dataesr/scientific-paragraphs-categorization huggingface dataset:
Fluoxetine Impacts on Diatom Metabolisms and Pathways A first analysis of the FT-ICR-MS spectra from the cells exposed to different fluoxetine concentrations revealed a wide array of changes, both in terms of peak intensity and peak diversity (Figure  2 ). This is particularly evident in the mass spectra corresponding to the peaks obtained from the cells exposed to the highest fluoxetine dose (80 µg/L), where the lowest number of common compounds were detected between this treatment and the control conditions (0 µg/L). Moreover, the cells exposed to 80 µg/L also showed the highest number of exclusively detected peaks (933), when compared to the control condition. A total of 640 putative metabolites that were significantly up-or down-regulated following fluoxetine exposure were identified upon submission of the mass data of all surveyed samples to the MassTRIX platform, and several metabolic pathways were highlighted in the KEGG database (Figure  3 ).
Six major metabolic pathways were highlighted due to the high number of metabolites belonging to these pathways and identified by MassTRIX. These metabolic pathways are mostly linked to the photosynthetic carbon fixation (yellow pathway), glycolysis (blue pathway), vitamin metabolism (green pathway), carbohydrate metabolism (purple pathway), fatty acid and lipid metabolism (black pathway) and steroid metabolism (red pathway). veyed samples to the MassTRIX platform, and several metabolic pathways were highlighted in the KEGG database (Figure  3 ). Six major metabolic pathways were highlighted due to the high number of metabolites belonging to these pathways and identified by MassTRIX. These metabolic pathways are mostly linked to the photosynthetic carbon fixation (yellow pathway), glycolysis (blue pathway), vitamin metabolism (green pathway), carbohydrate metabolism (purple pathway), fatty acid and lipid metabolism (black pathway) and steroid metabolism (red pathway).
The direct impact of fluoxetine on serotonin remobilization has therefore significant impacts on the phenylpropanoid biosynthesis pathway. This is in line with our results, where the phenylpropanoid biosynthesis pathway appears to be significantly down-regulated at high fluoxetine concentrations, indicating that although fluoxetine is designed to inhibit serotonin re-uptake in the nervous system synaptic spaces, the action of this antidepressant is also observable in algae cells in a similar way. Beyond the undeniable value of state-of-the-art untargeted FT-ICR-MS metabolomics to unravel the metabolic mechanisms behind fluoxetine toxicity in non-target organisms, such as marine diatoms, this technique also provides large datasets that can be used for fluoxetine biomarker discovery. In ecotoxicological terms and considering the diatom P.
Funding: The authors would like to thank  Fundação para a Ciência e a Tecnologia (FCT)  for funding the research at  MARE  ( UIDB/04292/2020  and  UIDP/04292/2020 ),  ARNET-Aquatic Research Network Associated Laboratory  ( LA/P/0069/2020 ),  BioISI  ( UID/MULTI/04046/2019 ) and via project grant  PTDC/CTA-AMB/30056/2017  (OPTOX). B. Duarte ,  V. F. Fonseca  and  M. Sousa Silva  were supported by researcher contracts ( CEEC-IND/00511/2017 ,  2021. 00244. CEECIND  and  CEECIND/02246/2017 , respectively). M. T. Cabrita is supported by a DL-57 researcher contract. We also acknowledge the support from the  Portuguese Mass Spectrometry Network  ( LISBOA-01-0145-FEDER-022125 ) and the  Project EU_FT-ICR_MS , funded by the  Europe and Union 's  Horizon 2020 research and innovation program  under grant agreement no.
Note that in the close vicinity of the GH events of Uganda and Burundi, large sediment-loaded riverbeds are visible. This is a consequence of the GH events that contributed directly to the transport of extra material to the rivers, increasing not only their sediment content but also their lateral mobility. These river dynamics are not included in our analysis. The two panels at the lower left depict the location of the GH sites (S2 imagery). Image credit: contains modified Copernicus Sentinel data (2022), processed with Google Earth Engine. ALOS 3D DEM data provided by Japan Aerospace Exploration Agency (JAXA). manually mapped Burundi GH event inventory to be similar or more accurate since we use a combination of S2 and Planet satellite data and a completely manual detection workflow. The date of GH event occurrences is determined from local media and field observations, and if not available from these resources it is determined by the first and last available imagery from S2 and Planet imagery.
