dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements I am grateful to  Florin Bilbiie  (editor) and to two anonymous referees for their helpful comments. Special thanks go to  Marianne Guille  and  Sébastien Lotz  for their constant guidance and advice. I am also grateful to members of the  Laboratoire d'Economie Mathematique et de Microeconomie  (LEMMA), and to participants of the  Nanterre University 7th PhD Conference in International Macroeconomics and Financial Econometrics in Paris , the  GdRE 35th International Symposium on Money, Banking and Finance in Aix-en-Provence  and the  Italian Econometrics Society  (SIdE) 2019 meeting in Lecce.
Expression of KEP-coding genes Our initial work on the arbuscular mycorrhizal fungus R. irregularis allowed the identification of seven KEPs with differential expressions  [13]. We thus searched for gene. They correspond to groups of repeated motifs with a similar sequence, identified in at least four species. Similarity was determined by manual curation. For each fungal species, the conserved motif and its number of repetition is indicated. Motif 4 in C. graminicola was cropped to highlight its conserved sequence. Branches of the tree where collapsed when species displayed no conserved peptide. In the case of Basidiomycota and Aspergillus groups, branches were collapsed for space saving (only some species of these groups indeed contain a conserved peptide). Refer to Additional file 2 Table  S2  for detailed information expression datasets in the literature to more largely confirm the importance of KEPs.
Indeed, yeasts in this subphylum display a greater proportion of SPs than their filamentous counterparts. This is in sharp contrast to what was observed in Ascomycota. We also observed that these yeasts from the Basidiomycota tend to produce longer peptides through KEP processing than the other Dikarya, indicating that they differently evolved the use of these peculiar proteins. Also, filamentous fungi from the Ascomycota display a greater percentage of SPs than filamentous species from the Basidiomycota. The high proportion of plant pathogen species in filamentous Ascomycota can explain this difference. In our dataset, among 56 plant pathogens, 45 are filamentous Ascomycota. Besides, the observation that Chytridiomycota have a greater amount of SPs than Mucoromycotina  [27]  is also supported by our data.
Data description and classification Publicly available protein catalogs of 250 fungal species were retrieved from the JGI database. Eighty-four, 145, and 21 datasets corresponded respectively to Basidiomycota, Ascomycota and early diverging fungi. Lifestyles were sorted so that 11, 94, 127 and 18 fungi were respectively classified as endophytes, pathogens, saprotrophs and beneficial symbionts. We also separated filamentous fungi (182 species) from unicellular, yeast or yeast-like fungi (68 species). The information concerning the 250 species, together with publication records for each genome, is displayed in Additional file 1 Table  S1a.
Search for KEX1, KEX2 and STE13 conservation We performed a BLAST analysis to search for the presence of KEX2 but also KEX1 and STE13, two other proteases involved in the maturation of Ascomycota α-type pheromones. While KEX2 cleaves at the C-terminus of the KR/RR/KK dipeptide, KEX1 cleaves at its N-terminus. STE13 removes additional dipeptides following the cleavage site of KEX2 with a preference for XP or XA dipeptides. We used Saccharomyces cerevisiae protein sequences (KEX1, YGL203C; KEX2, YNL238W; STE13, YOR219C) as queries in a BLASTP analysis against NCBI refSeq_protein database, with a threshold e-value of 0. 001. Only 15 available protein catalogs displayed a lack of at least one of these proteins (Additional file 1 Table  S1b ). KEX2 was for example absent in Microsporidia species, in two Taphrinomycotina, two Agaricomycotina and one Ustilaginomycotina species.
Pipeline description The following pipeline is illustrated in Additional file 4 Figure  S1  and the script can be downloaded from the link "Download repeatSearch" in Additional file 2 Table  S2  (see Additional file 2 Table  S2  legend for detail). After protein catalog retrieval from the JGI, proteins present in exact duplicates were removed. SignalP4. 0 [42]  was used first and unselected proteins were then scanned with SignalP3. 0 [43]. Proteins selected by either software were retained. We then discarded membrane-located proteins using TMHMM  [44]. Sig-nalP3. 0, SignalP4. 0 and TMHMM were used with default parameters. Protein sequences deleted of their signal peptide were then cleaved in silico at every KR or RR site. Proteins giving at least four fragments of at least two amino acids were selected.
