dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, the problematic is different because the sources are non axi-symmetrical and 3D displacements can be computed. Since, this study is of a fissure eruption, the source of unrest is a fracture, which is a non axi-symmetrical source. Non uniqueness in the surface response may be caused by material properties  [24, 25]  but for a given material response, there is a unique surface displacement for each individual fracture. Moreover InSAR provides displacements along several ascending and descending LOS and GNSS gives 3D displacements, thus the combined use of these two types of data can be used to describe 3D displacements  [26]. Non unique source determination comes from inverse modeling, which inherently accounts for errors in data and models. The main sources of error in InSAR and GNSS data derive from atmospheric conditions (water content of the troposphere and ionosphere) and models are simplifications of a complex reality: the number of sources is unknown and their shapes are probably more complex than assumed.
A previous study of the  26  May 2016 eruption at Piton de la Fournaise  [16]  showed that, despite the very dense network of permanent GNSS stations present on this volcano, magma propagation could only be successfully tracked when adding a priori information on the intrusion shape provided by the inversion of co-eruptive interferograms. This conclusion was reached using the intermediate Sentinel-1 acquisition, which allowed different strategies to invert GNSS data to be quantitatively evaluated. Here, we extend our previous study on the combined use of InSAR and GNSS data and we investigate the usefulness of GNSS time series in resolving the non-unique inverse models obtained from InSAR data. Time series of displacements, whether provided by continuous GNSS and tilt measurements  [27]  or by InSAR  [28] , are usually analyzed using kinematic inversions, where amplitudes of dislocation sources  [29]  are determined from minimizing the sum of the L2 norm of the weighted residual on displacements and spatially smoothed dislocation amplitudes.
This requires as many parameters as there are dislocations, which leads to statistically less likely solutions  [30]  and solutions physically less meaningful than pressure sources  [31]. In our previous study  [16] , we addressed these issues by determining the pressurized area corresponding to the active intrusion, where the a priori intrusion was derived from InSAR and a circular pressure source was projected onto the mesh representing this intrusion. This method had two drawbacks: 4 parameters were needed when the number of permanent GNSS measurements is 30 and a projection from a plane on the intrusion geometry was needed, which could exclude part of the intrusion and thus induce errors if the intrusion geometry was curved. In this study, we focus on the methodological aspects of jointly inverting InSAR and GNSS (campaign and time series) data covering the May 2016 Piton de la Fournaise eruption in order to track magma propagation.
Because of the non-unique nature of inversions, data covering the whole eruption leads to two families of equally well fitting intrusion models. The influence of the data weightings on the inverted intrusion is also studied. We next use times series of GNSS data to determine the dynamics of the magma propagation for these families of models and to investigate if time series help in the determination of the most likely family of models. Finally, three methods are compared to track magma in the intrusion, one with no a priori and two using the a priori geometry determined from data covering the whole eruption. One of these methods is a new strategy, which uses a graph-based representation of meshes to define the pressurized part of the intrusion. Results from this new method are compared with previous results.
After 10 days of minor inflation and a low seismicity rate, a seismic crisis started on May 25 at 19:40 (all times are U. T. C) accompanied by rapid deformation, which corresponded to magma transfer towards the surface  [16]. At 04:05 on May 26, a fissure opened, 2800 m to the southeast of the summit cone, at an elevation of 1890 m producing lava fountaining and a 0. 5 Mm 3 lava flow, which is an unusually small volume for a Piton de la Fournaise eruption  [8]. The eruption stopped after 27 h. InSAR acquisitions and GNSS measurements provide an extensive dataset which can be used to understand the magma transfer during this eruption  [16]. Seismic data are also available. However, in this case earthquakes being too small and shallow, precise relocation does not allow to track magma at the depth indicated by InSAR and GNSS data  [16, 17].
