dataesr/scientific-paragraphs-categorization huggingface dataset:
g. Arendt and others, 2008). New geodetic and geophysical data present a unique opportunity to investigate a decade of change over the Kaskawulsh Glacier. The first objective of this study is therefore to compute the geodetic mass balance using recently acquired SPOT5/6/7 data to assess glacier mass balance. The second objective is to assess the state of dynamic adjustment to the mass (im)balance and to estimate committed mass loss from the Kaskawulsh Glacier. We do this by comparing measured ice fluxesestimated using data from the first spatially extensive icepenetrating radar (IPR) survey of the glacier and NASA MEaSUREs ITS_LIVE surface velocities  (Gardner and others, 2019)  to balance fluxes determined using a fully distributed massbalance model. Hence, we explore discrepancies between internal mass redistribution and climate-driven surface mass-balance change to evaluate the current extent of this dynamic adjustment.
Potential direct clear-sky radiation Potential direct clear-sky radiation I (Eqn (2)) is calculated at 0. 5 h intervals using a combination of the ArcGIS Solar Analyst toolbox and a custom adaptation of the python PyEPHEM astronomical calculations module to assign local time to the calculated radiation values (see Supplementary materials). Radiation is calculated across the 200 m grid for clear-sky conditions by incorporating a fixed atmospheric transmissivity of 0. 75  (Hock, 1998 (Hock, , 1999)). This use of calculated radiation values is insufficient for modelling mass balance several decades into the past or future  (Wild and others, 2005; Huss and others, 2009) , but conforms with the observation of minimal sensitivity of ablation to temporal changes in the potential solar radiation on the multi-annual timescales of our study  (Vincent and Six, 2013).
Meteorological variables Temperature and precipitation inputs to the downscaling routine (described below) are obtained from the National Centre for Environmental Prediction's (NCEP) North American Regional Reanalysis (NARR) product  (Mesinger and others, 2006). The NARR product comprises multiple atmospheric and surface climate variables at high temporal (3 hourly) and moderate spatial (32 km at 60 °N) resolution for the North American continent between 1979 and present. Three-hourly temperature and geopotential data at 29 discrete pressure levels in the atmosphere are used as inputs for the temperature downscaling. Daily total surface precipitation data are used as inputs for the precipitation downscaling.
Eight Automatic Weather Station (AWS) temperature records are available from four stations belonging to the SFU Glaciology Group, two belonging to the University of Ottawa Laboratory for Cryospheric Research and two operated by Environment Canada. AWS temperature records are used to obtain monthly bias corrections for the downscaled temperatures (Fig. 4 ). Monthly mean temperatures for each AWS location are determined for the time intervals over which data are available within the study period. The minimum AWS record length is seven years. A Δ change method is used to calculate a bias correction  (Hay and others, 2000; Clarke and others, 2015) : T c (x, y, t) = T ds (x, y, t) + DT(t), (4) where T c (x, y, t) is the bias-corrected temperature at position x, Journal of Glaciology y and time t, T ds (x, y, t) is the temperature at the same position and time downscaled from the NARR data and ΔT(t) is the difference between the mean monthly downscaled temperature and mean monthly AWS temperatures, linearly interpolated to daily values.
Ice fluxes We use new IPR data, along with the NASA MEaSUREs ITS_LIVE surface velocities  (Gardner and others, 2019) , to estimate the observed 2007-18 ice fluxes at nine gates in the ablation area of Kaskawulsh Glacier. The flux gates (Fig. 8a ) are roughly perpendicular to the direction of ice flow, with five spanning the main trunk of the glacier and four spanning the major tributaries (North Arm, Central Arm, Stairway Glacier, South Arm). Ice-flux estimates are confined to the ablation area by the radar-data coverage. We compare the observed fluxes to balance fluxes at the same locations obtained using the modelled surface mass balance described above. Below we describe the determination of glacier cross-sectional area based on collection, processing and interpretation of IPR data, followed by the estimation of depth-averaged velocities using the NASA MEaSUREs ITS_LIVE surface-velocity dataset.
