceptibility periods identi ed, UrbanSat was signi cantly correlated with brain volumetric development in the mPFC-ROI (rho=0.
Discussion Using a remote-sensing satellite-measure, 'UrbanSat', we characterized the relation of population-density, a proxy of urbanicity, with brain structure, function and behavior during childhood and adolescence in large datasets in China and Europe. We provide converging evidence for association of UrbanSat during childhood and adolescence with GMV and SA of the mPFC and aDMN, but not with CT and FA. The mPFC and aDMN mediate the correlation between UrbanSat and improved perspective-taking and increased depression-symptoms. We also found positive correlations of UrbanSat during childhood with cerebellar volume, which mediated the association with perspective-taking and depression-symptoms. We are extending previous observations reporting an association of depression-symptoms with urban settings  17  , by demonstrating the stability of this observation in different geographical and sociocultural regions, and by discovering possible underlying brain mechanisms and susceptibilityperiods during childhood and adolescent development.
Our results are suggestive of a cumulative effect of urbanicity on brain and behavior, whereby participants born or migrating to the city at an earlier age had more pronounced effects than those who become city dwellers later. Given that CHIMGEN participants were students who moved to cities for their studies, we do not have any data on people who after spending some years in the city moved back to the country side. We also do not have data to distinguish possible short but extreme exposure to urban life, in utero or during susceptibility periods from a moderate continuous exposure. We found several shared effects of high population-density in urban settings on brain structure and function as well as on social-cognition and mental-health in both CHIMGEN and IMAGEN, indicating their generalization to other sociocultural conditions and geographies.
Air pollution induces neuroin ammation in the brain, leading to the damage and loss of neural tissue in the prefrontal cortex  35  and may provoke depression-symptoms  41. Thus, urban upbringing may cause affective and anxiety symptoms by way of both, increased social stress and pollution. Remotely-sense satellite data play a critical role in monitoring the Earth's surface to track environmental conditions that are intimately related to human health  10. Satellite data are applied to map urbanization, poverty, climate change and pollution, as well as spread of infectious disease  10. Our study extends the application of remote-sensing satellite data and provides a method to characterize and monitor spatial and temporal patterns of risk for mental disorders. In the optimized CFA models, the four satellite-measures contributing to UrbanSat showed different factor-loadings.
Thus, UrbanSat provides a unique opportunity to identify the cumulative effects and susceptibility-periods of urbanicity on brain and behavior. However, we note that UrbanSat cannot unravel environmental pathways and their interactions that cause the aversive effects of urban living. This is a task for subsequent studies with access to su cient ground level data for comprehensive characterization of causal environmental pathways that underlie the observed correlations. In the current work, we have provided proof of principle establishing the use of satellite-data to inform the relation between urban environment, brain and behavior. As our approach can be extended and generalized to other geographies and is easy to implement even in the absence of detailed or directly comparable ground level data, it may be relevant for public health, policy and urban planning globally.
These scores are summed across the set of trees, and normalized to take values between 0 and 1, with the sum of all scores equal to 1. A set of 300 GPLTR models was done and we reported the symptoms as ranked by the DDIS. The logistic regression and the GPLTR models were performed without replacing missing data. Statistical analyses were performed using R software  [18]. Regression tree analyses were performed with the 'GPLTR' R package  [16].
Remark 1. 5. The iterative construction of operators D k , which is quite involved, has been implemented in the software Mathematica in  [Ros20b] , thanks to which the coefficients a k can be immediately computed. Before presenting further results, let us comment the proof of Theorem 1. 1. Savo's method amounts to study the quantity F (t, r) = Ω(r) u(t, x)dω(x), where Ω(r) = {δ > r}. Upon appropriate localization to deal with the non-smoothness of Ω(r) for large r, it turns out that F (t, r) satisfies a non-homogeneous one-dimensional heat equation on the half-line [0, ∞), with Neumann boundary condition at the origin. Then, the whole asymptotics of Theorem 1. 1 and the expression of the coefficients are obtained by iterating the corresponding Duhamel's formula. Some non-trivial modifications must be implemented to adapt this technique to the sub-Riemannian setting.
Then, in Section 5, we develop the asymptotic expansion of the heat content, concluding the proof of Theorem 1. 1 and Proposition 1. 2. In Section 6, we show how to obtain the coefficients through a Riemannian approximation, proving Theorem 1. 6. Finally, in Section 7 we show that, in presence of a characteristic point, the integrand of the coefficient a 5 is not locally integrable, proving Theorem 1. 7. Acknowledgments. This work was supported by the Grants ANR-15-CE40-0018, ANR-18-CE40-0012 of the ANR, and the Project VINCI 2019 ref. c2-1212. A previous proof of Theorem 3. 1 employed Riemannian approximations and the classical Li-Yau heat kernel estimate. We thank Emmanuel Trelat for stimulating discussions that led to the current simpler proof, using instead a hypoelliptic version of Kac's principle.
Cell-to-cell communication can be inferred from ligand-receptor expression in cell transcriptomic datasets. However, important challenges remain: global integration of cell-to-cell communication; biological interpretation; and application to individual cell population transcriptomic profiles. We develop ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand-receptor interactions accounting for multiple subunits expression; 2) quantification of communication scores; 3) the possibility to connect a cell population of interest with 31 reference human cell types; and 4) three visualization modes to facilitate biological interpretation. We apply ICELLNET to three datasets generated through RNA-seq, single-cell RNA-seq, and microarray. ICELLNET reveals autocrine IL-10 control of human dendritic cell communication with up to 12 cell types.
manual curation was performed based on a rigorous literature screening of original articles, applying the following criteria: 1) robustness of the findings, 2) consistency with international classifications and nomenclature, 3) experimental validation of the functionality of the ligand-receptor interaction (see Methods). We also used consensus reviews from leaders in the field, in particular for cytokines  3, [14] [15] [16]. This helped solving controversies on how to classify some molecules, which have features of different molecular families. We did not include putative interactions based on protein-protein interaction predictions, as it is done in some other databases  9. This led to the integration of 380 ligand-receptor interactions into the ICELLNET database (Supplementary Data 1). Whenever relevant, we took into account the multiple subunits of the ligands and the receptors (Fig.
1a ). Interactions were classified into 6 major families of communication molecules, with a strong emphasis on inflammatory and immune processes: Growth factors, Cytokines, Chemokines, Immune Checkpoints, Notch signaling, and Antigen binding (Fig. 1b  and Supplementary Data 1). Other families such as hormones or adhesion molecules were more scarcely represented. In order to simplify the subsequent graphical visualization, these were grouped as other in our current classification (Fig. 1b  and Supplementary Data 1). Cytokine-receptor pairs were mapped in an exhaustive manner, by exploiting a series of reference articles and consensus classifications. They represent 50% of the total interactions included in the database (194 interactions), and were further classified into 7 sub-families according to structural protein motifs: type 1 cytokines, type 2 cytokines, IL-1 family, IL-17 family, TNF family, TGF-ß family, and RTK cytokines  3, [14] [15] [16]  (Fig.