dataesr/scientific-paragraphs-categorization huggingface dataset:
Local data collection, quality control and data analysis standardization The methods for data extraction at each institution have been previously described. 1 Briefly, each institution executed Structured Query Language (SQL) queries on local clinical databases, using Informatics for Integrating Biology and the Bedside (i2b2) platform, Observational Medical Outcomes Partnership (OMOP) Common Data Model, Epic Clarity, or other clinical database infrastructures, to extract patient-level data. Data fields extracted included demographics characteristics, clinical course, medication classes, International Statistical Classification of Diseases and Related Health Problems, 9th or 10th Edition (ICD-9 or ICD-10) diagnoses codes, and laboratory values identified by logical identifier names and codes (LOINC) at an individual patient level in a format standardized across the 4CE Consortium.
Patient level data were strictly kept to each institution for executing analytical code locally, and no patient-level data were transmitted between sites. To ensure consistency and quality control across all participating institutions, a R package was run at each institution to verify file and column names, data types, code values and ranges, and presence of duplicate values. 9 The functions for quality check from the R package were also implemented in our analytical R package to ensure adequate data quality prior to data analyses. The R package used for quality check can be accessed at https://github. com/covidclinical/Phase2. 1DataRPackage. To minimize variations in analytical techniques arising from package version differences in R, most institutions used a standardised Docker image containing Microsoft R Open (R version 4.
73 m 2 and 60 mL/min/ 1. 73 m 2 in a 60-year-old non-Black male by the 2009 CKD-EPI eGFR equation), and the timing of baseline sCr measurement, were also included in supplemental models. Estimated coefficients for covariates with low event incidences (<5 events) would not be computed at the institution level, and the number of institutions and sample sizes contributing to each estimated coefficient are reported. Data from National University Hospital Singapore was excluded from mortality analysis as no mortality events occurred. For kidney function recovery and kidney function impairment, competing risk analyses was performed with death as the competing event and censoring at the last available electronic record. Cumulative incidence curves with 95% CI for secondary outcomes were plotted from aggregated life tables and compared with Gray's test.
Cause-specific Cox models were constructed for each secondary outcome with the same covariates for mortality analysis as outlined above. Cause-specific Cox regression was chosen as the study was focused on investigating long-term secondary outcomes in the group of patients surviving their initial COVID-19 admission. To account for differences in inter-institution demographic and comorbidity distribution, a randomeffects meta-analysis with the DerSimonian-Laird method was performed to pool estimates for each covariate. Statistical analysis was done in R version 4. 0. 2 (R Project for Statistical Computing). Analytical source code is available on GitHub (https://github. com/ covidclinical/Phase2. 1AKIRPackage). A two-sided pvalue of <0. 05 was considered statistically significant. To adjust for multiple comparisons in multivariable models for mortality and kidney function recovery, Bonferroni correction was applied with a p-value of <0.
Mann and Czechowski (2021)  modeled the dust production by collisional fragmentation near the Sun and the dust trajectories were calculated with included radiation pressure and Lorentz force terms. Mann and Czechowski (2021)  showed that the observed impacts largely agrees with the model calculations for dust > 100 nm and the differences are possibly due to the influence of smaller particles, of local and temporal variations and of other dust components, such as dust in bound orbits and interstellar dust. In this work, we analyze data acquired by the Solar Orbiter. The spacecraft orbits the Sun in an elliptic orbit with a period of approximately 6 months. At perihelion, the Solar Orbiter reaches a minimum solar distance of 0. 28 AU, just within the perihelion of the Mercury orbit. The expected mission duration is 7 years, with a possible 3 year extension.
Machine Learning Classification of Time Series Data In this article, we present a machine learning-based framework as a novel method for detecting dust impact signals in radio and plasma waves instrument data. Machine learning methods, in particular neural networks in the recent decade, have been extensively used for challenging time series classification problems, such as: speech recognition  (Trosten et al. , 2019) , heart rate monitoring  (Wickstrøm et al. , 2022)  and human activity classification  (Villar et al. , 2016). A neural network has previously been used for selecting the signals of interest observed by the WAVES instrument on board the Wind spacecraft  (Bougeret et al. , 1995). While an unsupervised method (self-organizing maps) was used for identifying and categorizing plasma waves in the magnetic field data observed by the MMS 1 spacecraft  (Vech and Malaspina, 2021).