IPR data collection Ground-based IPR data were collected in 2018 and 2019 with a ruggedised BSI IceRadar system  (Mingo and Flowers, 2010; Mingo and others, 2020) , comprising a Narod and Clarke (1994) impulse transmitter (from Bennest Enterprises Ltd. ) with a ± 600 V pulse and a pulse repetition frequency of 512 MHz. The receiving unit employs a 12-bit digitiser (Pico 4227), an integrated single-frequency global positioning system (GPS) unit (Garmin NMEA GPS18x) and Blue Systems Integrated IceRadar Acquisition Software. The GPS unit is used only to obtain horizontal coordinates. Receiver and transmitter are connected to identical sets of resistively loaded dipole antennas of 5 MHz centre frequency which were towed in-line at ∼30 m separation during the common-offset surveys. During data acquisition, we collected 1024 stacks every 2-3 s at walking speed.
Depth-averaged velocities At each transect, cross-glacier depth-averaged velocity profiles (i. e. u(y)) are generated using surface-velocity data and assumptions about flow partitioning between sliding and deformation. Surface velocities are obtained from the NASA MEaSUREs Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project  (Gardner and others, 2019). These data are generated using Landsat 4, 5, 7 and 8 imagery and auto-RIFT feature tracking  (Gardner and others, 2018)  to produce annual velocity mosaics. At each of our flux gates, we extract annual surface velocity profiles from the 240 m×240 m gridded ITS_LIVE dataset for the 2007-18 study period. From the 2007-18 profiles we compute a 12-year mean velocity profile at each transect (Fig. 8 ). We consider three velocity models, which respectively give rise to lower, higher and Table  1.
Errors also arise in the calculation of ice fluxes. There are three major sources of uncertainty in our calculation: that associated with (1) ice depth, due to processing and interpretation of the radar data, (2) surface velocity, arising from inter-annual variability evident in the ITS_LIVE data and (3) the velocity-depth profile, owing to the unknown partitioning of surface velocity between deformation and sliding. The latter is the largest. Inconsistency also arises from using radar data collected in 2018-19 to compute 2007-18 fluxes, given the nearly pervasive thinning observed from 2007 to 2018 (Fig. 2 ). Entertaining the possibility that Q ref (Table  3 ) may not be the correct representation of flux at each gate, we explore the impact of substituting Q low ± σ (no sliding) and Q high ± σ (plug flow) for Q ref.
42 m w. e. (Q bal-0. 42 , red). Dark red/blue shading is standard deviation of balance fluxes for the 12 simulations that satisfy both stages of model tuning for debris-present case. Light red/blue shading is the uncertainty for each balance flux determined from the uncertainties on model accumulation and melt rates. Shading is continuous between flux gates only to assist in visual interpretation; not all flux gates are connected as suggested by shading. and Climate Change Canada), less than has been estimated is required to offset the effects of warming (e. g. De  Woul and Hock, 2005). With the anticipated warming yet to come by 2100, the glacier mass-loss rate and committed retreat we have estimated based on 2007-18 data are lower than should be expected for mid-to late-21st century climate conditions.
Acknowledgments. Permission to conduct field work was granted by the  Kluane First Nation (KFN) ,  Parks Canada and Yukon Government. We are grateful to  S. Williams ,  L. Goodwin  and  S. Shaw  for logistical support, and  D. Bigelow ,  C. Wong  ( Parks Canada ) and  U. Fischer  for assistance in the field. L. Copland kindly provided temperature data used in the NARR bias corrections. We thank  L. Mingo  for assistance with interpretation of radar data,  A. Adhikari  for carrying out snowline picks,  A. Morin  for performing calculations involving the Farinotti and others (2019) dataset and  H. Jiskoot  for helpful discussions related to model tuning. SPOT6/7 data were obtained thanks to public funds received in the framework of  GEOSUD, a project  ( ANR-10-EQPX-20 ) of the  programme 'Investissements d'Avenir ' managed by the  French National Research Agency.
Data Availability. The Kaskawulsh Glacier outline was obtained from https://www. glims. org/maps/glims, the Nasa ITS_LIVE surface velocities were obtained from https://nsidc. org/apps/itslive/, the NARR data used as input to the mass balance model were obtained from https://www. ncdc. noaa. gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr, and the SFU Glaciology Group AWS temperature records are available at https://vault. sfu. ca/index. php/s/Io7LCSrcGuvTmo9. Further data available upon request.