InSAR Data Synthetic Aperture Radar (SAR) images are systematically acquired by various satellites (Sentinel-1 in Strimap Mode, Cosmo Skymed, ALOS2, TerraSar-X)  [38]  within the framework of the national monitoring service, OI 2. For the 26 May 2016 event, images acquired by both Sentinel-1 and Cosmo-Skymed are available. All interferograms were produced using the DIAPASON software  [39] , removing the orbital contribution based on the precise orbit state vectors provided by Space Agencies as well as the topographic contribution using a 5 m Lidar DEM, produced by the French Geographic Institute (IGN) in 2008-2009. The interferograms were unwrapped using the Snaphu algorithm  [40]. They were then detrended and shifted such that a null displacement in the Enclos Fouqué caldera (see location on Figure  1 ) was used as a reference.
GNSS Data Surface displacements of Piton de la Fournaise volcano are continuously monitored by a permanent GNSS network consisting of 24 stations, operated by the Piton de la Fournaise Volcanological Observatory (OVPF). In this study, we used only the 10 stations located in the summit part of the Enclos Fouqué area, which were close enough to the intrusion to capture the unrest associated with the eruption (see Figure  1 ). Processing was carried out with the GAMIT/GLOBK software package  [43]  to determine a daily position for each station. The station REUN, located 17 km away from the volcano was used as a reference to correct for Earth plate motion. Time evolution of two GNSS baselines located at the summit is shown on Figure  2. No significant deformation was recorded during the period spanned by InSAR data prior to and after 25-26 May 2016.
A Young's Modulus of 5 GPa and a Poisson's ratio of 0. 25 were assumed for the Piton de la Fournaise edifice, following Cayol and Cornet  [46] , Fukushima et al. [48]. To take advantage of the whole data set acquired during this eruption, we developed a strategy for inversion that consisted of several steps (Figure  4 ). First, we performed a global inversion that is, an inversion of the cumulated InSAR and GNSS displacements recorded between two dates: one before the eruptive crisis onset, that is, before the onset of the magma propagation and one after the eruption had ended. In this study, we refer to this global inversion as the "static inversion" as it is intended to reproduce the final geometry of the magma that was trapped at depth after the eruption had resumed. Second, we performed a temporal inversion, that is, inversions of the cumulated displacements for 29 time intervals between 20:40 on 25 May and 4:00 on 26 May, from GNSS time series.
Inversion and Data Weighting Through the Covariance Matrix Inversion of ground displacements to determine the best-fitting deformation model requires minimization of the difference between the observations and the model results. This difference is called a misfit and we calculated it in a least square sense (L2-norm) as : χ 2 = (d 0 -G(m)) T C d -1 (d 0 -G(m)) (1) where d o represents the vector of observed displacements, G(m) is the vector of modeled displacements with m the set of model parameters and G represents the model prediction. C d is the covariance matrix for the data, which accounts for correlated noise related to the atmospheric contribution to InSAR data and for the variance of GNSS data. The atmospheric contribution results in correlated random noise, which can be characterized by the autocorrelation function or covariance function  [48, 50] , expressed as: C(r) = σ 2 d exp(-r/a) ( 2 ) where r is the distance between two points, σ 2 d is the noise variance and a is the correlation length.
Following the study of Fukushima et al. [51] , the data covariance matrix C SAR d can be computed from this function using a noise variance σ 2 d = 5 × 10 -4 m 2 and a correlation distance a = 850 m. This results in a diagonal dominant matrix where diagonal terms correspond to σ 2 d. In addition to InSAR data, GNSS measurements were used from both permanent and campaign networks. We assumed there was no correlation between the GNSS stations, so that the covariance matrix C GNSS d was diagonal and was a function of the variance. C GNSS d = diag σ 2 d ( 3 ) Typical magnitude of the GNSS variance was σ 2 d = 2 × 10 -4 m 2 for horizontal components and σ 2 d = 9 × 10 -4 m 2 for vertical components. The data vector d o and covariance matrix on data C d combining InSAR and GNSS data are given, respectively, as : d o = d o GNSS d o SAR (4) C d = C GNSS d 0 0 C SAR d ( 5 ) Inversions consist of two stages : a search and an appraisal stage.