, 2000). Additionally, a change in atmosphere (precipitation events, ionospheric disturbances) can dynamically influence the coherence values  (Rocca et al. , 2000; Jacquemart and Tiampo, 2021). To better assess the ability to detect GH timing, it is essential to understand the dynamic factors controlling the behaviour of the signal. We derive precipitation estimates from the Global Precipitation Measurement (GPM) Level 3 Integrated Multi-satellite Retrievals for GPM (IMERG) final daily (10 km spatial resolution) dataset that has been validated through rain gauge data within the area  (Nakulopa et al. 2022). General vegetation patterns per GH event are visualised using the normalised difference vegetation index (NDVI;  Tucker, 1979). NDVI time series are derived from the Landsat 8 (30 m spatial resolution) archive and processed within the GEE environment.
e. edge pixels that contain both impacted and non-impacted landscape) since we do not know the effect of these pixels on the SAR time series and GH event timing estimations. This allows us to establish baseline results. The ascending and descending track data are processed separately throughout the two workflows. Amplitude and coherence time series are generated by averaging the values within the identified impacted area per image (Fig. 3 , step 3), and the SAC time series are generated by applying the SAC method (Fig. 3 , step 2; Sect. 3. 2) within both workflows. The resulting time series are normalised using the time series average to improve comparability. Additionally, we make an effort to remove the seasonal influence and atmospheric effect on the amplitude and coherence time series by subtracting the regional amplitude and coherence trend (i.
3,  Step 4). The algorithm was set to predict only one breakpoint since we aim to detect the most significant change in the time series. The output of the applied binary segmentation change detection algorithm is a value that represents the location of an image within the image stack. The date of this image is extracted and assigned as the earliest date after the GH event occurrence. This applies for the amplitude and SAC time series. However, since coherence is based on image pairs, it would identify the image pair right after the GH event. We therefore assign the first date from this image pair as the earliest date after the GH event occurrence. On the complete GH event scale (timing workflow 1) this results in two dates (from ascending and descending track) per data product (amplitude, detrended amplitude, SAC, coherence, detrended coherence).
Additionally, in its current form, the methodology does not account for the GH events that occur within a time span that is longer than the acquisition time (> 6-12 d) of S1 images (i. e. multi-temporal GH events). In that case one would require a time window of occurrence, rather than a specific date. The methodology can be adapted to allow for it to derive a time window of GH occurrence. This could mainly be done following the GH event scale, where the start and the end date of the GH event inducing change within the SAR time series (applicable for all data products) should be indicative of the time window of GH event occurrence. However, this remains to be investigated. 4. The open-access S1 satellite with its high resolution, high repeat time, and global coverage proves to be an excellent data product for estimating GH event timing and allows for our developed methodology to be applied on every region of the world.
The use of our methodology with different satellite products (e. g. Constellation of small Satellites for the Mediterranean basin Observation-SkyMed (COSMO-SkyMed), upcoming NASA-Indian Space Research Organisation SAR (NISAR) satellite) is not straightforward. Different available SAR satellite products operate in different bands with their own characteristics (e. g. X-band for COSMO-SkyMed;  Covello et al. , 2010 , L-band for NISAR; NISAR, 2018) that will likely have implications on the ability for accurate GH event timing estimation. For example, the varying vegetation penetration depths associated with different SAR bands  (Dzurisin, 2006)  will likely have an influence on the impact of seasonal vegetation dynamics on the SAR time series as observed for our GH events (Fig. 6 ). 5. The methodology can benefit (in terms of data availability, scalability, and processing time) from implementation on a cloud computing service.
