dataesr/scientific-paragraphs-categorization huggingface dataset:
4. Predictive capacity refers to the ability to forecast the future state of a system to some specific level of accuracy using a computational or mathematical model  [100]. In this regard, non-formal models such as state-and-transition models are not predictive as they rely only on observed states and transitions between them and do not infer unobserved states. Building on the vast literature and a long tradition in systems biology starting with [109,110], the aforementioned limitations of current formalisms led us to develop an innovative dynamical modeling framework in ecology called ecological discrete-event networks (EDEN) and freely available (https://github. com/fpom/ecco, accessed on 15 September 2023). Note that some model types may have undefined/unapplicable properties. For instance, despite their non-deterministic nature, the properties "synchronous" or "non-synchronous" are not rigorously applicable to STMs.
Funding This work was supported by the  London Arts and Humanities Partnership  (project  526079 -"Neanderthal subsistence in Portugal: Small and large prey consumption during the Marine Isotope Stage 5 "), the  Institute of Archaeology, University College London ,  IoA Awards  (travel grant),  Fundação para a Ciência e Tecnologia, Portugal  ( projects PTDC/HIS-ARQ/098164/2008  -" Middle Paleolithic Archaeology of the Almonda Karstic System , "  PTDC/HAR-ARQ/30413/2017  -" Archaeology and Evolution of Early Humans in the Western Façade of Iberia , "  UIDB/00698/2020 , and  UIDP/00698/2020 ). Additional financial support has been provided by MN's two-year postdoc contract for project " Neanderthal and Anatomically Modern Human interactions with small prey in Atlantic Iberia  throughout the changing environments of the Pleistocene", as part of the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skodowska-Curie  grant agreement  101034349 , and from the  State Research Agency of the Spanish Ministry of Science and Innovation through the Program Maria de Maeztu Unit of Excellence  ( CEX2019-000945-M ).
Background: Intermediate and high-risk non-muscle-invasive bladder cancer (NMIBC) is typically managed with transurethral resection of the bladder tumour (TURBT) followed by intravesical Bacillus Calmette-Guérin (BCG) immunotherapy; however, NMIBC patients can become refractory or unresponsive to BCG treatment, and/or progress to muscle-invasive bladder cancer (MIBC). Healthcare resource utilization (HCRU) and costs in these patient populations are high. Methods: A retrospective longitudinal cohort design of adult (≥18 years) patients with bladder cancer and BCG treatment (01/ 01/2012-31/12/2017) was conducted using data from a representative subset of the German statutory health insurance database. During the follow-up period after last BCG, patients were categorized into subgroups of No further NMIBC treatment, Continuous treatment for NMIBC, or MIBC evidence; HCRU and costs were tabulated for each subgroup and for the entire cohort.
Data Source This study used administrative claims from 1/1/2010 to 31/12/2019, extracted from a subset of the German statutory health insurance (GKV-Spitzenverband, Gesetzliche Krankenversicherung) database. This subset includes data for approximately 5 million insured persons from different sickness funds and presents good representativeness in terms of age/sex distribution and geography versus total GKV. 22 The anonymized data set includes detailed prescription, sickness record, inpatient hospitalization, outpatient treatment, special services, and registration/deregistration (including due to death) data. This allowed a minimum of 2-year baseline period prior to index date during which patients' characteristics were described. Patients with metastases (ICD-10 codes C77*, C78*, C79*, C80*) or cancers to the liver (C22*), lung (C34*), and bone (C40*, C41*) at baseline were excluded.
Healthcare Resource Utilization HCRU data were captured for the following categories: inpatient care (overnight stays and day cases); outpatient care; medication used; and sick leave. Procedures related to BC treatment were also captured separately: systemic treatment; intravesical treatment; radiotherapy; and surgery (TURBT and cystectomy). HCRU was presented for the entire cohort and per each outcome subgroup category, per patient and per patient-year.
