dataesr/scientific-paragraphs-categorization huggingface dataset:
1D , Dataset S1, ANOVA with post hoc Tukey HSD ((honestly significant difference)]. In contrast, CA23, a strain that had previously been passaged in the murine GI tract exhibited significantly higher gut colonization fitness relative to the other 24 strains (Fig. 1D ). This higher fitness was observed both in samples obtained from fecal pellets and from the stomach or cecum (Fig. 1D ). Interestingly, CA23 also showed higher relative fitness in the kidney, liver and brain in a systemic infection model (Fig. 1E ). Bar-seq experiments therefore identified a single outlier strain with higher competitive fitness in colonizing both the GI tract and internal organs (data from individual mice shown in SI Appendix, Fig. S3  C-E and Dataset S1). We next examined the fitness of CA23 relative to its parental strain CA10 by performing head-to-head competition experiments in GI colonization and systemic infection models.
Genomic Analysis of Original and Microevolved SC5314 Strains. Sequencing of the collection of 25 SC5314 strains was performed and compared to the C. albicans reference genome (SC5314 assembly  22). This showed that versions of SC5314 obtained from different labs had few major differences (Fig. 2  A and B and SI Appendix, Fig. S5A  and Dataset S3 ). However, one lab's version of SC5314, CA09, contained large loss of heterozygosity (LOH) events located on chromosomes 2, 3, and 5 (Fig. 2  B and C and SI Appendix, Fig. S5 A  and B ). Here, the Chr 2 LOH encompassed ~1. 69 Mbp of this chromosome (75. 6%), while the other LOH events spanned ~0. 2 Mbp (11. 4%) of Chr 3 and ~0. 19 Mbp (15. 7%) of Chr 5. In total, these large LOH events affected 4,160 heterozygous positions located in 593 coding regions highlighting that lab-tolab variation between SC5314 isolates exists.
S10A ). The collection of SC5314 strains together with the CA23 derivatives were gavaged together into mice and Bar-seq and CFU analysis were carried out on fecal samples and GI organs (SI Appendix, Fig. S10 B-D ). After 1 d, analysis of fecal pellets showed that strains CA28/CA29 (1 × NRG1) were present at significantly lower levels than CA23 (3 × NRG1) and CA26/CA27 (2 × NRG1), while there was no significant difference between CA23 and CA26/CA27 at this time point (Fig. 5  A and B and SI Appendix, Fig. S10B  and Dataset S2). At days 3 and 7, all four NRG1-depleted strains (CA26-CA29) showed reduced colonization fitness relative to CA23 as observed both in fecal samples and GI organs (Fig. 5  A and B and SI Appendix, Fig. S10  B and C and Table  S4 ). Indeed, at these time points, the relative fitness of CA26-CA29 was comparable to that of euploid CA10 rather than that of their immediate parent, CA23 (Fig.
5B  and SI Appendix, Fig. S10  B and C and Table  S4  and Dataset S2). These results establish that changes in NRG1 gene dosage determine both filamentation properties and commensal fitness in the GI tract. To further corroborate the competitive fitness phenotypes of CA23 (3 × NRG1) vs. CA26 (2 × NRG1) from the Bar-seq experiment, a head-to-head competition experiment was performed between these strains in the murine GI model. Notably, CA23 exhibited increased fitness over CA26 by day 9 of the experiment and this difference was then maintained to the experimental end point (Fig. 5C  and SI Appendix, Fig. S10E ). These results establish that the presence of an extra copy of NRG1 enhances C. albicans colonization fitness in the GI tract both when examined in a pool of strains and in direct head-to-head competitions.
The PCR was carried out using Phusion polymerase with dimethyl sulfoxide (DMSO) added. The following PCR conditions were utilized: 95 °C for 5 min, 95 °C for 20 s, 60 °C for 20 s, 72 °C for 20 s, steps 2 to 4 repeated for 34 cycles, and 72 °C for 5 min. All PCRs resulted in an amplicon of 250 bp. The multiple PCR samples from the same gDNA were pooled together and resolved on a 2% agarose gel, which was followed by gel purification to generate the final Bar-seq libraries. The concentration of different libraries was determined by Nanodrop and qPCR, and sizes were confirmed on a bioanalyzer. All the libraries were pooled in equimolar concentrations and sequenced on a Miseq sequencer (Novogene). The demultiplexing was performed on the data files considering the different i5 and i7 combinations, which were further processed using Qiime tools (version PNAS 2023 Vol.