For this work however, the 3 RPW antenna signals are all converted to monopole electric field signals ( Ē1 , Ē2 , Ē3 ) by the following conversion: Ē1 = CH 3 -CH 2 = V 2 -V SC L 2 L2 - V 2 -V SC L 2 L2 - V 1 -V SC L 1 L1 = V 1 -V SC L 1 L1 (4) Ē2 = CH 3 = V 2 -V SC L 2 L2 (5) Ē3 = CH 3 -CH 2 -CH 1 = Ē1 -CH 1 = V 1 -V SC L 1 L1 - V 1 -V SC L 1 L1 - V 3 -V SC L 3 L3 = V 3 -V SC L 3 L3 (6) 2. 2 The Triggered Snapshot WaveForms (TSWF) data product and the TDS Classifier For this project, we use the Triggered Snapshot WaveForms (TSWF) data product, processed with software version 2. 1. 1 and acquired over a one and a half year period, spanning between June 15, 2020, to December 16, 2021. The TSWF data product consists of signal packets (63 ms snapshots) that are downlinked only if the classification algorithm on-board the Solar Orbiter is triggered.
The accuracy of the on-board classification algorithm is therefore important in order to optimize the data transfer and provide reliable data products for statistical analysis. The input to the on-board classification algorithm, hereafter named the TDS classifier or the TDS classification algorithm, is the 63 ms signal packet, while the output is categorized into one out of three labels: dust, wave or other. The TDS classifier assigns the label based on 3 extracted features. 1. The snapshot peak amplitude 2. The ratio of the peak amplitude to the median of the signal 3. The bandwidth of the main spectral peak identified in the Fourier spectrum The signal label is then determined by comparing the extracted feature values against configurable thresholds. For more detailed descriptions of the TDS classifier, see  Maksimovic et al.
Acknowledgements. This work is supported by the  Research Council of Norway  (grant number  262941 ). A. K. thanks  Audun Theodorsen  for aiding the motivation and objective of the article. In addition, A. K thanks  Juha Vierienen ,  Björn Gustavsson  and  Patrick Guio  for helpful discussions. S. K. is supported by the  Tromsø Research Foundation  under grant  19_SG_AT. J. V. , D. P. and J. S. acknowledge the support of  Czech Science Foundation  grant  22-10775S.
Code and data availability. The code used for this work, the trained classifiers and the training and testing data is available at: https:// github. com/AndreasKvammen/ML_dust_detection. The Triggered Snapshot WaveForms (TSWF) data files can be downloaded at: https: //rpw. lesia. obspm. fr/roc/data/pub/solo/rpw/data/L2/tds_wf_e/.
The SCS configuration includes 63 river mouths. Daily data were provided by the National Hydro-Meteorological Service of Vietnam for 11 rivers flowing in northern and central Vietnam (including the Red river). Monthly climatology runoff issued from the CLS -INDESO project were provided for the other rivers, including the Mekong river and Pearl river  (Tranchant et al. , 2016). The atmospheric forcing is calculated from the bulk formulae of Large and Yeager (2004) using the European Centre for Medium Range Weather Forecasts (ECMWF) operational forecasts at 1/8° horizontal resolution and 3 hours temporal resolution, available at https://www. ecmwf. int/, last access 18 May 2023. Open boundary tidal conditions are prescribed from FES2014b, the 2015 release of the FES (Finite Element Solution) global tide model  (Carrere et al.
Satellite data To evaluate the modeled SST, we use daily OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis) outputs for the period 2009 -2018, available at https://data. nodc. noaa. gov/ghrsst/L4/GLOB/UKMO/OSTIA/ (last access 18 May 2023). OSTIA is a GHRSST (Group for High Resolution Sea Surface Temperature) Level 4 SST daily product built from multiple spatial sensors and drifting and moored buoys data, with a horizontal resolution of 1/18°. Regarding the SSS, we use outputs from the 9-day-averaged de-biased SMOS (Soil Moisture and Ocean Salinity) SSS Level 3 version 3, developed by  Boutin et al. (2016). It has a resolution of 25 km and is available for the period 2010 To evaluate the SLA and surface geostrophic currents, we use daily 1/4° global ocean gridded L4 sea surface heights in delayedtime of CMEMS, available at: https://data.
marine. copernicus. eu/product/SEALEVEL_GLO_PHY_L4_MY_008_047/description (last access 18 May 2023). This altimetry product (hereafter called ALTI) is generated using data from different altimeter missions and covers the period from 1993 up to present  (Ablain et al. , 2015; Ray and Zaron, 2016). For model-data comparison, we extracted the daily altimetric SLA on the period of simulation  (2009 -2018)  and removed at each point of each dataset (model and altimetry) the temporal average over 2009 -2018.
