dataesr/scientific-paragraphs-categorization huggingface dataset:
Physical principle of the OHC change calculation In the space geodetic approach, the OHC change is estimated from the thermal expansion of the seawater observed from satellite data and in situ data. Thermal expansion corresponds to the change in water density due to temperature change, and is also called TSSL change. The TSSL change is retrieved from the sea level budget equation where the total sea level change (∆SL) is the sum of the manometric sea level (∆M AN ) and the steric sea level change (∆SSL) expressed in meters. The latter can be decomposed in two terms, the thermal expansion change (∆T SSL) and the halosteric sea level change (∆HSSL). Thermal expansion is derived at every timestep and spatial coordinate following Eq. (  1 ): ∆T SSL(lon, lat, t) = ∆SL(lon, lat, t) -∆M AN (lon, lat, t) -∆HSSL(lon, lat, t) (1) The TSSL change is converted into OHC change with the use of the  where TSSL, the thermosteric sea level referenced to a physical thermosteric sea level (defined at 0°Celsius and 35 PSU), and OHC, the ocean heat content, are both integrated over the whole water column.
Total sea level change The total sea level change at regional scales is derived from the sea level products operationally generated by the Copernicus Climate Change Service (C3S). This dataset, fully described in  Legeais et al. (2021)  is highly stable and specifically designed for climate applications. It provides daily sea level anomalies grids at a 1/4°spatial resolution. It is based on reference altimeter missions (TopEx/Poseidon, Jason-1,2,3 and S6-MF very soon). Complementary missions (ERS-1,2, Envisat, Cryosat, SARAL/Altika, Sentinel-3A) are used to increase spatial coverage. C3S provides total sea level anomalies (SLA) around a mean sea surface (MSS) above the reference mean sea-surface computed over 1993-2012. The C3S sea level grids were also corrected for the glacial isostatic adjustment (GIA) applying a regional correction from an ensemble average of 27 ICE-5G model viscosity variations (Spada and Melini 2019).
Halosteric sea level change The halosteric sea level (HSSL) change is estimated from in situ temperature (T) and salinity (S) data. The computation of the sea level variations due to salinity variations relies on the thermodynamic equation of seawater  (McDougall and Barker, 2011). The HSSL component is estimated from the combination of two in situ temperature and salinity datasets. From 0 m to 2000 m depth, the HSSL is calculated with the ISAS20 temperature and salinity datasets  (Kolodziejczyk et al. , 2021). The ISAS procedure and product is described in  (Gaillard et al. , 2016). It is based on the measurements from the Argo and Deep Argo floats (note that at the time of the ISAS20 computation, few of Deep Argo profiles reached the data quality standard required to be implemented into the optimal interpolation tool, personnal communication from Nicolas Kolodziejcyk).
Recent studies highlight that in situ salinity datasets from Argo present a drift since 2016 due to anomalies on conductivity sensors  (Wong et al. , 2020). Barnoud et al. (2021)  showed that this drift affects the global mean HSSL estimates. The impact of this halosteric drift on the regional OHC change is still unknown and difficult to quantify. Currently the only uncertainties associated with HSSL that are available are formal uncertainties from temperature and salinity profiles estimated in EN4. 2. 2. l09 and ISAS20 datasets. In this present study, these uncertainties have not been propagated to the HSSL change.
IEEH The IEEH is also computed with the combination of ISAS20 and EN4. 2. 2. l09 in situ datasets which were already depicted in section 3. To propagate and estimate the uncertainties on the regional OHC change from the space geodetic data, the uncertainty of the IEEH must be taken into account. We use the IEEH uncertainty estimated at global scale in  Marti et al. (2022)  and apply it everywhere as a constant. The global estimate results from the dispersion between several global IEEH estimated from 11 Argo solutions (see  Marti et al. (2022) ). As the IEEH is not an intensive parameter (i. e. it can not be averaged from regional scale to global scale), a scaling factor is used to convert the global uncertainty into regional one with a cross product: e IEEH (lat, lon) = IEEH(lat, lon) * e IEEH global IEEH global (4) where e IEEH (lat, lon) is the uncertainty at a given latitude and longitude, IEEH(lat, lon) is the values of the regional IEEH, e IEEH global = 5.