To our knowledge, only one study has examined the real-world costs following BCG treatment among patients with NMIBC. Patients with high-grade NMIBC (2008-2015) in the US SEER-Medicare database who progressed had higher hospitalization admissions and more outpatient/emergency visits; mean annualized costs per patient after BCG were double for patients who progressed (USD $65,668) compared with patients who did not (USD $29,780). 26 he present study's strengths include the use of recent data from a representative subset of GKV German data; demographic distribution is similar to the general population, and results are generalisable to the German population. However, miscoding or coding delays may have introduced biases in this study. Cancer staging, morphology and grading were unavailable in this data source; hence, the true disease stage was unknown.
Total bias is a measure of the overall accuracy of a measurement, while residual bias measures the remaining error after accounting for known sources of error. Both are important in understanding the reliability and accuracy of a measurement. However, in this paper, we discuss residual bias, as it takes into account the many corrections that have been put into place in the baselines. To reflect and document changes in the baselines' residual biases, which are all merged in our large analyses, we will provide an overview of our dataset's subsets characteristics in Sect. 4. The Aeolus Validation Experiment (AboVE) is a part of the French contribution to the Aeolus calibration and validation (cal/val) program, which included a series of intensive measurement campaigns at mid-latitude and tropical stations, Observatoire de Haute-Provence (OHP), and at the high-altitude Maido observatory at Réunion island, OPAR (Observatoire de Physique de l'Atmosphère de La Réunion).
The paper is organized as follows: in Sect. 2, the radiosondes, the ground-based lidars, and the designs and operation principles of ALADIN are briefly described with a focus on the commonalities and differences between the two wind lidar instruments and M10 radiosondes. In Sect. 3, an overview of the two validation campaigns is given, and the procedure of matching the different resolutions of the used datasets is explained. An extensive statistical study is then proposed in Sect. 4 through a statistical comparison of entire datasets over the different regions and time periods, as well as a longterm validation over both observatories. Additionally, single case studies are presented to give further insight into the error sources. Finally, the results are discussed in Sect. 5, followed by the conclusions in Sect.
Radiosondes The radiosonde (RS) wind measurements, based on a simple GPS tracking of the balloon position, offer high accuracy and vertical resolution, and their inherent errors (e. g. , instrument errors) are minor compared to satellite instrument errors. They are well suited to serve as a baseline dataset for the actual atmospheric state to validate the Aeolus HLOS winds. Radiosonde measurements are known to provide a solid reference against which other measurements can be validated (B. Sun et al. , 2010; Krisch et al. , 2017). Furthermore, radiosondes also provide guidance for observational strategies and requirements when collecting feedback from past collocations campaigns with similar instrumentation  (Iwai et al. , 2021; Baars et al. , 2020; Martin et al. , 2021). For each launch, it can be assumed that the observation errors are not correlated between the different radiosondes.
Rayleigh clear stands for clear skies. According to the Rayleigh approach, the winds are measured in regions showing absence of strong Mie backscatter. The presence of a high backscatter ratio would qualify the data as Rayleigh cloudy, as for cloudy/particle-loaded skies. Rayleigh cloudy products can also provide usable wind measurements. However, contamination of the Mie scattering must first be corrected, which is still in the experimental stage and is not within the scope of this study, therefore limiting our study only to the Rayleigh clear product. The Level 2B product provides an HLOS error estimation for each range bin in the observation profiles. The validity flag (de  Kloe et al. , 2016)  ensures the validity of the products. We also apply the quality control guidance L2B threshold from the Aeolus NWP impact experiments (Rennie and Isaksen, 2020), except we do not apply any HLOS estimated error threshold.
The reason for this choice is to allow for more data to be collocated and also to observe the satellite's behavior in the higher altitudes, where is it shown to have a higher error. In this study, we present data from baselines ranging from 2B02 and from 2B11 to 2B13, covering the period from September 2018 to January 2022. During the Aeolus commissioning phase, it was noted that pixels of ACCDs (accumulation charge-coupled devices) with an increased dark current were present in the memory zone of both ACCDs in the detector unit of ALADIN  (Reitebuch et al. , 2020; Kanitz et al. , 2019b). These pixels are called hot pixels, and their increased dark current can have a time-variable magnitude. The results presented by  Weiler et al. (2019a)  revealed that by May 2020 6 % of ACCD pixels could be classified as hot pixels.
