dataesr/scientific-paragraphs-categorization huggingface dataset:
The lower compression at the outer side allows easier Li intercalation and less resistance for electrode expansion. Figure  4b  shows the almost linear intensity reduction for the Xrays and increase for the neutrons of the cathode during cell discharge. To determine the Li intercalation over the Li x MnO 2 electrode length, the interval of the unrolling software was reduced to the single electrode thickness and expanded in length over the whole electrode, as shown in the horizontal cut through the X-ray tomogram in Fig. 1d. Figures  5  and 6  represent three of the unrolled cathodes, from the pristine to the partly discharged state, of the X-ray and neutron datasets, respectively. In the case of the X-ray images, the nickel mesh was removed via thresholding to avoid corruptions of the Li x MnO 2 by the different intensity values.
The larger intensity reduction at the outer electrode side can be explained by being less confined and more susceptible to expansion. After unrolling the neutron datasets, the electrodes were split into three sections at different heights of about 4. 8 mm, each labelled with top, middle, or bottom, see Fig. 6. The slices were then combined through the electrode thickness to obtain a higher SNR. The same procedure was carried out for the whole electrode to analyse the intensity variation at the three different electrode heights of the cell and compare them to the mean value taken from the bulk. Figure  6b  shows the averaged electrodes of the pristine (CR2-00), partly discharged to -225. 71 mAh (CR2-03) and the more discharged to -580. 55 mAh (CR2-06) SoC with the corresponding greyscale intensity line plots.
However, despite the behaviour of the two cells investigated here being very similar, we cannot say for certain that their behaviour is statistically significant and representative of all cells operated under similar conditions. Discussion 4D CT data were obtained from two primary CR2 Li/MnO 2 cells while they were discharged under identical conditions. Due to the complementary nature of the two probing methods, one cell was examined using operando X-ray CT and the other using in situ neutron CT. The multi-modal characterisation was then assessed using a novel virtual electrode unrolling technique. X-ray CT allowed for the quantification of mechanical degradation effects such as electrode cracking from the electrode bending process during cell manufacturing. Whereas neutron CT gives information about the electrochemistry such as Li-ion transport and intercalation, electrolyte wetting of the cell and consumption or gas formation by, for example, electrolyte decay.
The tomographies are labelled from CR2-000 to CR2-102. For the current investigation, seven tomograms were used for the data analysis starting with the pristine SoC at the discharge time 0 s and expanding time intervals between each tomography (300 s, 900 s, 1500 s, 2100 s, 3000 s, 3900 s). The tomograms are marked by blue squares in the discharge curve in Fig. 1b. Figure  2a  shows orthogonal slices of three of the 3D reconstructed X-ray tomograms, in the pristine state, partly discharged to -240. 65 and -604. 90 mAh. Neutron tomograms were taken at the V7 CONRAD-2 imaging beamline at HZB. Tomograms were collected in the pristine SoC and after with an increasing time interval between each tomography (300 s, 900 s, 1500 s, 2100 s, 3000 s, 3900 s, 5100 s). The discharge process was interrupted during tomography acquisition due to the long exposure time of 8 h per tomogram.
Based on the larger field-of-view (FOV), the whole cell size could be imaged which is displayed in Fig. 2b , as orthogonal slices of the reconstructed 3D images for different SoC. The discharge profile is given in Fig. 1c  and shows all eight discharge steps, here labelled with CR2-00, for the pristine state, to CR2-07 for the almost fully discharged cell: red squares mark the SoCs where neutron tomograms were recorded. 3D images can give a comprehensive insight into the structure, working principle and morphological changes of a cell. However, it can be challenging to extract meaningful spatial information from a spiral-wound structure. To facilitate interpretation of the spatial data along the electrode length, a plug-in from the Amira Software Package was applied, which was created by Konrad-Zuse-Zentrum Berlin (ZIB)  34  for virtually unrolling the spiral-wound structure and take measurements along its length.
The negative electrical contact was made by a copper wire glued with aluminium tape to the battery casing, which contacted the negative terminal, and by a cable soldered to a nickelplated crocodile clip connected to the positive terminal at the cell head. Both cables were led away downwards and upwards, respectively, out of the FOV and connected to a potentiostat (1000E, Gamry Instruments, USA). The first tomogram was taken in the pristine state before discharging and the following datasets were collected consecutively with an increasing time interval between each tomogram. The discharging process was made over a 4. 7 Ω resistance and interrupted for about 8 h for taking tomograms (see Fig. 1c ) over a 360°rotation with an angular increment of 0. 45°and 800 steps. For each angular step, three radiographies were made with 9 s of exposure time and 5 s readout time of the camera.