120 No. 11 e2218163120 https://doi. org/10. 1073/pnas. 2218163120 9 of 10 2020. 2)  (57). Data shown reflect analyses carried out with forward reads. Different strains were identified with the 20-bp unique barcodes using Qiime Cutadapt and visualized with Qiime2 View (https://qiime2. org). The competitive index was determined for each sample by dividing the values obtained for individual samples by those obtained for the corresponding samples in the initial inoculation pool. Statistical significance was assessed using ANOVA with post hoc Tukey HSD tests by comparing each strain to CA10, CA23, CA26, CA27, CA28, and CA29. Statistical analysis used the package in R (Datasets S1 and S2).
5 °C/s. A Biorad qRT-PCR mix was used to set up reactions that were run on a Biorad CFX Maestro 384-well machine and analyzed with the Biorad CFX Maestro software. RNA Isolation and Gene Expression Analysis. Gene expression analyses were performed by qRT-PCR. C. albicans strains were grown overnight in liquid YPD at 30 °C. Strains were then cultured in YPD at 30 °C (200 rpm) for 4 h. Cells were pelleted and washed three times with sterile PBS. Cell pellets were stored at -80 °C until processed for RNA isolation. RNA was isolated from Candida cells using the RiboPure Yeast RNA isolation kit (Ambion). Total RNA obtained after the elution step was subjected to DNase treatment using Turbo DNaseI. RNA concentration and quality was determined using a Nanodrop. One microgram total RNA was converted to cDNA using the iScript kit (Biorad).
Samples were sequenced using an Illumina HiSeq 2000 generating 101 bp paired reads. The genome sequences and General Feature Files for the SC5314 reference genome (haplotype A, version A22-s07-m01-r130) were downloaded from the Candida Genome Database (http://www. candidagenome. org/). Reads were aligned to the SC5314 reference genome (haplotype A chromosomes) using Minimap2 version 2. 17  (60). Sequence Alignment/Map tools (SAMtools) v1. 10 (r783) and Picard tools version 2. 23. 3 (http://broadinstitute. github. io/picard) were used to filter, sort, and convert the SAM files. Single nucleotide polymorphisms (SNPs) were called using the Genome Analysis Toolkit (GATK) version 3. 6  (61, 62) , according to the GATK Best Practices. SNPs were filtered using the following parameters: VariantFiltration, QualByDepth (QD) < 2.
0, LowQD, ReadPosRankSum < -8. 0, LowRankSum, FisherStrand (FS) > 60. 0, HightFS, MQRankSum < 12. 5, MQRankSum, MQ < 40. 0, LowMQ, HaplotypeScore > 13. 0. Coverage levels were also calculated using GATK, with an average of 168. 2X across samples. A dataset of 8,264 confident SNPs across the SC5314 isolates was created. Besides passing GATK's filters, we also classified heterozygous positions as those with an allelic ratio of the number of alternative allele reads/total number of reads comprised between 20% and 80% and homozygous positions as those with an allelic ratio of the number of alternative allele reads/total number of reads of >80% and <20%. RAxML version 8. 2. 12 (  63 ) with the GTRCAT model of substitution and 1,000 bootstraps was used to infer phylogenetic relationships between the SC5314 isolates using the dataset of 8,264 confident SNPs.
To estimate chromosome copy number, the read alignment depth was calculated for 1 kbp windows across each genome and normalized to the average depth per genome. The normalized alignment depth for each 1 kbp was averaged across 10 kbp windows and plotted using GraphPad Prism 9. Relative changes in ploidy were identified, including whole chromosome and segmental aneuploidy larger than 0. 1 Mbp. To identify changes in heterozygosity, the number of heterozygous positions was calculated per 1 kbp window using vcftools  (64)  and the genotypes assigned with GATK. Heterozygosity levels were averaged across 10 kbp windows and plotted using GraphPad Prism 9. Loss of heterozygosity (LOH) tracts were confirmed by visual inspection in IGV  (65). Data, Materials, and Software Availability. Plasmids used in this study will be made available at Addgene (for Addgene ID, see SI Appendix, Table  S1 ).
