dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources and measurements We analysed data from the AMD module of the FRB! outcomes registry. Data were obtained from each visit, including the VA (best of corrected uncorrected and pinhole), the activity of the underlying choroidal neovascularization (CNV) lesion, treatment given, procedures and ocular adverse events. VA scores were converted as the number of letters read on a logarithm of the minimum angle of resolution (logMAR) VA standard ETDRS chart  (Dong et al. 2003). The activity of the CNV lesion (active or inactive) was graded by the treating physician based on findings from clinical examination according to a definition provided in the data collection screen from optical coherence tomography (OCT) and dye-based fundus fluorescein angiography, alone or in combination, at each visit.
Discussion This FRB! observational database analysis assessed the incidence, cumulative rate, risk factors and 12-month outcomes of SMH with loss of vision over 10 years in nAMD eyes treated with VEGF inhibitors in daily practice. The estimated incidence of SMH with loss of vision was approximately 8 per 10 000 IVTs and 5 per year per 1000 nAMD patients treated with VEGF inhibitors in routine clinical practice. Few studies have investigated the incidence and rate of this uncommon and potentially severe complication of nAMD. A nationwide prospective observational study using SOSU (Scottish Ophthalmic Surveillance Unit) found an overall incidence of 5. 4 SMH (defined as all newly diagnosed cases of fovea-involving SMH of size >2 disc diameters) per million per annum  (Al-Hity et al. 2019). In relation to the number of patients with exudative AMD (about 1.
The strength of this study is that it provides additional real-world data on the incidence, risk factors and longterm outcomes of SMH with loss of vision in nAMD. The FRB! system includes quality assurance measures that eliminate out-of-range and missing data  (Gillies et al. 2014 ). Our study reported treatment outcomes of SMH up to 12 months compared with matched controls. We acknowledge some weaknesses that are inherent in observational studies. First, the estimated incidence may be underestimated due to information bias since cases were self-reported. Second, there was a lack of randomization of initial management and treatment decisions were made without a study protocol. The selection of cases and management may differ among physicians and the reasons cannot be known from our data. Furthermore, we were only able to obtain these additional data on initial SMH management for 58% of our cases.
The FRB system did not collect systemic conditions and medications, notably antithrombotic agents, which may have influenced SMH development and outcomes. Duration of symptoms was also not collected in the FRB database, which is a significant visual prognosis factor in the treatment of SMH. Nevertheless, we reported outcomes of SMH with loss of vision as it is being managed in daily practice. To conclude, SMH is an uncommon complication in eyes treated with VEGF inhibitors for nAMD in routine clinical practice. Men and eyes with disciform scar CNV type at baseline were at a greater risk of developing SMH on treatment. Although they improved with treatment from the time of SMH, visual outcomes were poor with half of the cases losing at least 3 lines of vision 1 year later.
received honoraria from  Novartis ,  Bayer  and  Allergan  and is a member of the medical advisory boards for  Allergan ,  Novartis  and  Bayer. D. Barthelmes  has received research grants and travel expenses for Bayer and Novartis, acts as consultant for Alcon and is an inventor of the software used to collect the data for this analysis. C. Creuzot-Garcher has received grants from  Horus and Bayer , honoraria from  Novartis  and  Bayer  and is a member of the medical advisory boards for  Novartis, Bayer, Thea, Horus, Alcon and Allergan. M. C. Gillies  has received grants from  NHMRC , grants from  RANZCO Eye Foundation , grants and others from  Novartis  and grants and others from  Bayer  and is an inventor of the software used to collect the data for this analysis. F. Viola  is a medical advisor/consultant for  Bayer ,  Novartis ,  Roche.
The DPC is a multiangle sensor that detects atmospheric parameters. However, the retrieval of high-precision and high-spatial-resolution aerosol products from the DPC remains a great challenge due to the ill-posed nature of the problem. Thus, a novel aerosol optical depth (AOD) retrieval algorithm was proposed using visible surface reflectance relationships (VISRRs). The VISRR algorithm accounts for the surface anisotropy and needs neither a shortwave infrared band nor a surface reflectance database that can retrieve AOD over dark and bright land cover. Firstly, moderate-resolution imaging spectroradiometer (MODIS) surface reflectance (MYD09) products were used to derive the preceding surface reflectance relationships (SRRs), which are related to surface types, scattering angle, and normalized difference vegetation index (NDVI).