The three radiographies where combined to one projection by a median filter to reduce white spots, caused by scattered or fast neutrons and secondary γ-rays, which hit the camera chip. The data collection and discharging processes were controlled using trigger signals between the beamline control software (LabVIEW, National Instruments, USA) and the potentiostat. For data processing, the 'Remove Outliers' and median filter of ImageJ Fiji  38  were used to clean the 16-bit images from remaining white spots 3D reconstructions were performed using Octopus 8. 9 software (XRE, Gent, Belgium) with FBP reconstruction algorithm and beam hardening correction. Virtual unrolling of the battery electrodes. The access to uneven, folded or rolled surfaces within a sample is hardly possible. It is generally possible to extract small flattened areas out of a 3D volume, but these areas mostly do not deliver meaningful results.
Acknowledgements This work was carried out with funding from the  Faraday Institution  (faraday. ac. uk; EP/ S003053/1 ), grant number  FIRG001. We would like to acknowledge the  Royal Academy of Engineering  ( CiET1718\59 ) for financial support. This work was authored in part by the  National Renewable Energy Laboratory , operated by  Alliance for Sustainable Energy, LLC , for the  U. S. Department of Energy (DOE)  under Contract No. DE-AC36-08GO28308.
Calculation of Saturation Index Scaling formation was expected to be a function of mineral salt over-saturation at the membrane surface. The saturation index (SI) value is commonly used to assess the supersaturation degree of salts such as AaBb in the solution, and can be defined as  [21] : IAP SI log sp K   =       (7) [ ] [ ] IAP a b A B = ( 8 ) where IAP is the ionic activity product. K sp is the solubility product constant of the related salts. A positive SI value shows that the compound is over-saturated, which is related to a risk of scaling, whereas an SI value below zero corresponds to under-saturation of the solution  [21]. PHREEQC software  [23]  was used to calculate the SI values of different salts in the non-ideal aqueous solution.
-males: pREE (kcal/d) ¼ 88. 362 þ 13. 397 x W þ 479. 9 x H e 5. 677 x A -females: pREE (kcal/d) ¼ 447. 593 þ 9. 247 x W þ 309. Anthropometric measurements included body weight e measured with a medical balance e and height e measured with a stadiometer. Body mass index (BMI) was calculated as weight (kg)/ height (m 2 ). Weight loss in the last 6 months and Eastern Cooperative Oncology Group performance status was also recorded. Since REE depends mainly on lean body mass (LBM), LBM was estimated from CT-scans performed for diagnosis procedures within the month of REE measurement. Skeletal muscle area (SMA) at the third lumbar vertebra (L3) was measured using ImageJ® software (NIH, USA) and LBM was calculated as  [24] : LBM (kg) ¼ L3 SMA (cm 2 ) x 0. 30 þ 6. 06. For comparative analyses, mREE/LBM ratio was dichotomized into 2 groups ("high" and "low") according to its median value.
Statistical analyses Calculations were performed using R statistical software (version 3. 5. 1, R Stats Package). Comparisons between groups were performed with Student ttest for quantitative variables and with chi-square test for qualitative variables. Overall survival (OS) was defined as the time elapsed between evaluation and death or last follow-up visit. Survival curves were obtained with Kaplan-Meier estimates and compared with logrank test. We calculated that we would need to enroll 57 patients in normo-and hypermetabolic groups to show a 30% difference of overall survival at 12 months (65% vs 35% respectively) with a twosided 5% significance level and a 90% statistical power. Cox proportional hazards regression was used to identify clinical and biological variables associated with OS.
Discussion In this prospective cohort of 144 metastatic NSCLC patients, our data show that hypermetabolism is associated with decreased survival. To the best of our knowledge, the current study is the first to describe energy expenditure as an independent prognostic factor in multivariable models in cancer patients. So far, only few studies have described the relationship between REE and survival in cancer patients. Most of them suggest that elevated REE is associated with poor outcome  [10, 14, 16]. However, none has evaluated the prognostic value of REE in comparison with standard prognostic factors. In a previous study, we have compared overall survival of metastatic cancer patients according to REE measured before chemotherapy initiation. Hypermetabolic patients showed a shorter overall survival compared with normometabolic patients  [10, 14].
