dataesr/scientific-paragraphs-categorization huggingface dataset:
Further work is needed to determine the mechanistic differences in uptake of lipid-bound and lipid-free aSyn monomer in different cell types. The two-fold increase in uptake of lipid-associated fibrillar aSyn that we show is likely to increase stress to neurons as reported for the uptake of lipid-free aSyn fibrils  72. We further characterised the SV lipid-bound fibrils to determine whether structural differences or simply the lipid-   5 ). d The signal intensity of neutron scattering from the lipid bilayer in the presence of aSyn fibrils over 20 h shows the loss of signal after 11 h (grey-green), no clear signal can be observed from 12-20 h (blue-pink), each line represents data collected each hour, data have been offset for clarity. Data for each condition were acquired once. e. i SV (0.
Each acquisition lasted 15 min, and 2 successive acquisitions of a sample were usually 15 min apart (except on occasions where controls were also measured). The program Grasp version 8. 20b (Charles Dewhurst, ILL) was used to reduce the data, accounting for detectors parallaxes, flat fields and efficiencies, normalizing by acquisition time (flux is constant), using transmissions (measured at the beginning and the end of the kinetics), subtracting the contribution from an empty cuvette, and obtaining the absolute scale from the attenuated direct beam measurement, which was also used to deduce the instrumental resolution. Modelling of the data was performed in SASView (http://www. sasview. org), using the Guinier-Porod model  58. The scattering intensity, I(q), is derived from independent contributions of the Guinier form in Eq.
The depletion beam was spatially shaped into a vortex beam by a spatial light modulator (X10468 02, Hamamatsu Photonics, Hamamatsu City, Japan) and the beams were recombined using a shortpass dichroic mirror (FF720-SDi01, Semrock, NY, USA). Imaging was performed using a commercial point scanning microscope (Abberior Instruments, Göttingen, Germany) comprising the microscope frame (IX83, Olympus, Shinjiuku, Japan), a set of galvanometer mirrors (Quad scanner, Abberior Instruments) and a detection unit. A ×100/1. 4 NA oil immersion objective (UPLSAPO 100XO, Olympus) and the Inspector software was used for data acquisition (Andreas Schönle, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany). Fluorescence emission was descanned, focused onto a pinhole and detected using an avalanche photodiode (SPCM-AQRH, Excelitas Technologies, Waltham, USA).
Fluorescence measurements were taken using a FLUOstar Omega plate reader (BMG LABTECH GmbH, Ortenberg, Germany). The plates were incubated at 37 °C with double orbital shaking at 200 rpm for five minutes before each read every hour for 310 h. Excitation was set at 440 nm with 20 flashes, and the ThT fluorescence intensity was measured at 480 nm emission with a 1100 gain setting. ThT assays were repeated three times using three wells for each condition. Data were normalised to the well with the maximum fluorescence intensity for each plate and the average was calculated for all experiments. A linear trend line was fitted along the exponential phase of each ThT fluorescence curve for the 9 wells per sample condition. The trend line was used to calculate the nucleation rate, where the intercept crossed the x axis (t lag ) and the elongation rate, as the slope of the exponential phase (k) using Eq.
(3). Data was analysed in GraphPad Prism 8. y ¼ kx À tlag ð3Þ Determining the remaining monomer concentration of aSyn after ThT-based assays using analytical size exclusion chromatography. To calculate the remaining aSyn monomer concentration in each well after ThT-based assays, size exclusion chromatography was performed on a high-pressure liquid chromatography (SEC) system. The contents of each well after the ThT-based assay were centrifuged at 21k × g for 20 min and the supernatant from each well was added to individual aliquots in the autosampler of the Agilent 1260 Infinity HPLC system (Agilent Technologies). Acquired AFM images were computationally flattened on Nanoscope Analysis 9. 4 (Bruker) before import into MATLAB (MathWorks, Natick, MA, USA) using the MATLAB toolkit for Bruker.
Batch analysis was performed using an in-house MATLAB script. A combination of manual and automatic segmentation of individual fibrils was performed. The height profile from each fibril was smoothed and characterised as either smooth or periodic as by a peak/trough search algorithm. For calculation of average fibril height, the mean across the whole fibril length and of the peaks were used for smooth and periodic fibrils, respectively. For periodic fibrils, the mean height of its peaks and troughs were additionally calculated. Coherent Raman scattering microscopy and stimulated Raman scattering microscopy. Coherent Raman scattering microscopy (coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS)) was performed on a Leica SP8 CARS laser scanning microscope with the additional SRS option (Leica Microsystems, Mannheim, Germany).