Furthermore, to solve the problem of the NDVI being susceptible to the atmosphere, an innovative method based on an iterative atmospheric correction was proposed to provide a realistic NDVI. The VISRR algorithm was then applied to the thirteen months of DPC multiangle data over the China region. AOD product comparison between the DPC and MODIS showed that they had similar spatial distribution, but the DPC had both high spatial resolution and coverage. The validation between the ground-based sites and the retrieval results showed that the DPC AOD performed best, with a Pearson correlation coefficient (R) of 0. 88, a root mean square error (RMSE) of 0. 17, and a good fraction (Gfrac) of 62. 7%. Then, the uncertainties regarding the AOD products were discussed for future improvements. Our results revealed that the VISRR algorithm is an effective method for retrieving reliable, simultaneously high-spatial-resolution and full-surface-coverage AOD data with good accuracy.
Correspondingly, based on the characteristics of these sensors, different specific aerosol retrieval algorithms have been proposed. The basic principle of satellitebased aerosol retrieval is to decouple the surface and atmosphere signals. However, satellitebased aerosol retrieval is an ill-posed problem due to the unknowns being more numerous than the measurements  [17]. Therefore, no matter which algorithm is implemented, it must solve two basic problems: the prior estimation of the surface reflectance (SR) and the hypothesis of the aerosol components. There are two common tools for solving the first of these problems: spectral SR relationships (SRRs) and SR databases (SRDs). For example, the dark target (DT) method, an official MODIS aerosol retrieval algorithm, uses the simple SRRs between shortwave infrared (SWIR) and visible (VIS) bands as the a priori constraints over dark surface types (i.
[49]. When combined with the fine-model AOD, e. g. , our previous fine-model aerosol algorithm 'spectral neutrality of surface polarized reflectance' (SNOSPR), based on multiangle polarized data  [30] , the fine-model aerosol fraction (FMF) can be calculated, which can characterize the weight of aerosols from anthropogenic and natural sources. This paper is structured as follows: In Section 2, the study area and datasets are presented. Section 3 introduces the basic principles of aerosol retrieval based on satellite remote sensing and the VISRR algorithm. Then, in Section 4, the spatial distribution of daily and average DPC AOD products is presented and compared with that of MODIS products. Then, all aerosol products are validated with ground-based (i. e. , AERONET and SONET) products, and the uncertainties regarding the AOD are analyzed and discussed.
Thus, satellite aerosol remote sensing over China is necessary and has always been a research hotspot. However, due to the complexity of the aerosol sources and land cover, aerosol retrieval over China is still a great challenge. The DPC is the first Chinese multiangle polarized satellite sensor. A two-day revisit period enables the DPC to effectively monitor the temporal variation in aerosols, and it crosses the equator at about 13:30 local solar time. A more detailed introduction to the DPC can be found in Li et al. [15]. According to Zhu et al. [51] , the calibration uncertainties of the DPC are about 1-7% depending on the wavelength and view zenith angle. This study used thirteen months (March 2019 to March 2020) of DPC multiangle multispectral (443, 490, 670, and 865 nm) intensity data to retrieve AOD using the VISRR algorithm.
Elevation Data Global Multiresolution Terrain Elevation Data 2010 (GMTED2010) is a global elevation model developed by the U. S. Geological Survey (USGS, https://usgs. gov/) (accessed on 15 August 2022) and National Geospatial Intelligence Agency (NGA), which has three spatial resolutions of about 1 km, 500 m, and 250 m  [52]. In this study, GMTED2010 data were used to determine the altitude and correct the influence of Rayleigh scattering for the DPC.
Spectral Library The USGS spectral library is a reference database containing seven categories of reflectance spectra (artificial materials, coatings, liquids, minerals, organic compounds, soils and mixtures, and vegetation) measured using laboratory, field, and airborne spectrometers  [53]. The Spectral Library Version07b (Splib07b) datasets were used to analyze the characteristic variety of the surface spectra between different bands, in order to adjust the spectral response function (SRF) differences between DPC and MODIS for corresponding bands.