In the search stage, the misfit is minimized using a near-neighborhood inversion algorithm  [52]. In the appraisal stage, model uncertainties are estimated by resampling the population of models obtained in the search stage following the Bayesian inference  [53]. Because the misfit function is a L2-norm, it favors models that explain the largest displacements. The matrix covariance characterizing the data is used to account for errors and intrinsic noise affecting the data. A proper definition of the covariance matrix should weight data according to their own errors in the inversion process. However, InSAR data are projections of a single displacement field along the LOS direction. Due to this projection, a displacement vector will produce LOS displacement of different amplitudes on ascending and descending LOS.
Since a null LOS displacement can mean either no displacement or displacement orthogonal to the LOS, there is no reason to give a lower weighting to datum of lower amplitude. To give equal weighting to each dataset and avoid overweighting one LOS over the other or the GNSS data when they were added, we chose to conduct inversions in which misfits of the different datasets were scaled by their reference values. We defined the reference value as the value computed from a null model χ 2 re f as χ 2 re f = d o T C d -1 d o (6) This defined the misfit relative to this reference value χ 2 (%) as χ 2 (%) = χ 2 χ 2 re f × 100 (7) This value being independent of the total amplitude of displacements, it is useful for comparing inversion results at different time steps of the temporal inversion. We conducted inversions according to a weighted misfit scaling each dataset by its own reference misfit: χ 2 w =   N ∑ i=1 χ 2 i χ 2 i re f + χ 2 GNSS χ 2 GNSS re f   × χ 2 re f N + 1 ( 8 ) where i refers to the i th interferogram and N is the total number of interferograms.
This scaling is equivalent to applying different scaling factors of χ 2 i re f /χ 2 re f with i = 1,. , N and χ 2 GNSS re f /χ 2 re f to the covariance matrices of the InSAR and the GNSS data, respectively. In terms of Bayesian inference, this scaling gives the same likelihood to each of the InSAR and GNSS datasets and each of these datasets has the same contribution to the posterior probability density distribution.
We chose to invert the interferograms directly rather than use the 3D components reconstruction of the displacement field in order to reduce the errors induced from using several interferograms spanning different time periods. We decided not to use the whole dataset because the use of different data in the same LOS could have led to over weighting of one LOS. In total we run seven inversions (Table  1 ). With inversions 01, 02a and 02b, we checked whether the inflation during the 10 days of unrest could be neglected by comparing the influence of interferograms S1 A and S1 A2 on the inversion results (Table  1 ). In inversion 04a, 04b and 05, we tested whether the addition of GNSS data, which adds constraints to the north-south displacement, led to a different, better constrained source. Inversions 02a and 02b (and 04a and 04b) were conducted with the same initial conditions in terms of data used, weighting and parametrization, in order to test the variability of the inversion due to the random search.
Lastly, in inversions 03, 04a and 04b, we tested the weighting method to investigate if different inverted sources were obtained and to gain insight into the origin of discrepancies between independent datasets. 4 ) and (  5 ), 'No' means that no specific additional weighting is applied whereas χ 2 re f means that the misfit is scaled by the misfit of the null model), geometry of the best model (for families definition see Section 5. 1. 1) and % of Explained data (see Equation (  9 )) computed for each interferogram spanning the eruption. Numbers in bold are for data used in the inversion, values in small italics are computed a posteriori for data not used in the inversion. Inv01 was computed with InSAR data S1 A2, which does not cover the pre-eruptive period. Inv. 01 to 03 were computed without including the GNSS data.
Results The static inversion results were evaluated based on the percentage of explained data (%Ed), which were computed per dataset or as an overall. For the ith dataset (either a given interferogram or the GNSS data), %Ed i is evaluated as: %Ed i =   1 - u obs i -u mod i T u obs i -u mod i u obs i T u obs i    * 100 (9) where u i obs and u i mod are the data and model vectors corresponding to all pixels in the masked interferograms or to all GNSS measurements. This value is independent of the covariance matrix, the subsampling and the dataset used to run the inversion. Based on this definition, the closer to 100%, the better the data fit.