Validation products The space geodetic product is validated in the North Atlantic region at two test sites. First, the product is validated in the subtropical region with two products: an optimal interpolation product based on data from Argo floats and produced by the National Oceanography Centre (hereinafter referred to as NOC Argo OI) and T/S data from RAPID-MOCHA (RAPID-Meridional Eastern and Western boundaries as well as at the mooring located west of the Mid Atlantic Ridge. OHC was then estimated across each of the two sections using the NOC Argo OI data, calibrated against RAPID observations at the boundaries of each section, relying on the main assumption that the two datasets are equal at these locations except for an additive bias. The geodetic OHC product is further validated in the Subpolar North Atlantic with two in situ datasets: repeat hydrography along the A25-Ovide section  (Mercier et al.
Since TSSL is the differences between SL, MAN and HSSL, Σ T SSL is obtained by summing the variance-covariance matrices of the errors of SL, MAN and HSSL: Σ T SSL = Σ SL + Σ M AN + Σ HSSL (7) Note that Equation 7 implicitly assumes that the errors of SL, MAN and HSSL are uncorrelated with each other. However, in practice a few similar geophysical corrections are applied on both SL and MAN which can generate correlated errors not quantified to date. However, we expect these correlated errors to be small because the system error measurements are independent and there are few potential sources of correlated errors (e. g. GIA and ocean tide). Moreover, if observing systems are different and do not allow the observation of the ocean with the same spatio-temporal sampling, errors could be introduced while interpolating these datasets to the same spatio-temporal resolution.
Space geodetic SSL change A processing chain was specifically developed in order to calculate the OHC change from space geodetic measurements and in situ products, following the physical principle from section 2. The first step consists in pre-processing the grids of input data over the full period. All grids are interpolated on a 1°and a monthly resolution. The space geodetic approach gives the estimation of the SSL change following : ∆SSL(lon, lat, t) = ∆SL(lon, lat, t) -∆M AN (lon, lat, t) (11) Then, Atlantic Ocean trends of the dilatation of seawater are estimated from April 2002 to December 2020 following the mathematical statement in section 4. 1. SSL change from space geodetic data are validated against SSL change estimated with independent in situ data from NOC Argo OI, ISAS21 and OVIDE.
TSSL and OHC change The TSSL change is calculated from SSL change derived from space geodetic data and HSSL change derived from in situ data (Eq. 1, Figure  A1 ). For the computation of TSSL changes, SSL changes and HSSL changes have to be referenced to the same date. We set arbitrarily the reference date t0 to January 2005. SSL and HSSL datasets described in section 3, do not have ∆T SSL(lon, lat, t) = ∆SSL(lon, lat, t) -∆HSSL(lon, lat, t) -(∆SSL(lon, lat, t0) -∆HSSL(lon, lat, t0)) (12) Note that we tested the sensitivity to the choice of time reference for TSSL and OHC changes and we found there is no effect on TSSL trends and a weak effect on OHC changes (<1% difference). Once the Thermosteric sea level change has been estimated at regional scale, we estimate the OHC change by using the Argo-derived IEEH coefficient described in section 3.
In the Subpolar North Atlantic region To evaluate the accuracy of the geodetic product in the Subpolar North Atlantic region, the geodetic estimate of the OHC trends are compared to ISAS21 OHC trends (Figures  4a  and 4b ). The large-scale distribution of OHC trends is consistent in the geodetic and the insitu products with cooling patterns north of 55°N and at the west European basin and warming patterns at 35°-50°N and 70°-30°W. However, both the negative OHC trend in the northern subpolar gyre and the positive OHC trend in the northwestern subtropical gyre appear larger in the geodetic estimate than in the ISAS21 estimate ~-2-5 W m -2. Unraveling the causes of such differences is challenging. They could be due, for instance, to the limited availability of T and S data below and mid 2014 (see the comparison against OVIDE OHC estimate), for instance.
In the Sutropical North Atlantic region OHC trends estimated in each 1°grid cell of the NOC Argo OI dataset are shown in Figure  5b. On the overall, the OHC trends' pattern from the NOC Argo OI dataset are very similar to the geodetic OHC trends' pattern (5a) with an homogeneous positive trend marked in the Northwest by highly positive trends, and in the Northwest and Northeast by slightly negative trends. Additionally, the average magnitude of the space geodetic dataset is very similar to the NOC Argo OI dataset (2. 9 W m -2 vs 2. 3 W m -2 ), although it does appear to be slightly higher, particularly at latitudes towards the centre of the analysed region. The dashed blue and black lines are OHC trends lines respectively from the space geodetic and ISAS21 products. Following the approach described in the methods section, spatially integrated estimates of OHC anomaly were produced across the Western basin of the subtropical North Atlantic at 26.