Similarly, the LUTs of CWV and O 3 gas transmission were also built by the 6SV RT model. The value of CWV was set in the range of 0 to 7 g/cm 2 with an interval of 0. 5, and the value of O 3 was set in the range of 0 to 5 cm-atm with an interval of 0. 5. In this study, the prior knowledge of BRDF shape (Equation (  3 )) was assumed to be unchanged for one month and independent of bands  [62, 65]. When the LUTs and related auxiliary data were ready, the VISRR algorithm could be performed based on the multiangle multispectral DPC intensity data. All procedures were applied to the individual pixels, and the output result at each pixel was recorded. The schematic flowchart is shown in Figure  5 , and the specific retrieval steps are described below.
(a) Preprocessing the input data The input parameters of the DPC included the measured TOA reflectance (R Meas TOA (λ)) of 443, 490, 670, and 865 nm, with N (maximum of 12) multiangle (θ s , θ v , ∅), and the auxiliary datasets included month-averaged MCD43D19-21, MYD08_D3, GMTED2010, MCD12Q1, and the LUTs. The first step was the identification of the land surface type for each pixel using the MCD12Q1 products; second was the recognition of cloud-contaminated pixels, as only cloud-free and land (except for permanent snow, ice, or inland water) pixels were further processed in the next step; and third was to match and extract the auxiliary data according to the longitude, latitude, the corresponding geometric factor α 1 and volumetric factor α 2 of the BRDF shape, the daily O 3 and CWV, and the elevation from the month-averaged MCD43D19-20, MYD08_D3, and GMTED2010 products.
Conclusions In this study, an innovative algorithm called VISRR was proposed and applied to DPC multiangle intensity data to simultaneously retrieve AOD at an intrinsic spatial resolution (3. 3 km) over dark and bright land cover. The VISRR algorithm considers surface anisotropy and neither requires a shortwave infrared band nor establishes SRDs. The VISRR's previous SRRs were built based on long-time-series MYD09 products and.
Conclusions In this study, an innovative algorithm called VISRR was proposed and applied to DPC multiangle intensity data to simultaneously retrieve AOD at an intrinsic spatial resolution (3. 3 km) over dark and bright land cover. The VISRR algorithm considers surface anisotropy and neither requires a shortwave infrared band nor establishes SRDs. The VISRR's previous SRRs were built based on long-time-series MYD09 products and consideration of surface types, SCA, and NDVI. Meanwhile, to address the issue of the NDVI being susceptible to the atmosphere, an innovative iterative AC method was proposed to obtain realistic NDVI values. Then, by combining the fundamental aerosol models across China with a LUT method, the proposed algorithm was applied to thirteen months of DPC multiangle intensity data.
Acknowledgments: The authors would like to express our gratitude to  AOE Baotou, Beijing, Beijing PKU, XiangHe and Taipei CWB AERONET  sites for ground-based data. The authors would also like to thank the  Chinese National Space Administration Earth Observation and Data Center  for providing the DPC/GF-5 data,  NASA  for providing the MODIS products data, and  USGS  for providing the GMTED2010 and Splib07b datasets. The authors deeply appreciate the detailed comments and valuable suggestions from the anonymous reviewers.
26 Figure 5. 265 Figure 5. Flowchart of the main steps of the VISRR aerosol and surface retrieval algorithm. (a) Preprocessing the input data The input parameters of the DPC included the measured TOA reflectance (   ()) of 443, 490, 670, and 865 nm, with N (maximum of 12) multiangle (  ,   , ∅), and the auxiliary datasets included month-averaged MCD43D19-21, MYD08_D3, GMTED2010, MCD12Q1, and the LUTs.
Acknowledgements Authors wish to thank the  Scientific Council members of IABERD (Christophe Badie ,  Paco Barquinero ,  Christina Beinke ,  Jean-Michel Dolo ,  Paola Fattibene ,  Farrah Flegal ,  Angelina Kinoshita ,  Ulrike Kulka ,  Tomisato Miura ,  Alexander Romanyukha ,  Shin Toyoda ,  Marco Valente , and  Albrecht Wieser ) for their comments and revisions in the preparation of this manuscript.