Reconstructed images were then further analysed in a separate MATLAB script to quantify the major axis length (i. e. , longest dimension) and eccentricity. Aggregate segmentation was performed by applying an intensity threshold, and aSyn uptake was normalised to soma area to account for differences in cell size. Statistics and reproducibility. dSTORM images were collected for three experiments and a total of 23 cells were analysed per condition. A one-way ANOVA with Holm-Šídák tests was performed using Graph Pad Prism 8. For AFM analysis, data were collected on fibril length and periodicity for 32 aSyn only fibrils and 53 aSyn fibrils grown in the presence of SV from two experimental repeats. SRS/CARS data were acquired from two experimental samples and for two fields of view from each. Data were normalised in Origin Pro.
Acknowledgements We would like to thank  Institut Laue-Langevin  for allocating us beam-time and  Dr Sylvian Prévost  for help preparing the samples for SANS. We would like to thank  Dr Karin Müller  and  Dr Filomena Gallo  at the  Cambridge Advance Imaging Centre  for help with sample preparation for  TEM. ScopeM (ETH Zürich)  is acknowledged for access to the Coherent Raman microscope. T. M. M. acknowledges support of the  Ernest Oppenheimer Fund through provision of his Oppenheimer Research Fellowship. O. V. was supported by the  Engineering and Physical Sciences Research Council  ( EP/L015889/1 ) and the  Horizon Europe Program  ( 101064246 ). C. W. C. was jointly funded by the  Cambridge Trust  and  Wolfson College  for her PhD. G. S. K. S. acknowledges fundingfrom the  Wellcome Trust  ( 065807/Z/01/Z ) ( 203249/Z/16/Z ), the  UK Medical Research Council (MRC)  ( MR/ K02292X/1 ),  Michael J Fox Foundation  ( 16238  and  022159 ) and  Infinitus China Ltd.
Data availability Raw data are available at the University of Cambridge Repository https://doi. org/10. 17863/CAM. 87759. SANS data acquired at the Institut Laue-Langevin, Grenoble, France are available at https://doi. org/10. 5291/ILL-DATA. 8-03-999. In-house MATLAB code for analysis of % fluorescence in the soma, length and eccentricity of aSyn in i  3  Neurons and AFM fibril length and periodicity is available on request.
North Brazil Current (NBC) rings are believed to play a key role in the Atlantic Ocean circulation and climate. Here, we use a new collection of high-resolution in-situ observations acquired during the EUREC 4 A-OA field experiment together with satellite altimetry to define, with unprecedented detail, the structure and evolution of these eddies. In-situ observations reveal a more complex structure than previously documented. In particular, we highlight a measurable impact of the Amazon outflow in creating a barrier layer over a large portion of the eddies. We show that this unprecedented data set allows us to estimate the accuracy of satellite altimetry gridded fields. The geostrophic velocities derived from satellite altimetry turn out to be considerably lower (up to 50% in amplitude) than the values measured by current meters.
They applied AMEDA to the absolute sea surface height (SSH) and associated geostrophic current fields at 50 m depth provided by the "Global Observed Ocean Physics Temperature Salinity Heights and Currents Reprocessing"-ARMOR3D  [49, 50]. ARMOR3D is an observational-based product that combines statistical satellite SLA, SST, and sea surface salinity (SSS) data sets with in-situ vertical profiles of temperature and salinity to construct a weekly global 3D field of temperature, salinity, and geostrophic velocities at 1/4°spatial resolution. However, the accuracy of ARMOR3D in representing the vertical mesoscale structure of the ocean has not been fully evaluated, especially in the WNTA. The calculated parameters are eddy lifetime (weeks), maximum azimuthal velocity V max (m. s -1 ) and its associated radius R max (km), the amplitude (cm), the kinetic energy of the eddy EKE (cm 2.
