dataesr/scientific-paragraphs-categorization huggingface dataset:
3  and Methods). This approach suggests that the flow acceleration propagated more than 200 km inland from 2016 to 2022 (Fig. 2d ) and is fully consistent with high-precision GNSS-derived accelerations (Fig. 2a-c ). The speed-up of the NEGIS triggered by the gradual retreat of ZI's terminus (Extended Data Fig. 1  and Methods) is also responsible for dynamic thinning  4. We use satellite and airborne altimetry data to detect changes in the ice surface elevation along the main trunk. We estimate annual elevation change rates over the ice surface from April 2011 to April 2021 using radar altimetry data from ESA's Earth Explorer CryoSat-2 mission 31. To better resolve elevation changes along the ice-sheet margin, we use laser altimetry observations from NASA's Operation IceBridge Airborne Topographic Mapper (ATM) flights from April 2011 to April 2019 (ref.
32 ) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data from October 2018 to April 2021 (refs. 33, 34 ). We find a thinning of more than 30 m from 2011 to 2021 near the ZI calving front (Fig. 3j ). The entire lower portion of ZI (between 0 and 50 km inland) thinned between 8 and 35 m over the past decade (Fig. 3k ). Between 100 and 200 km upstream along the main trunk, the NEGIS thinned between 2 and 3 m from 2011 to 2021. Thinning is traced up to 250 km inland and the pattern of thinning suggests a large amount of thinning along the main trunk of the NEGIS. The neighbouring glacier, NG, likewise shows considerable thinning of 5-15 m near the grounding line.
GNSS data processing We process the GNSS data using the GIPSY-OASIS software package with high-precision kinematic data processing methods  27  and with ambiguity resolution using the orbit and clock products of the Jet Propulsion Laboratory ( JPL). We use GIPSY-OASIS version 6. 4, which was developed at the JPL  30. We use JPL final orbit products, which include satellite orbits, satellite clock parameters and Earth orientation parameters. The orbit products take the satellite antenna phase centre offsets into account. The atmospheric delay parameters are modelled using the Vienna Mapping Function 1 (VMF1) with VMF1 grid nominals  54. Corrections are applied to remove the solid Earth tide and ocean tidal loading. The amplitudes and phases of the main ocean tidal loading terms are calculated using the Automatic Loading Provider (http:// holt.
oso. chalmers. se/loading/), which is applied to the FES2014b ocean tide model including correction for the centre of mass motion of the Earth owing to the ocean tides. The site coordinates are computed in the IGS14 frame  55. We convert the Cartesian coordinates at 15-s intervals into local up, north and east coordinates for each GNSS site monitored at the NEGIS surface. An example of a 15-s solution is shown in Extended Data Fig. 2a-c.
Surface speed and acceleration from mosaics based on ESA Sentinel-1 SAR offset tracking We derive ice speeds from mosaics based on ESA Sentinel-1 SAR offset tracking obtained from https://dataverse01. geus. dk/dataverse/ Ice_velocity. The ice velocity maps of the NEGIS are derived from the intensity tracking of the ESA Sentinel-1 data with a 12-day repeat; the operational interferometric post-processing chain is applied for the analysis  29. We use all available speed mosaics (provided on a grid with a spatial resolution of 500 m) and associated standard deviation of the underlying shift maps generated by the offset tracking to create a time series of speed for each grid point (Extended Data Fig. 3b-d ). We then remove outliers. To remove outliers, we fit and remove a trend to each time series of speed and estimate the mean.
Downhill correction for GNSS We use mosaics based on ESA Sentinel-1 to estimate the downhill correction of the flow acceleration at GNSS stations. We estimate the time series of the ice speed at two pixels on a velocity map on a 0. 5 × 0. 5-km grid. Pixel 1 is located at the GNSS starting position (when the station was deployed) and pixel 2 is located at the GNSS end-point position. The difference between the two time series is used to estimate the downhill correction for the flow acceleration. The uncertainty level in Extended Data Fig. 3a  is ±0. 7 m year -2. However, two pixels close to each other (such as 1,000 m apart) experience almost the same noise. Thus, when estimating the difference between two neighbouring time series, the noise is reduced to ±0. 3 m year -2. To estimate the downhill correction of the flow acceleration, we fit a trend to the time series of the differences between two pixels.