Measures of banking sector vulnerability. As there is no universally accepted empirical measure of banking sector vulnerability, we employ six alternative variables commonly used in the literature. First, a simple way of measuring the potential effect of benign neglect on financing conditions and financial instability more generally is to focus on credit volatility. In essence, the higher the credit volatility, the more unstable financing is for households and firms. This variable is calculated as a five-year moving variance on quarterly credit data, which come from the International Monetary Fund's International Financial Statistics (IFS) database. Our second measure is the credit-to-GDP gap. This is one of the most widely accepted proxies for banking and financial imbalances among policymakers and academics.
We compute the credit-to-GDP gap as the difference between the credit-to-GDP ratio and its Hodrick-Prescott (HP) filter trend. Credit refers to domestic loans provided by financial corporations to the household and private non-financial corporate sector. Data come from the World Bank's Global Financial Development (GFD) database. The next four variables that we consider as proxies for banking sector vulnerability concern the structure of banks' balance sheets. They are taken from the GFD database. The first is the credit-to-deposit ratio, which measures the banking sector's funding stability. This ratio increases if credit creation is higher than deposit growth and decreases in the opposite case. Thus a higher ratio indicates there is more wholesale funding in the capital structure and is a signal of excessive bank leverage.
As argued by  Levieuge and Lucotte (2014) , it is also important to control for demand and supply shocks because they can impact the output gap and inflation variabilities, and thus the value of the CON S index. Thus, by considering the variance of demand and supply shocks, we control for inflation and output gap volatilities not necessarily reflecting a conscious willingness by the central bank to prioritize inflation stabilization. We then take the heterogeneity of the country sample into account by considering real GDP per capita as an indicator of the level of development. This variable is taken from the World Bank's World Development Indicators (WDI) database. The second set of control variables is intended to capture the degree of banking competition because this can affect the risk-taking behaviour of financial intermediaries and, in turn, banking sector vulnerability.
We measure the level of banking competition using two proxies commonly employed in the banking literature. The first is the Lerner index, which measures the degree of market power of the banks and is thus an inverse proxy for bank competition. A low value (the minimum is 0) indicates a high degree of competition, while a high value (the maximum is 1) indicates a low competitive environment. The second proxy we consider is a measure of bank concentration. This corresponds to the assets of the three largest commercial banks as a share of total commercial banking assets. As with the Lerner index, bank concentration is an inverse proxy for competition because a concentrated market structure is expected to be associated with higher prices and profits, reflecting an uncompetitive context. These two variables are obtained from the GFD database.
Due to data availability, it is important to note that the three specifications that we consider cover different time periods. The specification (1) covers the period from 1980 to 2012. The specification (2) covers the period from 1996 to 2012, as the competition and concentration measures provided by GFD database are available since 1996. Finally, the specification (3), which includes all control variables, covers the period from 1996 to 2005, because the measure of banking sector supervision provided by  Abiad et al. (2010)  are only available until 2005  13. For all the specifications reported from Tables  1 to 3 , we find a robust relationship between the measure of inflation aversion of the central bank and the level of banking sector vulnerability. Except specification (3), with the capital-to-asset ratio as the endogenous variable, the coefficients associated with the CON S index are significant at the conventional levels, with a sign that validates the benign neglect hypothesis.
By this way, we take explicitly into account the fact that common shocks are not the same for OECD and non-OECD countries. Results that we obtain concerning the CON S and CON S_W indexes are not impacted by this change 15. Second, we include a dummy variable capturing the subprime crisis. However, since the banking sectors of economies around the world have not been impacted in the same way by the subprime crisis, we do not consider the same dummy variable for all countries of our sample. As our main research question concerns the fragility of the banking sector, we prefer to consider a subprime crisis dummy variable equal to 1 for countries which have known at the same period a systemic banking crisis, and 0 15 Results are available upon request. otherwise. To this end, we refer to the comprehensive database on systemic banking crises compiled by  Laeven and Valencia (2013)   16.