Strains generated in the Bennett laboratory are available at FGSC [Fungal Genetics Stock Center (Manhattan, Kansas, USA), fgsc. net, SI Appendix, Table  S1 ]. The 25 SC5314 genome assemblies are available at National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) as BioProject PRJNA860611  (66). The Bar-seq raw reads are available at NCBI SRA as BioProject PRJNA891290 (analysis of 25 strains by Bar-seq are files PK1-PK36 and the set of 29 strains by Bar-seq are files PK37-PK58)  (67).
ACKNOWLEDGMENTS. We acknowledge the generous gifts of SC5314 isolates from the  Medical Research Council, Centre for Medical Mycology ,  Judith Berman ,  Joseph Bliss ,  Stephanie Diezmann ,  Scott Filler ,  Deborah Hogan ,  Ilse Jacobsen ,  Damian Krysan ,  Joachim Morschhäuser , and  Julian Naglik. We also thank  Matthew Hirakawa  ( Sandia National Laboratories ) for help with barcode construct design. I. V. E. is a  Canadian Institute for Advanced Research (CIFAR)   Azrieli Global Scholar  in the CIFAR Program Fungal Kingdom: Threats & Opportunities and was supported by grants from the  French National Research Agency and Institut Pasteur. R. J. B. is supported by  NIH  grants  AI66869 ,  AI41893 , and  AI081704.
This paper analyses the performance of SAR-based results to support emergency management operations during the volcanic crisis of La Palma island. Tajogaite eruption began on September 19th 2021 affecting a high populated area and the surrounding farmland, used for different agricultural purposes. SAR datasets obtained from Sentinel-1, COSMO-SkyMED, PAZ-TerraSAR-X and Capella satellites, were processed to calculate amplitude, coherence and differential interferograms. These results have permitted to map the morphology of the volcanic cone, the evolution of lava flows and the regional displacements. The comparison of the results obtained with the different satellites has been useful to understand their performance during an emergency scenario triggered by a volcanic eruption. COSMO-SkyMED has provided the most useful information for the analysis of the cone morphology and the lava flows.
Implementing monitoring programs is therefore essential to provide civil authorities with useful and easy-to-understand data and products to analyze the evolution of volcanic hazards during the emergency and to make effective risk-based decisions. Earth Observation data constitute a well suited source of information for this purpose, because they allow to remotely monitor the affected areas providing a good cost-effective spatial coverage. Specifically, active Synthetic Aperture Radar (SAR) sensors have the capability of operating night and day and seeing through meteorological cloud cover. SAR can also penetrate through relatively dense ash/aerosols plumes, which is a major asset to provide observations during the course of volcanic eruptions. The pixels of SAR images contain an amplitude and a phase component, both of which provide information relevant to volcano monitoring  (Ebmeier et al.
, 2004) , movement of faults/tectonic structures  (Gudmundsson et al. , 2016)  and caldera subsidence  (Shreve et al. , 2019)  Nowadays there is a great amount of SAR data available from different satellite missions, with different image extent, spatial resolution, radar bands and revisit time, which determines their suitability to monitor the above mentioned phenomena. For instance, several works studying volcanic eruptions have used multi-sensor satellite monitoring to investigate different processes. Arnold et al. (2017)  studied the extrusion rates at El Reventador Volcano in Ecuador, between 2011 and 2016, using X-band SAR data from TanDEM-X to calculate topographic changes and C-band data from RADARSAT-2 to retrieve lava flow extent (by exploiting backscattering changes) maps and lava flow thickness (by exploiting radar shadow).
In the work by  Xu and Jónsson (2014) , the 2007-2008 volcanic eruption on Jebel at Tair island (Red Sea) was analyzed with multisensor SAR interferometry. They mapped the lava areal extent and evolution with ALOS-1 interferometric coherence data (with limited time resolution due to the long revisit time of the satellite) and estimated the volume of the new lava flows by comparing pre-eruptive DEM generated with L-band data and post-eruptive DEM generated with TanDEM-X data. They studied the co-eruptive surface deformation and dyke modelling using ALOS-1, ERS and ENVISAT interferograms and additionally estimated the post-emplacement lava flow deformation due to cooling using ten consecutive ALOS interferograms with a chain stacking approach  (Biggs et al. , 2007). In general, TanDEM-X is better suited for DEM generation, whereas Sentinel-1 usually performs better when monitoring shallow intrusions of large areas with a high frequency and medium coherence loss, and high-resolution SAR data typically offer better performance for mapping morphological features.