Background: First-line Helicobacter pylori (H. pylori) treatments have been relatively well evaluated; however, it remains necessary to identify the most effective rescue treatments. Our aim was to assess the effectiveness and safety of H. pylori regimens containing rifabutin. METHODS: International multicentre prospective non-interventional European Registry on H. pylori Management (Hp-EuReg). Patients treated with rifabutin were registered in AEG-REDCap e-CRF from 2013 to 2021. Modified intention-to-treat and per-protocol analyses were performed. Data were subject to quality control. Results: Overall, 500 patients included in the Hp-EuReg were treated with rifabutin (mean age 52 years, 72% female, 63% with dyspepsia, 4% with peptic ulcer). Culture was performed in 63% of cases: dual resistance (to both clarithromycin and metronidazole) was reported in 46% of the cases, and triple resistance (to clarithromycin, metronidazole, and levofloxacin) in 39%.
European Registry on H. pylori Management (Hp-EuReg) Hp-EuReg is an international, multicentre, prospective, non-interventional registry that has been recording information on the management of H. pylori infection since 2013. Hp-EuReg has a Scientific Committee that ensures coherence, continued quality and scientific integrity of the analyses performed and manuscripts produced. Additionally, the Hp-EuReg protocol  [16]  selected national coordinators in the 30 participating countries, where gastroenterologists are currently recruited at approximately 300 centres to provide their contribution to the study. The investigators assemble and upload a series of variables and outcomes into the registry's database (REDCap) using an Electronic Case Report Form (e-CRF). The variables include: the patient's demographic information; any previous attempts for eradication and the treatments employed; the outcomes of any treatment, recording details such as compliance, cure rate, follow-up, and any reported AE.
The REDCap database [17] is managed and hosted by the "Asociación Española de Gastroenterología" (AEG, Madrid, Spain, www. aegastro. es, last accessed 10 March 2022), a non-profit Scientific and Medical Society that focuses on Gastroenterology research. The study was conducted according to the guidelines of the 1975 Declaration of Helsinki and was approved in 2012 by the Ethics Committee of La Princesa University Hospital (Madrid, Spain), that acted as reference Institutional Review Board, was classified by the Spanish Drug and Health Product Agency, and was prospectively registered at ClinicalTrials. gov (NCT02328131).
Data Analysis Data from June 2013 to November 2021 were extracted, and a quality review was performed on all the records included for each country and centre. The PPI dose used for H. pylori eradication treatment was grouped into three categories as reported by Graham  [18]  and Kirchheiner  [19] : Low dose, if the potency of PPI was between 4. 5 and 27 mg omeprazole equivalents when given twice daily; standard dose, for PPI between 32 and 40 mg omeprazole equivalents when given twice daily; and high dose, for PPI between 54 and 128 mg omeprazole equivalents when given twice daily. Treatment duration was evaluated according to three categories: 10, 12 and 14 days, based on the most frequent treatment durations.
Institutional Review Board Statement: The study was conducted according to the guidelines of the 1975 Declaration of Helsinki and was approved in 2012 by the  Ethics Committee of La Princesa University Hospital (Madrid, Spain ), that acted as reference Institutional Review Board, was classified by the  Spanish Drug and Health Product Agency , and was prospectively registered at ClinicalTrials. gov( NCT02328131 ). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
9 kg, 0. 8 m wingspan) were performed by  Mansour et al. , (2011)   [31]. The MASC UAV (5-7. 5 kg, 2. 6-3. 4 m wingspan) was developed for atmospheric research and used in the wind energy domain. It was operated upstream and downstream from 100 m hub-height wind turbines with distinct rotor diameters (82, 100, and 110 m) in order to collect data on the flow and wake from as close as one rotor diameter during a field campaign in 2013  [15]. In addition, the horizontal wind speed measured by the MASC was compared to LIDAR and mast measurements, which showed good agreement, even though it was not perfect because of spatial and temporal biases. Afterwards, Rautenberg et al. , (2019) operated the MASC UAV to measure the wind and turbulence in the vicinity of a wind turbine (125 m hub height, 100 m rotor diameter) at the JadeWind Park site (Varel, Germany).