More importantly, we still observe a significant relationship between the CON S (or CON S_W ) index and our alternative measures of banking sector vulnerability. This confirms our initial findings that central banks' preferences are an important driver of the fragility of the banking sector. Alternative control variables. We assess whether our baseline results are sensitive to the set of control variables by considering some alternative control variables. First, following Demirgüç-Kunt and Detragiache (1998), we replace demand and supply shocks with the annual growth rate of real GDP and the annual inflation rate. These two variables are taken from the WDI database and constitute an alternative approach to capturing macroeconomic shocks that may adversely affect the economy and the banking system and, in turn, drive financial imbalances.
Second, we consider two alternative proxies for banking competition. We replace the Lerner index with the Boone index. As the Lerner index, the Boone index is a non-structural competition measure and is taken from the GFD database. Despite the intensive academic debate between the proponents of the Lerner index and those of the Boone index, some recent empirical papers in the banking literature use the Boone index as a measure of bank competition (see, e. g. Schaeck and Čihák, 2014; Van Leuvensteijn et al. , 2011). In the same way, we consider an alternative measure of bank concentration, defined as the assets of the five largest commercial banks, rather than the three largest, as a share of total assets of the banking sector  19. Third, we re-estimate our baseline model by replacing the banking supervision index with a measure of de jure supervisory power to give a more complete picture of prudential regulation.
Controlling for other characteristics of the banking sector. We extend the set of control variables by considering different features of the banking industry. First, following the recent empirical literature having investigated the competition-efficiencystability nexus, we consider additional control variables proxying the efficiency of the banking sector. Indeed, as shown by  Schaeck and Čihák (2014) , efficiency is the transmission mechanism through which competition contributes to stability. The proxies for banking sector efficiency that we consider are the bank cost to income ratio and the bank overhead costs to total assets ratio. They are taken form the GFD database. The bank cost to income ratio is defined as the operating expenses of a bank as a share of sum of net-interest revenue and other operating income, while the bank overhead costs to total assets ratio represents the operating expenses of a bank as a share of the value of all assets held.
These two variables are expressed in percentage terms. The World Bank computes them by considering for each country the median of the bank-level ratios. Second, we control for the diversification of income sources in the banking industry, i. e. interest vs non-interest income 21. Indeed, the link between the income diversification and bank risk has attracted increasing attention in academic research 22. Our measure of income diversification is taken from the GFD database, and is defined as the ratio of bank non-interest income to total income. As for the measures of banking sector efficiency, the World Bank considers for each country the median of the bank-level ratios. Third, we control for the relative importance of the foreign bank presence in domestic banking systems. As well-documented by  Claessens and Van Horen (2014) , there is an extensive debate in the academic literature concerning the potential costs and benefits of foreign bank ownership, particularly in terms of financial stability.
The variable that we consider to measure the relative importance of the foreign bank presence is taken from the GFD database, and corresponds to the percentage of the number of foreign owned banks to the number of the total banks in the host economy. A bank is defined as foreign-owned if 50 % or more of its shares are owned by foreigners  23. Fourth, we take into account the fact that risk may be different for governmentowned banks and privately-owned banks, and then, the fact that the ownership structure of the banking sector may impact its vulnerability (see, e. g. Barth et al. , 2004; La Porta et al. , 2002). Following the existing macro literature, we measure the relative importance of government ownership in the banking industry as the share of stateowned or state controlled bank assets on the total banking sector assets.