Moreover, CT-scans are performed in routine diagnosis and follow-up work-up in cancer patients, and thus offer a convenient solution for determining LBM in this population. Missing data in LBM assessment are another limitation of the current study. However, the reasons for missing data, i. e. CT-images not available in medical records or not including L3 vertrebra-are not related to energy expenditure, and in the subgroup analysis, mREE/LBM showed good agreement with mREE/pREE ratio. The number of lost to follow-up patients (9%) represents another limitation of our study. However, reasons for lost to followup did not depend from REE. Indeed, no significant differences in mREE/pREE ratio were found between lost to follow-up patients and the rest of the cohort. Thus, lost to follow-up patients were unlikely to impact the interaction of REE with survival and with other prognostic factors.
Statistical analysis Statistical analyses were performed using R v3. 4. 2 (package R, deseq2). Population characteristics were expressed as medians and percentages. The quantitative variables of absolute concentrations (CFU per gram of feces or CFU per millilitre) and relative abundance (in log 10 or percentage) were expressed as means and medians (minimum and maximum values). The statistical tests used were Student t test and Pearson tests. The significance level was set at a value of 0. 05. Figures were designed using ggplot2 and colours were choose using colorbrewer2 (https://colorbrewer2. org).
, 2002) , dust atmospheric residence time  (Betzer et al. , 1988) , as well as the strength and trajectories of transporting winds  (Kohfeld et al. , 2013). Changes in dust flux can be seen in the variabilities of geochemical and isotopic compositions (e. g. , rare earth elements (REE) and Neodymium (Nd) isotopes,  Vanneste et al. , 2016 Vanneste et al. , , 2015)). Amsterdam Island is remote and nearly free from human disturbance, which makes it an ideal location to measure dust flux and provenance, and determine past changes in wind dynamics  (Gaudichet et al. , 1989). The specific objectives of this study were to: (1) investigate the dust flux in peat core from Amsterdam Island; (2) identify the provenance of the dust by comparing core samples with a reference dataset of dust geochemistry and isotopic signatures from the Southern Hemisphere; (3) interpret these measurements in terms of past changes in the dynamics of the SWW at their northern limit in the Indian ocean, through comparison with other Southern Hemisphere (SH) records; and (4) probe the anthropogenic impact on the Southern Hemisphere based on the dust variability in Amsterdam Island peat core.
Sample collection and preparation A 5 m-long peat sequence (AMS14-PB01A, 37°50. 742´S, 77°32. 898´E, Fig. 1c ) was collected from the center of the caldera peatland at 738 m a. s. l. (Fig. 1 ; Fig. S1 ) in December 2014 using a 50 cm long Russian corer with an internal diameter of 10 cm  (Belokopytov and Beresnevich 1955; De Vleeschouwer et al. , 2014). Two further cores (AMS14-PB01B, 4. 15 m length; AMS14-PB01C, top 1 m length) were collected and archived. Cores were photographed, described, wrapped in plastic film and PVC tubes, and shipped to France refrigerated at +4 °C. Cores were frozen and subsequently sliced at roughly 1-cm resolution using the sub-sampling procedure described in  De Vleeschouwer et al. , (2010)  and  Givelet et al. , (2004). To study the chemical and isotopic provenance of dust in the core, we assembled a reference dataset consisting of nine top soil samples from this island, representative of local dust sources (Fig.
Specific reference dust/soil samples in this study were collected from (1) local; (2) Sua Pan (Botswana) and (3) Etosha Pan (Namibia) in Southern Africa (Fig. 1a, c ). More information on Southern African dust/soil and local AMS soil are given in the supplementary information Text S2, Table  S1  and Table S2 , respectively. Dataset of Southern American surface sediments (including Puna-Altiplano-Plateau, Central Western Argentina and Patagonia) are from  Gili et al. , (2017).
Blanks were carbonate and charcoal (for details see  Delqué-Količ et al. , 2013; Moreau et al. , 2013). The surface of the core was dated with 210 Pb using the Constant Rate Supply model  (Appleby, 2001) , together with four post-bomb radiocarbon dates  (Goodsite et al. , 2001; Hua, 2009). The 210 Pb Constant Rate Supply model has been validated by 137 Cs and 241 Am measurements  (See Li et al. , 2017 for details). An age-depth model was generated from a combination of radiocarbon dating, post-bomb and 210 Pb dating with the Bacon package version 2. 2 updated in 2018  (Blaauw and Christen, 2011)  within R software version 3. 5 (R development Core Team, 2013) using the SHCal13 calibration curve  (Hogg et al. , 2013). For modelling, the core was divided into 101 sections. The prior settings were default 10 year/cm for accumulation rate (gamma distribution with shape 1.