Elevation changes from CryoSat-2 To estimate elevation changes over the ice surface, we use a regular grid with a resolution of 500 × 500 m that covers the NEGIS. We denote the centre of each grid point as C(x 0 , y 0 ). For each grid point, we select CryoSat-2 data with coordinates P(x i , y i ), with a maximum distance of 1,000 m from C. The CryoSat-2 data points with coordinates P(x i , y i ) have an elevation h i measured at time t i. The index i denotes the ith data point. We use all available CryoSat-2 data measured between July 2010 and July 2021 to create surface elevation time series at each grid point C. Previous studies have used a third-order polynomial equation to describe changes in the elevation and a third-order polynomial equation to describe the shape of the surface  2, 56, 57.
Elevation changes from ICESat-2 We use ICESat-2 data from October 2018 to June 2021 and estimate elevation changes over the ice surface  33. We use the ICESat-2 Algorithm Theoretical Basis Document for Land Ice Along-Track Height (ATL06) Release 004, which was retrieved from https://nsidc. org/data/atl06 (ref. 58 ). We estimate elevation changes using the same method described in the previous section. However, to describe surface changes over about 2. 5 years, we fit a third-order polynomial (as opposed to the seventh-order polynomial used for CryoSat-2 data) and we also use a third-order polynomial equation to describe the shape of the surface and a seasonal term to account for the annual surface changes. Extended Data Fig. 5a  shows the grid points on the NEGIS where we have successfully estimated the ICESat-2 elevation time series.
Elevation changes from NASA's Operation IceBridge ATM flights We estimate elevation changes using NASA's ATM surveys in Greenland from spring 2011 to spring 2019 (ref. 32 ). The ATM flights are mainly concentrated along the margin of the GrIS. To estimate elevation changes, we take the height difference between overlapping points from two different campaigns, that is, we take the height differences between the 2011 survey and the 2012 survey, between the 2012 survey and the 2013 survey etc. However, it should be noted that no survey was conducted in spring 2020. Extended Data Fig. 6  shows ATM flight lines over the NEGIS. Extended Data Fig. 6  (lower-right panel) shows an example of elevation changes at the overlapping points from 2016 to 2017. The largest elevation change is observed near the glacier margin.
Gridded elevation changes and their uncertainties It is important to note that we do not merge CryoSat-2, ICESat-2 and NASA's ATM surveys when we create elevation time series. Instead, we estimate the annual elevation change rates from April to April for each dataset independently and then merge the rates. The observed annual elevation change rates from CryoSat-2, ICESat-2 and NASA's ATM surveys are used to interpolate elevation change rates onto a regular grid of 1 × 1 km. The interpolation is performed using the ordinary kriging method  59, 60. We use the observed annual elevation change rates to estimate an empirical semi-variogram. We fit a model variogram to the empirical semi-variogram to take the spatial correlation of elevation change rates into account. For each grid point, we estimate the elevation change rate dh i,krig and the associated error σ i,krig.
Acknowledgements We thank  B. O. Petersen  from the  Joint Arctic Command (Denmark)  for coordination of the logistical support, which allowed us to carry out fieldwork in northeast Greenland. S. A. K. acknowledges support from the  Danish Council for Independent Research  (grant no. 1026-00085B ),  Villum Experiment  (grant no. 40718 ), the  Danish Ministry of Climate, Energy and Utilities  (project no. 2019-4542 ) and  Carlsbergfondet  (grant no. CF14-0145 ). Y. C. is supported by the  JPL Strategic Research and Technology Development Program.
Data availability CryoSat-2 data are available at https://earth. esa. int/eogateway/catalog/ cryosat-products. NASA's Operation IceBridge ATM data from April 2011 to April 2019 are available at https://nsidc. org/data/ilatm2. The ICESat-2 data are available at https://nsidc. org/data/icesat-2. Ice surface velocity and BedMachine Greenland are freely available from the National Snow and Ice Data Center (NSIDC). RACMO SMB and runoff information can be accessed at https://zenodo. org/record/3367211#. YyrIObTP23B, and MAR SMB and runoff information is available at https://mar. cnrs. fr. Ocean thermal forcing data are available at https://doi. org/10. 7280/D1667W. The ice speed obtained from mosaics based on ESA Sentinel-1 SAR offset tracking is available at https:// dataverse01. geus. dk/dataverse/Ice_velocity.