In-situ data More than 12 600 Argo profiles were collected in the SCS between 2009 and 2018 (see Fig. 1b ), available from https://data-argo. ifremer. fr/geo/pacific_ocean/ (http://doi. org/10. 17882/42182, last access 18 May 2023). The ALIS R/V crossed the SCS from 10 May to 28 July 2014 (see Fig. 1b ), measuring SST and SSS every 6 s by the vessel-mounted Seabird SBE21 thermosalinometer (hereafter called TSG-Alis data). Under the framework of a cooperative Vietnam -US international research program  (Rogowski et al. , 2019) , a Seaglider sg206 was deployed on 22 January 2017 until 16 May 2017 in the SCS (see Fig. 1b ). It collected 555 vertical profiles of conductivity, temperature and pressure from an unpumped Sea-Bird Electronics CTD (SBE 41CP). Conductivity, temperature and depth were sampled at 5 s intervals in the upper 150 m, corresponding to a resolution finer than 1 m, and between 55 -100 s below.
Statistical evaluation The simulated dataset S and observational dataset O (of the same size N) are compared using three statistical parameters: the bias, the Normalized Root Mean Square Error (NRMSE) and the Pearson correlation coefficient R: 𝐵𝑖𝑎𝑠 = 𝑆 ̅ -𝑂 ̅ (Eq. 13) 𝑁𝑅𝑀𝑆𝐸 = √ 1 𝑁 ∑ (𝑆𝑖-𝑂𝑖) 2 𝑛 𝑖=1 (𝑂 𝑚𝑎𝑥 -𝑂 𝑚𝑖𝑛 ) (Eq. 14) 𝑅 = ∑ (𝑂 𝑖 -𝑂 ̅ )(𝑆 𝑖 -𝑆 ̅ ) 𝑁 𝑖=1 ∑ (𝑂 𝑖 -𝑂 ̅ ) 2 𝑁 𝑖=1 ∑ (𝑆 𝑖 -𝑆 ̅ ) 2 𝑁 𝑖=1 (Eq. 15) Where Si and Oi are respectively the simulated and observed series, and 𝑆 ̅ and 𝑂 ̅ their mean values. In Sect. 4, we use the same statistical evaluation methods for the comparison between online and offline computation on lateral fluxes: 𝑆 𝑖 , 𝑂 𝑖 , 𝑆 ̅ and 𝑂 ̅ are respectively replaced by 𝑂𝐹 𝑖 (the offline fluxes series), 𝑂𝑁 𝑖 (the online fluxes series), 𝑂𝐹 ̅̅̅̅ and 𝑂𝑁 ̅̅̅̅ (the corresponding mean values).
Annual cycle The SST annual cycle (Fig. 3a ) is very well simulated, with a highly significant correlation (R=0. 99 and p-value p < 0. 01, corresponding to a significant level higher than 99%), and a small NRMSE (0. 05) between the model outputs and OSTIA, and a slight bias of -0. 18°C. In both datasets, the monthly climatological cycle of SST reaches its maximum value in May/June (spring-summer) and decreases to its minimum in January/February (winter). This monthly climatological SST agrees with the study of  Kumar et al. (2010) , who observed the same SST annual cycle by analyzing hydrographic WOA data  (World Ocean Atlas, 2005). The simulated SSS seasonal cycle (Fig. 3c ) also shows a good agreement with SMOS data, with a highly significant correlation of 0. 89 (p <0. 01), a low NRMSE equal to 19%, and a slight negative bias (-0.
04). In both model and data, the average SSS is maximum in April (spring) with values of 33. 47 in the model and 33. 52 in SMOS, and minimum from September to December (autumn) with 33. 07 in the model and 33. 17 in SMOS. This significant seasonal variation of SSS in the SCS, with high salinity in winter-spring and low salinity in summer-autumn was also obtained by  Kumar et al. (2010)  and  Zeng et al. (2014). The annual cycle of SLA obtained with the model and ALTI data during the period 2009 -2018 shows a minimum value in spring-summer (June) with -0. 033 m both for the model and ALTI (Fig. 3e ). The SLA reaches its highest value in winter (December) with 0. 039 m and 0. 049 m respectively for the model and ALTI. The model outputs and the altimeter measurements have a highly significant correlation (R=0.