Approximately 13 % of pixels will be concerned by this issue at the end of the mission's extended life in spring 2023, assuming the hot-pixel generation rate does not change  (Weiler et al. , 2019a). Meanwhile, a hot-pixel correction has been in place for Aeolus data since 14 June 2019. Keeping the hot-pixel appearance rate under a minimum is of the utmost importance: it can greatly affect the collocations between Aeolus and the reference instruments, making them harder to estimate the nominal behavior of the satellite (See Sect. 4. 1). The wind is observed orthogonal to the satellite ground track, pointing 35 • off-nadir, away from the Sun  (Lux et al. , 2020a, their Fig. 5 ). (H)LOS means (horizontal) line of sight. A single wind component, called vLOS, is measured along the satellite's line of sight (LOS).
Adaptation of the measurement grid Two significant aspects must be considered for adequate comparison of the radiosonde wind profiles with the Aeolus wind data. First, the two instruments' different horizontal and vertical resolutions necessitate an adaptation of the radiosondes' measurement grid to that of Aeolus. Aeolus' data format consists of 24 vertical range bins that divide the atmosphere, resulting in wind profiles that can be obtained between 0 and 30 km, displaying a vertical resolution between 250 and 2 km  (Reitebuch et al. , 2014). The distribution of these 24 range bins is defined through a dedicated range bin setting (RBS). The main reason for adding or changing a RBS is to address a specific need, such as better sampling at specific heights. The RBS can therefore vary depending on the latitude and the time, which is all adjusted operationally.
In this study, we operate with the bins of varying sizes (from 500 to 1500 m) and a vertical range from the ground up to a maximum altitude (varying between 17. 8 and 28. 7 km). The radiosonde data have a vertical range up to 35 km, depending on the balloon burst altitude, and the vertical resolution is approximately 5 m at the typical rate of climb of 5 m s -1. The ground-based lidars have a vertical range of up to 75 km and a resolution of 120 m. Since the instruments have around 5000 measurement points for the radiosondes down to 200 measurements points for the ground-based lidars, compared to the satellite's 24 (at best, if all the measurements have passed quality checks) measurements for a single profile, a downsampling of these two reference datasets is required. Each Aeolus profile is used as a reference for the collocated profiles downsampling, meaning that the averaging grid is specific to each satellite observation.
Pearson's correlation coefficient, R, between the HLOS Aeolus wind component and the HLOS instrument component sample is calculated using Eq. (  6 ), where xi and yi represent the sample i point of Aeolus and the instrument, respectively. x and y represent the mean wind component of the datasets. R = N i=1 (xi -x) (yi -y) N i=1 (xi -x) 2 N i=1 (yi -y) 2 (6) The scaled MAD is identical to the standard deviation (Eq. 4) if the analyzed data follow a normal distribution. In addition to the metrics presented above, a least-square line fit to the respective datasets is performed to also provide the slope and the approximated bias, which we refer to as the intercept. https://doi. org/10. 5194/amt-16-997-2023 Atmos. Meas. Tech. , 16, 997-1016, 2023.
AboVE OHP The AboVE-OHP campaigns took place from 6 to 14 January 2019 (AboVE OHP 1) and from 29 November to 14 December 2021 (AboVE OHP 2). Additional measurements were conducted on 20 and 21 December 2021 and 10, 17, and 24 January 2022. The ANX 4. 5 orbit, active during AboVE OHP 1, allowed for collocations with both ascending and descending orbits, within 100 to 150 km. The AboVE2-OHP benefited from the satellite orbit modification: the ANX 2. 0 orbit enables collocations within 50 km twice per week for ascending and descending orbits as seen in Fig. 2. Similar to the AboVE-Maido-2 campaign, the measurements included dusk-to-dawn coverage, shorter measurements, and radiosondes ascents. The satellite's AboVE2-OHP range bin setting is the same as the AboVE-Maido-2 RBS, since the goal is to compare the two regions' datasets and differentiate biases between the ones inherent to the setting and the ones due to the geographical location.