Acknowledgments: A preliminary version of this paper benefitted from insightful review and inspiring comments by  Paul R. Bown. We wish to warmly thank  Ghislaine Broillet  for the preparation of smear slides for calcareous nannofossil analysis and  Zaminrizkavan Co. , Ltd. for assistance during fieldwork and petrographic studies. This work is a contribution to the  IGCP-655  project ( UNESCO-IUGS ). The research of B. Bádenas is supported by  Group E18: Aragosaurus: Recursos Geológicos y Paleoambientales.
These experts have been asked to identify up to 15 key publications outlining current scientific state-of-the-art techniques, advanced breeding methods and potential strategies for crop improvement with a focus on plant traits that are by implication heritable and/or transferable and can be exploited to increase plant yield potential, yield stability and nutritional quality. A further emphasis was made on species that serve as model plant species, species with major agricultural value and species that are already important in Europe to meet the future crop productivity needs (see Crop Selection Box). Subsequently, a literature survey and database have been developed, making this information on plant traits, technologies, genes and methods exploitable (https://cropb ooste r-p. wur. nl). In addition, an effort has been made to capture existing knowledge about studies on underutilized terrestrial crops and marine macrophytes (seaweeds).
Within the database construction, each publication entry is accompanied by a survey capturing in detail the determinants of the different traits. For example, the data collection for a publication regarding nutritional quality could run as follows (Figure  2 ): selection of the nutrient class (e. g. , proteins, vitamins or minerals), further specification of categories and subcategories (e. g. , macronutrients, micronutrients or specific nutrients) and finally collection of the physical and/or environmental factors affecting this trait. The experts could indicate whether the gene/trait under consideration affected multiple plant determinants, e. g. , a genes or pathway that has an impact on both nutritional quality and yield potential. In this way, it is possible to identify trends and commonalities between multiple database entries, identified traits and specified technologies.
With the help of the database, several key opportunities to improve crop production and quality have been identified  (Baekelandt et al. , 2022; Burgess et al. , 2022; Gojon et al. , 2022; Scharff et al. , 2021) , e. g. , exploring the wild germplasm of crops for desirable traits, investigating alternative protein sources using non-traditional and ancient grain species, and reducing or even eliminating anti-nutritional factors in plants by means of biotechnological approaches. Finally, the panel of experts belonging to participant institutions from the CropBooster-P consortium, ensuring a broad representation of knowledge about species, research fields, approaches and technologies, have queried the database. This already resulted in several reviews describing relevant genes, traits and technologies of interest and presenting a prospective view on breeding options that could be exploited in distinct research fields with respect to crop improvement  (Baekelandt et al.
, 2022; Burgess et al. , 2022; Gojon et al. , 2022; Scharff et al. , 2021). These are, however, only the tip of the iceberg, and the flexibility of the database allows to further extract specific information, such as which traits have been underexplored in which crop species, as well as reveal useful connections across species and plant traits, e. g. , with regard to the role of specific genes in multiple plant developmental processes. The CropBooster-P database can therefore be considered as an expert resource including recent advances and/or key findings with regard to traits that have been successfully selected/changed and that could contribute to the future-proofing of our crops in terms of increasing plant yield, sustainability and nutritional quality. Within the project, the database served as a foundation because it is both a scientifically sound overview of the current state-of-the-art in distinct research fields, as well as a resource with which to engage in discussion with stakeholder groups  (Nair et al.
Crop Selection Box Algae Laminaria, Porphyra, Ulva lactuca Forage grasses Lolium perenne (ryegrass), Medicago sativa (alfalfa) Grain staples Triticum aestivum (wheat), Zea mays (maize) N 2 fixers Glycine max (soybean), Pisum sativum (pea) Oilseed Helianthus annuus (sunflower), Brassica rapa (rapeseed) Vegetables Solanum lycopersicum (tomato), Lactuca sativa (lettuce) Fibre and lignocellulosic crops Populus (poplar), Miscanthus sinensis (miscanthus) Root staples Solanum tuberosum (potato), Beta vulgaris (sugar beet) Model plants Arabidopsis thaliana (arabidopsis), Nicotiana tabacum (tobacco) Others (e. g. , fruits) Vitis vinifera (grape), Pome fruits. Crop species selection includes species distributed in ten main classes (in bold). The example crops presented here, for each crop category, were considered priority crops for the CropBooster-P data collection.