5°N spanning longitudes from 76. 74°W to 50. 57°W, and across the Eastern Basin between longitudes 50. 57°W and 16. 23°W. Figure  5c  shows the temporal evolution of depth-integrated OHC anomaly estimated from the RAPID array at 26. 5°N, spatially integrated across the Western basin of the North Atlantic (black). Equivalent space geodetic OHC anomalies are shown in blue. Trends are shown with dashed lines. The comparison of the RAPID data against space geodetic OHC data reveals fairly good temporal agreement in the OHC time series between both datasets, with trends of 5. 21 W m -2 and 6. 01 W m -2 respectively. Using a similar arrangement as the one described 335 above, Figure  5d  shows the temporal evolution of OHC anomaly integrated across the Eastern basin calculated using the space geodetic OHC anomalies assessed against RAPID.
Note that currently, our estimate of the uncertainties is likely underestimated for several reasons. First, the uncertainties are given at annual timescales. A possible improvement would be to provide a variance-covariance matrix at monthly timescales but this might lead to larger variance as variability in the ocean increases with frequency, and is particularly large at mesoscale (1-3 months). Second, uncertainties of halosteric sea level due to salinity variations have not been estimated at regional scale. It has to be estimated properly from temperature and salinity uncertainties scale. A comparison of several Argo products provides an estimate of the regional uncertainty in IEEH due to the differences between different Argo datasets. This comparison shows regional difference in IEEH below 1%.
One way to estimate the North Atlantic MHT would be to develop a regional ocean heat budget following  Mayer et al. (2022). In situ measurements such as those derived from RAPID section and OSNAP (Overturning in the Subpolar North Atlantic Program) array would complement the results and ultimately allow analysis of MHT variability and investigation of its cause. Applied at all basin scales, the space geodetic approach will support studies on the spatial distribution of energy and the heat transport within the ocean. In combination with Argo estimates of the upper layer OHC, it will also provide estimates of the OHC in deep-ocean layers. Table  1. Description of the contents of the dataset. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests.
Variables Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. the EO4 Society, Atlantic Regional Initiative, 4D-Atlantic-OHC Project (ESA Contract No. 4000134928/21/I-NB). Annaig Prigent (LOP-S/IFREMER) is warmly acknowledged for the computation of ISAS21 data set. Anne Barnoud and Julia Pfeffer are also thanked for their feedback on the first draft of this article.
Data availability. OHC change at regional scales or "4DAtlantic" product from space altimetry and space gravimetry was produced by Magellium/LEGOS and distributed by AVISO+ (https://aviso. altimetry. fr) with support from ESA. The results of this study were obtained with the first version of the dataset (V1. 0). It is available online at https://doi. org/10. 24400/527896/a01-2022. 012  (Magellium/LEGOS, 2022). The content of the dataset is described in Table  1. The complete associated documentation is available on the website (experimental dataset description and algorithm theoretical basis document). Further information and documentation are also available on the project website (https://www. 4datlantic-ohc. org/).
https://doi. org/10. 5194/essd-2023-236 Preprint. Discussion started: 26 July 2023 c Author(s) 2023. CC BY 4. 0 License. Overturning Circulation and Heatflux Array). The NOC Argo OI dataset is a gridded product generated by the NOC using optimal interpolation of Argo profile data including T and S over the depths 0-2000m. The spatial resolution of NOC Argo OI is 1°in latitude/longitude and 20m in depth with a temporal resolution of 10 days. In-situ estimates of depth-integrated OHC were computed from the gridded T and S using thermodynamic routines included in the Gibbs SeaWater (GSW) oceanographic library over the period April 2004 to September 2021. The RAPID programme aims to quantify the Atlantic Meridional Overturning Circulation (AMOC) using an array of moored instruments at a latitude of ∼26.