Conclusion We established a regionally applicable methodology to automatically determine GH event timing from SAR images that can be applied without prior knowledge of the GH event. We successfully assessed the use of multiple SAR-derived data products in their ability to accurately detect GH event timing in contrasting landscapes. We show that landslides and flash floods can be detected and studied together; hence, we open new perspectives in the study of multi-hazards that can subsequently aid in hazard assessment, early warning, and disaster risk reduction strategies. Our methodology has the potential to be combined with existing spatial detection methods to support inventory creation and boost GH event research in remote inaccessible areas such as the African cloud-covered tropics. From a data processing point of view, the methodology is established around an unprecedented analysis of various SAR products coming from Sentinel-1 (S1) images.
Acknowledgements. This study was supported by the  Belgian Science Policy Office (BELSPO)  through the  PAStECA  project ( BR/165/A3/PASTECA ) entitled " Historical Aerial Photographs and Archives to Assess Environmental Changes in Central Africa " (http://pasteca. africamuseum. be, last access: 7 November 2022) and the  GEOTROP  project ( B2/223/P1/ GEOTROP ) entitled " GEOmorphic hazards and compound events in a changing Tropical East Africa ". The compilation of the inventory data benefited from field-based insight and discussion with  Arthur Depicker ,  Josué Mugisho Bachinyaga ,  John Sekajugo , and  Judith Uwihirwe. PlanetScope data provided by the  European Space Agency. Financial support. This research has been supported by the  Belgian Federal Science Policy Office  through two grants (grant nos.
A. A. J. Deijns et al. :  Timing landslide and flash flood events from SAR satellite Code and data availability. Sentinel-1 data are provided open access by ESA and retrieved from ASF DAAC (https://search. asf. alaska. edu/#/; Copernicus, 2022a). Sentinel-2 data are provided open access by ESA and retrieved from Google Earth Engine (https://developers. google. com/earth-engine/datasets; Copernicus, 2022b). Landsat 8 data are provided open access by the US Geological Survey and retrieved from Google Earth Engine (https://developers. google. com/earth-engine/datasets; US Geological Survey, 2022). The Python scripts for the GH event timing estimation, sensitivity analysis, and precipitation analysis and the Google Earth Engine code for vegetation analysis can be accessed at https://doi. org/10.
Seismic data In July and August 2019, we conducted an ocean bottom seismometer (OBS) passive seismic experiment (Fig. 1b ) during the SMARTIES cruise  43. A network of 19 OBSs was deployed, covering the Romanche transform fault (TF) for ~140 km and the Mid-Atlantic Ridge (MAR) for ~120 km of their lengths, to record continuous seismicity data for ~21 days, with instrument spacing of ~30 km (Fig. 1b ). We detected initial earthquake arrivals automatically using a short-term-average/long-term-average trigger algorithm within the SEISAN package  44  by analyzing the vertical components from 17 useful OBSs. In total, 760 earthquakes were detected and registered into a SEISAN database, each of which was detected by more than five arrivals and was then checked manually.
Earthquake location We used the nonlinear oct-tree search algorithm of the NonLinLoc program  46  to locate initial earthquake hypocentres, of which the maximum likelihood solution was selected as the preferred result. NonLinLoc estimates a 3-D error ellipsoid (68% confidence) from the posterior density function scatter samples  46. Iterative calculations of station corrections (Extended Data Fig. 5 ) were used to find the best solution as well as to remove the 3-D effects when the average RMS misfit yields a minimum (Extended Data Fig. 3b ). At the same time, we removed the S-wave delays, which may be caused by the unconsolidated sediments  9  , from the original S-onsets before the inversion  14. After removing S-wave delays, double-difference relocations for 364 well-constrained events were determined using the hypoDD program  47.
All 364 earthquakes were detected on more than six OBSs, with RMS residuals of <0. 25 s, uncertainties of <5 km, and azimuthal gaps of <270°. We performed the relocation using the differential travel times from the original catalog and waveform cross-correlation data. Station corrections (Extended Data Fig. 5 ) obtained by the NonLinLoc program were successfully applied. A minimum of 6 catalog links per event pair was required to form a continuous cluster. Five iterations were carried out, with a maximum event separation of 6 km. As a result, 276 events were well relocated, and they replaced the NonLinLoc locations in the final catalog. As a result, 317 events are located along the MAR and 197 events along the Romanche TF (Extended Data Fig. 1b ), with an updated average horizontal uncertainty of ~2.