| Data source: Cohort of hospitalized patients with drug-induced AKIs A single-center retrospective study ("IRA-PMSI") from January 1, 2019, to June 30, 2019, was performed to build a cohort of hospitalized patients suffering from AKIs based on PMSI extraction and chart review. The IRA-PMSI study's objectives and procedures were approved by an independent ethics committee (CPP Nord Ouest II, Amiens, France; reference: RCB 2020-A00556-33), and the study was registered at ClinicalTrials. gov (NCT04923750). The primary objective of the IRA-PMSI study was to assess the ability of several ICD-10 codes to identify AKIs. Results have already been published, such as description of the patient's selection which we described in the Data S1  [7]. The present report describes one of the IRA-PMSI study's secondary objectives: to describe the characteristics of drug-induced AKIs and to assess their risk factors.
| Clinical and demographic information For each identified AKI, we collected several clinical and demographic information from the patients' EMRs. The demographic variables included age, sex, and BMI. The comorbidities considered as risk factors for AKI were hypertension, diabetes, dyslipidaemia, heart disease, cancer, gout, CKD (stage ≤3A, [estimated glomerular filtration rate (eGFR) ≥ 45 mL/min/1. 73 m 2 ], 3B [eGFR between 30 and 44 mL/min/1. 73 m 2 ], 4 [eGFR between 15 and 29 mL/min/1. 73 m 2 ], or 5 and not on dialysis [eGFR <15 mL/min/1. 73 m 2 ]), nephrectomy, kidney transplant, previous AKI, and a history of ADRs. Lastly, clinical data related to the episode of AKI were gathered: the medical unit, KDIGO grade, length of hospital stay, use of dialysis or not, and outcome.
| Statistics Descriptive statistics were used to characterize the study population. Patients were divided according to whether the AKI was drug-induced or not and then whether the injury was acquired in the community or in hospital. Groups were compared using a chi-squared test or Fisher's test, depending on the data distribution. The prescription drugs administered to patients with CA-AKI versus HA-AKI were assessed using a chisquared test or Fisher's test. To compare drug-induced AKIs with non-druginduced AKIs, we calculated the crude odds ratio (OR) [95% confidence interval (CI)] and the P value. Next, we used multivariate logistic regression to model the relationship between risk factors and drug-induced AKI, by taking the non-drug-induced AKI group as a reference. The potential adjustment variables were age, sex, obesity (defined as a BMI ≥ 30 kg/m 2 ), and risk factors for AKI (i.
g. , CKD) and prescription medications. Our multivariate analysis identified several significant risk factors for drug-induced AKI: female sex, CKD, and previous ADR. These findings were consistent with the literature data  [11]. A sex difference in ADRs has been well documented  [39] ; Moyer et al. found a 1. 6-fold-greater OR in women than in men, for example  [40]. Historically, women were less likely than men to be included in clinical trials. Moreover, it has been reported that specific physiological features of women (i. e. , hormonal status) can alter the pharmacokinetics and pharmacodynamics of medications  [40] , and this influences the response to injury and/or treatment  [41]. Indeed, (the World Health Organization's global database of individual case safety reports) found that women (and especially those of child-bearing age) reported more ADRs than men did  [42].
Thirdly, we did not collect information on the use of over-the-counter drugs or self-medication; this might have led us to underestimate exposure to certain drugs such as NSAIDs. Indeed, analgesics like NSAIDs are frequently used for self-medication, and the continuous use of NSAID (alone or combined with other drugs) for pain relief over long periods of time is associated with the development of progressive kidney disease or AKI  [51]. Information on self-medication rarely recorded in hospital discharge summary databases like the PMSI  [52]  and is not systematically recorded in EMRs. Furthermore, AKI cases were evaluated by a single adjudicator; a manually review of all EMRs was a timeconsuming process that prevented a second evaluation. We did not have access to urine output, which could have led to an underestimation of AKI cases.
| Study sites and bromeliads Sites and bromeliad characteristics are given in Table  1. These sites naturally vary in the amount and distribution of rainfall. Minimum and maximum monthly averages for ambient precipitation and air temperature were obtained from an open access compilation of high-resolution (30 arcsec, ~1 km) climate data  (CHELSA, Karger et al. , 2017). Ambient rainfall amplitude was calculated as the difference between maximum and minimum monthly precipitation. Because bromeliad species differed across sites, they were matched in terms of growth form (i. e. crateriform rosettes sensu  Mori et al. , 1997). At each site, we selected 30 plants belonging to the most common tank bromeliad species in the area. We used measurements of vegetative traits (number of leaves, plant diameter in cm) and maximum water volume in ml (the difference between a known volume of water used to fill the empty bromeliad and the remaining volume when the bromeliad overflowed) to select plants that were similar in size and complexity within a site.