However, although many studies devoted to the generation of these products from SAR data can be found in literature, there is a lack of studies focusing on the generation of these products for operational use in the event of an emergency, which should meet some particular requirements (e. g. quick availability of the satellite data, availability of experts to generate the needed products, which must further be easily understandable for the decision-making authorities, etc. ). A particular need we found during La Palma 2021 emergency, was the development of a clear and straightforward method to help decide which data are best suited for each application (depending on different factors such as spatial resolution, revisit time, availability, etc. ). A method based on the critical evaluation from a real experience would thus be of great value.
This eruption, which affected densely populated areas of the island and lasted 85 days, caused more than 800 million euros in damages and the evacuation of more than 7000 people  (Cabildo La Palma, 2022). In this paper, we present 3 SAR-derived products that IGME team produced during La Palma emergency to identify potential geohazards associated to the eruption. Specifically, the aims of these products were to monitor 1) the evolution of the morphology of the volcanic edifice; 2) the extension of lava flows and 3) the surface ground deformation. We then evaluate the performance/operativity of the 5 different SAR dataset that we used for emergency support (Capella, COSMO-SkyMed -Spotlight, COSMO-SkyMed -Stripmap, PAZ and Sentinel-1), considering both technical and accessibility factors.
SAR data and validation data During the Tajogaite eruption the IGME team used images from four different SAR constellations: Sentinel 1 A/B (S-1), PAZ-TerraSAR-X (PAZ-TSX), COSMO-SkyMed (CSK) and Capella. The different platforms allow to perform different analysis adapted to the best combination of resolution, revisiting time and wavelength available. S-1 is a constellation of two satellites developed by the European Space Agency (ESA) that provide C-band SAR images of medium-high resolution (5 × 20 m), wide sensing area (250 km width) and a repeating cycle of 6 days  (Torres et al. , 2012). During the eruption period (September -December 2021), 25 images in ascending and 25 in descending trajectory were acquired (Fig. 3 ), meaning one acquisition each 6 days per trajectory (Table  1 ). They were freely available to download at the Copernicus Open Access Hub two hours after each acquisition.
, 2019; Shevchenko et al. , 2021). In the work of  Dualeh et al. (2021)  a summary of case studies classified by the type of eruption, studied process and method is presented. SAR visual analyses of amplitude data are independent of the satellite revisiting period and data can be directly compared with that from other satellites or UAV. Changes in the features and backscattering generated by the different acquisition geometries and incidence angles must be considered during the analysis of the results. During La Palma eruption four datasets from different satellites were available, all of them with capacity to perform an amplitude analysis. Images from Sentinel-1, CSK and PAZ were processed using ESA SNAP software  (Veci et al. , 2014)  applying the processing chain shown in Fig. 4. Images were first calibrated and filtered to facilitate comparison and reduce speckle effects.
However, GEP tools are semi-automatic and unsupervised, so it is convenient to validate the results or, at least, compare them with other data. To obtain two datasets for cross-comparison, we used the  processors P-SBAS and SNAPPING. Multitemporal InSAR results were obtained at the end of the eruption, but these tools can be helpful also during the emergency to retrieve mean velocity results (where atmospheric and other nuisance artifacts are mitigated) in a fast way. Moreover, SNAPPING offers the possibility of easily adding newer SAR images to the multitemporal InSAR processing as it uses a Single Reference approach. P-SBAS processing chain provides a parallel computing solution  (Casu et al. , 2014; Manunta et al. , 2019)  for the SBAS approach  (Berardino et al. , 2002)  and it was developed to handle massive SAR archives from current satellites.
The main application in La Palma eruption was the visual recognition of the cone new features and evolution, covered by four different satellites (Capella, CSK, PAZ and Sentinel-1) with two acquisition geometries (except for Capella) and different modes (CSK Spotlight and Stripmap). This diversity of data allowed to compare their usefulness to analyze the cone evolution. PAZ data provided 71 Stripmap images with a period of 11 days in 6 different tracks. The high resolution of those data (3 m pixel) allowed to monitor the evolution of the cone during the whole eruption and represented the main reference data to describe the cone morphological evolution (Fig. 8 ). Fig. 8a  show the first image of the cone after the beginning of the event (27/09/2021). Although the eruption started at the SE of the image, the eruptive fissure propagated towards the NW where the activity was focus.