Statistical comparison of Aeolus with collocated data In this subsection, we statistically analyze the comparisons before discussing Aeolus' capacities and performance at dif-ferent altitudes. The dataset presented consists of the combined measurements from both cal/val sites, including all time periods. For this analysis, we will study the mean bias as a function of altitude, along with the number of collocated data points in Fig. 3. Additionally, we will present the Aeolus Rayleigh wind values plotted in Fig. 4  against the corresponding values of the reference instruments downsampled to match the Aeolus height resolution (as discussed in Sect. 2. 3). We provide an overview of all the validation cases in Tables  1  and 2. Concerning the two reference instruments, a point-by-point comparison shows a mean bias of 0.
1 m s -1 between the wind profile of the lidar and that of the radiosonde, with a standard deviation of 2. 3 m s -1. The Aeolus instrument settings and ground processing have significantly changed during its mission. These will affect statistical properties like bias and standard deviation/MAD. In addition to the combined statistics, we provide in Table  3  a splitting of the results, presenting them separately for the different baselines. Also, near-real-time and reprocessing results are separated, i. e. , baseline 11 (introduced in near-real-time processing on 8 October 2020) and the baseline 11 results before that date (based on reprocessed Aeolus data). The split is needed since the reprocessing used different calibration data than the near-real-time processing, along with several changes in the correction.
The main refinements in the reprocessed dataset are improvements in the hot-pixel correction by a more careful application of the method, along with using the data of the day itself rather than the day before for the M1 telescope temperature correction. The newer baseline should expect improved overall and local biases (ESA, 2021b). Within the dataset, distances to collocation vary between 22. 1 and 207 km for ascending orbits and between 54 and 241. 4 km for descending orbits. Therefore, the spatial offsets are highly variable, depending on the orbit phase and the as- cending node crossing (ANX) setting: for ascending orbits, the average distance to collocation is 67. 4 km for radiosondes and 79. 1 km for lidars, whereas for descending orbits, the averages are 161. 4 and 147. 5 km, respectively.
Such a difference does not allow for comparing the whole dataset as a unified set of collocations. The reference measurements were separated into ascending and descending orbital phases to account for that disparity. Figure  3a  and b show the mean bias as a function of altitude, with the shading representing the standard deviation. From 5 km to approximately 22 km, the bias lies within the ±5 m s -1 range but increases as soon as the upper bins mark is reached. The data below 5 km have a larger standard deviation, which is consistent with what  Guo et al. (2021)  reports on the increased wind speed differences in the 2-3 km range for descending orbits. The same calculations were then realized, only this time within the 5-22 km window (we refer to it as the "altitude range method"). The results show that removing the higher bins decreases the random error from 5.
, 2014). Table  5  shows the previously mentioned data and the different methods for comparison. The all-data method means that the instrument is collocated within the closest overall Aeolus overpass. The altitude range method cuts the data outside the 5 to 22 km range to account for possible RBS issues and non-optimal satellite coverage area. The last method aggregates every eligible profile (within the last 2 h) into one, averaged, single collocation. The goal was to see if the uppermost-bin issue would become insignificant after a certain amount of averaging, hence making the 22+ km range data available for usage. So far, both methods show encouraging data, reducing the scaled MAD for both instruments at any orbit type. Putting these numbers into perspective, for the radiosondes, a scaled MAD of 4.
Case-based analysis In this subsection, we further discuss Aeolus' capacities and performance with the help of four specific case studies. The goal is to provide an overview of the measurements by choosing representative collocations, each helping to show specificities present in the dataset. It shows that in the same conditions (i. e. , spatial/temporal offsets) Aeolus can behave very differently. These measurements are part of a more extensive statistical analysis presented in the above subsection. The Fig. 5  shows the HLOS wind velocity profiles measured by the radiosonde (black), the ground-based lidar (red), and the space lidar (blue). The shadings represent the measurement error for each data point, and the radiosondes' uncertainty is between 0. 4 and 1 m s -1  (Dirksen et al. , 2014) , which is smaller than the ground-based lidar (2.