Considering only the InSAR data, the sum of the reference misfit on each interferogram leads to a total reference misfit of χ 2 i re f = 3070, while the reference misfits for all ascending interferograms and all descending interferograms are 970 and 2100, respectively. Thus, although subsampled interferograms have a balanced number of points, the descending interferograms account for 2/3 of the total misfit. This higher weight simply results from the displacement pattern and amplitude and from the projection of this displacement along the LOS (Figure  8 ). To counteract the influence of the weighting, we normalized the data by their reference misfit χ 2 re f. This weighting is equivalent to a normalization of the covariance matrix. Without this normalization, each dataset has uncertainties simply estimated from the data  [57].
By altering this normalization factor, we used the values coming from the data uncertainties to evaluate the relative weighting of the data from a single dataset. However, we artificially considered that each dataset was equally likely. Results from inversions 03, 04a and 04b processed with this new weighting (Table  1 ) show an increase of the misfit on ascending data by a few percent, confirming that this weighting is efficient. Doing so, the calculated geometry remains the same and the inversion process is not able to improve the fit for the ascending data, even at the expense of the fit on the descending data. 6 ) and weight of each dataset in the computation of the misfit. The top three lines correspond to inversions conducted without the campaign and continous GNSS data, while the bottom three lines include the GNSS data.
A Need for Geometrical a Priori to Invert for the GNSS Time Series We developed three methods of source parametrization to invert the whole GNSS time series. Taking advantage of the SAR acquisition during the eruptive crisis, we compared inversion results using either continuous GNSS data, InSAR data (S1 D1) or both datasets covering the first part of the eruption (until 1:45) with each method (Table  4 ). The Ellipse method does not have any a priori on the source location. The inversion of cumulated displacement from GNSS time series data leads to a very good fit (96%) of these data (Table  4 ). However, the fit on the S1 D1 interferogram falls to 22%. The horizontal location of the deformation source (Figure  9 ) is comparable to the source determined by the static inversion for the whole eruption.
GNSS Temporal Inversion Solves the Conundrum of Non Unique Static Inversion In this study, we also showed that GNSS time series data help to choose between equally likely models determined from InSAR. Regardless of whether ascending and descending InSAR data have equal weighting or weighting coming from the data amplitudes or whether GNSS data were used or not, two families of equally well fitting models were obtained from the inversion of data covering the whole eruption (Table  1 ). These non unique results arise from inherent uncertainties pertaining to the data and the models. Here, we show that GNSS time series data can resolve the ambiguity between these families. In fact, the family of sources corresponding to a sill that turns into a dike leads to a source consistent with the displacement time series from GNSS and to a more meaningful propagation of the magma: magma starts beneath the summit and moves towards the ground surface in a way that is compatible with observations at this volcano  [58].
Conclusions InSAR and GNSS data provide complementary information on the spatial distribution of displacements and their temporal evolution, respectively. Combining these data, we successfully inverted the source of observed displacements in order to track magma transport in the volcanic edifice of Piton de la Fournaise. We showed that four different LOS are sufficient to invert the static geometry of the emplaced magma intrusion. The addition of campaign and permanent GNSS displacement covering the same period does not remove the ambiguity pertaining to the non-uniqueness of inversions. Inversion of InSAR data covering a time period shorter to the other InSAR data indicates a pre-eruptive phase of deformation which is not detectible in the longer time period. Systematic discrepancies in data fitting between ascending and descending LOS are indicative of seaward flank displacement accompanying the magma intrusion.
Funding: The PhD Fellowship of D. S is funded by the  French Ministry for Higher Education and Research. This research was partly supported by the  INSU-CNRS ALEAS program , by the  ANR  through the  SLIDEVOLC project  (contract  ANR-16-CE04-004-01 ) as well as the  MagmaPropagator project  (contract  ANR-18-CE92-0037-01 ), by the  French Government Laboratory of Excellence  initiative  ANR-10-LABX-00006 , the  CNES  through the  CNES  project  AssimSAR , the  Région Auvergne  and the  European Regional Development Fund.