Twenty-five images from Spotlight mode provided very high-resolution data (1 m pixel) improving the morphological changes recognition. Fig. 9  shows the comparison between PAZ and CSK images acquired at the same time in both different orbit directions. Thanks to the finer resolution of CSK, the vents location and position of the slopes and cliffs could be more clearly identified and characterized with visual inspection. Also, possible anomalies or instabilities could have been early detected. Information provided by UAV images was highly enhanced using SAR information since first have severe difficulties during the most active periods (Fig. 9e ). Data from Capella constellation provided the highest resolution (less than 0. 5 m pixel) with the possibility to define cone and lava flow features (Fig.
17a ). However, its orbit changes still reduce their operability during emergencies. On the opposite side, Sentinel-1 data is the most stable and fast-responsive data, but the medium resolution (15 m pixel) of the Interferometric Wideswath (IW) mode decrease the usefulness of this data for morphological analysis (Fig. 17d ). During the emergency the SAR amplitude images represented an important complementary source of information, as they were obtained at any weather conditions, overpassing the volcanic plume, and some were acquired during the night, when other options like UAV were unavailable. Once processed, they were analyzed to detect new features in the cone area that could reveal changes of its morphology. This objective was achieved mainly using the high-resolution data from Capella and CSK Spotlight but also CSK and PAZ Stripmap mode.
Lava flow detection Fast delineation of lava flows over large areas helps to optimize emergency services, as inhabitants evacuation, and reduce the possible damages. Monitoring of fresh lavas over older ones provide data about flow direction and possible spills over unaffected areas. Limiting their use, SAR images for MTC must be interferometric pairs, reducing the data availability. During the first stage of the eruption the event was covered by PAZ images with a temporal difference of 11 days. MTC results shows in blue and green the greatest changes between the two images compared. Fig. 10a  shows the initial changes with the appearance of the main cone and lava flows. It is important to highlight that the changes on the    interferometric data with time gaps of even 1 day. The high temporal resolution reduces the temporal decorrelation, and allowed to detect the active lava channels with great changes inside the slowly changing old lava flows.
This comparison confirms the ability of MTC maps not only to detect the active lava channels but also the side and distal lobes, difficult to identify in the optical inspection. Even if optical data from UAV which was essential during the emergency, it had troublesome to assess the activity of lavas, especially in the distal lobes. That kind of data also requires several flights over the area, expensive in time and machine wearing. SAR data provides a complete overview of the area with short processing times. Accurate demarcation of the lava flow perimeter during the emergency was daily updated by several organizations using direct inspection, UAV, SAR and Optical images. For this reason, the MTC maps generated by the InSARLab team were not used for this task. On the other side, the active lavas location obtained analyzing the MTC maps were a newly developed result that had to be confirmed and validated during the emergency process.
, 2022). This model is also coherent with the depth and location evolution of the earthquakes (Fig. Supl. 1; IGN, 2022a). After the beginning of the eruption, displacements were monitored using regular Sentinel-1 acquisitions each 6 days in both geometries including 20 interferograms on each (Fig. Supl. 2 and 3). Interferograms were analyzed joint with information from in-situ monitoring data available (GNSS stations, extensometers, tiltmeters), visual information from field staff and other data from satellites (Pleiades, VIIRS). All the anomalies detected after the co-eruptive interferograms showed no significant signals to be considered as confirmed displacement. The results are confirmed by the stable trend measured by IGN GNSS stations. One of those permanent stations (LP03) registered 6 episodes of sudden vertical displacement (Fig.
16 ). They were not observed in the Sentinel-1 Distal inactive areas of the lava flows began to cool without new lava intakes, showing the first coherent data in the edges on October 21st (Fig. 14a ) and all over the lava flows after the eruption end on December 13th (Fig. 14b ). Interferograms after that date reveals a continuous subsiding trend in the lava flows according to their natural contraction while progressively cooling. Interferometric results from Sentinel-1 were obtained each time a new image was ready to use. Once available at the Sentinel-1 repositories, around 1 h after acquisition, the shortest interferometric pair was calculated and analyzed. Results and first conclusions were reported to the in-situ field team. Displacement anomalies were compared with GNSS data. Local anomalies detected in-situ were communicated to the remote sensing team and analyzed in the interferometric results using both Sentinel-1 and high-resolution data available.