| Environmental and biological data Q table). Species pool's richness was the total number of species found at the site level. Functional redundancy (FR) refers to the repetition of trait combinations by different species and is calculated as the difference between the Simpson index of species diversity and Rao's quadratic entropy (RaoQ) based on their functional dissimilarity  (De Bello et al. , 2007). Functional redundancy was calculated at the site (i. e. species pool) level from the sum of abundances of species in 30 bromeliads and the corresponding Q table. The FR metric was calculated using the package syncsa in R software.
| Statistical analyses Site-specific RLQ analyses were used to explore covariation between environmental variables and traits. The RLQ analysis is a double inertia analysis of environment (R) and trait modalities tables (Q) with a link expressed by a contingency table of species abundance data (L)  (Dray et al. , 2014). Prior to each RLQ analysis, the L table was log(x + 1)-transformed to downweight abundant species and underwent correspondence analysis (CA). The R and Q tables underwent separate principal component analyses (PCA) with the bromeliad scores from the CA as row weights and CA species scores as column weights. The results of these three separate ordinations were used as inputs in the RLQ analysis, a three-table ordination method which maximizes covariation between environmental variables and species traits.
Bromeliad and species scores in the RLQ were evaluated against the score obtained from the separate analyses of the R, L and Q tables. The total inertia of each RLQ analysis (the sum of eigenvalues) was used as a multivariate statistic to measure the correlation between the trait modalities table and the table of environmental variables  (Dray & Legendre, 2008). Linear and exponential models were then used to test any relationship between total was not contingent on the specific precipitation variable selected (here the amplitude of rainfall), we also tested relationships between inertia and all precipitation-related variables available from the bioclimatic database CHELSA  (Karger et al. , 2017). We assessed the overall significance of each RLQ analysis using to establish overall significance.
| What drives functional community responses to precipitation treatments? Our six sites varied substantially in the total amount of variance explained by trait-environment associations, varying from 42% to 88% for axis 1 of the site-specific RLQs, and from 6% to 37% in axis 2 (Table  2 ). There was no significant relationship between the strength of the trait-environment associations (technically the total inertia of RLQs) and natural rainfall amplitude (Figure  1a , p > 0. 05). More generally, the strength of trait-environment associations did not correlate with any of the precipitation-related variables available from the bioclimatic database (data not shown). By contrast, the strength of trait-environment associations increased with the number of species in the local pools (Figure  1b , negative exponential adjusted R 2 = 0.
Funding information Nível Superior , Grant/Award Number:  PNPD-CAPES 2013/0877  and  PNPD-CAPES 2014/04603-4 ;  Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada ; CESAB;  FAPESP , Grant/Award Number:  2012/51143-3  and  2019/08474-8 ;  Agence Nationale de la Recherche , Grant/Award Number:  ANR-10-LABX-25-01  and  ANR-12-BSV7-0022-01 ;  Secretaría de Ciencia y Tecnología de la Universidad Nacional de Rosario , Grant/Award Number:  AGR-139 ;  COLCIENCIAS, Grant/Award.
DATA AVA I L A B I L I T Y S TAT E M E N T The environment and species data are available from Zenodo at https://doi. org/10. 5281/zenodo. 1124951 (Srivastava et al. , 2020b). The trait data are available from the Knowledge Network for Biocomplexity at https://knb. ecoin forma tics. org/#view/ doi:10. 5063/F1VD6WMF (Céréghino, Srivastava, et al. , 2018).
Askival is a light-toned, coarsely crystalline float rock, which was identified near the base of Vera Rubin Ridge in Gale crater. We have studied Askival, principally with the ChemCam instrument but also using APXS compositional data and MAHLI images. Askival and an earlier identified sample, Bindi, represent two rare examples of feldspathic cumulate float rocks in Gale crater with >65% relict plagioclase. Bindi appears unaltered whereas Askival shows textural and compositional signatures of silicification, along with alkali remobilization and hydration. Askival likely experienced multiple stages of alteration, occurring first through acidic hydrolysis of metal cations, followed by deposition of silica and possible phyllosilicates at low T and neutral-alkaline pH. Through laserinduced breakdown spectroscopy compositional analyses and normative calculations, we suggest that an assemblage of Fe-Mg silicates including amphibole and pyroxene, Fe phases, and possibly Mg-rich phyllosilicate are present.