Magnitudes Earthquake magnitudes were determined using the local magnitude scale ML 49 : ML = lgA + 1. 11 lg (D)+0. 00189 D -2. 09. The maximum amplitude is measured on a seismogram simulating the original Wood-Anderson seismogram using the SEISAN package  44. D is the hypocentral distance in kilometers. The magnitude completeness (MC, 1. 5) and B-value (0. 87) are also calculated using the ZMAP software  50  (Extended Data Fig. 8a ). Earthquakes were divided into three groups, i. e. , events along the TF, in the ridge-transform intersection (RTI), and along the MAR (Extended Data Fig. 8b-d ). Their B-values were also determined, with small differences from each other (0. 89-0. 93) (Extended Data Fig. 8 ).
Focal mechanism solution We used the P-phase first-motion polarities to determine focal mechanisms using the HASH package  51  , and these polarities were picked from unfiltered earthquake waveform data on the vertical component. As previously noted  14  , the obtained mechanism solutions are not very robust, mainly due to the large spacing of OBSs, the present MAR axis (0. 08°S,16. 38°W)  38 (Supplementary Table  5 ). The MORBs measured in this study contain Ba 329. 96-334. 21 ppm, Nb 46. 94-48. 48 ppm, and Rb 19. 52-25. 27 ppm (Fig. 4 , Supplementary Table  5 ). The CO2/Ba ratios of melt inclusion from the MORBs are believed to be a good proxy for the CO2 concentration  [34] [35] [36] [37]. If we use a constant CO2/Ba ratio (81. 3 ± 23)  35  , the estimated CO2 concentration in the undegassed mantle melt would be ~2.
16 km can reach 40 × 200 × 200 = 1. 6 km 2 , equivalent to ~2. 1% of volume change in the deep seismogenic zone resulting from the CO2 degassing. Apparently, this value is overestimated as many earthquakes can originate in the same volume space. If we use the average fault length (~100 m), the estimated average amount of volume change by CO2 degassing would be ~0. 5%. useful discussions. Funding: This research was funded by the ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015), the French government under the program "Investissements d'Avenir", the Regional Council of Brittany (SAD programme), the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013), and the ERC Advanced Grant agreement no. 339442_TransAtlanticILAB.
Code availability The SEISAN software  44  used to pick phases is available at https://www. geo. uib. no/seismo/SOFTWARE/SEISAN/. The NonLinLoc code 46 used for earthquake location is available at http://alomax. free. fr/nlloc/. The HypoDD program (version 1. 3) 47 used for double-difference earthquake relocation is available at https://www. ldeo. columbia. edu/~felixw/hypoDD. html. The ZMAP software 50 used for catalog analysis to obtain B-value and magnitude completeness is available at https://github. com/swiss-seismological-service/zmap7. The HASH software 51 (version 1. 2) used for determining focal mechanisms solution is available at https://www. usgs. gov/node/279393. The Global Mapper used for structural analysis is available at https://www. bluemarblegeo. com/global-mapper/. The GMT 6 toolbox 57 used for graphing is available at https://www.
events at depths of 10 - 1. Data Fig. 2 Statistics 1012 16 km can reach 40 × 200 × 200 = 1. 6 km 2 , equivalent to ~2. 1% of volume change in the deep seismogenic zone resulting from the CO2 degassing. Apparently, this value is overestimated as many earthquakes can originate in the same volume space. If we use the average fault length (~100 m), the estimated average amount of volume change by CO2 degassing would be ~0. 5%. useful discussions. Funding: This research was funded by the ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015), the French government under the program "Investissements d'Avenir", the Regional Council of Brittany (SAD programme), the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013), and the ERC Advanced Grant agreement no.