2000m depth in ISAS21, and the relaxation towards the climatology in those deep layers. Time series of monthly and full-depth OHC interpolated along the OVIDE section during 2002-2020 from both geodetic product and ISAS21 are shown in Figure 4c. The corresponding hydrography-based OHC estimates from the 9 OVIDE hydrography surveys are superimposed (red dots). A good correlation of r = 0. 83 (at 95% CL) is found between the full-depth monthly OHC time series, as well as with hydrography-based OHC change estimates from OVIDE dataset (r = 0. 6 at 95% CL). However, OHC changes along the OVIDE line appear overestimated in the range of ~-7. 5x10 8 to 10 9 J m -2 by the space geodetic product in 2008, early 2013, late 2014 to mid 2015 and late 2017 (see the comparison against ISAS21 OHC estimate).
measurements over the 19-year period from April 2002 to December 2020. Together with the estimation of the halosteric sea level variations from in situ data, the space geodetic dataset allows to estimate the thermal expansion of the ocean. With the use of the integrated expansion efficiency of heat coefficient derived from in situ data, thermal expansion is converted into an estimation of the ocean heat content. The estimation of the steric sea level change was first validated with in situ gridded datasets. The results have shown overall good consistency and correlations between the geodetic estimation over the subtropical region with in situ estimation from NOC Argo OI and over the subpolar region with in situ estimation from ISAS21 and OVIDE. The results in terms of ocean https://doi. org/10.
The latter indication was not approved in Brazil and Colombia  [17, 19]. In recent years, real-world studies have reported the effectiveness of ceftazidime-avibactam against MDR Gram-negative bacterial infections on carbapenem-resistant Enterobacteriaceae (CRE) across the United States (USA) and Europe, but these studies were often limited to certain types of infection, or specific countries  [20, 21]. This non-interventional medical chart review study was conducted to collect comprehensive real-world data examining the span of indications, treatment characteristics, and efficacy and safety of ceftazidime-avibactam in real-world clinical settings in Europe and LATAM. The primary objective of the EZTEAM study was to describe the patterns of use of ceftazidime-avibactam in real-world practice, with particular focus on its indications and use for the treatment of infections due to aerobic Gram-negative organisms with limited treatment options, available microbiological evidence, and use in combination regimens.
Study Design This non-interventional medical chart review study was conducted at 30 European sites (Austria, France, Germany, Greece, Italy, Spain, the United Kingdom [UK], and Russia) and 15 hospital sites in LATAM (Argentina, Brazil, and Colombia). No drugs were supplied for this study. All data were collected through the abstraction of hospital medical records. Indication, treatments, hospital length of stay (LOS), readmissions, and clinical outcomes (refer to Supplementary Table  S1  for definitions of clinical outcomes) were collected in patients who were exposed to ceftazidime-avibactam for at least 72 h. Data collected for patients who were exposed to ceftazidime-avibactam for less than 72 h were limited to indication, use of ceftazidime-avibactam, adverse events, and mortality. This study was conducted in 45 enrolling hospital sites after receiving approval from the independent ethics committees where applicable by local regulation (a list of participating sites is provided in Supplementary Table  S6 ), as well as in accordance with the Helsinki Declaration of 1964 and its later amendments.
Data Collection Collected data included patient characteristics, clinical and microbiological characteristics of the infection (by local laboratory and any standards used by the site), treatment patterns (dose, frequency, duration of infusion, start and stop dates, associated antibiotics if any), effectiveness, and safety of ceftazidime-avibactam, medical resource utilization during hospital stay, mortality and hospital readmissions up to 60 days post-discharge or a censoring event (death, withdrawal, or lost-to-follow-up, whichever occurred first). Post-discharge information on hospital readmissions or survival status not available via medical chart abstraction was obtained by contacting the patient or their legal representative by phone. The study data collection and assessment schedule are described in Fig.
Statistical Analyses The full analysis set (FAS) contained all enrolled patients meeting the study eligibility criteria. Descriptive and effectiveness analyses were performed for patients exposed for at least 72 h (FAS72?). Safety data was analyzed for all patients enrolled. The results were presented overall and stratified by indication (cIAI, cUTI, HAP/VAP, and others, including patients with BSI/sepsis). Continuous/quantitative variables were summarized using the number of observations, mean, standard deviation (SD), median, and range as appropriate. Categorical/qualitative data were presented using frequency counts and percentages with exact 95% confidence intervals (CIs). Univariate logistic regression was modeled for clinical success versus clinical failure excluding clinical indeterminate outcome to investigate the association between potential factors and clinical success at initial hospitalization (refer to Supplementary Table  S1  for definitions of clinical outcomes).