[26]  used the META2. 0 DT global dataset of eddy trajectories (distributed by AVISO-https://www. aviso. altimetry. fr/en/data/products/ (accessed on 1 January 2023)), based on SLA maps, and found 128 rings from 1993 to 2016, meaning a mean generation rate of 5. 3 ± 1. 5 rings/year. Although our results remain comparable, ref. [25, 26]  detected on average more rings than TOEddies between 1993 and 2016. Moreover, Ref. [22]  defined 34 anticyclonic eddies formed in the NBC retroflection from October 1992 to December 1998, using the upper layer thickness computed from TOPEX/POSEIDON sea surface height anomalies (SSHa) combined with historical hydrographic data. During the exact same period of time, Ref. [25]  identified 29 rings, whereas we detected 26 by applying TOEddies. Ref. [10] , using SeaWifs ocean color imagery, identified 18 NBC rings from 1997 to 2000 as does TOEddies, whereas  [25]  found only 16 for the same period of time.
They reveal a sensible difference (up to 50% in intensity) from the estimates of surface geostrophic velocities derived from satellite altimetry. This difference increases for velocities closer to the surface. Using the 27 years of the TOEddies database, we produced new NBC rings statistics. We identified a mean generation rate of 4. 5 ± 1. 1 rings per year, with more rings shed in boreal spring, and less in boreal autumn. Our estimates compare well, albeit they somewhat differ, with previous estimates from satellite altimetry and ocean color. By comparing TOEddies ring detections with EUREC 4 A-OA and published in-situ data, we show that only the surface-intensified variety of rings can be identified in a robust way from satellite altimetry to allow for statistics. NBC rings radius, azimuthal velocities, amplitudes, and EKE have their highest values near the generation region, the NBC retroflection.
Acknowledgments: This research has been supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreements no. 817578  (TRIATLAS), the  Centre National d'Etudes Spatiales  through the  TOEddies  and  EUREC 4 A-OA projects , the  French national program LEFE INSU , the  IFREMER , the  French vessel research fleet , the  French research infrastructures AERIS  and  ODATIS ,  IPSL , the  Chaire Chanel program  of the  Geosciences Department at ENS , and the  EUREC 4 A-OA JPI Ocean and Climate program  and by  TRIATLAS H2020 EU programme. We also warmly thank the captain and crew of  RVs Atalante  and  Maria S. Merian.
Funding: This research has been supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreements no. 817578  (TRIATLAS), the  Centre National d'Etudes Spatiales  through the  TOEddies  and  EUREC 4 A-OA projects , the  French national program LEFE INSU , the  IFREMER , the  French vessel research fleet , the  French research infrastructures AERIS  and  ODATIS ,  IPSL , the  Chaire Chanel program  of the  Geosciences Department at ENS , and the  EUREC 4 A-OA JPI Ocean and Climate program  and by  TRIATLAS H2020 EU program.
, 2001). Over the past 3 decades, multiple scatterometer missions have been launched with the aim of obtaining full and repeated global coverage  (Ulaby et al. , 2014) , such as the Quick Scatterometer (QSCAT;  Ku-band; from 1999 to 2009) , the Oceansat-2 Scatterometer (OSCAT;  Ku-band; since 2009) , and the Advanced SCATterometer (ASCAT; C-band; since 2007). Among these sensors, both ERS-1/-2 and ASCAT operate at the C-band frequency but have a temporal gap of about 6 years (i. e. be- tween  2001 and 2007). Filling this time gap would lead to the first global C-band scatterometer data set with continuous observations for the past 3 decades (since 1992). Moreover, this data set could, in principle, be further extended because ASCAT is still operational, and similar C-band radar missions have been secured for the future (such as the Sentinel radar series and the MetOp Second Generation satellite mission;  Malenovský et al.
9 km resolution in the period of  1992 -2001 and QSCAT (1999 -2009) )  and AS-CAT (2007-now) images of 4. 45 km resolution have been made publicly available. To guarantee a long time span from 1992 onwards, we aggregated QSCAT and ASCAT images to 8. 9 km to be consistent with the resolution of ERS-1/-2 images. We chose to produce a monthly radar data set in this study because daily scatterometer images do not provide full global coverage and also because the higher spatial resolution was achieved at the cost of reduced temporal resolution, with daily images only being available for polar regions (https://www. scp. byu. edu/docs/ EnhancedFAQ. html, last access: 20 January 2023). Besides, the monthly time resolution has been frequently adopted by previous global-scale studies  (Sun et al. , 2018).