Methods: We analyzed MASK-air® data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7 days. We firstly used k-means clustering algorithms for longitudinal data to define clusters of weeks according to the trajectories of reported daily rhinitis symptoms. Clustering was applied separately for weeks when medication was reported or not. We compared obtained clusters on symptoms and rhinitis medication patterns. We then used the latent class mixture model to assess the robustness of results.
| Study design We performed a longitudinal analysis using the MASK-air® database. We identified the weeks for which patients had answered to the MASK-air® daily monitoring questionnaire on all 7 days. To identify patterns of allergic rhinitis weeks, we applied cluster analysis methods for longitudinal data separately for weeks during which: (i) medication was used for at least 1 day and (ii) medication was not used on any day. The phenotypic characteristics of the different clusters were subsequently compared.
| Data analysis Categorical variables were described by absolute and relative frequencies, while continuous variables were described by means and standard deviations (SDs) or medians and interquartile ranges (IQRs). When responding to the MASK-air® daily monitoring questionnaire, it is not possible to skip any of the questions and data are saved only after the final answer. This precludes any missing data. Since selective attrition is common in MASK-air® (given that many patients use the app for only a few days and/or intermittently  6, 8, 9  ), days from complete weeks were compared with all days of the full MASK-air® database on the characteristics of patients, VAS data and frequency of medication use. The computation of effect sizes was based on standardized differences. Longitudinal k-means (an adaptation of k-means clustering algorithms for longitudinal data trajectories) was applied to cluster weeks during which medication was used for at least 1 day.
| Characteristics of the assessed days We analyzed a total of 16,177 complete weeks of which 8981 (55. 5%) involved the use of medication (Figure  S1 ). These weeks corresponded to 113,239 days of MASK-air® use provided by a total of 2590 users (mean age ± SD = 39. 1 ± 13. 7 years) (Table  1 ; Table  S1 ). There were 61,299 (54. 1%) days for women. Differences between assessed days and days from the full MASK-air® database were very small (Table  1 ). Days with single medication involved, most commonly, the use of an antihistamine (14. 2%), an intranasal steroid (8. 4%) or azelastinefluticasone (3. 3%). Regarding co-medication schemes, intranasal steroids + antihistamines were registered in 8. 6% of the days and azelastine-fluticasone + other medication in 3. 5% (Table  1 ).
| Limitations and strengths As in any cohort study, there are several biases to be considered. 20  the present study, selection biases mostly concern the fact that MASK-air® users may not be representative of the general population with allergic rhinitis (being younger and, potentially, more affluent and with higher access to care). Nevertheless, the impact of such selection biases may be limited. In a preliminary study, we found that data from patients aged between 65 and 74 years are similar to those of younger patients regarding symptoms and medication use patterns (Taborda-Barata et al. , submitted). Symptom and medication use patterns observed in MASK-air® were also consistent with patterns observed from other data sources including online searches on Google Trends, antihistamine sales and classical epidemiologic studies.
21 On the other hand, the assessed sample appears representative of the full MASK-air® database, as the characteristics of the assessed days/users are similar to those of the entire database (Table  1 ), although the existence of unmeasured confounders (e. g. , related to the severity or to the quality of/access to medical care) explaining differences in MASK-air® reporting patterns cannot be discarded. Observational data are also subject to information biases. 22 While the tools for symptom measurement have been largely studied in MASK-air®,  6, 8, 9, 23  users may often provide incorrect information on medication use or on their profile data. Another limitation of this study concerns the impossibility of assessing the effect of medication. Even though we assessed consecutive days of data reporting and compared medication patterns across different clusters, we were not able to assess the modifying effect of different medication classes on reported symptoms.
We were also not able to measure potentially-relevant variables for cluster characterisation (which could help, at least partly, to explain differences in symptoms and drug use patterns across clusters), such as patients' ease of access to the healthcare system. This study also has important strengths. It is the first study to assess MASK-air® data in a longitudinal way, pointing to the possibility of assessing relevant amounts of real-world longitudinal data from patients with allergic rhinitis. Another strength concerns the fact that clusters were obtained based on VAS global, which displays high concurrent validity and reliability as well as moderate responsiveness. 24 Finally, the obtained clusters may not only have face validity (as they correspond-according to a consensus of ARIA experts involved in this study-to medically relevant and recognizable week patterns) but also construct validity.