Unfortunately, there  does not yet exist in the literature a database on banking sector supervision and regulation that covers a longer time span. Due to data availability, please also note that, for each specification, the time period depends on the endogenous variable considered. The credit volatility, the credit-to-GDP gap, and the credit-to-deposit ratio cover the period from 1980 to 2012, while the other measures of banking sector vulnerability cover the period 1998 to 2012.
g. ,  Corley et al. 2018; Pieters et al. 2011). However, M 3 has a 70 m/pixel maximum resolution  (Green et al. 2011 ) and Diviner infrared spectrometer has an average resolution of 250 m/pixel  (Paige et al. 2010a). Therefore, each reported lithology and associated chemical features represent an average over the entire pixel, only painting a broad-scale view of these complex geological features. Complementary to remote-sensing datasets, rovers on the lunar surface have the potential to make detailed, localised, in situ geological investigations. For instance, China's rover Yutu-2 (part of the Chang'E-4 mission) recently reported orthopyroxene-and olivine-   Prettyman et al. 2006). Data for these elements have been selected to µg g gw abundances of Th > 2µg g -1 , FeO > 8 wt. % and TiO 2 > 2 wt.
%. These data are overlain on a Clementine albedo map of the Moon in an orthographic projection. Locations of the Apollo (A) and Luna (L) landing sites are from  Wagner et al. (2017). The locations of the Imbrium basin and South Pole-Aitken basin are also indicated bearing lithologies within the Von Kármán crater (situated within the SPA basin;  Li et al. 2019). Such a mineral assemblage, together with the geological context of this crater (i. e. material on the Von Kármán crater floor is thought to have been excavated from 72 km below the SPA floor;  Huang et al. 2018) , led  Li et al. (2019)  to a suggestion that these lithologies could represent excavated mantle material. This is significant because no mantle rocks have conclusively been identified in the Apollo or Luna collections. However, without laboratory-based geochemical measurements of returned samples, the true provenance of these lithologies and what they tell us about the Moon (for instance, testing predicted lunar magma ocean crystallisation sequences) cannot be unambiguously established.
Geophysical Insights The Apollo Lunar Surface Experiments Packages (ALSEP) deployed across the Moon's surface by astronauts on the Apollo 12, 14, 15, 16 and 17 missions are one of the key legacies of the Apollo programme. These included active and passive seismic experiments, gravimeters, magnetometers, and heat flow probes at the Apollo 15, 16 and 17 landing sites, for example (e. g. ,  Eichelman and Lauderdale 1974; Wieczorek et al. 2006; Jaumann et al. 2012; Weber 2014). This lunar network of seismometers recorded ca. 13000 catalogued events between 1969 and 1977 (e. g. ,  Nakamura et al. 2008) , including nonnatural events related to impacts of booster rockets and lunar modules from Apollo spacecraft. Natural seismic sources on the Moon have been divided into three categories: meteoroid impacts, shallow moonquakes and deep moonquakes.
6 kg of material as of August 2019, based on samples classified in the Meteoritical Bulletin Database, they now outweigh the total mass of samples collected by Apollo and Luna missions. Moreover, meteorites sample the lunar surface in a more global way than sample-return missions, and, therefore, are likely to provide more representative sampling of the lunar surface, both spatially and temporally. However, meteorites pose challenges for paleomagnetism. All lunar meteorites are "finds", meteorites found at the Earth surface long after their fall, typically several kyr and up to several hundred kyr for Antarctic meteorites. The magnetic carriers of lunar rocks are Ni-poor iron-nickel alloys that are prone to weathering on Earth. Weathering of magnetic carriers leads to the loss of the primary magnetization, and, worse, possible acquisition of magnetization (chemical remanent magnetisation) in the Earth field.
Summary Despite a large body of research over the past five decades, many new discoveries are being made through continued laboratory investigations of lunar meteorites and samples returned by Apollo and Luna missions. Insights from recent orbital and landed lunar missions have allowed integration of laboratory data acquired on returned samples to address some of the outstanding questions related to the Moon's origin and evolution. At the same time, new fundamental questions have arisen, which can only be addressed by future sample-return missions from areas of the Moon identified as being more representative than was the case for Apollo and Luna missions. As we enter into a new era of sample-return missions from various Solar System bodies, and with an increasing interest in lunar exploration among the worldwide community, new samples returned from the Moon will have immense potential to address key science questions not just related to the Moon but also to the inner Solar System.