The resulting merged radar data set, named long-term, high-resolution scatterometer (LHScat), is publicly available in netCDF format at https://doi. org/10. 6084/m9. figshare. 20407857 (Tao et al. , 2023). LHScat will be constantly updated to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions. Below, we provide a detailed illustration on the source data, methods, quality, and validation of the LHScat data set.
Data and methods 2. 1 ERS-1/-2, QSCAT, and ASCAT data Scatterometers were originally designed to measure wind speed and direction, particularly over oceans. However, their data have also been found to be useful for land applications such as soil moisture estimation, rainfall estimation, and forest monitoring. Here we analysed spaceborne scatterometer data from the ERS-1/-2, QSCAT, and ASCAT sensors (Fig. 1 ; Table  1 ). The backscatter of the radar signal, usually expressed in decibels (dB), is a function of the sensor parameters (frequency, polarization, look angle, and spatial resolution) and the dielectric and geometric properties of the scattering objects. 9, 10. 7, 18. 7, 23. 8, Dec 2002 Oct 2003 36. 5, 50. 3, 52. 8, 89. 0 AMSR-2 Radiometer 6. 9, 7. 3, 10. 7, 18. 7, May 2012 -23. 8, 36.
ERS-1/-2 images were acquired in vertical (V)-polarization mode and were usually gridded at 25 or 12. 5 km resolutions  (Frison and Mougin, 1996). The SeaWinds scatterometer (13. 4 GHz; Ku-band) on board QSCAT was launched by NASA in 1999 and collected data in full mode until November 2009. It provides normalized cross-sectional backscatter values at fixed incidence angles of 46 • in the horizontal (H)-polarization mode and 54. 1 • in V-polarization mode. Its ascending and descending orbits cross the Equator at 06:00 and 18:00 LST (local standard time), respectively. The QSCAT images are normally delivered at a resolution of 22 km × 22 km  (Tsai et al. , 2000). ASCAT, on board the Meteorological Operational (MetOp) series of satellites, was launched in October 2006 as a successor of the ERS-1/-2 scatterometers.
Data preprocessing We first aggregated QSCAT and ASCAT images of the BYU version at the resolution of ERS-1/-2 images, namely 8. 9 km per pixel. Ascending path QSCAT and ASCAT images were used. The ascending path time of QSCAT acquisition (06:00 LT) is before sunrise, and the ascending path time of ASCAT (21:30 LT) is well after sunset, so both reflect nighttime land surface conditions. The ERS-1/-2 images of the BYU version are generated by combining the images of all paths to ensure the highest possible spatial and temporal coverages; we therefore used the all-path ERS-1/-2 images. ERS-2 signals during August 1996-June 1997 were increased by 0. 2 dB to account for the sensor calibration bias  (Crapolicchio and Lecomte, 2003). V-polarization QSCAT images were merged with Vpolarization ERS-1/-2 and ASCAT images.
H-polarization QSCAT images were also tried, but very similar merged signals were obtained. The BYU data centre provides images synthesized from acquisitions made over periods of 17, 3, and 4 consecutive days for ERS-1/-2, QSCAT, and ASCAT, respectively. For all three sensors, images acquired within a month were averaged. ERS-1/-2 and ASCAT observations of the BYU version were normalized to a common 40 • incidence angle to be free of angle influence on the observations. Monthly signals exceeding 3 standard deviations from the long-term mean were consider to be outliers. Some AS-CAT images were found to have strip patterns. Fortunately, all the strips were characterized by regions with a low number of radar observations and thus can be masked by thresholding for a minimum number of observations, which was set to 20  (Tao et al.
55 to 0. 91 (Fig. 4c ). To summarize, combining monthly climatic variables and decision tree regression modelling, we corrected the monthly signal differences, pixel by pixel, using the following steps: 1. For each pixel, a decision tree regression model was built taking the monthly signal differences during the overlapping periods  (i. e. 1999-2001, and 2007-2009)  as a dependent variable and monthly ERA5-Land rainfall, snow depth, and skin temperature (0. 1 × 0. lution; Muñoz-Sabater, 2019) as explanatory variables. We used the MATLAB function fitrtree to implement the decision tree modelling (The MathWorks, Inc. ). 2. After tree construction, cross-validation procedures were used to avoid overfitting. We increased the value for the MinLeafSize parameter from 1 to 30, with a step size of 1, and calculated the cross-validated errors.