RESULTS A total of 569 patients met the eligibility criteria, of whom 516 (90. 7%) and 53 patients (9. 3%) were assigned to the FAS72? and FAS72-(patients exposed for less than 72 h) data sets, respectively. Out of 516 patients in the FAS72? group, 354 patients were enrolled from the European region and 162 patients Fig. 1  Study design. 1 Indicates outcome that was examined for patients exposed to ceftazidime-avibactam for C 72 h. 2 Information about hospital readmissions or death after hospital discharge not available via medical chart abstraction was ascertained by contacting the patient or their legal representative by phone [ 60 days after hospital discharge. Patients followed from ceftazidime-avibactam initiation until 60 days after hospital discharge or another censoring event (in-hospital death, withdrawal from the study, or loss-to-follow-up, whichever occurs first).
Data had been abstracted from patient medical charts. Note: Data collection began 01 Jan 2018 or since the date of the launch if after 01 Jan 2018 from the LATAM region. Unless specified otherwise, all the results presented herein are for patients included in the FAS72? data set. Baseline characteristics of patients in the FAS72-data set are available in Supplementary Table  S2. For the 516 patients treated by ceftazidime-avibactam for at least 72 h, the main indications were HAP/VAP (22. 1%), cUTI (20. 0%), BSI (18. 8%), and cIAI (17. 4%) (Table  1  The majority of patients had HAIs (n = 354, 68. 6%), followed by healthcare-associated infections (n = 116, 22. 5%) and communityacquired infections (n = 46, 8. 9%). Healthcareassociated infections were most frequently observed among patients with cUTI (n = 41, 39.
Ceftazidime-avibactam was also well tolerated in patients who had MDR Gram-negative bacteria infections including CRE and MDR Pseudomonas spp. , and also in patients with multiple comorbidities, severely or critically ill patients, or those with bacteremia  [28]. The current study displayed several strengths. This was a large-scale study conducted in 11 countries pertaining to two regions: Europe (including Russia) and LATAM, enrolling 569 patients. Collected data represented patients' medical records from routine clinical practice in their respective countries. Compared to restrictive eligibility criteria in randomized controlled trials, this real-world evidence study allowed selection of heterogeneous patient populations with various comorbidities and concomitant treatments. Ceftazidime-avibactam was used per the approved prescribing information (dose, frequency, duration of infusion), with the recommended dose adjustments in patients with renal impairment.
ACKNOWLEDGEMENTS All authors would like to thank  Nathalie Baillon-Plot (Pfizer P. I. O. , Paris, France)  for her support and contribution in this study. Funding. The EZTEAM study was funded by  Pfizer , however, no payments or honoraria were made to the authors in respect to manuscript preparation. Pfizer also funded the journal's Rapid Service fees. Authorship. All authors meet the International Committee of Medical Journal Editors (ICMJE) authorship guidelines for this manuscript. Disclosures. Alex Soriano  has received honoraria for lectures and advisory boards from  Pfizer ,  MSD ,  Menarini ,  Shionogi , and  Angelini. Philippe Montravers has received research funds for lectures and advisory boards for Menarini,  MSD  and  Pfizer. Galina Klyasova  has received honoraria for lectures from  Pfizer ,  MSD ,  Gilead.
Reduction of JWST NIRSpec data We analyze JWST NIRSpec observations of Ganymede obtained as part of the Solar System ERS program #1373  (79). The observations were taken using the IFU, F290LP filter, and G395H high-resolution grating, which provided a resolving power of ~2700 and a wavelength coverage of 2. 9 to 5. 2 μm. Details concerning the dates, geometries, and exposure times associated with each visit are given in Table  1. As of the time of this analysis, the standard JWST pipeline (version #1. 8. 5) fails to appropriately combine the multiple dithered observations of Ganymede, does not correct for 1/f frequency-dependent noise in the detectors, and artificially induces low-tomedium frequency wave-like structures in the spectra of targets observed with the IFU. Much of this latter problem appears in the "Cu-beBuild" step of the analysis, where the data from individual onedimensional slits are interpolated and combined into a two-dimensional image at every wavelength.