Background: We need high-quality data to assess the determinants for COVID-19 severity in people with MS (PwMS). Several studies have recently emerged but there is great benefit in aligning data collection efforts at a global scale. Objectives: Our mission is to scale-up COVID-19 data collection efforts and provide the MS community with data-driven insights as soon as possible. Methods: Numerous stakeholders were brought together. Small dedicated interdisciplinary task forces were created to speed-up the formulation of the study design and work plan. First step was to agree upon a COVID-19 MS core data set. Second, we worked on providing a user-friendly and rapid pipeline to share COVID-19 data at a global scale.
Results: The COVID-19 MS core data set was agreed within 48 hours. To date, 23 data collection partners are involved and the first data imports have been performed successfully. Data processing and analysis is an on-going process. Conclusions: We reached a consensus on a core data set and established data sharing processes with multiple partners to address an urgent need for information to guide clinical practice. First results show that partners are motivated to share data to attain the ultimate joint goal: better understand the effect of COVID-19 in PwMS.
Global alignment on a COVID-19 in MS core data set To prepare the draft COVID-19 in MS core data set, we started with identifying common areas of interest in emerging and active data collection efforts on COVID-19 in MS. A final list of variables was agreed to within 48 hours in a global consensus-building teleconference. The core variable set can be downloaded via the MSDA website (https://msdataalliance. com/covid-19/for-ms-registers-and-data-custodians/). The following groups of variables were deemed important: COVID-19 infection, COVID-19 severity, COVID-19 treatment, demographic information, MS history and severity, information on DMT use and comorbidities and selected lifestyle behaviours, particularly smoking. During the consensus-building teleconference, we agreed on the importance of including both patient-reported and clinicianreported data to facilitate the inclusion of both milder and more severe COVID-19 cases in MS.
Implementation of the COVID-19 in MS core data set in as many existing and emerging registries or cohorts as possible Registries and cohorts are able to deliver the highest data quality. Such high-quality data are needed to deliver fine-tuned results during, but particularly after, the pandemic. Some of these initiatives are already available for international COVID-19 and MS data collection and have implemented standardized safety protocols to address post-authorization safety studies (PASS). 3 Table  1  summarizes the current list of initiatives that have or are planning to implement the COVID-19 in MS core data set (as of 10 May 2020). This list is expanding rapidly and is continuously updated on our websites. We also provide an option to directly enter data into our central platform (a distinction is made between a clinician-reported fast module for data entry by healthcare professionals (https://platform.
Is there a difference in COVID-19 outcomes between untreated PwMS and PwMS on DMT? 4. Does the type of treatment have an effect on COVID-19 outcomes? With the data being collected, which will focus on reported cases of COVID-19 among patients and not assess the background populations, we need to give priority to analyses on cases only, most importantly to identify factors predicting outcome once an infection is suspected or confirmed. In contrast, we believe indepth analyses of population-based data sources including history and follow-up information will be required to address some research questions, such as risks of COVID-19 in the MS population and contributing factors such as DMTs or the long-term effect on MS progression of having had a COVID-19 infection, which will require a longer term follow-up.
Nevertheless, we were able to motivate numerous stakeholders in the field of MS (neurologists, patient organizations, researchers, registries, etc. ) to join our effort in bringing together data from patients, clinicians, registries and other COVID-19 in MS initiatives. We aim to continuously improve our approach while we move ahead. We hope the data sharing approach used here can be used as a template for different stakeholders to work together on an international scale also outside of the scope of the COVID-19 crisis. Indeed, there are many other urgent research questions that need to be addressed that require scaling-up real-world data research at a global scale.
Acknowledgements We would like to thank the sponsors of the Multiple Sclerosis Data Alliance and the  Multiple Sclerosis International Federation  for providing financial support. Special thanks goes to QMENTA for kindly providing us with the central platform. The authors thank everyone who actively participated in any of the global teleconferences, brainstorms and task force meetings. We would like to thank all the  PwMS  and healthcare professionals who already contributed to the different data collection efforts. We also want to thank  Jan Samyn ,  Margo Heremans ,  Angela Hooper ,  Jorina Nickmans ,  Gunther Meyer  and  Victoria Gilbert  to support us with ensuring the operational power we needed to speed-up our progress. Genzyme,  Merck-Serono, Biogen Idec and Roche. P. R. R. is shareholder, employee and member of board of directors of QMENTA.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The operational costs linked to this study are funded by the Multiple Sclerosis International Federation (MSIF) and the Multiple Sclerosis Data Alliance (MSDA), acting under the umbrella of the European Charcot Foundation (ECF). The MSDA receives income from a range of corporate sponsors, recently including Biogen, Bristol-Myers Squibb (formerly Celgene), Canopy Growth Corporation, Genzyme, Icometrix, Merck, Mylan, Novartis, QMENTA, Quanterix and Roche. MSIF receives income from a range of corporate sponsors, recently including Biogen, Bristol-Myers Squibb (formerly Celgene), Genzyme, Med-Day, Merck, Mylan, Novartis and Roche.