INTRODUCTION A series of igneous float rocks have been identified in Gale crater that consist predominantly of basalts and trachybasalts  (Cousin et al. , 2017; Edwards et al. , 2017)  but with other examples of evolved bulk rock compositions having total alkali contents up to 14 wt%  (Sautter et al. , 2015; Schmidt et al. , 2013). These samples are likely the result of fractionation of basaltic magma and are reminiscent of intraplate magmatism on Earth  (Edwards et al. , 2017; Udry et al. , 2018). However, one sample-named Askival-is anomalous and represents a light-toned, coarsely crystalline float rock which was identified on sol 2018, at the Bressay boulder field locality, near the base of Vera Rubin Ridge (Figs. 1  and 2 ). The Curiosity rover was able to obtain MastCam, Mars Hand Lens Imager (MAHLI), and ChemCam Remote Micro-Imager (RMI) imagery at high resolution and color, together with ChemCam laser-induced breakdown spectroscopy (LIBS) and Alpha Particle X-ray Spectrometer (APXS) compositional data on Askival.
Data show an extremely high silica-rich composition that is unlikely to be the result of purely igneous processes. We describe the Askival sample using the rover data with the aim of distinguishing between alteration overprints and its original mineral assemblage and compositional signatures. To do this, we compare to previously described samples interpreted as pristine feldspar-rich igneous rocks, in particular the Bindi sample from sol 544  (Cousin et al. , 2017; Edwards et al. , 2017)  (location shown on Fig. 1 ). Bindi is believed to be a feldspathic cumulate related to the basalt-trachybasalt igneous rocks in Gale. A key part of the investigation of Askival is to reconstruct alteration processes that significantly modified its original mineralogy. We use ChemCam laser spots to analyze the sub-mm scale mineral intergrowths within the rock.
We attempt to constrain the primary and secondary mineral assemblages in Askival through comparison of LIBS data with candidate mineral and normative compositions. On the basis of the compositional and textural data informing phase equilibria modeling, we also consider the stability of different mineral assemblages for the mafic phases in Askival. We also discuss the processes that altered Askival. This is an important issue as Askival is the first feldspathic rock found on Mars with such evidence for alteration. Based on the discovery of its possible unaltered counterparts, it has been proposed that feldspar-rich rocks could be a large fraction of the crust in the ancient highlands  (Sautter et al. , 2015). Feldspathic rocks have been challenging to identify from orbit, due to their paucity of spectral signatures.
Acknowledgments-JCB and DB acknowledge funding from  UKSA  and an  STFC  PhD studentship (DB). JS acknowledges funding from the  Expanding Excellence in England  ( E3 ) fund. AC, WR, OG, OF, VS, and PP acknowledge CNES support for their work on ChemCam aboard Curiosity. RCW acknowledges NASA contracts #  NNH15A35I  and  NNH13ZDA018O. CCB acknowledges funding from  USRA. We thank the engineering and science teams of  Mars Science Laboratory  for the successful operations and acquisition of ChemCam and other data. We also thank two anonymous reviewers for their comments, which were helpful in improving the manuscript.
These contrasting trends suggest that tuna Hg may be sensitive to spatiotemporal changes in anthropogenic Hg emissions and ocean uptake and/or regional variations in biogeochemical processes or foraging ecology that affect tuna exposure to MeHg. Studies of large spatial coverage are therefore needed to investigate the control and relative importance of atmospheric Hg sources, oceanic Hg methylation, and foraging ecology on tuna Hg concentrations. Here, we report the highest spatially resolved dataset of Hg concentrations of skipjack tuna, with records from six distinct areas of the Pacific Ocean (n = 661, from ∼45°N to 25°S, Fig. 1A ). As the third most consumed marine fish species worldwide  (20) , it is a species of high commercial and human health importance. Although classified as highly migratory, this epipelagic and fastgrowing tuna species shows relatively restricted movements and high site fidelity rates in the Pacific Ocean  (21, 22)  and thus should be able to reflect spatial MeHg patterns in surface water food webs.