Also shown in Fig. 6  are the cal/val campaign observations. For this comparison, we used only Aeolus data between 5 and 22 km to exclude the uppermost bins characterized by the largest errors (Sect. 4. 2). As previously mentioned, both sites have a similar average distance to the collocation; however, the time offset is larger for the Nîmes site by 2. 5 h. As demonstrated in Sect. 4. 1, the distance to collocation does not significantly impact the bias of the collocations. As such, this explains why both time series share a very similar trend and show the same variations specific to mission events (FM-A laser power loss, FM-B switch, etc. ). We note that the standard deviation at Nîmes is higher by 1. 5 m s -1 on average compared to that at Réunion. Because the distance to collocation is the same at both sites, this higher random error can be explained by the larger time offset.
Acknowledgements. We gratefully thank the personnel of OPAR station ( Eric Golubic ,  Patrick Hernandez , and  Louis Mottet ) and the  personnel of station Gérard Mégie at OHP  ( Frederic Gomez ,  Francois Dolon ,  Pierre Da Conceicao ,  Francois Huppert , and others) for conducting the radiosonde launches and lidar operation. The work related to Aeolus validation has been performed in the frame of  Aeolus Scientific Calibration and Validation Team (ACVT)  activities. We thank three anonymous referees for their constructive remarks that helped improve the manuscript. Financial support. The upgrade of OHP and OPAR wind lidars was financially supported by  CNES  ( Centre National d'Etudes Spatiales ) as well as through  EU   FP7 ARISE  and  H2020 ARISE2  projects. The work by  Mathieu Ratynski  was carried out under a PhD fellowship co-funded by  CNES  and  ACRI-ST.
Code availability. The code used in this study can be found at https://doi. org/10. 5281/zenodo. 7477104 (Ratynski, 2022b). Data availability. Aeolus data are publicly available through the Aeolus online dissemination system (https://aeolus-ds. eo. esa. int/ oads/access/, last access: 9 February 2022; EASA, 2022). The OHP lidar data can be obtained through the NDACC lidar database at https://ndacc. larc. nasa. gov/ (last access: 9 February 2022; NDACC, 2022). The Météo-France radiosonde data are free available at https://donneespubliques. meteofrance. fr/?fond= produit&id_produit=97&id_rubrique=33 (last access: 9 February 2022; Meteo France, 2022). The dataset used for the realization of this study can be found at https://doi. org/10. 5281/zenodo. 7477063 (Ratynski, 2022a). Supplement. The supplement related to this article is available online at: https://doi.
G. D. was partially supported by the  European Community   H2020  grant  GHAIA 777822 , and the  Simons Foundation  grant  601941 , GD. C. J. was partially supported by the  Simons Collaborations in MPS  grant  563916 , SM and  Max Engelstein 's  NSF  grant  2000288. A. J. was supported by the  Simons Foundation  grant  601941 , GD. //jep. centre-mersenne. org/.
Study design and patients As part of the study CKRP203A2105 (NCT01830010), BM biopsies have been systematically collected (during 2014-2016) pre-transplant and one and three months post-transplant. The subjects had been diagnosed with a hematological malignancy, underwent allo-HCT and were administered 3 mg mocravimod for 111 days in combination with cyclosporine A (CsA) or tacrolimus (Tac) as GvHD prophylaxis (Table  1 ). All subjects except the first one, who received fludarabine + 2 Gy total body irradiation conditioning, were fully myeloablatively conditioned. The follow-up period was one-year post-transplant. BM biopsies were performed to assess the remission state of the underlying hematological malignancy by cytology, histology, immunohistochemistry, and flow cytometry. In total, biopsies from 9 patients, who had received mocravimod were analyzed.
Within the here analyzed dataset, the incidence of grade II-IV aGvHD was not significantly lower in mocravimod-treated patients (44 %) compared to controls (50 %; Fig. 3A ). Studies with fingolimod have previously suggested an analogous to our observed change in Th phenotypes; i. e. , downregulation of main Th1 and Th17 regulators and upregulation of exhaustion markers  [23]. Importantly, total CD4 + T cells, which are powerful GvL mediators, were present at higher numbers in BM biopsies from mocravimod-treated patients compared to controls. This probably translated into reduced relapse rates when mocravimod is given, with 33 % of patients relapsing compared to 60 % in the control. Our data is limited by the fact that the underlying hematological malignancies, remission state before allo-HCT, and conditioning regimen differed between groups.