METHODS Thirty-one patients were operated on between November 2012 and March 2019 at Toulouse and Strasbourg University hospitals. The same procedure was taught by the senior author (BC) to all teams. For 29 of the patients, 1 LAP was sufficient to cover the defect; for 2 patients, we combined LAP with another flap (a second LAP and 2 superior gluteal artery perforator flaps [S-GAPs]). Indications were diverse: neurosurgical complications, extensive burns, oncodermatology, and ballistic injury. Table  1  summarizes all the data in this series. All perforator arteries were identified preoperatively using an acoustic handheld Doppler (HADECO Bidop ES-100 8 MHz). Color Doppler identification was also performed where there was any doubt about the perforator path. Surgery performed under general anesthesia, with patients in prone position.
LC-MS Analysis Analysis of the LC-MS carried out, as described in Drouet et al. [37] , on a Water 2695 Alliance coupled with a single quadrupole mass spectrometer ZQ (Waters-Micromass, Manchester, UK). LC-ESI-MS (liquid chromatography-electrospray ionization-mass spectrometry) MassLynx 4. 0 software (Waters-Micromass, Manchester, UK) was used to acquire and process data. The separation was conducted using a linear gradient: from a mixture of 10:90 (v/v) to 100:0 (v/v) of solvent A (methanol) and solvent B (water + 0. 05% (v/v) formic acid) at a flow rate of 1. 00 mL/min during a run of 1 h. The detection was set at 350 nm. Before injection, extracts were centrifuged 15 min at 5000× g (Heraeus Biofuge Stratos, Thermo Scientific, Illkirch, France) and the supernatant extract was filtered through 0.
Their coded levels and experimental values are shown in Table  1. The ranges of each variables were determined based on single parameter evaluation preliminary experiments. In comparison, based on the results obtained from these preliminary experiments, the influences of the liquid/solid ratio and extraction temperature parameters were negligible (i. e. , no significant impact) and were fixed to 25:1 mL/g DW and 45 • C, respectively (data not shown). In silico, the 27 different bath conditions (run ID) for the full factorial design were determined and randomized (run order). The respective independent process variables for each batch condition are shown in Table  2. Each batch condition was tested in separate triplicates. Table  3  presents the TFC from the N. lotus stamens resulting from these extraction conditions.
Conceptualization, D. T. and C. H. ; methodology, D. T. , S. D. , A. K. and C. H. ; software, D. T. , S. D. and C. H. ; validation, D. T. and C. H. ; formal analysis, D. T. and C. H. ; investigation, D. T. , S. D. , A. K. and C. H. ; resources, D. T. and C. H. ; data curation, D. T. and C. H. ; writing-original draft preparation, D. T. and C. H. ; writing-review and editing, D. T. , S. D. , A. K. and C. H. ; visualization, D. T. , S. D. and C. H. ; supervision, D. T. and C. H. ; project administration, D. T. and C. H. ; funding acquisition, D. T. and C. H. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Mahidol University. This research was supported by Cosmetosciences, a global training and research program dedicated to the cosmetic industry.
Acknowledgments: This research project is supported by  Mahidol University. D. T. gratefully acknowledges the financial support from  Mahidol University. D. T. and C. H. gratefully acknowledges the support of the  French government via the French Embassy in Thailand  in the form of Junior Research Fellowship Program 2018. C. H. and D. T. gratefully acknowledges the support of  Campus France  through the  PHC SIAM  (PNPIA, Project  44926WK ). S. D. acknowledges research fellowship of  Loire Valley Region.