Acknowledgments This work is a part of LOTUS international joint laboratory (lotus. usth. edu. vn). Numerical simulations were performed using CALMIP HPC facilities (project P13120) and the cluster OCCIGEN from the  CINES group  (project  DARI A0080110098 ). Glider data were collected by the  Vietnam Center for Oceanography (CFO, VASI)  in the framework of the  US-Vietnam collaboration project:  " Gulf of Tonkin Circulation study"  ( NICOP N62909-15-1-2018 ), financed by the  Office of Naval Research (ONR). This work is also supported by the  Vietnam Academy of Science and Technology , grant code  CSCL17. 03/23-24. Fang, G. ,  Susanto , R. D. , Wirasantosa, S. , Qiao, F. , Supangat, A. , Fan, B. , Wei, Z. , Sulistiyo, B. , and Li, S. : Volume, heat, and freshwater transports from the  South China Sea  to Indonesian seas in the boreal winter of 2007-2008, J Geophys Res Oceans, 115, 1-11, https://doi.
A simulation was performed over the recent 10-year period 2009 -2018, using three hourly atmospheric forcing, daily lateral oceanic boundary forcing, nine tidal forcing components and real-time or climatology data for 63 river discharge points. The ability of the model to reproduce the characteristics of circulation and water masses over the SCS was evaluated through a thorough comparison with available satellite and in-situ observation datasets. The model shows high similarity with surface satellite data and in-situ observations in terms of tidal representation and of seasonal https://doi. org/10. 5194/egusphere-2023-547 Preprint. Discussion started: 1 June 2023 c Author(s) 2023. CC BY 4. 0 License. cycle, interannual variability and spatial distribution of surface characteristics and circulation (SST, SSS, SLA and associated geostrophic currents), with low biases and highly significant spatial and temporal correlations.
Data about analgesic consumption was collected through our medical prescription and delivery software: Orbis (Agfa Healthcare, Mortsel, Belgium). Morphine consumption was converted to oral morphine equivalents using the guidelines published by the Centers for Disease Control and Prevention  (14). Postoperative complications were classi ed according to the Clavien-Dindo classi cation system as minor (grade I-II) or major (grade IIIA and IIIB-IV) 22  (15).
Statistical analysis The population was divided into two groups according to the surgical approach, i. e. , robotic (robotic group) or conventional (conventional group) laparoscopy. Patients were also categorized according to the type of analgesics they consumed, i. e. , opioids or non-opioid analgesics. Univariate analysis was performed using the Student's t test for normally distributed data and the Chi Square test for categorical data. The Wilcoxon test was used for continuous variables and Fisher's exact test for categorical variables. All reported p-values were 2-sided. Signi cant difference was denoted when p<0. 05. All statistical analysis was performed using commercially available software (RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.
Our results are partially in agreement with those of previous studies showing no difference in analgesic consumption between robotic vs conventional laparoscopy  (9). However, our data contrast with the results of Mangalath et al  (23)  reporting lower analgesic use during the rst postoperative 24 hours following robotic surgery. This apparent discrepancy can be explained by the limited sample size of Mangalath et al's study which only included 24 patients in the robotic group, after exclusion of four, and 24 patients in the laparoscopic group, after exclusion of 37. Beyond the surgical approach, postoperative analgesic consumption should be analyzed in the speci c context of endometriosis. Time-to-diagnosis for patients with endometriosis can be as long as 12 years during which time they often experience considerable pain (1).
The Brier score is usually employed to verify the binary forecast. However, it has limitations because of the unbalanced categories in our case. The extreme-event dataset is composed of 95 % zeros and 5 % ones. Large parts of the forecast and observation datasets match because of the large presence of "0" values (daily precipitation lower than the 95th percentile) in both datasets. To address this issue, we also use a binary loss index focusing on extremes ("1" values). We assess the extreme events by proposing and using a simple extension of the binary loss score as introduced by  Legrand et al. (2022)  to ensemble forecasts. This metric considers only the case of the occurrence of an extreme event in the forecast or in the observation or in both but not the non-events (see  Sect. 2. 3. 2).
Data We use ECMWF's S2S precipitation hindcast data  (cycle 47r2; ECMWF, 2021; Vitart, 2020; ECMWF, 2022a)  from 2001 to 2020. They are composed of 11 ensemble members, initialized twice a week and run for 46 d. We focus on Europe, in the spatial box [30 • N, 72 • N] × [-15 • E, 49. 5 • E]. The hindcast period covers 20 years with 2080 forecast initializations between 4 January 2001 and 30 December 2020 (twice a week, on Monday and Thursday). The data were downloaded at the model spectral resolution O320 (ECMWF, 2022b, c) and regridded for the analysis to a 0. 5 • × 0. 5 • regular grid using a first-order conservative remapping  (Jones, 1999; CDO, 2018). ERA5 precipitation  (Hersbach et al. , 2019)  is used here as the verification dataset. The choice of a reanalysis dataset is motivated by its continuous spatial and temporal availability and by it avoiding the uncertainties due to the inherent spatial sparsity of weather station networks  (Hofstra et al.