Geochronology Radiocarbon and calibrated ages are shown in Table  1. The 210 Pb, 137 Cs and 241 Am results are published in  Li et al. , (2017). The 210 Pb Constant Rate Supply model is in good agreement with post-bomb 14 C ages. The age-depth model of the core was based on a combination of radiocarbon dating, post-bomb and 210 Pb dating (Fig. 2c ; Fig. S4 ). The model shows that the peat sequence spans the last 6. 6 kyr (Table  S6 ), with a mean accumulation rate of 0. 76 mm yr -1. Table  1. Radiocarbon dating results from Amsterdam Island peat sequence. Four samples were dated to a post-bomb period  (Goodsite et al. , 2001; Hua, 2009; Spalding et al. , 2005)  and the results calibrated using SH zone 1-2 calibration curve  (Hua et al. , 2013)  by Calibomb software  (Stuiver and Reimer, 2003).
Dust provenance Studies on atmospheric deposition at AMS, using 222 Rn, 210 Pb and air mass back trajectories suggested some continental inputs to AMS, in particular from Southern Africa  (Angot et al. , 2014; Gaudichet et al. , 1989; Polian et al. , 1986) , and Southern South America (Fig. S2 ). Comparison of ɛNd values in the AMS peat sequence with the reference datasets of the potential dust source end-members (local, Southern Africa, and Southern South America) show that ɛNd values of the peat samples lie between the local and continental dust sources (Fig. 5 , Fig. S7 ). This also suggests that AMS can potentially receive dust from local, Southern African, and Southern South American sources. The AMS soil samples display slightly higher Eu/Eu* (median 1. 24, Fig. 5 ) and lower La/Yb N ratios (median 0.
Acknowledgements: Many thanks for the support of the  Mission 66 of Amsterdam Island , without which the field trip could not be possible. We are grateful to  Svante Björck ,  Bart Klink  and  Elisabeth Michel  for the peat sample coring. A very special thanks to  Alain Quivoron  and  Hubert Launay  for their continuous directive and logistical support during and after fieldwork on Amsterdam Island. Combined fieldwork was funded by the  French Polar Institute  ( IPEV, Brest, France ) through the  IPEV Programmes 1066 "PARAD"  (to  F. De Vleeschouwer ) and 1065 PALATIO (to  N. Van  der.
Sampling Strategy The measurement strategy had two components (Table  1 ). Firstly, a 1. 5-yearlong (August 2017 to December 2018) quasi-monthly monitoring of the hydrology at a fixed station, 150 m deep, located in the valley (FS in Figure  1 ). This point was chosen considering that it represents a transitional place between the Beagle Channel and Ushuaia Bay, being part of the deep glacial valley. The variables measured included physical (temperature, salinity, density) and bio-optical (DO, Chl-a fluorescence, turbidity) parameters. Secondly, spatial monitoring of hydrological and hydrodynamic structures across and along the axis of the bay and valley (Figure  1 ) was carried out in winter (August 2017), summer (January 2018) and autumn (April 2018). Additional data on the concentration and size of suspended particles and POC concentration were also acquired during that period.
Hydrological Data A multiparameter JFE ASTD 102 RINKO profiler was used to collect surface to bottom profiles at 10 Hz of pressure, temperature, conductivity, DO, Chl-a fluorescence, and turbidity data. DO was measured with a fast-responding fluorescence sensor. The oxygen sensor was adjusted routinely following a twopoint calibration (0% in Na2SO3 solution; 100% bubbled water for 30 min) as recommended by the manufacturer. Additionally, water samples were collected at selected depths during surveys using a Niskin bottle, to conduct oxygen analysis by Winkler titration Winkler DO (mg/L) = 0. 81 × DO sensor (R 2 = 0. 96, N = 27). Chl-a fluorescence concentration and turbidity were measured with a fluorometer and a backscattering sensor, respectively. Raw data were transformed into engineering units using the software and calibration sheets provided by the manufacturer and later processed using the Sea-Bird Data Processing Software to obtain 1-dbar binned data.