5 dB. Since the ESA ERS-2 data have a resolution of 25 km, we aggregated our merged radar signals to that resolution. For each month between 2001 and 2011, pixels with available ESA ERS-2 observations were located, and their ERS-2 signals were averaged across pixels. Because the footprints of the ESA ERS-2 observations are not fixed temporally, different months have a different subset of pixels. Our merged radar time series from the same pixels were then averaged and compared with the ESA ERS-2 signal mean. Months with too few pixels (< 100) having ESA ERS-2 observations were not considered. This increases the strictness of the comparison in the sense that there is an additional spatial variation in pixels embedded within the radar time series.
Importance of the predictor variables We found that the most important predictors calculated by the MATLAB function predictorImportance (Fig. 8 ) were almost identical to the predictors used in the first splits of the decision trees (Fig. S4 ). We therefore illustrated the variable importance below, using the results presented in Fig. 8  (i. e. those calculated with predictorImportance). For 33. 3 % of all the pixels, signal differences were most accurately predicted by rainfall (hereafter referred to as Type 1 pixels; Fig. 8 ). This type of pixel was mainly found in the Southern Hemisphere, particularly in tropical regions. In the Northern Hemisphere, such pixels were primarily located in the low and middle latitudes (Fig. 8 ). For 57. 8 % of all the pixels, the signal differences were most accurately predicted by skin temperature (hereafter referred to as Type 2 pixels; Fig.
Rescaling the radar time series The purpose of this project was to create the first global long-term radar backscatter data set with a consistent C-band signal dynamic. C-band ERS-1/-2 (1992-2001) and ASCAT (2007 onwards) signals were bridged by  Ku-band QSCAT (1999 -2009)  signals. Observations overlapped between the three sensors, which allowed us to rescale the signal times series. The CDF matching technique has been a classical signal rescaling method  (Liu et al. , 2009 (Liu et al. , , 2011)). 2012). In these previous studies, the overlapping periods among sensors are relatively long, with some even exceeding 10 years. In contrast, neither the ERS-2-QSCAT nor the QSCAT-ASCAT overlapping periods span more than 3 years. The rescaled QSCAT signals by CDF could therefore be biased, due, for instance, to deforestation in southern Amazonia (Fig.
, 2010 (Brocca et al. , , 2013;; Draper et al. , 2009). We here further demonstrated its capability of rescaling microwave signals with a short overlapping period (∼ 3 years). Additionally, the results shown in Fig. 3  suggest that this method is robust in response to both the trends and sudden changes in the radar signal. Merging the time series of satellite observations has been an important https://doi. org/10. 5194/essd-15-1577-2023 Earth Syst. Sci. Data, 15, 1577-1596, 2023   [1999] [2000] [2001] [2007] [2008] [2009]. The variable importance was calculated from the decision tree regression model using the MATLAB function predictorImportance. For pixel Types 1, 2, and 3, the most important variables are monthly precipitation, skin temperature, and snow depth, respectively. yet challenging task in Earth science studies.
Signal quality and merging mechanism After rescaling the radar time series from different sensors, monthly signal differences were corrected by modelling them from climatic variables (namely precipitation, skin temperature, and snow depth). The quality of the merged radar signals was assessed against the ESA ERS-2 data set. Highly similar monthly time series were obtained, suggesting high accuracy for the merging procedure. Why did rainfall, skin temperature, and snow depth successfully predict the signal differences? The main reason is that the Ku-band signal has a lower penetrating ability in comparison to the C-band signal because of its shorter wavelength. In regions with a strong rainfall, such as the tropics, Ku-band signals are more impacted by raindrops and the intercepted water on leaf surfaces, thus showing different seasonal patterns with C-band signals (Fig.
The Ku-band signal in-to estimate snow depth and snow water equivalent. One of the classical methods is based on the fact that microwaves at different frequencies respond differently to snow cover. For instance, the  Chang et al. (1982)  equation utilizes the channel differences between low-(such as 19 GHz) and high-(such as 37 GHz) frequency brightness temperatures observed by passive microwave sensors. Here, we found similar signal differences between low-(C-band) and high-(Ku-band) frequency radar signals. Since several radar sensors at different frequencies are operating, efforts could be made to create products of snow depth or snow water equivalent by combining radar signals of different frequencies such as QSCAT and ASCAT. It is also worth noting that, although climatic data were used to merge radar signals into a single time series, this does not mean that our final radar signals contain mainly climate information.