Binary loss index Legrand et al. (  2022 ) studied in detail a risk function defined as the ratio between (i) the empirical probability of having an extreme event in either the observation dataset or the forecast dataset and (ii) the empirical probability of having an extreme event in the observations or the forecast (including having an event in both datasets). In our context, the risk function can be written as follows: R (u) (X) = P(X (u) = Y (u) ) P(Y (u) = 1 or X (u) = 1) , (3) where Y (u) is the binary observation with Y (u) = 0 (Y (u) = 1) if the observed daily precipitation is lower (greater) than a certain threshold u and X (u) is the binary forecast with X (u)  = 0 (X (u) = 1) if the predicted daily precipitation is lower (greater) than u. The risk function R (u) focuses on how well the 1 values (extreme-event days) match between observation and forecast.
Daily and local comparison We begin by discussing the forecast skill at the daily and grid-point scale. BLI indicates more skill during the extended winter (skill for up to 11 d and many regions with a last skillful day greater than 7 d) than during the extended summer (last skillful day below 6 d for most grid points); see Fig. 5. https://doi. org/10. 5194/nhess-23-2857-2023 Nat. Hazards Earth Syst. Sci. , 23, 2857-2871, 2023 Figure  3. Illustration of the weekly aggregation of extremes at one grid point. During week 1, the forecast predicts one extreme and one extreme is observed. For both the forecast and the observation, the number of extreme events in the 7 d window is greater than or equal to 1: E t 1 = 1 for the two datasets. For both datasets, the number of events in the 7 d window is lower than n for n ≥ 2: E t n = 0 for the two datasets.
During week 2, one extreme is observed but the forecast predicts two events. For both datasets, the number of extreme events in the 7 d window is greater than or equal to 1: E t 1 = 1 for the two datasets. For the observation, the number of events in the 7 d window is lower than 2 (E t 2 = 0), and this number is greater than or equal to 2 for the forecast (E t 2 = 1). For the configuration with n ≥ 3, E t n = 0 for both datasets. Figure  4. Illustration of the spatial aggregation of extremes in one neighborhood. The forecast indicates two extremes in the spatial neighborhood, and three events are observed. For both datasets, the number of extreme events in the neighborhood is greater than or equal to 1 (E s 1 = 1) and greater than or equal to 2 (E s 2 = 1). For three events or more, E s 3 = 0 for the forecast and E s 3 = 1 for the observation.
For four events or more, E s n = 0 for both datasets. Regions with high skill are Norway, the Alps and the western half of the Iberian Peninsula in the extended winter and the Bay of Biscay, the south of France, Norway, central Europe and the south of the Alps in the extended summer. The BLI skill score is less conservative than the Brier skill score; however the spatial patterns are similar for the two metrics (Fig. B1  in the Appendix). That is, the last skillful day for the Brier skill score is overall smaller than the last skillful day for BLI, but both the Brier score and BLI show the same regions with high and low skill of the forecast for precipitation extremes, in summer and winter.
Code and data availability. The codes used for the data analysis are available on GitHub (https://github. com/ PauRiv/S2S_verif_precip, last access: 21 August 2023; https://doi. org/10. 5281/zenodo. 8260765, Rivoire, 2023). The ECMWF's S2S hindcast data are available on the ECMWF platform (https://apps. ecmwf. int/datasets/data/ s2s-reforecasts-instantaneous-accum-ecmf/levtype=sfc/type=cf/, cycle 47r2; ECMWF, 2023).
7, 8  The βc cytokine family is a subfamily of the type I cytokine glycoprotein family, characterized by four helical bundle, namely α-helix A to D (see Figure  1B ). It includes also interleukin-5 (IL-5) and granulocyte-macrophage colony stimulating factor (GM-CSF). The associated βc receptor comprises heterodimeric glycoproteins: an α subunit and the shared βc subunit as described earlier. 5  The α receptor subunit also comprises IL-5Rα or CD125 for F I G U R E 1 IL-3 and IL-3Rα proteins. 3D  tructures and schematic representation of (A) human IL-3 and (B) human IL-3Rα both taken from PDB ID: 5UV8. (C) A general view of the IL-3Rα/IL-3 interaction and (D) a zoom over the predicted conserved or hot regions represented with dots. These conserved residues are R128 c , T131 c , and F132 c of IL-3, and S203 c , F232 c and N233 c of IL-3Rα (the " c " character designates a conserved residue).