Genome sequencing, assembly, and repetitive element identification Library construction (180 bases-insert library) and genome sequencing (Illumina HiSeq platform) were performed at Eurofins Genomics (Ebersberg, Germany). The quality of the 101-bp Illumina reads was assessed using FastQC v0. 11. 3 (http://www. bioinformatics. bbsrc. ac. uk/projects/ fastqc), and low-quality sequences were removed by Trimmomatic  (Bolger et al. 2014 ) and adaptors were removed by BBDuk from the BBMap package (https://sourceforge. net/ projects/bbmap/). High-quality reads were assembled using SPAdes genome assembler v3. 10. 0  (Bankevich et al. 2012)  with default parameters for Illumina paired-end reads. Repeated elements were identified and masked using RepeatMasker (http://www. repeatmasker. org) and to identify copies of de novo repeats identified by RepeatModeler (http:// www.
Gene prediction For gene prediction, the software Augustus  (Stanke and Waack 2003)  was initially used for generating training gene sets and therefore used as input the models of each organism produced for Genemark-ES v4. 30  (Lomsadze et al. 2005). The trained models were used again by Augustus  (Stanke and Waack 2003)  for the gene prediction in the masked genomic sequences. In addition to protein-coding genes, tRNAs were predicted by tRNAscan-SE v1. 23  (Lowe and Eddy 1997). The protein sequences were functionally annotated by InterProScan (database version 5. 27-66. 0) and BLAST against Swiss-Prot database.
Functional annotation To predict CYP450s, we identified proteins carrying the PFAM domain PF00067 and compared, by using BLAST, their sequences against the CYP previously described by  Teixeira et al. (2017). Putative CYP450 genes were classified into families and subfamilies as recommended by the International P450 Nomenclature Committee  (Nelson 2006)  using the BLAST identity cutoff of 35% for families and 55% for subfamilies. CYP proteins that could not be assigned to families or subfamilies based on the International P450 Nomenclature Committee were aligned and subjected to phylogenetic analyses, as described elsewhere  (Chen et al. 2014a). Carbohydrate-Active Enzymes (CAZymes) were identified using the dbCAN2 web server  (Zhang et al. 2018). For comparative studies, we repeated the same analyses using the 24 previously sequenced black yeasts  (Teixeira et al.
2015) , fungal version 4. 1. 0 (https:// fungismash. secondarymetabolites. org/). Membrane transport proteins were identified and classified as described elsewhere  (Teixeira et al. 2017). Briefly, protein sequences were compared using BLASTP (E-value threshold 1e-05) against the transporter sequences available at Transporter Classification Database (TCDB)  (Saier et al. 2014). To predict changes in size of protein families, functional d o m a i n s w e r e c o u n t e d a n d c o m p i l e d f o r 2 1 Herpotrichiellaceae and in the domatia-associated species. Significant contractions of specific InterPro domain were determined by Fisher's exact test using the Python library SciPy.
Mitochondrial genome extraction and annotation Mitochondrial genomes were extracted by aligning the contigs generated from the whole genome assembly against the reference mitochondrial genome of Exophiala dermatitidis NIH/ UT8656 (Genbank NW_008751656) using NUCmer (default GenBank ID QQXO00000000 QRBJ00000000 QQSL00000000 QQXN00000000 parameters)  (Kurtz et al. 2004 ). Mitochondrial genome annotations were done using MFannot (http://megasun. bch. umontreal. ca/cgi-bin/mfannot/mfannotInterface. pl) and manually adjusted. Annotation of tRNA genes was improved using tRNAscan-SE  (Lowe and Eddy 1997).
Gene family expansions and contractions By comparing the content of protein functional domains identified in the Chaetothyriales colonizing ant domatia against the family Herpotrichiellaceae, we verified 23 significant (Fisher's exact test, q value < 0. 05) gene family contractions (Table  2 ). Such contractions are unique among the Chaetothyriales and provide specific genomic signatures of black domatia-associated fungi. Several contracted families belong to classes of enzymes thought to confer the metabolic plasticity previously described in Herpotrichiellaceae, including cytochrome P450s, transcription factors, alcohol dehydrogenases, and membrane transporters of the superfamily major facilitator, such as sugar transporter-like (Table  2 ). Protein domains associated with the metabolism of volatile organic compounds (VOC), such as the S-hydroxymethylglutathione (IPR006913), as well as flavin monooxygenase-like (IPR020946), which constitute a family of xenobioticmetabolizing enzymes, were also found depleted, but not absent, in this group of fungi.