Author Contributions: Conceptualization, D. T. and C. H. ; methodology, D. T. , S. D. , A. K. and C. H. ; software, D. T. , S. D. and C. H. ; validation, D. T. and C. H. ; formal analysis, D. T. and C. H. ; investigation, D. T. , S. D. , A. K. and C. H. ; resources, D. T. and C. H. ; data curation, D. T. and C. H. ; writing-original draft preparation, D. T. and C. H. ; writing-review and editing, D. T. , S. D. , A. K. and C. H. ; visualization, D. T. , S. D. and C. H. ; supervision, D. T. and C. H. ; project administration, D. T. and C. H. ; funding acquisition, D. T. and C. H. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Mahidol University. This research was supported by Cosmetosciences, a global training and research program dedicated to the cosmetic industry.
7, and a medium effect size (f = 0. 25) is 0. 81. Additionally, the sensitivity of the tests for an alpha level of 0. 05 and a power of 0. 90 was sufficient for detecting a medium effect size (f = 0. 28) for the interaction. All the analyses were carried out by using the statistical package SPSS version 20. 0 (SPSS, IBM, Armonk, NY, USA) and the G Power software (version 3. 1. 9. 2, Heinrich Heine University, Düsseldorf, Germany). The level of statistical significance was set at 0. 05.
Patients and methods Adolescent females undergoing treatment for moderate to severe AIS at a single institution (n = 20) were recruited for the AIS study group. The location, type, and direction (side of curve convexity) of the curves was recorded for all subjects (see Table  1 ). Adolescent and young adult females (n = 19) without scoliosis were recruited as a control group. To see the full data set of demographics and data dictionary used, please refer to S1.
Definitive diagnosis of acute ischemic stroke should have been confirmed on MRI performed 24 to 72 hours after admission. Consecutive patients with a known non-migraine status admitted to NVICU during the same period, using the same diagnostic assessment were used as a control population. Exclusion criteria were age under 18 years old, other cerebral lesion diagnosed on initial CT, contrast allergy, history of severe renal failure, pregnancy and non-available imaging (MRI or CT perfusion). The study protocol was approved by the Ethics Committee of Rouen University Hospital. We would be wiling to share the de-identified Excel database, which includes all the clinical and radiological data of the participants upon reasonable request According to the ICHD-3β diagnostic criteria, patients were classified as migraine with or without aura.
4/ Imaging analysis All images (CT and MRI) were analysed by the same neuroradiologist (AC) blinded to the migraine status, using a GE Healthcare workstation (ADW 4. 6, GE Healthcare, Waukesha, Wis. , USA). Regarding CTA analysis, we recorded whether there was intracranial arterial occlusion. A proximal occlusion was defined as occlusion within the first 3 segments of the intracranial arterial system. Regarding perfusion CT, all perfusion maps were processed using OLEA SPHERE ® software (Olea Medical ® , La Ciotat, France). Cerebral blood volume (CBV), cerebral blood flow (CBF), mean-transit-time (MTT) and Time to maximum (Tmax) maps were generated for each patient using block-circulant Singular Value Decomposition (oSVD) deconvolution method  [9]  -the concentration-time curves of tissue were deconvoluted by the arterial input function (AIF) to obtain a residue function.
5/ Statistical analyses We performed univariate analysis to compare clinical and imaging data between the two groups. Fisher's exact test and Chi square were used for categorical data, and the Mann-Whitney U test was used for continuous data. In order to assess the association between migraine status and the infarct growth, we used a stepwise logistic regression. All variables assumed to be relevant or potentially implying confusion bias were fed into a multivariable regression model. All statistical analyses were performed using SPSS 16. 0. The threshold for statistical significance was set at p ≤ 0. 05.
Such candidates would represent interesting targets/biomarkers of ARI resistance. Therefore, we used our proteomic dataset to identify proteins that were consistently modulated in all three ARI-resistant cell lines, in both 2D and 3D conditions. We generated a proteomic panel consisting of 13 candidates that were differentially regulated upon resistance to AR inhibition, independent of the type of inhibitor or the culture conditions (Fig. 5a , Table  1 ). Identification of well-established factors associated with CRPC such as prostate specific antigen (PSA) or prostate specific membrane antigen (PSMA/FOLH1) strengthened the validity of the proteins identified. Of note, we identified several candidates, mainly enzymes involved in redox processes, which have not yet been reported in prostate cancer (Table  1 ).