Acknowledgements We thank the organisers of the " Role of Sample Return in Addressing Major Outstanding Questions in Planetary Sciences " workshop that took place at the  International Space Institute in Bern in February 2018. Jim Head  and two anonymous reviewers are thanked for their thorough and insightful evaluation of this manuscript. For financial support, we are grateful to the  UK Science and Technology Facilities Council (STFC)  (grants  ST/P005225/1  to RT,  ST/M001253/1  to KHJ, and  ST/P000657/1  to MA), the  Royal Society  (grant  RS/UF140190  to KHJ), the  European Commission Horizon 2020 Research and Innovation programme (Marie Skłodowska-Curie Actions Fellowship  grant  794287  to JFS), the  Agence Nationale de la Recherche  (project Maglune  ANR-14-CE33-0012  to JG), the  NASA   Solar System Workings program  (grant  NNX15AL62G  to BPW) and the  NASA Solar System Exploration Virtual Institute  (grant  NNA14AB01A  to BPW).
The added value of combining InSAR and GNSS data, characterized by good spatial coverage and high temporal resolution, respectively, is evaluated based on a specific event: the propagation of the magma intrusion leading to the 26 May 2016 eruption at Piton de la Fournaise volcano (Reunion Island, France). Surface displacement is a non linear function of the geometry and location of the pressurized source of unrest, so inversions use a random search, based on a neighborhood algorithm, combined with a boundary element modeling method. We first invert InSAR and GNSS data spanning the whole event (propagation phase and eruption) to determine the final geometry of the intrusion. Random search conducted in the inversion results in two best-fit model families with similar data fits. Adding the same time-period GNSS dataset to the inversions does not significantly modify the results.
Even when weighting data to provide even contributions, the fit is systematically better for descending than ascending interferograms, which might indicate an eastward flank motion. Then, we invert the GNSS time series in order to derive information on the propagation dynamics, validating our approach using a SAR image acquired during the propagation phase. We show that the GNSS time series can only be used to correctly track the magma propagation when the final intrusion geometry derived from InSAR and GNSS measurements is used as an a priori. A new method to extract part of a mesh, based on the representation of meshes as graphs, better explains the data and better accounts for the opening of the eruptive fissure than a method based on the projection of a circular pressure sources. Finally, we demonstrate that the temporal inversion of GNSS data strongly favors one family of models over an other for the final intrusion, removing the ambiguity inherent in the inversion of InSAR data.
The still relatively poor temporal resolution of Synthetic Aperture Radar (SAR) data remains a clear limitation for their use in tracking magma propagation. This phase, which is crucial to understanding the dynamics of magma emplacement, is usually recorded by continuous, ground-based sensors such as GNSS stations, tiltmeters or seismometers for example, References  [9, [13] [14] [15]. It is not always possible to place ground-based captors in ideal locations with respect to the intrusion due to field conditions  [16]. Moreover, seismicity does not always permit to track magma propagation, specifically when microearthquakes are too shallow and have a too small magnitude  [17]. However, SAR temporal resolution has improved appreciably: the usual revisit time was reduced from one month to 6 days with the launch of the Sentinel-1 satellite constellation.
There are a few cases for which SAR images have been acquired during a propagation phase and they all yield exceptional results. One early example is the sill emplacement preceding the 1999 Eyjafjallajökull eruption. The SAR acquisition was favored by a long-lived propagation phase probably linked to limited magma driving pressure  [10]. Similarly, a SAR image acquired during the 2009 eruption at Fernandina volcano, Galapagos  [18]  provided insight into the complex transition from circumferential intrusions to radial eruptive fissures. Also the May 2016 eruption at Piton de la Fournaise volcano was recorded by an intermediate Sentinel-1 acquisition, which occurred only 2 h before the onset of the eruption. With the increase in the number of radar imaging satellites, InSAR data are available with different Lines-of-Sight (LOS) and different precisions depending on the radar wavelength  [19].