Derived parameters (potential temperature, salinity, potential density anomaly, density ratio) were derived according to the TEOS-10 equations. Hydrographic data were processed according to routine procedures and displayed using the Ocean Data View software  [16]. On several occasions, near-bottom water samples were collected together with CTD profiles and filtered on 0. 4 μm Whatman Nuclepore Polycarbonate filters. In situ volumetric concentrations of suspended particulate material (SPM) were estimated from dry weights of particulate matter. A simple regression equation was derived between SPM concentration and turbidity measurements for the optical backscatter sensor: SPM(mg/L) = 1. 17 × Turbidity(FTU) + 0. 25 (R 2 = 0. 88, N = 17), which can be used to approximate the observed concentrations.
PSDs were then normalized with Vctot and expressed in %. During the August 2017 survey, a digital holographic camera (Sequoia LISST-HOLO, 0. 2 Hz sampling rate) was used in parallel to the LISST-100X to study the volume and number concentrations, and the shape of large particles, within the range 20-2000 μm  [21]. Two steps of 5 min, 2 m below the surface and 2 m above the bottom were carried out for each profile to collect enough images (>150), which were combined to obtain a representative estimation of particle composition in the surface bottom layers. PSD was estimated using a Matlab based image analysis processing (based on the estimate of the spherical equivalent diameter), and the size spectrum was expressed in μL/L for each of the 50 size classes logarithmically spaced from 20 to 2000 μm.
Hydrodynamical Data A Teledyne RDI workhorse 300 KHz ADCP was used to measure currents along the cross-bay section (B1-B9 in Figure  1 ). The ADCP was mounted on an aluminum structure attached to a dinghy, which moved at a speed between 1 and 2 m/s along the section. The boat's trajectory was tracked by GPS. The ADCP was programmed to sample at 1 Hz with a vertical resolution of 4 m, and took measurements of current speed and direction from 7 m depth to 4-8 m above the bottom (signals from the 4 ADCP beams, each with an orthogonal angle of 20°, are typically affected by side-lobe interference in the lowest 6% of the depth). The section was repeated 4 to 5 times resulting in cross-profiles 3950-4250 m and 36-63 min long. For operational reasons, each survey could not extend beyond 5 h and cover a complete tidal cycle.
It used the bottom tracking to derive absolute speed and direction, and bottom depth. Velocity data were linearly extrapolated in the unmeasured zones to the bed and the surface. Data from the different transects were projected on the average cross-section plane using an orthogonal translation and interpolated on a regular grid. The arithmetic average of the velocity components of all transects was computed at each node of the grid. Water discharge was calculated as the flow perpendicular to the mean cross-section. The renewal time in the northern part of Ushuaia Bay delimited by the section was estimated as the ratio of the volume of the domain (evaluated at 3. 4 × 10 8 m 3 by integrating bathymetric data interpolated on a regular grid) and the water flow entering the bay and perpendicular to the section.
A short-term mooring line was deployed between 2 December and 11 December 2019 on a 60 m bottom in the valley axis (54°48. 316 S-68°14. 972 W, Figure  1 ) to describe the variability of tidal currents and determine the residual current at the water vein entering the shallow bay. The currents were measured with a Nortek Aquadopp acoustic current meter, positioned at a depth of 4 m, with 2 min burst sampling at 1 Hz every 10 min. The tidal data (sea-level anomaly) are from the Ushuaia gauging station maintained by the Argentinian Naval Hydrographic Service and have been retrieved from the IOC sea-level station monitoring facility (http://www. iocsealevelmonitoring. org/).
Funding: This research was funded by the  ECOS-SUD program  in collaboration with  SeCyT  ( Secretaría de Ciencia y Tecnología ) for the project  HYDRO-USHUAIA. This study was also supported by the  Agencia Nacional de Promoción Científica y Tecnológica  ( PICT 2014/3106 ),  Consejo Nacional de Investigaciones Científicas y Técnicas  (P-UE 2016:  22920160100077CO CADIC , and the  UPVD 's  BQR program.
Post-processing of experimental data The experiments have a duration of 4 s starting with the injection until all the liquidsolid mixture is injected and dispersed out of the field of view. During the experiment, the horizontal flow was set at a constant velocity of 0. 15 m/s and the particles injection velocity was set at 0. 95 m/s. Those values were defined only to allow getting a sufficiently good visualization for image post-processing. The main objective of the experiment is to validate the potential of an Euler-Euler modeling approach with a modified-kε turbulence model to predict correctly the complex physical phenomena observed experimentally. The data processing was done using Matlab software. As presented earlier, the post-processing consists of computing the concentration fields by using the pixel intensity levels of each image obtained.